diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ar.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ar.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ar.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.de.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.de.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.de.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.es.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.es.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.es.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fa.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fa.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fa.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fr.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fr.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.fr.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ru.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ru.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a.ru.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ar.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ar.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ar.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.de.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.de.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.de.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.es.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.es.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.es.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fa.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fa.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fa.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fr.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fr.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.fr.jpg and /dev/null differ diff --git a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ru.jpg b/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ru.jpg deleted file mode 100644 index 7112c185..00000000 Binary files a/translated_images/1200px-Girl_and_cat.89bd70951181e86a088f41f72415b6324ba4fe506148c6f18dca4e2ef3079ec3.ru.jpg and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.ar.png b/translated_images/AND-OR-Tree.5592d2c70187f283.ar.png deleted file mode 100644 index 9e863d49..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.ar.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.de.png b/translated_images/AND-OR-Tree.5592d2c70187f283.de.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.de.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.es.png b/translated_images/AND-OR-Tree.5592d2c70187f283.es.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.es.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.fa.png b/translated_images/AND-OR-Tree.5592d2c70187f283.fa.png deleted file mode 100644 index d3f31551..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.fa.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.fr.png b/translated_images/AND-OR-Tree.5592d2c70187f283.fr.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.fr.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283.ru.png b/translated_images/AND-OR-Tree.5592d2c70187f283.ru.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283.ru.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ar.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ar.png deleted file mode 100644 index 9e863d49..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ar.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.de.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.de.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.de.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.es.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.es.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.es.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fa.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fa.png deleted file mode 100644 index d3f31551..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fa.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fr.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fr.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.fr.png and /dev/null differ diff --git a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ru.png b/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ru.png deleted file mode 100644 index 20b7de28..00000000 Binary files a/translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ru.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.ar.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.ar.png deleted file mode 100644 index 7979891d..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.ar.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.de.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.de.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.de.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.es.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.es.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.es.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.fa.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.fa.png deleted file mode 100644 index be722d9d..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.fa.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.fr.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.fr.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.fr.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb2215.ru.png b/translated_images/ComputeGraph.463c9d8ebdcb2215.ru.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb2215.ru.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ar.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ar.png deleted file mode 100644 index 7979891d..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ar.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.de.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.de.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.de.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.es.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.es.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.es.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fa.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fa.png deleted file mode 100644 index be722d9d..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fa.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fr.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fr.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.fr.png and /dev/null differ diff --git a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ru.png b/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ru.png deleted file mode 100644 index 8779e767..00000000 Binary files a/translated_images/ComputeGraph.463c9d8ebdcb22158e0563fcc50171dfbf3cb40443b597bc642b0f34ac23abbf.ru.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.ar.png b/translated_images/ComputeGraphGrad.4626252c0de03507.ar.png deleted file mode 100644 index 8974ed9f..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.ar.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.de.png b/translated_images/ComputeGraphGrad.4626252c0de03507.de.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.de.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.es.png b/translated_images/ComputeGraphGrad.4626252c0de03507.es.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.es.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.fa.png b/translated_images/ComputeGraphGrad.4626252c0de03507.fa.png deleted file mode 100644 index 3ae7e2a9..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.fa.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.fr.png b/translated_images/ComputeGraphGrad.4626252c0de03507.fr.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.fr.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de03507.ru.png b/translated_images/ComputeGraphGrad.4626252c0de03507.ru.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de03507.ru.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ar.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ar.png deleted file mode 100644 index 8974ed9f..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ar.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.de.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.de.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.de.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.es.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.es.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.es.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fa.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fa.png deleted file mode 100644 index 3ae7e2a9..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fa.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fr.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fr.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.fr.png and /dev/null differ diff --git a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ru.png b/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ru.png deleted file mode 100644 index 662cdc4d..00000000 Binary files a/translated_images/ComputeGraphGrad.4626252c0de035075e5cd2b7f71b776d5e3e8f64f2dc472b4420d3fdfaf53ba8.ru.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.ar.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.ar.png deleted file mode 100644 index 8d48d3b3..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.ar.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.de.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.de.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.de.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.es.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.es.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.es.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.fa.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.fa.png deleted file mode 100644 index 30665cdb..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.fa.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.fr.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.fr.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.fr.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d6.ru.png b/translated_images/CoreferenceResolution.861924d6d384a7d6.ru.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d6.ru.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ar.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ar.png deleted file mode 100644 index 8d48d3b3..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ar.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.de.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.de.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.de.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.es.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.es.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.es.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fa.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fa.png deleted file mode 100644 index 30665cdb..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fa.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fr.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fr.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.fr.png and /dev/null differ diff --git a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ru.png b/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ru.png deleted file mode 100644 index ec2b7711..00000000 Binary files a/translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ru.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ar.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ar.png deleted file mode 100644 index ff5d8b83..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ar.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.de.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.de.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.de.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.es.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.es.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.es.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fa.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fa.png deleted file mode 100644 index 82f3ef62..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fa.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fr.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fr.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.fr.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ru.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ru.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ru.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ar.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ar.png deleted file mode 100644 index ff5d8b83..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ar.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.de.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.de.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.de.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.es.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.es.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.es.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fa.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fa.png deleted file mode 100644 index 82f3ef62..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fa.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fr.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fr.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.fr.png and /dev/null differ diff --git a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ru.png b/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ru.png deleted file mode 100644 index 55a70bba..00000000 Binary files a/translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3ae9ff8f06546ffcdff1347fa032b8e4b3d70b9e4ad0bf506.ru.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ar.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ar.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ar.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.de.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.de.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.de.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.es.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.es.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.es.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fa.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fa.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fa.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fr.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fr.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.fr.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ru.png b/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ru.png deleted file mode 100644 index 817ac42d..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c94d2b8e9337d8a006f2a7a90e067f3c34a8c6425ea905f490.ru.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ar.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ar.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ar.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.de.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.de.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.de.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.es.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.es.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.es.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fa.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fa.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fa.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fr.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fr.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.fr.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ru.png b/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ru.png deleted file mode 100644 index 2d43fa37..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbfb6ddc71819ae0ba025e10144c73ad0ce906933e5382d081d.ru.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ar.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ar.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ar.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.de.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.de.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.de.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.es.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.es.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.es.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fa.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fa.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fa.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fr.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fr.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.fr.png and /dev/null differ diff --git a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ru.png b/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ru.png deleted file mode 100644 index 8072fbe4..00000000 Binary files a/translated_images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d952ff424cc6533143559f9d97298020f64ebac91356cbb416.ru.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ar.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ar.png deleted file mode 100644 index 281b6576..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ar.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.de.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.de.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.de.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.es.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.es.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.es.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fa.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fa.png deleted file mode 100644 index eccf149a..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fa.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fr.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fr.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.fr.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ru.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ru.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5b.ru.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ar.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ar.png deleted file mode 100644 index 281b6576..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ar.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.de.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.de.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.de.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.es.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.es.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.es.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fa.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fa.png deleted file mode 100644 index eccf149a..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fa.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fr.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fr.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.fr.png and /dev/null differ diff --git a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ru.png b/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ru.png deleted file mode 100644 index 130b81a8..00000000 Binary files a/translated_images/DIKW_Pyramid.94126f7d2bd8db5be71c6f1658b94bd3c85342e3cb827913b556b0414d358340.ru.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ar.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ar.png deleted file mode 100644 index 608285e4..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ar.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.de.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.de.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.de.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.es.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.es.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.es.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fa.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fa.png deleted file mode 100644 index 4e1951eb..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fa.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fr.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fr.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fr.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ru.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ru.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ru.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ar.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ar.png deleted file mode 100644 index 608285e4..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ar.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.de.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.de.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.de.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.es.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.es.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.es.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fa.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fa.png deleted file mode 100644 index 4e1951eb..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fa.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fr.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fr.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.fr.png and /dev/null differ diff --git a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ru.png b/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ru.png deleted file mode 100644 index 5c1eceef..00000000 Binary files a/translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ru.png and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ar.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ar.jpg deleted file mode 100644 index facb2684..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ar.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.de.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.de.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.de.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.es.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.es.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.es.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fa.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fa.jpg deleted file mode 100644 index 32c735f1..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fa.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fr.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fr.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.fr.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ru.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ru.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.ru.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ar.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ar.jpg deleted file mode 100644 index facb2684..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ar.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.de.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.de.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.de.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.es.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.es.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.es.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fa.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fa.jpg deleted file mode 100644 index 32c735f1..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fa.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fr.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fr.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.fr.jpg and /dev/null differ diff --git a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ru.jpg b/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ru.jpg deleted file mode 100644 index aa71e3ef..00000000 Binary files a/translated_images/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9fb32bab6ae07e40faa202be1935c0fbd8c2bc2600a87bcb1.ru.jpg and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.ar.png b/translated_images/NetLogo-Main.32653711ec1a01b3.ar.png deleted file mode 100644 index 3d949dde..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.ar.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.de.png b/translated_images/NetLogo-Main.32653711ec1a01b3.de.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.de.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.es.png b/translated_images/NetLogo-Main.32653711ec1a01b3.es.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.es.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.fa.png b/translated_images/NetLogo-Main.32653711ec1a01b3.fa.png deleted file mode 100644 index 76a5bee1..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.fa.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.fr.png b/translated_images/NetLogo-Main.32653711ec1a01b3.fr.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.fr.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3.ru.png b/translated_images/NetLogo-Main.32653711ec1a01b3.ru.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3.ru.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ar.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ar.png deleted file mode 100644 index 3d949dde..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ar.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.de.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.de.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.de.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.es.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.es.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.es.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fa.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fa.png deleted file mode 100644 index 76a5bee1..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fa.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fr.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fr.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.fr.png and /dev/null differ diff --git a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ru.png b/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ru.png deleted file mode 100644 index c2f12530..00000000 Binary files a/translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ru.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.ar.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.ar.png deleted file mode 100644 index 860161f0..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.ar.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.de.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.de.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.de.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.es.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.es.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.es.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.fa.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.fa.png deleted file mode 100644 index e4377f37..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.fa.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.fr.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.fr.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.fr.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c05.ru.png b/translated_images/NetLogo-ModelLib.efe023afb4763c05.ru.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c05.ru.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ar.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ar.png deleted file mode 100644 index 860161f0..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ar.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.de.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.de.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.de.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.es.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.es.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.es.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fa.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fa.png deleted file mode 100644 index e4377f37..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fa.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fr.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fr.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.fr.png and /dev/null differ diff --git a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ru.png b/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ru.png deleted file mode 100644 index bd1a3da8..00000000 Binary files a/translated_images/NetLogo-ModelLib.efe023afb4763c059704a8ac0e2cd5e51889b117e8eac02aaa5334cfe1c52c13.ru.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.ar.png b/translated_images/NeuroArch.4e17cdba5e445721.ar.png deleted file mode 100644 index 4ddc3079..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.ar.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.de.png b/translated_images/NeuroArch.4e17cdba5e445721.de.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.de.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.es.png b/translated_images/NeuroArch.4e17cdba5e445721.es.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.es.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.fa.png b/translated_images/NeuroArch.4e17cdba5e445721.fa.png deleted file mode 100644 index 9645b759..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.fa.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.fr.png b/translated_images/NeuroArch.4e17cdba5e445721.fr.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.fr.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721.ru.png b/translated_images/NeuroArch.4e17cdba5e445721.ru.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721.ru.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ar.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ar.png deleted file mode 100644 index 4ddc3079..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ar.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.de.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.de.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.de.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.es.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.es.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.es.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fa.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fa.png deleted file mode 100644 index 9645b759..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fa.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fr.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fr.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.fr.png and /dev/null differ diff --git a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ru.png b/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ru.png deleted file mode 100644 index f5f9fcee..00000000 Binary files a/translated_images/NeuroArch.4e17cdba5e445721ad0a94a738c6f1178fc9c5f36486fb0f8fccfd61964d528c.ru.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ar.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ar.png deleted file mode 100644 index 53a29c17..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ar.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.de.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.de.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.de.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.es.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.es.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.es.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fa.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fa.png deleted file mode 100644 index 72b30fec..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fa.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fr.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fr.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.fr.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ru.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ru.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.ru.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ar.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ar.png deleted file mode 100644 index 53a29c17..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ar.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.de.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.de.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.de.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.es.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.es.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.es.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fa.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fa.png deleted file mode 100644 index 72b30fec..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fa.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fr.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fr.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.fr.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ru.png b/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ru.png deleted file mode 100644 index 01ea0d27..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecall.d2ef5b6081a0b094b5242ff6aa810ef25285f5a4e0efcc604c31708798fd51ee.ru.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ar.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ar.png deleted file mode 100644 index f2f75e64..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ar.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.de.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.de.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.de.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.es.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.es.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.es.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fa.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fa.png deleted file mode 100644 index bb86e197..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fa.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fr.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fr.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.fr.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ru.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ru.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.ru.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ar.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ar.png deleted file mode 100644 index f2f75e64..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ar.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.de.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.de.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.de.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.es.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.es.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.es.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fa.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fa.png deleted file mode 100644 index bb86e197..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fa.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fr.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fr.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.fr.png and /dev/null differ diff --git a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ru.png b/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ru.png deleted file mode 100644 index cc69c43a..00000000 Binary files a/translated_images/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a6f0db4193ce61830ea03e505138410480fc16f9051694a40.ru.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.ar.png b/translated_images/Overfitting.408ad91cd90b4371.ar.png deleted file mode 100644 index 1312d6f8..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.ar.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.de.png b/translated_images/Overfitting.408ad91cd90b4371.de.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.de.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.es.png b/translated_images/Overfitting.408ad91cd90b4371.es.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.es.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.fa.png b/translated_images/Overfitting.408ad91cd90b4371.fa.png deleted file mode 100644 index cbcc63cc..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.fa.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.fr.png b/translated_images/Overfitting.408ad91cd90b4371.fr.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.fr.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371.ru.png b/translated_images/Overfitting.408ad91cd90b4371.ru.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371.ru.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ar.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ar.png deleted file mode 100644 index 1312d6f8..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ar.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.de.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.de.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.de.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.es.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.es.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.es.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fa.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fa.png deleted file mode 100644 index cbcc63cc..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fa.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fr.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fr.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.fr.png and /dev/null differ diff --git a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ru.png b/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ru.png deleted file mode 100644 index ed20f884..00000000 Binary files a/translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ru.png and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ar.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ar.jpg deleted file mode 100644 index 262d4559..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ar.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.de.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.de.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.de.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.es.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.es.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.es.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fa.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fa.jpg deleted file mode 100644 index 262d4559..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fa.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fr.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fr.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.fr.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ru.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ru.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d.ru.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ar.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ar.jpg deleted file mode 100644 index 262d4559..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ar.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.de.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.de.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.de.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.es.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.es.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.es.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fa.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fa.jpg deleted file mode 100644 index 262d4559..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fa.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fr.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fr.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.fr.jpg and /dev/null differ diff --git a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ru.jpg b/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ru.jpg deleted file mode 100644 index 6067f6b5..00000000 Binary files a/translated_images/Rosenblatt-wikipedia.294821b285ac796d2281a556fe3de9a7aa328e34cdd25f3ac9deefa0982ea2df.ru.jpg and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ar.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ar.png deleted file mode 100644 index dc2f8ad1..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ar.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.de.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.de.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.de.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.es.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.es.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.es.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fa.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fa.png deleted file mode 100644 index 28d8f8db..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fa.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fr.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fr.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fr.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ru.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ru.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ru.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ar.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ar.png deleted file mode 100644 index dc2f8ad1..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ar.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.de.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.de.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.de.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.es.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.es.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.es.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fa.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fa.png deleted file mode 100644 index 28d8f8db..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fa.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fr.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fr.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.fr.png and /dev/null differ diff --git a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ru.png b/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ru.png deleted file mode 100644 index 32b95965..00000000 Binary files a/translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ar.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ar.png deleted file mode 100644 index 04699092..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.de.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.de.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.de.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.es.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.es.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.es.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fa.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fa.png deleted file mode 100644 index 04699092..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fr.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fr.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ru.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ru.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ar.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ar.png deleted file mode 100644 index 04699092..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.de.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.de.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.de.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.es.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.es.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.es.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fa.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fa.png deleted file mode 100644 index 04699092..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fr.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fr.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ru.png b/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ru.png deleted file mode 100644 index c3c23780..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ar.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ar.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.de.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.de.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.de.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.es.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.es.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.es.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fa.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fa.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fr.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fr.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ru.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ru.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ar.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ar.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.de.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.de.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.de.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.es.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.es.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.es.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fa.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fa.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fr.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fr.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ru.png b/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ru.png deleted file mode 100644 index 0e739ec7..00000000 Binary files a/translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ar.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ar.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.de.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.de.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.de.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.es.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.es.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.es.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fa.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fa.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fr.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fr.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ru.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ru.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ru.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ar.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ar.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ar.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.de.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.de.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.de.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.es.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.es.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.es.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fa.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fa.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fa.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fr.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fr.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.fr.png and /dev/null differ diff --git a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ru.png b/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ru.png deleted file mode 100644 index c2daf0e1..00000000 Binary files a/translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.ru.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.ar.png b/translated_images/aae.9d418a990fc75bf9.ar.png deleted file mode 100644 index 321ec860..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.ar.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.de.png b/translated_images/aae.9d418a990fc75bf9.de.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.de.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.es.png b/translated_images/aae.9d418a990fc75bf9.es.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.es.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.fa.png b/translated_images/aae.9d418a990fc75bf9.fa.png deleted file mode 100644 index d10fcca2..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.fa.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.fr.png b/translated_images/aae.9d418a990fc75bf9.fr.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.fr.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9.ru.png b/translated_images/aae.9d418a990fc75bf9.ru.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9.ru.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ar.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ar.png deleted file mode 100644 index 321ec860..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ar.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.de.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.de.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.de.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.es.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.es.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.es.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fa.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fa.png deleted file mode 100644 index d10fcca2..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fa.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fr.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fr.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.fr.png and /dev/null differ diff --git a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ru.png b/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ru.png deleted file mode 100644 index 02a776b6..00000000 Binary files a/translated_images/aae.9d418a990fc75bf9204b701eb6998206fd1b986643cc4e9b899aabdd27504702.ru.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.ar.png b/translated_images/activation-func.b4924007c7ce7764.ar.png deleted file mode 100644 index 4e9cf42e..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.ar.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.de.png b/translated_images/activation-func.b4924007c7ce7764.de.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.de.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.es.png b/translated_images/activation-func.b4924007c7ce7764.es.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.es.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.fa.png b/translated_images/activation-func.b4924007c7ce7764.fa.png deleted file mode 100644 index 86017bde..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.fa.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.fr.png b/translated_images/activation-func.b4924007c7ce7764.fr.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.fr.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce7764.ru.png b/translated_images/activation-func.b4924007c7ce7764.ru.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce7764.ru.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ar.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ar.png deleted file mode 100644 index 4e9cf42e..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ar.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.de.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.de.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.de.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.es.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.es.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.es.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fa.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fa.png deleted file mode 100644 index 86017bde..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fa.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fr.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fr.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.fr.png and /dev/null differ diff --git a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ru.png b/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ru.png deleted file mode 100644 index 1e85371c..00000000 Binary files a/translated_images/activation-func.b4924007c7ce77648d4b1dd3e81c689453204902c096c626735c7b53688cdc63.ru.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.ar.png b/translated_images/adversarial-dog.d9fc7773b0142b89.ar.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.ar.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.de.png b/translated_images/adversarial-dog.d9fc7773b0142b89.de.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.de.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.es.png b/translated_images/adversarial-dog.d9fc7773b0142b89.es.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.es.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.fa.png b/translated_images/adversarial-dog.d9fc7773b0142b89.fa.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.fa.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.fr.png b/translated_images/adversarial-dog.d9fc7773b0142b89.fr.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.fr.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89.ru.png b/translated_images/adversarial-dog.d9fc7773b0142b89.ru.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89.ru.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ar.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ar.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ar.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.de.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.de.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.de.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.es.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.es.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.es.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fa.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fa.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fa.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fr.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fr.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.fr.png and /dev/null differ diff --git a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ru.png b/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ru.png deleted file mode 100644 index 2da4f4e2..00000000 Binary files a/translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ru.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.ar.png b/translated_images/ai-computervision.6506ebebac3fbf76.ar.png deleted file mode 100644 index ce7d0fbf..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.ar.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.de.png b/translated_images/ai-computervision.6506ebebac3fbf76.de.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.de.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.es.png b/translated_images/ai-computervision.6506ebebac3fbf76.es.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.es.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.fa.png b/translated_images/ai-computervision.6506ebebac3fbf76.fa.png deleted file mode 100644 index 349a0b8e..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.fa.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.fr.png b/translated_images/ai-computervision.6506ebebac3fbf76.fr.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.fr.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76.ru.png b/translated_images/ai-computervision.6506ebebac3fbf76.ru.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76.ru.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ar.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ar.png deleted file mode 100644 index ce7d0fbf..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ar.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.de.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.de.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.de.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.es.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.es.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.es.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fa.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fa.png deleted file mode 100644 index 349a0b8e..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fa.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fr.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fr.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.fr.png and /dev/null differ diff --git a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ru.png b/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ru.png deleted file mode 100644 index fe8eea04..00000000 Binary files a/translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.ru.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.ar.png b/translated_images/ai-for-beginners.b354ca904b22cd70.ar.png deleted file mode 100644 index 237aa2c7..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.ar.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.de.png b/translated_images/ai-for-beginners.b354ca904b22cd70.de.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.de.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.es.png b/translated_images/ai-for-beginners.b354ca904b22cd70.es.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.es.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.fa.png b/translated_images/ai-for-beginners.b354ca904b22cd70.fa.png deleted file mode 100644 index 604bad35..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.fa.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.fr.png b/translated_images/ai-for-beginners.b354ca904b22cd70.fr.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.fr.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70.ru.png b/translated_images/ai-for-beginners.b354ca904b22cd70.ru.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70.ru.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ar.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ar.png deleted file mode 100644 index 237aa2c7..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ar.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.de.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.de.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.de.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.es.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.es.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.es.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fa.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fa.png deleted file mode 100644 index 604bad35..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fa.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fr.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fr.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.fr.png and /dev/null differ diff --git a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ru.png b/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ru.png deleted file mode 100644 index 03831c19..00000000 Binary files a/translated_images/ai-for-beginners.b354ca904b22cd70ef0d0df7b0c8633af55878f9aba882d9b9971d12badff175.ru.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.ar.png b/translated_images/ai-intro.bf28d1ac4235881c.ar.png deleted file mode 100644 index 50a3bb79..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.ar.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.de.png b/translated_images/ai-intro.bf28d1ac4235881c.de.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.de.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.es.png b/translated_images/ai-intro.bf28d1ac4235881c.es.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.es.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.fa.png b/translated_images/ai-intro.bf28d1ac4235881c.fa.png deleted file mode 100644 index 3eb64956..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.fa.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.fr.png b/translated_images/ai-intro.bf28d1ac4235881c.fr.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.fr.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c.ru.png b/translated_images/ai-intro.bf28d1ac4235881c.ru.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c.ru.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ar.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ar.png deleted file mode 100644 index 50a3bb79..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ar.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.de.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.de.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.de.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.es.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.es.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.es.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fa.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fa.png deleted file mode 100644 index 3eb64956..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fa.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fr.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fr.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.fr.png and /dev/null differ diff --git a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ru.png b/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ru.png deleted file mode 100644 index 46a466ad..00000000 Binary files a/translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ru.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ar.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ar.png deleted file mode 100644 index 3fa89020..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ar.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.de.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.de.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.de.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.es.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.es.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.es.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fa.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fa.png deleted file mode 100644 index 7e577f4a..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fa.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fr.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fr.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.fr.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ru.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ru.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e83.ru.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ar.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ar.png deleted file mode 100644 index 3fa89020..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ar.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.de.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.de.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.de.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.es.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.es.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.es.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fa.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fa.png deleted file mode 100644 index 7e577f4a..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fa.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fr.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fr.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.fr.png and /dev/null differ diff --git a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ru.png b/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ru.png deleted file mode 100644 index ddb504db..00000000 Binary files a/translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.ru.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.ar.png b/translated_images/ai-nlp.b22dcb8ca4707cea.ar.png deleted file mode 100644 index 7b91361b..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.ar.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.de.png b/translated_images/ai-nlp.b22dcb8ca4707cea.de.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.de.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.es.png b/translated_images/ai-nlp.b22dcb8ca4707cea.es.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.es.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.fa.png b/translated_images/ai-nlp.b22dcb8ca4707cea.fa.png deleted file mode 100644 index 9190cb5c..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.fa.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.fr.png b/translated_images/ai-nlp.b22dcb8ca4707cea.fr.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.fr.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707cea.ru.png b/translated_images/ai-nlp.b22dcb8ca4707cea.ru.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707cea.ru.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ar.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ar.png deleted file mode 100644 index 7b91361b..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ar.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.de.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.de.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.de.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.es.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.es.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.es.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fa.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fa.png deleted file mode 100644 index 9190cb5c..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fa.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fr.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fr.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.fr.png and /dev/null differ diff --git a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ru.png b/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ru.png deleted file mode 100644 index 7706c398..00000000 Binary files a/translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ru.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.ar.png b/translated_images/ai-overview.0857791951d19500.ar.png deleted file mode 100644 index 8e921b9d..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.ar.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.de.png b/translated_images/ai-overview.0857791951d19500.de.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.de.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.es.png b/translated_images/ai-overview.0857791951d19500.es.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.es.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.fa.png b/translated_images/ai-overview.0857791951d19500.fa.png deleted file mode 100644 index 4aee3d6b..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.fa.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.fr.png b/translated_images/ai-overview.0857791951d19500.fr.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.fr.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500.ru.png b/translated_images/ai-overview.0857791951d19500.ru.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500.ru.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ar.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ar.png deleted file mode 100644 index 8e921b9d..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ar.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.de.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.de.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.de.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.es.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.es.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.es.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fa.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fa.png deleted file mode 100644 index 4aee3d6b..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fa.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fr.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fr.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.fr.png and /dev/null differ diff --git a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ru.png b/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ru.png deleted file mode 100644 index d0159570..00000000 Binary files a/translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ru.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.ar.png b/translated_images/ai-symbolic.715a30cb610411a6.ar.png deleted file mode 100644 index 7f523873..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.ar.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.de.png b/translated_images/ai-symbolic.715a30cb610411a6.de.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.de.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.es.png b/translated_images/ai-symbolic.715a30cb610411a6.es.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.es.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.fa.png b/translated_images/ai-symbolic.715a30cb610411a6.fa.png deleted file mode 100644 index c181f732..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.fa.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.fr.png b/translated_images/ai-symbolic.715a30cb610411a6.fr.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.fr.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6.ru.png b/translated_images/ai-symbolic.715a30cb610411a6.ru.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6.ru.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ar.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ar.png deleted file mode 100644 index 7f523873..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ar.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.de.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.de.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.de.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.es.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.es.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.es.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fa.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fa.png deleted file mode 100644 index c181f732..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fa.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fr.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fr.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.fr.png and /dev/null differ diff --git a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ru.png b/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ru.png deleted file mode 100644 index 7ec69f54..00000000 Binary files a/translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ru.png and /dev/null differ diff --git a/translated_images/ar/.co-op-translator.json b/translated_images/ar/.co-op-translator.json new file mode 100644 index 00000000..4cadc7f6 --- /dev/null +++ b/translated_images/ar/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:05+00:00", + "source_file": "images/favicon.png", + "language_code": "ar" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:34+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "ar" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:05:26+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "ar" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:06:11+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "ar" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:28+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "ar" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:07:00+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "ar" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:42+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "ar" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:08:25+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "ar" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:39+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "ar" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:43+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "ar" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:48+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "ar" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:55+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "ar" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:09:04+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "ar" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:15+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "ar" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:36+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "ar" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:43+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "ar" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:56+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "ar" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:07+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "ar" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:20+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "ar" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:25+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "ar" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:28+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "ar" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:44+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "ar" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:10:59+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "ar" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "ar" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:12+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "ar" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:15+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "ar" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:20+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "ar" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "ar" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:23+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "ar" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:29+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "ar" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "ar" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:33+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "ar" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:38+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "ar" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:42+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "ar" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:44+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "ar" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:47+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "ar" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:51+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "ar" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "ar" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:12:07+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "ar" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:17+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "ar" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:27+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "ar" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:34+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "ar" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:40+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "ar" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:51+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "ar" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:55+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "ar" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:13:05+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "ar" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:19+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "ar" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:27+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "ar" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:42+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "ar" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:14:09+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "ar" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:20+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "ar" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:51+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "ar" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:15:01+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "ar" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:06+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "ar" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:18+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "ar" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:28+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "ar" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:42+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "ar" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:52+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "ar" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:57+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "ar" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:59+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "ar" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:12+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "ar" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:43+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "ar" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:32+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "ar" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:41+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "ar" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:45+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "ar" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:50+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "ar" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:18:12+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "ar" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:18+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "ar" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:21+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "ar" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:36+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "ar" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:19:06+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "ar" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:20+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "ar" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:20:37+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "ar" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:21:25+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "ar" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:45+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "ar" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:22:00+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "ar" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:22+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "ar" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:23:23+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "ar" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:43+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "ar" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:49+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "ar" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:53+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "ar" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:24:02+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "ar" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:18+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "ar" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:26+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "ar" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:34+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "ar" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:43+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "ar" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:54+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "ar" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:25:01+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "ar" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:08+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "ar" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:12+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "ar" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:17+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "ar" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:21+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "ar" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:33+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "ar" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:38+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "ar" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:50+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "ar" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:55+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "ar" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:58+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "ar" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:03+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "ar" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:04+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "ar" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:06+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "ar" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:11+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "ar" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:21+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "ar" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:26:58+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "ar" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:18+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "ar" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:28+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "ar" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:44+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "ar" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:28:08+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "ar" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:30+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "ar" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:52+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "ar" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:29:06+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "ar" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:24+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "ar" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:43+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "ar" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:29:58+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "ar" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:29+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "ar" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:41+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "ar" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:53+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "ar" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:31:01+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "ar" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:15+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "ar" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:26+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "ar" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:38+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "ar" + } +} \ No newline at end of file diff --git a/translated_images/ar/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/ar/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/ar/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/ar/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/ar/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..9813a237 Binary files /dev/null and b/translated_images/ar/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/ar/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/ar/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..ab1d3407 Binary files /dev/null and b/translated_images/ar/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/ar/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/ar/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..aaec301d Binary files /dev/null and b/translated_images/ar/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/ar/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/ar/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..b5e61c35 Binary files /dev/null and b/translated_images/ar/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/ar/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/ar/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..502e52ae Binary files /dev/null and b/translated_images/ar/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/ar/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/ar/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/ar/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/ar/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/ar/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/ar/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/ar/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/ar/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/ar/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/ar/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/ar/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..1e3b21ee Binary files /dev/null and b/translated_images/ar/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/ar/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/ar/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..f57b8b9c Binary files /dev/null and b/translated_images/ar/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/ar/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/ar/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..a3e1c9b1 Binary files /dev/null and b/translated_images/ar/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/ar/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/ar/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..b9d88abe Binary files /dev/null and b/translated_images/ar/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/ar/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/ar/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..f2d6cc47 Binary files /dev/null and b/translated_images/ar/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/ar/NeuroArch.4e17cdba5e445721.webp b/translated_images/ar/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..45fa3c8e Binary files /dev/null and b/translated_images/ar/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/ar/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/ar/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..d2a58931 Binary files /dev/null and b/translated_images/ar/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/ar/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/ar/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..0f927905 Binary files /dev/null and b/translated_images/ar/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/ar/Overfitting.408ad91cd90b4371.webp b/translated_images/ar/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..85c4500f Binary files /dev/null and b/translated_images/ar/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/ar/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/ar/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..349016c2 Binary files /dev/null and b/translated_images/ar/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/ar/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/ar/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..e295dd77 Binary files /dev/null and b/translated_images/ar/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/ar/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/ar/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/ar/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/ar/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/ar/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/ar/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/ar/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/ar/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/ar/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/ar/aae.9d418a990fc75bf9.webp b/translated_images/ar/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..9c98d5e1 Binary files /dev/null and b/translated_images/ar/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/ar/activation-func.b4924007c7ce7764.webp b/translated_images/ar/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..de9ea423 Binary files /dev/null and b/translated_images/ar/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/ar/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/ar/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/ar/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/ar/ai-computervision.6506ebebac3fbf76.webp b/translated_images/ar/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..7c5f852c Binary files /dev/null and b/translated_images/ar/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/ar/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/ar/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..49788062 Binary files /dev/null and b/translated_images/ar/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/ar/ai-intro.bf28d1ac4235881c.webp b/translated_images/ar/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..cba2c64a Binary files /dev/null and b/translated_images/ar/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/ar/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/ar/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..dd99c347 Binary files /dev/null and b/translated_images/ar/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/ar/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/ar/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..192ffc77 Binary files /dev/null and b/translated_images/ar/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/ar/ai-overview.0857791951d19500.webp b/translated_images/ar/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..3c1171ad Binary files /dev/null and b/translated_images/ar/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/ar/ai-symbolic.715a30cb610411a6.webp b/translated_images/ar/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..1c6514ec Binary files /dev/null and b/translated_images/ar/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/ar/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/ar/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..1605b4ba Binary files /dev/null and b/translated_images/ar/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/ar/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/ar/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..c8701ba9 Binary files /dev/null and b/translated_images/ar/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/ar/artneuron.1a5daa88d20ebe6f.webp b/translated_images/ar/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..1ccce6d9 Binary files /dev/null and b/translated_images/ar/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/ar/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/ar/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..370342cd Binary files /dev/null and b/translated_images/ar/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..53815717 Binary files /dev/null and b/translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/ar/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/ar/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..3ee62209 Binary files /dev/null and b/translated_images/ar/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..41a0b9c7 Binary files /dev/null and b/translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/ar/bot-ner.4b09235dbb0ad275.webp b/translated_images/ar/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..470cdd65 Binary files /dev/null and b/translated_images/ar/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/ar/bow.3811869cff59368d.webp b/translated_images/ar/bow.3811869cff59368d.webp new file mode 100644 index 00000000..00f304a5 Binary files /dev/null and b/translated_images/ar/bow.3811869cff59368d.webp differ diff --git a/translated_images/ar/braille-result.46530fea020b03c7.webp b/translated_images/ar/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/ar/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/ar/braille-symbols.0159185ab69d5339.webp b/translated_images/ar/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..eb160ef3 Binary files /dev/null and b/translated_images/ar/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/ar/braille.341962ff76b1bd70.webp b/translated_images/ar/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/ar/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/ar/cartpole.f52a67f27e058170.webp b/translated_images/ar/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/ar/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/ar/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/ar/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..b67563f2 Binary files /dev/null and b/translated_images/ar/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/ar/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/ar/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..39daab2d Binary files /dev/null and b/translated_images/ar/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/ar/clip-class.3af42ef0b2b19369.webp b/translated_images/ar/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..dfc6dd46 Binary files /dev/null and b/translated_images/ar/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/ar/cnn-pyramid.85915455759ef0ce.webp b/translated_images/ar/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..49c0c239 Binary files /dev/null and b/translated_images/ar/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/ar/coco-examples.71bc60380fa6cceb.webp b/translated_images/ar/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..d236ba9d Binary files /dev/null and b/translated_images/ar/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/ar/convolutionExample.634615d5ff7081e0.webp b/translated_images/ar/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/ar/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/ar/data.50b2a9d5484bdbf0.webp b/translated_images/ar/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..5d2fa9ce Binary files /dev/null and b/translated_images/ar/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/ar/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/ar/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..27b76703 Binary files /dev/null and b/translated_images/ar/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/ar/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/ar/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/ar/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/ar/dsh_age.d212a30d4e54fb5f.webp b/translated_images/ar/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/ar/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..f9898336 Binary files /dev/null and b/translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..e3fde247 Binary files /dev/null and b/translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..da7e5e7b Binary files /dev/null and b/translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/ar/f-rcnn.3cda6d9bb4188875.webp b/translated_images/ar/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..351b92a7 Binary files /dev/null and b/translated_images/ar/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/ar/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/ar/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..77e9f540 Binary files /dev/null and b/translated_images/ar/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/ar/favicon.37b561214b36d454.webp b/translated_images/ar/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/ar/favicon.37b561214b36d454.webp differ diff --git a/translated_images/ar/features.6291f9c7ba3a0b95.webp b/translated_images/ar/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..1ce3e7de Binary files /dev/null and b/translated_images/ar/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/ar/filter-horiz.59b80ed4feb946ef.webp b/translated_images/ar/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..7b4a2f8d Binary files /dev/null and b/translated_images/ar/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/ar/filter-vert.b7148390ca0bc356.webp b/translated_images/ar/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..aebbcd8b Binary files /dev/null and b/translated_images/ar/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/ar/frame-difference.706f805491a0883c.webp b/translated_images/ar/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..864747f6 Binary files /dev/null and b/translated_images/ar/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/ar/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/ar/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..beeb59d2 Binary files /dev/null and b/translated_images/ar/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/ar/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/ar/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..e6f0ad19 Binary files /dev/null and b/translated_images/ar/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/ar/history-of-ai.7e83efa70b537f5a.webp b/translated_images/ar/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..5b72e702 Binary files /dev/null and b/translated_images/ar/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/ar/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/ar/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..e4c4aa24 Binary files /dev/null and b/translated_images/ar/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/ar/ideal-cat.203dd4597643d6b0.webp b/translated_images/ar/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/ar/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/ar/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/ar/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/ar/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/ar/image.896e8254a2c60d45.webp b/translated_images/ar/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/ar/image.896e8254a2c60d45.webp differ diff --git a/translated_images/ar/inception.a6605b85bcbc6f52.webp b/translated_images/ar/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..1a83883c Binary files /dev/null and b/translated_images/ar/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/ar/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/ar/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..c44808f8 Binary files /dev/null and b/translated_images/ar/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/ar/iou_equation.9a4751d40fff4e11.webp b/translated_images/ar/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..8a395c48 Binary files /dev/null and b/translated_images/ar/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..528c9cf9 Binary files /dev/null and b/translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/ar/knowledge-spectrum.b60df631852c0217.webp b/translated_images/ar/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..37a8c7df Binary files /dev/null and b/translated_images/ar/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/ar/lmfilters.ea9e4868a82cf74c.webp b/translated_images/ar/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/ar/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/ar/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/ar/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..7796578a Binary files /dev/null and b/translated_images/ar/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/ar/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/ar/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..43aa0797 Binary files /dev/null and b/translated_images/ar/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..74bd5118 Binary files /dev/null and b/translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/ar/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/ar/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/ar/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/ar/navi.2f20b727910110ea.webp b/translated_images/ar/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..3c19f246 Binary files /dev/null and b/translated_images/ar/navi.2f20b727910110ea.webp differ diff --git a/translated_images/ar/netout.1eb15eb76fd76731.webp b/translated_images/ar/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..df8d4451 Binary files /dev/null and b/translated_images/ar/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/ar/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/ar/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..5af2b3e5 Binary files /dev/null and b/translated_images/ar/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/ar/optical.1f4a94464579a83a.webp b/translated_images/ar/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/ar/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/ar/original-dog.8f68a67d2fe0911f.webp b/translated_images/ar/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/ar/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/ar/overfit.a0bd57f717c15769.webp b/translated_images/ar/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/ar/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/ar/overfit1.f24b71c6f652e59e.webp b/translated_images/ar/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..495f4c00 Binary files /dev/null and b/translated_images/ar/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/ar/overfit2.131f5800ae10ca5e.webp b/translated_images/ar/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..8dd690b9 Binary files /dev/null and b/translated_images/ar/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/ar/palm-movement.341495f0e9c47da3.webp b/translated_images/ar/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/ar/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/ar/photo-cat.8c8e8fb760ffe457.webp b/translated_images/ar/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/ar/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/ar/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/ar/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..3dfb239a Binary files /dev/null and b/translated_images/ar/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/ar/protege.274177ceeac13b38.webp b/translated_images/ar/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..e97b52fb Binary files /dev/null and b/translated_images/ar/protege.274177ceeac13b38.webp differ diff --git a/translated_images/ar/r-fcn.13eb88158b99a3da.webp b/translated_images/ar/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..c1cc5364 Binary files /dev/null and b/translated_images/ar/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/ar/rcnn1.cae407020dfb1d1f.webp b/translated_images/ar/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/ar/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/ar/rcnn2.2d9530bb83516484.webp b/translated_images/ar/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..cec91d02 Binary files /dev/null and b/translated_images/ar/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/ar/resnet-block.aba4ccbcc0944434.webp b/translated_images/ar/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..e94e914f Binary files /dev/null and b/translated_images/ar/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/ar/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/ar/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..f7462d50 Binary files /dev/null and b/translated_images/ar/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/ar/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/ar/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/ar/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/ar/rnn-generate.56c54afb52f9781d.webp b/translated_images/ar/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..deba5d0d Binary files /dev/null and b/translated_images/ar/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/ar/rnn.27f5c29c53d727b5.webp b/translated_images/ar/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..35a7bef1 Binary files /dev/null and b/translated_images/ar/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/ar/segm.92442f2cb42ff4fa.webp b/translated_images/ar/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..9a6baee4 Binary files /dev/null and b/translated_images/ar/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/ar/segnet.87377542aa5a8b76.webp b/translated_images/ar/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..c4d3b17d Binary files /dev/null and b/translated_images/ar/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/ar/style.5293e85e077ab22c.webp b/translated_images/ar/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/ar/style.5293e85e077ab22c.webp differ diff --git a/translated_images/ar/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/ar/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..cc61f2d2 Binary files /dev/null and b/translated_images/ar/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/ar/transformer-layer.905e14747ca4e7d5.webp b/translated_images/ar/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..dcbd89c8 Binary files /dev/null and b/translated_images/ar/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/ar/triplet-complex.32094972c7b4441b.webp b/translated_images/ar/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..eef90fe1 Binary files /dev/null and b/translated_images/ar/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/ar/triplet.4b9b332587593298.webp b/translated_images/ar/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..8239c4b0 Binary files /dev/null and b/translated_images/ar/triplet.4b9b332587593298.webp differ diff --git a/translated_images/ar/turing-test-evol.4184696701293ead.webp b/translated_images/ar/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..d4f18b8d Binary files /dev/null and b/translated_images/ar/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/ar/unet.3adb555bf39d3657.webp b/translated_images/ar/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..60b0d94c Binary files /dev/null and b/translated_images/ar/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/ar/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/ar/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..711fb20e Binary files /dev/null and b/translated_images/ar/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/ar/vae.464c465a5b6a9e25.webp b/translated_images/ar/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..c99f583f Binary files /dev/null and b/translated_images/ar/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/ar/vaemnist-diag.694315f775d5d666.webp b/translated_images/ar/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/ar/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/ar/vaemnist.cab9e602dc08dc50.webp b/translated_images/ar/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..fcbab5f3 Binary files /dev/null and b/translated_images/ar/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/ar/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/ar/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..294b37a3 Binary files /dev/null and b/translated_images/ar/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/ar/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/ar/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..1eb1e3f9 Binary files /dev/null and b/translated_images/ar/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/ar/vqgan.5027fe05051dfa31.webp b/translated_images/ar/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..624de8f7 Binary files /dev/null and b/translated_images/ar/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/ar/yolo.a2648ec82ee8bb4e.webp b/translated_images/ar/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..db53099a Binary files /dev/null and b/translated_images/ar/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.ar.png b/translated_images/arch-human.5d4d35f1bba3ab1c.ar.png deleted file mode 100644 index 8a4d1ab1..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.ar.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.de.png b/translated_images/arch-human.5d4d35f1bba3ab1c.de.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.de.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.es.png b/translated_images/arch-human.5d4d35f1bba3ab1c.es.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.es.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.fa.png b/translated_images/arch-human.5d4d35f1bba3ab1c.fa.png deleted file mode 100644 index 3a9e0977..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.fa.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.fr.png b/translated_images/arch-human.5d4d35f1bba3ab1c.fr.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.fr.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1c.ru.png b/translated_images/arch-human.5d4d35f1bba3ab1c.ru.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1c.ru.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ar.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ar.png deleted file mode 100644 index 8a4d1ab1..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ar.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.de.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.de.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.de.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.es.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.es.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.es.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fa.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fa.png deleted file mode 100644 index 3a9e0977..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fa.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fr.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fr.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.fr.png and /dev/null differ diff --git a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ru.png b/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ru.png deleted file mode 100644 index b1b6a8d7..00000000 Binary files a/translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ru.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.ar.png b/translated_images/arch-kbs.3ec5c150b09fa8da.ar.png deleted file mode 100644 index dc063585..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.ar.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.de.png b/translated_images/arch-kbs.3ec5c150b09fa8da.de.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.de.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.es.png b/translated_images/arch-kbs.3ec5c150b09fa8da.es.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.es.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.fa.png b/translated_images/arch-kbs.3ec5c150b09fa8da.fa.png deleted file mode 100644 index 4dfb5577..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.fa.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.fr.png b/translated_images/arch-kbs.3ec5c150b09fa8da.fr.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.fr.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8da.ru.png b/translated_images/arch-kbs.3ec5c150b09fa8da.ru.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8da.ru.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ar.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ar.png deleted file mode 100644 index dc063585..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ar.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.de.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.de.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.de.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.es.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.es.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.es.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fa.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fa.png deleted file mode 100644 index 4dfb5577..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fa.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fr.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fr.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.fr.png and /dev/null differ diff --git a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ru.png b/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ru.png deleted file mode 100644 index 71d63ff5..00000000 Binary files a/translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ru.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.ar.png b/translated_images/artneuron.1a5daa88d20ebe6f.ar.png deleted file mode 100644 index 5a4c89d5..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.ar.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.de.png b/translated_images/artneuron.1a5daa88d20ebe6f.de.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.de.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.es.png b/translated_images/artneuron.1a5daa88d20ebe6f.es.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.es.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.fa.png b/translated_images/artneuron.1a5daa88d20ebe6f.fa.png deleted file mode 100644 index 4cd8b110..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.fa.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.fr.png b/translated_images/artneuron.1a5daa88d20ebe6f.fr.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.fr.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f.ru.png b/translated_images/artneuron.1a5daa88d20ebe6f.ru.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f.ru.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ar.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ar.png deleted file mode 100644 index 5a4c89d5..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ar.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.de.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.de.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.de.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.es.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.es.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.es.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fa.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fa.png deleted file mode 100644 index 4cd8b110..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fa.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fr.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fr.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.fr.png and /dev/null differ diff --git a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ru.png b/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ru.png deleted file mode 100644 index bc515ea3..00000000 Binary files a/translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ru.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ar.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ar.png deleted file mode 100644 index 2f1fb499..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ar.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.de.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.de.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.de.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.es.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.es.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.es.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fa.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fa.png deleted file mode 100644 index 8d5601bb..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fa.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fr.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fr.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.fr.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ru.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ru.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ff.ru.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ar.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ar.png deleted file mode 100644 index 2f1fb499..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ar.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.de.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.de.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.de.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.es.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.es.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.es.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fa.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fa.png deleted file mode 100644 index 8d5601bb..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fa.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fr.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fr.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.fr.png and /dev/null differ diff --git a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ru.png b/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ru.png deleted file mode 100644 index 17e68933..00000000 Binary files a/translated_images/ascii-character-map.18ed6aa7f3b0a7ffeb29db95be05a245b6a20712bb2c3a9963c1ec7e9df70358.ru.png and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg deleted file mode 100644 index 2e144d49..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.de.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.de.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.de.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.es.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.es.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.es.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fa.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fa.jpg deleted file mode 100644 index 29423d52..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fa.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fr.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fr.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fr.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ar.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ar.jpg deleted file mode 100644 index 2e144d49..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ar.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.de.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.de.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.de.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.es.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.es.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.es.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fa.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fa.jpg deleted file mode 100644 index 29423d52..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fa.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fr.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fr.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.fr.jpg and /dev/null differ diff --git a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ru.jpg b/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ru.jpg deleted file mode 100644 index 91cef019..00000000 Binary files a/translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ru.jpg and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.ar.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.ar.png deleted file mode 100644 index 624f1085..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.ar.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.de.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.de.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.de.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.es.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.es.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.es.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.fa.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.fa.png deleted file mode 100644 index 2ad46bb1..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.fa.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.fr.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.fr.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.fr.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba9.ru.png b/translated_images/bag-of-words-example.606fc1738f1d7ba9.ru.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba9.ru.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ar.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ar.png deleted file mode 100644 index 624f1085..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ar.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.de.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.de.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.de.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.es.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.es.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.es.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fa.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fa.png deleted file mode 100644 index 2ad46bb1..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fa.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fr.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fr.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.fr.png and /dev/null differ diff --git a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ru.png b/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ru.png deleted file mode 100644 index df73f38d..00000000 Binary files a/translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.ru.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png deleted file mode 100644 index b634d7f2..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.de.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.de.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.de.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.es.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.es.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.es.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.fa.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.fa.png deleted file mode 100644 index 6554488f..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.fa.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.fr.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.fr.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.fr.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ar.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ar.png deleted file mode 100644 index b634d7f2..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ar.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.de.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.de.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.de.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.es.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.es.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.es.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fa.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fa.png deleted file mode 100644 index 6554488f..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fa.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fr.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fr.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.fr.png and /dev/null differ diff --git a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ru.png b/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ru.png deleted file mode 100644 index 729e59e0..00000000 Binary files a/translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.ru.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.ar.png b/translated_images/bot-ner.4b09235dbb0ad275.ar.png deleted file mode 100644 index d2d3d9ed..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.ar.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.de.png b/translated_images/bot-ner.4b09235dbb0ad275.de.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.de.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.es.png b/translated_images/bot-ner.4b09235dbb0ad275.es.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.es.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.fa.png b/translated_images/bot-ner.4b09235dbb0ad275.fa.png deleted file mode 100644 index daf52e94..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.fa.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.fr.png b/translated_images/bot-ner.4b09235dbb0ad275.fr.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.fr.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad275.ru.png b/translated_images/bot-ner.4b09235dbb0ad275.ru.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad275.ru.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ar.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ar.png deleted file mode 100644 index d2d3d9ed..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ar.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.de.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.de.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.de.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.es.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.es.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.es.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fa.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fa.png deleted file mode 100644 index daf52e94..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fa.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fr.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fr.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.fr.png and /dev/null differ diff --git a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ru.png b/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ru.png deleted file mode 100644 index abbf4141..00000000 Binary files a/translated_images/bot-ner.4b09235dbb0ad2754ec1f54c8c797f902cbb0b45ac90b0cfc8287343cef8df2f.ru.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.ar.png b/translated_images/bow.3811869cff59368d.ar.png deleted file mode 100644 index 4f30493c..00000000 Binary files a/translated_images/bow.3811869cff59368d.ar.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.de.png b/translated_images/bow.3811869cff59368d.de.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d.de.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.es.png b/translated_images/bow.3811869cff59368d.es.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d.es.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.fa.png b/translated_images/bow.3811869cff59368d.fa.png deleted file mode 100644 index a1438da4..00000000 Binary files a/translated_images/bow.3811869cff59368d.fa.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.fr.png b/translated_images/bow.3811869cff59368d.fr.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d.fr.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d.ru.png b/translated_images/bow.3811869cff59368d.ru.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d.ru.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ar.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ar.png deleted file mode 100644 index 4f30493c..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ar.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.de.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.de.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.de.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.es.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.es.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.es.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fa.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fa.png deleted file mode 100644 index a1438da4..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fa.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fr.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fr.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.fr.png and /dev/null differ diff --git a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ru.png b/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ru.png deleted file mode 100644 index 8969ff98..00000000 Binary files a/translated_images/bow.3811869cff59368d951c7a765ed20ebeaf7d10680eb602ea7c5312fb22f7b7ad.ru.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.ar.png b/translated_images/braille-result.46530fea020b03c7.ar.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.ar.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.de.png b/translated_images/braille-result.46530fea020b03c7.de.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.de.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.es.png b/translated_images/braille-result.46530fea020b03c7.es.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.es.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.fa.png b/translated_images/braille-result.46530fea020b03c7.fa.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.fa.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.fr.png b/translated_images/braille-result.46530fea020b03c7.fr.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.fr.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c7.ru.png b/translated_images/braille-result.46530fea020b03c7.ru.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c7.ru.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ar.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ar.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ar.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.de.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.de.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.de.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.es.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.es.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.es.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fa.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fa.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fa.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fr.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fr.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.fr.png and /dev/null differ diff --git a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ru.png b/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ru.png deleted file mode 100644 index 82fa8e43..00000000 Binary files a/translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ru.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.ar.png b/translated_images/braille-symbols.0159185ab69d5339.ar.png deleted file mode 100644 index 36dc7b2a..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.ar.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.de.png b/translated_images/braille-symbols.0159185ab69d5339.de.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.de.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.es.png b/translated_images/braille-symbols.0159185ab69d5339.es.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.es.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.fa.png b/translated_images/braille-symbols.0159185ab69d5339.fa.png deleted file mode 100644 index f1f372ea..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.fa.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.fr.png b/translated_images/braille-symbols.0159185ab69d5339.fr.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.fr.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d5339.ru.png b/translated_images/braille-symbols.0159185ab69d5339.ru.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d5339.ru.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ar.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ar.png deleted file mode 100644 index 36dc7b2a..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ar.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.de.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.de.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.de.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.es.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.es.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.es.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fa.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fa.png deleted file mode 100644 index f1f372ea..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fa.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fr.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fr.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.fr.png and /dev/null differ diff --git a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ru.png b/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ru.png deleted file mode 100644 index dbf3f5af..00000000 Binary files a/translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ru.png and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.ar.jpeg b/translated_images/braille.341962ff76b1bd70.ar.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.ar.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.de.jpeg b/translated_images/braille.341962ff76b1bd70.de.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.de.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.es.jpeg b/translated_images/braille.341962ff76b1bd70.es.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.es.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.fa.jpeg b/translated_images/braille.341962ff76b1bd70.fa.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.fa.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.fr.jpeg b/translated_images/braille.341962ff76b1bd70.fr.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.fr.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd70.ru.jpeg b/translated_images/braille.341962ff76b1bd70.ru.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd70.ru.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ar.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ar.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ar.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.de.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.de.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.de.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.es.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.es.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.es.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fa.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fa.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fa.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fr.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fr.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.fr.jpeg and /dev/null differ diff --git a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ru.jpeg b/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ru.jpeg deleted file mode 100644 index 5d3ee5e4..00000000 Binary files a/translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ru.jpeg and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.ar.png b/translated_images/cartpole.f52a67f27e058170.ar.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.ar.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.de.png b/translated_images/cartpole.f52a67f27e058170.de.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.de.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.es.png b/translated_images/cartpole.f52a67f27e058170.es.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.es.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.fa.png b/translated_images/cartpole.f52a67f27e058170.fa.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.fa.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.fr.png b/translated_images/cartpole.f52a67f27e058170.fr.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.fr.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170.ru.png b/translated_images/cartpole.f52a67f27e058170.ru.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170.ru.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ar.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ar.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ar.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.de.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.de.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.de.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.es.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.es.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.es.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fa.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fa.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fa.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fr.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fr.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.fr.png and /dev/null differ diff --git a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ru.png b/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ru.png deleted file mode 100644 index 76b66c47..00000000 Binary files a/translated_images/cartpole.f52a67f27e058170c25efc1bca8375b60906570ea757fe8d7ef04ae8e53df29d.ru.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ar.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ar.png deleted file mode 100644 index 76f325ae..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ar.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.de.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.de.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.de.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.es.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.es.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.es.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fa.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fa.png deleted file mode 100644 index 6c85bbc3..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fa.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fr.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fr.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.fr.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ru.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ru.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f0.ru.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ar.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ar.png deleted file mode 100644 index 76f325ae..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ar.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.de.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.de.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.de.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.es.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.es.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.es.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fa.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fa.png deleted file mode 100644 index 6c85bbc3..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fa.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fr.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fr.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.fr.png and /dev/null differ diff --git a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ru.png b/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ru.png deleted file mode 100644 index b64c223f..00000000 Binary files a/translated_images/catsdogsdataset.a7aff8e6085fd3f062a882179ac5e5a9e652f82ca35c5f5a94f96f526da34498.ru.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.ar.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.ar.png deleted file mode 100644 index ccd726f6..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.ar.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.de.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.de.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.de.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.es.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.es.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.es.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.fa.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.fa.png deleted file mode 100644 index a8175245..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.fa.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.fr.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.fr.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.fr.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be.ru.png b/translated_images/clip-arch.b3dbf20b4e8ed8be.ru.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be.ru.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ar.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ar.png deleted file mode 100644 index ccd726f6..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ar.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.de.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.de.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.de.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.es.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.es.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.es.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fa.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fa.png deleted file mode 100644 index a8175245..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fa.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fr.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fr.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.fr.png and /dev/null differ diff --git a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ru.png b/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ru.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.ru.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.ar.png b/translated_images/clip-class.3af42ef0b2b19369.ar.png deleted file mode 100644 index 18e1b77f..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.ar.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.de.png b/translated_images/clip-class.3af42ef0b2b19369.de.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.de.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.es.png b/translated_images/clip-class.3af42ef0b2b19369.es.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.es.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.fa.png b/translated_images/clip-class.3af42ef0b2b19369.fa.png deleted file mode 100644 index 1cc89b55..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.fa.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.fr.png b/translated_images/clip-class.3af42ef0b2b19369.fr.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.fr.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369.ru.png b/translated_images/clip-class.3af42ef0b2b19369.ru.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369.ru.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ar.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ar.png deleted file mode 100644 index 18e1b77f..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ar.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.de.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.de.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.de.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.es.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.es.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.es.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fa.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fa.png deleted file mode 100644 index 1cc89b55..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fa.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fr.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fr.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.fr.png and /dev/null differ diff --git a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ru.png b/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ru.png deleted file mode 100644 index fba8ec26..00000000 Binary files a/translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.ru.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.ar.png b/translated_images/cnn-pyramid.85915455759ef0ce.ar.png deleted file mode 100644 index b08b93ce..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.ar.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.de.png b/translated_images/cnn-pyramid.85915455759ef0ce.de.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.de.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.es.png b/translated_images/cnn-pyramid.85915455759ef0ce.es.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.es.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.fa.png b/translated_images/cnn-pyramid.85915455759ef0ce.fa.png deleted file mode 100644 index 4665453c..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.fa.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.fr.png b/translated_images/cnn-pyramid.85915455759ef0ce.fr.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.fr.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce.ru.png b/translated_images/cnn-pyramid.85915455759ef0ce.ru.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce.ru.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ar.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ar.png deleted file mode 100644 index b08b93ce..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ar.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.de.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.de.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.de.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.es.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.es.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.es.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fa.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fa.png deleted file mode 100644 index 4665453c..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fa.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fr.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fr.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.fr.png and /dev/null differ diff --git a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ru.png b/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ru.png deleted file mode 100644 index dfac1513..00000000 Binary files a/translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.ru.png and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.ar.jpg b/translated_images/coco-examples.71bc60380fa6cceb.ar.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.ar.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.de.jpg b/translated_images/coco-examples.71bc60380fa6cceb.de.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.de.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.es.jpg b/translated_images/coco-examples.71bc60380fa6cceb.es.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.es.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.fa.jpg b/translated_images/coco-examples.71bc60380fa6cceb.fa.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.fa.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.fr.jpg b/translated_images/coco-examples.71bc60380fa6cceb.fr.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.fr.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb.ru.jpg b/translated_images/coco-examples.71bc60380fa6cceb.ru.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb.ru.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ar.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ar.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ar.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.de.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.de.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.de.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.es.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.es.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.es.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fa.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fa.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fa.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fr.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fr.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.fr.jpg and /dev/null differ diff --git a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ru.jpg b/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ru.jpg deleted file mode 100644 index 87e2c770..00000000 Binary files a/translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ru.jpg and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.ar.png b/translated_images/convolutionExample.634615d5ff7081e0.ar.png deleted file mode 100644 index daaaecab..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.ar.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.de.png b/translated_images/convolutionExample.634615d5ff7081e0.de.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.de.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.es.png b/translated_images/convolutionExample.634615d5ff7081e0.es.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.es.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.fa.png b/translated_images/convolutionExample.634615d5ff7081e0.fa.png deleted file mode 100644 index ad54c9fe..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.fa.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.fr.png b/translated_images/convolutionExample.634615d5ff7081e0.fr.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.fr.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0.ru.png b/translated_images/convolutionExample.634615d5ff7081e0.ru.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0.ru.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ar.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ar.png deleted file mode 100644 index daaaecab..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ar.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.de.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.de.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.de.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.es.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.es.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.es.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fa.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fa.png deleted file mode 100644 index ad54c9fe..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fa.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fr.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fr.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.fr.png and /dev/null differ diff --git a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ru.png b/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ru.png deleted file mode 100644 index 3f306d46..00000000 Binary files a/translated_images/convolutionExample.634615d5ff7081e0b73416b9e398f9eade32856616386fdc0cf412ce898cc192.ru.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.ar.png b/translated_images/data.50b2a9d5484bdbf0.ar.png deleted file mode 100644 index 6562e7e9..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.ar.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.de.png b/translated_images/data.50b2a9d5484bdbf0.de.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.de.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.es.png b/translated_images/data.50b2a9d5484bdbf0.es.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.es.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.fa.png b/translated_images/data.50b2a9d5484bdbf0.fa.png deleted file mode 100644 index 0fd6a645..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.fa.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.fr.png b/translated_images/data.50b2a9d5484bdbf0.fr.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.fr.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0.ru.png b/translated_images/data.50b2a9d5484bdbf0.ru.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0.ru.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ar.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ar.png deleted file mode 100644 index 6562e7e9..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ar.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.de.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.de.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.de.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.es.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.es.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.es.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fa.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fa.png deleted file mode 100644 index 0fd6a645..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fa.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fr.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fr.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.fr.png and /dev/null differ diff --git a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ru.png b/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ru.png deleted file mode 100644 index adc9b475..00000000 Binary files a/translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ru.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.ar.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.ar.png deleted file mode 100644 index 73630508..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.ar.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.de.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.de.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.de.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.es.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.es.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.es.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.fa.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.fa.png deleted file mode 100644 index f11b96c3..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.fa.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.fr.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.fr.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.fr.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5.ru.png b/translated_images/dcgan_generator.b500988ec2bc8ba5.ru.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5.ru.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ar.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ar.png deleted file mode 100644 index 73630508..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ar.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.de.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.de.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.de.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.es.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.es.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.es.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fa.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fa.png deleted file mode 100644 index f11b96c3..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fa.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fr.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fr.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.fr.png and /dev/null differ diff --git a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ru.png b/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ru.png deleted file mode 100644 index 13064830..00000000 Binary files a/translated_images/dcgan_generator.b500988ec2bc8ba5cb38dae855fece348933171227c0536743b9c3b599f33f3d.ru.png and /dev/null differ diff --git a/translated_images/de/.co-op-translator.json b/translated_images/de/.co-op-translator.json new file mode 100644 index 00000000..ffc90e50 --- /dev/null +++ b/translated_images/de/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:04+00:00", + "source_file": "images/favicon.png", + "language_code": "de" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:22+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "de" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:05:07+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "de" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:05:56+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "de" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:24+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "de" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:06:48+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "de" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:27+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "de" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:08:10+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "de" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:36+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "de" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:42+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "de" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:46+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "de" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:53+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "de" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:09:01+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "de" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:10+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "de" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:27+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "de" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:42+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "de" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:51+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "de" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:03+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "de" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:15+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "de" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:24+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "de" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:27+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "de" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:38+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "de" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:10:55+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "de" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "de" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:08+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "de" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:15+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "de" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:19+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "de" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "de" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:23+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "de" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:27+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "de" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "de" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:32+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "de" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:36+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "de" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:41+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "de" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:44+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "de" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:46+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "de" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:50+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "de" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:53+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "de" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:12:02+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "de" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:14+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "de" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:24+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "de" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:31+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "de" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:38+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "de" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:47+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "de" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "de" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:13:00+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "de" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:14+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "de" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:24+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "de" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:36+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "de" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:14:00+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "de" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:18+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "de" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:39+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "de" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:14:59+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "de" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:04+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "de" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:13+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "de" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:25+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "de" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:37+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "de" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:49+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "de" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:56+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "de" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "de" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:06+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "de" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:31+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "de" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:17+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "de" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:41+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "de" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:44+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "de" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:48+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "de" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:18:02+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "de" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:18+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "de" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:20+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "de" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:30+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "de" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:18:56+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "de" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:17+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "de" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:20:07+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "de" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:21:12+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "de" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:40+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "de" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:21:55+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "de" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:14+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "de" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:22:59+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "de" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:39+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "de" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:48+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "de" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:52+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "de" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:23:59+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "de" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:12+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "de" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:24+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "de" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:31+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "de" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:40+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "de" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:50+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "de" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:24:59+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "de" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:05+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "de" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:11+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "de" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:15+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "de" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:20+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "de" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:28+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "de" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:37+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "de" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:46+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "de" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:54+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "de" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:57+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "de" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:02+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "de" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:04+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "de" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:05+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "de" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:09+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "de" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:18+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "de" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:26:44+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "de" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:13+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "de" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:25+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "de" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:38+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "de" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:27:59+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "de" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:22+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "de" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:45+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "de" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:29:01+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "de" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:18+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "de" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:37+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "de" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:29:53+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "de" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:17+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "de" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:39+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "de" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:49+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "de" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:30:59+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "de" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:09+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "de" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:23+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "de" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:34+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "de" + } +} \ No newline at end of file diff --git a/translated_images/de/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/de/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/de/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/de/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/de/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..ac038473 Binary files /dev/null and b/translated_images/de/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/de/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/de/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..0b2cbe3e Binary files /dev/null and b/translated_images/de/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/de/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/de/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..2032959d Binary files /dev/null and b/translated_images/de/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/de/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/de/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..2cb9a607 Binary files /dev/null and b/translated_images/de/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/de/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/de/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..1ec39b01 Binary files /dev/null and b/translated_images/de/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/de/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/de/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/de/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/de/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/de/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/de/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/de/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/de/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/de/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/de/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/de/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..9cf68e98 Binary files /dev/null and b/translated_images/de/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/de/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/de/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..695f7191 Binary files /dev/null and b/translated_images/de/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/de/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/de/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..46489154 Binary files /dev/null and b/translated_images/de/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/de/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/de/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..be56bd4d Binary files /dev/null and b/translated_images/de/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/de/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/de/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..e05c5388 Binary files /dev/null and b/translated_images/de/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/de/NeuroArch.4e17cdba5e445721.webp b/translated_images/de/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..df625d05 Binary files /dev/null and b/translated_images/de/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/de/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/de/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..b2bd5872 Binary files /dev/null and b/translated_images/de/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/de/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/de/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..0c3d172b Binary files /dev/null and b/translated_images/de/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/de/Overfitting.408ad91cd90b4371.webp b/translated_images/de/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..a0df0968 Binary files /dev/null and b/translated_images/de/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/de/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/de/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..47d35f0f Binary files /dev/null and b/translated_images/de/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/de/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/de/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..e5b3ba34 Binary files /dev/null and b/translated_images/de/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/de/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/de/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/de/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/de/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/de/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/de/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/de/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/de/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/de/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/de/aae.9d418a990fc75bf9.webp b/translated_images/de/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..5699d020 Binary files /dev/null and b/translated_images/de/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/de/activation-func.b4924007c7ce7764.webp b/translated_images/de/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..8138807f Binary files /dev/null and b/translated_images/de/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/de/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/de/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/de/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/de/ai-computervision.6506ebebac3fbf76.webp b/translated_images/de/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..dfdbee10 Binary files /dev/null and b/translated_images/de/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/de/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/de/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..012036a5 Binary files /dev/null and b/translated_images/de/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/de/ai-intro.bf28d1ac4235881c.webp b/translated_images/de/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..5a191761 Binary files /dev/null and b/translated_images/de/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/de/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/de/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..1ecb00f6 Binary files /dev/null and b/translated_images/de/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/de/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/de/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..e90b1e4c Binary files /dev/null and b/translated_images/de/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/de/ai-overview.0857791951d19500.webp b/translated_images/de/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..a0f0b41e Binary files /dev/null and b/translated_images/de/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/de/ai-symbolic.715a30cb610411a6.webp b/translated_images/de/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..a1106ec3 Binary files /dev/null and b/translated_images/de/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/de/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/de/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..bfbc319e Binary files /dev/null and b/translated_images/de/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/de/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/de/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..f1e66f79 Binary files /dev/null and b/translated_images/de/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/de/artneuron.1a5daa88d20ebe6f.webp b/translated_images/de/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..9e300afa Binary files /dev/null and b/translated_images/de/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/de/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/de/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..a8c09768 Binary files /dev/null and b/translated_images/de/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/de/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/de/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..00450b3e Binary files /dev/null and b/translated_images/de/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/de/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/de/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..654dd782 Binary files /dev/null and b/translated_images/de/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/de/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/de/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..1969e068 Binary files /dev/null and b/translated_images/de/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/de/bot-ner.4b09235dbb0ad275.webp b/translated_images/de/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..d1e9907a Binary files /dev/null and b/translated_images/de/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/de/bow.3811869cff59368d.webp b/translated_images/de/bow.3811869cff59368d.webp new file mode 100644 index 00000000..ee8b1447 Binary files /dev/null and b/translated_images/de/bow.3811869cff59368d.webp differ diff --git a/translated_images/de/braille-result.46530fea020b03c7.webp b/translated_images/de/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/de/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/de/braille-symbols.0159185ab69d5339.webp b/translated_images/de/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..c39ac558 Binary files /dev/null and b/translated_images/de/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/de/braille.341962ff76b1bd70.webp b/translated_images/de/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/de/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/de/cartpole.f52a67f27e058170.webp b/translated_images/de/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/de/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/de/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/de/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..df16aa63 Binary files /dev/null and b/translated_images/de/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/de/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/de/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..165116ce Binary files /dev/null and b/translated_images/de/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/de/clip-class.3af42ef0b2b19369.webp b/translated_images/de/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..0ec30531 Binary files /dev/null and b/translated_images/de/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/de/cnn-pyramid.85915455759ef0ce.webp b/translated_images/de/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..5054cdad Binary files /dev/null and b/translated_images/de/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/de/coco-examples.71bc60380fa6cceb.webp b/translated_images/de/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..00f789f9 Binary files /dev/null and b/translated_images/de/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/de/convolutionExample.634615d5ff7081e0.webp b/translated_images/de/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/de/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/de/data.50b2a9d5484bdbf0.webp b/translated_images/de/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..14031e91 Binary files /dev/null and b/translated_images/de/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/de/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/de/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..0efb4889 Binary files /dev/null and b/translated_images/de/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/de/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/de/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/de/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/de/dsh_age.d212a30d4e54fb5f.webp b/translated_images/de/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/de/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/de/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/de/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..edb160de Binary files /dev/null and b/translated_images/de/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/de/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/de/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..570dfd10 Binary files /dev/null and b/translated_images/de/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/de/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/de/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..368ccd2e Binary files /dev/null and b/translated_images/de/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/de/f-rcnn.3cda6d9bb4188875.webp b/translated_images/de/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..45187d75 Binary files /dev/null and b/translated_images/de/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/de/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/de/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..4fdd314a Binary files /dev/null and b/translated_images/de/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/de/favicon.37b561214b36d454.webp b/translated_images/de/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/de/favicon.37b561214b36d454.webp differ diff --git a/translated_images/de/features.6291f9c7ba3a0b95.webp b/translated_images/de/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..03679af2 Binary files /dev/null and b/translated_images/de/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/de/filter-horiz.59b80ed4feb946ef.webp b/translated_images/de/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..143c9e79 Binary files /dev/null and b/translated_images/de/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/de/filter-vert.b7148390ca0bc356.webp b/translated_images/de/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..562c3e00 Binary files /dev/null and b/translated_images/de/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/de/frame-difference.706f805491a0883c.webp b/translated_images/de/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..7498e40c Binary files /dev/null and b/translated_images/de/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/de/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/de/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..c30fd501 Binary files /dev/null and b/translated_images/de/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/de/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/de/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..3710de09 Binary files /dev/null and b/translated_images/de/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/de/history-of-ai.7e83efa70b537f5a.webp b/translated_images/de/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..ceee74c7 Binary files /dev/null and b/translated_images/de/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/de/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/de/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..89f5667a Binary files /dev/null and b/translated_images/de/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/de/ideal-cat.203dd4597643d6b0.webp b/translated_images/de/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/de/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/de/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/de/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/de/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/de/image.896e8254a2c60d45.webp b/translated_images/de/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/de/image.896e8254a2c60d45.webp differ diff --git a/translated_images/de/inception.a6605b85bcbc6f52.webp b/translated_images/de/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..dc3ef75e Binary files /dev/null and b/translated_images/de/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/de/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/de/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..0118c218 Binary files /dev/null and b/translated_images/de/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/de/iou_equation.9a4751d40fff4e11.webp b/translated_images/de/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..12597107 Binary files /dev/null and b/translated_images/de/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/de/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/de/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..1f86d52d Binary files /dev/null and b/translated_images/de/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/de/knowledge-spectrum.b60df631852c0217.webp b/translated_images/de/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..922f7baf Binary files /dev/null and b/translated_images/de/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/de/lmfilters.ea9e4868a82cf74c.webp b/translated_images/de/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/de/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/de/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/de/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..57d39b93 Binary files /dev/null and b/translated_images/de/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/de/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/de/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..8773c19e Binary files /dev/null and b/translated_images/de/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/de/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/de/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..ef7337db Binary files /dev/null and b/translated_images/de/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/de/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/de/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/de/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/de/navi.2f20b727910110ea.webp b/translated_images/de/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..e9254c25 Binary files /dev/null and b/translated_images/de/navi.2f20b727910110ea.webp differ diff --git a/translated_images/de/netout.1eb15eb76fd76731.webp b/translated_images/de/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..dca51483 Binary files /dev/null and b/translated_images/de/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/de/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/de/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..b3b7b2e7 Binary files /dev/null and b/translated_images/de/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/de/optical.1f4a94464579a83a.webp b/translated_images/de/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/de/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/de/original-dog.8f68a67d2fe0911f.webp b/translated_images/de/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/de/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/de/overfit.a0bd57f717c15769.webp b/translated_images/de/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/de/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/de/overfit1.f24b71c6f652e59e.webp b/translated_images/de/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..de604b48 Binary files /dev/null and b/translated_images/de/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/de/overfit2.131f5800ae10ca5e.webp b/translated_images/de/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..8dd690b9 Binary files /dev/null and b/translated_images/de/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/de/palm-movement.341495f0e9c47da3.webp b/translated_images/de/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/de/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/de/photo-cat.8c8e8fb760ffe457.webp b/translated_images/de/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/de/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/de/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/de/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..10eb00bf Binary files /dev/null and b/translated_images/de/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/de/protege.274177ceeac13b38.webp b/translated_images/de/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..d8357bb4 Binary files /dev/null and b/translated_images/de/protege.274177ceeac13b38.webp differ diff --git a/translated_images/de/r-fcn.13eb88158b99a3da.webp b/translated_images/de/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..80d60e50 Binary files /dev/null and b/translated_images/de/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/de/rcnn1.cae407020dfb1d1f.webp b/translated_images/de/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/de/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/de/rcnn2.2d9530bb83516484.webp b/translated_images/de/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..5559976c Binary files /dev/null and b/translated_images/de/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/de/resnet-block.aba4ccbcc0944434.webp b/translated_images/de/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..7488045a Binary files /dev/null and b/translated_images/de/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/de/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/de/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..283c5ff1 Binary files /dev/null and b/translated_images/de/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/de/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/de/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/de/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/de/rnn-generate.56c54afb52f9781d.webp b/translated_images/de/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..b593742f Binary files /dev/null and b/translated_images/de/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/de/rnn.27f5c29c53d727b5.webp b/translated_images/de/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..36ff1b34 Binary files /dev/null and b/translated_images/de/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/de/segm.92442f2cb42ff4fa.webp b/translated_images/de/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..a30f266d Binary files /dev/null and b/translated_images/de/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/de/segnet.87377542aa5a8b76.webp b/translated_images/de/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..c7d28a2a Binary files /dev/null and b/translated_images/de/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/de/style.5293e85e077ab22c.webp b/translated_images/de/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/de/style.5293e85e077ab22c.webp differ diff --git a/translated_images/de/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/de/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..24687ae0 Binary files /dev/null and b/translated_images/de/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/de/transformer-layer.905e14747ca4e7d5.webp b/translated_images/de/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..4e6b8eef Binary files /dev/null and b/translated_images/de/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/de/triplet-complex.32094972c7b4441b.webp b/translated_images/de/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..535b25b3 Binary files /dev/null and b/translated_images/de/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/de/triplet.4b9b332587593298.webp b/translated_images/de/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..5267c8f6 Binary files /dev/null and b/translated_images/de/triplet.4b9b332587593298.webp differ diff --git a/translated_images/de/turing-test-evol.4184696701293ead.webp b/translated_images/de/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..05f0b72e Binary files /dev/null and b/translated_images/de/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/de/unet.3adb555bf39d3657.webp b/translated_images/de/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..6d6489ff Binary files /dev/null and b/translated_images/de/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/de/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/de/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..a72007b9 Binary files /dev/null and b/translated_images/de/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/de/vae.464c465a5b6a9e25.webp b/translated_images/de/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..a194cfe7 Binary files /dev/null and b/translated_images/de/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/de/vaemnist-diag.694315f775d5d666.webp b/translated_images/de/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/de/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/de/vaemnist.cab9e602dc08dc50.webp b/translated_images/de/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..2e523d00 Binary files /dev/null and b/translated_images/de/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/de/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/de/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..5bd4d5f9 Binary files /dev/null and b/translated_images/de/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/de/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/de/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..632a2189 Binary files /dev/null and b/translated_images/de/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/de/vqgan.5027fe05051dfa31.webp b/translated_images/de/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..03a460ee Binary files /dev/null and b/translated_images/de/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/de/yolo.a2648ec82ee8bb4e.webp b/translated_images/de/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..90d30b35 Binary files /dev/null and b/translated_images/de/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.ar.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.ar.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.ar.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.de.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.de.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.de.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.es.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.es.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.es.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.fa.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.fa.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.fa.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.fr.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.fr.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.fr.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93.ru.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93.ru.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93.ru.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ar.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ar.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ar.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.de.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.de.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.de.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.es.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.es.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.es.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fa.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fa.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fa.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fr.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fr.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.fr.jpg and /dev/null differ diff --git a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ru.jpg b/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ru.jpg deleted file mode 100644 index 1ec22269..00000000 Binary files a/translated_images/dog-from-unsplash.426f9fbca2febc93f70499f8330874380bd6f2184b4fb986bac7220826d11a00.ru.jpg and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.ar.png b/translated_images/dsh_age.d212a30d4e54fb5f.ar.png deleted file mode 100644 index 2355616a..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.ar.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.de.png b/translated_images/dsh_age.d212a30d4e54fb5f.de.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.de.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.es.png b/translated_images/dsh_age.d212a30d4e54fb5f.es.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.es.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.fa.png b/translated_images/dsh_age.d212a30d4e54fb5f.fa.png deleted file mode 100644 index 2355616a..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.fa.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.fr.png b/translated_images/dsh_age.d212a30d4e54fb5f.fr.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.fr.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f.ru.png b/translated_images/dsh_age.d212a30d4e54fb5f.ru.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f.ru.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ar.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ar.png deleted file mode 100644 index 2355616a..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ar.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.de.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.de.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.de.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.es.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.es.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.es.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fa.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fa.png deleted file mode 100644 index 2355616a..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fa.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fr.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fr.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.fr.png and /dev/null differ diff --git a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ru.png b/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ru.png deleted file mode 100644 index 8a028317..00000000 Binary files a/translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ru.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png deleted file mode 100644 index c5727cd7..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.de.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.de.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.de.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.es.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.es.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.es.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.fa.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.fa.png deleted file mode 100644 index 77f6c428..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.fa.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.fr.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.fr.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.fr.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png b/translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ar.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ar.png deleted file mode 100644 index c5727cd7..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ar.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.de.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.de.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.de.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.es.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.es.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.es.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fa.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fa.png deleted file mode 100644 index 77f6c428..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fa.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fr.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fr.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.fr.png and /dev/null differ diff --git a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ru.png b/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ru.png deleted file mode 100644 index 48e3a77a..00000000 Binary files a/translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.ru.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.ar.png b/translated_images/encoder-decoder-attention.7a726296894fb567.ar.png deleted file mode 100644 index 1fc32744..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.ar.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.de.png b/translated_images/encoder-decoder-attention.7a726296894fb567.de.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.de.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.es.png b/translated_images/encoder-decoder-attention.7a726296894fb567.es.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.es.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.fa.png b/translated_images/encoder-decoder-attention.7a726296894fb567.fa.png deleted file mode 100644 index 5eeef654..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.fa.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.fr.png b/translated_images/encoder-decoder-attention.7a726296894fb567.fr.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.fr.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567.ru.png b/translated_images/encoder-decoder-attention.7a726296894fb567.ru.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567.ru.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ar.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ar.png deleted file mode 100644 index 1fc32744..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ar.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.de.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.de.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.de.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.es.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.es.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.es.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fa.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fa.png deleted file mode 100644 index 5eeef654..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fa.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fr.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fr.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.fr.png and /dev/null differ diff --git a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ru.png b/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ru.png deleted file mode 100644 index bea72c38..00000000 Binary files a/translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.ru.png and /dev/null differ diff --git a/translated_images/es/.co-op-translator.json b/translated_images/es/.co-op-translator.json new file mode 100644 index 00000000..acf96629 --- /dev/null +++ b/translated_images/es/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:04+00:00", + "source_file": "images/favicon.png", + "language_code": "es" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:16+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "es" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:04:59+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "es" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:05:48+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "es" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:22+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "es" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:06:42+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "es" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:20+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "es" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:08:04+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "es" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:35+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "es" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:41+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "es" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:45+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "es" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:51+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "es" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:08:59+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "es" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:09+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "es" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:23+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "es" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:41+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "es" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:48+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "es" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:02+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "es" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:13+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "es" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:23+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "es" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:27+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "es" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:35+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "es" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:10:53+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "es" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "es" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:06+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "es" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:15+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "es" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:18+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "es" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "es" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:23+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "es" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:26+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "es" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "es" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:32+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "es" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:35+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "es" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:40+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "es" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:43+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "es" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:45+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "es" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:49+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "es" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:52+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "es" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:11:59+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "es" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:13+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "es" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:23+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "es" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:30+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "es" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:37+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "es" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:45+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "es" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "es" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:12:58+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "es" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:12+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "es" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:23+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "es" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:33+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "es" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:13:55+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "es" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:17+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "es" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:33+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "es" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:14:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "es" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:04+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "es" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:11+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "es" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:24+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "es" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:35+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "es" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:48+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "es" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:55+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "es" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "es" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:04+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "es" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:26+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "es" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:10+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "es" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:41+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "es" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:43+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "es" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:47+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "es" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:17:58+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "es" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:17+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "es" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:19+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "es" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:28+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "es" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:18:51+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "es" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:15+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "es" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:19:52+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "es" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:21:05+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "es" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:38+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "es" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:21:53+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "es" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:10+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "es" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:22:49+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "es" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:37+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "es" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:47+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "es" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:52+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "es" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:23:58+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "es" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:09+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "es" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:23+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "es" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:30+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "es" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:38+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "es" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:48+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "es" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:24:58+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "es" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:04+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "es" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:10+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "es" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:15+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "es" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:19+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "es" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:26+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "es" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:37+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "es" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:44+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "es" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:53+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "es" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:57+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "es" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:00+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "es" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:04+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "es" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:05+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "es" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:08+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "es" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:16+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "es" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:26:37+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "es" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:10+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "es" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:24+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "es" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:35+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "es" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:27:55+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "es" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:19+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "es" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:40+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "es" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:28:59+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "es" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:15+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "es" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:34+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "es" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:29:51+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "es" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:12+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "es" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:37+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "es" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:48+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "es" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:30:57+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "es" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:07+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "es" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:21+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "es" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:32+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "es" + } +} \ No newline at end of file diff --git a/translated_images/es/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/es/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/es/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/es/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/es/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..ddad536d Binary files /dev/null and b/translated_images/es/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/es/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/es/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..3ae66128 Binary files /dev/null and b/translated_images/es/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/es/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/es/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..4d2f0b83 Binary files /dev/null and b/translated_images/es/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/es/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/es/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..7882193a Binary files /dev/null and b/translated_images/es/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/es/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/es/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..bbafcf55 Binary files /dev/null and b/translated_images/es/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/es/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/es/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/es/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/es/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/es/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/es/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/es/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/es/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/es/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/es/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/es/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..5d796888 Binary files /dev/null and b/translated_images/es/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/es/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/es/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..ee865b0c Binary files /dev/null and b/translated_images/es/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/es/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/es/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..46489154 Binary files /dev/null and b/translated_images/es/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/es/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/es/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..428e6a56 Binary files /dev/null and b/translated_images/es/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/es/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/es/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..2257cbe7 Binary files /dev/null and b/translated_images/es/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/es/NeuroArch.4e17cdba5e445721.webp b/translated_images/es/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..be7082c8 Binary files /dev/null and b/translated_images/es/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/es/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/es/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..1737dca1 Binary files /dev/null and b/translated_images/es/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/es/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/es/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..a0b53be7 Binary files /dev/null and b/translated_images/es/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/es/Overfitting.408ad91cd90b4371.webp b/translated_images/es/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..e4e836de Binary files /dev/null and b/translated_images/es/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/es/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/es/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..f80aec4e Binary files /dev/null and b/translated_images/es/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/es/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/es/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..de7dc217 Binary files /dev/null and b/translated_images/es/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/es/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/es/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/es/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/es/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/es/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/es/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/es/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/es/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/es/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/es/aae.9d418a990fc75bf9.webp b/translated_images/es/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..971200cb Binary files /dev/null and b/translated_images/es/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/es/activation-func.b4924007c7ce7764.webp b/translated_images/es/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..8138807f Binary files /dev/null and b/translated_images/es/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/es/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/es/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/es/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/es/ai-computervision.6506ebebac3fbf76.webp b/translated_images/es/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..5dfa9594 Binary files /dev/null and b/translated_images/es/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/es/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/es/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..8980ad3a Binary files /dev/null and b/translated_images/es/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/es/ai-intro.bf28d1ac4235881c.webp b/translated_images/es/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..ec888e80 Binary files /dev/null and b/translated_images/es/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/es/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/es/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..db2c1a40 Binary files /dev/null and b/translated_images/es/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/es/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/es/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..bdf26f3f Binary files /dev/null and b/translated_images/es/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/es/ai-overview.0857791951d19500.webp b/translated_images/es/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..521b27e8 Binary files /dev/null and b/translated_images/es/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/es/ai-symbolic.715a30cb610411a6.webp b/translated_images/es/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..690060e9 Binary files /dev/null and b/translated_images/es/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/es/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/es/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..1b3abcd2 Binary files /dev/null and b/translated_images/es/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/es/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/es/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..743f94b1 Binary files /dev/null and b/translated_images/es/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/es/artneuron.1a5daa88d20ebe6f.webp b/translated_images/es/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..9e300afa Binary files /dev/null and b/translated_images/es/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/es/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/es/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..ba618900 Binary files /dev/null and b/translated_images/es/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/es/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/es/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..8c89046c Binary files /dev/null and b/translated_images/es/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/es/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/es/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..065c5964 Binary files /dev/null and b/translated_images/es/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/es/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/es/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..5655b1de Binary files /dev/null and b/translated_images/es/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/es/bot-ner.4b09235dbb0ad275.webp b/translated_images/es/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..12b6c622 Binary files /dev/null and b/translated_images/es/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/es/bow.3811869cff59368d.webp b/translated_images/es/bow.3811869cff59368d.webp new file mode 100644 index 00000000..d38fbc5c Binary files /dev/null and b/translated_images/es/bow.3811869cff59368d.webp differ diff --git a/translated_images/es/braille-result.46530fea020b03c7.webp b/translated_images/es/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/es/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/es/braille-symbols.0159185ab69d5339.webp b/translated_images/es/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..bc715eb0 Binary files /dev/null and b/translated_images/es/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/es/braille.341962ff76b1bd70.webp b/translated_images/es/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/es/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/es/cartpole.f52a67f27e058170.webp b/translated_images/es/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/es/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/es/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/es/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..0f513fb6 Binary files /dev/null and b/translated_images/es/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/es/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/es/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..4823586e Binary files /dev/null and b/translated_images/es/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/es/clip-class.3af42ef0b2b19369.webp b/translated_images/es/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..ee7128c3 Binary files /dev/null and b/translated_images/es/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/es/cnn-pyramid.85915455759ef0ce.webp b/translated_images/es/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..32eb4d87 Binary files /dev/null and b/translated_images/es/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/es/coco-examples.71bc60380fa6cceb.webp b/translated_images/es/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..10424e72 Binary files /dev/null and b/translated_images/es/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/es/convolutionExample.634615d5ff7081e0.webp b/translated_images/es/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/es/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/es/data.50b2a9d5484bdbf0.webp b/translated_images/es/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..9578faf3 Binary files /dev/null and b/translated_images/es/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/es/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/es/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..62992e07 Binary files /dev/null and b/translated_images/es/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/es/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/es/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/es/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/es/dsh_age.d212a30d4e54fb5f.webp b/translated_images/es/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/es/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/es/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/es/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..640c510d Binary files /dev/null and b/translated_images/es/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/es/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/es/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..2000def0 Binary files /dev/null and b/translated_images/es/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/es/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/es/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..74955382 Binary files /dev/null and b/translated_images/es/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/es/f-rcnn.3cda6d9bb4188875.webp b/translated_images/es/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..60862860 Binary files /dev/null and b/translated_images/es/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/es/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/es/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..c41ef045 Binary files /dev/null and b/translated_images/es/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/es/favicon.37b561214b36d454.webp b/translated_images/es/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/es/favicon.37b561214b36d454.webp differ diff --git a/translated_images/es/features.6291f9c7ba3a0b95.webp b/translated_images/es/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..03679af2 Binary files /dev/null and b/translated_images/es/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/es/filter-horiz.59b80ed4feb946ef.webp b/translated_images/es/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..78123739 Binary files /dev/null and b/translated_images/es/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/es/filter-vert.b7148390ca0bc356.webp b/translated_images/es/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..133982bf Binary files /dev/null and b/translated_images/es/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/es/frame-difference.706f805491a0883c.webp b/translated_images/es/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..13b06d26 Binary files /dev/null and b/translated_images/es/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/es/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/es/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..32004749 Binary files /dev/null and b/translated_images/es/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/es/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/es/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..90507657 Binary files /dev/null and b/translated_images/es/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/es/history-of-ai.7e83efa70b537f5a.webp b/translated_images/es/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..0b8c4fd5 Binary files /dev/null and b/translated_images/es/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/es/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/es/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..f8a6c554 Binary files /dev/null and b/translated_images/es/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/es/ideal-cat.203dd4597643d6b0.webp b/translated_images/es/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/es/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/es/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/es/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/es/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/es/image.896e8254a2c60d45.webp b/translated_images/es/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/es/image.896e8254a2c60d45.webp differ diff --git a/translated_images/es/inception.a6605b85bcbc6f52.webp b/translated_images/es/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..e570684d Binary files /dev/null and b/translated_images/es/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/es/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/es/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..5714dd25 Binary files /dev/null and b/translated_images/es/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/es/iou_equation.9a4751d40fff4e11.webp b/translated_images/es/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..c2beb28b Binary files /dev/null and b/translated_images/es/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/es/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/es/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..58c195e0 Binary files /dev/null and b/translated_images/es/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/es/knowledge-spectrum.b60df631852c0217.webp b/translated_images/es/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..5ba63061 Binary files /dev/null and b/translated_images/es/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/es/lmfilters.ea9e4868a82cf74c.webp b/translated_images/es/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/es/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/es/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/es/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..022ae457 Binary files /dev/null and b/translated_images/es/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/es/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/es/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..a9dbfe7a Binary files /dev/null and b/translated_images/es/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/es/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/es/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..146fc2d7 Binary files /dev/null and b/translated_images/es/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/es/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/es/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/es/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/es/navi.2f20b727910110ea.webp b/translated_images/es/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..e9254c25 Binary files /dev/null and b/translated_images/es/navi.2f20b727910110ea.webp differ diff --git a/translated_images/es/netout.1eb15eb76fd76731.webp b/translated_images/es/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..3206efc4 Binary files /dev/null and b/translated_images/es/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/es/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/es/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..e47fc003 Binary files /dev/null and b/translated_images/es/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/es/optical.1f4a94464579a83a.webp b/translated_images/es/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/es/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/es/original-dog.8f68a67d2fe0911f.webp b/translated_images/es/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/es/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/es/overfit.a0bd57f717c15769.webp b/translated_images/es/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/es/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/es/overfit1.f24b71c6f652e59e.webp b/translated_images/es/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..de604b48 Binary files /dev/null and b/translated_images/es/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/es/overfit2.131f5800ae10ca5e.webp b/translated_images/es/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..8dd690b9 Binary files /dev/null and b/translated_images/es/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/es/palm-movement.341495f0e9c47da3.webp b/translated_images/es/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/es/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/es/photo-cat.8c8e8fb760ffe457.webp b/translated_images/es/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/es/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/es/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/es/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..42f71832 Binary files /dev/null and b/translated_images/es/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/es/protege.274177ceeac13b38.webp b/translated_images/es/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..52830476 Binary files /dev/null and b/translated_images/es/protege.274177ceeac13b38.webp differ diff --git a/translated_images/es/r-fcn.13eb88158b99a3da.webp b/translated_images/es/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..0e93055d Binary files /dev/null and b/translated_images/es/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/es/rcnn1.cae407020dfb1d1f.webp b/translated_images/es/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/es/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/es/rcnn2.2d9530bb83516484.webp b/translated_images/es/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..aed68b94 Binary files /dev/null and b/translated_images/es/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/es/resnet-block.aba4ccbcc0944434.webp b/translated_images/es/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..5fff04cb Binary files /dev/null and b/translated_images/es/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/es/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/es/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..51542f3a Binary files /dev/null and b/translated_images/es/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/es/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/es/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/es/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/es/rnn-generate.56c54afb52f9781d.webp b/translated_images/es/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..265037b6 Binary files /dev/null and b/translated_images/es/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/es/rnn.27f5c29c53d727b5.webp b/translated_images/es/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..855e4bf3 Binary files /dev/null and b/translated_images/es/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/es/segm.92442f2cb42ff4fa.webp b/translated_images/es/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..f02fcab4 Binary files /dev/null and b/translated_images/es/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/es/segnet.87377542aa5a8b76.webp b/translated_images/es/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..41e55fcc Binary files /dev/null and b/translated_images/es/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/es/style.5293e85e077ab22c.webp b/translated_images/es/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/es/style.5293e85e077ab22c.webp differ diff --git a/translated_images/es/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/es/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..777d6f9f Binary files /dev/null and b/translated_images/es/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/es/transformer-layer.905e14747ca4e7d5.webp b/translated_images/es/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..14a9a0e2 Binary files /dev/null and b/translated_images/es/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/es/triplet-complex.32094972c7b4441b.webp b/translated_images/es/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..b91dde94 Binary files /dev/null and b/translated_images/es/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/es/triplet.4b9b332587593298.webp b/translated_images/es/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..e3bce183 Binary files /dev/null and b/translated_images/es/triplet.4b9b332587593298.webp differ diff --git a/translated_images/es/turing-test-evol.4184696701293ead.webp b/translated_images/es/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..ceb7bd2b Binary files /dev/null and b/translated_images/es/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/es/unet.3adb555bf39d3657.webp b/translated_images/es/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..64c08f33 Binary files /dev/null and b/translated_images/es/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/es/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/es/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..859ad39d Binary files /dev/null and b/translated_images/es/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/es/vae.464c465a5b6a9e25.webp b/translated_images/es/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..e74d89f5 Binary files /dev/null and b/translated_images/es/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/es/vaemnist-diag.694315f775d5d666.webp b/translated_images/es/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/es/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/es/vaemnist.cab9e602dc08dc50.webp b/translated_images/es/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..2e523d00 Binary files /dev/null and b/translated_images/es/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/es/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/es/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..63aae4d0 Binary files /dev/null and b/translated_images/es/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/es/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/es/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..46e4222a Binary files /dev/null and b/translated_images/es/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/es/vqgan.5027fe05051dfa31.webp b/translated_images/es/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..9063f316 Binary files /dev/null and b/translated_images/es/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/es/yolo.a2648ec82ee8bb4e.webp b/translated_images/es/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..7391909d Binary files /dev/null and b/translated_images/es/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png deleted file mode 100644 index 6f8bcaf6..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.de.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.de.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.de.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.es.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.es.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.es.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fa.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fa.png deleted file mode 100644 index 4aaeb9f2..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fa.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fr.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fr.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fr.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ar.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ar.png deleted file mode 100644 index 6f8bcaf6..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ar.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.de.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.de.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.de.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.es.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.es.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.es.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fa.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fa.png deleted file mode 100644 index 4aaeb9f2..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fa.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fr.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fr.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.fr.png and /dev/null differ diff --git a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ru.png b/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ru.png deleted file mode 100644 index 714cd817..00000000 Binary files a/translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.ru.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.ar.png b/translated_images/f-rcnn.3cda6d9bb4188875.ar.png deleted file mode 100644 index 75737026..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.ar.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.de.png b/translated_images/f-rcnn.3cda6d9bb4188875.de.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.de.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.es.png b/translated_images/f-rcnn.3cda6d9bb4188875.es.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.es.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.fa.png b/translated_images/f-rcnn.3cda6d9bb4188875.fa.png deleted file mode 100644 index ab3324c6..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.fa.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.fr.png b/translated_images/f-rcnn.3cda6d9bb4188875.fr.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.fr.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb4188875.ru.png b/translated_images/f-rcnn.3cda6d9bb4188875.ru.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb4188875.ru.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ar.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ar.png deleted file mode 100644 index 75737026..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ar.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.de.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.de.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.de.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.es.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.es.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.es.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fa.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fa.png deleted file mode 100644 index ab3324c6..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fa.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fr.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fr.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.fr.png and /dev/null differ diff --git a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ru.png b/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ru.png deleted file mode 100644 index ef56edfa..00000000 Binary files a/translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ru.png and /dev/null differ diff --git a/translated_images/fa/.co-op-translator.json b/translated_images/fa/.co-op-translator.json new file mode 100644 index 00000000..70f53938 --- /dev/null +++ b/translated_images/fa/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:05+00:00", + "source_file": "images/favicon.png", + "language_code": "fa" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:40+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "fa" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:05:35+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "fa" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:06:19+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "fa" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:30+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "fa" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:07:06+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "fa" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:50+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "fa" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:08:32+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "fa" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:40+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "fa" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:43+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "fa" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:49+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "fa" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:56+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "fa" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:09:05+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "fa" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:17+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "fa" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:40+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "fa" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:44+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "fa" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:59+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "fa" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:08+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "fa" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:22+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "fa" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:26+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "fa" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:28+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "fa" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:48+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "fa" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "fa" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "fa" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:14+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "fa" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:16+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "fa" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:21+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "fa" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "fa" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:23+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "fa" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "fa" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "fa" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:33+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "fa" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:39+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "fa" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:43+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "fa" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:44+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "fa" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:48+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "fa" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:51+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "fa" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "fa" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:12:09+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "fa" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:19+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "fa" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:29+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "fa" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:36+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "fa" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:40+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "fa" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:53+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "fa" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:55+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "fa" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:13:08+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "fa" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:21+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "fa" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:28+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "fa" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:46+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "fa" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:14:14+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "fa" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:21+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "fa" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:56+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "fa" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:15:02+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "fa" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:07+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "fa" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:21+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "fa" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:30+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "fa" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:45+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "fa" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:54+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "fa" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "fa" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:59+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "fa" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:15+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "fa" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:55+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "fa" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:40+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "fa" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:41+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "fa" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:45+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "fa" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:50+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "fa" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:18:17+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "fa" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:18+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "fa" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:22+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "fa" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:40+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "fa" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:19:11+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "fa" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:22+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "fa" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:20:53+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "fa" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:21:32+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "fa" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:48+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "fa" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:22:03+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "fa" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:26+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "fa" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:23:34+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "fa" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:45+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "fa" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:50+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "fa" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:54+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "fa" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:24:03+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "fa" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:21+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "fa" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:27+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "fa" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:36+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "fa" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:44+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "fa" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:57+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "fa" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:25:02+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "fa" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:09+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "fa" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:13+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "fa" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:18+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "fa" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:22+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "fa" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:35+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "fa" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:39+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "fa" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:52+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "fa" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:56+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "fa" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:59+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "fa" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:03+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "fa" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:04+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "fa" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:06+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "fa" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:13+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "fa" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:23+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "fa" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:27:04+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "fa" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:22+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "fa" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:29+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "fa" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:47+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "fa" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:28:12+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "fa" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:33+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "fa" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:55+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "fa" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:29:08+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "fa" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:28+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "fa" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:46+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "fa" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:30:01+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "fa" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:35+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "fa" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:43+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "fa" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:55+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "fa" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:31:03+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "fa" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:17+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "fa" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:28+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "fa" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:40+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "fa" + } +} \ No newline at end of file diff --git a/translated_images/fa/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/fa/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/fa/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/fa/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/fa/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..cccce910 Binary files /dev/null and b/translated_images/fa/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/fa/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/fa/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..9e6521c7 Binary files /dev/null and b/translated_images/fa/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/fa/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/fa/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..edcb42f6 Binary files /dev/null and b/translated_images/fa/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/fa/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/fa/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..16bfa2f3 Binary files /dev/null and b/translated_images/fa/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/fa/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/fa/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..18c1abab Binary files /dev/null and b/translated_images/fa/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/fa/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/fa/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/fa/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/fa/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/fa/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/fa/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/fa/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/fa/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/fa/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/fa/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/fa/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..ca176b97 Binary files /dev/null and b/translated_images/fa/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/fa/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/fa/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..5b214ffe Binary files /dev/null and b/translated_images/fa/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/fa/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/fa/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..d76defbb Binary files /dev/null and b/translated_images/fa/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/fa/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/fa/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..a7a2c385 Binary files /dev/null and b/translated_images/fa/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/fa/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/fa/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..4cec9a6b Binary files /dev/null and b/translated_images/fa/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/fa/NeuroArch.4e17cdba5e445721.webp b/translated_images/fa/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..a6fd71fb Binary files /dev/null and b/translated_images/fa/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/fa/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/fa/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..f831f84f Binary files /dev/null and b/translated_images/fa/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/fa/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/fa/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..07dcf1d3 Binary files /dev/null and b/translated_images/fa/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/fa/Overfitting.408ad91cd90b4371.webp b/translated_images/fa/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..feb2d40b Binary files /dev/null and b/translated_images/fa/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/fa/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/fa/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..6fde9a54 Binary files /dev/null and b/translated_images/fa/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/fa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/fa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..43750624 Binary files /dev/null and b/translated_images/fa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/fa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/fa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/fa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/fa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/fa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/fa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/fa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/fa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/fa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/fa/aae.9d418a990fc75bf9.webp b/translated_images/fa/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..858071e9 Binary files /dev/null and b/translated_images/fa/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/fa/activation-func.b4924007c7ce7764.webp b/translated_images/fa/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..de9ea423 Binary files /dev/null and b/translated_images/fa/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/fa/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/fa/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/fa/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/fa/ai-computervision.6506ebebac3fbf76.webp b/translated_images/fa/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..f9caebb2 Binary files /dev/null and b/translated_images/fa/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/fa/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/fa/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..17d51fdb Binary files /dev/null and b/translated_images/fa/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/fa/ai-intro.bf28d1ac4235881c.webp b/translated_images/fa/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..13368136 Binary files /dev/null and b/translated_images/fa/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/fa/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/fa/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..4ddd19c6 Binary files /dev/null and b/translated_images/fa/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/fa/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/fa/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..5719dc8b Binary files /dev/null and b/translated_images/fa/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/fa/ai-overview.0857791951d19500.webp b/translated_images/fa/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..3c73fef6 Binary files /dev/null and b/translated_images/fa/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/fa/ai-symbolic.715a30cb610411a6.webp b/translated_images/fa/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..db4cbd2f Binary files /dev/null and b/translated_images/fa/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/fa/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/fa/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..ca4c92a9 Binary files /dev/null and b/translated_images/fa/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/fa/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/fa/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..1c4416ef Binary files /dev/null and b/translated_images/fa/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/fa/artneuron.1a5daa88d20ebe6f.webp b/translated_images/fa/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..1ccce6d9 Binary files /dev/null and b/translated_images/fa/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/fa/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/fa/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..4ad692de Binary files /dev/null and b/translated_images/fa/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/fa/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/fa/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..0ff4d88b Binary files /dev/null and b/translated_images/fa/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/fa/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/fa/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..a7af520d Binary files /dev/null and b/translated_images/fa/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/fa/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/fa/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..ab234b6e Binary files /dev/null and b/translated_images/fa/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/fa/bot-ner.4b09235dbb0ad275.webp b/translated_images/fa/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..b82e3b66 Binary files /dev/null and b/translated_images/fa/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/fa/bow.3811869cff59368d.webp b/translated_images/fa/bow.3811869cff59368d.webp new file mode 100644 index 00000000..79aac80d Binary files /dev/null and b/translated_images/fa/bow.3811869cff59368d.webp differ diff --git a/translated_images/fa/braille-result.46530fea020b03c7.webp b/translated_images/fa/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/fa/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/fa/braille-symbols.0159185ab69d5339.webp b/translated_images/fa/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..9bda658c Binary files /dev/null and b/translated_images/fa/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/fa/braille.341962ff76b1bd70.webp b/translated_images/fa/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/fa/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/fa/cartpole.f52a67f27e058170.webp b/translated_images/fa/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/fa/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/fa/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/fa/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..016a3370 Binary files /dev/null and b/translated_images/fa/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/fa/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/fa/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..6545b953 Binary files /dev/null and b/translated_images/fa/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/fa/clip-class.3af42ef0b2b19369.webp b/translated_images/fa/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..c90c5631 Binary files /dev/null and b/translated_images/fa/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/fa/cnn-pyramid.85915455759ef0ce.webp b/translated_images/fa/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..6e365e47 Binary files /dev/null and b/translated_images/fa/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/fa/coco-examples.71bc60380fa6cceb.webp b/translated_images/fa/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..b81ce848 Binary files /dev/null and b/translated_images/fa/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/fa/convolutionExample.634615d5ff7081e0.webp b/translated_images/fa/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/fa/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/fa/data.50b2a9d5484bdbf0.webp b/translated_images/fa/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..12d090ca Binary files /dev/null and b/translated_images/fa/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/fa/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/fa/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..d3daf1a9 Binary files /dev/null and b/translated_images/fa/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/fa/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/fa/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/fa/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/fa/dsh_age.d212a30d4e54fb5f.webp b/translated_images/fa/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/fa/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/fa/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/fa/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..62310f12 Binary files /dev/null and b/translated_images/fa/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/fa/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/fa/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..3085c3e6 Binary files /dev/null and b/translated_images/fa/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/fa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/fa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..be637514 Binary files /dev/null and b/translated_images/fa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/fa/f-rcnn.3cda6d9bb4188875.webp b/translated_images/fa/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..f8bcfa34 Binary files /dev/null and b/translated_images/fa/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/fa/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/fa/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..ae01ab19 Binary files /dev/null and b/translated_images/fa/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/fa/favicon.37b561214b36d454.webp b/translated_images/fa/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/fa/favicon.37b561214b36d454.webp differ diff --git a/translated_images/fa/features.6291f9c7ba3a0b95.webp b/translated_images/fa/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..1ce3e7de Binary files /dev/null and b/translated_images/fa/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/fa/filter-horiz.59b80ed4feb946ef.webp b/translated_images/fa/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..011ba58d Binary files /dev/null and b/translated_images/fa/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/fa/filter-vert.b7148390ca0bc356.webp b/translated_images/fa/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..18eee32d Binary files /dev/null and b/translated_images/fa/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/fa/frame-difference.706f805491a0883c.webp b/translated_images/fa/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..7aed8494 Binary files /dev/null and b/translated_images/fa/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/fa/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/fa/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..f378b8bf Binary files /dev/null and b/translated_images/fa/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/fa/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/fa/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..c2d6bbb2 Binary files /dev/null and b/translated_images/fa/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/fa/history-of-ai.7e83efa70b537f5a.webp b/translated_images/fa/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..4d28f916 Binary files /dev/null and b/translated_images/fa/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/fa/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/fa/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..1ba588e1 Binary files /dev/null and b/translated_images/fa/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/fa/ideal-cat.203dd4597643d6b0.webp b/translated_images/fa/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/fa/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/fa/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/fa/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/fa/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/fa/image.896e8254a2c60d45.webp b/translated_images/fa/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/fa/image.896e8254a2c60d45.webp differ diff --git a/translated_images/fa/inception.a6605b85bcbc6f52.webp b/translated_images/fa/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..e2df28a4 Binary files /dev/null and b/translated_images/fa/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/fa/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/fa/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..0326579f Binary files /dev/null and b/translated_images/fa/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/fa/iou_equation.9a4751d40fff4e11.webp b/translated_images/fa/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..7f159af5 Binary files /dev/null and b/translated_images/fa/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/fa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/fa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..58877c53 Binary files /dev/null and b/translated_images/fa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/fa/knowledge-spectrum.b60df631852c0217.webp b/translated_images/fa/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..92648cb1 Binary files /dev/null and b/translated_images/fa/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/fa/lmfilters.ea9e4868a82cf74c.webp b/translated_images/fa/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/fa/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/fa/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/fa/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..d74579b5 Binary files /dev/null and b/translated_images/fa/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/fa/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/fa/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..8fd5d05b Binary files /dev/null and b/translated_images/fa/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/fa/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/fa/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..c183792e Binary files /dev/null and b/translated_images/fa/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/fa/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/fa/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/fa/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/fa/navi.2f20b727910110ea.webp b/translated_images/fa/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..743fc188 Binary files /dev/null and b/translated_images/fa/navi.2f20b727910110ea.webp differ diff --git a/translated_images/fa/netout.1eb15eb76fd76731.webp b/translated_images/fa/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..1f4f780e Binary files /dev/null and b/translated_images/fa/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/fa/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/fa/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..a2d4ed64 Binary files /dev/null and b/translated_images/fa/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/fa/optical.1f4a94464579a83a.webp b/translated_images/fa/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/fa/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/fa/original-dog.8f68a67d2fe0911f.webp b/translated_images/fa/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/fa/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/fa/overfit.a0bd57f717c15769.webp b/translated_images/fa/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/fa/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/fa/overfit1.f24b71c6f652e59e.webp b/translated_images/fa/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..fb5d998f Binary files /dev/null and b/translated_images/fa/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/fa/overfit2.131f5800ae10ca5e.webp b/translated_images/fa/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..ce5bb1fd Binary files /dev/null and b/translated_images/fa/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/fa/palm-movement.341495f0e9c47da3.webp b/translated_images/fa/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/fa/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/fa/photo-cat.8c8e8fb760ffe457.webp b/translated_images/fa/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/fa/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/fa/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/fa/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..55600075 Binary files /dev/null and b/translated_images/fa/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/fa/protege.274177ceeac13b38.webp b/translated_images/fa/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..476cbb0b Binary files /dev/null and b/translated_images/fa/protege.274177ceeac13b38.webp differ diff --git a/translated_images/fa/r-fcn.13eb88158b99a3da.webp b/translated_images/fa/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..436fa4f6 Binary files /dev/null and b/translated_images/fa/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/fa/rcnn1.cae407020dfb1d1f.webp b/translated_images/fa/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/fa/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/fa/rcnn2.2d9530bb83516484.webp b/translated_images/fa/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..9fbdc2d3 Binary files /dev/null and b/translated_images/fa/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/fa/resnet-block.aba4ccbcc0944434.webp b/translated_images/fa/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..2de94e35 Binary files /dev/null and b/translated_images/fa/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/fa/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/fa/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..ebbb9982 Binary files /dev/null and b/translated_images/fa/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/fa/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/fa/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/fa/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/fa/rnn-generate.56c54afb52f9781d.webp b/translated_images/fa/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..570917d9 Binary files /dev/null and b/translated_images/fa/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/fa/rnn.27f5c29c53d727b5.webp b/translated_images/fa/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..f534f34c Binary files /dev/null and b/translated_images/fa/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/fa/segm.92442f2cb42ff4fa.webp b/translated_images/fa/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..29c1fc7c Binary files /dev/null and b/translated_images/fa/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/fa/segnet.87377542aa5a8b76.webp b/translated_images/fa/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..365b2aa7 Binary files /dev/null and b/translated_images/fa/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/fa/style.5293e85e077ab22c.webp b/translated_images/fa/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/fa/style.5293e85e077ab22c.webp differ diff --git a/translated_images/fa/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/fa/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..3ac37601 Binary files /dev/null and b/translated_images/fa/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/fa/transformer-layer.905e14747ca4e7d5.webp b/translated_images/fa/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..b77d3f43 Binary files /dev/null and b/translated_images/fa/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/fa/triplet-complex.32094972c7b4441b.webp b/translated_images/fa/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..cd34da72 Binary files /dev/null and b/translated_images/fa/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/fa/triplet.4b9b332587593298.webp b/translated_images/fa/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..18fef1a2 Binary files /dev/null and b/translated_images/fa/triplet.4b9b332587593298.webp differ diff --git a/translated_images/fa/turing-test-evol.4184696701293ead.webp b/translated_images/fa/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..adfb0a6a Binary files /dev/null and b/translated_images/fa/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/fa/unet.3adb555bf39d3657.webp b/translated_images/fa/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..33e06a24 Binary files /dev/null and b/translated_images/fa/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/fa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/fa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..b7ab58a1 Binary files /dev/null and b/translated_images/fa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/fa/vae.464c465a5b6a9e25.webp b/translated_images/fa/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..bd7dad90 Binary files /dev/null and b/translated_images/fa/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/fa/vaemnist-diag.694315f775d5d666.webp b/translated_images/fa/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/fa/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/fa/vaemnist.cab9e602dc08dc50.webp b/translated_images/fa/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..fcbab5f3 Binary files /dev/null and b/translated_images/fa/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/fa/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/fa/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..f1bb7dc5 Binary files /dev/null and b/translated_images/fa/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/fa/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/fa/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..41a5e5b7 Binary files /dev/null and b/translated_images/fa/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/fa/vqgan.5027fe05051dfa31.webp b/translated_images/fa/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..fe8e9be6 Binary files /dev/null and b/translated_images/fa/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/fa/yolo.a2648ec82ee8bb4e.webp b/translated_images/fa/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..35e9a957 Binary files /dev/null and b/translated_images/fa/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.ar.png b/translated_images/faster-rcnn.8d46c099b87ef30a.ar.png deleted file mode 100644 index 494c2672..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.ar.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.de.png b/translated_images/faster-rcnn.8d46c099b87ef30a.de.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.de.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.es.png b/translated_images/faster-rcnn.8d46c099b87ef30a.es.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.es.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.fa.png b/translated_images/faster-rcnn.8d46c099b87ef30a.fa.png deleted file mode 100644 index ad454a56..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.fa.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.fr.png b/translated_images/faster-rcnn.8d46c099b87ef30a.fr.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.fr.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30a.ru.png b/translated_images/faster-rcnn.8d46c099b87ef30a.ru.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30a.ru.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ar.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ar.png deleted file mode 100644 index 494c2672..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ar.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.de.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.de.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.de.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.es.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.es.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.es.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fa.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fa.png deleted file mode 100644 index ad454a56..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fa.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fr.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fr.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.fr.png and /dev/null differ diff --git a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ru.png b/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ru.png deleted file mode 100644 index 2bf38420..00000000 Binary files a/translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ru.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.ar.png b/translated_images/favicon.37b561214b36d454.ar.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.ar.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.de.png b/translated_images/favicon.37b561214b36d454.de.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.de.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.es.png b/translated_images/favicon.37b561214b36d454.es.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.es.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.fa.png b/translated_images/favicon.37b561214b36d454.fa.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.fa.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.fr.png b/translated_images/favicon.37b561214b36d454.fr.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.fr.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454.ru.png b/translated_images/favicon.37b561214b36d454.ru.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454.ru.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ar.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ar.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ar.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.de.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.de.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.de.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.es.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.es.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.es.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fa.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fa.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fa.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fr.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fr.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.fr.png and /dev/null differ diff --git a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ru.png b/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ru.png deleted file mode 100644 index 26e0ae43..00000000 Binary files a/translated_images/favicon.37b561214b36d454f9fd1f725d77f310fe256eb88f2a0ae08b9cb18aeb30650c.ru.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.ar.png b/translated_images/features.6291f9c7ba3a0b95.ar.png deleted file mode 100644 index c97e068f..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.ar.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.de.png b/translated_images/features.6291f9c7ba3a0b95.de.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.de.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.es.png b/translated_images/features.6291f9c7ba3a0b95.es.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.es.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.fa.png b/translated_images/features.6291f9c7ba3a0b95.fa.png deleted file mode 100644 index 11ad1fcc..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.fa.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.fr.png b/translated_images/features.6291f9c7ba3a0b95.fr.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.fr.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b95.ru.png b/translated_images/features.6291f9c7ba3a0b95.ru.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b95.ru.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ar.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ar.png deleted file mode 100644 index c97e068f..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ar.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.de.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.de.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.de.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.es.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.es.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.es.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fa.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fa.png deleted file mode 100644 index 11ad1fcc..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fa.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fr.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fr.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.fr.png and /dev/null differ diff --git a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ru.png b/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ru.png deleted file mode 100644 index 4671d226..00000000 Binary files a/translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ru.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.ar.png b/translated_images/filter-horiz.59b80ed4feb946ef.ar.png deleted file mode 100644 index 8814b91c..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.ar.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.de.png b/translated_images/filter-horiz.59b80ed4feb946ef.de.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.de.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.es.png b/translated_images/filter-horiz.59b80ed4feb946ef.es.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.es.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.fa.png b/translated_images/filter-horiz.59b80ed4feb946ef.fa.png deleted file mode 100644 index ebf1e9ad..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.fa.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.fr.png b/translated_images/filter-horiz.59b80ed4feb946ef.fr.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.fr.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946ef.ru.png b/translated_images/filter-horiz.59b80ed4feb946ef.ru.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946ef.ru.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ar.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ar.png deleted file mode 100644 index 8814b91c..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ar.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.de.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.de.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.de.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.es.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.es.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.es.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fa.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fa.png deleted file mode 100644 index ebf1e9ad..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fa.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fr.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fr.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.fr.png and /dev/null differ diff --git a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ru.png b/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ru.png deleted file mode 100644 index 9b7e97ff..00000000 Binary files a/translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ru.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.ar.png b/translated_images/filter-vert.b7148390ca0bc356.ar.png deleted file mode 100644 index bffa0bb2..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.ar.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.de.png b/translated_images/filter-vert.b7148390ca0bc356.de.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.de.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.es.png b/translated_images/filter-vert.b7148390ca0bc356.es.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.es.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.fa.png b/translated_images/filter-vert.b7148390ca0bc356.fa.png deleted file mode 100644 index 664b6948..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.fa.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.fr.png b/translated_images/filter-vert.b7148390ca0bc356.fr.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.fr.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356.ru.png b/translated_images/filter-vert.b7148390ca0bc356.ru.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356.ru.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ar.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ar.png deleted file mode 100644 index bffa0bb2..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ar.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.de.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.de.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.de.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.es.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.es.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.es.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fa.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fa.png deleted file mode 100644 index 664b6948..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fa.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fr.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fr.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.fr.png and /dev/null differ diff --git a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ru.png b/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ru.png deleted file mode 100644 index 00ef919a..00000000 Binary files a/translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ru.png and /dev/null differ diff --git a/translated_images/fr/.co-op-translator.json b/translated_images/fr/.co-op-translator.json new file mode 100644 index 00000000..143d6cb1 --- /dev/null +++ b/translated_images/fr/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:04+00:00", + "source_file": "images/favicon.png", + "language_code": "fr" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:11+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "fr" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:04:50+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "fr" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:05:42+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "fr" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:21+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "fr" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:06:36+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "fr" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:14+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "fr" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:07:57+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "fr" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:33+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "fr" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:41+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "fr" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:44+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "fr" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:50+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "fr" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:08:58+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "fr" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:07+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "fr" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:20+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "fr" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:41+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "fr" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:46+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "fr" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:00+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "fr" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:11+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "fr" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:23+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "fr" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:26+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "fr" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:32+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "fr" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:10:51+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "fr" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "fr" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:04+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "fr" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:14+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "fr" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:17+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "fr" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "fr" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "fr" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:24+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "fr" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "fr" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:32+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "fr" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:34+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "fr" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:39+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "fr" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:43+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "fr" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:45+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "fr" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:48+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "fr" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:52+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "fr" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:11:56+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "fr" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:11+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "fr" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:21+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "fr" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:30+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "fr" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:36+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "fr" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:42+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "fr" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "fr" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:12:57+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "fr" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:10+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "fr" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:22+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "fr" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:30+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "fr" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:13:50+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "fr" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:16+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "fr" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:27+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "fr" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:14:57+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "fr" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:02+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "fr" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:09+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "fr" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:22+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "fr" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:32+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "fr" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:46+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "fr" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:54+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "fr" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "fr" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:01+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "fr" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:21+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "fr" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:03+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "fr" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:40+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "fr" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:42+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "fr" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:46+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "fr" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:17:54+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "fr" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:17+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "fr" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:19+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "fr" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:25+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "fr" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:18:45+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "fr" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:13+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "fr" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:19:37+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "fr" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:20:59+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "fr" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:35+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "fr" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:21:50+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "fr" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:06+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "fr" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:22:36+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "fr" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:36+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "fr" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:46+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "fr" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:50+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "fr" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:23:55+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "fr" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:06+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "fr" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:22+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "fr" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:29+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "fr" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:37+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "fr" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:46+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "fr" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:24:57+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "fr" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:03+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "fr" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:10+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "fr" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:14+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "fr" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:18+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "fr" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:24+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "fr" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:36+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "fr" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:41+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "fr" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:52+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "fr" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:56+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "fr" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:00+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "fr" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:03+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "fr" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:05+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "fr" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:07+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "fr" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:15+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "fr" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:26:30+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "fr" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:07+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "fr" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:23+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "fr" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:32+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "fr" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:27:51+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "fr" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:16+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "fr" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:37+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "fr" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:28:57+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "fr" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:11+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "fr" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:31+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "fr" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:29:49+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "fr" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:06+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "fr" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:36+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "fr" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:46+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "fr" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:30:56+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "fr" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:05+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "fr" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:19+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "fr" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:30+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "fr" + } +} \ No newline at end of file diff --git a/translated_images/fr/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/fr/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/fr/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/fr/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/fr/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..643156a2 Binary files /dev/null and b/translated_images/fr/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/fr/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/fr/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..d3ee9698 Binary files /dev/null and b/translated_images/fr/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/fr/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/fr/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..7725a67b Binary files /dev/null and b/translated_images/fr/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/fr/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/fr/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..edf171b6 Binary files /dev/null and b/translated_images/fr/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/fr/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/fr/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..35b99249 Binary files /dev/null and b/translated_images/fr/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/fr/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/fr/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/fr/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/fr/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/fr/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/fr/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/fr/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/fr/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/fr/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/fr/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/fr/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..3cf3ef0c Binary files /dev/null and b/translated_images/fr/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/fr/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/fr/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..462c1f2b Binary files /dev/null and b/translated_images/fr/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/fr/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/fr/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..46489154 Binary files /dev/null and b/translated_images/fr/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/fr/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/fr/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..1da7d749 Binary files /dev/null and b/translated_images/fr/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/fr/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/fr/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..7cbe3d4e Binary files /dev/null and b/translated_images/fr/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/fr/NeuroArch.4e17cdba5e445721.webp b/translated_images/fr/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..e4d19d89 Binary files /dev/null and b/translated_images/fr/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/fr/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/fr/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..2cce41d6 Binary files /dev/null and b/translated_images/fr/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/fr/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/fr/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..0bd43ffa Binary files /dev/null and b/translated_images/fr/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/fr/Overfitting.408ad91cd90b4371.webp b/translated_images/fr/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..918d3db1 Binary files /dev/null and b/translated_images/fr/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/fr/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/fr/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..ee733c23 Binary files /dev/null and b/translated_images/fr/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/fr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/fr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..65fa9cb6 Binary files /dev/null and b/translated_images/fr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/fr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/fr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/fr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/fr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/fr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/fr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/fr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/fr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/fr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/fr/aae.9d418a990fc75bf9.webp b/translated_images/fr/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..9d78aee5 Binary files /dev/null and b/translated_images/fr/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/fr/activation-func.b4924007c7ce7764.webp b/translated_images/fr/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..8138807f Binary files /dev/null and b/translated_images/fr/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/fr/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/fr/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/fr/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/fr/ai-computervision.6506ebebac3fbf76.webp b/translated_images/fr/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..5ff77a2f Binary files /dev/null and b/translated_images/fr/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/fr/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/fr/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..eacb188d Binary files /dev/null and b/translated_images/fr/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/fr/ai-intro.bf28d1ac4235881c.webp b/translated_images/fr/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..82885bf0 Binary files /dev/null and b/translated_images/fr/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/fr/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/fr/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..a82a0cc2 Binary files /dev/null and b/translated_images/fr/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/fr/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/fr/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..fcbe44a1 Binary files /dev/null and b/translated_images/fr/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/fr/ai-overview.0857791951d19500.webp b/translated_images/fr/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..0cf11548 Binary files /dev/null and b/translated_images/fr/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/fr/ai-symbolic.715a30cb610411a6.webp b/translated_images/fr/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..6b246999 Binary files /dev/null and b/translated_images/fr/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/fr/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/fr/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..965b72d1 Binary files /dev/null and b/translated_images/fr/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/fr/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/fr/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..c80bfb15 Binary files /dev/null and b/translated_images/fr/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/fr/artneuron.1a5daa88d20ebe6f.webp b/translated_images/fr/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..9e300afa Binary files /dev/null and b/translated_images/fr/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/fr/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/fr/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..e5c58323 Binary files /dev/null and b/translated_images/fr/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/fr/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/fr/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..f3897ac6 Binary files /dev/null and b/translated_images/fr/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/fr/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/fr/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..1ebf58d6 Binary files /dev/null and b/translated_images/fr/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/fr/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/fr/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..c20bd00d Binary files /dev/null and b/translated_images/fr/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/fr/bot-ner.4b09235dbb0ad275.webp b/translated_images/fr/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..0deef35f Binary files /dev/null and b/translated_images/fr/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/fr/bow.3811869cff59368d.webp b/translated_images/fr/bow.3811869cff59368d.webp new file mode 100644 index 00000000..6060d759 Binary files /dev/null and b/translated_images/fr/bow.3811869cff59368d.webp differ diff --git a/translated_images/fr/braille-result.46530fea020b03c7.webp b/translated_images/fr/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/fr/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/fr/braille-symbols.0159185ab69d5339.webp b/translated_images/fr/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..f68546de Binary files /dev/null and b/translated_images/fr/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/fr/braille.341962ff76b1bd70.webp b/translated_images/fr/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/fr/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/fr/cartpole.f52a67f27e058170.webp b/translated_images/fr/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/fr/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/fr/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/fr/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..a58aa8f1 Binary files /dev/null and b/translated_images/fr/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/fr/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/fr/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..1687a933 Binary files /dev/null and b/translated_images/fr/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/fr/clip-class.3af42ef0b2b19369.webp b/translated_images/fr/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..0b9a3d88 Binary files /dev/null and b/translated_images/fr/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/fr/cnn-pyramid.85915455759ef0ce.webp b/translated_images/fr/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..c15601f4 Binary files /dev/null and b/translated_images/fr/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/fr/coco-examples.71bc60380fa6cceb.webp b/translated_images/fr/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..14db8e82 Binary files /dev/null and b/translated_images/fr/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/fr/convolutionExample.634615d5ff7081e0.webp b/translated_images/fr/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/fr/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/fr/data.50b2a9d5484bdbf0.webp b/translated_images/fr/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..81008aef Binary files /dev/null and b/translated_images/fr/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/fr/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/fr/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..85a6f0b5 Binary files /dev/null and b/translated_images/fr/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/fr/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/fr/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/fr/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/fr/dsh_age.d212a30d4e54fb5f.webp b/translated_images/fr/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/fr/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/fr/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/fr/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..dc883a6d Binary files /dev/null and b/translated_images/fr/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/fr/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/fr/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..98781867 Binary files /dev/null and b/translated_images/fr/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/fr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/fr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..410f5a9f Binary files /dev/null and b/translated_images/fr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/fr/f-rcnn.3cda6d9bb4188875.webp b/translated_images/fr/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..cb5ee25b Binary files /dev/null and b/translated_images/fr/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/fr/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/fr/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..738086bf Binary files /dev/null and b/translated_images/fr/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/fr/favicon.37b561214b36d454.webp b/translated_images/fr/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/fr/favicon.37b561214b36d454.webp differ diff --git a/translated_images/fr/features.6291f9c7ba3a0b95.webp b/translated_images/fr/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..03679af2 Binary files /dev/null and b/translated_images/fr/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/fr/filter-horiz.59b80ed4feb946ef.webp b/translated_images/fr/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..ca8e4bbf Binary files /dev/null and b/translated_images/fr/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/fr/filter-vert.b7148390ca0bc356.webp b/translated_images/fr/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..d200934f Binary files /dev/null and b/translated_images/fr/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/fr/frame-difference.706f805491a0883c.webp b/translated_images/fr/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..204aefdd Binary files /dev/null and b/translated_images/fr/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/fr/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/fr/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..da5bfbe2 Binary files /dev/null and b/translated_images/fr/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/fr/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/fr/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..cd18ce69 Binary files /dev/null and b/translated_images/fr/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/fr/history-of-ai.7e83efa70b537f5a.webp b/translated_images/fr/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..e5ec0718 Binary files /dev/null and b/translated_images/fr/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/fr/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/fr/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..88e658ed Binary files /dev/null and b/translated_images/fr/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/fr/ideal-cat.203dd4597643d6b0.webp b/translated_images/fr/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/fr/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/fr/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/fr/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/fr/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/fr/image.896e8254a2c60d45.webp b/translated_images/fr/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/fr/image.896e8254a2c60d45.webp differ diff --git a/translated_images/fr/inception.a6605b85bcbc6f52.webp b/translated_images/fr/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..d4475058 Binary files /dev/null and b/translated_images/fr/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/fr/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/fr/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..422e408e Binary files /dev/null and b/translated_images/fr/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/fr/iou_equation.9a4751d40fff4e11.webp b/translated_images/fr/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..bdf7ff24 Binary files /dev/null and b/translated_images/fr/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/fr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/fr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..21eea41f Binary files /dev/null and b/translated_images/fr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/fr/knowledge-spectrum.b60df631852c0217.webp b/translated_images/fr/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..25c8dc0d Binary files /dev/null and b/translated_images/fr/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/fr/lmfilters.ea9e4868a82cf74c.webp b/translated_images/fr/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/fr/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/fr/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/fr/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..6c79ceb5 Binary files /dev/null and b/translated_images/fr/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/fr/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/fr/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..dafedb09 Binary files /dev/null and b/translated_images/fr/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/fr/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/fr/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..bcd7e7b1 Binary files /dev/null and b/translated_images/fr/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/fr/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/fr/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/fr/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/fr/navi.2f20b727910110ea.webp b/translated_images/fr/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..e9254c25 Binary files /dev/null and b/translated_images/fr/navi.2f20b727910110ea.webp differ diff --git a/translated_images/fr/netout.1eb15eb76fd76731.webp b/translated_images/fr/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..252a6b73 Binary files /dev/null and b/translated_images/fr/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/fr/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/fr/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..2145169d Binary files /dev/null and b/translated_images/fr/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/fr/optical.1f4a94464579a83a.webp b/translated_images/fr/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/fr/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/fr/original-dog.8f68a67d2fe0911f.webp b/translated_images/fr/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/fr/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/fr/overfit.a0bd57f717c15769.webp b/translated_images/fr/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/fr/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/fr/overfit1.f24b71c6f652e59e.webp b/translated_images/fr/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..de604b48 Binary files /dev/null and b/translated_images/fr/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/fr/overfit2.131f5800ae10ca5e.webp b/translated_images/fr/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..8dd690b9 Binary files /dev/null and b/translated_images/fr/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/fr/palm-movement.341495f0e9c47da3.webp b/translated_images/fr/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/fr/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/fr/photo-cat.8c8e8fb760ffe457.webp b/translated_images/fr/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/fr/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/fr/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/fr/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..058ad6f1 Binary files /dev/null and b/translated_images/fr/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/fr/protege.274177ceeac13b38.webp b/translated_images/fr/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..f9c459ba Binary files /dev/null and b/translated_images/fr/protege.274177ceeac13b38.webp differ diff --git a/translated_images/fr/r-fcn.13eb88158b99a3da.webp b/translated_images/fr/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..ee1f552b Binary files /dev/null and b/translated_images/fr/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/fr/rcnn1.cae407020dfb1d1f.webp b/translated_images/fr/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/fr/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/fr/rcnn2.2d9530bb83516484.webp b/translated_images/fr/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..a32ff872 Binary files /dev/null and b/translated_images/fr/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/fr/resnet-block.aba4ccbcc0944434.webp b/translated_images/fr/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..791f17f2 Binary files /dev/null and b/translated_images/fr/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/fr/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/fr/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..5c5a3547 Binary files /dev/null and b/translated_images/fr/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/fr/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/fr/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/fr/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/fr/rnn-generate.56c54afb52f9781d.webp b/translated_images/fr/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..ca60928f Binary files /dev/null and b/translated_images/fr/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/fr/rnn.27f5c29c53d727b5.webp b/translated_images/fr/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..54168ba9 Binary files /dev/null and b/translated_images/fr/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/fr/segm.92442f2cb42ff4fa.webp b/translated_images/fr/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..db24edb9 Binary files /dev/null and b/translated_images/fr/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/fr/segnet.87377542aa5a8b76.webp b/translated_images/fr/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..cac7c05a Binary files /dev/null and b/translated_images/fr/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/fr/style.5293e85e077ab22c.webp b/translated_images/fr/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/fr/style.5293e85e077ab22c.webp differ diff --git a/translated_images/fr/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/fr/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..5dbe4dbe Binary files /dev/null and b/translated_images/fr/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/fr/transformer-layer.905e14747ca4e7d5.webp b/translated_images/fr/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..b41524d3 Binary files /dev/null and b/translated_images/fr/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/fr/triplet-complex.32094972c7b4441b.webp b/translated_images/fr/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..3dcaafb7 Binary files /dev/null and b/translated_images/fr/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/fr/triplet.4b9b332587593298.webp b/translated_images/fr/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..4e0c295e Binary files /dev/null and b/translated_images/fr/triplet.4b9b332587593298.webp differ diff --git a/translated_images/fr/turing-test-evol.4184696701293ead.webp b/translated_images/fr/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..f9af1297 Binary files /dev/null and b/translated_images/fr/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/fr/unet.3adb555bf39d3657.webp b/translated_images/fr/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..55cf7d95 Binary files /dev/null and b/translated_images/fr/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/fr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/fr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..535f6624 Binary files /dev/null and b/translated_images/fr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/fr/vae.464c465a5b6a9e25.webp b/translated_images/fr/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..2fe41120 Binary files /dev/null and b/translated_images/fr/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/fr/vaemnist-diag.694315f775d5d666.webp b/translated_images/fr/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/fr/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/fr/vaemnist.cab9e602dc08dc50.webp b/translated_images/fr/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..2e523d00 Binary files /dev/null and b/translated_images/fr/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/fr/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/fr/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..58ee44dd Binary files /dev/null and b/translated_images/fr/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/fr/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/fr/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..7efaed10 Binary files /dev/null and b/translated_images/fr/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/fr/vqgan.5027fe05051dfa31.webp b/translated_images/fr/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..9e1588ed Binary files /dev/null and b/translated_images/fr/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/fr/yolo.a2648ec82ee8bb4e.webp b/translated_images/fr/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..d902f00f Binary files /dev/null and b/translated_images/fr/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/frame-difference.706f805491a0883c.ar.png b/translated_images/frame-difference.706f805491a0883c.ar.png deleted file mode 100644 index 33a1622e..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.ar.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c.de.png b/translated_images/frame-difference.706f805491a0883c.de.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.de.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c.es.png b/translated_images/frame-difference.706f805491a0883c.es.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.es.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c.fa.png b/translated_images/frame-difference.706f805491a0883c.fa.png deleted file mode 100644 index cf1d687b..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.fa.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c.fr.png b/translated_images/frame-difference.706f805491a0883c.fr.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.fr.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c.ru.png b/translated_images/frame-difference.706f805491a0883c.ru.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c.ru.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ar.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ar.png deleted file mode 100644 index 33a1622e..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ar.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.de.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.de.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.de.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.es.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.es.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.es.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fa.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fa.png deleted file mode 100644 index cf1d687b..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fa.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fr.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fr.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.fr.png and /dev/null differ diff --git a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ru.png b/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ru.png deleted file mode 100644 index f2570cf8..00000000 Binary files a/translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ru.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.ar.png b/translated_images/gan_arch_detail.46b95fd366f8e543.ar.png deleted file mode 100644 index 12ab0fe4..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.ar.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.de.png b/translated_images/gan_arch_detail.46b95fd366f8e543.de.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.de.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.es.png b/translated_images/gan_arch_detail.46b95fd366f8e543.es.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.es.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.fa.png b/translated_images/gan_arch_detail.46b95fd366f8e543.fa.png deleted file mode 100644 index 0f51a6d3..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.fa.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.fr.png b/translated_images/gan_arch_detail.46b95fd366f8e543.fr.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.fr.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543.ru.png b/translated_images/gan_arch_detail.46b95fd366f8e543.ru.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543.ru.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ar.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ar.png deleted file mode 100644 index 12ab0fe4..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ar.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.de.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.de.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.de.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.es.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.es.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.es.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fa.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fa.png deleted file mode 100644 index 0f51a6d3..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fa.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fr.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fr.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.fr.png and /dev/null differ diff --git a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ru.png b/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ru.png deleted file mode 100644 index a7f42288..00000000 Binary files a/translated_images/gan_arch_detail.46b95fd366f8e543170264fe07e516683ac3e5bb699392c35449e99590a11063.ru.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.ar.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.ar.png deleted file mode 100644 index 76f98fed..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.ar.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.de.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.de.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.de.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.es.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.es.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.es.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.fa.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.fa.png deleted file mode 100644 index e27d50a4..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.fa.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.fr.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.fr.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.fr.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d69.ru.png b/translated_images/gan_architecture.8f3a5ab62b8d5d69.ru.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d69.ru.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ar.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ar.png deleted file mode 100644 index 76f98fed..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ar.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.de.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.de.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.de.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.es.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.es.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.es.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fa.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fa.png deleted file mode 100644 index e27d50a4..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fa.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fr.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fr.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.fr.png and /dev/null differ diff --git a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ru.png b/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ru.png deleted file mode 100644 index 6bde8cbc..00000000 Binary files a/translated_images/gan_architecture.8f3a5ab62b8d5d698a91f7668017d8e09b81c5d8e7cc99bdb23979d90a0c475e.ru.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.ar.png b/translated_images/history-of-ai.7e83efa70b537f5a.ar.png deleted file mode 100644 index af67f7bd..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.ar.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.de.png b/translated_images/history-of-ai.7e83efa70b537f5a.de.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.de.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.es.png b/translated_images/history-of-ai.7e83efa70b537f5a.es.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.es.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.fa.png b/translated_images/history-of-ai.7e83efa70b537f5a.fa.png deleted file mode 100644 index 953dd2cb..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.fa.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.fr.png b/translated_images/history-of-ai.7e83efa70b537f5a.fr.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.fr.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a.ru.png b/translated_images/history-of-ai.7e83efa70b537f5a.ru.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a.ru.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ar.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ar.png deleted file mode 100644 index af67f7bd..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ar.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.de.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.de.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.de.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.es.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.es.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.es.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fa.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fa.png deleted file mode 100644 index 953dd2cb..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fa.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fr.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fr.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.fr.png and /dev/null differ diff --git a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ru.png b/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ru.png deleted file mode 100644 index e7f46c5a..00000000 Binary files a/translated_images/history-of-ai.7e83efa70b537f5a0264357672b0884cf3a220fbafe35c65d70b2c3805f7bf5e.ru.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.ar.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.ar.png deleted file mode 100644 index 6f03747f..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.ar.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.de.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.de.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.de.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.es.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.es.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.es.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.fa.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.fa.png deleted file mode 100644 index b421882e..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.fa.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.fr.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.fr.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.fr.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044.ru.png b/translated_images/ideal-cat-loop.999fbb8ff306e044.ru.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044.ru.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ar.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ar.png deleted file mode 100644 index 6f03747f..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ar.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.de.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.de.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.de.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.es.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.es.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.es.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fa.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fa.png deleted file mode 100644 index b421882e..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fa.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fr.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fr.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.fr.png and /dev/null differ diff --git a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ru.png b/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ru.png deleted file mode 100644 index da33b981..00000000 Binary files a/translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ru.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.ar.png b/translated_images/ideal-cat.203dd4597643d6b0.ar.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.ar.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.de.png b/translated_images/ideal-cat.203dd4597643d6b0.de.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.de.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.es.png b/translated_images/ideal-cat.203dd4597643d6b0.es.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.es.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.fa.png b/translated_images/ideal-cat.203dd4597643d6b0.fa.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.fa.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.fr.png b/translated_images/ideal-cat.203dd4597643d6b0.fr.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.fr.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0.ru.png b/translated_images/ideal-cat.203dd4597643d6b0.ru.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0.ru.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ar.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ar.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ar.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.de.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.de.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.de.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.es.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.es.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.es.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fa.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fa.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fa.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fr.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fr.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.fr.png and /dev/null differ diff --git a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ru.png b/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ru.png deleted file mode 100644 index 54e79786..00000000 Binary files a/translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ru.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.ar.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.ar.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.ar.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.de.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.de.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.de.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.es.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.es.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.es.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.fa.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.fa.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.fa.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.fr.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.fr.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.fr.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a.ru.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a.ru.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a.ru.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ar.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ar.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ar.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.de.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.de.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.de.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.es.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.es.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.es.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fa.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fa.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fa.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fr.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fr.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.fr.png and /dev/null differ diff --git a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ru.png b/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ru.png deleted file mode 100644 index 0422909f..00000000 Binary files a/translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ru.png and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.ar.jpg b/translated_images/image.896e8254a2c60d45.ar.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.ar.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.de.jpg b/translated_images/image.896e8254a2c60d45.de.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.de.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.es.jpg b/translated_images/image.896e8254a2c60d45.es.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.es.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.fa.jpg b/translated_images/image.896e8254a2c60d45.fa.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.fa.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.fr.jpg b/translated_images/image.896e8254a2c60d45.fr.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.fr.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d45.ru.jpg b/translated_images/image.896e8254a2c60d45.ru.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d45.ru.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ar.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ar.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ar.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.de.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.de.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.de.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.es.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.es.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.es.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fa.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fa.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fa.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fr.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fr.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.fr.jpg and /dev/null differ diff --git a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ru.jpg b/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ru.jpg deleted file mode 100644 index 599c8388..00000000 Binary files a/translated_images/image.896e8254a2c60d451dbc648a499c3ed5d4b596bea265a3ca7ea581b5bc50d32f.ru.jpg and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.ar.png b/translated_images/inception.a6605b85bcbc6f52.ar.png deleted file mode 100644 index ea3c1be0..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.ar.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.de.png b/translated_images/inception.a6605b85bcbc6f52.de.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.de.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.es.png b/translated_images/inception.a6605b85bcbc6f52.es.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.es.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.fa.png b/translated_images/inception.a6605b85bcbc6f52.fa.png deleted file mode 100644 index fdf2634c..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.fa.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.fr.png b/translated_images/inception.a6605b85bcbc6f52.fr.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.fr.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52.ru.png b/translated_images/inception.a6605b85bcbc6f52.ru.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52.ru.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ar.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ar.png deleted file mode 100644 index ea3c1be0..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ar.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.de.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.de.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.de.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.es.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.es.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.es.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fa.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fa.png deleted file mode 100644 index fdf2634c..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fa.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fr.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fr.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.fr.png and /dev/null differ diff --git a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ru.png b/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ru.png deleted file mode 100644 index e0def5a5..00000000 Binary files a/translated_images/inception.a6605b85bcbc6f52490ec55e68109dd41924cba9d7e1007453b4cdf554199c8d.ru.png and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.ar.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.ar.jpeg deleted file mode 100644 index 91d1ad5a..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.ar.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.de.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.de.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.de.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.es.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.es.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.es.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.fa.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.fa.jpeg deleted file mode 100644 index fd4bf8c3..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.fa.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.fr.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.fr.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.fr.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd45.ru.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd45.ru.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd45.ru.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ar.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ar.jpeg deleted file mode 100644 index 91d1ad5a..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ar.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.de.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.de.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.de.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.es.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.es.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.es.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fa.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fa.jpeg deleted file mode 100644 index fd4bf8c3..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fa.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fr.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fr.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.fr.jpeg and /dev/null differ diff --git a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ru.jpeg b/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ru.jpeg deleted file mode 100644 index fb0e780f..00000000 Binary files a/translated_images/instance_vs_semantic.eee9812bebf8cd450cdef4caaed2d4dd9c6c3b671e0c65d8aef312758ebb7b89.ru.jpeg and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.ar.png b/translated_images/iou_equation.9a4751d40fff4e11.ar.png deleted file mode 100644 index eaaa8292..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.ar.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.de.png b/translated_images/iou_equation.9a4751d40fff4e11.de.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.de.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.es.png b/translated_images/iou_equation.9a4751d40fff4e11.es.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.es.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.fa.png b/translated_images/iou_equation.9a4751d40fff4e11.fa.png deleted file mode 100644 index 00862cce..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.fa.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.fr.png b/translated_images/iou_equation.9a4751d40fff4e11.fr.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.fr.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e11.ru.png b/translated_images/iou_equation.9a4751d40fff4e11.ru.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e11.ru.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ar.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ar.png deleted file mode 100644 index eaaa8292..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ar.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.de.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.de.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.de.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.es.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.es.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.es.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fa.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fa.png deleted file mode 100644 index 00862cce..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fa.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fr.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fr.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.fr.png and /dev/null differ diff --git a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ru.png b/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ru.png deleted file mode 100644 index 1f7dc380..00000000 Binary files a/translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ru.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png deleted file mode 100644 index 4d68b7f4..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.de.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.de.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.de.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.es.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.es.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.es.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fa.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fa.png deleted file mode 100644 index b6163a61..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fa.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fr.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fr.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fr.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ar.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ar.png deleted file mode 100644 index 4d68b7f4..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ar.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.de.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.de.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.de.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.es.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.es.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.es.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fa.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fa.png deleted file mode 100644 index b6163a61..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fa.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fr.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fr.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.fr.png and /dev/null differ diff --git a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ru.png b/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ru.png deleted file mode 100644 index eee9c653..00000000 Binary files a/translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ru.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.ar.png b/translated_images/knowledge-spectrum.b60df631852c0217.ar.png deleted file mode 100644 index 809f6623..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.ar.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.de.png b/translated_images/knowledge-spectrum.b60df631852c0217.de.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.de.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.es.png b/translated_images/knowledge-spectrum.b60df631852c0217.es.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.es.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.fa.png b/translated_images/knowledge-spectrum.b60df631852c0217.fa.png deleted file mode 100644 index ca5c4523..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.fa.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.fr.png b/translated_images/knowledge-spectrum.b60df631852c0217.fr.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.fr.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217.ru.png b/translated_images/knowledge-spectrum.b60df631852c0217.ru.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217.ru.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ar.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ar.png deleted file mode 100644 index 809f6623..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ar.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.de.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.de.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.de.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.es.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.es.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.es.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fa.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fa.png deleted file mode 100644 index ca5c4523..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fa.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fr.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fr.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.fr.png and /dev/null differ diff --git a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ru.png b/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ru.png deleted file mode 100644 index f34df277..00000000 Binary files a/translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ru.png and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.ar.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.ar.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.ar.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.de.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.de.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.de.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.es.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.es.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.es.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.fa.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.fa.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.fa.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.fr.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.fr.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.fr.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74c.ru.jpg b/translated_images/lmfilters.ea9e4868a82cf74c.ru.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74c.ru.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ar.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ar.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ar.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.de.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.de.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.de.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.es.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.es.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.es.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fa.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fa.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fa.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fr.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fr.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.fr.jpg and /dev/null differ diff --git a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ru.jpg b/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ru.jpg deleted file mode 100644 index 4edba1d7..00000000 Binary files a/translated_images/lmfilters.ea9e4868a82cf74cdca05121b1128f0122297cf2879501cd06956beb88ff96a2.ru.jpg and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.ar.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.ar.png deleted file mode 100644 index e1fa6919..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.ar.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.de.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.de.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.de.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.es.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.es.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.es.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.fa.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.fa.png deleted file mode 100644 index b586c643..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.fa.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.fr.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.fr.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.fr.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d.ru.png b/translated_images/ml-for-beginners.9e4fed176fd5817d.ru.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d.ru.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ar.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ar.png deleted file mode 100644 index e1fa6919..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ar.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.de.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.de.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.de.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.es.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.es.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.es.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fa.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fa.png deleted file mode 100644 index b586c643..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fa.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fr.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fr.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.fr.png and /dev/null differ diff --git a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ru.png b/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ru.png deleted file mode 100644 index 3df7d73b..00000000 Binary files a/translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.ru.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.ar.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.ar.png deleted file mode 100644 index 7e859b3d..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.ar.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.de.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.de.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.de.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.es.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.es.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.es.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.fa.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.fa.png deleted file mode 100644 index 6cbe9b13..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.fa.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.fr.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.fr.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.fr.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b.ru.png b/translated_images/mountaincar.f7b7a7f6d4f9933b.ru.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b.ru.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ar.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ar.png deleted file mode 100644 index 7e859b3d..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ar.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.de.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.de.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.de.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.es.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.es.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.es.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fa.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fa.png deleted file mode 100644 index 6cbe9b13..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fa.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fr.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fr.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.fr.png and /dev/null differ diff --git a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ru.png b/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ru.png deleted file mode 100644 index f84dfd1a..00000000 Binary files a/translated_images/mountaincar.f7b7a7f6d4f9933b31a5fb3453b9c026aa0d65f6644bb03513a955590aae1bc4.ru.png and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg deleted file mode 100644 index 62c1cc5f..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.de.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.de.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.de.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.es.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.es.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.es.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fa.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fa.jpg deleted file mode 100644 index 6cc4eba9..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fa.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fr.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fr.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.fr.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ar.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ar.jpg deleted file mode 100644 index 62c1cc5f..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ar.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.de.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.de.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.de.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.es.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.es.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.es.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fa.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fa.jpg deleted file mode 100644 index 6cc4eba9..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fa.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fr.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fr.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.fr.jpg and /dev/null differ diff --git a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ru.jpg b/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ru.jpg deleted file mode 100644 index 61cfc567..00000000 Binary files a/translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ru.jpg and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.ar.png b/translated_images/naive-detection.e7f1ba220ccd08c6.ar.png deleted file mode 100644 index fe75de3e..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.ar.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.de.png b/translated_images/naive-detection.e7f1ba220ccd08c6.de.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.de.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.es.png b/translated_images/naive-detection.e7f1ba220ccd08c6.es.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.es.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.fa.png b/translated_images/naive-detection.e7f1ba220ccd08c6.fa.png deleted file mode 100644 index 6f9429f7..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.fa.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.fr.png b/translated_images/naive-detection.e7f1ba220ccd08c6.fr.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.fr.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c6.ru.png b/translated_images/naive-detection.e7f1ba220ccd08c6.ru.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c6.ru.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ar.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ar.png deleted file mode 100644 index fe75de3e..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ar.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.de.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.de.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.de.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.es.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.es.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.es.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fa.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fa.png deleted file mode 100644 index 6f9429f7..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fa.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fr.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fr.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.fr.png and /dev/null differ diff --git a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ru.png b/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ru.png deleted file mode 100644 index 0979d36f..00000000 Binary files a/translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ru.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.ar.png b/translated_images/navi.2f20b727910110ea.ar.png deleted file mode 100644 index 9fe23e7e..00000000 Binary files a/translated_images/navi.2f20b727910110ea.ar.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.de.png b/translated_images/navi.2f20b727910110ea.de.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea.de.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.es.png b/translated_images/navi.2f20b727910110ea.es.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea.es.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.fa.png b/translated_images/navi.2f20b727910110ea.fa.png deleted file mode 100644 index 9fe23e7e..00000000 Binary files a/translated_images/navi.2f20b727910110ea.fa.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.fr.png b/translated_images/navi.2f20b727910110ea.fr.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea.fr.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea.ru.png b/translated_images/navi.2f20b727910110ea.ru.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea.ru.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ar.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ar.png deleted file mode 100644 index 9fe23e7e..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ar.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.de.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.de.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.de.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.es.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.es.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.es.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fa.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fa.png deleted file mode 100644 index 9fe23e7e..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fa.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fr.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fr.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.fr.png and /dev/null differ diff --git a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ru.png b/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ru.png deleted file mode 100644 index d98c5694..00000000 Binary files a/translated_images/navi.2f20b727910110ea593fa03a2491f2ba1b25c62c97b33c692bcf01917a1f333f.ru.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.ar.png b/translated_images/netout.1eb15eb76fd76731.ar.png deleted file mode 100644 index a8a741a5..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.ar.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.de.png b/translated_images/netout.1eb15eb76fd76731.de.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.de.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.es.png b/translated_images/netout.1eb15eb76fd76731.es.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.es.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.fa.png b/translated_images/netout.1eb15eb76fd76731.fa.png deleted file mode 100644 index b266a879..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.fa.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.fr.png b/translated_images/netout.1eb15eb76fd76731.fr.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.fr.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd76731.ru.png b/translated_images/netout.1eb15eb76fd76731.ru.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd76731.ru.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ar.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ar.png deleted file mode 100644 index a8a741a5..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ar.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.de.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.de.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.de.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.es.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.es.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.es.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fa.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fa.png deleted file mode 100644 index b266a879..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fa.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fr.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fr.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.fr.png and /dev/null differ diff --git a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ru.png b/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ru.png deleted file mode 100644 index ea31c5bd..00000000 Binary files a/translated_images/netout.1eb15eb76fd767313e067719f400cec4b0e5090239c3e997c29f6789d4c3c263.ru.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ar.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ar.png deleted file mode 100644 index 0b137cb7..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ar.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.de.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.de.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.de.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.es.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.es.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.es.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fa.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fa.png deleted file mode 100644 index 05cf3a85..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fa.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fr.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fr.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.fr.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ru.png b/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ru.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46ee.ru.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ar.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ar.png deleted file mode 100644 index 0b137cb7..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ar.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.de.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.de.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.de.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.es.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.es.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.es.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fa.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fa.png deleted file mode 100644 index 05cf3a85..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fa.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fr.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fr.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.fr.png and /dev/null differ diff --git a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ru.png b/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ru.png deleted file mode 100644 index 61d9e1f5..00000000 Binary files a/translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ru.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.ar.png b/translated_images/optical.1f4a94464579a83a.ar.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.ar.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.de.png b/translated_images/optical.1f4a94464579a83a.de.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.de.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.es.png b/translated_images/optical.1f4a94464579a83a.es.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.es.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.fa.png b/translated_images/optical.1f4a94464579a83a.fa.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.fa.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.fr.png b/translated_images/optical.1f4a94464579a83a.fr.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.fr.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a.ru.png b/translated_images/optical.1f4a94464579a83a.ru.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a.ru.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ar.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ar.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ar.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.de.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.de.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.de.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.es.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.es.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.es.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fa.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fa.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fa.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fr.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fr.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.fr.png and /dev/null differ diff --git a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ru.png b/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ru.png deleted file mode 100644 index f6d867c3..00000000 Binary files a/translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ru.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.ar.png b/translated_images/original-dog.8f68a67d2fe0911f.ar.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.ar.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.de.png b/translated_images/original-dog.8f68a67d2fe0911f.de.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.de.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.es.png b/translated_images/original-dog.8f68a67d2fe0911f.es.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.es.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.fa.png b/translated_images/original-dog.8f68a67d2fe0911f.fa.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.fa.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.fr.png b/translated_images/original-dog.8f68a67d2fe0911f.fr.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.fr.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f.ru.png b/translated_images/original-dog.8f68a67d2fe0911f.ru.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f.ru.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ar.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ar.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ar.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.de.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.de.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.de.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.es.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.es.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.es.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fa.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fa.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fa.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fr.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fr.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.fr.png and /dev/null differ diff --git a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ru.png b/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ru.png deleted file mode 100644 index da672bbd..00000000 Binary files a/translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ru.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.ar.png b/translated_images/overfit.a0bd57f717c15769.ar.png deleted file mode 100644 index 0f36f0ee..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.ar.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.de.png b/translated_images/overfit.a0bd57f717c15769.de.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.de.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.es.png b/translated_images/overfit.a0bd57f717c15769.es.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.es.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.fa.png b/translated_images/overfit.a0bd57f717c15769.fa.png deleted file mode 100644 index 0f36f0ee..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.fa.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.fr.png b/translated_images/overfit.a0bd57f717c15769.fr.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.fr.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c15769.ru.png b/translated_images/overfit.a0bd57f717c15769.ru.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c15769.ru.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ar.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ar.png deleted file mode 100644 index 0f36f0ee..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ar.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.de.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.de.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.de.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.es.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.es.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.es.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fa.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fa.png deleted file mode 100644 index 0f36f0ee..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fa.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fr.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fr.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.fr.png and /dev/null differ diff --git a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ru.png b/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ru.png deleted file mode 100644 index 760da1ed..00000000 Binary files a/translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ru.png and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.ar.jpg b/translated_images/overfit1.f24b71c6f652e59e.ar.jpg deleted file mode 100644 index 773d40f2..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.ar.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.de.jpg b/translated_images/overfit1.f24b71c6f652e59e.de.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.de.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.es.jpg b/translated_images/overfit1.f24b71c6f652e59e.es.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.es.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.fa.jpg b/translated_images/overfit1.f24b71c6f652e59e.fa.jpg deleted file mode 100644 index d23c0e86..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.fa.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.fr.jpg b/translated_images/overfit1.f24b71c6f652e59e.fr.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.fr.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e.ru.jpg b/translated_images/overfit1.f24b71c6f652e59e.ru.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e.ru.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ar.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ar.jpg deleted file mode 100644 index 773d40f2..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ar.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.de.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.de.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.de.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.es.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.es.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.es.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fa.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fa.jpg deleted file mode 100644 index d23c0e86..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fa.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fr.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fr.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.fr.jpg and /dev/null differ diff --git a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ru.jpg b/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ru.jpg deleted file mode 100644 index f61a8ffd..00000000 Binary files a/translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ru.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.ar.jpg b/translated_images/overfit2.131f5800ae10ca5e.ar.jpg deleted file mode 100644 index e70a2d7d..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.ar.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.de.jpg b/translated_images/overfit2.131f5800ae10ca5e.de.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.de.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.es.jpg b/translated_images/overfit2.131f5800ae10ca5e.es.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.es.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.fa.jpg b/translated_images/overfit2.131f5800ae10ca5e.fa.jpg deleted file mode 100644 index 55ec9dbb..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.fa.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.fr.jpg b/translated_images/overfit2.131f5800ae10ca5e.fr.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.fr.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e.ru.jpg b/translated_images/overfit2.131f5800ae10ca5e.ru.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e.ru.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ar.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ar.jpg deleted file mode 100644 index e70a2d7d..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ar.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.de.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.de.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.de.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.es.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.es.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.es.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fa.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fa.jpg deleted file mode 100644 index 55ec9dbb..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fa.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fr.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fr.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.fr.jpg and /dev/null differ diff --git a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ru.jpg b/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ru.jpg deleted file mode 100644 index 56803185..00000000 Binary files a/translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ru.jpg and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.ar.png b/translated_images/palm-movement.341495f0e9c47da3.ar.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.ar.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.de.png b/translated_images/palm-movement.341495f0e9c47da3.de.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.de.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.es.png b/translated_images/palm-movement.341495f0e9c47da3.es.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.es.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.fa.png b/translated_images/palm-movement.341495f0e9c47da3.fa.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.fa.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.fr.png b/translated_images/palm-movement.341495f0e9c47da3.fr.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.fr.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da3.ru.png b/translated_images/palm-movement.341495f0e9c47da3.ru.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da3.ru.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ar.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ar.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ar.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.de.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.de.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.de.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.es.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.es.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.es.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fa.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fa.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fa.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fr.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fr.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.fr.png and /dev/null differ diff --git a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ru.png b/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ru.png deleted file mode 100644 index bb70c019..00000000 Binary files a/translated_images/palm-movement.341495f0e9c47da39cc1f99626822a1d20203aa33ff89a86a068f14bea133e84.ru.png and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.ar.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.ar.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.ar.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.de.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.de.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.de.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.es.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.es.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.es.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.fa.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.fa.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.fa.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.fr.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.fr.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.fr.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe457.ru.jpg b/translated_images/photo-cat.8c8e8fb760ffe457.ru.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe457.ru.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ar.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ar.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ar.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.de.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.de.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.de.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.es.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.es.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.es.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fa.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fa.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fa.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fr.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fr.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.fr.jpg and /dev/null differ diff --git a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ru.jpg b/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ru.jpg deleted file mode 100644 index 776f24aa..00000000 Binary files a/translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ru.jpg and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.ar.png b/translated_images/pos-embedding.e41ce9b6cf6078af.ar.png deleted file mode 100644 index f2bb2b6b..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.ar.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.de.png b/translated_images/pos-embedding.e41ce9b6cf6078af.de.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.de.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.es.png b/translated_images/pos-embedding.e41ce9b6cf6078af.es.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.es.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.fa.png b/translated_images/pos-embedding.e41ce9b6cf6078af.fa.png deleted file mode 100644 index 14b10e54..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.fa.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.fr.png b/translated_images/pos-embedding.e41ce9b6cf6078af.fr.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.fr.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078af.ru.png b/translated_images/pos-embedding.e41ce9b6cf6078af.ru.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078af.ru.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ar.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ar.png deleted file mode 100644 index f2bb2b6b..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ar.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.de.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.de.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.de.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.es.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.es.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.es.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fa.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fa.png deleted file mode 100644 index 14b10e54..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fa.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fr.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fr.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.fr.png and /dev/null differ diff --git a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ru.png b/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ru.png deleted file mode 100644 index 1b8e8df5..00000000 Binary files a/translated_images/pos-embedding.e41ce9b6cf6078afd28da02f27e33ac7026ed4c156491df7ad9aa96be7c194bb.ru.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.ar.png b/translated_images/protege.274177ceeac13b38.ar.png deleted file mode 100644 index fc4d7664..00000000 Binary files a/translated_images/protege.274177ceeac13b38.ar.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.de.png b/translated_images/protege.274177ceeac13b38.de.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38.de.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.es.png b/translated_images/protege.274177ceeac13b38.es.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38.es.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.fa.png b/translated_images/protege.274177ceeac13b38.fa.png deleted file mode 100644 index 799ae332..00000000 Binary files a/translated_images/protege.274177ceeac13b38.fa.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.fr.png b/translated_images/protege.274177ceeac13b38.fr.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38.fr.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38.ru.png b/translated_images/protege.274177ceeac13b38.ru.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38.ru.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ar.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ar.png deleted file mode 100644 index fc4d7664..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ar.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.de.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.de.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.de.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.es.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.es.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.es.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fa.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fa.png deleted file mode 100644 index 799ae332..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fa.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fr.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fr.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.fr.png and /dev/null differ diff --git a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ru.png b/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ru.png deleted file mode 100644 index 06bcee87..00000000 Binary files a/translated_images/protege.274177ceeac13b38094bc425073776bb0d2525620ad6261b9d9760ebd2a8e322.ru.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.ar.png b/translated_images/r-fcn.13eb88158b99a3da.ar.png deleted file mode 100644 index 48737624..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.ar.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.de.png b/translated_images/r-fcn.13eb88158b99a3da.de.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.de.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.es.png b/translated_images/r-fcn.13eb88158b99a3da.es.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.es.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.fa.png b/translated_images/r-fcn.13eb88158b99a3da.fa.png deleted file mode 100644 index d6b41f9b..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.fa.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.fr.png b/translated_images/r-fcn.13eb88158b99a3da.fr.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.fr.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da.ru.png b/translated_images/r-fcn.13eb88158b99a3da.ru.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da.ru.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ar.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ar.png deleted file mode 100644 index 48737624..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ar.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.de.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.de.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.de.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.es.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.es.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.es.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fa.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fa.png deleted file mode 100644 index d6b41f9b..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fa.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fr.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fr.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.fr.png and /dev/null differ diff --git a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ru.png b/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ru.png deleted file mode 100644 index 244a46d6..00000000 Binary files a/translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ru.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.ar.png b/translated_images/rcnn1.cae407020dfb1d1f.ar.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.ar.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.de.png b/translated_images/rcnn1.cae407020dfb1d1f.de.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.de.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.es.png b/translated_images/rcnn1.cae407020dfb1d1f.es.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.es.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.fa.png b/translated_images/rcnn1.cae407020dfb1d1f.fa.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.fa.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.fr.png b/translated_images/rcnn1.cae407020dfb1d1f.fr.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.fr.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1f.ru.png b/translated_images/rcnn1.cae407020dfb1d1f.ru.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1f.ru.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ar.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ar.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ar.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.de.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.de.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.de.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.es.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.es.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.es.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fa.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fa.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fa.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fr.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fr.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.fr.png and /dev/null differ diff --git a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ru.png b/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ru.png deleted file mode 100644 index 50f70d5b..00000000 Binary files a/translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ru.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.ar.png b/translated_images/rcnn2.2d9530bb83516484.ar.png deleted file mode 100644 index 6c86c6b9..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.ar.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.de.png b/translated_images/rcnn2.2d9530bb83516484.de.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.de.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.es.png b/translated_images/rcnn2.2d9530bb83516484.es.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.es.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.fa.png b/translated_images/rcnn2.2d9530bb83516484.fa.png deleted file mode 100644 index a2dd851a..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.fa.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.fr.png b/translated_images/rcnn2.2d9530bb83516484.fr.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.fr.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484.ru.png b/translated_images/rcnn2.2d9530bb83516484.ru.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484.ru.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ar.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ar.png deleted file mode 100644 index 6c86c6b9..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ar.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.de.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.de.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.de.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.es.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.es.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.es.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fa.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fa.png deleted file mode 100644 index a2dd851a..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fa.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fr.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fr.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.fr.png and /dev/null differ diff --git a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ru.png b/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ru.png deleted file mode 100644 index fcad5ec0..00000000 Binary files a/translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ru.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.ar.png b/translated_images/resnet-block.aba4ccbcc0944434.ar.png deleted file mode 100644 index 3ea639de..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.ar.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.de.png b/translated_images/resnet-block.aba4ccbcc0944434.de.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.de.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.es.png b/translated_images/resnet-block.aba4ccbcc0944434.es.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.es.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.fa.png b/translated_images/resnet-block.aba4ccbcc0944434.fa.png deleted file mode 100644 index 665a80d7..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.fa.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.fr.png b/translated_images/resnet-block.aba4ccbcc0944434.fr.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.fr.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc0944434.ru.png b/translated_images/resnet-block.aba4ccbcc0944434.ru.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc0944434.ru.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ar.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ar.png deleted file mode 100644 index 3ea639de..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ar.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.de.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.de.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.de.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.es.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.es.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.es.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fa.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fa.png deleted file mode 100644 index 665a80d7..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fa.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fr.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fr.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.fr.png and /dev/null differ diff --git a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ru.png b/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ru.png deleted file mode 100644 index 970d8ef0..00000000 Binary files a/translated_images/resnet-block.aba4ccbcc094443477d7bee189d44fed695c852d710a702462d6b809155d959a.ru.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.ar.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.ar.png deleted file mode 100644 index 5fa16fd5..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.ar.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.de.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.de.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.de.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.es.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.es.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.es.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.fa.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.fa.png deleted file mode 100644 index c8eea61c..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.fa.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.fr.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.fr.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.fr.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294b.ru.png b/translated_images/rnn-anatomy.79ee3f3920b3294b.ru.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294b.ru.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ar.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ar.png deleted file mode 100644 index 5fa16fd5..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ar.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.de.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.de.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.de.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.es.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.es.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.es.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fa.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fa.png deleted file mode 100644 index c8eea61c..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fa.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fr.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fr.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.fr.png and /dev/null differ diff --git a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ru.png b/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ru.png deleted file mode 100644 index 554cb468..00000000 Binary files a/translated_images/rnn-anatomy.79ee3f3920b3294bd52e9543ef36102dbca3cecf6d4d78d8c63bf0bbc95fdc90.ru.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.ar.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.ar.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.ar.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.de.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.de.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.de.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.es.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.es.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.es.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.fa.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.fa.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.fa.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.fr.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.fr.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.fr.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eea.ru.png b/translated_images/rnn-generate-inf.5168dc65e0370eea.ru.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eea.ru.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ar.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ar.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ar.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.de.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.de.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.de.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.es.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.es.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.es.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fa.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fa.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fa.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fr.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fr.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.fr.png and /dev/null differ diff --git a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ru.png b/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ru.png deleted file mode 100644 index 0498e867..00000000 Binary files a/translated_images/rnn-generate-inf.5168dc65e0370eeab36f83885ba6b5bf56265698de5ddbd8648dc3653e1f0b9b.ru.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.ar.png b/translated_images/rnn-generate.56c54afb52f9781d.ar.png deleted file mode 100644 index 864cc4fe..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.ar.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.de.png b/translated_images/rnn-generate.56c54afb52f9781d.de.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.de.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.es.png b/translated_images/rnn-generate.56c54afb52f9781d.es.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.es.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.fa.png b/translated_images/rnn-generate.56c54afb52f9781d.fa.png deleted file mode 100644 index 52ae38a8..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.fa.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.fr.png b/translated_images/rnn-generate.56c54afb52f9781d.fr.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.fr.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d.ru.png b/translated_images/rnn-generate.56c54afb52f9781d.ru.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d.ru.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ar.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ar.png deleted file mode 100644 index 864cc4fe..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ar.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.de.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.de.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.de.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.es.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.es.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.es.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fa.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fa.png deleted file mode 100644 index 52ae38a8..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fa.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fr.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fr.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.fr.png and /dev/null differ diff --git a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ru.png b/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ru.png deleted file mode 100644 index f79ced50..00000000 Binary files a/translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ru.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.ar.png b/translated_images/rnn.27f5c29c53d727b5.ar.png deleted file mode 100644 index 8e52793b..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.ar.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.de.png b/translated_images/rnn.27f5c29c53d727b5.de.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.de.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.es.png b/translated_images/rnn.27f5c29c53d727b5.es.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.es.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.fa.png b/translated_images/rnn.27f5c29c53d727b5.fa.png deleted file mode 100644 index 3552d256..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.fa.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.fr.png b/translated_images/rnn.27f5c29c53d727b5.fr.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.fr.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b5.ru.png b/translated_images/rnn.27f5c29c53d727b5.ru.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b5.ru.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ar.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ar.png deleted file mode 100644 index 8e52793b..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ar.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.de.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.de.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.de.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.es.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.es.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.es.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fa.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fa.png deleted file mode 100644 index 3552d256..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fa.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fr.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fr.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.fr.png and /dev/null differ diff --git a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ru.png b/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ru.png deleted file mode 100644 index 6dc149a8..00000000 Binary files a/translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ru.png and /dev/null differ diff --git a/translated_images/ru/.co-op-translator.json b/translated_images/ru/.co-op-translator.json new file mode 100644 index 00000000..ef0f6a0f --- /dev/null +++ b/translated_images/ru/.co-op-translator.json @@ -0,0 +1,722 @@ +{ + "favicon.37b561214b36d454.webp": { + "original_hash": "228faa6584f8ba1f7e9a75e3200112e9", + "translation_date": "2026-01-15T11:04:04+00:00", + "source_file": "images/favicon.png", + "language_code": "ru" + }, + "ai-nlp.b22dcb8ca4707cea.webp": { + "original_hash": "fc9259e7fd3bcbc2ce15909701a4c284", + "translation_date": "2026-01-15T11:04:28+00:00", + "source_file": "lessons/sketchnotes/ai-nlp.png", + "language_code": "ru" + }, + "ai-symbolic.715a30cb610411a6.webp": { + "original_hash": "69d3628566f680ac8543651aa6fd520c", + "translation_date": "2026-01-15T11:05:15+00:00", + "source_file": "lessons/sketchnotes/ai-symbolic.png", + "language_code": "ru" + }, + "ai-computervision.6506ebebac3fbf76.webp": { + "original_hash": "69793d04780af37a3f32c9aa5c2d1ad8", + "translation_date": "2026-01-15T11:06:03+00:00", + "source_file": "lessons/sketchnotes/ai-computervision.png", + "language_code": "ru" + }, + "ai-for-beginners.b354ca904b22cd70.webp": { + "original_hash": "4b6f076798ec72ec7736b2e9039730e3", + "translation_date": "2026-01-15T11:06:26+00:00", + "source_file": "lessons/sketchnotes/ai-for-beginners.png", + "language_code": "ru" + }, + "ai-overview.0857791951d19500.webp": { + "original_hash": "7206f99da5c2b99d21581f946a660235", + "translation_date": "2026-01-15T11:06:54+00:00", + "source_file": "lessons/sketchnotes/ai-overview.png", + "language_code": "ru" + }, + "ai-intro.bf28d1ac4235881c.webp": { + "original_hash": "9d1e65416cc17cf9a373a1cff01f04a4", + "translation_date": "2026-01-15T11:07:35+00:00", + "source_file": "lessons/sketchnotes/ai-intro.png", + "language_code": "ru" + }, + "ai-neuralnetworks.1c687ae40bc86e83.webp": { + "original_hash": "6426ed635d4beb0ee499d634dfb61d65", + "translation_date": "2026-01-15T11:08:17+00:00", + "source_file": "lessons/sketchnotes/ai-neuralnetworks.png", + "language_code": "ru" + }, + "vae.464c465a5b6a9e25.webp": { + "original_hash": "0b658c7862077e139162c756bcb98071", + "translation_date": "2026-01-15T11:08:37+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vae.png", + "language_code": "ru" + }, + "vaemnist-diag.694315f775d5d666.webp": { + "original_hash": "0652f5a95005348c6b1533438103dfcf", + "translation_date": "2026-01-15T11:08:42+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist-diag.png", + "language_code": "ru" + }, + "autoencoder_schema.5e6fc9ad98a5eb61.webp": { + "original_hash": "3fb68572368c18bc334ef8d6dec0fee5", + "translation_date": "2026-01-15T11:08:47+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/autoencoder_schema.jpg", + "language_code": "ru" + }, + "vaemnist.cab9e602dc08dc50.webp": { + "original_hash": "8159b2f649e6dd8efa071455d6f222b6", + "translation_date": "2026-01-15T11:08:54+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/vaemnist.png", + "language_code": "ru" + }, + "aae.9d418a990fc75bf9.webp": { + "original_hash": "92f89b37641f659a7d25c91d13076f69", + "translation_date": "2026-01-15T11:09:02+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/images/aae.png", + "language_code": "ru" + }, + "instance_vs_semantic.eee9812bebf8cd45.webp": { + "original_hash": "f21d7fdd7090fd9f8a3c1178a8521076", + "translation_date": "2026-01-15T11:09:13+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/instance_vs_semantic.jpeg", + "language_code": "ru" + }, + "unet.3adb555bf39d3657.webp": { + "original_hash": "49a151c11708ee9a3e94d21448146bf8", + "translation_date": "2026-01-15T11:09:32+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/unet.png", + "language_code": "ru" + }, + "navi.2f20b727910110ea.webp": { + "original_hash": "fd3a1b48f7317e152edb2d1d943da24e", + "translation_date": "2026-01-15T11:09:42+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/navi.png", + "language_code": "ru" + }, + "segm.92442f2cb42ff4fa.webp": { + "original_hash": "87d6cbfe87db8fd64b45fa75ba863e60", + "translation_date": "2026-01-15T11:09:53+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segm.png", + "language_code": "ru" + }, + "segnet.87377542aa5a8b76.webp": { + "original_hash": "fa6d2d499aa2ae589a14caede7534561", + "translation_date": "2026-01-15T11:10:05+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/images/segnet.png", + "language_code": "ru" + }, + "gan_architecture.8f3a5ab62b8d5d69.webp": { + "original_hash": "4993c22b32d24c9d2d087645c2a90d81", + "translation_date": "2026-01-15T11:10:18+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_architecture.png", + "language_code": "ru" + }, + "style.5293e85e077ab22c.webp": { + "original_hash": "f797246177c68cc33bcdf7abae8c9b68", + "translation_date": "2026-01-15T11:10:25+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/style.jpg", + "language_code": "ru" + }, + "image.896e8254a2c60d45.webp": { + "original_hash": "0033165481f191386403b85801811833", + "translation_date": "2026-01-15T11:10:28+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/image.jpg", + "language_code": "ru" + }, + "gan_arch_detail.46b95fd366f8e543.webp": { + "original_hash": "ba3b25748ca02c3e725e9a1969ae5c5d", + "translation_date": "2026-01-15T11:10:41+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/gan_arch_detail.png", + "language_code": "ru" + }, + "dcgan_generator.b500988ec2bc8ba5.webp": { + "original_hash": "a11642e50f68e41c69b1580c9b724ef1", + "translation_date": "2026-01-15T11:10:57+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/images/dcgan_generator.png", + "language_code": "ru" + }, + "ideal-zebra.7f70e8b54ee15a7a.webp": { + "original_hash": "6944fb46795f714f0ce8e856c405498a", + "translation_date": "2026-01-15T11:11:02+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-zebra.png", + "language_code": "ru" + }, + "ideal-cat-loop.999fbb8ff306e044.webp": { + "original_hash": "c54e983c591431338bf8ddac9ab8415b", + "translation_date": "2026-01-15T11:11:10+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat-loop.png", + "language_code": "ru" + }, + "dog-from-unsplash.426f9fbca2febc93.webp": { + "original_hash": "9669f6d7c9c8b74efdd1a82a262d1766", + "translation_date": "2026-01-15T11:11:15+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/dog-from-unsplash.jpg", + "language_code": "ru" + }, + "features.6291f9c7ba3a0b95.webp": { + "original_hash": "e2727bfacc1156e045d1879bbe86e189", + "translation_date": "2026-01-15T11:11:20+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/features.png", + "language_code": "ru" + }, + "ideal-cat.203dd4597643d6b0.webp": { + "original_hash": "5b44760be530a91be18a5cceebbc2cc3", + "translation_date": "2026-01-15T11:11:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/ideal-cat.png", + "language_code": "ru" + }, + "adversarial-dog.d9fc7773b0142b89.webp": { + "original_hash": "cf7a833a6f82bdf5049180a09f9bf8f6", + "translation_date": "2026-01-15T11:11:23+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/adversarial-dog.png", + "language_code": "ru" + }, + "catsdogsdataset.a7aff8e6085fd3f0.webp": { + "original_hash": "3039ba35434f67e69ccbb44d9e6ee141", + "translation_date": "2026-01-15T11:11:28+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/catsdogsdataset.png", + "language_code": "ru" + }, + "original-dog.8f68a67d2fe0911f.webp": { + "original_hash": "3ed859629b4141f735fd0d3c1941f520", + "translation_date": "2026-01-15T11:11:31+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/images/original-dog.png", + "language_code": "ru" + }, + "braille.341962ff76b1bd70.webp": { + "original_hash": "ef1d59d4fb17f1f019c18be162c5759a", + "translation_date": "2026-01-15T11:11:33+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/data/braille.jpeg", + "language_code": "ru" + }, + "braille-symbols.0159185ab69d5339.webp": { + "original_hash": "5a7fddf66299dbb4eae490b631ee3a1c", + "translation_date": "2026-01-15T11:11:37+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-symbols.png", + "language_code": "ru" + }, + "frame-difference.706f805491a0883c.webp": { + "original_hash": "a141e72f08e1b8f7451392889af90072", + "translation_date": "2026-01-15T11:11:41+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/frame-difference.png", + "language_code": "ru" + }, + "braille-result.46530fea020b03c7.webp": { + "original_hash": "7c069b6ddda38dcd0b535a840b954ee1", + "translation_date": "2026-01-15T11:11:44+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/braille-result.png", + "language_code": "ru" + }, + "palm-movement.341495f0e9c47da3.webp": { + "original_hash": "100780b2e1d5adff2739adf58fc18404", + "translation_date": "2026-01-15T11:11:47+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/palm-movement.png", + "language_code": "ru" + }, + "optical.1f4a94464579a83a.webp": { + "original_hash": "1be97f7f2f58285c9960a90e5d5ae337", + "translation_date": "2026-01-15T11:11:50+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/images/optical.png", + "language_code": "ru" + }, + "1200px-Girl_and_cat.89bd70951181e86a.webp": { + "original_hash": "aa8cdeaa9beaad5a06610236cf22f296", + "translation_date": "2026-01-15T11:11:53+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/1200px-Girl_and_cat.jpg", + "language_code": "ru" + }, + "ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp": { + "original_hash": "f31ea979f0ceabdf198899985ac84c37", + "translation_date": "2026-01-15T11:12:04+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecallIoU.png", + "language_code": "ru" + }, + "naive-detection.e7f1ba220ccd08c6.webp": { + "original_hash": "4c96961c12a09f8e055546ed2e7de470", + "translation_date": "2026-01-15T11:12:16+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/naive-detection.png", + "language_code": "ru" + }, + "ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp": { + "original_hash": "6704085c522f47ce42ad8402dafe6429", + "translation_date": "2026-01-15T11:12:26+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/ObjDetectionPrecisionRecall.png", + "language_code": "ru" + }, + "iou_equation.9a4751d40fff4e11.webp": { + "original_hash": "0a27f4ab37ead8d77cdf94a0bbc99e4a", + "translation_date": "2026-01-15T11:12:32+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/iou_equation.png", + "language_code": "ru" + }, + "coco-examples.71bc60380fa6cceb.webp": { + "original_hash": "bcfe5693147ca18d88cf43b1d2d5d6d7", + "translation_date": "2026-01-15T11:12:39+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/coco-examples.jpg", + "language_code": "ru" + }, + "f-rcnn.3cda6d9bb4188875.webp": { + "original_hash": "fdafedad20701c95bdcf6bb923822ab1", + "translation_date": "2026-01-15T11:12:49+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/f-rcnn.png", + "language_code": "ru" + }, + "rcnn1.cae407020dfb1d1f.webp": { + "original_hash": "9cf0ea86d9dadf06a31cd109efc49796", + "translation_date": "2026-01-15T11:12:54+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn1.png", + "language_code": "ru" + }, + "faster-rcnn.8d46c099b87ef30a.webp": { + "original_hash": "067e2ca96344fd6d0490e0530a922b36", + "translation_date": "2026-01-15T11:13:03+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/faster-rcnn.png", + "language_code": "ru" + }, + "r-fcn.13eb88158b99a3da.webp": { + "original_hash": "38cd0e0105546e56fa17b711d445195f", + "translation_date": "2026-01-15T11:13:17+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/r-fcn.png", + "language_code": "ru" + }, + "yolo.a2648ec82ee8bb4e.webp": { + "original_hash": "e8835638234f5c21fcf36fdede6457f4", + "translation_date": "2026-01-15T11:13:26+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/yolo.png", + "language_code": "ru" + }, + "rcnn2.2d9530bb83516484.webp": { + "original_hash": "cc44ad151b6d88e2838db475e7ab5dff", + "translation_date": "2026-01-15T11:13:39+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/rcnn2.png", + "language_code": "ru" + }, + "Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp": { + "original_hash": "456419e07e4f1b5ab90c6f6d36d9817a", + "translation_date": "2026-01-15T11:14:04+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/images/Screen_Shot_2016-11-17_at_11.14.54_AM.png", + "language_code": "ru" + }, + "resnet-block.aba4ccbcc0944434.webp": { + "original_hash": "cf2131c7cb1053d6d2559ef83df057a8", + "translation_date": "2026-01-15T11:14:19+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/resnet-block.png", + "language_code": "ru" + }, + "cnn-pyramid.85915455759ef0ce.webp": { + "original_hash": "7233ded4a920332806363d704bfb59a6", + "translation_date": "2026-01-15T11:14:45+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/cnn-pyramid.png", + "language_code": "ru" + }, + "FeatureExtractionCNN.d9b456cbdae7cb64.webp": { + "original_hash": "c9bde577c8b279b5199a8b294ee9494f", + "translation_date": "2026-01-15T11:15:00+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/FeatureExtractionCNN.png", + "language_code": "ru" + }, + "filter-horiz.59b80ed4feb946ef.webp": { + "original_hash": "b081df9e2042850983a46ff817b88d55", + "translation_date": "2026-01-15T11:15:05+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-horiz.png", + "language_code": "ru" + }, + "vgg-16-arch1.d901a5583b3a51ba.webp": { + "original_hash": "5b0f835d04dc20d097a0b89c88339966", + "translation_date": "2026-01-15T11:15:16+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch1.jpg", + "language_code": "ru" + }, + "vgg-16-arch.64ff2137f50dd49f.webp": { + "original_hash": "9fc348503d0ec03875e4b46f896f3aa9", + "translation_date": "2026-01-15T11:15:27+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/vgg-16-arch.jpg", + "language_code": "ru" + }, + "convolutionExample.634615d5ff7081e0.webp": { + "original_hash": "5412e092848cb3cd4fa527aba3be0545", + "translation_date": "2026-01-15T11:15:39+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/convolutionExample.png", + "language_code": "ru" + }, + "inception.a6605b85bcbc6f52.webp": { + "original_hash": "ebf0f6b0257fffb906b532d86fce2a9c", + "translation_date": "2026-01-15T11:15:51+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/inception.png", + "language_code": "ru" + }, + "filter-vert.b7148390ca0bc356.webp": { + "original_hash": "bef527fda3ae7113ec9ebb3ab74a6298", + "translation_date": "2026-01-15T11:15:57+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/filter-vert.png", + "language_code": "ru" + }, + "lmfilters.ea9e4868a82cf74c.webp": { + "original_hash": "aa029ea2c45afbac3026a0a558eeec43", + "translation_date": "2026-01-15T11:15:59+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/images/lmfilters.jpg", + "language_code": "ru" + }, + "data.50b2a9d5484bdbf0.webp": { + "original_hash": "e501593d25b15c8f8860fe11e3f1c4a7", + "translation_date": "2026-01-15T11:16:09+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/images/data.png", + "language_code": "ru" + }, + "NetLogo-ModelLib.efe023afb4763c05.webp": { + "original_hash": "edd5b28318e2f43274a4541408110fb9", + "translation_date": "2026-01-15T11:16:37+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-ModelLib.png", + "language_code": "ru" + }, + "NetLogo-Main.32653711ec1a01b3.webp": { + "original_hash": "83a9fe96394620fc703a5f2de2d15ed1", + "translation_date": "2026-01-15T11:17:24+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/images/NetLogo-Main.png", + "language_code": "ru" + }, + "cartpole.f52a67f27e058170.webp": { + "original_hash": "5399242e127ea1c18aa6ee405daecf51", + "translation_date": "2026-01-15T11:17:41+00:00", + "source_file": "lessons/6-Other/22-DeepRL/images/cartpole.png", + "language_code": "ru" + }, + "mountaincar.f7b7a7f6d4f9933b.webp": { + "original_hash": "61c868cc389dc92bd2b402ea490cd162", + "translation_date": "2026-01-15T11:17:44+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/images/mountaincar.png", + "language_code": "ru" + }, + "ml-for-beginners.9e4fed176fd5817d.webp": { + "original_hash": "cd606a24083e039082b0486fc8382823", + "translation_date": "2026-01-15T11:17:49+00:00", + "source_file": "lessons/1-Intro/images/ml-for-beginners.png", + "language_code": "ru" + }, + "history-of-ai.7e83efa70b537f5a.webp": { + "original_hash": "644e648b98fcb10fb9713452ff02934d", + "translation_date": "2026-01-15T11:18:06+00:00", + "source_file": "lessons/1-Intro/images/history-of-ai.png", + "language_code": "ru" + }, + "photo-cat.8c8e8fb760ffe457.webp": { + "original_hash": "29d10fef28d240c48995ef9dd23e477a", + "translation_date": "2026-01-15T11:18:18+00:00", + "source_file": "lessons/1-Intro/images/photo-cat.jpg", + "language_code": "ru" + }, + "dsh_age.d212a30d4e54fb5f.webp": { + "original_hash": "61b9b2e2e29d9be9ae8f2edd8fa14ed4", + "translation_date": "2026-01-15T11:18:21+00:00", + "source_file": "lessons/1-Intro/images/dsh_age.png", + "language_code": "ru" + }, + "turing-test-evol.4184696701293ead.webp": { + "original_hash": "80d11b1074d61e5d6e4a207f72902e89", + "translation_date": "2026-01-15T11:18:33+00:00", + "source_file": "lessons/1-Intro/images/turing-test-evol.png", + "language_code": "ru" + }, + "knowledge-spectrum.b60df631852c0217.webp": { + "original_hash": "71b049ee26a22a68996034585436ca59", + "translation_date": "2026-01-15T11:19:01+00:00", + "source_file": "lessons/2-Symbolic/images/knowledge-spectrum.png", + "language_code": "ru" + }, + "DIKW_Pyramid.94126f7d2bd8db5b.webp": { + "original_hash": "ad410ec7f25454482fd4516c4a46fc67", + "translation_date": "2026-01-15T11:19:18+00:00", + "source_file": "lessons/2-Symbolic/images/DIKW_Pyramid.png", + "language_code": "ru" + }, + "AND-OR-Tree.5592d2c70187f283.webp": { + "original_hash": "2674b6cf8f768491c6b5a167f272a002", + "translation_date": "2026-01-15T11:20:22+00:00", + "source_file": "lessons/2-Symbolic/images/AND-OR-Tree.png", + "language_code": "ru" + }, + "triplet-complex.32094972c7b4441b.webp": { + "original_hash": "56a2e05839a141c311db52655b37f8ad", + "translation_date": "2026-01-15T11:21:18+00:00", + "source_file": "lessons/2-Symbolic/images/triplet-complex.png", + "language_code": "ru" + }, + "arch-human.5d4d35f1bba3ab1c.webp": { + "original_hash": "3b41c1a38a69fb1104f5ddc48163538d", + "translation_date": "2026-01-15T11:21:43+00:00", + "source_file": "lessons/2-Symbolic/images/arch-human.png", + "language_code": "ru" + }, + "arch-kbs.3ec5c150b09fa8da.webp": { + "original_hash": "f470e6ec071db529cd3149052cfeb8ff", + "translation_date": "2026-01-15T11:21:57+00:00", + "source_file": "lessons/2-Symbolic/images/arch-kbs.png", + "language_code": "ru" + }, + "triplet.4b9b332587593298.webp": { + "original_hash": "302e53ea355ffb556d99e3962d0a6516", + "translation_date": "2026-01-15T11:22:18+00:00", + "source_file": "lessons/2-Symbolic/images/triplet.png", + "language_code": "ru" + }, + "protege.274177ceeac13b38.webp": { + "original_hash": "56d5a5aba9b9e89a927f7fdc42ba16f5", + "translation_date": "2026-01-15T11:23:11+00:00", + "source_file": "lessons/2-Symbolic/images/protege.png", + "language_code": "ru" + }, + "synapse-wikipedia.ed20a9e4726ea1c6.webp": { + "original_hash": "88212730ecd9b0c8b848c319951427d8", + "translation_date": "2026-01-15T11:23:41+00:00", + "source_file": "lessons/3-NeuralNetworks/images/synapse-wikipedia.jpg", + "language_code": "ru" + }, + "overfit2.131f5800ae10ca5e.webp": { + "original_hash": "eb04381b2f222518ec400b98ebf441b2", + "translation_date": "2026-01-15T11:23:48+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit2.jpg", + "language_code": "ru" + }, + "netout.1eb15eb76fd76731.webp": { + "original_hash": "187261e2b5036746ee9d9106b1f7df1b", + "translation_date": "2026-01-15T11:23:53+00:00", + "source_file": "lessons/3-NeuralNetworks/images/netout.png", + "language_code": "ru" + }, + "artneuron.1a5daa88d20ebe6f.webp": { + "original_hash": "c2d8af8285e1eee1cf6c5ba4762f70ab", + "translation_date": "2026-01-15T11:24:00+00:00", + "source_file": "lessons/3-NeuralNetworks/images/artneuron.png", + "language_code": "ru" + }, + "Overfitting.408ad91cd90b4371.webp": { + "original_hash": "82c3203fefc4ef74609e8ab61c1c683b", + "translation_date": "2026-01-15T11:24:15+00:00", + "source_file": "lessons/3-NeuralNetworks/images/Overfitting.png", + "language_code": "ru" + }, + "overfit1.f24b71c6f652e59e.webp": { + "original_hash": "76155698e85340d9dfff8e0a628d25c1", + "translation_date": "2026-01-15T11:24:25+00:00", + "source_file": "lessons/3-NeuralNetworks/images/overfit1.jpg", + "language_code": "ru" + }, + "NeuroArch.4e17cdba5e445721.webp": { + "original_hash": "5a3b1a4c04e6b6b5a65574d5ea92a272", + "translation_date": "2026-01-15T11:24:33+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/NeuroArch.png", + "language_code": "ru" + }, + "ComputeGraphGrad.4626252c0de03507.webp": { + "original_hash": "b0bf679245a237b1e7785ae2357a5376", + "translation_date": "2026-01-15T11:24:41+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraphGrad.png", + "language_code": "ru" + }, + "Cross-Entropy-Loss.dc7ba633d2467ef3.webp": { + "original_hash": "11dd0fd77a272755d0ef5db9f71217da", + "translation_date": "2026-01-15T11:24:52+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/Cross-Entropy-Loss.png", + "language_code": "ru" + }, + "ComputeGraph.463c9d8ebdcb2215.webp": { + "original_hash": "7af66742d6bede114f971689745bedf2", + "translation_date": "2026-01-15T11:25:00+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/ComputeGraph.png", + "language_code": "ru" + }, + "overfit.a0bd57f717c15769.webp": { + "original_hash": "810408efec3fc8cb44a2b4bc53466a6b", + "translation_date": "2026-01-15T11:25:06+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/images/overfit.png", + "language_code": "ru" + }, + "Rosenblatt-wikipedia.294821b285ac796d.webp": { + "original_hash": "c21d9b47d0106208a0f35a920d6bff66", + "translation_date": "2026-01-15T11:25:12+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Rosenblatt-wikipedia.jpg", + "language_code": "ru" + }, + "Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp": { + "original_hash": "2cc48ff435c89ca1e162872a42b4813c", + "translation_date": "2026-01-15T11:25:16+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/Mark_I_perceptron_wikipedia.jpg", + "language_code": "ru" + }, + "activation-func.b4924007c7ce7764.webp": { + "original_hash": "8d357884343ec923611a319d4e910b99", + "translation_date": "2026-01-15T11:25:21+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/images/activation-func.png", + "language_code": "ru" + }, + "clip-class.3af42ef0b2b19369.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:31+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-class.png", + "language_code": "ru" + }, + "a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp": { + "original_hash": "9c386a6fd2c04edbd78dc9d688b59872", + "translation_date": "2026-01-15T11:25:38+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_old_male_teacher_of_math.png", + "language_code": "ru" + }, + "clip-arch.b3dbf20b4e8ed8be.webp": { + "original_hash": "bdd1638453069a216e843e1a8fd70db0", + "translation_date": "2026-01-15T11:25:48+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/clip-arch.png", + "language_code": "ru" + }, + "DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp": { + "original_hash": "e300d75d78f6050bc7b83df091f95775", + "translation_date": "2026-01-15T11:25:55+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.png", + "language_code": "ru" + }, + "DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp": { + "original_hash": "dd063fda04db937ab7210064af9d6151", + "translation_date": "2026-01-15T11:25:58+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.png", + "language_code": "ru" + }, + "DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp": { + "original_hash": "2cc9ab0daf8e1af7071fbc9eaf84970b", + "translation_date": "2026-01-15T11:26:02+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.png", + "language_code": "ru" + }, + "a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp": { + "original_hash": "ec89cbb600b14e86f353a347506554ba", + "translation_date": "2026-01-15T11:26:04+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.png", + "language_code": "ru" + }, + "a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp": { + "original_hash": "7064cd71a562523998203c756842c96d", + "translation_date": "2026-01-15T11:26:06+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.png", + "language_code": "ru" + }, + "vqgan.5027fe05051dfa31.webp": { + "original_hash": "845e5593c927fd3c3c125a90d0f8eb87", + "translation_date": "2026-01-15T11:26:10+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/images/vqgan.png", + "language_code": "ru" + }, + "bag-of-words-example.606fc1738f1d7ba9.webp": { + "original_hash": "a8fa84622ff35939e498d06fac3fa515", + "translation_date": "2026-01-15T11:26:20+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bag-of-words-example.png", + "language_code": "ru" + }, + "bow.3811869cff59368d.webp": { + "original_hash": "bfcb38af6b1cbc8c2e86101127b642ac", + "translation_date": "2026-01-15T11:26:51+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/bow.png", + "language_code": "ru" + }, + "ascii-character-map.18ed6aa7f3b0a7ff.webp": { + "original_hash": "afc5ff2601be320926d89f1ad897eda8", + "translation_date": "2026-01-15T11:27:15+00:00", + "source_file": "lessons/5-NLP/13-TextRep/images/ascii-character-map.png", + "language_code": "ru" + }, + "multi-layer-lstm.dd975e29bb2a59fe.webp": { + "original_hash": "27b6355fd86c9e8e6d443015653be763", + "translation_date": "2026-01-15T11:27:27+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/multi-layer-lstm.jpg", + "language_code": "ru" + }, + "rnn-anatomy.79ee3f3920b3294b.webp": { + "original_hash": "cac3c7102f2bc410efd6230b3bc58c63", + "translation_date": "2026-01-15T11:27:41+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn-anatomy.png", + "language_code": "ru" + }, + "rnn.27f5c29c53d727b5.webp": { + "original_hash": "bed0f1884d1a2c2620e6ba741af994b8", + "translation_date": "2026-01-15T11:28:04+00:00", + "source_file": "lessons/5-NLP/16-RNN/images/rnn.png", + "language_code": "ru" + }, + "encoder-decoder-attention.7a726296894fb567.webp": { + "original_hash": "e0546cbc8252d764df7f7212b371f0ed", + "translation_date": "2026-01-15T11:28:26+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/encoder-decoder-attention.png", + "language_code": "ru" + }, + "jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp": { + "original_hash": "47f7de9ada12b89dcfa75d81eb0aaf15", + "translation_date": "2026-01-15T11:28:49+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/jalammarBERT-language-modeling-masked-lm.png", + "language_code": "ru" + }, + "bahdanau-fig3.09ba2d37f202a6af.webp": { + "original_hash": "b5cfbb9cad3b0d3fbdcc63dcd2ed514f", + "translation_date": "2026-01-15T11:29:03+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/bahdanau-fig3.png", + "language_code": "ru" + }, + "pos-embedding.e41ce9b6cf6078af.webp": { + "original_hash": "f0910082dcac063f3baae2e1f28b4e30", + "translation_date": "2026-01-15T11:29:21+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/pos-embedding.png", + "language_code": "ru" + }, + "transformer-layer.905e14747ca4e7d5.webp": { + "original_hash": "dc9e2e401fa5e5c36a322b6721476ccd", + "translation_date": "2026-01-15T11:29:40+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/transformer-layer.png", + "language_code": "ru" + }, + "CoreferenceResolution.861924d6d384a7d6.webp": { + "original_hash": "25b5719ec70aaa44858076c16f7c208f", + "translation_date": "2026-01-15T11:29:56+00:00", + "source_file": "lessons/5-NLP/18-Transformers/images/CoreferenceResolution.png", + "language_code": "ru" + }, + "bot-ner.4b09235dbb0ad275.webp": { + "original_hash": "5e61aca4f94182c7e7d509b8d2de3c9f", + "translation_date": "2026-01-15T11:30:23+00:00", + "source_file": "lessons/5-NLP/19-NER/images/bot-ner.png", + "language_code": "ru" + }, + "unreasonable-effectiveness-of-rnn.541ead816778f42d.webp": { + "original_hash": "2a37bd4e9b12bcc19e045eaf22fea4e5", + "translation_date": "2026-01-15T11:30:40+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/unreasonable-effectiveness-of-rnn.jpg", + "language_code": "ru" + }, + "rnn-generate.56c54afb52f9781d.webp": { + "original_hash": "67332ada2d0603921bba37c06956c70e", + "translation_date": "2026-01-15T11:30:51+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate.png", + "language_code": "ru" + }, + "rnn-generate-inf.5168dc65e0370eea.webp": { + "original_hash": "d3ed5c87de90c01b0b77ca02580b3707", + "translation_date": "2026-01-15T11:31:00+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/images/rnn-generate-inf.png", + "language_code": "ru" + }, + "embedding-classifier-example.b77f021a7ee67eee.webp": { + "original_hash": "0c4233111f66d9c6925af5b97f9519a0", + "translation_date": "2026-01-15T11:31:12+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/embedding-classifier-example.png", + "language_code": "ru" + }, + "offset-sequence-representation.eb73fcefb29b46ee.webp": { + "original_hash": "52e7632a2b668957b903bb2607e953fe", + "translation_date": "2026-01-15T11:31:25+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/offset-sequence-representation.png", + "language_code": "ru" + }, + "example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp": { + "original_hash": "6e976f26b9bb4c7a021187e834132123", + "translation_date": "2026-01-15T11:31:36+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/images/example-algorithms-for-converting-words-to-vectors.png", + "language_code": "ru" + } +} \ No newline at end of file diff --git a/translated_images/ru/1200px-Girl_and_cat.89bd70951181e86a.webp b/translated_images/ru/1200px-Girl_and_cat.89bd70951181e86a.webp new file mode 100644 index 00000000..47042c75 Binary files /dev/null and b/translated_images/ru/1200px-Girl_and_cat.89bd70951181e86a.webp differ diff --git a/translated_images/ru/AND-OR-Tree.5592d2c70187f283.webp b/translated_images/ru/AND-OR-Tree.5592d2c70187f283.webp new file mode 100644 index 00000000..19bd9691 Binary files /dev/null and b/translated_images/ru/AND-OR-Tree.5592d2c70187f283.webp differ diff --git a/translated_images/ru/ComputeGraph.463c9d8ebdcb2215.webp b/translated_images/ru/ComputeGraph.463c9d8ebdcb2215.webp new file mode 100644 index 00000000..83c6797a Binary files /dev/null and b/translated_images/ru/ComputeGraph.463c9d8ebdcb2215.webp differ diff --git a/translated_images/ru/ComputeGraphGrad.4626252c0de03507.webp b/translated_images/ru/ComputeGraphGrad.4626252c0de03507.webp new file mode 100644 index 00000000..b23d1191 Binary files /dev/null and b/translated_images/ru/ComputeGraphGrad.4626252c0de03507.webp differ diff --git a/translated_images/ru/CoreferenceResolution.861924d6d384a7d6.webp b/translated_images/ru/CoreferenceResolution.861924d6d384a7d6.webp new file mode 100644 index 00000000..4f8556fd Binary files /dev/null and b/translated_images/ru/CoreferenceResolution.861924d6d384a7d6.webp differ diff --git a/translated_images/ru/Cross-Entropy-Loss.dc7ba633d2467ef3.webp b/translated_images/ru/Cross-Entropy-Loss.dc7ba633d2467ef3.webp new file mode 100644 index 00000000..b9ce74a8 Binary files /dev/null and b/translated_images/ru/Cross-Entropy-Loss.dc7ba633d2467ef3.webp differ diff --git a/translated_images/ru/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp b/translated_images/ru/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp new file mode 100644 index 00000000..222db344 Binary files /dev/null and b/translated_images/ru/DALL·E 2023-06-20 15.56.56 - a closeup watercolor portrait of young male teacher of literature with a book.56499a0c282407c9.webp differ diff --git a/translated_images/ru/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp b/translated_images/ru/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp new file mode 100644 index 00000000..f3b973c5 Binary files /dev/null and b/translated_images/ru/DALL·E 2023-06-20 15.57.43 - a closeup oil portrait of young female teacher of computer science with a computer.081e8aed073e2fbf.webp differ diff --git a/translated_images/ru/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp b/translated_images/ru/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp new file mode 100644 index 00000000..b8f4286a Binary files /dev/null and b/translated_images/ru/DALL·E 2023-06-20 15.58.42 - a closeup oil portrait of old male teacher of mathematics in front of blackboard.534175cf58a361d9.webp differ diff --git a/translated_images/ru/DIKW_Pyramid.94126f7d2bd8db5b.webp b/translated_images/ru/DIKW_Pyramid.94126f7d2bd8db5b.webp new file mode 100644 index 00000000..1597ea85 Binary files /dev/null and b/translated_images/ru/DIKW_Pyramid.94126f7d2bd8db5b.webp differ diff --git a/translated_images/ru/FeatureExtractionCNN.d9b456cbdae7cb64.webp b/translated_images/ru/FeatureExtractionCNN.d9b456cbdae7cb64.webp new file mode 100644 index 00000000..4a71c7c8 Binary files /dev/null and b/translated_images/ru/FeatureExtractionCNN.d9b456cbdae7cb64.webp differ diff --git a/translated_images/ru/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp b/translated_images/ru/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp new file mode 100644 index 00000000..41d4fa84 Binary files /dev/null and b/translated_images/ru/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp differ diff --git a/translated_images/ru/NetLogo-Main.32653711ec1a01b3.webp b/translated_images/ru/NetLogo-Main.32653711ec1a01b3.webp new file mode 100644 index 00000000..67ac8f31 Binary files /dev/null and b/translated_images/ru/NetLogo-Main.32653711ec1a01b3.webp differ diff --git a/translated_images/ru/NetLogo-ModelLib.efe023afb4763c05.webp b/translated_images/ru/NetLogo-ModelLib.efe023afb4763c05.webp new file mode 100644 index 00000000..7ba32f17 Binary files /dev/null and b/translated_images/ru/NetLogo-ModelLib.efe023afb4763c05.webp differ diff --git a/translated_images/ru/NeuroArch.4e17cdba5e445721.webp b/translated_images/ru/NeuroArch.4e17cdba5e445721.webp new file mode 100644 index 00000000..cfde5422 Binary files /dev/null and b/translated_images/ru/NeuroArch.4e17cdba5e445721.webp differ diff --git a/translated_images/ru/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp b/translated_images/ru/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp new file mode 100644 index 00000000..4b3320a0 Binary files /dev/null and b/translated_images/ru/ObjDetectionPrecisionRecall.d2ef5b6081a0b094.webp differ diff --git a/translated_images/ru/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp b/translated_images/ru/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp new file mode 100644 index 00000000..82a99d38 Binary files /dev/null and b/translated_images/ru/ObjDetectionPrecisionRecallIoU.96b4116fcd30ce5a.webp differ diff --git a/translated_images/ru/Overfitting.408ad91cd90b4371.webp b/translated_images/ru/Overfitting.408ad91cd90b4371.webp new file mode 100644 index 00000000..78b8c02d Binary files /dev/null and b/translated_images/ru/Overfitting.408ad91cd90b4371.webp differ diff --git a/translated_images/ru/Rosenblatt-wikipedia.294821b285ac796d.webp b/translated_images/ru/Rosenblatt-wikipedia.294821b285ac796d.webp new file mode 100644 index 00000000..eaf330c0 Binary files /dev/null and b/translated_images/ru/Rosenblatt-wikipedia.294821b285ac796d.webp differ diff --git a/translated_images/ru/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp b/translated_images/ru/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp new file mode 100644 index 00000000..d3238e0b Binary files /dev/null and b/translated_images/ru/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp differ diff --git a/translated_images/ru/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp b/translated_images/ru/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp new file mode 100644 index 00000000..ee032762 Binary files /dev/null and b/translated_images/ru/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp differ diff --git a/translated_images/ru/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp b/translated_images/ru/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp new file mode 100644 index 00000000..52ce29a9 Binary files /dev/null and b/translated_images/ru/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp differ diff --git a/translated_images/ru/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp b/translated_images/ru/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp new file mode 100644 index 00000000..fba04870 Binary files /dev/null and b/translated_images/ru/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp differ diff --git a/translated_images/ru/aae.9d418a990fc75bf9.webp b/translated_images/ru/aae.9d418a990fc75bf9.webp new file mode 100644 index 00000000..7899c46c Binary files /dev/null and b/translated_images/ru/aae.9d418a990fc75bf9.webp differ diff --git a/translated_images/ru/activation-func.b4924007c7ce7764.webp b/translated_images/ru/activation-func.b4924007c7ce7764.webp new file mode 100644 index 00000000..8138807f Binary files /dev/null and b/translated_images/ru/activation-func.b4924007c7ce7764.webp differ diff --git a/translated_images/ru/adversarial-dog.d9fc7773b0142b89.webp b/translated_images/ru/adversarial-dog.d9fc7773b0142b89.webp new file mode 100644 index 00000000..8816fc84 Binary files /dev/null and b/translated_images/ru/adversarial-dog.d9fc7773b0142b89.webp differ diff --git a/translated_images/ru/ai-computervision.6506ebebac3fbf76.webp b/translated_images/ru/ai-computervision.6506ebebac3fbf76.webp new file mode 100644 index 00000000..9b7f7e02 Binary files /dev/null and b/translated_images/ru/ai-computervision.6506ebebac3fbf76.webp differ diff --git a/translated_images/ru/ai-for-beginners.b354ca904b22cd70.webp b/translated_images/ru/ai-for-beginners.b354ca904b22cd70.webp new file mode 100644 index 00000000..562870a2 Binary files /dev/null and b/translated_images/ru/ai-for-beginners.b354ca904b22cd70.webp differ diff --git a/translated_images/ru/ai-intro.bf28d1ac4235881c.webp b/translated_images/ru/ai-intro.bf28d1ac4235881c.webp new file mode 100644 index 00000000..d73894c9 Binary files /dev/null and b/translated_images/ru/ai-intro.bf28d1ac4235881c.webp differ diff --git a/translated_images/ru/ai-neuralnetworks.1c687ae40bc86e83.webp b/translated_images/ru/ai-neuralnetworks.1c687ae40bc86e83.webp new file mode 100644 index 00000000..ddfe47eb Binary files /dev/null and b/translated_images/ru/ai-neuralnetworks.1c687ae40bc86e83.webp differ diff --git a/translated_images/ru/ai-nlp.b22dcb8ca4707cea.webp b/translated_images/ru/ai-nlp.b22dcb8ca4707cea.webp new file mode 100644 index 00000000..3520fca6 Binary files /dev/null and b/translated_images/ru/ai-nlp.b22dcb8ca4707cea.webp differ diff --git a/translated_images/ru/ai-overview.0857791951d19500.webp b/translated_images/ru/ai-overview.0857791951d19500.webp new file mode 100644 index 00000000..5fc3506e Binary files /dev/null and b/translated_images/ru/ai-overview.0857791951d19500.webp differ diff --git a/translated_images/ru/ai-symbolic.715a30cb610411a6.webp b/translated_images/ru/ai-symbolic.715a30cb610411a6.webp new file mode 100644 index 00000000..5fdc9289 Binary files /dev/null and b/translated_images/ru/ai-symbolic.715a30cb610411a6.webp differ diff --git a/translated_images/ru/arch-human.5d4d35f1bba3ab1c.webp b/translated_images/ru/arch-human.5d4d35f1bba3ab1c.webp new file mode 100644 index 00000000..c4713e73 Binary files /dev/null and b/translated_images/ru/arch-human.5d4d35f1bba3ab1c.webp differ diff --git a/translated_images/ru/arch-kbs.3ec5c150b09fa8da.webp b/translated_images/ru/arch-kbs.3ec5c150b09fa8da.webp new file mode 100644 index 00000000..2f49067c Binary files /dev/null and b/translated_images/ru/arch-kbs.3ec5c150b09fa8da.webp differ diff --git a/translated_images/ru/artneuron.1a5daa88d20ebe6f.webp b/translated_images/ru/artneuron.1a5daa88d20ebe6f.webp new file mode 100644 index 00000000..9e300afa Binary files /dev/null and b/translated_images/ru/artneuron.1a5daa88d20ebe6f.webp differ diff --git a/translated_images/ru/ascii-character-map.18ed6aa7f3b0a7ff.webp b/translated_images/ru/ascii-character-map.18ed6aa7f3b0a7ff.webp new file mode 100644 index 00000000..aecce96a Binary files /dev/null and b/translated_images/ru/ascii-character-map.18ed6aa7f3b0a7ff.webp differ diff --git a/translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp b/translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp new file mode 100644 index 00000000..ba50e329 Binary files /dev/null and b/translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp differ diff --git a/translated_images/ru/bag-of-words-example.606fc1738f1d7ba9.webp b/translated_images/ru/bag-of-words-example.606fc1738f1d7ba9.webp new file mode 100644 index 00000000..89561211 Binary files /dev/null and b/translated_images/ru/bag-of-words-example.606fc1738f1d7ba9.webp differ diff --git a/translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp b/translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp new file mode 100644 index 00000000..95e01876 Binary files /dev/null and b/translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp differ diff --git a/translated_images/ru/bot-ner.4b09235dbb0ad275.webp b/translated_images/ru/bot-ner.4b09235dbb0ad275.webp new file mode 100644 index 00000000..0e730cf5 Binary files /dev/null and b/translated_images/ru/bot-ner.4b09235dbb0ad275.webp differ diff --git a/translated_images/ru/bow.3811869cff59368d.webp b/translated_images/ru/bow.3811869cff59368d.webp new file mode 100644 index 00000000..5673b392 Binary files /dev/null and b/translated_images/ru/bow.3811869cff59368d.webp differ diff --git a/translated_images/ru/braille-result.46530fea020b03c7.webp b/translated_images/ru/braille-result.46530fea020b03c7.webp new file mode 100644 index 00000000..0235d8f6 Binary files /dev/null and b/translated_images/ru/braille-result.46530fea020b03c7.webp differ diff --git a/translated_images/ru/braille-symbols.0159185ab69d5339.webp b/translated_images/ru/braille-symbols.0159185ab69d5339.webp new file mode 100644 index 00000000..839390de Binary files /dev/null and b/translated_images/ru/braille-symbols.0159185ab69d5339.webp differ diff --git a/translated_images/ru/braille.341962ff76b1bd70.webp b/translated_images/ru/braille.341962ff76b1bd70.webp new file mode 100644 index 00000000..6751696b Binary files /dev/null and b/translated_images/ru/braille.341962ff76b1bd70.webp differ diff --git a/translated_images/ru/cartpole.f52a67f27e058170.webp b/translated_images/ru/cartpole.f52a67f27e058170.webp new file mode 100644 index 00000000..848d3ee7 Binary files /dev/null and b/translated_images/ru/cartpole.f52a67f27e058170.webp differ diff --git a/translated_images/ru/catsdogsdataset.a7aff8e6085fd3f0.webp b/translated_images/ru/catsdogsdataset.a7aff8e6085fd3f0.webp new file mode 100644 index 00000000..3a251be4 Binary files /dev/null and b/translated_images/ru/catsdogsdataset.a7aff8e6085fd3f0.webp differ diff --git a/translated_images/ru/clip-arch.b3dbf20b4e8ed8be.webp b/translated_images/ru/clip-arch.b3dbf20b4e8ed8be.webp new file mode 100644 index 00000000..d94a2030 Binary files /dev/null and b/translated_images/ru/clip-arch.b3dbf20b4e8ed8be.webp differ diff --git a/translated_images/ru/clip-class.3af42ef0b2b19369.webp b/translated_images/ru/clip-class.3af42ef0b2b19369.webp new file mode 100644 index 00000000..dc734014 Binary files /dev/null and b/translated_images/ru/clip-class.3af42ef0b2b19369.webp differ diff --git a/translated_images/ru/cnn-pyramid.85915455759ef0ce.webp b/translated_images/ru/cnn-pyramid.85915455759ef0ce.webp new file mode 100644 index 00000000..1cbf4f36 Binary files /dev/null and b/translated_images/ru/cnn-pyramid.85915455759ef0ce.webp differ diff --git a/translated_images/ru/coco-examples.71bc60380fa6cceb.webp b/translated_images/ru/coco-examples.71bc60380fa6cceb.webp new file mode 100644 index 00000000..5a1bf6a9 Binary files /dev/null and b/translated_images/ru/coco-examples.71bc60380fa6cceb.webp differ diff --git a/translated_images/ru/convolutionExample.634615d5ff7081e0.webp b/translated_images/ru/convolutionExample.634615d5ff7081e0.webp new file mode 100644 index 00000000..571cbb9c Binary files /dev/null and b/translated_images/ru/convolutionExample.634615d5ff7081e0.webp differ diff --git a/translated_images/ru/data.50b2a9d5484bdbf0.webp b/translated_images/ru/data.50b2a9d5484bdbf0.webp new file mode 100644 index 00000000..c9525ef5 Binary files /dev/null and b/translated_images/ru/data.50b2a9d5484bdbf0.webp differ diff --git a/translated_images/ru/dcgan_generator.b500988ec2bc8ba5.webp b/translated_images/ru/dcgan_generator.b500988ec2bc8ba5.webp new file mode 100644 index 00000000..b78ed82d Binary files /dev/null and b/translated_images/ru/dcgan_generator.b500988ec2bc8ba5.webp differ diff --git a/translated_images/ru/dog-from-unsplash.426f9fbca2febc93.webp b/translated_images/ru/dog-from-unsplash.426f9fbca2febc93.webp new file mode 100644 index 00000000..2d2fef68 Binary files /dev/null and b/translated_images/ru/dog-from-unsplash.426f9fbca2febc93.webp differ diff --git a/translated_images/ru/dsh_age.d212a30d4e54fb5f.webp b/translated_images/ru/dsh_age.d212a30d4e54fb5f.webp new file mode 100644 index 00000000..98793010 Binary files /dev/null and b/translated_images/ru/dsh_age.d212a30d4e54fb5f.webp differ diff --git a/translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp b/translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp new file mode 100644 index 00000000..081d3c8f Binary files /dev/null and b/translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp differ diff --git a/translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp b/translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp new file mode 100644 index 00000000..4e8c7951 Binary files /dev/null and b/translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp differ diff --git a/translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp b/translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp new file mode 100644 index 00000000..21ec9a57 Binary files /dev/null and b/translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp differ diff --git a/translated_images/ru/f-rcnn.3cda6d9bb4188875.webp b/translated_images/ru/f-rcnn.3cda6d9bb4188875.webp new file mode 100644 index 00000000..4b847b3f Binary files /dev/null and b/translated_images/ru/f-rcnn.3cda6d9bb4188875.webp differ diff --git a/translated_images/ru/faster-rcnn.8d46c099b87ef30a.webp b/translated_images/ru/faster-rcnn.8d46c099b87ef30a.webp new file mode 100644 index 00000000..f345a0eb Binary files /dev/null and b/translated_images/ru/faster-rcnn.8d46c099b87ef30a.webp differ diff --git a/translated_images/ru/favicon.37b561214b36d454.webp b/translated_images/ru/favicon.37b561214b36d454.webp new file mode 100644 index 00000000..48a53960 Binary files /dev/null and b/translated_images/ru/favicon.37b561214b36d454.webp differ diff --git a/translated_images/ru/features.6291f9c7ba3a0b95.webp b/translated_images/ru/features.6291f9c7ba3a0b95.webp new file mode 100644 index 00000000..03679af2 Binary files /dev/null and b/translated_images/ru/features.6291f9c7ba3a0b95.webp differ diff --git a/translated_images/ru/filter-horiz.59b80ed4feb946ef.webp b/translated_images/ru/filter-horiz.59b80ed4feb946ef.webp new file mode 100644 index 00000000..6be71d03 Binary files /dev/null and b/translated_images/ru/filter-horiz.59b80ed4feb946ef.webp differ diff --git a/translated_images/ru/filter-vert.b7148390ca0bc356.webp b/translated_images/ru/filter-vert.b7148390ca0bc356.webp new file mode 100644 index 00000000..0c89fc40 Binary files /dev/null and b/translated_images/ru/filter-vert.b7148390ca0bc356.webp differ diff --git a/translated_images/ru/frame-difference.706f805491a0883c.webp b/translated_images/ru/frame-difference.706f805491a0883c.webp new file mode 100644 index 00000000..e7a302ad Binary files /dev/null and b/translated_images/ru/frame-difference.706f805491a0883c.webp differ diff --git a/translated_images/ru/gan_arch_detail.46b95fd366f8e543.webp b/translated_images/ru/gan_arch_detail.46b95fd366f8e543.webp new file mode 100644 index 00000000..34b0325b Binary files /dev/null and b/translated_images/ru/gan_arch_detail.46b95fd366f8e543.webp differ diff --git a/translated_images/ru/gan_architecture.8f3a5ab62b8d5d69.webp b/translated_images/ru/gan_architecture.8f3a5ab62b8d5d69.webp new file mode 100644 index 00000000..4c7d3b3c Binary files /dev/null and b/translated_images/ru/gan_architecture.8f3a5ab62b8d5d69.webp differ diff --git a/translated_images/ru/history-of-ai.7e83efa70b537f5a.webp b/translated_images/ru/history-of-ai.7e83efa70b537f5a.webp new file mode 100644 index 00000000..0d31920a Binary files /dev/null and b/translated_images/ru/history-of-ai.7e83efa70b537f5a.webp differ diff --git a/translated_images/ru/ideal-cat-loop.999fbb8ff306e044.webp b/translated_images/ru/ideal-cat-loop.999fbb8ff306e044.webp new file mode 100644 index 00000000..278668e5 Binary files /dev/null and b/translated_images/ru/ideal-cat-loop.999fbb8ff306e044.webp differ diff --git a/translated_images/ru/ideal-cat.203dd4597643d6b0.webp b/translated_images/ru/ideal-cat.203dd4597643d6b0.webp new file mode 100644 index 00000000..a8d167b1 Binary files /dev/null and b/translated_images/ru/ideal-cat.203dd4597643d6b0.webp differ diff --git a/translated_images/ru/ideal-zebra.7f70e8b54ee15a7a.webp b/translated_images/ru/ideal-zebra.7f70e8b54ee15a7a.webp new file mode 100644 index 00000000..be349483 Binary files /dev/null and b/translated_images/ru/ideal-zebra.7f70e8b54ee15a7a.webp differ diff --git a/translated_images/ru/image.896e8254a2c60d45.webp b/translated_images/ru/image.896e8254a2c60d45.webp new file mode 100644 index 00000000..d2b9d3ce Binary files /dev/null and b/translated_images/ru/image.896e8254a2c60d45.webp differ diff --git a/translated_images/ru/inception.a6605b85bcbc6f52.webp b/translated_images/ru/inception.a6605b85bcbc6f52.webp new file mode 100644 index 00000000..ca1d8058 Binary files /dev/null and b/translated_images/ru/inception.a6605b85bcbc6f52.webp differ diff --git a/translated_images/ru/instance_vs_semantic.eee9812bebf8cd45.webp b/translated_images/ru/instance_vs_semantic.eee9812bebf8cd45.webp new file mode 100644 index 00000000..a9d519ac Binary files /dev/null and b/translated_images/ru/instance_vs_semantic.eee9812bebf8cd45.webp differ diff --git a/translated_images/ru/iou_equation.9a4751d40fff4e11.webp b/translated_images/ru/iou_equation.9a4751d40fff4e11.webp new file mode 100644 index 00000000..bd1cd539 Binary files /dev/null and b/translated_images/ru/iou_equation.9a4751d40fff4e11.webp differ diff --git a/translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp b/translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp new file mode 100644 index 00000000..8f33bab3 Binary files /dev/null and b/translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp differ diff --git a/translated_images/ru/knowledge-spectrum.b60df631852c0217.webp b/translated_images/ru/knowledge-spectrum.b60df631852c0217.webp new file mode 100644 index 00000000..e3c58633 Binary files /dev/null and b/translated_images/ru/knowledge-spectrum.b60df631852c0217.webp differ diff --git a/translated_images/ru/lmfilters.ea9e4868a82cf74c.webp b/translated_images/ru/lmfilters.ea9e4868a82cf74c.webp new file mode 100644 index 00000000..73254d4b Binary files /dev/null and b/translated_images/ru/lmfilters.ea9e4868a82cf74c.webp differ diff --git a/translated_images/ru/ml-for-beginners.9e4fed176fd5817d.webp b/translated_images/ru/ml-for-beginners.9e4fed176fd5817d.webp new file mode 100644 index 00000000..aa2603e3 Binary files /dev/null and b/translated_images/ru/ml-for-beginners.9e4fed176fd5817d.webp differ diff --git a/translated_images/ru/mountaincar.f7b7a7f6d4f9933b.webp b/translated_images/ru/mountaincar.f7b7a7f6d4f9933b.webp new file mode 100644 index 00000000..f988acb9 Binary files /dev/null and b/translated_images/ru/mountaincar.f7b7a7f6d4f9933b.webp differ diff --git a/translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp b/translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp new file mode 100644 index 00000000..d2a1858a Binary files /dev/null and b/translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp differ diff --git a/translated_images/ru/naive-detection.e7f1ba220ccd08c6.webp b/translated_images/ru/naive-detection.e7f1ba220ccd08c6.webp new file mode 100644 index 00000000..6bf3e9da Binary files /dev/null and b/translated_images/ru/naive-detection.e7f1ba220ccd08c6.webp differ diff --git a/translated_images/ru/navi.2f20b727910110ea.webp b/translated_images/ru/navi.2f20b727910110ea.webp new file mode 100644 index 00000000..e9254c25 Binary files /dev/null and b/translated_images/ru/navi.2f20b727910110ea.webp differ diff --git a/translated_images/ru/netout.1eb15eb76fd76731.webp b/translated_images/ru/netout.1eb15eb76fd76731.webp new file mode 100644 index 00000000..252a6b73 Binary files /dev/null and b/translated_images/ru/netout.1eb15eb76fd76731.webp differ diff --git a/translated_images/ru/offset-sequence-representation.eb73fcefb29b46ee.webp b/translated_images/ru/offset-sequence-representation.eb73fcefb29b46ee.webp new file mode 100644 index 00000000..ef16690d Binary files /dev/null and b/translated_images/ru/offset-sequence-representation.eb73fcefb29b46ee.webp differ diff --git a/translated_images/ru/optical.1f4a94464579a83a.webp b/translated_images/ru/optical.1f4a94464579a83a.webp new file mode 100644 index 00000000..c37dddad Binary files /dev/null and b/translated_images/ru/optical.1f4a94464579a83a.webp differ diff --git a/translated_images/ru/original-dog.8f68a67d2fe0911f.webp b/translated_images/ru/original-dog.8f68a67d2fe0911f.webp new file mode 100644 index 00000000..2a002703 Binary files /dev/null and b/translated_images/ru/original-dog.8f68a67d2fe0911f.webp differ diff --git a/translated_images/ru/overfit.a0bd57f717c15769.webp b/translated_images/ru/overfit.a0bd57f717c15769.webp new file mode 100644 index 00000000..8e30e2ed Binary files /dev/null and b/translated_images/ru/overfit.a0bd57f717c15769.webp differ diff --git a/translated_images/ru/overfit1.f24b71c6f652e59e.webp b/translated_images/ru/overfit1.f24b71c6f652e59e.webp new file mode 100644 index 00000000..de604b48 Binary files /dev/null and b/translated_images/ru/overfit1.f24b71c6f652e59e.webp differ diff --git a/translated_images/ru/overfit2.131f5800ae10ca5e.webp b/translated_images/ru/overfit2.131f5800ae10ca5e.webp new file mode 100644 index 00000000..b01d4f97 Binary files /dev/null and b/translated_images/ru/overfit2.131f5800ae10ca5e.webp differ diff --git a/translated_images/ru/palm-movement.341495f0e9c47da3.webp b/translated_images/ru/palm-movement.341495f0e9c47da3.webp new file mode 100644 index 00000000..0071936c Binary files /dev/null and b/translated_images/ru/palm-movement.341495f0e9c47da3.webp differ diff --git a/translated_images/ru/photo-cat.8c8e8fb760ffe457.webp b/translated_images/ru/photo-cat.8c8e8fb760ffe457.webp new file mode 100644 index 00000000..2998a349 Binary files /dev/null and b/translated_images/ru/photo-cat.8c8e8fb760ffe457.webp differ diff --git a/translated_images/ru/pos-embedding.e41ce9b6cf6078af.webp b/translated_images/ru/pos-embedding.e41ce9b6cf6078af.webp new file mode 100644 index 00000000..b86f06e5 Binary files /dev/null and b/translated_images/ru/pos-embedding.e41ce9b6cf6078af.webp differ diff --git a/translated_images/ru/protege.274177ceeac13b38.webp b/translated_images/ru/protege.274177ceeac13b38.webp new file mode 100644 index 00000000..2fbb4734 Binary files /dev/null and b/translated_images/ru/protege.274177ceeac13b38.webp differ diff --git a/translated_images/ru/r-fcn.13eb88158b99a3da.webp b/translated_images/ru/r-fcn.13eb88158b99a3da.webp new file mode 100644 index 00000000..04776b2a Binary files /dev/null and b/translated_images/ru/r-fcn.13eb88158b99a3da.webp differ diff --git a/translated_images/ru/rcnn1.cae407020dfb1d1f.webp b/translated_images/ru/rcnn1.cae407020dfb1d1f.webp new file mode 100644 index 00000000..1c949129 Binary files /dev/null and b/translated_images/ru/rcnn1.cae407020dfb1d1f.webp differ diff --git a/translated_images/ru/rcnn2.2d9530bb83516484.webp b/translated_images/ru/rcnn2.2d9530bb83516484.webp new file mode 100644 index 00000000..4f55ff55 Binary files /dev/null and b/translated_images/ru/rcnn2.2d9530bb83516484.webp differ diff --git a/translated_images/ru/resnet-block.aba4ccbcc0944434.webp b/translated_images/ru/resnet-block.aba4ccbcc0944434.webp new file mode 100644 index 00000000..4c5b8876 Binary files /dev/null and b/translated_images/ru/resnet-block.aba4ccbcc0944434.webp differ diff --git a/translated_images/ru/rnn-anatomy.79ee3f3920b3294b.webp b/translated_images/ru/rnn-anatomy.79ee3f3920b3294b.webp new file mode 100644 index 00000000..5c5a3547 Binary files /dev/null and b/translated_images/ru/rnn-anatomy.79ee3f3920b3294b.webp differ diff --git a/translated_images/ru/rnn-generate-inf.5168dc65e0370eea.webp b/translated_images/ru/rnn-generate-inf.5168dc65e0370eea.webp new file mode 100644 index 00000000..191d8694 Binary files /dev/null and b/translated_images/ru/rnn-generate-inf.5168dc65e0370eea.webp differ diff --git a/translated_images/ru/rnn-generate.56c54afb52f9781d.webp b/translated_images/ru/rnn-generate.56c54afb52f9781d.webp new file mode 100644 index 00000000..570917d9 Binary files /dev/null and b/translated_images/ru/rnn-generate.56c54afb52f9781d.webp differ diff --git a/translated_images/ru/rnn.27f5c29c53d727b5.webp b/translated_images/ru/rnn.27f5c29c53d727b5.webp new file mode 100644 index 00000000..0ec98bd7 Binary files /dev/null and b/translated_images/ru/rnn.27f5c29c53d727b5.webp differ diff --git a/translated_images/ru/segm.92442f2cb42ff4fa.webp b/translated_images/ru/segm.92442f2cb42ff4fa.webp new file mode 100644 index 00000000..36533d5a Binary files /dev/null and b/translated_images/ru/segm.92442f2cb42ff4fa.webp differ diff --git a/translated_images/ru/segnet.87377542aa5a8b76.webp b/translated_images/ru/segnet.87377542aa5a8b76.webp new file mode 100644 index 00000000..fbe6bebd Binary files /dev/null and b/translated_images/ru/segnet.87377542aa5a8b76.webp differ diff --git a/translated_images/ru/style.5293e85e077ab22c.webp b/translated_images/ru/style.5293e85e077ab22c.webp new file mode 100644 index 00000000..39af2a0e Binary files /dev/null and b/translated_images/ru/style.5293e85e077ab22c.webp differ diff --git a/translated_images/ru/synapse-wikipedia.ed20a9e4726ea1c6.webp b/translated_images/ru/synapse-wikipedia.ed20a9e4726ea1c6.webp new file mode 100644 index 00000000..682a48e4 Binary files /dev/null and b/translated_images/ru/synapse-wikipedia.ed20a9e4726ea1c6.webp differ diff --git a/translated_images/ru/transformer-layer.905e14747ca4e7d5.webp b/translated_images/ru/transformer-layer.905e14747ca4e7d5.webp new file mode 100644 index 00000000..d9448de6 Binary files /dev/null and b/translated_images/ru/transformer-layer.905e14747ca4e7d5.webp differ diff --git a/translated_images/ru/triplet-complex.32094972c7b4441b.webp b/translated_images/ru/triplet-complex.32094972c7b4441b.webp new file mode 100644 index 00000000..4dd6ca1e Binary files /dev/null and b/translated_images/ru/triplet-complex.32094972c7b4441b.webp differ diff --git a/translated_images/ru/triplet.4b9b332587593298.webp b/translated_images/ru/triplet.4b9b332587593298.webp new file mode 100644 index 00000000..b052337a Binary files /dev/null and b/translated_images/ru/triplet.4b9b332587593298.webp differ diff --git a/translated_images/ru/turing-test-evol.4184696701293ead.webp b/translated_images/ru/turing-test-evol.4184696701293ead.webp new file mode 100644 index 00000000..3a6a5f79 Binary files /dev/null and b/translated_images/ru/turing-test-evol.4184696701293ead.webp differ diff --git a/translated_images/ru/unet.3adb555bf39d3657.webp b/translated_images/ru/unet.3adb555bf39d3657.webp new file mode 100644 index 00000000..db77b002 Binary files /dev/null and b/translated_images/ru/unet.3adb555bf39d3657.webp differ diff --git a/translated_images/ru/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp b/translated_images/ru/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp new file mode 100644 index 00000000..b957ba78 Binary files /dev/null and b/translated_images/ru/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp differ diff --git a/translated_images/ru/vae.464c465a5b6a9e25.webp b/translated_images/ru/vae.464c465a5b6a9e25.webp new file mode 100644 index 00000000..03f72a9c Binary files /dev/null and b/translated_images/ru/vae.464c465a5b6a9e25.webp differ diff --git a/translated_images/ru/vaemnist-diag.694315f775d5d666.webp b/translated_images/ru/vaemnist-diag.694315f775d5d666.webp new file mode 100644 index 00000000..49b53b6d Binary files /dev/null and b/translated_images/ru/vaemnist-diag.694315f775d5d666.webp differ diff --git a/translated_images/ru/vaemnist.cab9e602dc08dc50.webp b/translated_images/ru/vaemnist.cab9e602dc08dc50.webp new file mode 100644 index 00000000..2e523d00 Binary files /dev/null and b/translated_images/ru/vaemnist.cab9e602dc08dc50.webp differ diff --git a/translated_images/ru/vgg-16-arch.64ff2137f50dd49f.webp b/translated_images/ru/vgg-16-arch.64ff2137f50dd49f.webp new file mode 100644 index 00000000..440db07a Binary files /dev/null and b/translated_images/ru/vgg-16-arch.64ff2137f50dd49f.webp differ diff --git a/translated_images/ru/vgg-16-arch1.d901a5583b3a51ba.webp b/translated_images/ru/vgg-16-arch1.d901a5583b3a51ba.webp new file mode 100644 index 00000000..637017cf Binary files /dev/null and b/translated_images/ru/vgg-16-arch1.d901a5583b3a51ba.webp differ diff --git a/translated_images/ru/vqgan.5027fe05051dfa31.webp b/translated_images/ru/vqgan.5027fe05051dfa31.webp new file mode 100644 index 00000000..18c96026 Binary files /dev/null and b/translated_images/ru/vqgan.5027fe05051dfa31.webp differ diff --git a/translated_images/ru/yolo.a2648ec82ee8bb4e.webp b/translated_images/ru/yolo.a2648ec82ee8bb4e.webp new file mode 100644 index 00000000..09a97596 Binary files /dev/null and b/translated_images/ru/yolo.a2648ec82ee8bb4e.webp differ diff --git a/translated_images/segm.92442f2cb42ff4fa.ar.png b/translated_images/segm.92442f2cb42ff4fa.ar.png deleted file mode 100644 index f9f84f39..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.ar.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa.de.png b/translated_images/segm.92442f2cb42ff4fa.de.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.de.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa.es.png b/translated_images/segm.92442f2cb42ff4fa.es.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.es.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa.fa.png b/translated_images/segm.92442f2cb42ff4fa.fa.png deleted file mode 100644 index 623d954f..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.fa.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa.fr.png b/translated_images/segm.92442f2cb42ff4fa.fr.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.fr.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa.ru.png b/translated_images/segm.92442f2cb42ff4fa.ru.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa.ru.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ar.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ar.png deleted file mode 100644 index f9f84f39..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ar.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.de.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.de.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.de.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.es.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.es.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.es.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fa.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fa.png deleted file mode 100644 index 623d954f..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fa.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fr.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fr.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.fr.png and /dev/null differ diff --git a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ru.png b/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ru.png deleted file mode 100644 index 70ab2c9a..00000000 Binary files a/translated_images/segm.92442f2cb42ff4fa650fee858a3a02f55cf46825d9602115130e9e95a52a8526.ru.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.ar.png b/translated_images/segnet.87377542aa5a8b76.ar.png deleted file mode 100644 index 09c44ae1..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.ar.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.de.png b/translated_images/segnet.87377542aa5a8b76.de.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.de.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.es.png b/translated_images/segnet.87377542aa5a8b76.es.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.es.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.fa.png b/translated_images/segnet.87377542aa5a8b76.fa.png deleted file mode 100644 index 53e757aa..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.fa.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.fr.png b/translated_images/segnet.87377542aa5a8b76.fr.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.fr.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76.ru.png b/translated_images/segnet.87377542aa5a8b76.ru.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76.ru.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ar.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ar.png deleted file mode 100644 index 09c44ae1..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ar.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.de.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.de.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.de.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.es.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.es.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.es.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fa.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fa.png deleted file mode 100644 index 53e757aa..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fa.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fr.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fr.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.fr.png and /dev/null differ diff --git a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ru.png b/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ru.png deleted file mode 100644 index 99e82b8a..00000000 Binary files a/translated_images/segnet.87377542aa5a8b76c6e19b715e86daec8da5c1124df41ab6064d8409b8579be6.ru.png and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.ar.jpg b/translated_images/style.5293e85e077ab22c.ar.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.ar.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.de.jpg b/translated_images/style.5293e85e077ab22c.de.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.de.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.es.jpg b/translated_images/style.5293e85e077ab22c.es.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.es.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.fa.jpg b/translated_images/style.5293e85e077ab22c.fa.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.fa.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.fr.jpg b/translated_images/style.5293e85e077ab22c.fr.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.fr.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22c.ru.jpg b/translated_images/style.5293e85e077ab22c.ru.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22c.ru.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ar.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ar.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ar.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.de.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.de.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.de.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.es.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.es.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.es.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fa.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fa.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fa.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fr.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fr.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.fr.jpg and /dev/null differ diff --git a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ru.jpg b/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ru.jpg deleted file mode 100644 index 24361692..00000000 Binary files a/translated_images/style.5293e85e077ab22cf0e8f920132b79bd9a6d6f957085ef20079ec5eee96405c3.ru.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ar.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ar.jpg deleted file mode 100644 index 1722866c..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ar.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.de.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.de.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.de.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.es.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.es.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.es.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fa.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fa.jpg deleted file mode 100644 index efc4d8f4..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fa.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fr.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fr.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fr.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ru.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ru.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ru.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ar.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ar.jpg deleted file mode 100644 index 1722866c..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ar.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.de.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.de.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.de.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.es.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.es.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.es.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fa.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fa.jpg deleted file mode 100644 index efc4d8f4..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fa.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fr.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fr.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.fr.jpg and /dev/null differ diff --git a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ru.jpg b/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ru.jpg deleted file mode 100644 index 352902a5..00000000 Binary files a/translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ru.jpg and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.ar.png b/translated_images/transformer-layer.905e14747ca4e7d5.ar.png deleted file mode 100644 index d3e1f350..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.ar.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.de.png b/translated_images/transformer-layer.905e14747ca4e7d5.de.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.de.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.es.png b/translated_images/transformer-layer.905e14747ca4e7d5.es.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.es.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.fa.png b/translated_images/transformer-layer.905e14747ca4e7d5.fa.png deleted file mode 100644 index cc844367..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.fa.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.fr.png b/translated_images/transformer-layer.905e14747ca4e7d5.fr.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.fr.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5.ru.png b/translated_images/transformer-layer.905e14747ca4e7d5.ru.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5.ru.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ar.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ar.png deleted file mode 100644 index d3e1f350..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ar.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.de.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.de.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.de.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.es.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.es.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.es.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fa.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fa.png deleted file mode 100644 index cc844367..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fa.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fr.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fr.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.fr.png and /dev/null differ diff --git a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ru.png b/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ru.png deleted file mode 100644 index 06395901..00000000 Binary files a/translated_images/transformer-layer.905e14747ca4e7d5cf1409e8bf8944c9b1d6e4f5ce3ab167918af65c4904d727.ru.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.ar.png b/translated_images/triplet-complex.32094972c7b4441b.ar.png deleted file mode 100644 index e3487e24..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.ar.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.de.png b/translated_images/triplet-complex.32094972c7b4441b.de.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.de.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.es.png b/translated_images/triplet-complex.32094972c7b4441b.es.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.es.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.fa.png b/translated_images/triplet-complex.32094972c7b4441b.fa.png deleted file mode 100644 index 1e6692c6..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.fa.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.fr.png b/translated_images/triplet-complex.32094972c7b4441b.fr.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.fr.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b.ru.png b/translated_images/triplet-complex.32094972c7b4441b.ru.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b.ru.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ar.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ar.png deleted file mode 100644 index e3487e24..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ar.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.de.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.de.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.de.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.es.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.es.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.es.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fa.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fa.png deleted file mode 100644 index 1e6692c6..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fa.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fr.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fr.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.fr.png and /dev/null differ diff --git a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ru.png b/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ru.png deleted file mode 100644 index d95a6f12..00000000 Binary files a/translated_images/triplet-complex.32094972c7b4441b844bd85e683ba8eedc08af12177160f11584452698f29ace.ru.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.ar.png b/translated_images/triplet.4b9b332587593298.ar.png deleted file mode 100644 index 14cffc56..00000000 Binary files a/translated_images/triplet.4b9b332587593298.ar.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.de.png b/translated_images/triplet.4b9b332587593298.de.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298.de.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.es.png b/translated_images/triplet.4b9b332587593298.es.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298.es.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.fa.png b/translated_images/triplet.4b9b332587593298.fa.png deleted file mode 100644 index 56ffcc61..00000000 Binary files a/translated_images/triplet.4b9b332587593298.fa.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.fr.png b/translated_images/triplet.4b9b332587593298.fr.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298.fr.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298.ru.png b/translated_images/triplet.4b9b332587593298.ru.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298.ru.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ar.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ar.png deleted file mode 100644 index 14cffc56..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ar.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.de.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.de.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.de.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.es.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.es.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.es.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fa.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fa.png deleted file mode 100644 index 56ffcc61..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fa.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fr.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fr.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.fr.png and /dev/null differ diff --git a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ru.png b/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ru.png deleted file mode 100644 index 50d14194..00000000 Binary files a/translated_images/triplet.4b9b332587593298b31846eb5cf341d8f7e48da76e6692dbb7cf0fcf2fd5ab38.ru.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.ar.png b/translated_images/turing-test-evol.4184696701293ead.ar.png deleted file mode 100644 index ed4034be..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.ar.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.de.png b/translated_images/turing-test-evol.4184696701293ead.de.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.de.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.es.png b/translated_images/turing-test-evol.4184696701293ead.es.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.es.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.fa.png b/translated_images/turing-test-evol.4184696701293ead.fa.png deleted file mode 100644 index dcee303f..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.fa.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.fr.png b/translated_images/turing-test-evol.4184696701293ead.fr.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.fr.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead.ru.png b/translated_images/turing-test-evol.4184696701293ead.ru.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead.ru.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ar.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ar.png deleted file mode 100644 index ed4034be..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ar.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.de.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.de.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.de.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.es.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.es.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.es.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fa.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fa.png deleted file mode 100644 index dcee303f..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fa.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fr.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fr.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.fr.png and /dev/null differ diff --git a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ru.png b/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ru.png deleted file mode 100644 index 767279a8..00000000 Binary files a/translated_images/turing-test-evol.4184696701293ead6de6e6441a659c62f0b119b342456987f531005f43be0b6d.ru.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.ar.png b/translated_images/unet.3adb555bf39d3657.ar.png deleted file mode 100644 index 3f7a456c..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.ar.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.de.png b/translated_images/unet.3adb555bf39d3657.de.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.de.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.es.png b/translated_images/unet.3adb555bf39d3657.es.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.es.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.fa.png b/translated_images/unet.3adb555bf39d3657.fa.png deleted file mode 100644 index dac2fcf8..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.fa.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.fr.png b/translated_images/unet.3adb555bf39d3657.fr.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.fr.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657.ru.png b/translated_images/unet.3adb555bf39d3657.ru.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657.ru.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ar.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ar.png deleted file mode 100644 index 3f7a456c..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ar.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.de.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.de.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.de.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.es.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.es.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.es.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fa.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fa.png deleted file mode 100644 index dac2fcf8..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fa.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fr.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fr.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.fr.png and /dev/null differ diff --git a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ru.png b/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ru.png deleted file mode 100644 index ac19ba86..00000000 Binary files a/translated_images/unet.3adb555bf39d3657bf535321c7ee153dbdd32895189e915e014229a9c79cf1e3.ru.png and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ar.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ar.jpg deleted file mode 100644 index 9fcd0d02..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ar.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.de.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.de.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.de.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.es.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.es.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.es.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fa.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fa.jpg deleted file mode 100644 index 5e9eefe2..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fa.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fr.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fr.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fr.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ru.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ru.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ru.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ar.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ar.jpg deleted file mode 100644 index 9fcd0d02..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ar.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.de.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.de.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.de.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.es.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.es.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.es.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fa.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fa.jpg deleted file mode 100644 index 5e9eefe2..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fa.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fr.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fr.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.fr.jpg and /dev/null differ diff --git a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ru.jpg b/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ru.jpg deleted file mode 100644 index 93a86c53..00000000 Binary files a/translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ru.jpg and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.ar.png b/translated_images/vae.464c465a5b6a9e25.ar.png deleted file mode 100644 index dc963bb9..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.ar.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.de.png b/translated_images/vae.464c465a5b6a9e25.de.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.de.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.es.png b/translated_images/vae.464c465a5b6a9e25.es.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.es.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.fa.png b/translated_images/vae.464c465a5b6a9e25.fa.png deleted file mode 100644 index 290a44ff..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.fa.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.fr.png b/translated_images/vae.464c465a5b6a9e25.fr.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.fr.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e25.ru.png b/translated_images/vae.464c465a5b6a9e25.ru.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e25.ru.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ar.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ar.png deleted file mode 100644 index dc963bb9..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ar.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.de.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.de.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.de.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.es.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.es.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.es.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fa.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fa.png deleted file mode 100644 index 290a44ff..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fa.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fr.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fr.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.fr.png and /dev/null differ diff --git a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ru.png b/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ru.png deleted file mode 100644 index d77ae3f6..00000000 Binary files a/translated_images/vae.464c465a5b6a9e253be65a8cb3be1724832cbde57ece3912ddc962b199472a89.ru.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.ar.png b/translated_images/vaemnist-diag.694315f775d5d666.ar.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.ar.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.de.png b/translated_images/vaemnist-diag.694315f775d5d666.de.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.de.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.es.png b/translated_images/vaemnist-diag.694315f775d5d666.es.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.es.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.fa.png b/translated_images/vaemnist-diag.694315f775d5d666.fa.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.fa.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.fr.png b/translated_images/vaemnist-diag.694315f775d5d666.fr.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.fr.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666.ru.png b/translated_images/vaemnist-diag.694315f775d5d666.ru.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666.ru.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ar.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ar.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ar.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.de.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.de.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.de.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.es.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.es.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.es.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fa.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fa.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fa.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fr.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fr.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.fr.png and /dev/null differ diff --git a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ru.png b/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ru.png deleted file mode 100644 index 6d15b15b..00000000 Binary files a/translated_images/vaemnist-diag.694315f775d5d666b02fb54f8fc7c64db65a9d126a16c2fdb8683cf9726f9ff5.ru.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.ar.png b/translated_images/vaemnist.cab9e602dc08dc50.ar.png deleted file mode 100644 index d10ea5ff..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.ar.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.de.png b/translated_images/vaemnist.cab9e602dc08dc50.de.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.de.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.es.png b/translated_images/vaemnist.cab9e602dc08dc50.es.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.es.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.fa.png b/translated_images/vaemnist.cab9e602dc08dc50.fa.png deleted file mode 100644 index 1f694a4a..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.fa.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.fr.png b/translated_images/vaemnist.cab9e602dc08dc50.fr.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.fr.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc50.ru.png b/translated_images/vaemnist.cab9e602dc08dc50.ru.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc50.ru.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ar.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ar.png deleted file mode 100644 index d10ea5ff..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ar.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.de.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.de.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.de.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.es.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.es.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.es.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fa.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fa.png deleted file mode 100644 index 1f694a4a..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fa.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fr.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fr.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.fr.png and /dev/null differ diff --git a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ru.png b/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ru.png deleted file mode 100644 index c215d37e..00000000 Binary files a/translated_images/vaemnist.cab9e602dc08dc5066ce14e005889d6b53ca5bcaf16e35c28dbf8cd40c304de1.ru.png and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.ar.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.ar.jpg deleted file mode 100644 index c39bb351..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.ar.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.de.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.de.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.de.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.es.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.es.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.es.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.fa.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.fa.jpg deleted file mode 100644 index 7a9cdd6e..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.fa.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.fr.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.fr.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.fr.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49f.ru.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49f.ru.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49f.ru.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ar.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ar.jpg deleted file mode 100644 index c39bb351..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ar.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.de.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.de.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.de.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.es.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.es.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.es.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fa.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fa.jpg deleted file mode 100644 index 7a9cdd6e..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fa.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fr.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fr.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.fr.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ru.jpg b/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ru.jpg deleted file mode 100644 index 29b4a5e6..00000000 Binary files a/translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ru.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.ar.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.ar.jpg deleted file mode 100644 index 0a22beff..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.ar.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.de.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.de.jpg deleted file mode 100644 index d89fcc90..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.de.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.es.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.es.jpg deleted file mode 100644 index 7d7adb89..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.es.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.fa.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.fa.jpg deleted file mode 100644 index beb00a3b..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.fa.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.fr.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.fr.jpg deleted file mode 100644 index d3ddac32..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.fr.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51ba.ru.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51ba.ru.jpg deleted file mode 100644 index dda4c334..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51ba.ru.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ar.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ar.jpg deleted file mode 100644 index 0a22beff..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ar.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.de.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.de.jpg deleted file mode 100644 index d89fcc90..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.de.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.es.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.es.jpg deleted file mode 100644 index 7d7adb89..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.es.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fa.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fa.jpg deleted file mode 100644 index beb00a3b..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fa.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fr.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fr.jpg deleted file mode 100644 index d3ddac32..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.fr.jpg and /dev/null differ diff --git a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ru.jpg b/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ru.jpg deleted file mode 100644 index dda4c334..00000000 Binary files a/translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ru.jpg and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.ar.png b/translated_images/vqgan.5027fe05051dfa31.ar.png deleted file mode 100644 index 1d9bf866..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.ar.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.de.png b/translated_images/vqgan.5027fe05051dfa31.de.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.de.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.es.png b/translated_images/vqgan.5027fe05051dfa31.es.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.es.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.fa.png b/translated_images/vqgan.5027fe05051dfa31.fa.png deleted file mode 100644 index 1e5f2513..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.fa.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.fr.png b/translated_images/vqgan.5027fe05051dfa31.fr.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.fr.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa31.ru.png b/translated_images/vqgan.5027fe05051dfa31.ru.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa31.ru.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ar.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ar.png deleted file mode 100644 index 1d9bf866..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ar.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.de.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.de.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.de.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.es.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.es.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.es.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fa.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fa.png deleted file mode 100644 index 1e5f2513..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fa.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fr.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fr.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.fr.png and /dev/null differ diff --git a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ru.png b/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ru.png deleted file mode 100644 index 76f140d1..00000000 Binary files a/translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.ru.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.ar.png b/translated_images/yolo.a2648ec82ee8bb4e.ar.png deleted file mode 100644 index eab8ff03..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.ar.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.de.png b/translated_images/yolo.a2648ec82ee8bb4e.de.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.de.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.es.png b/translated_images/yolo.a2648ec82ee8bb4e.es.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.es.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.fa.png b/translated_images/yolo.a2648ec82ee8bb4e.fa.png deleted file mode 100644 index 84feeefc..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.fa.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.fr.png b/translated_images/yolo.a2648ec82ee8bb4e.fr.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.fr.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4e.ru.png b/translated_images/yolo.a2648ec82ee8bb4e.ru.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4e.ru.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ar.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ar.png deleted file mode 100644 index eab8ff03..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ar.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.de.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.de.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.de.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.es.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.es.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.es.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fa.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fa.png deleted file mode 100644 index 84feeefc..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fa.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fr.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fr.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.fr.png and /dev/null differ diff --git a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ru.png b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ru.png deleted file mode 100644 index 061350e2..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ru.png and /dev/null differ diff --git a/translations/ar/README.md b/translations/ar/README.md index 2e400995..22eb25c6 100644 --- a/translations/ar/README.md +++ b/translations/ar/README.md @@ -1,178 +1,178 @@ [![ترخيص GitHub](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) -[![المساهمون في GitHub](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) +[![مساهمو GitHub](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![قضايا GitHub](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) -[![طلبات السحب في GitHub](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) -[![الطلبات مرحب بها](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) +[![طلبات سحب GitHub](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) +[![مرحباً بطلبات السحب](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![المراقبون في GitHub](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) -[![التفرعات في GitHub](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) -[![النجوم في GitHub](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) -[![بايندر](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) -[![جيتر](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) +[![مشاهدو GitHub](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) +[![شوكات GitHub](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) +[![نجوم GitHub](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) +[![Gitter](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) -[![خادم ديسكورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) # الذكاء الاصطناعي للمبتدئين - منهج دراسي -|![مذكرة رسمية بواسطة @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ar.png)| +|![مخطط ملاحظات بواسطة @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/ar/ai-overview.0857791951d19500.webp)| |:---:| -| الذكاء الاصطناعي للمبتدئين - _مذكرة رسمية بواسطة [@girlie_mac](https://twitter.com/girlie_mac)_ | +| الذكاء الاصطناعي للمبتدئين - _مخطط ملاحظات بواسطة [@girlie_mac](https://twitter.com/girlie_mac)_ | -استكشف عالم **الذكاء الاصطناعي** مع منهجنا الدراسي الممتد 12 أسبوعًا، والمكوّن من 24 درسًا! يشمل دروسًا عملية، واختبارات، ومختبرات. المنهج مناسب للمبتدئين ويغطي أدوات مثل TensorFlow وPyTorch، بالإضافة إلى أخلاقيات الذكاء الاصطناعي. +استكشف عالم **الذكاء الاصطناعي** (AI) مع منهجنا الدراسي المكون من 12 أسبوعًا و24 درسًا! يتضمن دروسًا عملية، واختبارات، ومعامل. المنهج مناسب للمبتدئين ويغطي أدوات مثل TensorFlow وPyTorch، بالإضافة إلى الأخلاقيات في الذكاء الاصطناعي. ### 🌐 دعم متعدد اللغات -#### مدعوم عبر GitHub Action (آلي ودائم التحديث) +#### مدعوم عبر GitHub Action (مؤتمت ودوماً محدث) -[العربية](./README.md) | [البنغالية](../bn/README.md) | [البلغارية](../bg/README.md) | [البورمية (ميانمار)](../my/README.md) | [الصينية (المبسطة)](../zh/README.md) | [الصينية (التقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (التقليدية، ماكاو)](../mo/README.md) | [الصينية (التقليدية، تايوان)](../tw/README.md) | [الكرواتية](../hr/README.md) | [التشيكية](../cs/README.md) | [الدنماركية](../da/README.md) | [الهولندية](../nl/README.md) | [الإستونية](../et/README.md) | [الفنلندية](../fi/README.md) | [الفرنسية](../fr/README.md) | [الألمانية](../de/README.md) | [اليونانية](../el/README.md) | [العبرية](../he/README.md) | [الهندية](../hi/README.md) | [الهنغارية](../hu/README.md) | [الإندونيسية](../id/README.md) | [الإيطالية](../it/README.md) | [اليابانية](../ja/README.md) | [الكانادا](../kn/README.md) | [الكورية](../ko/README.md) | [اللتوانية](../lt/README.md) | [الماليزية](../ms/README.md) | [المالايالامية](../ml/README.md) | [الماراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [النيجيرية بيدجن](../pcm/README.md) | [النرويجية](../no/README.md) | [الفارسية (الإنجليزية)](../fa/README.md) | [البولندية](../pl/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البنجابية (جيرموخي)](../pa/README.md) | [الرومانية](../ro/README.md) | [الروسية](../ru/README.md) | [الصربية (السيريلية)](../sr/README.md) | [السلوفاكية](../sk/README.md) | [السلوفينية](../sl/README.md) | [الإسبانية](../es/README.md) | [السواحلية](../sw/README.md) | [السويدية](../sv/README.md) | [التاغالوغ (الفلبينية)](../tl/README.md) | [التاميلية](../ta/README.md) | [التيلغو](../te/README.md) | [التايلاندية](../th/README.md) | [التركية](../tr/README.md) | [الأوكرانية](../uk/README.md) | [الأردية](../ur/README.md) | [الفيتنامية](../vi/README.md) +[العربية](./README.md) | [البنغالية](../bn/README.md) | [البلغارية](../bg/README.md) | [البورمية (ميانمار)](../my/README.md) | [الصينية (المبسطة)](../zh/README.md) | [الصينية (التقليدية، هونغ كونغ)](../hk/README.md) | [الصينية (التقليدية، ماكاو)](../mo/README.md) | [الصينية (التقليدية، تايوان)](../tw/README.md) | [الكرواتية](../hr/README.md) | [التشيكية](../cs/README.md) | [الدنماركية](../da/README.md) | [الهولندية](../nl/README.md) | [الإستونية](../et/README.md) | [الفنلندية](../fi/README.md) | [الفرنسية](../fr/README.md) | [الألمانية](../de/README.md) | [اليونانية](../el/README.md) | [العبرية](../he/README.md) | [الهندية](../hi/README.md) | [الهنغارية](../hu/README.md) | [الإندونيسية](../id/README.md) | [الإيطالية](../it/README.md) | [اليابانية](../ja/README.md) | [الكانادا](../kn/README.md) | [الكورية](../ko/README.md) | [الليتوانية](../lt/README.md) | [الملايوية](../ms/README.md) | [المالايالامية](../ml/README.md) | [الماراثية](../mr/README.md) | [النيبالية](../ne/README.md) | [نجد محلية نيجيرية](../pcm/README.md) | [النرويجية](../no/README.md) | [الفارسية](../fa/README.md) | [البولندية](../pl/README.md) | [البرتغالية (البرازيل)](../br/README.md) | [البرتغالية (البرتغال)](../pt/README.md) | [البنجابية (غرموخي)](../pa/README.md) | [الرومانية](../ro/README.md) | [الروسية](../ru/README.md) | [الصربية (السيريلية)](../sr/README.md) | [السلوفاكية](../sk/README.md) | [السلوفينية](../sl/README.md) | [الإسبانية](../es/README.md) | [السواحلية](../sw/README.md) | [السويدية](../sv/README.md) | [التاغالوغ (الفلبينية)](../tl/README.md) | [التاميلية](../ta/README.md) | [التيلجو](../te/README.md) | [التايلاندية](../th/README.md) | [التركية](../tr/README.md) | [الأوكرانية](../uk/README.md) | [الأردية](../ur/README.md) | [الفيتنامية](../vi/README.md) -> **هل تفضل الاستنساخ محليًا؟** +> **هل تفضل النسخ محليًا؟** -> يحتوي هذا المستودع على أكثر من 50 ترجمة للغات تزيد بشكل كبير من حجم التنزيل. للاستنساخ بدون الترجمات، استخدم السحب الانتقائي: +> يتضمن هذا المستودع أكثر من 50 ترجمة للغات تزيد بشكل ملحوظ من حجم التنزيل. للنسخ بدون الترجمات، استخدم التفريغ الجزئي: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> هذا يمنحك كل ما تحتاجه لإكمال الدورة بتنزيل أسرع بكثير. +> هذا يمنحك كل ما تحتاجه لإكمال الدورة مع تحميل أسرع بكثير. **إذا كنت ترغب في دعم لغات ترجمة إضافية، فهي مدرجة [هنا](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## انضم إلى المجتمع -[![خادم ديسكورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) +[![منصة ديسكورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## ما ستتعلمه +## ما الذي ستتعلمه **[خريطة ذهنية للدورة](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** في هذا المنهج، ستتعلم: -* نهجًا مختلفًا للذكاء الاصطناعي، بما في ذلك النهج "القديم الجيد" الرمزي مع **تمثيل المعرفة** والاستدلال ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **الشبكات العصبية** و**التعلم العميق**، وهي جوهر الذكاء الاصطناعي الحديث. سنوضح المفاهيم وراء هذه المواضيع المهمة باستخدام التعليمات البرمجية في اثنين من أشهر الأطر - [TensorFlow](http://Tensorflow.org) و[PyTorch](http://pytorch.org). -* **الهياكل العصبية** للعمل مع الصور والنصوص. سنغطي النماذج الحديثة لكنها قد تكون قليلة بعض الشيء في أحدث التقنيات. -* نهجًا أقل شيوعًا في الذكاء الاصطناعي، مثل **الخوارزميات الجينية** و**أنظمة الوكلاء المتعددين**. +* طرق مختلفة للذكاء الاصطناعي، بما في ذلك النهج الرمزي "القديم الجيد" مع **تمثيل المعرفة** والاستدلال ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **الشبكات العصبية** و**التعلم العميق**، وهما جوهر الذكاء الاصطناعي الحديث. سنوضح المفاهيم وراء هذه الموضوعات المهمة باستخدام الكود في اثنين من أشهر الأُطُر - [TensorFlow](http://Tensorflow.org) و[PyTorch](http://pytorch.org). +* **الهياكل العصبية** للعمل مع الصور والنصوص. سنغطي النماذج الحديثة لكن قد ينقصنا بعض أحدث التقدّم. +* طرق ذكاء اصطناعي أقل شهرة، مثل **الخوارزميات الجينية** و**أنظمة الوكلاء المتعددة**. ما لن نغطيه في هذا المنهج: -> [اكتشف جميع الموارد الإضافية لهذه الدورة في مجموعة Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [اعثر على جميع الموارد الإضافية لهذه الدورة في مجموعة Microsoft Learn الخاصة بنا](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* الحالات التجارية لاستخدام **الذكاء الاصطناعي في الأعمال**. ننصح بأخذ مسار التعلم [مقدمة إلى الذكاء الاصطناعي لمستخدمي الأعمال](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) على Microsoft Learn، أو [مدرسة الأعمال للذكاء الاصطناعي](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)، التي تم تطويرها بالتعاون مع [INSEAD](https://www.insead.edu/). -* **التعلم الآلي الكلاسيكي**، الموصوف جيدًا في منهجنا [التعلم الآلي للمبتدئين](http://github.com/Microsoft/ML-for-Beginners). -* تطبيقات الذكاء الاصطناعي العملية المبنية باستخدام **[الخدمات المعرفية](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. لذا نوصي بدءًا من وحدات Microsoft Learn لـ [الرؤية](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، و[معالجة اللغة الطبيعية](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، و**[الذكاء الاصطناعي التوليدي مع خدمة Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** وغيرها. -* أُطُر سحابية محددة للتعلم الآلي، مثل [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)، أو [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). يُنصح باستخدام مسارات التعلم [إنشاء وتشغيل حلول التعلم الآلي باستخدام Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) و[إنشاء وتشغيل حلول التعلم الآلي باستخدام Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **الذكاء الاصطناعي التفاعلي** و**الروبوتات الحوارية**. هناك مسار تعلم منفصل [إنشاء حلول الذكاء الاصطناعي التفاعلية](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)، ويمكنك أيضًا الرجوع إلى [منشور المدونة هذا](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) لمزيد من التفاصيل. -* **الرياضيات العميقة** خلف التعلم العميق. لذلك نوصي بـ[التعلم العميق](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) بواسطة إيان جودفيلو ويوشوا بنجيو وآرون كورفيل، والمتوفر أيضًا عبر الإنترنت على [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* حالات استخدام **الذكاء الاصطناعي في الأعمال**. يمكن أن تفكر في متابعة مسار التعلم [مقدمة في الذكاء الاصطناعي لمستخدمي الأعمال](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) على Microsoft Learn، أو [مدرسة أعمال الذكاء الاصطناعي](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)، التي تم تطويرها بالتعاون مع [INSEAD](https://www.insead.edu/). +* **التعلم الآلي الكلاسيكي**، الذي موصوف جيدًا في منهجنا [التعلم الآلي للمبتدئين](http://github.com/Microsoft/ML-for-Beginners). +* تطبيقات الذكاء الاصطناعي العملية المبنية باستخدام **[خدمات الإدراك](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. لهذا، نوصي بالبدء في الوحدات التعليمية في Microsoft Learn المتعلقة بـ[الرؤية الحاسوبية](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، و[معالجة اللغة الطبيعية](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، و**[الذكاء الاصطناعي التوليدي مع خدمة Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** وغيرها. +* أُطُر السحابة الخاصة بالتعلم الآلي مثل [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)، أو [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). يمكن استخدام مسارات التعلم [بناء وتشغيل حلول التعلم الآلي باستخدام Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) و[بناء وتشغيل حلول التعلم الآلي باستخدام Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **الذكاء الاصطناعي التحاوري** و**الروبوتات المحادثة**. هناك مسار تعلم منفصل [إنشاء حلول ذكاء اصطناعي تحاورية](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)، ويمكنك أيضًا الرجوع إلى [هذه التدوينة](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) لمزيد من التفاصيل. +* **الرياضيات العميقة** وراء التعلم العميق. نوصي لذٰلك بـ[التعلم العميق](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) من تأليف إيان جودفيلو ويوشوا بنجيو وآرون كورفيل، المتوفر أيضًا على الإنترنت عبر [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -للحصول على مقدمة لطيفة حول مواضيع _الذكاء الاصطناعي في السحابة_ يمكنك التفكير في أخذ مسار التعلم [البدء مع الذكاء الاصطناعي على Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +للمقدمة بلطف عن موضوعات _الذكاء الاصطناعي في السحابة_ قد تنظر في متابعة [البدء مع الذكاء الاصطناعي على Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # المحتوى | | رابط الدرس | PyTorch/Keras/TensorFlow | المختبر | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [إعداد الدورة](./lessons/0-course-setup/setup.md) | [إعداد بيئة التطوير الخاصة بك](./lessons/0-course-setup/how-to-run.md) | | -| I | [**مقدمة إلى الذكاء الاصطناعي**](./lessons/1-Intro/README.md) | | | +| I | [**مقدمة في الذكاء الاصطناعي**](./lessons/1-Intro/README.md) | | | | 01 | [مقدمة وتاريخ الذكاء الاصطناعي](./lessons/1-Intro/README.md) | - | - | | II | **الذكاء الاصطناعي الرمزي** | -| 02 | [تمثيل المعرفة وأنظمة الخبراء](./lessons/2-Symbolic/README.md) | [أنظمة الخبراء](./lessons/2-Symbolic/Animals.ipynb) / [الأنتولوجيا](./lessons/2-Symbolic/FamilyOntology.ipynb) /[رسم بياني للمفاهيم](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**مقدمة إلى الشبكات العصبية**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [بيرسيترون](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [دفتر ملاحظات](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [معمل](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [بيرسيترون متعدد الطبقات وإنشاء إطار العمل الخاص بنا](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [دفتر ملاحظات](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [معمل](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [مقدمة في الأطر (بايتورتش/تينسرفلو) والإفراط في التعميم](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [بايتورتش](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [كيراس](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [تينسرفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [معمل](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**رؤية الحاسوب**](./lessons/4-ComputerVision/README.md) | [بايتورتش](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [تينسرفلو](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [استكشاف رؤية الحاسوب على مايكروسوفت آزور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [مقدمة في رؤية الحاسوب. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [دفتر ملاحظات](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [معمل](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [الشبكات العصبية الالتفافية](./lessons/4-ComputerVision/07-ConvNets/README.md) & [هندسات CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [بايتورتش](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[تينسرفلو](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [معمل](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [الشبكات المدربة مسبقًا والتعلم النقلي](./lessons/4-ComputerVision/08-TransferLearning/README.md) و [خدع التدريب](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [بايتورتش](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [تينسرفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [معمل](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [التشفير التلقائي و VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [بايتورتش](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [تينسرفلو](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [الشبكات التنافسية التوليدية ونقل النمط الفني](./lessons/4-ComputerVision/10-GANs/README.md) | [بايتورتش](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [تينسرفلو](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [الكشف عن الأشياء](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [تينسرفلو](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [معمل](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [التجزئة الدلالية. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [بايتورتش](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [تينسرفلو](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**معالجة اللغة الطبيعية**](./lessons/5-NLP/README.md) | [بايتورتش](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[تينسرفلو](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [استكشاف معالجة اللغة الطبيعية على مايكروسوفت آزور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [تمثيل النص. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [تضمينات الكلمات الدلالية. Word2Vec و GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [نمذجة اللغة. تدريب التضمينات الخاصة بك](./lessons/5-NLP/15-LanguageModeling/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [معمل](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [الشبكات العصبية المتكررة](./lessons/5-NLP/16-RNN/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [الشبكات التكرارية التوليدية](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [معمل](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| 18 | [المحوّلات. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [بايتورتش](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[تينسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [تمييز الكيانات المسماة](./lessons/5-NLP/19-NER/README.md) | [تينسرفلو](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [معمل](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [نماذج اللغة الكبيرة، برمجة المطالبات ومهام القليل من الأمثلة](./lessons/5-NLP/20-LangModels/README.md) | [بايتورتش](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **تقنيات أخرى في الذكاء الاصطناعي** || | +| 02 | [تمثيل المعرفة وأنظمة الخبراء](./lessons/2-Symbolic/README.md) | [أنظمة الخبراء](./lessons/2-Symbolic/Animals.ipynb) / [الأونتولوجيا](./lessons/2-Symbolic/FamilyOntology.ipynb) /[رسم المفاهيم](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**مقدمة في الشبكات العصبية**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [بيرسيبترون](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [دفتر ملاحظات](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [مختبر](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [بيرسيبترون متعدد الطبقات وإنشاء إطار العمل الخاص بنا](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [دفتر ملاحظات](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [مختبر](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [مقدمة حول الأُطُر (PyTorch/TensorFlow) وتجاوز التكيف](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [مختبر](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**الرؤية الحاسوبية**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [استكشاف الرؤية الحاسوبية على مايكروسوفت أزور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [مقدمة في الرؤية الحاسوبية. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [دفتر ملاحظات](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [مختبر](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [الشبكات العصبية التلافيفية](./lessons/4-ComputerVision/07-ConvNets/README.md) & [هندسات CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [مختبر](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [الشبكات المدربة مسبقًا والتعلم التحويلي](./lessons/4-ComputerVision/08-TransferLearning/README.md) و [حيل التدريب](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [مختبر](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [المشفّرات الذاتية و VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [الشبكات التوليدية التنافسية ونقل الأسلوب الفني](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [كشف الأجسام](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [مختبر](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [التجزئة الدلالية. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**معالجة اللغات الطبيعية**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [استكشاف معالجة اللغات الطبيعية على مايكروسوفت أزور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [تمثيل النص. الحقيبة/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [تضمينات الكلمات الدلالية. Word2Vec و GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [نمذجة اللغة. تدريب التضمينات الخاصة بك](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [مختبر](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [الشبكات العصبية المتكررة](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [الشبكات المتكررة التوليدية](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [مختبر](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [المحولات. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [التعرف على الكيانات المسماة](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [مختبر](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [نماذج اللغة الكبيرة، برمجة المطالبات ومهام القليل من اللقطات](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **تقنيات ذكاء اصطناعي أخرى** || | | 21 | [الخوارزميات الجينية](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [دفتر ملاحظات](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [التعلم المعزز العميق](./lessons/6-Other/22-DeepRL/README.md) | [بايتورتش](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[تينسرفلو](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [معمل](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [أنظمة الوكلاء المتعددة](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 22 | [التعلم المعزز العميق](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [مختبر](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [أنظمة متعددة الوكلاء](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **أخلاقيات الذكاء الاصطناعي** | | | -| 24 | [أخلاقيات الذكاء الاصطناعي والمسؤولية](./lessons/7-Ethics/README.md) | [مايكروسوفت ليرن: مبادئ الذكاء الاصطناعي المسؤول](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [أخلاقيات الذكاء الاصطناعي والذكاء الاصطناعي المسؤول](./lessons/7-Ethics/README.md) | [مايكروسوفت ليرن: مبادئ الذكاء الاصطناعي المسؤول](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **إضافات** | | | -| 25 | [الشبكات متعددة الوسائط، CLIP و VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [دفتر ملاحظات](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [الشبكات متعددة الأنماط، CLIP وVQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [دفتر ملاحظات](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## تحتوي كل درس على -* مواد للقراءة المسبقة -* دفاتر مُنفذة من جيوبتر، وغالبًا ما تكون خاصة بإطار عمل معين (**بايتورتش** أو **تينسرفلو**). يحتوي الدفتر التنفيذي أيضًا على الكثير من المواد النظرية، لذا لفهم الموضوع تحتاج إلى مراجعة نسخة واحدة على الأقل من الدفتر (سواء كانت بايتورتش أو تينسرفلو). -* **معامل** متوفرة لبعض المواضيع، تتيح لك فرصة تجربة تطبيق المادة التي تعلمتها على مشكلة محددة. -* بعض الأقسام تحتوي على روابط لوحدات [**مايكروسوفت ليرن**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) التي تغطي مواضيع ذات صلة. +* مادة للقراءة المسبقة +* دفاتر Jupyter قابلة للتنفيذ، غالبًا ما تكون محددة للإطار (**PyTorch** أو **TensorFlow**). يحتوي دفتر الملاحظات التنفيذي أيضًا على الكثير من المواد النظرية، لذا لفهم الموضوع تحتاج إلى المرور على الأقل بإصدار واحد من دفتر الملاحظات (سواء PyTorch أو TensorFlow). +* **مختبرات** متاحة لبعض المواضيع، والتي تعطيك فرصة لتجربة تطبيق المادة التي تعلمتها على مشكلة محددة. +* تحتوي بعض الأقسام على روابط إلى وحدات [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) التي تغطي المواضيع ذات الصلة. ## البدء -### 🎯 جديد على الذكاء الاصطناعي؟ ابدأ هنا! +### 🎯 جديد في الذكاء الاصطناعي؟ ابدأ من هنا! -إذا كنت جديدًا تمامًا على الذكاء الاصطناعي وتريد أمثلة عملية سريعة، تحقق من [**أمثلة للمبتدئين**](./examples/README.md)! تتضمن: +إذا كنت جديدًا تمامًا على الذكاء الاصطناعي وتريد أمثلة سريعة وعملية، تحقق من [**أمثلة مناسبة للمبتدئين**](./examples/README.md)! تشمل هذه: -- 🌟 **مرحبًا بالعالم الذكي** - برنامج الذكاء الاصطناعي الأول الخاص بك (التعرف على الأنماط) +- 🌟 **مرحبًا بالعالم الذكي** - أول برنامج ذكاء اصطناعي لك (التعرف على الأنماط) - 🧠 **شبكة عصبية بسيطة** - بناء شبكة عصبية من الصفر -- 🖼️ **مصنف الصور** - تصنيف الصور مع تعليقات مفصلة -- 💬 **تحليل مشاعر النص** - تحليل النصوص الإيجابية/السلبية +- 🖼️ **مُصنف الصور** - تصنيف الصور مع تعليقات تفصيلية +- 💬 **تحليل مشاعر النص** - تحليل النص الإيجابي/السلبي تم تصميم هذه الأمثلة لمساعدتك على فهم مفاهيم الذكاء الاصطناعي قبل الغوص في المنهج الكامل. ### 📚 إعداد المنهج الكامل -- لقد أنشأنا [درس الإعداد](./lessons/0-course-setup/setup.md) لمساعدتك في إعداد بيئة التطوير الخاصة بك. - للمعلمين، أنشأنا أيضًا [درس إعداد المنهج](./lessons/0-course-setup/for-teachers.md) لكم! -- كيفية [تشغيل الكود في VSCode أو Codepace](./lessons/0-course-setup/how-to-run.md) +- لقد أنشأنا [درس الإعداد](./lessons/0-course-setup/setup.md) لمساعدتك في إعداد بيئة التطوير الخاصة بك. - للمعلمين، أنشأنا أيضًا [درس إعداد المنهج](./lessons/0-course-setup/for-teachers.md)! +- كيفية [تشغيل الكود في VSCode أو Codespace](./lessons/0-course-setup/how-to-run.md) اتبع هذه الخطوات: -تفرع المستودع: انقر على زر "Fork" في أعلى يمين هذه الصفحة. +نسخ المستودع: انقر على زر "Fork" في الزاوية العلوية اليمنى من هذه الصفحة. -نسخ المستودع: `git clone https://github.com/microsoft/AI-For-Beginners.git` +استنساخ المستودع: `git clone https://github.com/microsoft/AI-For-Beginners.git` -لا تنسَ وضع نجمة (🌟) على هذا المستودع لتجده بسهولة لاحقًا. +لا تنسى تمييز (🌟) هذا المستودع لسهولة العثور عليه لاحقًا. -## تعرف على المتعلمين الآخرين +## تواصل مع متعلمين آخرين -انضم إلى [خادم الديسكورد الرسمي للذكاء الاصطناعي](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) للتعرف على متعلمين آخرين يأخذون هذا المساق والتواصل معهم والحصول على الدعم. +انضم إلى [خادم ديسكورد الرسمي للذكاء الاصطناعي](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) للقاء وتكوين شبكة علاقات مع متعلمين آخرين يدرسون هذا المساق و للحصول على الدعم. -إذا كان لديك ملاحظات على المنتج أو أسئلة أثناء البناء قم بزيارة منتدى مطوري [Azure AI Foundry](https://aka.ms/foundry/forum) +إذا كان لديك ملاحظات على المنتج أو أسئلة أثناء البناء، زور منتدى مطوري [Azure AI Foundry](https://aka.ms/foundry/forum) -## الاختبارات القصيرة +## الاختبارات -> **ملاحظة عن الاختبارات القصيرة**: جميع الاختبارات موجودة في مجلد Quiz-app في etc\quiz-app، أو [متاحة عبر الإنترنت هنا](https://ff-quizzes.netlify.app/) وهي مرتبطة من داخل الدروس، يمكن تشغيل تطبيق الاختبارات محليًا أو نشره على Azure؛ اتبع التعليمات في مجلد `quiz-app`. يتم تعريبها تدريجياً. +> **ملاحظة حول الاختبارات**: جميع الاختبارات موجودة في مجلد Quiz-app ضمن etc\quiz-app، أو [متوفرة عبر الإنترنت هنا](https://ff-quizzes.netlify.app/) وهي مرتبطة من داخل الدروس. يمكن تشغيل تطبيق الاختبار محليًا أو نشره على Azure؛ اتبع التعليمات في مجلد `quiz-app`. يتم تعريبها تدريجياً. -## مطلوب المساعدة +## نبحث عن مساعدتك -هل لديك اقتراحات أو وجدت أخطاء إملائية أو في الكود؟ ارفع مشكلة أو أنشئ طلب سحب. +هل لديك اقتراحات أو اكتشفت أخطاء إملائية أو برمجية؟ قم بفتح مشكلة أو إنشاء طلب سحب. -## الشكر الخاص +## شكر خاص -* **✍️ المؤلف الرئيسي:** [Dmitry Soshnikov](http://soshnikov.com)، دكتوراه -* **🔥 المحرر:** [Jen Looper](https://twitter.com/jenlooper)، دكتوراه -* **🎨 رسام الملاحظات التوضيحية:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ منشئ الاختبارات القصيرة:** [Lateefah Bello](https://github.com/CinnamonXI)، [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 المساهمون الأساسيون:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ المؤلف الأساسي:** [دميتري سوشنيكوف](http://soshnikov.com)، دكتوراه +* **🔥 المحرر:** [جين لوبير](https://twitter.com/jenlooper)، دكتوراه +* **🎨 رسام الملاحظات المصورة:** [تومومي إيمورا](https://twitter.com/girlie_mac) +* **✅ منشئ الاختبارات:** [لطيفة بيلو](https://github.com/CinnamonXI)، [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 المساهمون الأساسيون:** [إيفجيني بيششيك](https://github.com/Pe4enIks) ## مناهج أخرى -فريقنا ينتج مناهج أخرى! اطلع على: +ينتج فريقنا مناهج أخرى! اطلع على: ### LangChain @@ -181,24 +181,24 @@ CO_OP_TRANSLATOR_METADATA: --- -### Azure / Edge / MCP / الوكلاء +### Azure / Edge / MCP / Agents [![AZD للمبتدئين](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) [![Edge AI للمبتدئين](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP للمبتدئين](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![وكلاء الذكاء الاصطناعي للمبتدئين](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![عملاء الذكاء الاصطناعي للمبتدئين](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### سلسلة الذكاء الاصطناعي التوليدي [![الذكاء الاصطناعي التوليدي للمبتدئين](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![الذكاء الاصطناعي التوليدي (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![الذكاء الاصطناعي التوليدي (جافا)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![الذكاء الاصطناعي التوليدي (جافا سكريبت)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +[![الذكاء الاصطناعي التوليدي (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![الذكاء الاصطناعي التوليدي (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- ### التعلم الأساسي -[![التعلم الآلي للمبتدئين](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![تعلم الآلة للمبتدئين](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![علوم البيانات للمبتدئين](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![الذكاء الاصطناعي للمبتدئين](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) [![الأمن السيبراني للمبتدئين](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) @@ -208,25 +208,25 @@ CO_OP_TRANSLATOR_METADATA: --- -### سلسلة المساعد الذكي -[![المساعد الذكي للبرمجة المشتركة بالذكاء الاصطناعي](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![المساعد الذكي لـ C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![مغامرة المساعد الذكي](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +### سلسلة كوبيلوت +[![كوبيلوت للبرمجة المشتركة بالذكاء الاصطناعي](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![كوبيلوت لـ C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![مغامرة كوبيلوت](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## الحصول على المساعدة -إذا واجهت صعوبة أو كان لديك أي سؤال حول بناء تطبيقات الذكاء الاصطناعي. انضم إلى المتعلمين الآخرين والمطورين ذوي الخبرة في مناقشات حول MCP. إنها مجتمع داعم حيث تُرحب الأسئلة ويتم مشاركة المعرفة بحرية. +إذا علقت أو كان لديك أي أسئلة حول بناء تطبيقات الذكاء الاصطناعي، انضم إلى المتعلمين والزملاء المطورين ذوي الخبرة في مناقشات حول MCP. هي مجتمع داعم حيث الأسئلة مرحب بها والمعرفة تُشارك بحرية. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -إذا كان لديك ملاحظات أو أخطاء في المنتج أثناء البناء قم بزيارة: +إذا كان لديك ملاحظات على المنتج أو واجهت أخطاء أثناء البناء زر: -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![منتدى مطوري Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**إخلاء المسؤولية**: -تمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي والمعتمد. بالنسبة للمعلومات الحيوية، يُنصح بالترجمة الاحترافية بواسطة مترجم بشري. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة ناتجة عن استخدام هذه الترجمة. +**إخلاء مسؤولية**: +تمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). على الرغم من أننا نسعى لتحقيق الدقة، يرجى العلم بأن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي والموثوق. للمعلومات الحساسة أو الهامة، يوصى بترجمة احترافية من قبل مترجم بشري. نحن غير مسؤولين عن أي سوء فهم أو تفسير خاطئ ناتج عن استخدام هذه الترجمة. \ No newline at end of file diff --git a/translations/ar/lessons/0-course-setup/how-to-run.md b/translations/ar/lessons/0-course-setup/how-to-run.md index f18811cc..e05463ac 100644 --- a/translations/ar/lessons/0-course-setup/how-to-run.md +++ b/translations/ar/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # كيفية تشغيل الكود -تحتوي هذه المناهج على العديد من الأمثلة القابلة للتنفيذ والمعامل التي قد ترغب في تشغيلها. لتنفيذ ذلك، تحتاج إلى القدرة على تشغيل كود Python في دفاتر Jupyter المقدمة كجزء من هذه المناهج. لديك عدة خيارات لتشغيل الكود: +يتضمن هذا المنهج العديد من الأمثلة التنفيذية والمختبرات التي سترغب في تشغيلها. للقيام بذلك، تحتاج إلى القدرة على تنفيذ كود بايثون في دفاتر Jupyter التي تُقدم كجزء من هذا المنهج. لديك عدة خيارات لتشغيل الكود: ## التشغيل محليًا على جهاز الكمبيوتر الخاص بك -لتشغيل الكود محليًا على جهاز الكمبيوتر الخاص بك، ستحتاج إلى تثبيت نسخة من Python. أوصي شخصيًا بتثبيت **[miniconda](https://conda.io/en/latest/miniconda.html)** - وهو تثبيت خفيف الوزن يدعم مدير الحزم `conda` لإنشاء **بيئات افتراضية** مختلفة لـ Python. +لتشغيل الكود محليًا على جهاز الكمبيوتر الخاص بك، يلزم وجود تثبيت لبايثون. أحد التوصيات هو تثبيت **[miniconda](https://conda.io/en/latest/miniconda.html)** - وهو تثبيت خفيف الوزن يدعم مدير الحزم `conda` لإنشاء **بيئات افتراضية** مختلفة لبايثون. -بعد تثبيت miniconda، تحتاج إلى استنساخ المستودع وإنشاء بيئة افتراضية لاستخدامها في هذه الدورة: +بعد تثبيت miniconda، قم باستنساخ المستودع وأنشئ بيئة افتراضية لاستخدامها في هذه الدورة: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,53 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### استخدام Visual Studio Code مع إضافة Python +### استخدام Visual Studio Code مع امتداد بايثون -ربما تكون أفضل طريقة لاستخدام المناهج هي فتحها في [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) مع [إضافة Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +يُفضل استخدام هذا المنهج عند فتحه في [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) مع [امتداد بايثون](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **ملاحظة**: بمجرد استنساخ وفتح الدليل في VS Code، سيقترح عليك تلقائيًا تثبيت إضافات Python. ستحتاج أيضًا إلى تثبيت miniconda كما هو موضح أعلاه. +> **ملاحظة**: بمجرد استنساخ المستودع وفتح الدليل في VS Code، سيقترح عليك تلقائيًا تثبيت امتدادات بايثون. كما يجب عليك أيضًا تثبيت miniconda كما هو موضح أعلاه. -> **ملاحظة**: إذا اقترح عليك VS Code إعادة فتح المستودع في الحاوية، يجب رفض ذلك لاستخدام تثبيت Python المحلي. +> **ملاحظة**: إذا اقترح VS Code عليك إعادة فتح المستودع داخل حاوية، فيجب عليك رفض ذلك لاستخدام تثبيت بايثون المحلي. ### استخدام Jupyter في المتصفح -يمكنك أيضًا استخدام بيئة Jupyter مباشرة من المتصفح على جهاز الكمبيوتر الخاص بك. في الواقع، يوفر كل من Jupyter الكلاسيكي وJupyter Hub بيئة تطوير مريحة مع الإكمال التلقائي، تمييز الكود، وما إلى ذلك. +يمكنك أيضًا استخدام بيئة Jupyter من المتصفح على جهاز الكمبيوتر الخاص بك. يوفر كل من Jupyter الكلاسيكي وJupyterHub بيئة تطوير ملائمة مع الإكمال التلقائي، تمييز الكود، وغيرها. -لتشغيل Jupyter محليًا، انتقل إلى دليل الدورة، وقم بتنفيذ: +لبدء Jupyter محليًا، انتقل إلى دليل الدورة، ونفذ: ```bash jupyter notebook -``` -أو +``` +أو ```bash jupyterhub -``` -يمكنك بعد ذلك التنقل إلى أي من ملفات `.ipynb`، فتحها والبدء في العمل. +``` +يمكنك بعد ذلك التنقل إلى أي من ملفات `.ipynb`، وفتحها والبدء في العمل. ### التشغيل في الحاوية -بديل لتثبيت Python هو تشغيل الكود في الحاوية. نظرًا لأن مستودعنا يحتوي على مجلد `.devcontainer` خاص يوضح كيفية بناء حاوية لهذا المستودع، سيعرض عليك VS Code إعادة فتح الكود في الحاوية. سيتطلب ذلك تثبيت Docker، وسيكون أكثر تعقيدًا، لذا نوصي بهذا للمستخدمين الأكثر خبرة. +أحد البدائل لتثبيت بايثون هو تشغيل الكود داخل حاوية. نظرًا لأن مستودعنا يوفر مجلد `.devcontainer` خاص يشرح كيفية بناء حاوية لهذا المستودع، يقدم VS Code فرصة لإعادة فتح الكود داخل حاوية. سيتطلب هذا تثبيت Docker، وسيكون أكثر تعقيدًا، لذا نوصي بذلك للمستخدمين الأكثر خبرة. ## التشغيل في السحابة -إذا كنت لا ترغب في تثبيت Python محليًا، ولديك إمكانية الوصول إلى بعض الموارد السحابية - فإن خيارًا جيدًا هو تشغيل الكود في السحابة. هناك عدة طرق يمكنك القيام بذلك: +إذا لم ترغب في تثبيت بايثون محليًا، ولديك وصول إلى بعض موارد السحابة - فإن بديلًا جيدًا هو تشغيل الكود في السحابة. هناك عدة طرق يمكنك القيام بذلك: -* استخدام **[GitHub Codespaces](https://github.com/features/codespaces)**، وهو بيئة افتراضية يتم إنشاؤها لك على GitHub، ويمكن الوصول إليها من خلال واجهة المتصفح لـ VS Code. إذا كان لديك إمكانية الوصول إلى Codespaces، يمكنك فقط النقر على زر **Code** في المستودع، بدء تشغيل Codespace، والبدء في العمل فورًا. -* استخدام **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) هو مورد حوسبة مجاني مقدم في السحابة للأشخاص مثلك لتجربة بعض الأكواد على GitHub. هناك زر في الصفحة الرئيسية لفتح المستودع في Binder - يجب أن يأخذك هذا بسرعة إلى موقع Binder، الذي سيبني الحاوية الأساسية ويبدأ واجهة الويب لـ Jupyter لك بسلاسة. +* استخدام **[GitHub Codespaces](https://github.com/features/codespaces)**، وهي بيئة افتراضية تُنشأ لك على GitHub، يمكن الوصول إليها من خلال واجهة VS Code في المتصفح. إذا كان لديك وصول إلى Codespaces، يمكنك فقط الضغط على زر **Code** في المستودع، وبدء مساحة الكود، والبدء في التشغيل بسرعة. +* استخدام **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. يقدم [Binder](https://mybinder.org) موارد حوسبة مجانية مقدمة في السحابة للأشخاص مثلك لتجربة بعض الأكواد على GitHub. هناك زر في الصفحة الرئيسية لفتح المستودع في Binder - يجب أن يأخذك هذا بسرعة إلى موقع Binder، الذي سيبني حاوية أساسية ويبدأ واجهة ويب Jupyter بسلاسة. -> **ملاحظة**: لمنع سوء الاستخدام، يتم حظر الوصول إلى بعض الموارد عبر الإنترنت في Binder. قد يمنع هذا بعض الأكواد التي تعتمد على تنزيل النماذج و/أو مجموعات البيانات من الإنترنت العام. قد تحتاج إلى إيجاد حلول بديلة. أيضًا، موارد الحوسبة المقدمة من Binder محدودة جدًا، لذا سيكون التدريب بطيئًا، خاصة في الدروس الأكثر تعقيدًا لاحقًا. +> **ملاحظة**: لمنع سوء الاستخدام، يفرض Binder حجب بعض موارد الويب. قد يمنع هذا عمل بعض الأكواد التي تقوم بجلب نماذج و/أو مجموعات بيانات من الإنترنت العام. قد تحتاج إلى إيجاد حلول بديلة. أيضًا، موارد الحوسبة التي يقدمها Binder أساسية جدًا، لذا سيكون التدريب بطيئًا، خاصة في الدروس الأكثر تعقيدًا لاحقًا. ## التشغيل في السحابة مع دعم GPU -بعض الدروس المتقدمة في هذه المناهج ستستفيد بشكل كبير من دعم GPU، لأنه بدون ذلك سيكون التدريب بطيئًا للغاية. هناك بعض الخيارات التي يمكنك اتباعها، خاصة إذا كان لديك إمكانية الوصول إلى السحابة إما من خلال [Azure للطلاب](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste)، أو من خلال مؤسستك: +بعض الدروس اللاحقة في هذا المنهج ستستفيد كثيرًا من دعم GPU. فمثلاً، قد يكون تدريب النماذج بطيئًا جدًا بدون دعم GPU. هناك عدة خيارات يمكنك اتباعها، خاصة إذا كان لديك وصول إلى السحابة إما عبر [Azure للطلاب](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste)، أو من خلال مؤسستك: -* إنشاء [آلة افتراضية للعلوم البيانات](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) والاتصال بها عبر Jupyter. يمكنك بعد ذلك استنساخ المستودع مباشرة على الآلة، والبدء في التعلم. تحتوي الآلات الافتراضية من سلسلة NC على دعم GPU. +* إنشاء [جهاز افتراضي لعلوم البيانات](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) والاتصال به عبر Jupyter. يمكنك بعد ذلك استنساخ المستودع مباشرة على الجهاز، والبدء بالتعلم. تدعم الأجهزة الافتراضية من سلسلة NC دعم GPU. -> **ملاحظة**: بعض الاشتراكات، بما في ذلك Azure للطلاب، لا توفر دعم GPU بشكل افتراضي. قد تحتاج إلى طلب نوى GPU إضافية من خلال طلب دعم تقني. +> **ملاحظة**: بعض الاشتراكات، بما في ذلك Azure للطلاب، لا توفر دعم GPU بشكل افتراضي. قد تحتاج إلى طلب المزيد من أنوية GPU عبر طلب دعم فني. -* إنشاء [مساحة عمل Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ثم استخدام ميزة الدفاتر هناك. [هذا الفيديو](https://azure-for-academics.github.io/quickstart/azureml-papers/) يوضح كيفية استنساخ مستودع إلى دفتر Azure ML والبدء في استخدامه. +* إنشاء [مساحة عمل Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) ثم استخدام ميزة الدفتر هناك. يوضح [هذا الفيديو](https://azure-for-academics.github.io/quickstart/azureml-papers/) كيفية استنساخ مستودع في دفاتر Azure ML والبدء باستخدامه. -يمكنك أيضًا استخدام Google Colab، الذي يأتي مع بعض دعم GPU المجاني، وتحميل دفاتر Jupyter هناك لتنفيذها واحدة تلو الأخرى. +يمكنك أيضًا استخدام Google Colab، الذي يأتي مع دعم GPU مجاني محدود، ورفع دفاتر Jupyter هناك لتنفيذها واحدة تلو الأخرى. -**إخلاء المسؤولية**: -تم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الموثوق. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة. \ No newline at end of file +--- + + +**تنويه**: +تمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). على الرغم من أننا نسعى لضمان الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي والمعتمد. للمعلومات الحيوية، يُنصح بالاستعانة بالترجمة البشرية الاحترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسير خاطئ ينشأ عن استخدام هذه الترجمة. + \ No newline at end of file diff --git a/translations/ar/lessons/1-Intro/README.md b/translations/ar/lessons/1-Intro/README.md index 1736517e..63a1969a 100644 --- a/translations/ar/lessons/1-Intro/README.md +++ b/translations/ar/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مقدمة في الذكاء الاصطناعي -![ملخص محتوى مقدمة الذكاء الاصطناعي في رسم توضيحي](../../../../translated_images/ai-intro.bf28d1ac4235881c.ar.png) +![ملخص محتوى مقدمة الذكاء الاصطناعي في رسم توضيحي](../../../../translated_images/ar/ai-intro.bf28d1ac4235881c.webp) > رسم توضيحي بواسطة [تومومي إيمورا](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: في الأصل، اخترع [تشارلز باباج](https://en.wikipedia.org/wiki/Charles_Babbage) أجهزة الكمبيوتر لتعمل على الأرقام وفقًا لإجراءات محددة - وهو ما يُعرف بالخوارزمية. وعلى الرغم من أن أجهزة الكمبيوتر الحديثة أكثر تقدمًا بكثير من النموذج الأصلي الذي اقترح في القرن التاسع عشر، إلا أنها لا تزال تتبع نفس فكرة العمليات المحسوبة. وبالتالي، يمكن برمجة الكمبيوتر للقيام بشيء ما إذا كنا نعرف التسلسل الدقيق للخطوات التي نحتاج إلى القيام بها لتحقيق الهدف. -![صورة لشخص](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ar.png) +![صورة لشخص](../../../../translated_images/ar/dsh_age.d212a30d4e54fb5f.webp) > صورة بواسطة [فيكي سوشنيكوفا](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: إحدى المشكلات عند التعامل مع مصطلح **[الذكاء](https://en.wikipedia.org/wiki/Intelligence)** هي أنه لا يوجد تعريف واضح لهذا المصطلح. يمكن للمرء أن يجادل بأن الذكاء مرتبط بـ **التفكير المجرد** أو بـ **الإدراك الذاتي**، لكننا لا نستطيع تعريفه بشكل صحيح. -![صورة لقطة](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ar.jpg) +![صورة لقطة](../../../../translated_images/ar/photo-cat.8c8e8fb760ffe457.webp) > [صورة](https://unsplash.com/photos/75715CVEJhI) بواسطة [Amber Kipp](https://unsplash.com/@sadmax) من Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | ماذا عن التعلم الآلي؟ | | > |--------------|-----------| -> | جزء من الذكاء الاصطناعي يعتمد على تعلم الكمبيوتر لحل مشكلة بناءً على بعض البيانات يُعرف بـ **التعلم الآلي**. لن نناقش التعلم الآلي التقليدي في هذه الدورة - نوجهك إلى منهج منفصل [التعلم الآلي للمبتدئين](http://aka.ms/ml-beginners). | ![التعلم الآلي للمبتدئين](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ar.png) | +> | جزء من الذكاء الاصطناعي يعتمد على تعلم الكمبيوتر لحل مشكلة بناءً على بعض البيانات يُعرف بـ **التعلم الآلي**. لن نناقش التعلم الآلي التقليدي في هذه الدورة - نوجهك إلى منهج منفصل [التعلم الآلي للمبتدئين](http://aka.ms/ml-beginners). | ![التعلم الآلي للمبتدئين](../../../../translated_images/ar/ml-for-beginners.9e4fed176fd5817d.webp) | ## لمحة تاريخية عن الذكاء الاصطناعي بدأ الذكاء الاصطناعي كمجال في منتصف القرن العشرين. في البداية، كان النهج السائد هو الاستدلال الرمزي، وقد أدى إلى عدد من النجاحات المهمة، مثل الأنظمة الخبيرة - برامج الكمبيوتر التي كانت قادرة على العمل كخبير في بعض المجالات المحدودة. ومع ذلك، سرعان ما أصبح واضحًا أن هذا النهج لا يتوسع بشكل جيد. استخراج المعرفة من خبير، تمثيلها في الكمبيوتر، والحفاظ على قاعدة المعرفة دقيقة تبين أنه مهمة معقدة للغاية ومكلفة للغاية لتكون عملية في العديد من الحالات. أدى ذلك إلى ما يُعرف بـ [شتاء الذكاء الاصطناعي](https://en.wikipedia.org/wiki/AI_winter) في السبعينيات. -لمحة تاريخية عن الذكاء الاصطناعي +لمحة تاريخية عن الذكاء الاصطناعي > صورة بواسطة [ديمتري سوشنيكوف](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * المساعدون الحديثون، مثل Cortana، Siri أو Google Assistant، جميعهم أنظمة هجينة تستخدم الشبكات العصبية لتحويل الكلام إلى نص والتعرف على نيتنا، ثم تستخدم بعض الاستدلال أو الخوارزميات الصريحة لتنفيذ الإجراءات المطلوبة. * في المستقبل، قد نتوقع نموذجًا كاملًا يعتمد على الشبكات العصبية للتعامل مع الحوار بنفسه. تظهر عائلة الشبكات العصبية الحديثة GPT و[Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) نجاحًا كبيرًا في هذا المجال. -تطور اختبار تورينغ +تطور اختبار تورينغ > صورة بواسطة دميتري سوشنيكوف، [الصورة](https://unsplash.com/photos/r8LmVbUKgns) بواسطة [مارينا أبروسيموفا](https://unsplash.com/@abrosimova_marina_foto)، Unsplash ## أبحاث الذكاء الاصطناعي الحديثة diff --git a/translations/ar/lessons/2-Symbolic/Animals.ipynb b/translations/ar/lessons/2-Symbolic/Animals.ipynb index 7558bb2e..6240b247 100644 --- a/translations/ar/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ar/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "مثال من [منهج الذكاء الاصطناعي للمبتدئين](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "في هذا المثال، سنقوم بتنفيذ نظام بسيط قائم على المعرفة لتحديد الحيوان بناءً على بعض الخصائص الفيزيائية. يمكن تمثيل النظام بشجرة AND-OR التالية (هذه جزء من الشجرة الكاملة، ويمكننا بسهولة إضافة المزيد من القواعد):\n", + "في هذا المثال، سنقوم بتنفيذ نظام بسيط قائم على المعرفة لتحديد حيوان بناءً على بعض الخصائص الفيزيائية. يمكن تمثيل النظام بالشجرة AND-OR التالية (هذا جزء من الشجرة الكاملة، يمكننا بسهولة إضافة المزيد من القواعد):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ar.png)\n" + "![](../../../../../../translated_images/ar/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## نظام خبير خاص بنا مع الاستدلال العكسي\n", + "## قشرة أنظمة الخبراء الخاصة بنا مع الاستدلال العكسي\n", "\n", - "دعونا نحاول تعريف لغة بسيطة لتمثيل المعرفة تعتمد على قواعد الإنتاج. سنستخدم فئات Python ككلمات مفتاحية لتعريف القواعد. سيكون هناك بشكل أساسي ثلاثة أنواع من الفئات:\n", + "لنحاول تعريف لغة بسيطة لتمثيل المعرفة تعتمد على قواعد الإنتاج. سنستخدم فئات بايثون كالكلمات المفتاحية لتعريف القواعد. سيكون هناك 3 أنواع أساسية من الفئات:\n", "* `Ask` تمثل سؤالًا يحتاج إلى طرحه على المستخدم. تحتوي على مجموعة الإجابات الممكنة.\n", - "* `If` تمثل قاعدة، وهي مجرد صيغة مختصرة لتخزين محتوى القاعدة.\n", - "* `AND`/`OR` هما فئتان لتمثيل فروع AND/OR في الشجرة. تقوم فقط بتخزين قائمة الحجج بداخلها. لتبسيط الكود، يتم تعريف جميع الوظائف في الفئة الرئيسية `Content`.\n" + "* `If` تمثل قاعدة، وهي مجرد سكر نحوي لتخزين محتوى القاعدة\n", + "* `AND`/`OR` هما فئتان لتمثيل فروع AND/OR للشجرة. هما فقط تخزنان قائمة الوسائط بداخلها. لتبسيط الكود، تم تعريف كل الوظائف في الفئة الأصل `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "في نظامنا، ستحتوي الذاكرة العاملة على قائمة **الحقائق** كـ **أزواج السمة والقيمة**. يمكن تعريف قاعدة المعرفة كقاموس كبير يربط الإجراءات (حقائق جديدة يجب إدراجها في الذاكرة العاملة) بالشروط، معبرًا عنها بتعبيرات AND-OR. أيضًا، يمكن `سؤال` بعض الحقائق.\n" + "في نظامنا، تحتوي الذاكرة العاملة على قائمة من **الحقائق** كـ **أزواج سمات-قيمة**. يمكن تعريف قاعدة المعرفة على أنها قاموس واحد كبير يربط الإجراءات (حقائق جديدة يجب إدراجها في الذاكرة العاملة) بالشروط، المعبر عنها بتعابير AND-OR. أيضًا، يمكن `Ask` عن بعض الحقائق.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "لتنفيذ الاستنتاج العكسي، سنقوم بتعريف فئة `Knowledgebase`. ستحتوي على:\n", - "* `الذاكرة` العاملة - قاموس يربط السمات بالقيم\n", - "* قواعد `Knowledgebase` بالتنسيق المحدد أعلاه\n", + "لأداء الاستدلال العكسي، سنقوم بتعريف فئة `Knowledgebase`. ستحتوي على:\n", + "* `memory` العمل - قاموس يربط السمات بالقيم\n", + "* قواعد Knowledgebase بصيغة المعرفة أعلاه\n", "\n", "الطريقتان الرئيسيتان هما:\n", - "* `get` للحصول على قيمة السمة، مع تنفيذ الاستنتاج إذا لزم الأمر. على سبيل المثال، `get('color')` ستجلب قيمة خانة اللون (ستسأل إذا لزم الأمر، وتخزن القيمة للاستخدام لاحقًا في الذاكرة العاملة). إذا طلبنا `get('color:blue')`، ستسأل عن اللون، ثم تعيد قيمة `y`/`n` بناءً على اللون.\n", - "* `eval` ينفذ عملية الاستنتاج الفعلية، أي يتنقل عبر شجرة AND/OR، ويقيم الأهداف الفرعية، وما إلى ذلك.\n" + "* `get` للحصول على قيمة سمة، مع إجراء الاستدلال إذا لزم الأمر. على سبيل المثال، `get('color')` ستحصل على قيمة خانة اللون (ستطلب إذا لزم الأمر، وتخزن القيمة للاستخدام لاحقًا في الذاكرة العاملة). إذا طلبنا `get('color:blue')`، فسوف تطلب لونًا، ثم تعيد قيمة `y`/`n` اعتمادًا على اللون.\n", + "* `eval` تُجري الاستدلال الفعلي، أي تتنقل في شجرة AND/OR، تقييم الأهداف الفرعية، إلخ.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "الآن دعونا نحدد قاعدة المعرفة الخاصة بالحيوانات ونقوم بإجراء الاستشارة. لاحظ أن هذه المكالمة ستطرح عليك أسئلة. يمكنك الإجابة عن طريق كتابة `ن`/`ي` للأسئلة بنعم أو لا، أو بتحديد رقم (0..N) للأسئلة ذات الإجابات المتعددة الأطول.\n" + "الآن دعونا نُعرّف قاعدة معرفتنا عن الحيوانات ونُجري الاستشارة. لاحظ أن هذه المكالمة ستطرح عليك أسئلة. يمكنك الإجابة بكتابة `y`/`n` لأسئلة نعم-لا، أو بتحديد رقم (0..N) لأسئلة ذات إجابات متعددة الأطول.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## استخدام PyKnow للاستدلال الأمامي\n", + "## استخدام Experta للاستدلال الأمامي\n", "\n", - "في المثال التالي، سنحاول تنفيذ الاستدلال الأمامي باستخدام إحدى مكتبات تمثيل المعرفة، [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** هي مكتبة لإنشاء أنظمة استدلال أمامي في بايثون، وهي مصممة لتكون مشابهة للنظام الكلاسيكي القديم [CLIPS](http://www.clipsrules.net/index.html).\n", + "في المثال التالي، سنحاول تنفيذ الاستدلال الأمامي باستخدام إحدى مكتبات تمثيل المعرفة، [Experta](https://github.com/nilp0inter/experta). **Experta** هي مكتبة لإنشاء أنظمة الاستدلال الأمامي في بايثون، وهي مصممة لتكون مشابهة للنظام الكلاسيكي القديم [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "كان بإمكاننا أيضًا تنفيذ سلسلة الاستدلال الأمامي بأنفسنا دون مشاكل كبيرة، ولكن التطبيقات البسيطة عادةً ما تكون غير فعالة للغاية. ولتحقيق مطابقة أكثر كفاءة للقواعد، يتم استخدام خوارزمية خاصة تُسمى [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "كان بإمكاننا أيضًا تنفيذ السلسلة الأمامية بأنفسنا بدون مشاكل كثيرة، لكن التنفيذات البدائية عادةً ليست فعالة جدًا. من أجل مطابقة القواعد بشكل أكثر فعالية، يُستخدم خوارزمية خاصة تسمى [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "سنعرّف نظامنا كفئة ترث من `KnowledgeEngine`. يتم تعريف كل قاعدة بواسطة وظيفة منفصلة مع تعليق `@Rule`، الذي يحدد متى يجب تشغيل القاعدة. داخل القاعدة، يمكننا إضافة حقائق جديدة باستخدام وظيفة `declare`، وإضافة تلك الحقائق سيؤدي إلى استدعاء المزيد من القواعد بواسطة محرك الاستدلال الأمامي.\n" + "سنقوم بتعريف نظامنا كصف فرعي من `KnowledgeEngine`. يتم تعريف كل قاعدة بواسطة دالة منفصلة مع تعليمة `@Rule`، التي تحدد متى يجب أن يتم تطبيق القاعدة. داخل القاعدة، يمكننا إضافة حقائق جديدة باستخدام دالة `declare`، وإضافة هذه الحقائق سيؤدي إلى استدعاء بعض القواعد الأخرى بواسطة محرك الاستدلال الأمامي.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "بمجرد أن نحدد قاعدة المعرفة، نقوم بملء ذاكرة العمل ببعض الحقائق الأولية، ثم نستدعي طريقة `run()` لتنفيذ الاستدلال. يمكنك أن ترى كنتيجة أن حقائق جديدة مستنتجة تُضاف إلى ذاكرة العمل، بما في ذلك الحقيقة النهائية عن الحيوان (إذا قمنا بإعداد جميع الحقائق الأولية بشكل صحيح).\n" + "بمجرد أن نحدد قاعدة المعرفة، نقوم بملء ذاكرتنا العاملة ببعض الحقائق الأولية، ثم نستدعي طريقة `run()` لأداء الاستدلال. يمكنك أن ترى كنتيجة أنه تمت إضافة حقائق مستنتجة جديدة إلى الذاكرة العاملة، بما في ذلك الحقيقة النهائية حول الحيوان (إذا قمنا بإعداد جميع الحقائق الأولية بشكل صحيح).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**إخلاء المسؤولية**: \nتمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية هو المصدر الموثوق. للحصول على معلومات حساسة أو هامة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\n" + "---\n\n\n**تنويه**:\nتمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الرسمي والمعتمد. بالنسبة للمعلومات الحساسة أو الهامة، يُنصح بالاستعانة بالترجمة المهنية البشرية. نحن غير مسؤولين عن أي سوء فهم أو تفسير ناتج عن استخدام هذه الترجمة.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T03:39:27+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:39:30+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ar" } diff --git a/translations/ar/lessons/2-Symbolic/README.md b/translations/ar/lessons/2-Symbolic/README.md index 1d339140..28eed326 100644 --- a/translations/ar/lessons/2-Symbolic/README.md +++ b/translations/ar/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# تمثيل المعرفة وأنظمة الخبراء +# تمثيل المعرفة والأنظمة الخبيرة -![ملخص محتوى الذكاء الاصطناعي الرمزي](../../../../translated_images/ai-symbolic.715a30cb610411a6.ar.png) +![ملخص محتوى الذكاء الاصطناعي الرمزي](../../../../../../translated_images/ar/ai-symbolic.715a30cb610411a6.webp) -> رسم توضيحي بواسطة [Tomomi Imura](https://twitter.com/girlie_mac) +> مخطط مرسوم بواسطة [تومومي إيمورا](https://twitter.com/girlie_mac) -السعي وراء الذكاء الاصطناعي يعتمد على البحث عن المعرفة لفهم العالم بطريقة مشابهة للبشر. ولكن كيف يمكن تحقيق ذلك؟ +إن السعي نحو الذكاء الاصطناعي يستند إلى البحث عن المعرفة، لفهم العالم بطريقة مشابهة لكيفية فهم البشر له. لكن كيف يمكن القيام بذلك؟ -## [اختبار ما قبل المحاضرة](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [اختبار قبلي للمحاضرة](https://ff-quizzes.netlify.app/en/ai/quiz/3) -في الأيام الأولى للذكاء الاصطناعي، كان النهج العلوي لإنشاء أنظمة ذكية (الذي تمت مناقشته في الدرس السابق) شائعًا. الفكرة كانت استخراج المعرفة من البشر إلى شكل يمكن قراءته بواسطة الآلة، ثم استخدامها لحل المشكلات تلقائيًا. هذا النهج كان يعتمد على فكرتين رئيسيتين: +في الأيام الأولى للذكاء الاصطناعي، كان النهج من الأعلى إلى الأسفل لإنشاء أنظمة ذكية (المناقش في الدرس السابق) شائعًا. الفكرة كانت استخلاص المعرفة من الأشخاص إلى شكل يمكن للآلة قراءته، ثم استخدامها تلقائيًا لحل المشكلات. هذا النهج استند إلى فكرتين كبيرتين: -* تمثيل المعرفة -* الاستنتاج +* تمثيل المعرفة +* الاستدلال ## تمثيل المعرفة -أحد المفاهيم المهمة في الذكاء الاصطناعي الرمزي هو **المعرفة**. من المهم التمييز بين المعرفة و*المعلومات* أو *البيانات*. على سبيل المثال، يمكن القول إن الكتب تحتوي على معرفة، لأننا نستطيع دراسة الكتب لنصبح خبراء. ومع ذلك، ما تحتويه الكتب يُطلق عليه في الواقع *بيانات*، ومن خلال قراءة الكتب ودمج هذه البيانات في نموذجنا العقلي للعالم، نحول البيانات إلى معرفة. +أحد المفاهيم المهمة في الذكاء الاصطناعي الرمزي هو **المعرفة**. من المهم التمييز بين المعرفة وبين *المعلومات* أو *البيانات*. على سبيل المثال، يمكن القول أن الكتب تحتوي على معرفة، لأنه يمكن دراسة الكتب ويصبح المرء خبيرًا. ومع ذلك، ما تحويه الكتب في الحقيقة يسمى *بيانات*، وعن طريق قراءة الكتب ودمج هذه البيانات في نموذجنا للعالم نحول هذه البيانات إلى معرفة. -> ✅ **المعرفة** هي شيء موجود في أذهاننا ويمثل فهمنا للعالم. يتم الحصول عليها من خلال عملية **تعلم** نشطة، حيث يتم دمج أجزاء المعلومات التي نتلقاها في نموذجنا العقلي للعالم. +> ✅ **المعرفة** هي شيء موجود في رأسنا ويمثل فهمنا للعالم. يتم الحصول عليها من خلال عملية **تعلم** نشطة، تدمج قطع المعلومات التي نستقبلها في نموذجنا النشط للعالم. -غالبًا لا نحدد المعرفة بشكل صارم، ولكننا نربطها بمفاهيم أخرى باستخدام [هرم DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). يحتوي هذا الهرم على المفاهيم التالية: +غالبًا ما لا نحدد المعرفة بدقة، لكننا نربطها بمفاهيم أخرى ذات صلة باستخدام [هرم DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). يحتوي على المفاهيم التالية: -* **البيانات** هي شيء يتم تمثيله في وسائل مادية، مثل النصوص المكتوبة أو الكلمات المنطوقة. البيانات موجودة بشكل مستقل عن البشر ويمكن نقلها بينهم. -* **المعلومات** هي كيفية تفسيرنا للبيانات في أذهاننا. على سبيل المثال، عندما نسمع كلمة *كمبيوتر*، لدينا فهم معين لما تعنيه. -* **المعرفة** هي المعلومات التي يتم دمجها في نموذجنا العقلي للعالم. على سبيل المثال، بمجرد أن نتعلم ما هو الكمبيوتر، نبدأ في تكوين أفكار حول كيفية عمله، تكلفته، وما يمكن استخدامه فيه. هذه الشبكة من المفاهيم المترابطة تشكل معرفتنا. -* **الحكمة** هي مستوى آخر من فهمنا للعالم، وتمثل *المعرفة الفوقية*، مثل فكرة حول كيفية ومتى يجب استخدام المعرفة. +* **البيانات** هي شيء مُمثل في وسيط مادي، مثل نص مكتوب أو كلمات منطوقة. البيانات موجودة بشكل مستقل عن البشر ويمكن نقلها بينهم. +* **المعلومات** هي كيفية تفسيرنا للبيانات في رأسنا. على سبيل المثال، عندما نسمع كلمة *حاسوب*، يكون لدينا بعض الفهم لما هي. +* **المعرفة** هي المعلومات التي تُدمج في نموذجنا للعالم. على سبيل المثال، بمجرد أن نتعلم ما هو الحاسوب، نبدأ في تكوين بعض الأفكار عن كيفية عمله، كم يكلف، وما يمكن استخدامه له. هذه الشبكة من المفاهيم المترابطة تشكل معرفتنا. +* **الحكمة** هي مستوى آخر من فهمنا للعالم، وتمثل *المعرفة العليا*، مثل فكرة حول كيفية ومتى يجب استخدام المعرفة. - + -*الصورة [من ويكيبيديا](https://commons.wikimedia.org/w/index.php?curid=37705247)، بواسطة Longlivetheux - عمل خاص، CC BY-SA 4.0* +*صورة [من ويكيبيديا](https://commons.wikimedia.org/w/index.php?curid=37705247)، بواسطة Longlivetheux - عمل شخصي، رخصة CC BY-SA 4.0* -لذلك، مشكلة **تمثيل المعرفة** هي إيجاد طريقة فعالة لتمثيل المعرفة داخل الكمبيوتر في شكل بيانات، لجعلها قابلة للاستخدام تلقائيًا. يمكن النظر إلى هذا على أنه طيف: +وبالتالي، مشكلة **تمثيل المعرفة** هي إيجاد طريقة فعالة لتمثيل المعرفة داخل الحاسوب في شكل بيانات، لجعلها قابلة للاستخدام تلقائيًا. يمكن رؤية هذا كمجال: -![طيف تمثيل المعرفة](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ar.png) +![طيف تمثيل المعرفة](../../../../../../translated_images/ar/knowledge-spectrum.b60df631852c0217.webp) -> صورة بواسطة [Dmitry Soshnikov](http://soshnikov.com) +> صورة بواسطة [دميتري سوشنيكوف](http://soshnikov.com) -* على الجانب الأيسر، هناك أنواع بسيطة جدًا من تمثيلات المعرفة التي يمكن استخدامها بفعالية بواسطة الكمبيوتر. أبسطها هو التمثيل الخوارزمي، حيث يتم تمثيل المعرفة بواسطة برنامج كمبيوتر. ومع ذلك، هذا ليس أفضل طريقة لتمثيل المعرفة، لأنه غير مرن. المعرفة داخل أذهاننا غالبًا ما تكون غير خوارزمية. -* على الجانب الأيمن، هناك تمثيلات مثل النصوص الطبيعية. هذا النوع هو الأقوى، ولكنه لا يمكن استخدامه للاستنتاج التلقائي. +* إلى اليسار، هناك أنواع بسيطة جدًا من تمثيلات المعرفة التي يمكن للحواسيب استخدامها بفعالية. أبسطها هو التمثيل الخوارزمي، حيث تمثل المعرفة بواسطة برنامج حاسوبي. ومع ذلك، ليست هذه هي أفضل طريقة لتمثيل المعرفة، لأنها غير مرنة. المعرفة داخل رؤوسنا غالبًا ما تكون غير خوارزمية. +* إلى اليمين، هناك تمثيلات مثل النص الطبيعي. إنها الأقوى، لكنها لا يمكن استخدامها للاستدلال التلقائي. -> ✅ فكر لدقيقة حول كيفية تمثيلك للمعرفة في ذهنك وتحويلها إلى ملاحظات. هل هناك صيغة معينة تساعدك على الاحتفاظ بالمعلومات؟ +> ✅ فكر للحظة في كيفية تمثيلك للمعرفة في رأسك وتحويلها إلى ملاحظات. هل هناك صيغة معينة تعمل جيدًا معك للمساعدة في التذكر؟ -## تصنيف طرق تمثيل المعرفة في الكمبيوتر +## تصنيف تمثيلات المعرفة الحاسوبية -يمكننا تصنيف طرق تمثيل المعرفة المختلفة في الكمبيوتر إلى الفئات التالية: +يمكننا تصنيف طرق تمثيل المعرفة المختلفة في الحاسوب في الفئات التالية: -* **تمثيلات الشبكة** تعتمد على حقيقة أن لدينا شبكة من المفاهيم المترابطة داخل أذهاننا. يمكننا محاولة إعادة إنتاج نفس الشبكات كرسوم بيانية داخل الكمبيوتر - ما يسمى بـ **الشبكة الدلالية**. +* **التمثيلات الشبكية** تعتمد على حقيقة أن لدينا شبكة من المفاهيم المترابطة داخل رأسنا. يمكننا محاولة إعادة إنتاج نفس الشبكات كرسوم بيانية داخل الحاسوب - ما يسمى **الشبكة الدلالية**. -1. **ثلاثيات الكائن-السمة-القيمة** أو **أزواج السمة-القيمة**. بما أن الرسم البياني يمكن تمثيله داخل الكمبيوتر كقائمة من العقد والحواف، يمكننا تمثيل الشبكة الدلالية بقائمة من الثلاثيات التي تحتوي على الكائنات، السمات، والقيم. على سبيل المثال، يمكننا بناء الثلاثيات التالية حول لغات البرمجة: +1. **ثلاثيات الكائن-السمة-القيمة** أو **أزواج السمة والقيمة**. بما أن الرسم البياني يمكن تمثيله داخل الحاسوب كقائمة من العقد والحواف، يمكننا تمثيل شبكة دلالية كقائمة من الثلاثيات التي تحتوي على كائنات، سمات، وقيم. على سبيل المثال، نبني ثلاثيات حول لغات البرمجة كالتالي: الكائن | السمة | القيمة --------|-------|------ -Python | هي | لغة غير محددة النوع -Python | اخترعها | Guido van Rossum -Python | بناء الكتل | التراجع -لغة غير محددة النوع | لا تحتوي على | تعريفات النوع +-------|-----------|------ +Python | is | Untyped-Language +Python | invented-by | Guido van Rossum +Python | block-syntax | indentation +Untyped-Language | doesn't have | type definitions > ✅ فكر كيف يمكن استخدام الثلاثيات لتمثيل أنواع أخرى من المعرفة. -2. **التمثيلات الهرمية** تركز على حقيقة أننا غالبًا ما ننشئ تسلسلًا هرميًا للكائنات داخل أذهاننا. على سبيل المثال، نعلم أن الكناري هو طائر، وكل الطيور لديها أجنحة. لدينا أيضًا فكرة عن لون الكناري عادةً، وسرعة طيرانه. +2. **التمثيلات الهرمية** تؤكد على حقيقة أننا غالبًا ما نُنشئ تسلسلاً هرميًا للكائنات داخل رأسنا. على سبيل المثال، نعرف أن الكناري طائر، وكل الطيور لها أجنحة. لدينا أيضًا فكرة عن لون الكناري المعتاد وسرعة طيرانه. - - **تمثيل الإطار** يعتمد على تمثيل كل كائن أو فئة من الكائنات كـ **إطار** يحتوي على **فتحات**. الفتحات تحتوي على قيم افتراضية ممكنة، قيود على القيم، أو إجراءات مخزنة يمكن استدعاؤها للحصول على قيمة الفتحة. جميع الإطارات تشكل تسلسلًا هرميًا مشابهًا لتسلسل الكائنات في لغات البرمجة الكائنية. - - **السيناريوهات** هي نوع خاص من الإطارات التي تمثل مواقف معقدة يمكن أن تتطور مع الزمن. + - **تمثيل الإطار** يعتمد على تمثيل كل كائن أو فئة من الكائنات كـ **إطار** يحتوي على **فراغات**. للفراغات قيم افتراضية محتملة، قيود على القيم، أو إجراءات مخزنة يمكن استدعاؤها للحصول على قيمة الفراغ. جميع الإطارات تشكل تسلسلًا هرميًا مشابهًا لتسلسل الكائنات في لغات البرمجة كائنية التوجه. + - **السيناريوهات** هي نوع خاص من الإطارات تمثل مواقف معقدة يمكن أن تتطور مع الزمن. -**Python** +**بايثون** -الفتحة | القيمة | القيمة الافتراضية | النطاق --------|-------|----------------|------- -الاسم | Python | | -هي | لغة غير محددة النوع | | -حالة المتغير | | CamelCase | -طول البرنامج | | | 5-5000 سطر -بناء الكتل | التراجع | | +الفراغ | القيمة | القيمة الافتراضية | النطاق +-----|-------|---------------|---------- +Name | Python | | | +Is-A | Untyped-Language | | | +Variable Case | | CamelCase | | +Program Length | | | 5-5000 lines | +Block Syntax | Indent | | | -3. **التمثيلات الإجرائية** تعتمد على تمثيل المعرفة بقائمة من الإجراءات التي يمكن تنفيذها عند حدوث شرط معين. - - قواعد الإنتاج هي عبارات إذا-فإن التي تسمح لنا باستخلاص استنتاجات. على سبيل المثال، يمكن للطبيب أن يكون لديه قاعدة تقول **إذا** كان لدى المريض حمى شديدة **أو** مستوى مرتفع من بروتين C التفاعلي في اختبار الدم **فإن** لديه التهاب. بمجرد أن نواجه أحد الشروط، يمكننا استخلاص استنتاج حول الالتهاب، ثم استخدامه في الاستنتاجات اللاحقة. - - يمكن اعتبار الخوارزميات شكلًا آخر من التمثيلات الإجرائية، على الرغم من أنها نادرًا ما تُستخدم مباشرة في أنظمة المعرفة. +3. **التمثيلات الإجرائية** تعتمد على تمثيل المعرفة كقائمة من الإجراءات التي يمكن تنفيذها عند تحقق شرط معين. + - قواعد الإنتاج هي جمل شرط-نتيجة تسمح لنا باستخلاص الاستنتاجات. على سبيل المثال، لدى الطبيب قاعدة تقول: **إذا** كان للمريض حمى مرتفعة **أو** مستوى مرتفع من البروتين C التفاعلي في اختبار الدم **فإن** لديه التهاب. بمجرد مواجهة أحد الشروط، يمكننا التوصل إلى استنتاج عن وجود الالتهاب، ثم استخدامه في استدلالات لاحقة. + - يمكن اعتبار الخوارزميات شكلًا آخر من التمثيل الإجرائي، رغم أنها نادرًا ما تستخدم مباشرة في أنظمة المعرفة. -4. **المنطق** اقترحه أرسطو في الأصل كطريقة لتمثيل المعرفة البشرية العالمية. - - المنطق الحملي كنظرية رياضية غني جدًا ليكون قابلًا للحساب، لذلك يتم استخدام بعض الأجزاء منه عادةً، مثل العبارات Horn المستخدمة في Prolog. - - المنطق الوصفي هو عائلة من الأنظمة المنطقية المستخدمة لتمثيل واستنتاج التسلسلات الهرمية للكائنات وتمثيلات المعرفة الموزعة مثل *الويب الدلالي*. +4. **المنطق** اقترحه أرسطو في الأصل كطريقة لتمثيل المعرفة البشرية الشاملة. + - المنطق البروديكي باعتباره نظرية رياضية غني جدًا بحيث لا يمكن حسابه في العادة، لذلك يُستخدم عادةً مجموعة فرعية منه مثل جمل Horn المستخدمة في Prolog. + - المنطق الوصفي هو عائلة من الأنظمة المنطقية المستخدمة لتمثيل والاستدلال حول التسلسلات الهرمية للكائنات والتمثيلات المعرفة الموزعة مثل *الويب الدلالي*. -## أنظمة الخبراء +## الأنظمة الخبيرة -أحد النجاحات المبكرة للذكاء الاصطناعي الرمزي كانت ما يسمى بـ **أنظمة الخبراء** - أنظمة كمبيوتر مصممة للعمل كخبير في مجال مشكلة محدودة. كانت تعتمد على **قاعدة معرفة** مستخرجة من خبير أو أكثر من البشر، وتحتوي على **محرك استنتاج** يقوم ببعض الاستنتاجات بناءً عليها. +كان من أوائل نجاحات الذكاء الاصطناعي الرمزي ما يسمى بـ **الأنظمة الخبيرة** - أنظمة حاسوبية صُممت لتعمل كخبير في مجال مشكلة محدد. كانت تعتمد على **قاعدة معرفة** مستخلصة من خبير واحد أو أكثر، وكانت تحتوي على **محرك استدلال** يقوم ببعض الاستدلالات فوقها. -![هيكل الإنسان](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ar.png) | ![نظام قائم على المعرفة](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ar.png) ----------------------------------------------|------------------------------------------------ -هيكل مبسط للنظام العصبي البشري | هيكل نظام قائم على المعرفة +![الهيكل البشري](../../../../../../translated_images/ar/arch-human.5d4d35f1bba3ab1c.webp) | ![نظام قائم على المعرفة](../../../../../../translated_images/ar/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +الهيكل المبسط للنظام العصبي البشري | بنية نظام قائم على المعرفة -أنظمة الخبراء تُبنى مثل نظام الاستنتاج البشري، الذي يحتوي على **ذاكرة قصيرة المدى** و**ذاكرة طويلة المدى**. وبالمثل، في الأنظمة القائمة على المعرفة نميز بين المكونات التالية: +تُبنى الأنظمة الخبيرة مثل نظام الاستدلال البشري، الذي يحتوي على **ذاكرة قصيرة الأمد** و**ذاكرة طويلة الأمد**. وبالمثل، نميز في أنظمة المعرفة المكونات التالية: -* **ذاكرة المشكلة**: تحتوي على المعرفة حول المشكلة التي يتم حلها حاليًا، مثل درجة الحرارة أو ضغط الدم للمريض، وما إذا كان لديه التهاب أم لا. هذه المعرفة تُسمى أيضًا **المعرفة الثابتة**، لأنها تحتوي على لقطة لما نعرفه حاليًا عن المشكلة - ما يسمى *حالة المشكلة*. -* **قاعدة المعرفة**: تمثل المعرفة طويلة المدى حول مجال المشكلة. يتم استخراجها يدويًا من خبراء البشر، ولا تتغير من استشارة إلى أخرى. لأنها تسمح لنا بالتنقل من حالة مشكلة إلى أخرى، تُسمى أيضًا **المعرفة الديناميكية**. -* **محرك الاستنتاج**: ينظم العملية بأكملها للبحث في فضاء حالة المشكلة، وطرح الأسئلة على المستخدم عند الضرورة. كما أنه مسؤول عن إيجاد القواعد المناسبة لتطبيقها على كل حالة. +* **ذاكرة المشكلة**: تحتوي على المعرفة حول المشكلة التي يتم حلها حالياً، مثل درجة حرارة أو ضغط دم المريض، أو ما إذا كان لديه التهاب أم لا، إلخ. تُسمى هذه المعرفة أيضًا **المعرفة الثابتة**، لأنها تحتوي على لقطة لما نعرفه حاليًا عن المشكلة - ما يسمى *حالة المشكلة*. +* **قاعدة المعرفة**: تمثل المعرفة طويلة الأمد حول مجال المشكلة. تستخلص يدويًا من خبراء البشر، ولا تتغير من استشارة لأخرى. لأنّها تسمح لنا بالتنقل من حالة مشكلة إلى أخرى، تُسمى أيضًا **المعرفة الديناميكية**. +* **محرك الاستدلال**: ينظم عملية البحث في فضاء حالات المشكلة، ويطرح أسئلة على المستخدم عند الضرورة. كما أنه مسؤول عن إيجاد القواعد المناسبة التي ستطبق على كل حالة. -كمثال، دعونا نأخذ نظام خبير لتحديد الحيوان بناءً على خصائصه الفيزيائية: +كمثال، لننظر في النظام الخبير التالي لتحديد حيوان بناءً على خصائصه الفيزيائية: -![شجرة AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ar.png) +![شجرة AND-OR](../../../../../../translated_images/ar/AND-OR-Tree.5592d2c70187f283.webp) -> صورة بواسطة [Dmitry Soshnikov](http://soshnikov.com) +> صورة بواسطة [دميتري سوشنيكوف](http://soshnikov.com) -هذا الرسم البياني يُسمى **شجرة AND-OR**، وهو تمثيل رسومي لمجموعة من قواعد الإنتاج. رسم الشجرة مفيد في بداية استخراج المعرفة من الخبير. لتمثيل المعرفة داخل الكمبيوتر، من الأكثر ملاءمة استخدام القواعد: +تسمى هذه المخططات **شجرة AND-OR**، وهي تمثيل بياني لمجموعة من قواعد الإنتاج. رسم الشجرة مفيد في بداية استخراج المعرفة من الخبير. لتمثيل المعرفة داخل الحاسوب، من الأكثر ملاءمة استخدام القواعد: ``` IF the animal eats meat @@ -120,79 +120,79 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` - -يمكنك ملاحظة أن كل شرط على الجانب الأيسر من القاعدة والإجراء هو في الأساس ثلاثيات الكائن-السمة-القيمة (OAV). **الذاكرة العاملة** تحتوي على مجموعة من ثلاثيات OAV التي تتوافق مع المشكلة التي يتم حلها حاليًا. **محرك القواعد** يبحث عن القواعد التي يتم استيفاء شرطها ويطبقها، مضيفًا ثلاثية جديدة إلى الذاكرة العاملة. -> ✅ اكتب شجرة AND-OR خاصة بك حول موضوع تحبه! +يمكنك ملاحظة أن كل شرط في جانب القاعدة الأيسر والفعل هما في الأساس ثلاثيات كائن-سمة-قيمة (OAV). تحتوي **ذاكرة العمل** على مجموعة من ثلاثيات OAV التي تتعلق بالمشكلة التي تُحل حاليًا. تبحث **محرك القواعد** عن القواعد التي تُحقق شروطها وتطبقها، مضيفة ثلاثية جديدة إلى ذاكرة العمل. -### الاستنتاج الأمامي مقابل الاستنتاج الخلفي +> ✅ اكتب شجرة AND-OR خاصة بك عن موضوع تحبه! -العملية الموضحة أعلاه تُسمى **الاستنتاج الأمامي**. تبدأ ببعض البيانات الأولية حول المشكلة المتوفرة في الذاكرة العاملة، ثم تنفذ الحلقة الاستنتاجية التالية: +### الاستدلال الأمامي مقابل الاستدلال العكسي -1. إذا كانت السمة المستهدفة موجودة في الذاكرة العاملة - توقف وأعطِ النتيجة -2. ابحث عن جميع القواعد التي يتم استيفاء شرطها حاليًا - احصل على **مجموعة الصراع** من القواعد. -3. قم بـ **حل الصراع** - اختر قاعدة واحدة سيتم تنفيذها في هذه الخطوة. يمكن أن تكون هناك استراتيجيات مختلفة لحل الصراع: - - اختر أول قاعدة قابلة للتطبيق في قاعدة المعرفة - - اختر قاعدة عشوائية - - اختر قاعدة *أكثر تحديدًا*، أي التي تستوفي أكبر عدد من الشروط في الجانب الأيسر (LHS) -4. طبق القاعدة المختارة وأضف قطعة جديدة من المعرفة إلى حالة المشكلة +العملية الموصوفة أعلاه تسمى **الاستدلال الأمامي**. تبدأ ببعض البيانات الأولية حول المشكلة المتوفرة في ذاكرة العمل، ثم تنفذ حلقة الاستدلال التالية: + +1. إذا كانت السمة المستهدفة موجودة في ذاكرة العمل - توقف وأعط النتيجة +2. ابحث عن كل القواعد التي تُحقق شروطها حاليًا - استخرج **مجموعة الصراع** من القواعد. +3. قم بعملية **حل الصراعات** - اختر قاعدة واحدة ستُنفذ في هذه الخطوة. قد تكون هناك استراتيجيات مختلفة لحل الصراع: + - اختيار أول قاعدة قابلة للتطبيق في قاعدة المعرفة + - اختيار قاعدة عشوائية + - اختيار قاعدة *أكثر تحديدًا*، أي التي تحقق أكبر عدد من الشروط في الجانب الأيسر (LHS) +4. طبق القاعدة المختارة وأدخل قطعة معرفة جديدة في حالة المشكلة 5. كرر من الخطوة 1. -ومع ذلك، في بعض الحالات قد نرغب في البدء بمعرفة فارغة حول المشكلة، وطرح أسئلة تساعدنا في الوصول إلى النتيجة. على سبيل المثال، عند إجراء تشخيص طبي، عادةً لا نقوم بإجراء جميع التحاليل الطبية مسبقًا قبل بدء تشخيص المريض. بل نرغب في إجراء التحاليل عندما يكون القرار بحاجة إلى اتخاذه. +ولكن، في بعض الحالات قد نرغب أن نبدأ بمعرفة فارغة عن المشكلة، وطرح أسئلة تساعدنا في الوصول إلى الاستنتاج. على سبيل المثال، عند التشخيص الطبي، عادةً لا نجري جميع الفحوصات الطبية مقدمًا قبل بدء التشخيص. نرغب في إجراء الفحوصات عند الحاجة لاتخاذ قرار. -يمكن نمذجة هذه العملية باستخدام **الاستنتاج الخلفي**. يتم تحفيزه بواسطة **الهدف** - قيمة السمة التي نبحث عنها: +يمكن تمثيل هذه العملية باستخدام **الاستدلال العكسي**. يتم تحفيزه عن طريق **الهدف** - قيمة السمة التي نريد العثور عليها: -1. اختر جميع القواعد التي يمكن أن تعطينا قيمة الهدف (أي مع الهدف على الجانب الأيمن (RHS)) - مجموعة الصراع -1. إذا لم تكن هناك قواعد لهذه السمة، أو كانت هناك قاعدة تقول إنه يجب أن نسأل المستخدم عن القيمة - اسأل عنها، وإلا: -1. استخدم استراتيجية حل الصراع لاختيار قاعدة واحدة سنستخدمها كـ *فرضية* - سنحاول إثباتها -1. كرر العملية بشكل متكرر لجميع السمات في الجانب الأيسر من القاعدة، محاولًا إثباتها كأهداف -1. إذا فشلت العملية في أي نقطة - استخدم قاعدة أخرى في الخطوة 3. +1. اختر كل القواعد التي يمكن أن تعطي قيمة الهدف (أي مع الهدف في الجانب الأيمن (RHS)) - مجموعة صراع +2. إذا لم تكن هناك قواعد لهذه السمة، أو كانت هناك قاعدة تقول أننا يجب أن نسأل المستخدم عن القيمة - اسأل عنها، وإلا: +3. استخدم استراتيجية حل الصراع لاختيار قاعدة واحدة سنستخدمها كـ *فرضية* - سنحاول إثباتها +4. كرر العملية لكل السمات في الجانب الأيسر من القاعدة، محاولاً إثباتها كأهداف +5. إذا فشلت العملية في أي نقطة - استخدم قاعدة أخرى في الخطوة 3. -> ✅ في أي الحالات يكون الاستنتاج الأمامي أكثر ملاءمة؟ وماذا عن الاستنتاج الخلفي؟ +> ✅ في أي الحالات يكون الاستدلال الأمامي أكثر ملاءمة؟ ماذا عن الاستدلال العكسي؟ -### تنفيذ أنظمة الخبراء +### تنفيذ الأنظمة الخبيرة -يمكن تنفيذ أنظمة الخبراء باستخدام أدوات مختلفة: +يمكن تنفيذ الأنظمة الخبيرة باستخدام أدوات مختلفة: -* برمجتها مباشرةً في لغة برمجة عالية المستوى. هذه ليست أفضل فكرة، لأن الميزة الرئيسية للنظام القائم على المعرفة هي أن المعرفة منفصلة عن الاستنتاج، ومن المحتمل أن يكون خبير مجال المشكلة قادرًا على كتابة القواعد دون فهم تفاصيل عملية الاستنتاج. -* استخدام **قشرة أنظمة الخبراء**، أي نظام مصمم خصيصًا ليتم ملؤه بالمعرفة باستخدام لغة تمثيل المعرفة. +* برمجتها مباشرة في لغة برمجة عالية المستوى. هذه ليست أفضل فكرة، لأن الميزة الرئيسية لنظام قائم على المعرفة هي أن المعرفة منفصلة عن الاستدلال، ومن المحتمل أن يكون خبير مجال المشكلة قادرًا على كتابة القواعد دون فهم تفاصيل عملية الاستدلال. +* استخدام **قشرة الأنظمة الخبيرة**، أي نظام مصمم خصيصًا ليتم ملؤه بالمعرفة باستخدام لغة تمثيل المعرفة. -## ✍️ تمرين: استنتاج الحيوانات +## ✍️ تمرين: استدلال الحيوانات -راجع [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) للحصول على مثال حول تنفيذ نظام خبير للاستنتاج الأمامي والخلفي. +راجع [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) لمثال على تنفيذ نظام خبير بالاستدلال الأمامي والعكسي. -> **ملاحظة**: هذا المثال بسيط جدًا، ويعطي فقط فكرة عن شكل نظام خبير. بمجرد أن تبدأ في إنشاء مثل هذا النظام، ستلاحظ بعض السلوك *الذكي* منه فقط عندما تصل إلى عدد معين من القواعد، حوالي 200+. في مرحلة ما، تصبح القواعد معقدة جدًا بحيث يصعب الاحتفاظ بها جميعًا في الذهن، وفي هذه المرحلة قد تبدأ في التساؤل عن سبب اتخاذ النظام قرارات معينة. ومع ذلك، السمة المهمة للأنظمة القائمة على المعرفة هي أنه يمكنك دائمًا *شرح* بالضبط كيف تم اتخاذ أي من القرارات. +> **ملاحظة**: هذا المثال بسيط نسبيًا، ويعطي فقط فكرة عن شكل النظام الخبير. بمجرد أن تبدأ بإنشاء نظام مماثل، لاحظ أنك ستشاهد سلوكًا *ذكيًا* فقط بمجرد الوصول إلى عدد معين من القواعد، حوالي 200+. في نقطة ما، تصبح القواعد معقدة جدًا بحيث يصعب تذكرها كلها، وقد تتساءل لماذا يتخذ النظام قرارات معينة. ومع ذلك، الميزة المهمة لأنظمة المعرفة هي أنه يمكنك دائمًا *شرح* بالضبط كيف اتخذ أي قرار. ## الأنطولوجيات والويب الدلالي -في نهاية القرن العشرين، كانت هناك مبادرة لاستخدام تمثيل المعرفة لتوضيح موارد الإنترنت، بحيث يصبح من الممكن العثور على الموارد التي تتوافق مع استفسارات محددة جدًا. هذه الحركة كانت تُسمى **الويب الدلالي**، وكانت تعتمد على عدة مفاهيم: +في نهاية القرن العشرين، كانت هناك مبادرة لاستخدام تمثيل المعرفة لوضع تعليقات توضيحية على موارد الإنترنت، بحيث يمكن العثور على الموارد التي تتطابق مع استفسارات محددة جدًا. كانت هذه المبادرة تسمى **الويب الدلالي**، واعتمدت على عدة مفاهيم: -- تمثيل معرفة خاص يعتمد على **[المنطق الوصفي](https://en.wikipedia.org/wiki/Description_logic)** (DL). يشبه تمثيل المعرفة بالإطار، لأنه يبني تسلسلًا هرميًا للكائنات مع الخصائص، ولكنه يحتوي على دلالات منطقية رسمية واستنتاج. هناك عائلة كاملة من DLs التي توازن بين التعبيرية وتعقيد الخوارزميات للاستنتاج. -- تمثيل المعرفة الموزعة، حيث يتم تمثيل جميع المفاهيم بواسطة معرف URI عالمي، مما يجعل من الممكن إنشاء تسلسلات هرمية للمعرفة تمتد عبر الإنترنت. -- عائلة من اللغات القائمة على XML لوصف المعرفة: RDF (إطار وصف الموارد)، RDFS (مخطط RDF)، OWL (لغة الويب للأنطولوجيا). +- تمثيل خاص للمعرفة يعتمد على **[المنطق الوصفي](https://en.wikipedia.org/wiki/Description_logic)** (DL). وهو مشابه لتمثيل المعرفة بالإطار، لأنه يبني تسلسلًا هرميًا للكائنات مع خصائص، لكنه يمتلك دلالة منطقية رسمية والاستدلال. هناك عائلة كاملة من DLs توازن بين التعبيرية والتعقيد الخوارزمي للاستدلال. +- تمثيل المعرفة الموزع، حيث تمثل جميع المفاهيم بمعرف URI عالمي، مما يجعل من الممكن إنشاء تسلسلات معرفية تمتد عبر الإنترنت. +- مجموعة من اللغات المعتمدة على XML لوصف المعرفة: RDF (إطار وصف الموارد)، RDFS (مخطط RDF)، OWL (لغة الويب للأنتولوجيا). -المفهوم الأساسي في الويب الدلالي هو مفهوم **الأنطولوجيا**. يشير إلى تحديد واضح لمجال المشكلة باستخدام تمثيل معرفي رسمي. يمكن أن تكون الأنطولوجيا الأبسط مجرد تسلسل هرمي للأشياء في مجال المشكلة، ولكن الأنطولوجيات الأكثر تعقيدًا ستتضمن قواعد يمكن استخدامها للاستنتاج. +مفهوم أساسي في الويب الدلالي هو مفهوم **الأنتولوجيا**. يشير إلى مواصفة صريحة لمجال المشكلة باستخدام بعض تمثيلات المعرفة الرسمية. يمكن أن تكون الأبسط في الأنتولوجيا مجرد تسلسل هرمي للكائنات في مجال المشكلة، لكن الأنتولوجيات الأكثر تعقيدًا ستشمل قواعد يمكن استخدامها للاستدلال. -في الويب الدلالي، جميع التمثيلات تعتمد على ثلاثيات. يتم تحديد كل كائن وكل علاقة بشكل فريد بواسطة URI. على سبيل المثال، إذا أردنا توضيح حقيقة أن منهج الذكاء الاصطناعي هذا تم تطويره بواسطة Dmitry Soshnikov في 1 يناير 2022 - إليك الثلاثيات التي يمكننا استخدامها: +في الويب الدلالي، جميع التمثيلات مبنية على ثلاثيات. كل كائن وكل علاقة يتم تعريفها بشكل فريد بواسطة URI. على سبيل المثال، إذا أردنا التعبير عن حقيقة أن منهج الذكاء الاصطناعي هذا تم تطويره بواسطة ديمتري سوشنيكوف في 1 يناير 2022 - هذه هي الثلاثيات التي يمكننا استخدامها: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ هنا `http://www.example.com/terms/creation-date` و `http://purl.org/dc/elements/1.1/creator` هما URI معروفان ومقبولان عالميًا للتعبير عن مفاهيم *المُنشئ* و *تاريخ الإنشاء*. +> ✅ هنا `http://www.example.com/terms/creation-date` و `http://purl.org/dc/elements/1.1/creator` هما بعض الURI المعروفة والمقبولة عالميًا للتعبير عن مفاهيم *المنشئ* و *تاريخ الإنشاء*. -في حالة أكثر تعقيدًا، إذا أردنا تعريف قائمة من المُنشئين، يمكننا استخدام بعض الهياكل البيانية المحددة في RDF. +في حالة أكثر تعقيدًا، إذا أردنا تعريف قائمة بالمنشئين، يمكننا استخدام بعض هياكل البيانات المعرفة في RDF. - + -> الرسوم البيانية أعلاه بواسطة [Dmitry Soshnikov](http://soshnikov.com) +> المخططات أعلاه بواسطة [ديمتري سوشنيكوف](http://soshnikov.com) -تقدم بناء الويب الدلالي تباطأ إلى حد ما بسبب نجاح محركات البحث وتقنيات معالجة اللغة الطبيعية، التي تسمح باستخراج البيانات المنظمة من النصوص. ومع ذلك، في بعض المجالات لا تزال هناك جهود كبيرة للحفاظ على الأنطولوجيات وقواعد المعرفة. بعض المشاريع الجديرة بالملاحظة: +تقدم بناء الويب الدلالي تم تباطؤه إلى حد ما بسبب نجاح محركات البحث وتقنيات معالجة اللغة الطبيعية، التي تسمح باستخراج البيانات المُنظمة من النص. ومع ذلك، في بعض المجالات لا تزال هناك جهود كبيرة للحفاظ على الأنتولوجيات وقواعد المعرفة. بعض المشاريع التي تستحق الذكر: -* [WikiData](https://wikidata.org/) هي مجموعة من قواعد المعرفة القابلة للقراءة الآلية المرتبطة بـ Wikipedia. يتم استخراج معظم البيانات من *InfoBoxes* في صفحات Wikipedia، وهي أجزاء من المحتوى المنظم. يمكنك [الاستعلام](https://query.wikidata.org/) عن WikiData باستخدام SPARQL، وهي لغة استعلام خاصة بالويب الدلالي. إليك مثال على استعلام يعرض أكثر ألوان العيون شيوعًا بين البشر: +* [ويكي بيانات](https://wikidata.org/) هي مجموعة من قواعد المعرفة القابلة للقراءة آليًا المرتبطة بويكيبيديا. يتم استخراج معظم البيانات من *مربعات المعلومات* داخل صفحات ويكيبيديا، وهي مقاطع ذات محتوى منظم. يمكنك [استعلام](https://query.wikidata.org/) ويكي بيانات باستخدام SPARQL، وهي لغة استعلام خاصة بالويب الدلالي. هنا استعلام نموذجي يعرض أكثر ألوان العين شيوعًا بين البشر: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) هو جهد آخر مشابه لـ WikiData. +* [دي بي ويديا](https://www.dbpedia.org/) هو جهد مشابه لويكي بيانات. -> ✅ إذا كنت ترغب في تجربة بناء أنطولوجيات خاصة بك، أو فتح أنطولوجيات موجودة، هناك محرر أنطولوجيا بصري رائع يسمى [Protégé](https://protege.stanford.edu/). قم بتنزيله، أو استخدمه عبر الإنترنت. +> ✅ إذا كنت تريد تجربة بناء الأنتولوجيات بنفسك، أو فتح الأنتولوجيات الموجودة، هناك محرر أونتولوجيا بصري رائع يسمى [Protégé](https://protege.stanford.edu/). قم بتحميله، أو استخدمه عبر الإنترنت. - + -*محرر Web Protégé مفتوح مع أنطولوجيا عائلة رومانوف. لقطة شاشة بواسطة Dmitry Soshnikov* +*محرر ويب بروتيجي مفتوح مع أنتولوجيا عائلة رومانوف. لقطة شاشة بواسطة ديمتري سوشنيكوف* -## ✍️ تمرين: أنطولوجيا العائلة +## ✍️ تمرين: أنتولوجيا العائلة -راجع [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) للحصول على مثال حول استخدام تقنيات الويب الدلالي للاستنتاج حول العلاقات الأسرية. سنأخذ شجرة عائلة ممثلة بتنسيق GEDCOM الشائع وأنطولوجيا للعلاقات الأسرية ونبني رسمًا بيانيًا لجميع العلاقات الأسرية لمجموعة معينة من الأفراد. +انظر إلى [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) كمثال على استخدام تقنيات الويب الدلالي للاستدلال حول علاقات العائلة. سنأخذ شجرة عائلية ممثلة بتنسيق GEDCOM الشائع وأنتولوجيا علاقات العائلة ونبني رسمًا بيانيًا لكل علاقات العائلة لمجموعة معينة من الأفراد. -## رسم مفهوم Microsoft +## رسم المفاهيم من مايكروسوفت -في معظم الحالات، يتم إنشاء الأنطولوجيات بعناية يدويًا. ومع ذلك، من الممكن أيضًا **استخراج** الأنطولوجيات من البيانات غير المنظمة، على سبيل المثال، من النصوص الطبيعية. +في معظم الحالات، تُنشأ الأنتولوجيات بعناية يدويًا. ومع ذلك، من الممكن أيضًا **استخراج** الأنتولوجيات من البيانات غير المنظمة، على سبيل المثال، من النصوص الطبيعية. -تم إجراء محاولة كهذه بواسطة Microsoft Research، وأدت إلى [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +تم تنفيذ محاولة من هذا النوع بواسطة أبحاث مايكروسوفت، وأسفرت عن [رسم المفاهيم من مايكروسوفت](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -إنه مجموعة كبيرة من الكيانات مجمعة معًا باستخدام علاقة الوراثة `is-a`. يسمح بالإجابة على أسئلة مثل "ما هي Microsoft؟" - الإجابة تكون شيئًا مثل "شركة باحتمالية 0.87، وعلامة تجارية باحتمالية 0.75". +هو مجموعة كبيرة من الكيانات المجمعة معًا باستخدام علاقة الوراثة `is-a`. يسمح بالإجابة على أسئلة مثل "ما هو مايكروسوفت؟" - والإجابة تكون شيئًا مثل "شركة باحتمالية 0.87، وعلامة تجارية باحتمالية 0.75". -الرسم البياني متاح إما كواجهة برمجية REST API، أو كملف نصي كبير قابل للتنزيل يسرد جميع أزواج الكيانات. +الرسم متاح إما كواجهة REST API، أو كملف نصي كبير قابل للتنزيل يسرد جميع أزواج الكيانات. -## ✍️ تمرين: رسم مفهوم +## ✍️ تمرين: رسم المفاهيم -جرب دفتر [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) لترى كيف يمكننا استخدام Microsoft Concept Graph لتصنيف المقالات الإخبارية إلى عدة فئات. +جرب الدفتر [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) لترى كيف يمكننا استخدام رسم المفاهيم من مايكروسوفت لتجميع مقالات الأخبار في عدة فئات. ## الخاتمة -في الوقت الحاضر، غالبًا ما يُعتبر الذكاء الاصطناعي مرادفًا لـ *التعلم الآلي* أو *الشبكات العصبية*. ومع ذلك، فإن الإنسان يظهر أيضًا استنتاجًا صريحًا، وهو شيء لا يتم التعامل معه حاليًا بواسطة الشبكات العصبية. في المشاريع الواقعية، لا يزال يتم استخدام الاستنتاج الصريح لأداء المهام التي تتطلب تفسيرات، أو القدرة على تعديل سلوك النظام بطريقة محكومة. +في الوقت الحاضر، غالبًا ما يُعتبر الذكاء الاصطناعي مرادفًا لـ *تعلم الآلة* أو *الشبكات العصبية*. ومع ذلك، الإنسان يظهر أيضًا استدلالًا صريحًا، وهو أمر حاليًا لا تتعامل معه الشبكات العصبية. في المشاريع الواقعية، لا يزال يتم استخدام الاستدلال الصريح لأداء المهام التي تتطلب تفسيرات، أو القدرة على تعديل سلوك النظام بطريقة محكومة. ## 🚀 تحدي -في دفتر أنطولوجيا العائلة المرتبط بهذا الدرس، هناك فرصة لتجربة علاقات أسرية أخرى. حاول اكتشاف روابط جديدة بين الأشخاص في شجرة العائلة. +في دفتر الأنتولوجيا العائلية المرتبط بهذا الدرس، هناك فرصة للتجربة مع علاقات العائلة الأخرى. حاول اكتشاف روابط جديدة بين الأشخاص في شجرة العائلة. ## [اختبار ما بعد المحاضرة](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## المراجعة والدراسة الذاتية +## مراجعة ودراسة ذاتية -قم ببعض البحث على الإنترنت لاكتشاف المجالات التي حاول فيها البشر قياس المعرفة وتشفيرها. ألقِ نظرة على تصنيف بلوم، وارجع إلى التاريخ لتتعرف على كيفية محاولة البشر فهم عالمهم. استكشف عمل لينيوس لإنشاء تصنيف للكائنات الحية، وراقب الطريقة التي أنشأ بها ديمتري مندليف طريقة لوصف العناصر الكيميائية وتصنيفها. ما هي الأمثلة الأخرى المثيرة للاهتمام التي يمكنك العثور عليها؟ +قم ببعض الأبحاث على الإنترنت لاكتشاف المجالات التي حاول فيها البشر قياس وترميز المعرفة. اطلع على تصنيف بلوم، وارجع في التاريخ لتتعلم كيف حاول البشر فهم عالمهم. استكشف عمل لينيوس في إنشاء تصنيف للكائنات الحية، ولاحظ الطريقة التي أنشأ بها ديمتري مندليف طريقة لوصف وتصنيف العناصر الكيميائية. ما هي الأمثلة المثيرة الأخرى التي تستطيع العثور عليها؟ -**التكليف**: [بناء أنطولوجيا](assignment.md) +**المهمة**: [بناء أنتولوجيا](assignment.md) --- + +**تنويه**: +تمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى للدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو عدم دقة. يجب اعتبار المستند الأصلي بلغته الأصلية كمصدر موثوق. للمعلومات الهامة، يُنصح باستخدام ترجمة بشرية محترفة. لا نتحمل أي مسؤولية عن أي سوء فهم أو تفسير ناتج عن استخدام هذه الترجمة. + \ No newline at end of file diff --git a/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 7140650c..21f6f38e 100644 --- a/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -488,7 +488,7 @@ "\n", "في حالة وجود أكثر من فئتين، سيقوم softmax بتطبيع الاحتمالات عبر جميع الفئات. إليك مخططًا يوضح بنية الشبكة التي تقوم بتصنيف أرقام MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ar.png)\n" + "![MNIST Classifier](../../../../../translated_images/ar/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1256,7 +1256,7 @@ "* خسارة تدريب منخفضة - يمكن للنموذج تقريب بيانات التدريب بشكل جيد لأنه يمتلك قوة تعبيرية كافية.\n", "* يمكن أن تكون خسارة التحقق أعلى بكثير من خسارة التدريب وقد تبدأ في الزيادة أثناء التدريب - وهذا لأن النموذج \"يحفظ\" نقاط التدريب ويفقد \"الصورة العامة\".\n", "\n", - "![الإفراط في التخصيص](../../../../../translated_images/overfit.a0bd57f717c15769.ar.png)\n", + "![الإفراط في التخصيص](../../../../../translated_images/ar/overfit.a0bd57f717c15769.webp)\n", "\n", "> في هذه الصورة، `x` تمثل بيانات التدريب، و`o` تمثل بيانات التحقق. على اليسار - نموذج خطي (طبقة واحدة)، يقترب بشكل جيد من طبيعة البيانات. على اليمين - نموذج مفرط في التخصيص، يقترب بشكل مثالي من بيانات التدريب، لكنه يفقد المعنى مع أي بيانات أخرى (خطأ التحقق مرتفع جدًا).\n" ] diff --git a/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/README.md index 83a6b7d6..4afdef04 100644 --- a/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: لننظر إلى المشكلة التالية لتقريب 5 نقاط (ممثلة بـ `x` على الرسوم البيانية أدناه): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ar.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ar.jpg) +![linear](../../../../../translated_images/ar/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ar/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **نموذج خطي، 2 معلمات** | **نموذج غير خطي، 7 معلمات** خطأ التدريب = 5.3 | خطأ التدريب = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: كما ترى من الرسم البياني أعلاه، يمكن اكتشاف الإفراط في التكيف من خلال خطأ تدريب منخفض جدًا وخطأ تحقق عالي. عادةً أثناء التدريب، سنرى كلا من أخطاء التدريب والتحقق تبدأ في الانخفاض، ثم في مرحلة ما قد يتوقف خطأ التحقق عن الانخفاض ويبدأ في الارتفاع. سيكون هذا علامة على الإفراط في التكيف، ومؤشرًا على أنه يجب علينا على الأرجح التوقف عن التدريب في هذه المرحلة (أو على الأقل أخذ لقطة للنموذج). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ar.png) +![overfitting](../../../../../translated_images/ar/Overfitting.408ad91cd90b4371.webp) ## كيفية منع الإفراط في التكيف diff --git a/translations/ar/lessons/3-NeuralNetworks/README.md b/translations/ar/lessons/3-NeuralNetworks/README.md index 8e60afd0..debb94a4 100644 --- a/translations/ar/lessons/3-NeuralNetworks/README.md +++ b/translations/ar/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مقدمة في الشبكات العصبية -![ملخص محتوى مقدمة الشبكات العصبية في رسم توضيحي](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ar.png) +![ملخص محتوى مقدمة الشبكات العصبية في رسم توضيحي](../../../../translated_images/ar/ai-neuralnetworks.1c687ae40bc86e83.webp) كما ناقشنا في المقدمة، إحدى الطرق لتحقيق الذكاء هي تدريب **نموذج حاسوبي** أو **دماغ اصطناعي**. منذ منتصف القرن العشرين، حاول الباحثون استخدام نماذج رياضية مختلفة، حتى أثبت هذا الاتجاه نجاحًا كبيرًا في السنوات الأخيرة. تُعرف هذه النماذج الرياضية للدماغ باسم **الشبكات العصبية**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: من علم الأحياء، نعلم أن دماغنا يتكون من خلايا عصبية (الخلايا العصبية)، لكل منها عدة "مدخلات" (التشعبات) ومخرج واحد (المحور العصبي). يمكن لكل من التشعبات والمحاور العصبية نقل إشارات كهربائية، والروابط بينها — المعروفة باسم المشابك العصبية — يمكن أن تظهر درجات متفاوتة من التوصيل، والتي يتم تنظيمها بواسطة الناقلات العصبية. -![نموذج الخلية العصبية](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ar.jpg) | ![نموذج الخلية العصبية](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ar.png) +![نموذج الخلية العصبية](../../../../translated_images/ar/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![نموذج الخلية العصبية](../../../../translated_images/ar/artneuron.1a5daa88d20ebe6f.webp) ----|---- خلية عصبية حقيقية *([الصورة](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) من ويكيبيديا)* | خلية عصبية اصطناعية *(الصورة بواسطة المؤلف)* لذلك، فإن أبسط نموذج رياضي للخلية العصبية يحتوي على عدة مدخلات X1, ..., XN ومخرج Y، وسلسلة من الأوزان W1, ..., WN. يتم حساب المخرج كالتالي: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) حيث f هي **دالة التنشيط** غير الخطية. diff --git a/translations/ar/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ar/lessons/4-ComputerVision/06-IntroCV/README.md index 50e2b9a4..5d6211b0 100644 --- a/translations/ar/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ar/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **معالجة صورة لكتاب برايل**. نركز على كيفية استخدام التقسيم، اكتشاف الميزات، التحويل المنظوري، وتلاعبات NumPy لفصل رموز برايل الفردية لتصنيفها لاحقًا بواسطة شبكة عصبية. -![صورة برايل](../../../../../translated_images/braille.341962ff76b1bd70.ar.jpeg) | ![صورة برايل بعد المعالجة](../../../../../translated_images/braille-result.46530fea020b03c7.ar.png) | ![رموز برايل](../../../../../translated_images/braille-symbols.0159185ab69d5339.ar.png) +![صورة برايل](../../../../../translated_images/ar/braille.341962ff76b1bd70.webp) | ![صورة برايل بعد المعالجة](../../../../../translated_images/ar/braille-result.46530fea020b03c7.webp) | ![رموز برايل](../../../../../translated_images/ar/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > صورة من [OpenCV.ipynb](OpenCV.ipynb) * **اكتشاف الحركة في الفيديو باستخدام الفرق بين الإطارات**. إذا كانت الكاميرا ثابتة، فإن الإطارات من تغذية الكاميرا يجب أن تكون متشابهة جدًا مع بعضها البعض. نظرًا لأن الإطارات تمثل كمصفوفات، فقط عن طريق طرح تلك المصفوفات لإطارين متتاليين سنحصل على الفرق بين البكسلات، والذي يجب أن يكون منخفضًا للإطارات الثابتة، ويصبح أعلى بمجرد وجود حركة كبيرة في الصورة. -![صورة لإطارات الفيديو وفروق الإطارات](../../../../../translated_images/frame-difference.706f805491a0883c.ar.png) +![صورة لإطارات الفيديو وفروق الإطارات](../../../../../translated_images/ar/frame-difference.706f805491a0883c.webp) > صورة من [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **التدفق البصري الكثيف** يحسب مجال المتجه الذي يظهر لكل بكسل إلى أين يتحرك. - **التدفق البصري المتناثر** يعتمد على أخذ بعض الميزات المميزة في الصورة (مثل الحواف)، وبناء مسارها من إطار إلى إطار. -![صورة التدفق البصري](../../../../../translated_images/optical.1f4a94464579a83a.ar.png) +![صورة التدفق البصري](../../../../../translated_images/ar/optical.1f4a94464579a83a.webp) > صورة من [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ar/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ar/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 689e9344..d87a4c0f 100644 --- a/translations/ar/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ar/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 هي شبكة حققت دقة بنسبة 92.7% في تصنيف ImageNet top-5 في عام 2014. تحتوي على الهيكل الطبقي التالي: -![طبقات ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ar.jpg) +![طبقات ImageNet](../../../../../translated_images/ar/vgg-16-arch1.d901a5583b3a51ba.webp) كما ترى، تتبع VGG هيكل الهرم التقليدي، وهو عبارة عن سلسلة من طبقات الالتفاف والتجميع. -![هرم ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ar.jpg) +![هرم ImageNet](../../../../../translated_images/ar/vgg-16-arch.64ff2137f50dd49f.webp) > الصورة من [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b3565f90..770aa699 100644 --- a/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "لذلك، في شبكة عصبية تلافيفية نموذجية، ستكون هناك عدة طبقات تلافيفية، مع طبقات تجميع بينها لتقليل أبعاد الصورة. كما سنزيد عدد الفلاتر، لأنه مع تقدم الأنماط - هناك المزيد من التركيبات المثيرة للاهتمام التي نحتاج إلى البحث عنها.\n", "\n", - "![صورة تظهر عدة طبقات تلافيفية مع طبقات تجميع.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ar.png)\n", + "![صورة تظهر عدة طبقات تلافيفية مع طبقات تجميع.](../../../../../translated_images/ar/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "بسبب تقليل الأبعاد المكانية وزيادة أبعاد الميزات/الفلاتر، يُطلق على هذه البنية أيضًا **بنية الهرم**.\n" ] diff --git a/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 5f397e44..1ec3c896 100644 --- a/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ar/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "لذلك، في شبكة CNN النموذجية، ستكون هناك عدة طبقات تلافيفية، مع طبقات تجميع بينهما لتقليل أبعاد الصورة. كما سنزيد عدد المرشحات، لأنه مع تقدم الأنماط تصبح هناك المزيد من التركيبات المثيرة للاهتمام التي نحتاج إلى البحث عنها.\n", "\n", - "![صورة توضح عدة طبقات تلافيفية مع طبقات تجميع.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ar.png)\n", + "![صورة توضح عدة طبقات تلافيفية مع طبقات تجميع.](../../../../../translated_images/ar/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "بسبب تقليل الأبعاد المكانية وزيادة أبعاد الميزات/المرشحات، يُطلق على هذا التصميم أيضًا **تصميم الهرم**.\n" ] diff --git a/translations/ar/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ar/lessons/4-ComputerVision/07-ConvNets/README.md index 7a526bfc..22d47845 100644 --- a/translations/ar/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ar/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: لاستخراج الأنماط، سنستخدم مفهوم **المرشحات الالتفافية**. كما تعلم، يتم تمثيل الصورة بمصفوفة ثنائية الأبعاد، أو موتر ثلاثي الأبعاد مع عمق اللون. تطبيق المرشح يعني أننا نأخذ مصفوفة **نواة المرشح** صغيرة نسبيًا، ولكل بكسل في الصورة الأصلية نحسب المتوسط المرجح مع النقاط المجاورة. يمكننا تصور هذا كنافذة صغيرة تنزلق عبر الصورة بأكملها، وتقوم بتوسيط جميع البكسلات وفقًا للأوزان في مصفوفة نواة المرشح. -![مرشح الحافة العمودية](../../../../../translated_images/filter-vert.b7148390ca0bc356.ar.png) | ![مرشح الحافة الأفقية](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ar.png) +![مرشح الحافة العمودية](../../../../../translated_images/ar/filter-vert.b7148390ca0bc356.webp) | ![مرشح الحافة الأفقية](../../../../../translated_images/ar/filter-horiz.59b80ed4feb946ef.webp) ----|---- > الصورة بواسطة ديمتري سوشنيكوف @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * يمكننا تصميم الشبكة بطريقة تجعل المرشحات تُدرَّب تلقائيًا * يمكننا استخدام نفس النهج للعثور على الأنماط في الميزات عالية المستوى، وليس فقط في الصورة الأصلية. وبالتالي، تعمل عملية استخراج الميزات في الشبكات العصبية الالتفافية على تسلسل هرمي للميزات، بدءًا من تركيبات البكسلات منخفضة المستوى، وصولًا إلى تركيبات أعلى مستوى لأجزاء الصورة. -![استخراج الميزات الهرمي](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ar.png) +![استخراج الميزات الهرمي](../../../../../translated_images/ar/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > صورة من [ورقة بحثية بواسطة هيسلوب-لينش](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)، بناءً على [أبحاثهم](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: كمثال، دعونا نلقي نظرة على بنية VGG-16، وهي شبكة حققت دقة 92.7% في تصنيف ImageNet ضمن أفضل 5 في عام 2014: -![طبقات ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ar.jpg) +![طبقات ImageNet](../../../../../translated_images/ar/vgg-16-arch1.d901a5583b3a51ba.webp) -![هرم ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ar.jpg) +![هرم ImageNet](../../../../../translated_images/ar/vgg-16-arch.64ff2137f50dd49f.webp) > صورة من [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ar/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ar/lessons/4-ComputerVision/07-ConvNets/lab/README.md index df7449d4..ca96bb5a 100644 --- a/translations/ar/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ar/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: سنستخدم [مجموعة بيانات الحيوانات الأليفة من أكسفورد-IIIT](https://www.robots.ox.ac.uk/~vgg/data/pets/)، والتي تحتوي على صور لـ 37 سلالة مختلفة من الكلاب والقطط. -![مجموعة البيانات التي سنتعامل معها](../../../../../../translated_images/data.50b2a9d5484bdbf0.ar.png) +![مجموعة البيانات التي سنتعامل معها](../../../../../../translated_images/ar/data.50b2a9d5484bdbf0.webp) لتحميل مجموعة البيانات، استخدم هذا المقتطف البرمجي: diff --git a/translations/ar/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ar/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index d1b6b2b7..96515f5b 100644 --- a/translations/ar/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ar/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "لرؤية صورة القطة المثالية، سنبدأ بصورة ضوضاء عشوائية، وسنحاول استخدام تقنية تحسين الانحدار التدريجي لتعديل الصورة بحيث يتعرف عليها الشبكة كقطة.\n", "\n", - "![دورة التحسين](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ar.png)\n", + "![دورة التحسين](../../../../../translated_images/ar/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "هذه هي الصورة التي نبدأ بها:\n" ] diff --git a/translations/ar/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ar/lessons/4-ComputerVision/08-TransferLearning/README.md index 9e3de702..188e8fcf 100644 --- a/translations/ar/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ar/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: فيما يلي ميزات مستخرجة من صورة قطة بواسطة شبكة VGG-16: -![ميزات مستخرجة بواسطة VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ar.png) +![ميزات مستخرجة بواسطة VGG-16](../../../../../translated_images/ar/features.6291f9c7ba3a0b95.webp) ## مجموعة بيانات القطط مقابل الكلاب @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: أحد الأساليب التي يمكننا اتخاذها هو البدء بصورة عشوائية، ثم محاولة استخدام تقنية **تحسين الانحدار التدرجي** لتعديل تلك الصورة بطريقة تجعل الشبكة تبدأ في الاعتقاد بأنها قطة. -![حلقة تحسين الصورة](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ar.png) +![حلقة تحسين الصورة](../../../../../translated_images/ar/ideal-cat-loop.999fbb8ff306e044.webp) ومع ذلك، إذا قمنا بذلك، سنحصل على شيء مشابه جدًا للضوضاء العشوائية. هذا لأن *هناك العديد من الطرق لجعل الشبكة تعتقد أن الصورة المدخلة هي قطة*، بما في ذلك بعض الطرق التي لا معنى لها بصريًا. بينما تحتوي تلك الصور على الكثير من الأنماط النموذجية للقطة، لا يوجد شيء يقيدها لتكون مميزة بصريًا. لتحسين النتيجة، يمكننا إضافة مصطلح آخر إلى دالة الخسارة، يسمى **خسارة التباين**. إنه مقياس يظهر مدى تشابه البكسلات المجاورة للصورة. تقليل خسارة التباين يجعل الصورة أكثر نعومة، ويتخلص من الضوضاء - مما يكشف عن أنماط أكثر جاذبية بصريًا. فيما يلي مثال على هذه الصور "المثالية"، التي يتم تصنيفها كقطة وكحمار وحشي باحتمالية عالية: -![القطة المثالية](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ar.png) | ![الحمار الوحشي المثالي](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ar.png) +![القطة المثالية](../../../../../translated_images/ar/ideal-cat.203dd4597643d6b0.webp) | ![الحمار الوحشي المثالي](../../../../../translated_images/ar/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *القطة المثالية* | *الحمار الوحشي المثالي* يمكن استخدام نهج مشابه لتنفيذ ما يسمى بـ **الهجمات العدائية** على الشبكة العصبية. لنفترض أننا نريد خداع الشبكة العصبية وجعل الكلب يبدو كأنه قطة. إذا أخذنا صورة كلب، والتي يتم التعرف عليها من قبل الشبكة ككلب، يمكننا تعديلها قليلاً باستخدام تحسين الانحدار التدرجي، حتى تبدأ الشبكة في تصنيفها كقطة: -![صورة كلب](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ar.png) | ![صورة كلب مصنفة كقطة](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ar.png) +![صورة كلب](../../../../../translated_images/ar/original-dog.8f68a67d2fe0911f.webp) | ![صورة كلب مصنفة كقطة](../../../../../translated_images/ar/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *الصورة الأصلية للكلب* | *صورة كلب مصنفة كقطة* diff --git a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3117f4cf..928ed395 100644 --- a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "نظرًا لأننا ندرب المشفر التلقائي لالتقاط أكبر قدر ممكن من المعلومات من الصورة الأصلية لتحقيق إعادة بناء دقيقة، تحاول الشبكة العثور على أفضل **تمثيل** للصور المدخلة لالتقاط المعنى.\n", "\n", - "![مخطط المشفر التلقائي](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg)\n", + "![مخطط المشفر التلقائي](../../../../../translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> الصورة مأخوذة من [مدونة Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 10784cc1..bd7db8ce 100644 --- a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "نظرًا لأننا ندرب المشفر التلقائي لالتقاط أكبر قدر ممكن من المعلومات من الصورة الأصلية لإعادة البناء بدقة، تحاول الشبكة العثور على أفضل **تمثيل مضمن** لصور المدخلات لالتقاط المعنى.\n", "\n", - "![رسم تخطيطي للمشفر التلقائي](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg)\n", + "![رسم تخطيطي للمشفر التلقائي](../../../../../translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*الصورة مأخوذة من [مدونة Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/README.md index 26ab2699..c3cc0494 100644 --- a/translations/ar/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ar/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: بما أننا نقوم بتدريب الشبكة العصبية التلقائية لالتقاط أكبر قدر ممكن من المعلومات من الصورة الأصلية لإعادة البناء بدقة، تحاول الشبكة العثور على أفضل **تمثيل** للصور المدخلة لالتقاط المعنى. -![مخطط الشبكة العصبية التلقائية](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ar.jpg) +![مخطط الشبكة العصبية التلقائية](../../../../../translated_images/ar/autoencoder_schema.5e6fc9ad98a5eb61.webp) > الصورة من [مدونة Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ar/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ar/lessons/4-ComputerVision/11-ObjectDetection/README.md index 7d6319fa..ecb373cf 100644 --- a/translations/ar/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ar/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [اختبار ما قبل المحاضرة](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![الكشف عن الكائنات](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ar.png) +![الكشف عن الكائنات](../../../../../translated_images/ar/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > الصورة من [موقع YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. تشغيل تصنيف الصور على كل مربع. 3. يمكن اعتبار المربعات التي تؤدي إلى تنشيط عالي بما يكفي أنها تحتوي على الكائن المطلوب. -![الكشف البسيط عن الكائنات](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ar.png) +![الكشف البسيط عن الكائنات](../../../../../translated_images/ar/naive-detection.e7f1ba220ccd08c6.webp) > *الصورة من [دفتر التمارين](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 فئة * [COCO](http://cocodataset.org/#home) - الكائنات الشائعة في السياق. 80 فئة، صناديق محيطة وأقنعة تقسيم -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ar.jpg) +![COCO](../../../../../translated_images/ar/coco-examples.71bc60380fa6cceb.webp) ## مقاييس الكشف عن الكائنات @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: بينما من السهل قياس أداء الخوارزمية في تصنيف الصور، فإن الكشف عن الكائنات يتطلب قياس صحة الفئة وكذلك دقة موقع الصندوق المحيط المستنتج. بالنسبة للأخير، نستخدم ما يسمى **التقاطع على الاتحاد** (IoU)، الذي يقيس مدى تداخل صندوقين (أو منطقتين عشوائيتين). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ar.png) +![IoU](../../../../../translated_images/ar/iou_equation.9a4751d40fff4e11.webp) > *الشكل 2 من [هذه المقالة الممتازة عن IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) تستخدم [البحث الانتقائي](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) لإنشاء هيكل هرمي لمناطق الاهتمام (ROI)، التي يتم تمريرها بعد ذلك عبر مستخلصات ميزات CNN ومصنفات SVM لتحديد فئة الكائن، وانحدار خطي لتحديد إحداثيات *الصندوق المحيط*. [المقالة الرسمية](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ar.png) +![RCNN](../../../../../translated_images/ar/rcnn1.cae407020dfb1d1f.webp) > *الصورة من van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ar.png) +![RCNN-1](../../../../../translated_images/ar/rcnn2.2d9530bb83516484.webp) > *الصور من [هذه المقالة](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ هذا النهج مشابه لـ R-CNN، لكن المناطق يتم تحديدها بعد تطبيق طبقات الالتفاف. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ar.png) +![FRCNN](../../../../../translated_images/ar/f-rcnn.3cda6d9bb4188875.webp) > الصورة من [المقالة الرسمية](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)، [arXiv](https://arxiv.org/pdf/1504.08083.pdf)، 2015 @@ -118,7 +118,7 @@ $$ الفكرة الرئيسية لهذا النهج هي استخدام الشبكة العصبية للتنبؤ بمناطق الاهتمام - ما يسمى *شبكة اقتراح المناطق*. [المقالة](https://arxiv.org/pdf/1506.01497.pdf)، 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ar.png) +![FasterRCNN](../../../../../translated_images/ar/faster-rcnn.8d46c099b87ef30a.webp) > الصورة من [المقالة الرسمية](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. يتم معالجة الميزات بواسطة **خريطة النقاط الحساسة للموقع**. يتم تقسيم كل كائن من $C$ الفئات إلى مناطق $k\times k$، وندرب الشبكة للتنبؤ بأجزاء الكائنات. 3. لكل جزء من مناطق $k\times k$، تصوت جميع الشبكات لفئات الكائنات، ويتم اختيار فئة الكائن ذات التصويت الأعلى. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ar.png) +![r-fcn image](../../../../../translated_images/ar/r-fcn.13eb88158b99a3da.webp) > الصورة من [المقالة الرسمية](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO هو خوارزمية مرور واحد في الوقت الفعلي. ال * يتم تقسيم الصورة إلى مناطق $S\times S$. * لكل منطقة، **CNN** تتنبأ بـ $n$ كائنات محتملة، *إحداثيات الصندوق المحيط* و *الثقة*=*الاحتمالية* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ar.png) + ![YOLO](../../../../../translated_images/ar/yolo.a2648ec82ee8bb4e.webp) > الصورة من [المقالة الرسمية](https://arxiv.org/abs/1506.02640) diff --git a/translations/ar/lessons/4-ComputerVision/README.md b/translations/ar/lessons/4-ComputerVision/README.md index 289ed103..d17677a7 100644 --- a/translations/ar/lessons/4-ComputerVision/README.md +++ b/translations/ar/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # رؤية الحاسوب -![ملخص محتوى رؤية الحاسوب في رسم توضيحي](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ar.png) +![ملخص محتوى رؤية الحاسوب في رسم توضيحي](../../../../translated_images/ar/ai-computervision.6506ebebac3fbf76.webp) في هذا القسم سنتعلم عن: diff --git a/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index f4f5e13b..87cb70b5 100644 --- a/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**حقيبة الكلمات** (BoW) هي الطريقة الأكثر استخدامًا لتمثيل النصوص بشكل تقليدي باستخدام المتجهات. يتم ربط كل كلمة بمؤشر في المتجه، ويحتوي عنصر المتجه على عدد مرات ظهور الكلمة في مستند معين.\n", "\n", - "![صورة توضح كيفية تمثيل حقيبة الكلمات باستخدام المتجهات في الذاكرة.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ar.png)\n", + "![صورة توضح كيفية تمثيل حقيبة الكلمات باستخدام المتجهات في الذاكرة.](../../../../../translated_images/ar/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **ملاحظة**: يمكنك أيضًا التفكير في حقيبة الكلمات كجمع لجميع المتجهات المشفرة بطريقة \"واحد-ساخن\" للكلمات الفردية في النص.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 5b9a677e..9a6a28b9 100644 --- a/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ar/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**حقيبة الكلمات** (Bag-of-words أو BoW) هي أبسط طريقة لفهم تمثيل النصوص باستخدام المتجهات التقليدية. يتم ربط كل كلمة بمؤشر في المتجه، ويحتوي عنصر المتجه على عدد مرات ظهور كل كلمة في مستند معين.\n", "\n", - "![صورة توضح كيفية تمثيل حقيبة الكلمات باستخدام المتجهات في الذاكرة.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ar.png) \n", + "![صورة توضح كيفية تمثيل حقيبة الكلمات باستخدام المتجهات في الذاكرة.](../../../../../translated_images/ar/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **ملاحظة**: يمكنك أيضًا التفكير في حقيبة الكلمات على أنها مجموع جميع المتجهات المشفرة بطريقة \"واحد-ساخن\" لكل كلمة فردية في النص.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index ff27fe85..59d856d2 100644 --- a/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "باستخدام طبقة التضمين كأول طبقة في شبكتنا، يمكننا الانتقال من نموذج **bag-of-words** إلى نموذج **embedding bag**، حيث نقوم أولاً بتحويل كل كلمة في النص إلى التضمين المقابل لها، ثم نحسب وظيفة تجميعية معينة لجميع تلك التضمينات، مثل `sum` أو `average` أو `max`.\n", "\n", - "![صورة توضح مصنف تضمين لخمس كلمات متسلسلة.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png)\n", + "![صورة توضح مصنف تضمين لخمس كلمات متسلسلة.](../../../../../translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "شبكة المصنف العصبية الخاصة بنا ستبدأ بطبقة التضمين، ثم طبقة التجميع، ومصنف خطي فوقها:\n" ] @@ -176,7 +176,7 @@ "\n", "في البنية السابقة، كنا بحاجة إلى ملء جميع التسلسلات لتكون بنفس الطول لكي تتناسب مع دفعة صغيرة. هذه ليست الطريقة الأكثر كفاءة لتمثيل التسلسلات ذات الطول المتغير - هناك نهج آخر يتمثل في استخدام **متجه الإزاحة**، الذي يحتفظ بإزاحات جميع التسلسلات المخزنة في متجه كبير واحد.\n", "\n", - "![صورة توضح تمثيل تسلسل باستخدام الإزاحة](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ar.png)\n", + "![صورة توضح تمثيل تسلسل باستخدام الإزاحة](../../../../../translated_images/ar/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **ملاحظة**: في الصورة أعلاه، نعرض تسلسلًا من الأحرف، ولكن في مثالنا نحن نعمل مع تسلسلات من الكلمات. ومع ذلك، يبقى المبدأ العام لتمثيل التسلسلات باستخدام متجه الإزاحة كما هو.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW أسرع، بينما skip-gram أبطأ ولكنه يقوم بتمثيل الكلمات النادرة بشكل أفضل.\n", "\n", - "![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png)\n", + "![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "لتجربة تضمين word2vec المدرب مسبقًا على مجموعة بيانات أخبار Google، يمكننا استخدام مكتبة **gensim**. أدناه نجد الكلمات الأكثر تشابهًا مع 'neural'\n", "\n", diff --git a/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index e30e0047..5db3e978 100644 --- a/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ar/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "باستخدام طبقة التضمين كأول طبقة في شبكتنا، يمكننا الانتقال من نموذج حقيبة الكلمات إلى نموذج **حقيبة التضمينات**، حيث نقوم أولاً بتحويل كل كلمة في النص إلى التضمين المقابل لها، ثم نحسب دالة تجميع معينة على جميع تلك التضمينات، مثل `sum` أو `average` أو `max`.\n", "\n", - "![صورة توضح مصنف تضمين لخمس كلمات متتالية.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png)\n", + "![صورة توضح مصنف تضمين لخمس كلمات متتالية.](../../../../../translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "تتكون شبكة التصنيف العصبية الخاصة بنا من الطبقات التالية:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW أسرع، بينما التخطي المستمر أبطأ ولكنه يقوم بتمثيل الكلمات النادرة بشكل أفضل.\n", "\n", - "![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png)\n", + "![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "لتجربة تضمين Word2Vec المدرب مسبقًا على مجموعة بيانات أخبار Google، يمكننا استخدام مكتبة **gensim**. أدناه نجد الكلمات الأكثر تشابهًا مع كلمة 'neural'.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/14-Embeddings/README.md b/translations/ar/lessons/5-NLP/14-Embeddings/README.md index 4c7a16fd..352448ce 100644 --- a/translations/ar/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ar/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: باستخدام طبقة التضمين كطبقة أولى في شبكة المصنف الخاصة بنا، يمكننا الانتقال من نموذج حقيبة الكلمات إلى نموذج **حقيبة التضمينات**، حيث نقوم أولاً بتحويل كل كلمة في النص إلى التضمين المقابل لها، ثم نحسب دالة تجميعية على جميع هذه التضمينات، مثل `sum` أو `average` أو `max`. -![صورة توضح مصنف التضمين لخمس كلمات في تسلسل.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ar.png) +![صورة توضح مصنف التضمين لخمس كلمات في تسلسل.](../../../../../translated_images/ar/embedding-classifier-example.b77f021a7ee67eee.webp) > الصورة بواسطة المؤلف @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW أسرع، بينما التخطي المستمر أبطأ ولكنه يقوم بتمثيل الكلمات النادرة بشكل أفضل. -![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png) +![صورة توضح كلا من خوارزميات CBoW و Skip-Gram لتحويل الكلمات إلى متجهات.](../../../../../translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > الصورة من [هذه الورقة](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ar/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ar/lessons/5-NLP/15-LanguageModeling/README.md index e27ff257..87d506a7 100644 --- a/translations/ar/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ar/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **كيس الكلمات المستمر** (CBoW)، حيث نتوقع الرمز الأوسط $W_0$ في سلسلة الرموز $W_{-N}$, ..., $W_N$. * **Skip-gram**، حيث نتوقع مجموعة من الرموز المجاورة {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} من الرمز الأوسط $W_0$. -![صورة من ورقة بحثية حول تحويل الكلمات إلى متجهات](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ar.png) +![صورة من ورقة بحثية حول تحويل الكلمات إلى متجهات](../../../../../translated_images/ar/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > صورة مأخوذة من [هذه الورقة البحثية](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ar/lessons/5-NLP/16-RNN/README.md b/translations/ar/lessons/5-NLP/16-RNN/README.md index 33b030cc..25347eda 100644 --- a/translations/ar/lessons/5-NLP/16-RNN/README.md +++ b/translations/ar/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: للتقاط معنى تسلسل النصوص، نحتاج إلى استخدام بنية شبكة عصبية أخرى تُسمى **الشبكة العصبية المتكررة** أو RNN. في RNN، نقوم بتمرير الجملة عبر الشبكة رمزًا واحدًا في كل مرة، وتنتج الشبكة **حالة** معينة، والتي نمررها مرة أخرى إلى الشبكة مع الرمز التالي. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ar.png) +![RNN](../../../../../translated_images/ar/rnn.27f5c29c53d727b5.webp) > الصورة من إعداد المؤلف @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: الشبكة المتكررة، سواء كانت أحادية الاتجاه أو ثنائية الاتجاه، تلتقط أنماطًا معينة داخل التسلسل، ويمكنها تخزينها في متجه الحالة أو تمريرها إلى المخرج. كما هو الحال مع الشبكات الالتفافية، يمكننا بناء طبقة متكررة أخرى فوق الأولى لالتقاط أنماط ذات مستوى أعلى والبناء من الأنماط ذات المستوى المنخفض المستخرجة بواسطة الطبقة الأولى. يؤدي هذا إلى مفهوم **الشبكة العصبية المتكررة متعددة الطبقات** التي تتكون من شبكتين متكررتين أو أكثر، حيث يتم تمرير مخرج الطبقة السابقة إلى الطبقة التالية كمدخل. -![صورة تظهر شبكة عصبية متكررة متعددة الطبقات من نوع LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg) +![صورة تظهر شبكة عصبية متكررة متعددة الطبقات من نوع LSTM](../../../../../translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp) *الصورة من [هذا المنشور الرائع](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) بواسطة Fernando López* diff --git a/translations/ar/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ar/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 966aafd6..ee383793 100644 --- a/translations/ar/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ar/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "الشبكة المتكررة، سواء كانت أحادية الاتجاه أو ثنائية الاتجاه، تلتقط أنماطًا معينة داخل التسلسل، ويمكنها تخزينها في متجه الحالة أو تمريرها إلى الإخراج. كما هو الحال مع الشبكات الالتفافية، يمكننا بناء طبقة متكررة أخرى فوق الطبقة الأولى لالتقاط أنماط ذات مستوى أعلى، مبنية من الأنماط ذات المستوى الأدنى التي استخرجتها الطبقة الأولى. يقودنا هذا إلى مفهوم **الشبكات العصبية المتكررة متعددة الطبقات**، والتي تتكون من شبكتين أو أكثر من الشبكات المتكررة، حيث يتم تمرير إخراج الطبقة السابقة إلى الطبقة التالية كمدخل.\n", "\n", - "![صورة توضح شبكة عصبية متكررة طويلة وقصيرة المدى متعددة الطبقات](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg)\n", + "![صورة توضح شبكة عصبية متكررة طويلة وقصيرة المدى متعددة الطبقات](../../../../../translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*الصورة مأخوذة من [هذا المقال الرائع](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) بقلم Fernando López*\n", "\n", diff --git a/translations/ar/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ar/lessons/5-NLP/16-RNN/RNNTF.ipynb index c8726935..415b8a28 100644 --- a/translations/ar/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ar/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "لتمثيل معنى تسلسل النصوص، سنستخدم هيكلية شبكة عصبية تُعرف بـ **الشبكة العصبية التكرارية** أو RNN. عند استخدام RNN، نقوم بتمرير الجملة عبر الشبكة كلمة واحدة في كل مرة، وتنتج الشبكة **حالة** معينة، والتي نمررها مرة أخرى إلى الشبكة مع الكلمة التالية.\n", "\n", - "![صورة توضح مثالًا على توليد شبكة عصبية تكرارية.](../../../../../translated_images/rnn.27f5c29c53d727b5.ar.png)\n", + "![صورة توضح مثالًا على توليد شبكة عصبية تكرارية.](../../../../../translated_images/ar/rnn.27f5c29c53d727b5.webp)\n", "\n", "بالنظر إلى تسلسل الإدخال من الرموز $X_0,\\dots,X_n$، تقوم RNN بإنشاء تسلسل من كتل الشبكة العصبية، وتدرب هذا التسلسل من البداية إلى النهاية باستخدام الانتشار العكسي. كل كتلة شبكة تأخذ زوجًا $(X_i,S_i)$ كمدخل، وتنتج $S_{i+1}$ كنتيجة. الحالة النهائية $S_n$ أو الناتج $Y_n$ يتم تمريرها إلى مصنف خطي لإنتاج النتيجة. جميع كتل الشبكة تشترك في نفس الأوزان، ويتم تدريبها من البداية إلى النهاية باستخدام تمريرة انتشار عكسي واحدة.\n", "\n", @@ -371,7 +371,7 @@ "\n", "الشبكات العصبية المتكررة، سواء كانت أحادية الاتجاه أو ثنائية الاتجاه، تلتقط الأنماط داخل التسلسل وتخزنها في متجهات الحالة أو تُرجعها كمخرجات. كما هو الحال مع الشبكات الالتفافية، يمكننا بناء طبقة متكررة أخرى بعد الطبقة الأولى لالتقاط أنماط على مستوى أعلى، يتم بناؤها من الأنماط ذات المستوى الأدنى التي استخرجتها الطبقة الأولى. هذا يقودنا إلى مفهوم **الشبكة المتكررة متعددة الطبقات**، والتي تتكون من شبكتين متكررتين أو أكثر، حيث يتم تمرير مخرجات الطبقة السابقة إلى الطبقة التالية كمدخلات.\n", "\n", - "![صورة توضح شبكة عصبية متكررة طويلة وقصيرة المدى متعددة الطبقات](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ar.jpg)\n", + "![صورة توضح شبكة عصبية متكررة طويلة وقصيرة المدى متعددة الطبقات](../../../../../translated_images/ar/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*الصورة مأخوذة من [هذا المقال الرائع](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) بقلم فرناندو لوبيز.*\n", "\n", diff --git a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 2bcbf953..fade7de5 100644 --- a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "الطريقة التي سنقوم بها بتدريب شبكة RNN لتوليد النصوص هي كالتالي. في كل خطوة، سنأخذ سلسلة من الأحرف بطول `nchars`، ونطلب من الشبكة توليد الحرف التالي لكل حرف مدخل:\n", "\n", - "![صورة توضح مثالًا على توليد شبكة RNN لكلمة \"HELLO\".](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ar.png)\n", + "![صورة توضح مثالًا على توليد شبكة RNN لكلمة \"HELLO\".](../../../../../translated_images/ar/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "اعتمادًا على السيناريو الفعلي، قد نرغب أيضًا في تضمين بعض الأحرف الخاصة، مثل *نهاية السلسلة* ``. في حالتنا، نريد فقط تدريب الشبكة على توليد نصوص بلا نهاية، وبالتالي سنقوم بتثبيت حجم كل سلسلة ليكون مساويًا لعدد الرموز `nchars`. وبناءً على ذلك، كل مثال تدريبي سيتكون من `nchars` مدخلات و`nchars` مخرجات (وهي سلسلة المدخلات مزاحة بمقدار رمز واحد إلى اليسار). وستتكون الدفعة الصغيرة (minibatch) من عدة سلاسل من هذا النوع.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 77a95079..9a159398 100644 --- a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "الطريقة التي سنقوم بها بتدريب الشبكة العصبية المتكررة لتوليد عناوين الأخبار هي كالتالي. في كل خطوة، سنأخذ عنوانًا واحدًا، يتم إدخاله في الشبكة العصبية المتكررة، وبالنسبة لكل حرف مُدخل، سنطلب من الشبكة توليد الحرف التالي:\n", "\n", - "![صورة توضح مثالًا لتوليد كلمة \"HELLO\" باستخدام شبكة عصبية متكررة.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ar.png)\n", + "![صورة توضح مثالًا لتوليد كلمة \"HELLO\" باستخدام شبكة عصبية متكررة.](../../../../../translated_images/ar/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "بالنسبة للحرف الأخير في تسلسلنا، سنطلب من الشبكة توليد رمز ``.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/README.md index 0dfbfe78..1e85a1dd 100644 --- a/translations/ar/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ar/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: هذا يسمح ببنيات عصبية مختلفة كما هو موضح في الصورة أدناه: -![صورة تظهر أنماط الشبكات العصبية المتكررة الشائعة.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ar.jpg) +![صورة تظهر أنماط الشبكات العصبية المتكررة الشائعة.](../../../../../translated_images/ar/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > الصورة مأخوذة من منشور المدونة [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) بواسطة [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: سنقوم بتدريب RNN لتوليد النصوص خطوة بخطوة. في كل خطوة، سنأخذ تسلسلًا من الحروف بطول `nchars`، ونطلب من الشبكة توليد الحرف التالي لكل حرف إدخال: -![صورة تظهر مثالًا لتوليد RNN لكلمة 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ar.png) +![صورة تظهر مثالًا لتوليد RNN لكلمة 'HELLO'.](../../../../../translated_images/ar/rnn-generate.56c54afb52f9781d.webp) عند توليد النصوص (أثناء الاستدلال)، نبدأ ببعض **المحفزات**، التي تمر عبر خلايا RNN لتوليد حالتها الوسيطة، ومن هذه الحالة يبدأ التوليد. نقوم بتوليد حرف واحد في كل مرة، ونمرر الحالة والحرف المُولد إلى خلية RNN أخرى لتوليد الحرف التالي، حتى يتم توليد عدد كافٍ من الحروف. diff --git a/translations/ar/lessons/5-NLP/18-Transformers/README.md b/translations/ar/lessons/5-NLP/18-Transformers/README.md index 1760113e..0d0782ec 100644 --- a/translations/ar/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ar/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **آليات الانتباه** توفر وسيلة لتحديد وزن تأثير كل متجه إدخال على كل توقع مخرجات للشبكة العصبية المتكررة. يتم تنفيذ ذلك من خلال إنشاء اختصارات بين الحالات الوسيطة لشبكة الإدخال وشبكة المخرجات. بهذه الطريقة، عند إنشاء رمز المخرجات yt، سنأخذ في الاعتبار جميع الحالات المخفية للإدخال hi، مع معاملات وزن مختلفة αt,i. -![صورة توضح نموذج المشفّر/المفكّك مع طبقة انتباه إضافية](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ar.png) +![صورة توضح نموذج المشفّر/المفكّك مع طبقة انتباه إضافية](../../../../../translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp) > نموذج المشفّر-المفكّك مع آلية انتباه إضافية في [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)، مقتبس من [هذه المقالة](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) مصفوفة الانتباه {αi,j} تمثل درجة تأثير كلمات الإدخال على إنشاء كلمة معينة في تسلسل المخرجات. أدناه مثال على مثل هذه المصفوفة: -![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png) +![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp) > الشكل من [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (الشكل 3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: بعد ذلك، نحتاج إلى التقاط بعض الأنماط داخل تسلسلنا. لتحقيق ذلك، تستخدم المحولات آلية **الانتباه الذاتي**، وهي في الأساس انتباه يتم تطبيقه على نفس التسلسل كمدخلات ومخرجات. تطبيق الانتباه الذاتي يسمح لنا بأخذ **السياق** داخل الجملة في الاعتبار، ورؤية الكلمات التي ترتبط ببعضها البعض. على سبيل المثال، يسمح لنا برؤية الكلمات التي تشير إليها الإشارات المرجعية مثل *it*، وأيضًا أخذ السياق في الاعتبار: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ar.png) +![](../../../../../translated_images/ar/CoreferenceResolution.861924d6d384a7d6.webp) > الصورة من [مدونة Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (تمثيلات المشفّر ثنائية الاتجاه من المحولات) هو شبكة محولات متعددة الطبقات كبيرة جدًا تحتوي على 12 طبقة لـ *BERT-base*، و24 طبقة لـ *BERT-large*. يتم تدريب النموذج أولاً على مجموعة كبيرة من بيانات النصوص (ويكيبيديا + الكتب) باستخدام التدريب غير المراقب (توقع الكلمات المخفية في الجملة). أثناء التدريب الأولي، يمتص النموذج مستويات كبيرة من فهم اللغة التي يمكن استخدامها لاحقًا مع مجموعات بيانات أخرى باستخدام التخصيص. تُعرف هذه العملية بـ **التعلم بالنقل**. -![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png) +![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > الصورة [المصدر](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ar/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ar/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 01e8bcb7..d4a5b448 100644 --- a/translations/ar/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ar/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**آليات الانتباه** توفر وسيلة لوزن التأثير السياقي لكل متجه إدخال على كل توقع إخراج للشبكة العودية. يتم تنفيذ ذلك من خلال إنشاء اختصارات بين الحالات الوسيطة لشبكة الإدخال العودية وشبكة الإخراج العودية. بهذه الطريقة، عند توليد رمز الإخراج $y_t$، سنأخذ في الاعتبار جميع الحالات المخفية للإدخال $h_i$، مع معاملات وزن مختلفة $\\alpha_{t,i}$.\n", "\n", - "![صورة توضح نموذج الترميز/فك الترميز مع طبقة انتباه إضافية](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ar.png)\n", + "![صورة توضح نموذج الترميز/فك الترميز مع طبقة انتباه إضافية](../../../../../translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp)\n", "*نموذج الترميز-فك الترميز مع آلية الانتباه الإضافية في [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)، مقتبس من [هذا المقال](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "مصفوفة الانتباه $\\{\\alpha_{i,j}\\}$ تمثل الدرجة التي تلعب بها كلمات الإدخال دورًا في توليد كلمة معينة في تسلسل الإخراج. أدناه مثال على مثل هذه المصفوفة:\n", "\n", - "![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png)\n", + "![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*الصورة مأخوذة من [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (الشكل 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (تمثيلات الترميز ثنائية الاتجاه من المحولات) هو شبكة محولات متعددة الطبقات كبيرة جدًا تحتوي على 12 طبقة في *BERT-base*، و24 طبقة في *BERT-large*. يتم تدريب النموذج أولاً على مجموعة نصوص كبيرة (ويكيبيديا + كتب) باستخدام تدريب غير خاضع للإشراف (توقع الكلمات المحجوبة في الجملة). أثناء التدريب الأولي، يكتسب النموذج مستوى كبيرًا من فهم اللغة يمكن الاستفادة منه لاحقًا مع مجموعات بيانات أخرى باستخدام التخصيص الدقيق. تُعرف هذه العملية باسم **التعلم بالنقل**.\n", "\n", - "![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png)\n", + "![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "هناك العديد من التعديلات على بنية المحولات، بما في ذلك BERT، وDistilBERT، وBigBird، وOpenGPT3، والمزيد، التي يمكن تخصيصها. حزمة [HuggingFace](https://github.com/huggingface/) توفر مستودعًا لتدريب العديد من هذه البنى باستخدام PyTorch.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ar/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index a23d4db2..504c98f2 100644 --- a/translations/ar/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ar/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**آليات الانتباه** توفر وسيلة لوزن التأثير السياقي لكل متجه إدخال على كل توقع إخراج للشبكة العصبية المتكررة. يتم تنفيذ ذلك من خلال إنشاء اختصارات بين الحالات الوسيطة لشبكة الإدخال العصبية المتكررة وشبكة الإخراج العصبية المتكررة. بهذه الطريقة، عند توليد رمز الإخراج $y_t$، سنأخذ في الاعتبار جميع الحالات المخفية للإدخال $h_i$، مع معاملات وزن مختلفة $\\alpha_{t,i}$.\n", "\n", - "![صورة توضح نموذج الترميز/فك الترميز مع طبقة انتباه إضافية](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ar.png)\n", + "![صورة توضح نموذج الترميز/فك الترميز مع طبقة انتباه إضافية](../../../../../translated_images/ar/encoder-decoder-attention.7a726296894fb567.webp)\n", "*نموذج الترميز-فك الترميز مع آلية الانتباه الإضافية في [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)، مقتبس من [هذا المنشور](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "مصفوفة الانتباه $\\{\\alpha_{i,j}\\}$ تمثل الدرجة التي تلعب بها كلمات الإدخال دورًا في توليد كلمة معينة في تسلسل الإخراج. أدناه مثال على مثل هذه المصفوفة:\n", "\n", - "![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ar.png)\n", + "![صورة توضح محاذاة نموذج RNNsearch-50، مأخوذة من Bahdanau - arviz.org](../../../../../translated_images/ar/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*الصورة مأخوذة من [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (الشكل 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (تمثيلات التشفير ثنائية الاتجاه من المحولات) هو شبكة محولات متعددة الطبقات كبيرة جدًا تحتوي على 12 طبقة في *BERT-base*، و24 طبقة في *BERT-large*. يتم تدريب النموذج مبدئيًا على مجموعة بيانات نصية ضخمة (ويكيبيديا + كتب) باستخدام تدريب غير خاضع للإشراف (التنبؤ بالكلمات المحجوبة في الجملة). خلال مرحلة التدريب المبدئي، يكتسب النموذج مستوى كبيرًا من فهم اللغة، والذي يمكن استغلاله لاحقًا مع مجموعات بيانات أخرى باستخدام التخصيص الدقيق. تُعرف هذه العملية باسم **التعلم بالنقل**.\n", "\n", - "![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ar.png)\n", + "![صورة من http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ar/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "هناك العديد من التعديلات على بنية المحولات، بما في ذلك BERT، وDistilBERT، وBigBird، وOpenGPT3، والمزيد، والتي يمكن تخصيصها بدقة.\n", "\n", diff --git a/translations/ar/lessons/5-NLP/19-NER/README.md b/translations/ar/lessons/5-NLP/19-NER/README.md index b629d0eb..19e6daab 100644 --- a/translations/ar/lessons/5-NLP/19-NER/README.md +++ b/translations/ar/lessons/5-NLP/19-NER/README.md @@ -59,7 +59,7 @@ CO_OP_TRANSLATOR_METADATA: نظرًا لأننا بحاجة إلى بناء تطابق واحد لواحد بين الرموز والفئات، يمكننا تدريب نموذج شبكة عصبية **كثيرة إلى كثيرة** من الصورة التالية: -![صورة تظهر أنماط الشبكات العصبية المتكررة الشائعة.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ar.jpg) +![صورة تظهر أنماط الشبكات العصبية المتكررة الشائعة.](../../../../../translated_images/ar/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *الصورة مأخوذة من [هذه المقالة](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) بواسطة [أندريه كارباتي](http://karpathy.github.io/). نماذج تصنيف الرموز لـ NER تتوافق مع بنية الشبكة الموجودة في أقصى اليمين في هذه الصورة.* diff --git a/translations/ar/lessons/5-NLP/README.md b/translations/ar/lessons/5-NLP/README.md index 2ea60c03..1b0612fa 100644 --- a/translations/ar/lessons/5-NLP/README.md +++ b/translations/ar/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # معالجة اللغة الطبيعية -![ملخص مهام معالجة اللغة الطبيعية في رسم توضيحي](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ar.png) +![ملخص مهام معالجة اللغة الطبيعية في رسم توضيحي](../../../../translated_images/ar/ai-nlp.b22dcb8ca4707cea.webp) في هذا القسم، سنركز على استخدام الشبكات العصبية لمعالجة المهام المتعلقة بـ **معالجة اللغة الطبيعية (NLP)**. هناك العديد من مشاكل معالجة اللغة الطبيعية التي نرغب في أن تتمكن الحواسيب من حلها: diff --git a/translations/ar/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ar/lessons/6-Other/23-MultiagentSystems/README.md index e6a1a648..83884d04 100644 --- a/translations/ar/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ar/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ بعد فتح النموذج، يتم نقلك إلى الشاشة الرئيسية لـ NetLogo. هنا نموذج يصف سكان الذئاب والخراف، مع موارد محدودة (العشب). -![الشاشة الرئيسية لـ NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ar.png) +![الشاشة الرئيسية لـ NetLogo](../../../../../translated_images/ar/NetLogo-Main.32653711ec1a01b3.webp) > لقطة شاشة بواسطة Dmitry Soshnikov diff --git a/translations/ar/lessons/README.md b/translations/ar/lessons/README.md index 64bc830d..3d8154ef 100644 --- a/translations/ar/lessons/README.md +++ b/translations/ar/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # نظرة عامة -![نظرة عامة في رسم توضيحي](../../../translated_images/ai-overview.0857791951d19500.ar.png) +![نظرة عامة في رسم توضيحي](../../../translated_images/ar/ai-overview.0857791951d19500.webp) > رسم توضيحي بواسطة [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ar/lessons/X-Extras/X1-MultiModal/README.md b/translations/ar/lessons/X-Extras/X1-MultiModal/README.md index bbd15df6..6290c68d 100644 --- a/translations/ar/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ar/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: الفكرة الرئيسية لـ CLIP هي القدرة على مقارنة النصوص التوضيحية مع الصور وتحديد مدى تطابق الصورة مع النص. -![بنية CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ar.png) +![بنية CLIP](../../../../../translated_images/ar/clip-arch.b3dbf20b4e8ed8be.webp) > *الصورة مأخوذة من [هذه المقالة](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: لنفترض أننا بحاجة إلى تصنيف الصور بين، على سبيل المثال، القطط، الكلاب والبشر. في هذه الحالة، يمكننا إعطاء النموذج صورة وسلسلة من النصوص التوضيحية: "*صورة لقط*", "*صورة لكلب*", "*صورة لإنسان*". في المتجه الناتج الذي يحتوي على 3 احتمالات، نحتاج فقط إلى اختيار المؤشر الذي يحتوي على أعلى قيمة. -![CLIP لتصنيف الصور](../../../../../translated_images/clip-class.3af42ef0b2b19369.ar.png) +![CLIP لتصنيف الصور](../../../../../translated_images/ar/clip-class.3af42ef0b2b19369.webp) > *الصورة مأخوذة من [هذه المقالة](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ CO_OP_TRANSLATOR_METADATA: أحد الفروقات المهمة بين VQGAN وGAN التقليدية هو أن الأخيرة يمكنها إنتاج صورة جيدة من أي متجه مدخلات، بينما من المحتمل أن ينتج VQGAN صورة غير متماسكة. لذلك، نحتاج إلى توجيه عملية إنشاء الصورة بشكل أكبر، ويمكن القيام بذلك باستخدام CLIP. -![بنية VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.ar.png) +![بنية VQGAN+CLIP](../../../../../translated_images/ar/vqgan.5027fe05051dfa31.webp) لإنشاء صورة تتوافق مع نص توضيحي، نبدأ بمتجه ترميز عشوائي يتم تمريره عبر VQGAN لإنتاج صورة. ثم يتم استخدام CLIP لإنتاج دالة خسارة تُظهر مدى تطابق الصورة مع النص التوضيحي. الهدف بعد ذلك هو تقليل هذه الخسارة باستخدام الانتشار العكسي لتعديل معلمات متجه المدخلات. مكتبة رائعة تنفذ VQGAN+CLIP هي [Pixray](http://github.com/pixray/pixray). -![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ar.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ar.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ar.png) +![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- صورة تم إنشاؤها من النص *صورة قريبة بالألوان المائية لمعلم شاب للأدب يحمل كتابًا* | صورة تم إنشاؤها من النص *صورة قريبة بالزيت لمُدرسة شابة لعلوم الحاسوب مع جهاز كمبيوتر* | صورة تم إنشاؤها من النص *صورة قريبة بالزيت لمعلم مسن للرياضيات أمام السبورة* @@ -77,7 +77,7 @@ DALL-E هو نسخة من GPT-3 تم تدريبها لتوليد الصور من الفرق الرئيسي بين DALL-E 1 وDALL-E 2 هو أن الأخير يولد صورًا وفنونًا أكثر واقعية. أمثلة على الصور التي تم إنشاؤها باستخدام DALL-E: -![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ar.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ar.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ar.png) +![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![صورة تم إنتاجها بواسطة Pixray](../../../../../translated_images/ar/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- صورة تم إنشاؤها من النص *صورة قريبة بالألوان المائية لمعلم شاب للأدب يحمل كتابًا* | صورة تم إنشاؤها من النص *صورة قريبة بالزيت لمُدرسة شابة لعلوم الحاسوب مع جهاز كمبيوتر* | صورة تم إنشاؤها من النص *صورة قريبة بالزيت لمعلم مسن للرياضيات أمام السبورة* diff --git a/translations/bg/README.md b/translations/bg/README.md index a386a450..d2a2512f 100644 --- a/translations/bg/README.md +++ b/translations/bg/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Изкуствен интелект за начинаещи - Учебна програма -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.bg.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bg/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/bg/lessons/1-Intro/README.md b/translations/bg/lessons/1-Intro/README.md index 67c04bf0..7454b1c1 100644 --- a/translations/bg/lessons/1-Intro/README.md +++ b/translations/bg/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Въведение в AI -![Обобщение на съдържанието за въведение в AI в рисунка](../../../../translated_images/ai-intro.bf28d1ac4235881c.bg.png) +![Обобщение на съдържанието за въведение в AI в рисунка](../../../../translated_images/bg/ai-intro.bf28d1ac4235881c.png) > Рисунка от [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Първоначално компютрите са били изобретени от [Чарлз Бабидж](https://en.wikipedia.org/wiki/Charles_Babbage), за да работят с числа, следвайки добре дефинирана процедура – алгоритъм. Съвременните компютри, макар и значително по-усъвършенствани от оригиналния модел, предложен през 19-ти век, все още следват същата идея за контролирани изчисления. Следователно е възможно да програмираме компютър да извърши нещо, ако знаем точната последователност от стъпки, които трябва да изпълним, за да постигнем целта. -![Снимка на човек](../../../../translated_images/dsh_age.d212a30d4e54fb5f.bg.png) +![Снимка на човек](../../../../translated_images/bg/dsh_age.d212a30d4e54fb5f.png) > Снимка от [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Един от проблемите при работа с термина **[Интелигентност](https://en.wikipedia.org/wiki/Intelligence)** е, че няма ясно определение за този термин. Може да се твърди, че интелигентността е свързана с **абстрактно мислене** или със **самосъзнание**, но не можем да я дефинираме правилно. -![Снимка на котка](../../../../translated_images/photo-cat.8c8e8fb760ffe457.bg.jpg) +![Снимка на котка](../../../../translated_images/bg/photo-cat.8c8e8fb760ffe457.jpg) > [Снимка](https://unsplash.com/photos/75715CVEJhI) от [Amber Kipp](https://unsplash.com/@sadmax) от Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | А какво да кажем за ML? | | > |--------------|-----------| -> | Част от Изкуствения интелект, която се основава на компютърното обучение за решаване на проблем въз основа на някои данни, се нарича **Машинно обучение**. Няма да разглеждаме класическото машинно обучение в този курс – насочваме ви към отделната учебна програма [Машинно обучение за начинаещи](http://aka.ms/ml-beginners). | ![ML за начинаещи](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.bg.png) | +> | Част от Изкуствения интелект, която се основава на компютърното обучение за решаване на проблем въз основа на някои данни, се нарича **Машинно обучение**. Няма да разглеждаме класическото машинно обучение в този курс – насочваме ви към отделната учебна програма [Машинно обучение за начинаещи](http://aka.ms/ml-beginners). | ![ML за начинаещи](../../../../translated_images/bg/ml-for-beginners.9e4fed176fd5817d.png) | ## Кратка история на AI Изкуственият интелект започва като област в средата на двадесети век. Първоначално символичното разсъждение е преобладаващ подход и води до редица важни успехи, като експертни системи – компютърни програми, които могат да действат като експерт в някои ограничени проблемни области. Въпреки това скоро става ясно, че такъв подход не се мащабира добре. Извличането на знания от експерт, представянето им в компютър и поддържането на точна база от знания се оказва много сложна задача и твърде скъпа, за да бъде практична в много случаи. Това води до така наречената [AI зима](https://en.wikipedia.org/wiki/AI_winter) през 70-те години. -Кратка история на AI +Кратка история на AI > Изображение от [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bg/lessons/2-Symbolic/Animals.ipynb b/translations/bg/lessons/2-Symbolic/Animals.ipynb index 9e6bf06f..1e8a2cd8 100644 --- a/translations/bg/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bg/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "В този пример ще изградим проста система, базирана на знания, за определяне на животно въз основа на някои физически характеристики. Системата може да бъде представена чрез следното AND-OR дърво (това е част от цялото дърво, лесно можем да добавим още правила):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.bg.png)\n" + "![](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/bg/lessons/2-Symbolic/README.md b/translations/bg/lessons/2-Symbolic/README.md index 67cdc4a4..cc7257bd 100644 --- a/translations/bg/lessons/2-Symbolic/README.md +++ b/translations/bg/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представяне на знания и експертни системи -![Обобщение на съдържанието за символен AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.bg.png) +![Обобщение на съдържанието за символен AI](../../../../translated_images/bg/ai-symbolic.715a30cb610411a6.png) > Скица от [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Следователно, проблемът с **представянето на знания** е да се намери ефективен начин за представяне на знанията вътре в компютър под формата на данни, за да бъдат автоматично използваеми. Това може да се разглежда като спектър: -![Спектър на представяне на знания](../../../../translated_images/knowledge-spectrum.b60df631852c0217.bg.png) +![Спектър на представяне на знания](../../../../translated_images/bg/knowledge-spectrum.b60df631852c0217.png) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | синтаксис на блокове | отстъп Един от ранните успехи на символния AI бяха така наречените **експертни системи** - компютърни системи, които бяха проектирани да действат като експерт в някаква ограничена проблемна област. Те се основаваха на **база знания**, извлечена от един или повече човешки експерти, и съдържаха **инференционен двигател**, който извършваше разсъждения върху нея. -![Човешка архитектура](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.bg.png) | ![Архитектура на система, базирана на знания](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.bg.png) +![Човешка архитектура](../../../../translated_images/bg/arch-human.5d4d35f1bba3ab1c.png) | ![Архитектура на система, базирана на знания](../../../../translated_images/bg/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Опростена структура на човешката нервна система | Архитектура на система, базирана на знания @@ -106,7 +106,7 @@ Python | синтаксис на блокове | отстъп Като пример, нека разгледаме следната експертна система за определяне на животно въз основа на неговите физически характеристики: -![AND-OR дърво](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.bg.png) +![AND-OR дърво](../../../../translated_images/bg/AND-OR-Tree.5592d2c70187f283.png) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index be63aac0..2bde2178 100644 --- a/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/bg/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "В случай че имаме повече от 2 класа, softmax ще нормализира вероятностите между всички тях. Ето диаграма на архитектурата на мрежата, която извършва класификация на цифри от MNIST:\n", "\n", - "![MNIST Класификатор](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.bg.png)\n" + "![MNIST Класификатор](../../../../../translated_images/bg/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Ниска загуба при обучението - моделът може добре да приближи обучаващите данни, защото има достатъчно изразителна мощност.\n", "* Загубата при валидация може да бъде много по-висока от загубата при обучение и може да започне да се увеличава по време на обучението - това е, защото моделът \"запомня\" обучаващите точки и губи \"общата картина\".\n", "\n", - "![Преобучаване](../../../../../translated_images/overfit.a0bd57f717c15769.bg.png)\n", + "![Преобучаване](../../../../../translated_images/bg/overfit.a0bd57f717c15769.png)\n", "\n", "> На тази картинка, `x` представлява обучаващи данни, `o` - валидационни данни. Ляво - линеен модел (еднослоен), той приближава естеството на данните доста добре. Дясно - преобучен модел, моделът перфектно приближава обучаващите данни, но спира да има смисъл с всякакви други данни (грешката при валидация е много висока).\n" ] diff --git a/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md index 9b4670f6..be71b717 100644 --- a/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/bg/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting е изключително важно понятие в машин Разгледайте следния проблем за апроксимация на 5 точки (представени с `x` на графиките по-долу): -![линеен модел](../../../../../translated_images/overfit1.f24b71c6f652e59e.bg.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.bg.jpg) +![линеен модел](../../../../../translated_images/bg/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/bg/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Линеен модел, 2 параметъра** | **Нелинеен модел, 7 параметъра** Грешка при обучение = 5.3 | Грешка при обучение = 0 @@ -79,7 +79,7 @@ Overfitting е изключително важно понятие в машин Както можете да видите от графиката по-горе, overfitting може да бъде открит чрез много ниска грешка при обучение и висока грешка при валидиране. Обикновено по време на обучение ще видим как грешките при обучение и валидиране започват да намаляват, но в даден момент грешката при валидиране може да спре да намалява и да започне да се увеличава. Това ще бъде знак за overfitting и индикатор, че вероятно трябва да спрем обучението на този етап (или поне да направим моментна снимка на модела). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.bg.png) +![overfitting](../../../../../translated_images/bg/Overfitting.408ad91cd90b4371.png) ## Как да предотвратим overfitting diff --git a/translations/bg/lessons/3-NeuralNetworks/README.md b/translations/bg/lessons/3-NeuralNetworks/README.md index 10fe4173..6ef25819 100644 --- a/translations/bg/lessons/3-NeuralNetworks/README.md +++ b/translations/bg/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Въведение в невронните мрежи -![Обобщение на съдържанието за въведение в невронните мрежи в рисунка](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.bg.png) +![Обобщение на съдържанието за въведение в невронните мрежи в рисунка](../../../../translated_images/bg/ai-neuralnetworks.1c687ae40bc86e83.png) Както обсъдихме във въведението, един от начините за постигане на интелигентност е чрез обучение на **компютърен модел** или **изкуствен мозък**. От средата на 20-ти век изследователите опитват различни математически модели, докато в последните години този подход не се оказа изключително успешен. Тези математически модели на мозъка се наричат **невронни мрежи**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: От биологията знаем, че нашият мозък се състои от нервни клетки (неврони), всяка от които има множество "входове" (дендрити) и един "изход" (аксон). Както дендритите, така и аксоните могат да провеждат електрически сигнали, а връзките между тях — известни като синапси — могат да имат различна степен на проводимост, която се регулира от невротрансмитери. -![Модел на неврон](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.bg.jpg) | ![Модел на неврон](../../../../translated_images/artneuron.1a5daa88d20ebe6f.bg.png) +![Модел на неврон](../../../../translated_images/bg/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модел на неврон](../../../../translated_images/bg/artneuron.1a5daa88d20ebe6f.png) ----|---- Реален неврон *([Изображение](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) от Wikipedia)* | Изкуствен неврон *(Изображение от автора)* Следователно, най-простият математически модел на неврон съдържа няколко входа X1, ..., XN и един изход Y, както и серия от тегла W1, ..., WN. Изходът се изчислява като: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) където f е някаква нелинейна **активационна функция**. diff --git a/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md index f65b9634..82232666 100644 --- a/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/bg/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Предварителна обработка на фотография на книга на Брайл**. Фокусираме се върху това как можем да използваме прагова обработка, откриване на характеристики, перспективна трансформация и манипулации с NumPy, за да отделим отделни символи на Брайл за по-нататъшна класификация от невронна мрежа. -![Изображение на Брайл](../../../../../translated_images/braille.341962ff76b1bd70.bg.jpeg) | ![Предварително обработено изображение на Брайл](../../../../../translated_images/braille-result.46530fea020b03c7.bg.png) | ![Символи на Брайл](../../../../../translated_images/braille-symbols.0159185ab69d5339.bg.png) +![Изображение на Брайл](../../../../../translated_images/bg/braille.341962ff76b1bd70.jpeg) | ![Предварително обработено изображение на Брайл](../../../../../translated_images/bg/braille-result.46530fea020b03c7.png) | ![Символи на Брайл](../../../../../translated_images/bg/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Изображение от [OpenCV.ipynb](OpenCV.ipynb) * **Откриване на движение във видео чрез разлика между кадри**. Ако камерата е фиксирана, тогава кадрите от камерата трябва да са доста подобни един на друг. Тъй като кадрите се представят като масиви, просто като извадите тези масиви за два последователни кадъра, ще получите разликата в пикселите, която трябва да е ниска за статични кадри и да стане по-висока, когато има значително движение в изображението. -![Изображение на видео кадри и разлики между кадри](../../../../../translated_images/frame-difference.706f805491a0883c.bg.png) +![Изображение на видео кадри и разлики между кадри](../../../../../translated_images/bg/frame-difference.706f805491a0883c.png) > Изображение от [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Плътен оптичен поток** изчислява векторно поле, което показва за всеки пиксел къде се движи. - **Рядък оптичен поток** се основава на вземане на някои отличителни характеристики в изображението (например ръбове) и изграждане на тяхната траектория от кадър на кадър. -![Изображение на оптичен поток](../../../../../translated_images/optical.1f4a94464579a83a.bg.png) +![Изображение на оптичен поток](../../../../../translated_images/bg/optical.1f4a94464579a83a.png) > Изображение от [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 109aada7..cd5fcba5 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 е мрежа, която постигна 92.7% точност в класификацията на ImageNet топ-5 през 2014 г. Тя има следната структура на слоевете: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.bg.jpg) +![ImageNet Layers](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.jpg) Както можете да видите, VGG следва традиционна пирамидална архитектура, която представлява последователност от слоеве за конволюция и пулуване. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.bg.jpg) +![ImageNet Pyramid](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.jpg) > Изображение от [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 2fbfa50d..b2930543 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Така в типичен CNN ще има няколко слоя на свиване, със слоеве за пулуване между тях, за да се намалят размерите на изображението. Също така ще увеличим броя на филтрите, защото с напредването на моделите има повече възможни интересни комбинации, които трябва да търсим.\n", "\n", - "![Изображение, показващо няколко слоя на свиване със слоеве за пулуване.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.bg.png)\n", + "![Изображение, показващо няколко слоя на свиване със слоеве за пулуване.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Поради намаляването на пространствените размери и увеличаването на размерите на характеристиките/филтрите, тази архитектура се нарича също **пирамидална архитектура**.\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 0abccfac..a8f0c6d6 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Така, в типичен CNN има няколко конволюционни слоя, със слоеве за пуллинг между тях, за да се намалят размерите на изображението. Също така увеличаваме броя на филтрите, защото с напредването на моделите има повече възможни интересни комбинации, които трябва да търсим.\n", "\n", - "![Изображение, показващо няколко конволюционни слоя със слоеве за пуллинг.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.bg.png)\n", + "![Изображение, показващо няколко конволюционни слоя със слоеве за пуллинг.](../../../../../translated_images/bg/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Поради намаляването на пространствените размери и увеличаването на размерите на характеристиките/филтрите, тази архитектура се нарича **пирамидална архитектура**.\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md index 288ecc40..85c8ab43 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: За да извлечем шаблони, ще използваме понятието за **конволюционни филтри**. Както знаете, едно изображение се представя чрез 2D-матрица или 3D-тензор с цветова дълбочина. Прилагането на филтър означава, че вземаме сравнително малка матрица, наречена **ядро на филтъра**, и за всеки пиксел в оригиналното изображение изчисляваме претегленото средно с неговите съседни точки. Можем да си представим това като малък прозорец, който се плъзга по цялото изображение и осреднява всички пиксели според теглата в матрицата на ядрото на филтъра. -![Филтър за вертикални ръбове](../../../../../translated_images/filter-vert.b7148390ca0bc356.bg.png) | ![Филтър за хоризонтални ръбове](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.bg.png) +![Филтър за вертикални ръбове](../../../../../translated_images/bg/filter-vert.b7148390ca0bc356.png) | ![Филтър за хоризонтални ръбове](../../../../../translated_images/bg/filter-horiz.59b80ed4feb946ef.png) ----|---- > Изображение от Дмитрий Сошников @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Можем да проектираме мрежата така, че филтрите да се обучават автоматично * Можем да използваме същия подход, за да намираме шаблони в характеристики на високо ниво, а не само в оригиналното изображение. Така извличането на характеристики в CNN работи на йерархия от характеристики, започвайки от нискоуровневи комбинации от пиксели до по-високо ниво комбинации от части на изображението. -![Йерархично извличане на характеристики](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.bg.png) +![Йерархично извличане на характеристики](../../../../../translated_images/bg/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Изображение от [статия на Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), базирано на [тяхното изследване](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Като пример, нека разгледаме архитектурата на VGG-16, мрежа, която постигна 92.7% точност в класификацията на ImageNet (топ 5) през 2014 г.: -![Слоеве на ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.bg.jpg) +![Слоеве на ImageNet](../../../../../translated_images/bg/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Пирамида на ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.bg.jpg) +![Пирамида на ImageNet](../../../../../translated_images/bg/vgg-16-arch.64ff2137f50dd49f.jpg) > Изображение от [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ef03b368..026e11a8 100644 --- a/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/bg/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Ще използваме [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), който съдържа изображения на 37 различни породи кучета и котки. -![Наборът от данни, с който ще работим](../../../../../../translated_images/data.50b2a9d5484bdbf0.bg.png) +![Наборът от данни, с който ще работим](../../../../../../translated_images/bg/data.50b2a9d5484bdbf0.png) За да изтеглите набора от данни, използвайте този кодов фрагмент: diff --git a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 896f26a6..c5697fbf 100644 --- a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "За да визуализираме идеалната котка, ще започнем с изображение от случайни шумове и ще се опитаме да използваме техниката на оптимизация чрез градиентен спуск, за да коригираме изображението така, че мрежата да разпознае котка.\n", "\n", - "![Цикъл на оптимизация](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.bg.png)\n", + "![Цикъл на оптимизация](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Ето нашето начално изображение:\n" ] diff --git a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md index fbe12086..256bf0c6 100644 --- a/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/bg/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ето примерни характеристики, извлечени от снимка на котка от мрежата VGG-16: -![Характеристики, извлечени от VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.bg.png) +![Характеристики, извлечени от VGG-16](../../../../../translated_images/bg/features.6291f9c7ba3a0b95.png) ## Набор от данни за котки и кучета @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Един подход, който можем да използваме, е да започнем с произволно изображение и след това да използваме техниката **оптимизация чрез градиентен спуск**, за да го коригираме така, че мрежата да започне да мисли, че това е котка. -![Цикъл на оптимизация на изображение](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.bg.png) +![Цикъл на оптимизация на изображение](../../../../../translated_images/bg/ideal-cat-loop.999fbb8ff306e044.png) Ако направим това, ще получим нещо много подобно на случаен шум. Това е така, защото *има много начини мрежата да мисли, че входното изображение е котка*, включително такива, които визуално не изглеждат логични. Докато тези изображения съдържат много модели, типични за котка, няма нищо, което да ги ограничава да бъдат визуално разпознаваеми. За да подобрим резултата, можем да добавим друг термин към функцията за загуба, наречен **variation loss**. Това е метрика, която показва колко сходни са съседните пиксели на изображението. Минимизирането на variation loss прави изображението по-гладко и премахва шума, разкривайки по-визуално привлекателни модели. Ето пример за такива "идеални" изображения, които се класифицират като котка и като зебра с висока вероятност: -![Идеална котка](../../../../../translated_images/ideal-cat.203dd4597643d6b0.bg.png) | ![Идеална зебра](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.bg.png) +![Идеална котка](../../../../../translated_images/bg/ideal-cat.203dd4597643d6b0.png) | ![Идеална зебра](../../../../../translated_images/bg/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Идеална котка* | *Идеална зебра* Подобен подход може да се използва за извършване на така наречените **адверсариални атаки** върху невронна мрежа. Да предположим, че искаме да заблудим невронната мрежа и да направим така, че куче да изглежда като котка. Ако вземем изображение на куче, което мрежата разпознава като куче, можем да го коригираме малко чрез оптимизация с градиентен спуск, докато мрежата започне да го класифицира като котка: -![Снимка на куче](../../../../../translated_images/original-dog.8f68a67d2fe0911f.bg.png) | ![Снимка на куче, класифицирано като котка](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.bg.png) +![Снимка на куче](../../../../../translated_images/bg/original-dog.8f68a67d2fe0911f.png) | ![Снимка на куче, класифицирано като котка](../../../../../translated_images/bg/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Оригинална снимка на куче* | *Снимка на куче, класифицирано като котка* diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index cbadbfab..856984f0 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Тъй като обучаваме автоенкодера да улавя възможно най-много информация от оригиналното изображение за точно възстановяване, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение.\n", "\n", - "![Диаграма на Автоенкодер](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bg.jpg)\n", + "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 1eb5b0d7..25eb239c 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Тъй като тренираме автоенкодера да улавя колкото се може повече информация от оригиналното изображение за точно възстановяване, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение.\n", "\n", - "![Диаграма на Автоенкодер](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bg.jpg)\n", + "![Диаграма на Автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md index 565f9620..ac2343db 100644 --- a/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/bg/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Тъй като обучаваме автоенкодера да улавя максимално много информация от оригиналното изображение за точна реконструкция, мрежата се опитва да намери най-доброто **вграждане** на входните изображения, за да улови тяхното значение. -![Диаграма на автоенкодер](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bg.jpg) +![Диаграма на автоенкодер](../../../../../translated_images/bg/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Изображение от [Keras блог](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md index 2a506aa2..10eaa9ec 100644 --- a/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/bg/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Тест преди лекцията](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Разпознаване на обекти](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.bg.png) +![Разпознаване на обекти](../../../../../translated_images/bg/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Изображение от [уебсайта на YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Извършваме класификация на изображения върху всяка част. 3. Тези части, които водят до достатъчно висока активация, могат да се считат за съдържащи търсения обект. -![Наивно разпознаване на обекти](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.bg.png) +![Наивно разпознаване на обекти](../../../../../translated_images/bg/naive-detection.e7f1ba220ccd08c6.png) > *Изображение от [тетрадката с упражнения](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 класа * [COCO](http://cocodataset.org/#home) - Общи обекти в контекст. 80 класа, рамки и маски за сегментация -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.bg.jpg) +![COCO](../../../../../translated_images/bg/coco-examples.71bc60380fa6cceb.jpg) ## Метрики за разпознаване на обекти @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Докато за класификация на изображения е лесно да се измери колко добре се представя алгоритъмът, за разпознаване на обекти трябва да измерим както коректността на класа, така и прецизността на местоположението на предвидената рамка. За последното използваме така наречената **Пресечна площ спрямо обединение** (IoU), която измерва колко добре се припокриват две рамки (или две произволни области). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.bg.png) +![IoU](../../../../../translated_images/bg/iou_equation.9a4751d40fff4e11.png) > *Фигура 2 от [този отличен блог пост за IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) използва [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), за да генерира йерархична структура от региони ROI, които след това се обработват чрез CNN екстрактори на характеристики и SVM-класификатори, за да се определи класът на обекта, и линейна регресия за определяне на координатите на *рамката*. [Официална статия](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.bg.png) +![RCNN](../../../../../translated_images/bg/rcnn1.cae407020dfb1d1f.png) > *Изображение от van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.bg.png) +![RCNN-1](../../../../../translated_images/bg/rcnn2.2d9530bb83516484.png) > *Изображения от [този блог](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ $$ Този подход е подобен на R-CNN, но регионите се определят след прилагане на слоевете за конволюция. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.bg.png) +![FRCNN](../../../../../translated_images/bg/f-rcnn.3cda6d9bb4188875.png) > Изображение от [официалната статия](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Основната идея на този подход е да се използва невронна мрежа за предсказване на ROI - така наречената *Мрежа за предложения на региони*. [Статия](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.bg.png) +![FasterRCNN](../../../../../translated_images/bg/faster-rcnn.8d46c099b87ef30a.png) > Изображение от [официалната статия](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Характеристиките се обработват от **Position-Sensitive Score Map**. Всеки обект от $C$ класове се разделя на $k\times k$ региони, и обучаваме мрежата да предсказва части от обекти. 3. За всяка част от $k\times k$ региони всички мрежи гласуват за класовете на обектите, и класът с максимален брой гласове се избира. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.bg.png) +![r-fcn image](../../../../../translated_images/bg/r-fcn.13eb88158b99a3da.png) > Изображение от [официалната статия](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO е алгоритъм за разпознаване в реално вре * Изображението се разделя на $S\times S$ региони. * За всеки регион **CNN** предсказва $n$ възможни обекти, координати на *рамката* и *увереност*=*вероятност* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.bg.png) + ![YOLO](../../../../../translated_images/bg/yolo.a2648ec82ee8bb4e.png) > Изображение от [официалната статия](https://arxiv.org/abs/1506.02640) diff --git a/translations/bg/lessons/4-ComputerVision/README.md b/translations/bg/lessons/4-ComputerVision/README.md index bc415d75..26592e12 100644 --- a/translations/bg/lessons/4-ComputerVision/README.md +++ b/translations/bg/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Компютърно зрение -![Обобщение на съдържанието за компютърно зрение в рисунка](../../../../translated_images/ai-computervision.6506ebebac3fbf76.bg.png) +![Обобщение на съдържанието за компютърно зрение в рисунка](../../../../translated_images/bg/ai-computervision.6506ebebac3fbf76.png) В тази секция ще научим за: diff --git a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 3742ec23..94ec4eaf 100644 --- a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Чанта с думи** (BoW) е най-често използваното традиционно векторно представяне. Всяка дума е свързана с индекс във вектора, а елементът на вектора съдържа броя на срещанията на дадена дума в конкретен документ.\n", "\n", - "![Изображение, показващо как представянето чрез \"чанта с думи\" се съхранява в паметта.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.bg.png) \n", + "![Изображение, показващо как представянето чрез \"чанта с думи\" се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Можете също да мислите за BoW като за сума от всички едноразрядно кодирани вектори за отделните думи в текста.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 70d8c812..259d7dca 100644 --- a/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/bg/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Чанта с думи** (BoW) е най-простото за разбиране традиционно представяне на вектор. Всяка дума е свързана с индекс във вектора, а елементът на вектора съдържа броя на срещанията на всяка дума в даден документ.\n", "\n", - "![Изображение, показващо как представянето чрез чанта с думи се съхранява в паметта.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.bg.png) \n", + "![Изображение, показващо как представянето чрез чанта с думи се съхранява в паметта.](../../../../../translated_images/bg/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Можете също да мислите за BoW като сума от всички едно-горещо-кодирани вектори за отделните думи в текста.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 124fe055..4153e266 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Използвайки слой за вграждане като първи слой в нашата мрежа, можем да преминем от модел на чанта с думи към модел на **чанта с вграждания**, където първо преобразуваме всяка дума в текста в съответното вграждане, а след това изчисляваме някаква агрегатна функция върху всички тези вграждания, като например `sum`, `average` или `max`.\n", "\n", - "![Изображение, показващо класификатор с вграждане за пет последователни думи.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bg.png)\n", + "![Изображение, показващо класификатор с вграждане за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Нашата невронна мрежа за класификация ще започне със слой за вграждане, след това слой за агрегиране и линеен класификатор върху него:\n" ] @@ -176,7 +176,7 @@ "\n", "В предишната архитектура трябваше да запълним всички последователности до еднаква дължина, за да ги включим в минипартида. Това не е най-ефективният начин за представяне на последователности с променлива дължина - друг подход би бил използването на **вектор на отместванията**, който съдържа отместванията на всички последователности, съхранени в един голям вектор.\n", "\n", - "![Изображение, показващо представяне на последователност с отмествания](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.bg.png)\n", + "![Изображение, показващо представяне на последователност с отмествания](../../../../../translated_images/bg/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: На изображението по-горе е показана последователност от символи, но в нашия пример работим с последователности от думи. Въпреки това, основният принцип на представяне на последователности с вектор на отмествания остава същият.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи.\n", "\n", - "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bg.png)\n", + "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "За да експериментираме с вграждания word2vec, предварително обучени върху набора от данни Google News, можем да използваме библиотеката **gensim**. По-долу намираме думите, които са най-близки до 'neural'.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index d46dc9a5..b06a13ef 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/bg/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Като използваме embedding слой като първи слой в нашата мрежа, можем да преминем от модел bag-of-words към модел **embedding bag**, където първо преобразуваме всяка дума в текста в съответния embedding, а след това изчисляваме някаква агрегираща функция върху всички тези embeddings, като например `sum`, `average` или `max`.\n", "\n", - "![Изображение, показващо embedding класификатор за пет последователни думи.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bg.png)\n", + "![Изображение, показващо embedding класификатор за пет последователни думи.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Нашата невронна мрежа за класификация се състои от следните слоеве:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи.\n", "\n", - "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bg.png)\n", + "![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "За да експериментираме с вграждането Word2Vec, предварително обучено върху набора от данни Google News, можем да използваме библиотеката **gensim**. По-долу намираме думите, които са най-близки до 'neural'.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/14-Embeddings/README.md b/translations/bg/lessons/5-NLP/14-Embeddings/README.md index 1b822711..dc91dfdf 100644 --- a/translations/bg/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/bg/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Използвайки слой за вграждане като първи слой в нашата мрежа за класификация, можем да преминем от модел на чанта от думи към модел на **чанта от вграждания**, където първо преобразуваме всяка дума в текста в съответното вграждане и след това изчисляваме някаква агрегираща функция върху всички тези вграждания, като `sum`, `average` или `max`. -![Изображение, показващо класификатор с вграждания за пет думи от последователност.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bg.png) +![Изображение, показващо класификатор с вграждания за пет думи от последователност.](../../../../../translated_images/bg/embedding-classifier-example.b77f021a7ee67eee.png) > Изображение от автора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW е по-бърз, докато скип-грам е по-бавен, но се справя по-добре с представянето на редки думи. -![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bg.png) +![Изображение, показващо алгоритмите CBoW и Skip-Gram за преобразуване на думи във вектори.](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Изображение от [тази статия](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md b/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md index 694e4cf5..24ff04a5 100644 --- a/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/bg/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Непрекъсната торба с думи** (CBoW), когато предсказваме средния токен $W_0$ в последователност от токени $W_{-N}$, ..., $W_N$. * **Skip-gram**, където предсказваме набор от съседни токени {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} от средния токен $W_0$. -![изображение от статия за преобразуване на думи във вектори](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bg.png) +![изображение от статия за преобразуване на думи във вектори](../../../../../translated_images/bg/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Изображение от [тази статия](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/bg/lessons/5-NLP/16-RNN/README.md b/translations/bg/lessons/5-NLP/16-RNN/README.md index 29fcd50e..c41a9c47 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/README.md +++ b/translations/bg/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: За да уловим значението на текстовата последователност, трябва да използваме друга архитектура на невронна мрежа, наречена **рекурентна невронна мрежа** или RNN. В RNN подаваме изречението през мрежата символ по символ, а мрежата произвежда някакво **състояние**, което след това подаваме отново на мрежата заедно със следващия символ. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.bg.png) +![RNN](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.png) > Изображение от автора @@ -61,7 +61,7 @@ LSTM мрежата е организирана по начин, подобен Рекурентната мрежа, независимо дали е еднопосочна или двунаправлена, улавя определени модели в рамките на последователността и може да ги съхранява в състоянието или да ги предава като изход. Както при конволюционните мрежи, можем да изградим друг рекурентен слой върху първия, за да уловим модели на по-високо ниво и да изградим от модели на ниско ниво, извлечени от първия слой. Това ни води до понятието за **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се подава на следващия слой като вход. -![Изображение, показващо многослойна LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bg.jpg) +![Изображение, показващо многослойна LSTM RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Изображение от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес* diff --git a/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 060a4ffb..8ac33881 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентната мрежа, независимо дали е еднопосочна или двунаправлена, улавя определени модели в рамките на последователността и може да ги съхранява в скрития вектор или да ги предава към изхода. Както при конволюционните мрежи, можем да изградим друг рекурентен слой върху първия, за да уловим модели на по-високо ниво, изградени от модели на ниско ниво, извлечени от първия слой. Това ни води до понятието **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се подава като вход към следващия слой.\n", "\n", - "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bg.jpg)\n", + "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Снимка от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес*\n", "\n", diff --git a/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb index 6cc0038b..5420fa4d 100644 --- a/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/bg/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "За да уловим значението на текстова последователност, ще използваме архитектура на невронна мрежа, наречена **рекурентна невронна мрежа** или RNN. Когато използваме RNN, подаваме изречението си през мрежата, една по една дума (токен), и мрежата произвежда някакво **състояние**, което след това подаваме отново на мрежата заедно със следващия токен.\n", "\n", - "![Изображение, показващо пример за генериране с рекурентна невронна мрежа.](../../../../../translated_images/rnn.27f5c29c53d727b5.bg.png)\n", + "![Изображение, показващо пример за генериране с рекурентна невронна мрежа.](../../../../../translated_images/bg/rnn.27f5c29c53d727b5.png)\n", "\n", "Дадена входна последователност от токени $X_0,\\dots,X_n$, RNN създава последователност от блокове на невронната мрежа и обучава тази последователност от край до край, използвайки обратно разпространение. Всеки блок на мрежата приема двойка $(X_i,S_i)$ като вход и произвежда $S_{i+1}$ като резултат. Финалното състояние $S_n$ или изходът $Y_n$ се подава към линеен класификатор, за да се произведе резултатът. Всички блокове на мрежата споделят едни и същи тегла и се обучават от край до край с един цикъл на обратно разпространение.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентните мрежи, еднопосочни или двунасочни, улавят модели в рамките на последователността и ги съхраняват в състояния или ги връщат като изход. Както при конволюционните мрежи, можем да изградим друг рекурентен слой след първия, за да уловим модели на по-високо ниво, изградени от модели на по-ниско ниво, извлечени от първия слой. Това ни води до понятието за **многослойна RNN**, която се състои от две или повече рекурентни мрежи, където изходът на предишния слой се предава на следващия слой като вход.\n", "\n", - "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bg.jpg)\n", + "![Изображение, показващо многослойна дългосрочно-краткосрочна памет RNN](../../../../../translated_images/bg/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Снимка от [този чудесен пост](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) от Фернандо Лопес.*\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index d6690bbe..5eed8380 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Начинът, по който ще обучим RNN да генерира текст, е следният. На всяка стъпка ще вземем последователност от символи с дължина `nchars` и ще помолим мрежата да генерира следващия изходен символ за всеки входен символ:\n", "\n", - "![Изображение, показващо пример за генериране на думата 'HELLO' от RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bg.png)\n", + "![Изображение, показващо пример за генериране на думата 'HELLO' от RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png)\n", "\n", "В зависимост от конкретния сценарий, може да искаме да включим и някои специални символи, като например *край на последователността* ``. В нашия случай просто искаме да обучим мрежата за безкрайно генериране на текст, затова ще фиксираме размера на всяка последователност да бъде равен на `nchars` токени. Следователно, всеки тренировъчен пример ще се състои от `nchars` входове и `nchars` изходи (които са входната последователност, изместена с един символ наляво). Минипартидата ще се състои от няколко такива последователности.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 632b613a..086ebd56 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Начинът, по който ще обучим RNN да генерира новинарски заглавия, е следният. На всяка стъпка ще вземем едно заглавие, което ще бъде подадено в RNN, и за всеки входен символ ще поискаме от мрежата да генерира следващия изходен символ:\n", "\n", - "![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bg.png)\n", + "![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png)\n", "\n", "За последния символ от нашата последователност ще поискаме от мрежата да генерира токен ``.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md index 5de2e311..4fb542d0 100644 --- a/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/bg/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Това позволява различни невронни архитектури, които са показани на изображението по-долу: -![Изображение, показващо общи модели на рекурентни невронни мрежи.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.bg.jpg) +![Изображение, показващо общи модели на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Изображение от блог пост [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) от [Андрей Карпати](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Ще обучим тази RNN да генерира текст стъпка по стъпка. На всяка стъпка ще вземем последователност от символи с дължина `nchars` и ще помолим мрежата да генерира следващия изходен символ за всеки входен символ: -![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bg.png) +![Изображение, показващо пример за генериране на думата 'HELLO' с RNN.](../../../../../translated_images/bg/rnn-generate.56c54afb52f9781d.png) При генериране на текст (по време на инференция) започваме с някакъв **подсказка**, която се предава през RNN клетките, за да генерира междинното си състояние, и след това от това състояние започва генерирането. Генерираме един символ наведнъж и предаваме състоянието и генерирания символ на друга RNN клетка, за да генерира следващия, докато генерираме достатъчно символи. diff --git a/translations/bg/lessons/5-NLP/18-Transformers/README.md b/translations/bg/lessons/5-NLP/18-Transformers/README.md index e81242d6..c2eb612f 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/README.md +++ b/translations/bg/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механизмите за внимание** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка прогноза на изхода на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входния RNN и изходния RNN. По този начин, когато генерираме изходния символ yt, ще вземем предвид всички скрити състояния на входа hi, с различни теглови коефициенти αt,i. -![Изображение, показващо модел кодировач/декодировач с добавен слой за внимание](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bg.png) +![Изображение, показващо модел кодировач/декодировач с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png) > Моделът кодировач-декодировач с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрицата за внимание {αi,j} представлява степента, до която определени входни думи играят роля в генерирането на дадена дума в изходната последователност. По-долу е пример за такава матрица: -![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bg.png) +![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png) > Фигура от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Следващата стъпка е да уловим някои модели в рамките на нашата последователност. За да направят това, трансформерите използват механизъм за **самовнимание**, който по същество е внимание, приложено към една и съща последователност като вход и изход. Прилагането на самовнимание ни позволява да вземем предвид **контекста** в рамките на изречението и да видим кои думи са взаимосвързани. Например, това ни позволява да видим кои думи се отнасят до кореференции, като *it*, и също така да вземем контекста предвид: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.bg.png) +![](../../../../../translated_images/bg/CoreferenceResolution.861924d6d384a7d6.png) > Изображение от [Блогът на Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформерна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (WikiPedia + книги) чрез неуправляемо обучение (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значителни нива на разбиране на езика, които след това могат да бъдат използвани с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**. -![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bg.png) +![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Изображение [източник](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 9d247d37..df1136ba 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/bg/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмите на вниманието** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка изходна прогноза на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входната RNN и изходната RNN. По този начин, при генериране на изходен символ $y_t$, ще вземем предвид всички входни скрити състояния $h_i$, с различни теглови коефициенти $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показващо модел encoder/decoder с добавен слой за внимание](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bg.png)\n", + "![Изображение, показващо модел encoder/decoder с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png)\n", "*Моделът encoder-decoder с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрицата на вниманието $\\{\\alpha_{i,j}\\}$ представлява степента, до която определени входни думи участват в генерирането на дадена дума в изходната последователност. По-долу е даден пример за такава матрица:\n", "\n", - "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bg.png)\n", + "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Фигура, взета от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформерна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (Wikipedia + книги) с помощта на неконтролирано обучение (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значително ниво на езиково разбиране, което след това може да бъде използвано с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**.\n", "\n", - "![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bg.png)\n", + "![изображение от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Съществуват много вариации на трансформерните архитектури, включително BERT, DistilBERT, BigBird, OpenGPT3 и други, които могат да бъдат фино настроени. Пакетът [HuggingFace](https://github.com/huggingface/) предоставя хранилище за обучение на много от тези архитектури с PyTorch.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index ab07690a..fcec9e21 100644 --- a/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/bg/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмите на внимание** предоставят начин за претегляне на контекстуалното влияние на всеки входен вектор върху всяка изходна прогноза на RNN. Това се реализира чрез създаване на преки връзки между междинните състояния на входната RNN и изходната RNN. По този начин, при генериране на изходен символ $y_t$, ще вземем предвид всички входни скрити състояния $h_i$, с различни теглови коефициенти $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показващо модел енкодер/декодер с добавен слой за внимание](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bg.png)\n", + "![Изображение, показващо модел енкодер/декодер с добавен слой за внимание](../../../../../translated_images/bg/encoder-decoder-attention.7a726296894fb567.png)\n", "*Моделът енкодер-декодер с механизъм за добавено внимание в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран от [този блог пост](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрицата на внимание $\\{\\alpha_{i,j}\\}$ представлява степента, до която определени входни думи играят роля в генерирането на дадена дума в изходната последователност. По-долу е даден пример за такава матрица:\n", "\n", - "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bg.png)\n", + "![Изображение, показващо пример за подравняване, намерено от RNNsearch-50, взето от Bahdanau - arviz.org](../../../../../translated_images/bg/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Фигура, взета от [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Фиг.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) е много голяма многослойна трансформаторна мрежа с 12 слоя за *BERT-base* и 24 за *BERT-large*. Моделът първо се предварително обучава върху голям корпус от текстови данни (WikiPedia + книги) чрез обучение без надзор (предсказване на маскирани думи в изречение). По време на предварителното обучение моделът усвоява значително ниво на езиково разбиране, което след това може да бъде използвано с други набори от данни чрез фина настройка. Този процес се нарича **трансферно обучение**.\n", "\n", - "![картина от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bg.png)\n", + "![картина от http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/bg/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Съществуват много вариации на трансформаторни архитектури, включително BERT, DistilBERT, BigBird, OpenGPT3 и други, които могат да бъдат фино настроени.\n", "\n", diff --git a/translations/bg/lessons/5-NLP/19-NER/README.md b/translations/bg/lessons/5-NLP/19-NER/README.md index e9ab74f0..311cc867 100644 --- a/translations/bg/lessons/5-NLP/19-NER/README.md +++ b/translations/bg/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Тъй като трябва да изградим едно към едно съответствие между токените и класовете, можем да обучим **много към много** невронен мрежов модел от тази картина: -![Изображение, показващо общи архитектури на рекурентни невронни мрежи.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.bg.jpg) +![Изображение, показващо общи архитектури на рекурентни невронни мрежи.](../../../../../translated_images/bg/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Изображение от [този блог пост](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) от [Андрей Карпати](http://karpathy.github.io/). Моделите за класификация на токени за NER съответстват на най-дясната архитектура на мрежата на тази картина.* diff --git a/translations/bg/lessons/5-NLP/README.md b/translations/bg/lessons/5-NLP/README.md index be1d013c..0db562c2 100644 --- a/translations/bg/lessons/5-NLP/README.md +++ b/translations/bg/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обработка на естествен език -![Обобщение на задачите в NLP в рисунка](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.bg.png) +![Обобщение на задачите в NLP в рисунка](../../../../translated_images/bg/ai-nlp.b22dcb8ca4707cea.png) В тази секция ще се фокусираме върху използването на невронни мрежи за решаване на задачи, свързани с **обработката на естествен език (NLP)**. Съществуват много NLP проблеми, които искаме компютрите да могат да решават: diff --git a/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md b/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md index 6e644fe1..bdfe761f 100644 --- a/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/bg/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ След като отворите модела, ще бъдете отведени до основния екран на NetLogo. Ето примерен модел, който описва популацията на вълци и овце, при наличието на ограничени ресурси (трева). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.bg.png) +![NetLogo Main Screen](../../../../../translated_images/bg/NetLogo-Main.32653711ec1a01b3.png) > Екранна снимка от Дмитрий Сошников diff --git a/translations/bg/lessons/README.md b/translations/bg/lessons/README.md index 58a379ae..c3e0d764 100644 --- a/translations/bg/lessons/README.md +++ b/translations/bg/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Преглед -![Преглед в рисунка](../../../translated_images/ai-overview.0857791951d19500.bg.png) +![Преглед в рисунка](../../../translated_images/bg/ai-overview.0857791951d19500.png) > Рисунка от [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/bg/lessons/X-Extras/X1-MultiModal/README.md b/translations/bg/lessons/X-Extras/X1-MultiModal/README.md index c8c3f530..6379dcc3 100644 --- a/translations/bg/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/bg/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Основната идея на CLIP е да може да сравнява текстови подсказки с изображение и да определя доколко изображението съответства на подсказката. -![Архитектура на CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.bg.png) +![Архитектура на CLIP](../../../../../translated_images/bg/clip-arch.b3dbf20b4e8ed8be.png) > *Снимка от [тази публикация в блог](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: Да предположим, че трябва да класифицираме изображения между, например, котки, кучета и хора. В този случай можем да подадем на модела изображение и серия от текстови подсказки: "*снимка на котка*", "*снимка на куче*", "*снимка на човек*". В получения вектор от 3 вероятности просто трябва да изберем индекса с най-висока стойност. -![CLIP за класификация на изображения](../../../../../translated_images/clip-class.3af42ef0b2b19369.bg.png) +![CLIP за класификация на изображения](../../../../../translated_images/bg/clip-class.3af42ef0b2b19369.png) > *Снимка от [тази публикация в блог](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ CLIP може също да се използва за **генериране н Една от важните разлики между VQGAN и традиционните GAN е, че последните могат да произведат прилично изображение от всеки входен вектор, докато VQGAN е по-вероятно да произведе изображение, което не е кохерентно. Затова трябва допълнително да насочим процеса на създаване на изображението, което може да се направи с помощта на CLIP. -![Архитектура на VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.bg.png) +![Архитектура на VQGAN+CLIP](../../../../../translated_images/bg/vqgan.5027fe05051dfa31.png) За да генерираме изображение, съответстващо на текстова подсказка, започваме с някакъв случаен вектор за кодиране, който се подава през VQGAN, за да се произведе изображение. След това CLIP се използва за създаване на функция на загуба, която показва доколко изображението съответства на текстовата подсказка. Целта е да минимизираме тази загуба, използвайки обратна пропагация за настройка на параметрите на входния вектор. Отлична библиотека, която реализира VQGAN+CLIP, е [Pixray](http://github.com/pixray/pixray). -![Снимка, генерирана от Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.bg.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.bg.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.bg.png) +![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Снимка, генерирана от подсказка *близък акварелен портрет на млад мъж учител по литература с книга* | Снимка, генерирана от подсказка *близък маслен портрет на млада жена учител по компютърни науки с компютър* | Снимка, генерирана от подсказка *близък маслен портрет на възрастен мъж учител по математика пред черна дъска* @@ -77,7 +77,7 @@ DALL-E е версия на GPT-3, обучена да генерира изоб Основната разлика между DALL.E 1 и 2 е, че вторият генерира по-реалистични изображения и изкуство. Примери за генериране на изображения с DALL-E: -![Снимка, генерирана от Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.bg.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.bg.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.bg.png) +![Снимка, генерирана от Pixray](../../../../../translated_images/bg/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Снимка, генерирана от Pixray](../../../../../translated_images/bg/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Снимка, генерирана от подсказка *близък акварелен портрет на млад мъж учител по литература с книга* | Снимка, генерирана от подсказка *близък маслен портрет на млада жена учител по компютърни науки с компютър* | Снимка, генерирана от подсказка *близък маслен портрет на възрастен мъж учител по математика пред черна дъска* diff --git a/translations/bn/README.md b/translations/bn/README.md index 20cd160d..2c7a1c1e 100644 --- a/translations/bn/README.md +++ b/translations/bn/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # আর্টিফিশিয়াল ইন্টেলিজেন্স ফর বিগিনার্স - একটি কারিকুলাম -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.bn.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/bn/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _স্কেচনোট [@girlie_mac](https://twitter.com/girlie_mac) দ্বারা_ | diff --git a/translations/bn/lessons/1-Intro/README.md b/translations/bn/lessons/1-Intro/README.md index 0739bd40..f6449e4b 100644 --- a/translations/bn/lessons/1-Intro/README.md +++ b/translations/bn/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI পরিচিতি -![AI পরিচিতির সামগ্রিক বিষয়বস্তুর একটি ডুডল](../../../../translated_images/ai-intro.bf28d1ac4235881c.bn.png) +![AI পরিচিতির সামগ্রিক বিষয়বস্তুর একটি ডুডল](../../../../translated_images/bn/ai-intro.bf28d1ac4235881c.png) > স্কেচনোট: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: মূলত, কম্পিউটার আবিষ্কার করেছিলেন [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) সংখ্যা নিয়ে কাজ করার জন্য, একটি সুস্পষ্ট প্রক্রিয়া অনুসরণ করে - একটি অ্যালগরিদম। আধুনিক কম্পিউটার, যদিও ১৯শ শতাব্দীতে প্রস্তাবিত মূল মডেলের তুলনায় অনেক বেশি উন্নত, তবুও নিয়ন্ত্রিত গণনার একই ধারণা অনুসরণ করে। তাই, যদি আমরা জানি লক্ষ্য অর্জনের জন্য প্রয়োজনীয় সঠিক ধাপগুলোর ক্রম, তাহলে কম্পিউটারকে কিছু করতে প্রোগ্রাম করা সম্ভব। -![একজন ব্যক্তির ছবি](../../../../translated_images/dsh_age.d212a30d4e54fb5f.bn.png) +![একজন ব্যক্তির ছবি](../../../../translated_images/bn/dsh_age.d212a30d4e54fb5f.png) > ছবি: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[বুদ্ধিমত্তা](https://en.wikipedia.org/wiki/Intelligence)** শব্দটি নিয়ে কাজ করার সময় একটি সমস্যা হলো এই শব্দটির কোনো স্পষ্ট সংজ্ঞা নেই। কেউ যুক্তি করতে পারে যে বুদ্ধিমত্তা **অমূর্ত চিন্তা** বা **আত্ম-সচেতনতার** সাথে সংযুক্ত, কিন্তু আমরা এটি সঠিকভাবে সংজ্ঞায়িত করতে পারি না। -![একটি বিড়ালের ছবি](../../../../translated_images/photo-cat.8c8e8fb760ffe457.bn.jpg) +![একটি বিড়ালের ছবি](../../../../translated_images/bn/photo-cat.8c8e8fb760ffe457.jpg) > [ছবি](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) Unsplash থেকে @@ -98,13 +98,13 @@ AGI নিয়ে কথা বলার সময় আমাদের এমন > | ML সম্পর্কে কী? | | > |--------------|-----------| -> | কৃত্রিম বুদ্ধিমত্তার অংশ যা কম্পিউটারকে কিছু ডেটার ভিত্তিতে একটি সমস্যা সমাধানে শেখানোর উপর ভিত্তি করে তাকে **মেশিন লার্নিং** বলা হয়। আমরা এই কোর্সে ক্লাসিক্যাল মেশিন লার্নিং বিবেচনা করব না - আমরা আপনাকে একটি পৃথক [Machine Learning for Beginners](http://aka.ms/ml-beginners) কারিকুলামে রেফার করছি। | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.bn.png) | +> | কৃত্রিম বুদ্ধিমত্তার অংশ যা কম্পিউটারকে কিছু ডেটার ভিত্তিতে একটি সমস্যা সমাধানে শেখানোর উপর ভিত্তি করে তাকে **মেশিন লার্নিং** বলা হয়। আমরা এই কোর্সে ক্লাসিক্যাল মেশিন লার্নিং বিবেচনা করব না - আমরা আপনাকে একটি পৃথক [Machine Learning for Beginners](http://aka.ms/ml-beginners) কারিকুলামে রেফার করছি। | ![ML for Beginners](../../../../translated_images/bn/ml-for-beginners.9e4fed176fd5817d.png) | ## AI এর সংক্ষিপ্ত ইতিহাস কৃত্রিম বুদ্ধিমত্তা একটি ক্ষেত্র হিসেবে শুরু হয়েছিল বিংশ শতাব্দীর মাঝামাঝি। প্রথমদিকে, প্রতীকী যুক্তি একটি প্রভাবশালী পদ্ধতি ছিল, এবং এটি কিছু গুরুত্বপূর্ণ সাফল্য অর্জন করেছিল, যেমন বিশেষজ্ঞ সিস্টেম – কম্পিউটার প্রোগ্রাম যা কিছু সীমিত সমস্যার ক্ষেত্রে একজন বিশেষজ্ঞের মতো কাজ করতে সক্ষম ছিল। তবে, শীঘ্রই এটি স্পষ্ট হয়ে যায় যে এই পদ্ধতি ভালোভাবে স্কেল করে না। একজন বিশেষজ্ঞ থেকে জ্ঞান বের করা, কম্পিউটারে উপস্থাপন করা, এবং সেই জ্ঞানভাণ্ডারকে সঠিক রাখা একটি অত্যন্ত জটিল কাজ এবং অনেক ক্ষেত্রে ব্যবহারিকভাবে খুব ব্যয়বহুল। এটি ১৯৭০-এর দশকে তথাকথিত [AI Winter](https://en.wikipedia.org/wiki/AI_winter) এর দিকে নিয়ে যায়। -AI এর সংক্ষিপ্ত ইতিহাস +AI এর সংক্ষিপ্ত ইতিহাস > ছবি: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/bn/lessons/2-Symbolic/Animals.ipynb b/translations/bn/lessons/2-Symbolic/Animals.ipynb index f60d3692..55977051 100644 --- a/translations/bn/lessons/2-Symbolic/Animals.ipynb +++ b/translations/bn/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "এই উদাহরণে, আমরা একটি সহজ জ্ঞান-ভিত্তিক সিস্টেম বাস্তবায়ন করব যা কিছু শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করবে। সিস্টেমটি নিম্নলিখিত AND-OR গাছ দ্বারা উপস্থাপন করা যেতে পারে (এটি পুরো গাছের একটি অংশ, আমরা সহজেই আরও কিছু নিয়ম যোগ করতে পারি):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.bn.png)\n" + "![](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/bn/lessons/2-Symbolic/README.md b/translations/bn/lessons/2-Symbolic/README.md index 1905792b..d90694b9 100644 --- a/translations/bn/lessons/2-Symbolic/README.md +++ b/translations/bn/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # জ্ঞান উপস্থাপন এবং বিশেষজ্ঞ সিস্টেম -![সিম্বলিক AI বিষয়বস্তুর সারাংশ](../../../../translated_images/ai-symbolic.715a30cb610411a6.bn.png) +![সিম্বলিক AI বিষয়বস্তুর সারাংশ](../../../../translated_images/bn/ai-symbolic.715a30cb610411a6.png) > স্কেচনোট [Tomomi Imura](https://twitter.com/girlie_mac) দ্বারা @@ -41,7 +41,7 @@ AI-এর প্রাথমিক দিনগুলোতে, বুদ্ধ তাই, **জ্ঞান উপস্থাপনের** সমস্যাটি হল কম্পিউটারের ভিতরে ডেটার আকারে জ্ঞান উপস্থাপন করার কার্যকর উপায় খুঁজে বের করা, যাতে এটি স্বয়ংক্রিয়ভাবে ব্যবহারযোগ্য হয়। এটি একটি স্পেকট্রামের মতো দেখা যেতে পারে: -![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../translated_images/knowledge-spectrum.b60df631852c0217.bn.png) +![জ্ঞান উপস্থাপনের স্পেকট্রাম](../../../../translated_images/bn/knowledge-spectrum.b60df631852c0217.png) > চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা @@ -94,7 +94,7 @@ Block Syntax | Indent | | | সিম্বলিক AI-এর প্রাথমিক সাফল্যগুলোর মধ্যে একটি ছিল তথাকথিত **বিশেষজ্ঞ সিস্টেম** - কম্পিউটার সিস্টেম যা একটি সীমিত সমস্যার ক্ষেত্রে একজন বিশেষজ্ঞের মতো কাজ করার জন্য ডিজাইন করা হয়েছিল। এগুলো একটি **জ্ঞানভিত্তি** এবং একটি **ইনফারেন্স ইঞ্জিন** নিয়ে গঠিত ছিল যা এর উপর যুক্তি করত। -![মানব স্থাপত্য](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.bn.png) | ![জ্ঞানভিত্তিক সিস্টেম](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.bn.png) +![মানব স্থাপত্য](../../../../translated_images/bn/arch-human.5d4d35f1bba3ab1c.png) | ![জ্ঞানভিত্তিক সিস্টেম](../../../../translated_images/bn/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ মানব স্নায়ুতন্ত্রের সরলীকৃত কাঠামো | জ্ঞানভিত্তিক সিস্টেমের স্থাপত্য @@ -106,7 +106,7 @@ Block Syntax | Indent | | | একটি উদাহরণ হিসেবে, চলুন একটি বিশেষজ্ঞ সিস্টেমের কথা বিবেচনা করি যা শারীরিক বৈশিষ্ট্যের উপর ভিত্তি করে একটি প্রাণী নির্ধারণ করে: -![AND-OR গাছ](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.bn.png) +![AND-OR গাছ](../../../../translated_images/bn/AND-OR-Tree.5592d2c70187f283.png) > চিত্র [Dmitry Soshnikov](http://soshnikov.com) দ্বারা diff --git a/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 230ac92e..c1f6f3d1 100644 --- a/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/bn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "যদি আমাদের ২টির বেশি শ্রেণি থাকে, তাহলে softmax সমস্ত শ্রেণির সম্ভাবনাগুলোকে স্বাভাবিকীকরণ করবে। এখানে একটি নেটওয়ার্ক আর্কিটেকচারের চিত্র দেওয়া হয়েছে যা MNIST ডিজিট শ্রেণিবিন্যাস করে: \n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.bn.png) \n" + "![MNIST Classifier](../../../../../translated_images/bn/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png) \n" ] }, { @@ -1259,7 +1259,7 @@ "* প্রশিক্ষণ ক্ষতি কম - মডেল প্রশিক্ষণ ডেটাকে ভালোভাবে অনুমান করতে পারে, কারণ এর যথেষ্ট প্রকাশক্ষমতা রয়েছে।\n", "* যাচাইকরণ ক্ষতি প্রশিক্ষণ ক্ষতির তুলনায় অনেক বেশি হতে পারে এবং প্রশিক্ষণের সময় বৃদ্ধি পেতে পারে - কারণ মডেল প্রশিক্ষণ পয়েন্টগুলোকে \"মনে রাখে\" এবং \"সামগ্রিক চিত্র\" হারিয়ে ফেলে।\n", "\n", - "![ওভারফিটিং](../../../../../translated_images/overfit.a0bd57f717c15769.bn.png)\n", + "![ওভারফিটিং](../../../../../translated_images/bn/overfit.a0bd57f717c15769.png)\n", "\n", "> এই ছবিতে, `x` প্রশিক্ষণ ডেটাকে নির্দেশ করে, `o` - যাচাইকরণ ডেটা। বামদিকে - লিনিয়ার মডেল (এক-লেয়ার), এটি ডেটার প্রকৃতিকে বেশ ভালোভাবে অনুমান করে। ডানদিকে - ওভারফিটেড মডেল, মডেল প্রশিক্ষণ ডেটাকে পুরোপুরি ভালোভাবে অনুমান করে, কিন্তু অন্য কোনো ডেটার ক্ষেত্রে অর্থহীন হয়ে যায় (যাচাইকরণ ত্রুটি খুব বেশি)।\n" ] diff --git a/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md index 2d407f67..37f92b43 100644 --- a/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/bn/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* নিচের ৫টি বিন্দু (গ্রাফে `x` দ্বারা চিহ্নিত) আনুমানিক করার সমস্যাটি বিবেচনা করুন: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.bn.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.bn.jpg) +![linear](../../../../../translated_images/bn/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/bn/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **লিনিয়ার মডেল, ২টি প্যারামিটার** | **নন-লিনিয়ার মডেল, ৭টি প্যারামিটার** প্রশিক্ষণ ত্রুটি = ৫.৩ | প্রশিক্ষণ ত্রুটি = ০ @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* উপরের গ্রাফ থেকে আপনি দেখতে পাচ্ছেন, ওভারফিটিং খুব কম প্রশিক্ষণ ত্রুটি এবং উচ্চ ভ্যালিডেশন ত্রুটি দ্বারা সনাক্ত করা যায়। সাধারণত প্রশিক্ষণের সময় আমরা দেখতে পাবো প্রশিক্ষণ এবং ভ্যালিডেশন ত্রুটি উভয়ই কমতে শুরু করে, এবং তারপর কোনো এক সময় ভ্যালিডেশন ত্রুটি কমা বন্ধ করে এবং বাড়তে শুরু করে। এটি ওভারফিটিংয়ের একটি চিহ্ন হবে এবং এটি নির্দেশ করবে যে আমাদের সম্ভবত এই সময়ে প্রশিক্ষণ বন্ধ করা উচিত (অথবা অন্তত মডেলের একটি স্ন্যাপশট নেওয়া উচিত)। -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.bn.png) +![overfitting](../../../../../translated_images/bn/Overfitting.408ad91cd90b4371.png) ## কিভাবে ওভারফিটিং প্রতিরোধ করবেন diff --git a/translations/bn/lessons/3-NeuralNetworks/README.md b/translations/bn/lessons/3-NeuralNetworks/README.md index 528baf7d..85692c04 100644 --- a/translations/bn/lessons/3-NeuralNetworks/README.md +++ b/translations/bn/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # নিউরাল নেটওয়ার্কের পরিচিতি -![নিউরাল নেটওয়ার্ক বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.bn.png) +![নিউরাল নেটওয়ার্ক বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-neuralnetworks.1c687ae40bc86e83.png) যেমনটি আমরা পরিচিতিতে আলোচনা করেছি, বুদ্ধিমত্তা অর্জনের একটি উপায় হল একটি **কম্পিউটার মডেল** বা একটি **কৃত্রিম মস্তিষ্ক** প্রশিক্ষণ দেওয়া। ২০শ শতাব্দীর মাঝামাঝি থেকে গবেষকরা বিভিন্ন গাণিতিক মডেল চেষ্টা করেছেন, এবং সাম্প্রতিক বছরগুলোতে এই দিকটি অত্যন্ত সফল প্রমাণিত হয়েছে। মস্তিষ্কের এই ধরনের গাণিতিক মডেলগুলোকে **নিউরাল নেটওয়ার্ক** বলা হয়। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: জীববিজ্ঞান থেকে আমরা জানি যে আমাদের মস্তিষ্ক নিউরাল কোষ (নিউরন) নিয়ে গঠিত, প্রতিটি কোষের একাধিক "ইনপুট" (ডেনড্রাইট) এবং একটি "আউটপুট" (অ্যাক্সন) থাকে। ডেনড্রাইট এবং অ্যাক্সন উভয়ই বৈদ্যুতিক সংকেত পরিচালনা করতে পারে, এবং তাদের মধ্যে সংযোগগুলো — যেগুলো সিন্যাপস নামে পরিচিত — বিভিন্ন মাত্রার পরিবাহিতা প্রদর্শন করতে পারে, যা নিউরোট্রান্সমিটার দ্বারা নিয়ন্ত্রিত হয়। -![একটি নিউরনের মডেল](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.bn.jpg) | ![একটি কৃত্রিম নিউরনের মডেল](../../../../translated_images/artneuron.1a5daa88d20ebe6f.bn.png) +![একটি নিউরনের মডেল](../../../../translated_images/bn/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![একটি কৃত্রিম নিউরনের মডেল](../../../../translated_images/bn/artneuron.1a5daa88d20ebe6f.png) ----|---- আসল নিউরন *([উইকিপিডিয়া থেকে ইমেজ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg))* | কৃত্রিম নিউরন *(লেখকের তৈরি ইমেজ)* তাই, একটি নিউরনের সবচেয়ে সহজ গাণিতিক মডেলটি কয়েকটি ইনপুট X1, ..., XN এবং একটি আউটপুট Y, এবং একটি সিরিজ ওজন W1, ..., WN নিয়ে গঠিত। আউটপুটটি নিম্নরূপ গণনা করা হয়: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) যেখানে f হল কিছু অরৈখিক **অ্যাক্টিভেশন ফাংশন**। diff --git a/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md index 33abe7f7..567655c6 100644 --- a/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/bn/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV ব্যবহার করে ভিডিও ফ্রেম-বাই * **ব্রেইল বইয়ের একটি ছবির প্রি-প্রসেসিং**। আমরা দেখিয়েছি কীভাবে থ্রেশহোল্ডিং, ফিচার ডিটেকশন, পার্সপেক্টিভ ট্রান্সফরমেশন এবং NumPy ম্যানিপুলেশন ব্যবহার করে ব্রেইল প্রতীকগুলোকে আলাদা করা যায়, যা পরে নিউরাল নেটওয়ার্ক দ্বারা শ্রেণীবদ্ধ করা হবে। -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.bn.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.bn.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.bn.png) +![Braille Image](../../../../../translated_images/bn/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/bn/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/bn/braille-symbols.0159185ab69d5339.png) ----|-----|----- > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে * **ভিডিওতে ফ্রেম ডিফারেন্স ব্যবহার করে গতিবিধি সনাক্তকরণ**। যদি ক্যামেরা স্থির থাকে, তাহলে ক্যামেরা ফিডের ফ্রেমগুলো একে অপরের সাথে বেশ মিল থাকবে। যেহেতু ফ্রেমগুলো অ্যারে হিসেবে উপস্থাপিত হয়, দুটি পরবর্তী ফ্রেমের জন্য সেই অ্যারেগুলোকে বিয়োগ করলেই আমরা পিক্সেল পার্থক্য পাব, যা স্থির ফ্রেমের জন্য কম হবে এবং ছবিতে উল্লেখযোগ্য গতিবিধি থাকলে বেশি হবে। -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.bn.png) +![Image of video frames and frame differences](../../../../../translated_images/bn/frame-difference.706f805491a0883c.png) > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে @@ -89,7 +89,7 @@ OpenCV ব্যবহার করে ভিডিও ফ্রেম-বাই - **ডেন্স অপটিক্যাল ফ্লো** প্রতিটি পিক্সেলের জন্য একটি ভেক্টর ক্ষেত্র গণনা করে যা দেখায় এটি কোথায় স্থানান্তরিত হচ্ছে। - **স্পার্স অপটিক্যাল ফ্লো** ছবিতে কিছু বৈশিষ্ট্যপূর্ণ বৈশিষ্ট্য (যেমন: প্রান্ত) গ্রহণ করে এবং ফ্রেম থেকে ফ্রেমে তাদের গতিপথ তৈরি করে। -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.bn.png) +![Image of Optical Flow](../../../../../translated_images/bn/optical.1f4a94464579a83a.png) > ছবি [OpenCV.ipynb](OpenCV.ipynb) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8ad44b37..8301d9a5 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 একটি নেটওয়ার্ক যা ২০১৪ সালে ImageNet টপ-৫ শ্রেণীবিন্যাসে ৯২.৭% সঠিকতা অর্জন করেছিল। এর স্তর কাঠামো নিম্নরূপ: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.bn.jpg) +![ImageNet Layers](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.jpg) যেমনটি আপনি দেখতে পাচ্ছেন, VGG একটি ঐতিহ্যবাহী পিরামিড আর্কিটেকচার অনুসরণ করে, যা কনভোলিউশন-পুলিং স্তরের একটি ক্রম। -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.bn.jpg) +![ImageNet Pyramid](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.jpg) > ছবি [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index ca3ef0f9..7240ff12 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "সুতরাং, একটি সাধারণ CNN-এ কয়েকটি কনভল্যুশনাল লেয়ার থাকবে, যার মধ্যে পুলিং লেয়ার থাকবে যা ছবির মাত্রা কমিয়ে দেবে। আমরা ফিল্টারের সংখ্যাও বাড়াবো, কারণ প্যাটার্ন যত উন্নত হয় - তত বেশি সম্ভাব্য আকর্ষণীয় সংমিশ্রণ থাকে যা আমাদের খুঁজে বের করতে হবে।\n", "\n", - "![কয়েকটি কনভল্যুশনাল লেয়ার এবং পুলিং লেয়ার দেখানো একটি চিত্র।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.bn.png)\n", + "![কয়েকটি কনভল্যুশনাল লেয়ার এবং পুলিং লেয়ার দেখানো একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "স্থানিক মাত্রা কমানো এবং ফিচার/ফিল্টারের মাত্রা বাড়ানোর কারণে, এই আর্কিটেকচারকে **পিরামিড আর্কিটেকচার**ও বলা হয়।\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 415c6d78..7ee73580 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "এইভাবে, একটি সাধারণ CNN-এ কয়েকটি কনভোলিউশনাল লেয়ার থাকে, যার মধ্যে পুলিং লেয়ারগুলো ছবির মাত্রা কমানোর জন্য ব্যবহৃত হয়। আমরা ফিল্টারের সংখ্যা বাড়িয়ে দিই, কারণ প্যাটার্নগুলো আরও উন্নত হওয়ার সাথে সাথে আরও বেশি সম্ভাব্য আকর্ষণীয় সংমিশ্রণ থাকে যা আমাদের খুঁজে বের করতে হয়।\n", "\n", - "![কনভোলিউশনাল লেয়ার এবং পুলিং লেয়ার নিয়ে একটি চিত্র।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.bn.png)\n", + "![কনভোলিউশনাল লেয়ার এবং পুলিং লেয়ার নিয়ে একটি চিত্র।](../../../../../translated_images/bn/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "স্থানিক মাত্রা কমানো এবং ফিচার/ফিল্টার মাত্রা বাড়ানোর কারণে, এই আর্কিটেকচারকে **পিরামিড আর্কিটেকচার** বলা হয়।\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md index f86f12c5..59d75968 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: প্যাটার্ন বের করার জন্য, আমরা **কনভোলিউশনাল ফিল্টার** ধারণাটি ব্যবহার করব। আপনি জানেন, একটি ছবি 2D-ম্যাট্রিক্স বা রঙের গভীরতা সহ 3D-টেনসর দ্বারা উপস্থাপিত হয়। একটি ফিল্টার প্রয়োগ করার অর্থ হলো আমরা একটি তুলনামূলকভাবে ছোট **ফিল্টার কের্নেল** ম্যাট্রিক্স নিই, এবং মূল ছবির প্রতিটি পিক্সেলের জন্য আমরা প্রতিবেশী পয়েন্টগুলির সাথে ওজনযুক্ত গড় গণনা করি। আমরা এটি একটি ছোট জানালা হিসেবে দেখতে পারি যা পুরো ছবির উপর দিয়ে স্লাইড করে এবং ফিল্টার কের্নেল ম্যাট্রিক্সের ওজন অনুযায়ী সমস্ত পিক্সেল গড় করে। -![ভার্টিকাল এজ ফিল্টার](../../../../../translated_images/filter-vert.b7148390ca0bc356.bn.png) | ![হরিজন্টাল এজ ফিল্টার](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.bn.png) +![ভার্টিকাল এজ ফিল্টার](../../../../../translated_images/bn/filter-vert.b7148390ca0bc356.png) | ![হরিজন্টাল এজ ফিল্টার](../../../../../translated_images/bn/filter-horiz.59b80ed4feb946ef.png) ----|---- > ছবি: দিমিত্রি সশনিকভ @@ -38,7 +38,7 @@ CNN যেভাবে কাজ করে তা নিম্নলিখিত * আমরা নেটওয়ার্কটিকে এমনভাবে ডিজাইন করতে পারি যাতে ফিল্টারগুলি স্বয়ংক্রিয়ভাবে প্রশিক্ষিত হয় * আমরা একই পদ্ধতি ব্যবহার করে উচ্চ-স্তরের বৈশিষ্ট্যগুলিতে প্যাটার্ন খুঁজে বের করতে পারি, শুধুমাত্র মূল ছবিতে নয়। সুতরাং CNN বৈশিষ্ট্য বের করার কাজ বৈশিষ্ট্যের একটি শ্রেণিবিন্যাসে কাজ করে, যা নিম্ন-স্তরের পিক্সেল সংমিশ্রণ থেকে শুরু করে ছবির অংশগুলির উচ্চ-স্তরের সংমিশ্রণে পৌঁছায়। -![হায়ারারকিকাল ফিচার এক্সট্রাকশন](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.bn.png) +![হায়ারারকিকাল ফিচার এক্সট্রাকশন](../../../../../translated_images/bn/FeatureExtractionCNN.d9b456cbdae7cb64.png) > ছবি: [হিসলপ-লিঞ্চের গবেষণাপত্র](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), তাদের [গবেষণার উপর ভিত্তি করে](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN যেভাবে কাজ করে তা নিম্নলিখিত উদাহরণস্বরূপ, আসুন VGG-16-এর আর্কিটেকচার দেখি, একটি নেটওয়ার্ক যা ২০১৪ সালে ImageNet-এর টপ-৫ শ্রেণীবিন্যাসে ৯২.৭% সঠিকতা অর্জন করেছিল: -![ইমেজনেট স্তর](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.bn.jpg) +![ইমেজনেট স্তর](../../../../../translated_images/bn/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ইমেজনেট পিরামিড](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.bn.jpg) +![ইমেজনেট পিরামিড](../../../../../translated_images/bn/vgg-16-arch.64ff2137f50dd49f.jpg) > ছবি: [রিসার্চগেট](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e28d91f9..cd14c714 100644 --- a/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/bn/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: আমরা [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ব্যবহার করব, যেখানে ৩৭টি ভিন্ন জাতের কুকুর এবং বিড়ালের ছবি রয়েছে। -![আমরা যে ডেটাসেট নিয়ে কাজ করব](../../../../../../translated_images/data.50b2a9d5484bdbf0.bn.png) +![আমরা যে ডেটাসেট নিয়ে কাজ করব](../../../../../../translated_images/bn/data.50b2a9d5484bdbf0.png) ডেটাসেট ডাউনলোড করতে, এই কোড স্নিপেটটি ব্যবহার করুন: diff --git a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b08a85d7..b6b13775 100644 --- a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "আদর্শ বিড়ালকে কল্পনা করার জন্য, আমরা একটি এলোমেলো শব্দযুক্ত ছবি দিয়ে শুরু করব এবং গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন কৌশল ব্যবহার করে ছবিটি সামঞ্জস্য করার চেষ্টা করব, যাতে নেটওয়ার্কটি বিড়ালকে চিনতে পারে।\n", "\n", - "![অপ্টিমাইজেশন লুপ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.bn.png)\n", + "![অপ্টিমাইজেশন লুপ](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "এখানে আমাদের শুরুর ছবি:\n" ] diff --git a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md index c62a1a00..991f7b7b 100644 --- a/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/bn/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras এবং PyTorch-এ কিছু সাধারণ আর্কিট এখানে VGG-16 নেটওয়ার্ক দ্বারা একটি বিড়ালের ছবির থেকে বের করা বৈশিষ্ট্যের উদাহরণ দেওয়া হয়েছে: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.bn.png) +![Features extracted by VGG-16](../../../../../translated_images/bn/features.6291f9c7ba3a0b95.png) ## বিড়াল বনাম কুকুর ডেটাসেট @@ -48,19 +48,19 @@ Keras এবং PyTorch-এ কিছু সাধারণ আর্কিট একটি পদ্ধতি হলো একটি র্যান্ডম ইমেজ দিয়ে শুরু করা এবং তারপর **গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন** পদ্ধতি ব্যবহার করে সেই ইমেজটি এমনভাবে পরিবর্তন করা যাতে নেটওয়ার্কটি মনে করে এটি একটি বিড়াল। -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.bn.png) +![Image Optimization Loop](../../../../../translated_images/bn/ideal-cat-loop.999fbb8ff306e044.png) তবে, যদি আমরা এটি করি, তাহলে আমরা এমন কিছু পাব যা র্যান্ডম নয়েজের মতো দেখায়। কারণ *নেটওয়ার্ককে মনে করানোর অনেক উপায় আছে যে ইনপুট ইমেজটি একটি বিড়াল*, যার মধ্যে কিছু ভিজুয়ালি অর্থপূর্ণ নয়। যদিও এই ইমেজগুলোতে বিড়ালের জন্য সাধারণ প্যাটার্ন থাকে, তবুও এগুলোকে ভিজুয়ালি আলাদা করার জন্য কিছু নেই। ফলাফল উন্নত করতে, আমরা লস ফাংশনে একটি নতুন টার্ম যোগ করতে পারি, যাকে **ভ্যারিয়েশন লস** বলা হয়। এটি একটি মেট্রিক যা দেখায় ইমেজের প্রতিবেশী পিক্সেলগুলো কতটা মিল। ভ্যারিয়েশন লস মিনিমাইজ করলে ইমেজটি মসৃণ হয় এবং নয়েজ দূর হয় - ফলে আরও ভিজুয়ালি আকর্ষণীয় প্যাটার্ন প্রকাশ পায়। এখানে এমন "আদর্শ" ইমেজের উদাহরণ দেওয়া হয়েছে, যা বিড়াল এবং জেব্রা হিসেবে উচ্চ সম্ভাবনায় শ্রেণীবদ্ধ করা হয়েছে: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.bn.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.bn.png) +![Ideal Cat](../../../../../translated_images/bn/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/bn/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *আদর্শ বিড়াল* | *আদর্শ জেব্রা* একই পদ্ধতি ব্যবহার করে তথাকথিত **অ্যাডভার্সিয়াল আক্রমণ** করা যেতে পারে নিউরাল নেটওয়ার্কে। ধরুন আমরা একটি কুকুরকে বিড়াল হিসেবে দেখাতে চাই। যদি আমরা কুকুরের একটি ছবি নিই, যা নেটওয়ার্ক দ্বারা কুকুর হিসেবে স্বীকৃত, আমরা তারপর এটি সামান্য পরিবর্তন করতে পারি গ্রেডিয়েন্ট ডিসেন্ট অপ্টিমাইজেশন ব্যবহার করে, যতক্ষণ না নেটওয়ার্ক এটি বিড়াল হিসেবে শ্রেণীবদ্ধ করে: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.bn.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.bn.png) +![Picture of a Dog](../../../../../translated_images/bn/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/bn/adversarial-dog.d9fc7773b0142b89.png) -----|----- *কুকুরের আসল ছবি* | *কুকুরের ছবি যা বিড়াল হিসেবে শ্রেণীবদ্ধ* diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 98b49eb2..b7ebef7e 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "যেহেতু আমরা অটোএনকোডারকে মূল ছবির যতটা সম্ভব তথ্য ধারণ করতে প্রশিক্ষণ দিচ্ছি সঠিক পুনর্গঠনের জন্য, নেটওয়ার্কটি ইনপুট ছবিগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে অর্থ ধারণ করার জন্য।\n", "\n", - "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bn.jpg)\n", + "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে\n", "\n", diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9a7f2e69..0f8f50e1 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "যেহেতু আমরা অটোএনকোডারকে মূল ছবির যতটা সম্ভব তথ্য ধারণ করতে প্রশিক্ষণ দিচ্ছি সঠিক পুনর্গঠনের জন্য, নেটওয়ার্কটি ইনপুট ছবিগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে অর্থ ধারণ করার জন্য।\n", "\n", - "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bn.jpg)\n", + "![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে*\n", "\n", diff --git a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md index c431d6fc..723b706e 100644 --- a/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/bn/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN প্রশিক্ষণের সময় একটি বড় সম আমরা যখন অটোএনকোডার প্রশিক্ষণ দিই, তখন এটি মূল ইমেজ থেকে যতটা সম্ভব তথ্য ধারণ করার চেষ্টা করে, যাতে সঠিকভাবে পুনর্গঠন করা যায়। নেটওয়ার্কটি ইনপুট ইমেজগুলোর সেরা **এম্বেডিং** খুঁজে বের করার চেষ্টা করে, যা অর্থপূর্ণ তথ্য ধারণ করে। -![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.bn.jpg) +![অটোএনকোডার ডায়াগ্রাম](../../../../../translated_images/bn/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ছবি [Keras ব্লগ](https://blog.keras.io/building-autoencoders-in-keras.html) থেকে diff --git a/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md index 22e80b9b..f92a5579 100644 --- a/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/bn/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [পূর্ব-লেকচার কুইজ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![অবজেক্ট ডিটেকশন](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.bn.png) +![অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ছবি [YOLO v2 ওয়েবসাইট](https://pjreddie.com/darknet/yolov2/) থেকে নেওয়া @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. প্রতিটি টাইলের উপর ইমেজ ক্লাসিফিকেশন চালান। 3. যেসব টাইল যথেষ্ট উচ্চ অ্যাক্টিভেশন দেখায়, সেগুলোতে কাঙ্ক্ষিত বস্তুটি রয়েছে বলে বিবেচনা করুন। -![সাধারণ অবজেক্ট ডিটেকশন](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.bn.png) +![সাধারণ অবজেক্ট ডিটেকশন](../../../../../translated_images/bn/naive-detection.e7f1ba220ccd08c6.png) > *ছবি [এক্সারসাইজ নোটবুক](ObjectDetection-TF.ipynb) থেকে নেওয়া* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - ২০টি ক্লাস * [COCO](http://cocodataset.org/#home) - সাধারণ বস্তুসমূহের প্রসঙ্গ। ৮০টি ক্লাস, বাউন্ডিং বক্স এবং সেগমেন্টেশন মাস্ক। -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.bn.jpg) +![COCO](../../../../../translated_images/bn/coco-examples.71bc60380fa6cceb.jpg) ## অবজেক্ট ডিটেকশন মেট্রিকস @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: যেখানে ইমেজ ক্লাসিফিকেশনের ক্ষেত্রে অ্যালগরিদম কতটা ভালো কাজ করছে তা পরিমাপ করা সহজ, অবজেক্ট ডিটেকশনের ক্ষেত্রে আমাদের ক্লাসের সঠিকতা এবং অনুমিত বাউন্ডিং বক্সের অবস্থানের নির্ভুলতা উভয়ই পরিমাপ করতে হবে। এর জন্য আমরা **ইন্টারসেকশন ওভার ইউনিয়ন** (IoU) ব্যবহার করি, যা দুটি বক্স (বা দুটি এলাকা) কতটা ওভারল্যাপ করছে তা পরিমাপ করে। -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.bn.png) +![IoU](../../../../../translated_images/bn/iou_equation.9a4751d40fff4e11.png) > *[এই চমৎকার ব্লগ পোস্ট](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) থেকে ফিগার ২* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ব্যবহার করে ROI অঞ্চলের একটি হায়ারারকিকাল স্ট্রাকচার তৈরি করে, যা পরে CNN ফিচার এক্সট্রাক্টর এবং SVM-ক্লাসিফায়ারগুলোর মাধ্যমে বস্তু ক্লাস নির্ধারণ করে এবং লিনিয়ার রিগ্রেশন ব্যবহার করে *বাউন্ডিং বক্স* এর কোঅর্ডিনেট নির্ধারণ করে। [অফিশিয়াল পেপার](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.bn.png) +![RCNN](../../../../../translated_images/bn/rcnn1.cae407020dfb1d1f.png) > *ছবি van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.bn.png) +![RCNN-1](../../../../../translated_images/bn/rcnn2.2d9530bb83516484.png) > *ছবি [এই ব্লগ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) থেকে নেওয়া* @@ -110,7 +110,7 @@ $$ এই পদ্ধতি R-CNN এর মতোই, তবে রিজনগুলো কনভোলিউশন লেয়ার প্রয়োগ করার পরে নির্ধারণ করা হয়। -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.bn.png) +![FRCNN](../../../../../translated_images/bn/f-rcnn.3cda6d9bb4188875.png) > ছবি [অফিশিয়াল পেপার](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), ২০১৫ @@ -118,7 +118,7 @@ $$ এই পদ্ধতির মূল ধারণাটি হল রিজন প্রেডিক্ট করতে নিউরাল নেটওয়ার্ক ব্যবহার করা - যাকে *রিজন প্রপোজাল নেটওয়ার্ক* বলা হয়। [পেপার](https://arxiv.org/pdf/1506.01497.pdf), ২০১৬ -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.bn.png) +![FasterRCNN](../../../../../translated_images/bn/faster-rcnn.8d46c099b87ef30a.png) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/pdf/1506.01497.pdf) থেকে নেওয়া @@ -130,7 +130,7 @@ $$ 2. ফিচারগুলো **পজিশন-সেনসিটিভ স্কোর ম্যাপ** দ্বারা প্রক্রিয়াকৃত হয়। $C$ ক্লাসের প্রতিটি অবজেক্টকে $k\times k$ অঞ্চলে ভাগ করা হয় এবং আমরা অবজেক্টের অংশগুলো প্রেডিক্ট করতে প্রশিক্ষণ দিই। 3. $k\times k$ অঞ্চলের প্রতিটি অংশের জন্য সমস্ত নেটওয়ার্ক অবজেক্ট ক্লাসের জন্য ভোট দেয় এবং সর্বাধিক ভোট পাওয়া অবজেক্ট ক্লাসটি নির্বাচিত হয়। -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.bn.png) +![r-fcn image](../../../../../translated_images/bn/r-fcn.13eb88158b99a3da.png) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/abs/1605.06409) থেকে নেওয়া @@ -141,7 +141,7 @@ YOLO একটি রিয়েলটাইম ওয়ান-পাস অ * ছবিটিকে $S\times S$ অঞ্চলে ভাগ করা হয়। * প্রতিটি অঞ্চলের জন্য, **CNN** $n$ সম্ভাব্য অবজেক্ট, *বাউন্ডিং বক্স* এর কোঅর্ডিনেট এবং *কনফিডেন্স*=*প্রোবাবিলিটি* * IoU প্রেডিক্ট করে। - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.bn.png) + ![YOLO](../../../../../translated_images/bn/yolo.a2648ec82ee8bb4e.png) > ছবি [অফিশিয়াল পেপার](https://arxiv.org/abs/1506.02640) থেকে নেওয়া diff --git a/translations/bn/lessons/4-ComputerVision/README.md b/translations/bn/lessons/4-ComputerVision/README.md index 246f9b5d..70dca50d 100644 --- a/translations/bn/lessons/4-ComputerVision/README.md +++ b/translations/bn/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # কম্পিউটার ভিশন -![কম্পিউটার ভিশন বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/ai-computervision.6506ebebac3fbf76.bn.png) +![কম্পিউটার ভিশন বিষয়বস্তুর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-computervision.6506ebebac3fbf76.png) এই অংশে আমরা শিখব: diff --git a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index db2302d8..77d6c4c9 100644 --- a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**শব্দের ব্যাগ** (BoW) ভেক্টর উপস্থাপনাটি সবচেয়ে বেশি ব্যবহৃত ঐতিহ্যবাহী ভেক্টর উপস্থাপনাগুলোর একটি। প্রতিটি শব্দ একটি ভেক্টর সূচকের সাথে যুক্ত থাকে, এবং ভেক্টরের উপাদানটি একটি নির্দিষ্ট ডকুমেন্টে সেই শব্দটির উপস্থিতির সংখ্যা ধারণ করে।\n", "\n", - "![একটি শব্দের ব্যাগ ভেক্টর উপস্থাপনাটি মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.bn.png) \n", + "![একটি শব্দের ব্যাগ ভেক্টর উপস্থাপনাটি মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-কে আপনি টেক্সটের পৃথক শব্দগুলোর জন্য এক-হট-এনকোডেড ভেক্টরগুলোর যোগফল হিসেবেও ভাবতে পারেন।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 2a43f388..9bb52a12 100644 --- a/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/bn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ব্যাগ-অফ-ওয়ার্ডস** (BoW) ভেক্টর উপস্থাপন হল সবচেয়ে সহজে বোঝা যায় এমন ঐতিহ্যবাহী ভেক্টর উপস্থাপন। প্রতিটি শব্দ একটি ভেক্টর সূচকের সাথে যুক্ত থাকে, এবং একটি ভেক্টর উপাদান একটি নির্দিষ্ট ডকুমেন্টে প্রতিটি শব্দের উপস্থিতির সংখ্যা ধারণ করে।\n", "\n", - "![ব্যাগ-অফ-ওয়ার্ডস ভেক্টর উপস্থাপন মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.bn.png) \n", + "![ব্যাগ-অফ-ওয়ার্ডস ভেক্টর উপস্থাপন মেমোরিতে কীভাবে উপস্থাপিত হয় তা দেখানো একটি চিত্র।](../../../../../translated_images/bn/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW কে আপনি টেক্সটের পৃথক শব্দগুলোর জন্য সমস্ত এক-হট-এনকোডেড ভেক্টরের যোগফল হিসেবেও ভাবতে পারেন।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 46f01561..27dc8c69 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "আমাদের নেটওয়ার্কে প্রথম লেয়ার হিসেবে এমবেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এমবেডিং ব্যাগ** মডেলে পরিবর্তন করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এমবেডিংয়ে রূপান্তর করি এবং তারপর সেই সমস্ত এমবেডিংয়ের উপর কিছু সামগ্রিক ফাংশন গণনা করি, যেমন `sum`, `average` বা `max`।\n", "\n", - "![পাঁচটি ক্রমের শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bn.png)\n", + "![পাঁচটি ক্রমের শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "আমাদের ক্লাসিফায়ার নিউরাল নেটওয়ার্কটি এমবেডিং লেয়ার দিয়ে শুরু হবে, তারপর অ্যাগ্রিগেশন লেয়ার, এবং তার উপরে একটি লিনিয়ার ক্লাসিফায়ার:\n" ] @@ -176,7 +176,7 @@ "\n", "পূর্ববর্তী আর্কিটেকচারে, সমস্ত সিকোয়েন্সকে একই দৈর্ঘ্যে প্যাড করতে হতো যাতে সেগুলোকে একটি মিনিব্যাচে ফিট করানো যায়। এটি ভেরিয়েবল দৈর্ঘ্যের সিকোয়েন্স উপস্থাপনার সবচেয়ে কার্যকর পদ্ধতি নয় - আরেকটি পদ্ধতি হতে পারে **অফসেট** ভেক্টর ব্যবহার করা, যা একটি বড় ভেক্টরে সংরক্ষিত সমস্ত সিকোয়েন্সের অফসেট ধারণ করবে।\n", "\n", - "![অফসেট সিকোয়েন্স উপস্থাপনা দেখানো একটি চিত্র](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.bn.png)\n", + "![অফসেট সিকোয়েন্স উপস্থাপনা দেখানো একটি চিত্র](../../../../../translated_images/bn/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: উপরের ছবিতে, আমরা একটি ক্যারেক্টারের সিকোয়েন্স দেখিয়েছি, কিন্তু আমাদের উদাহরণে আমরা শব্দের সিকোয়েন্স নিয়ে কাজ করছি। তবে, অফসেট ভেক্টর দিয়ে সিকোয়েন্স উপস্থাপনার সাধারণ নীতিটি একই থাকে।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW দ্রুততর, তবে স্কিপ-গ্রাম ধীর হলেও কম ঘন ঘন ব্যবহৃত শব্দগুলোর উপস্থাপনা করতে ভালো কাজ করে।\n", "\n", - "![CBoW এবং Skip-Gram অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ চিত্র।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bn.png)\n", + "![CBoW এবং Skip-Gram অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ডেটাসেটে প্রি-ট্রেন করা word2vec এম্বেডিং নিয়ে পরীক্ষা করার জন্য, আমরা **gensim** লাইব্রেরি ব্যবহার করতে পারি। নিচে 'neural' শব্দটির সাথে সবচেয়ে মিল থাকা শব্দগুলো খুঁজে বের করা হয়েছে:\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2874dc40..4747d3c1 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/bn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "আমাদের নেটওয়ার্কে প্রথম লেয়ার হিসেবে একটি এম্বেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এম্বেডিং ব্যাগ** মডেলে স্যুইচ করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এম্বেডিং-এ রূপান্তর করি এবং তারপর সেই এম্বেডিংগুলোর উপর একটি সমষ্টিগত ফাংশন (যেমন `sum`, `average` বা `max`) গণনা করি।\n", "\n", - "![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এম্বেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bn.png)\n", + "![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এম্বেডিং ক্লাসিফায়ার দেখানো চিত্র।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "আমাদের ক্লাসিফায়ার নিউরাল নেটওয়ার্কে নিম্নলিখিত লেয়ারগুলো অন্তর্ভুক্ত রয়েছে:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW দ্রুততর, এবং স্কিপ-গ্রাম ধীর হলেও এটি কম ঘন ঘন ব্যবহৃত শব্দগুলোর উপস্থাপনা আরও ভালোভাবে করে।\n", "\n", - "![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার একটি চিত্র।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bn.png)\n", + "![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার একটি চিত্র।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "গুগল নিউজ ডেটাসেটে প্রি-ট্রেইন করা Word2Vec এম্বেডিং নিয়ে পরীক্ষা করার জন্য, আমরা **gensim** লাইব্রেরি ব্যবহার করতে পারি। নিচে আমরা 'neural' শব্দটির সাথে সবচেয়ে সাদৃশ্যপূর্ণ শব্দগুলো খুঁজে বের করব।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/14-Embeddings/README.md b/translations/bn/lessons/5-NLP/14-Embeddings/README.md index af5d32c0..4764b215 100644 --- a/translations/bn/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/bn/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW বা TF/IDF ভিত্তিক ক্লাসিফায়ার প আমাদের ক্লাসিফায়ার নেটওয়ার্কে প্রথম লেয়ার হিসেবে একটি এমবেডিং লেয়ার ব্যবহার করে, আমরা ব্যাগ-অফ-ওয়ার্ডস থেকে **এমবেডিং ব্যাগ** মডেলে পরিবর্তন করতে পারি, যেখানে আমরা প্রথমে আমাদের টেক্সটের প্রতিটি শব্দকে সংশ্লিষ্ট এমবেডিংয়ে রূপান্তর করি এবং তারপর সেই এমবেডিংগুলোর উপর কিছু অ্যাগ্রিগেট ফাংশন গণনা করি, যেমন `sum`, `average` বা `max`। -![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো হয়েছে।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.bn.png) +![পাঁচটি সিকোয়েন্স শব্দের জন্য একটি এমবেডিং ক্লাসিফায়ার দেখানো হয়েছে।](../../../../../translated_images/bn/embedding-classifier-example.b77f021a7ee67eee.png) > লেখকের তৈরি চিত্র @@ -40,7 +40,7 @@ BoW বা TF/IDF ভিত্তিক ক্লাসিফায়ার প CBoW দ্রুততর, তবে স্কিপ-গ্রাম ধীরতর, কিন্তু কম ঘন ঘন ব্যবহৃত শব্দগুলোকে ভালোভাবে রিপ্রেজেন্ট করে। -![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ দেখানো হয়েছে।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bn.png) +![CBoW এবং স্কিপ-গ্রাম অ্যালগরিদমের মাধ্যমে শব্দগুলোকে ভেক্টরে রূপান্তর করার উদাহরণ দেখানো হয়েছে।](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [এই পেপার](https://arxiv.org/pdf/1301.3781.pdf) থেকে নেওয়া চিত্র diff --git a/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md b/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md index 1716ed36..d3d48e31 100644 --- a/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/bn/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **কন্টিনিউয়াস ব্যাগ-অফ-ওয়ার্ডস** (CBoW), যেখানে আমরা একটি টোকেন সিকোয়েন্স $W_{-N}$, ..., $W_N$ এর মধ্যে $W_0$ টোকেন পূর্বানুমান করি। * **স্কিপ-গ্রাম**, যেখানে আমরা মধ্যবর্তী টোকেন $W_0$ থেকে পার্শ্ববর্তী টোকেনগুলোর সেট {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} পূর্বানুমান করি। -![শব্দকে ভেক্টরে রূপান্তর করার পেপারের ছবি](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.bn.png) +![শব্দকে ভেক্টরে রূপান্তর করার পেপারের ছবি](../../../../../translated_images/bn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ছবি [এই পেপার](https://arxiv.org/pdf/1301.3781.pdf) থেকে diff --git a/translations/bn/lessons/5-NLP/16-RNN/README.md b/translations/bn/lessons/5-NLP/16-RNN/README.md index ef83c82e..c7e57c74 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/README.md +++ b/translations/bn/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: টেক্সট সিকোয়েন্সের অর্থ ধারণ করতে, আমাদের আরেকটি নিউরাল নেটওয়ার্ক আর্কিটেকচার ব্যবহার করতে হবে, যেটিকে **রিকারেন্ট নিউরাল নেটওয়ার্ক** বা RNN বলা হয়। RNN-এ, আমরা আমাদের বাক্যটি নেটওয়ার্কের মাধ্যমে একবারে একটি প্রতীক পাঠাই, এবং নেটওয়ার্ক কিছু **স্টেট** তৈরি করে, যা আমরা পরবর্তী প্রতীকের সাথে আবার নেটওয়ার্কে পাঠাই। -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.bn.png) +![RNN](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.png) > লেখকের তৈরি ছবি @@ -61,7 +61,7 @@ LSTM নেটওয়ার্ক RNN-এর মতোই সংগঠিত, একটি রিকারেন্ট নেটওয়ার্ক, একদিকে বা বাইডিরেকশনাল, একটি সিকোয়েন্সের নির্দিষ্ট প্যাটার্নগুলো ধারণ করে এবং সেগুলোকে স্টেট ভেক্টরে সংরক্ষণ করতে পারে বা আউটপুটে পাস করতে পারে। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম লেয়ার দ্বারা এক্সট্র্যাক্ট করা নিম্ন-স্তরের প্যাটার্ন থেকে উচ্চ-স্তরের প্যাটার্ন ক্যাপচার করতে প্রথম লেয়ারের উপরে আরেকটি রিকারেন্ট লেয়ার তৈরি করতে পারি। এটি আমাদের **মাল্টিলেয়ার RNN** ধারণায় নিয়ে যায়, যা দুটি বা তার বেশি রিকারেন্ট নেটওয়ার্ক নিয়ে গঠিত, যেখানে পূর্ববর্তী লেয়ারের আউটপুট পরবর্তী লেয়ারের ইনপুট হিসেবে পাঠানো হয়। -![মাল্টিলেয়ার LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bn.jpg) +![মাল্টিলেয়ার LSTM RNN](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg) *ছবি [এই অসাধারণ পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে ফার্নান্দো লোপেজের দ্বারা* diff --git a/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 1f251696..9589eb3d 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "পুনরাবৃত্ত নেটওয়ার্ক, একমুখী বা দ্বিমুখী, একটি সিকোয়েন্সের নির্দিষ্ট প্যাটার্নগুলো ধারণ করে এবং সেগুলো স্টেট ভেক্টরে সংরক্ষণ করতে পারে বা আউটপুটে পাস করতে পারে। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম স্তরের উপর আরেকটি পুনরাবৃত্ত স্তর তৈরি করতে পারি, যা নিম্ন-স্তরের প্যাটার্ন থেকে উচ্চ-স্তরের প্যাটার্ন ধারণ করে। এটি আমাদের **বহুস্তর RNN** ধারণার দিকে নিয়ে যায়, যা দুই বা তার বেশি পুনরাবৃত্ত নেটওয়ার্ক নিয়ে গঠিত, যেখানে পূর্ববর্তী স্তরের আউটপুট পরবর্তী স্তরের ইনপুট হিসেবে পাস করা হয়।\n", "\n", - "![একটি বহুস্তর দীর্ঘ-স্বল্প-মেয়াদী-মেমরি RNN দেখানো চিত্র](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bn.jpg)\n", + "![একটি বহুস্তর দীর্ঘ-স্বল্প-মেয়াদী-মেমরি RNN দেখানো চিত্র](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ছবি [এই অসাধারণ পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে, লেখক Fernando López*\n", "\n", diff --git a/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb index 385eb476..993a075d 100644 --- a/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/bn/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "একটি টেক্সট সিকোয়েন্সের অর্থ ধরতে, আমরা **পুনরাবৃত্তি নিউরাল নেটওয়ার্ক** বা RNN নামে একটি নিউরাল নেটওয়ার্ক আর্কিটেকচার ব্যবহার করব। RNN ব্যবহার করার সময়, আমরা আমাদের বাক্যটি নেটওয়ার্কের মধ্য দিয়ে একবারে একটি টোকেন পাঠাই, এবং নেটওয়ার্কটি কিছু **অবস্থা** তৈরি করে, যা আমরা পরবর্তী টোকেনের সাথে আবার নেটওয়ার্কে পাঠাই।\n", "\n", - "![একটি উদাহরণ পুনরাবৃত্তি নিউরাল নেটওয়ার্ক জেনারেশন দেখানো চিত্র।](../../../../../translated_images/rnn.27f5c29c53d727b5.bn.png)\n", + "![একটি উদাহরণ পুনরাবৃত্তি নিউরাল নেটওয়ার্ক জেনারেশন দেখানো চিত্র।](../../../../../translated_images/bn/rnn.27f5c29c53d727b5.png)\n", "\n", "টোকেনগুলির ইনপুট সিকোয়েন্স $X_0,\\dots,X_n$ দেওয়া হলে, RNN একটি নিউরাল নেটওয়ার্ক ব্লকের সিকোয়েন্স তৈরি করে এবং ব্যাকপ্রোপাগেশনের মাধ্যমে এই সিকোয়েন্সটি এন্ড-টু-এন্ড প্রশিক্ষণ দেয়। প্রতিটি নেটওয়ার্ক ব্লক $(X_i,S_i)$ জোড়াকে ইনপুট হিসাবে নেয় এবং ফলাফল হিসাবে $S_{i+1}$ তৈরি করে। চূড়ান্ত অবস্থা $S_n$ বা আউটপুট $Y_n$ একটি লিনিয়ার ক্লাসিফায়ারে যায় ফলাফল তৈরি করতে। সমস্ত নেটওয়ার্ক ব্লক একই ওজন ভাগ করে এবং একটি ব্যাকপ্রোপাগেশন পাস ব্যবহার করে এন্ড-টু-এন্ড প্রশিক্ষণ দেওয়া হয়।\n", "\n", @@ -371,7 +371,7 @@ "\n", "পুনরাবৃত্ত নেটওয়ার্ক, একমুখী বা দ্বিমুখী, সিকোয়েন্সের মধ্যে প্যাটার্নগুলো ধরে এবং সেগুলোকে স্টেট ভেক্টরে সংরক্ষণ করে বা আউটপুট হিসেবে ফেরত দেয়। কনভোলিউশনাল নেটওয়ার্কের মতো, আমরা প্রথম স্তরের মাধ্যমে নিম্ন স্তরের প্যাটার্নগুলো থেকে উচ্চ স্তরের প্যাটার্ন ধরার জন্য আরেকটি পুনরাবৃত্ত স্তর তৈরি করতে পারি। এটি আমাদের **বহুস্তর RNN** ধারণার দিকে নিয়ে যায়, যা দুটি বা তার বেশি পুনরাবৃত্ত নেটওয়ার্ক নিয়ে গঠিত, যেখানে আগের স্তরের আউটপুট পরবর্তী স্তরের ইনপুট হিসেবে ব্যবহৃত হয়।\n", "\n", - "![একটি বহুস্তর দীর্ঘ-মেয়াদী-মেমরি RNN-এর ছবি](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.bn.jpg)\n", + "![একটি বহুস্তর দীর্ঘ-মেয়াদী-মেমরি RNN-এর ছবি](../../../../../translated_images/bn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ছবি [এই চমৎকার পোস্ট](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) থেকে, লেখক Fernando López।*\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 42168a5f..5774f286 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "আমরা RNN-কে টেক্সট জেনারেট করার জন্য যেভাবে প্রশিক্ষণ দেব তা হলো নিম্নরূপ। প্রতিটি ধাপে, আমরা `nchars` দৈর্ঘ্যের একটি অক্ষরের ক্রম নেব এবং নেটওয়ার্ককে প্রতিটি ইনপুট অক্ষরের জন্য পরবর্তী আউটপুট অক্ষর তৈরি করতে বলব:\n", "\n", - "![চিত্রটি 'HELLO' শব্দের একটি উদাহরণ RNN জেনারেশন দেখাচ্ছে।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bn.png)\n", + "![চিত্রটি 'HELLO' শব্দের একটি উদাহরণ RNN জেনারেশন দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png)\n", "\n", "বাস্তব পরিস্থিতির উপর নির্ভর করে, আমরা কিছু বিশেষ অক্ষর অন্তর্ভুক্ত করতে চাইতে পারি, যেমন *end-of-sequence* ``। আমাদের ক্ষেত্রে, আমরা শুধু নেটওয়ার্ককে অবিরাম টেক্সট জেনারেশনের জন্য প্রশিক্ষণ দিতে চাই, তাই আমরা প্রতিটি ক্রমের আকারকে `nchars` টোকেনের সমান স্থির করব। ফলস্বরূপ, প্রতিটি প্রশিক্ষণ উদাহরণে থাকবে `nchars` ইনপুট এবং `nchars` আউটপুট (যা ইনপুট ক্রম এক প্রতীক বামে সরানো)। মিনিব্যাচে এমন কয়েকটি ক্রম থাকবে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 91ac0b25..5beed615 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "আমরা যেভাবে RNN প্রশিক্ষণ করব যাতে এটি সংবাদ শিরোনাম তৈরি করতে পারে তা হলো নিম্নরূপ। প্রতিটি ধাপে, আমরা একটি শিরোনাম নেব, যা RNN-এ প্রবেশ করানো হবে, এবং প্রতিটি ইনপুট অক্ষরের জন্য আমরা নেটওয়ার্ককে পরবর্তী আউটপুট অক্ষর তৈরি করতে বলব:\n", "\n", - "![চিত্রটি 'HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখাচ্ছে।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bn.png)\n", + "![চিত্রটি 'HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখাচ্ছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png)\n", "\n", "আমাদের ক্রমের শেষ অক্ষরের জন্য, আমরা নেটওয়ার্ককে `` টোকেন তৈরি করতে বলব।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md index ff1332d8..dbb04134 100644 --- a/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/bn/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) এবং তাদের গেটেড স এটি বিভিন্ন নিউরাল আর্কিটেকচারের জন্য অনুমতি দেয়, যা নিচের ছবিতে দেখানো হয়েছে: -![RNN-এর সাধারণ প্যাটার্ন দেখানো একটি ছবি।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.bn.jpg) +![RNN-এর সাধারণ প্যাটার্ন দেখানো একটি ছবি।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > ছবি ব্লগ পোস্ট [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) থেকে [Andrej Karpaty](http://karpathy.github.io/) দ্বারা @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) এবং তাদের গেটেড স আমরা এই RNN-কে ধাপে ধাপে টেক্সট তৈরি করতে প্রশিক্ষণ দেব। প্রতিটি ধাপে, আমরা `nchars` দৈর্ঘ্যের একটি চরিত্রের সিকোয়েন্স গ্রহণ করব এবং নেটওয়ার্ককে প্রতিটি ইনপুট চরিত্রের জন্য পরবর্তী আউটপুট চরিত্র তৈরি করতে বলব: -!['HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখানো হয়েছে।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.bn.png) +!['HELLO' শব্দটি তৈরি করার একটি উদাহরণ RNN দেখানো হয়েছে।](../../../../../translated_images/bn/rnn-generate.56c54afb52f9781d.png) যখন টেক্সট তৈরি করা হয় (ইনফারেন্সের সময়), আমরা কিছু **প্রম্পট** দিয়ে শুরু করি, যা RNN সেলগুলোর মাধ্যমে পাস করে তার মধ্যবর্তী স্টেট তৈরি করে, এবং তারপর এই স্টেট থেকে জেনারেশন শুরু হয়। আমরা একবারে একটি চরিত্র তৈরি করি এবং স্টেট এবং তৈরি করা চরিত্রকে অন্য RNN সেলে পাস করি পরবর্তী চরিত্র তৈরি করার জন্য, যতক্ষণ না আমরা পর্যাপ্ত চরিত্র তৈরি করি। diff --git a/translations/bn/lessons/5-NLP/18-Transformers/README.md b/translations/bn/lessons/5-NLP/18-Transformers/README.md index 91344d9a..053a8a49 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/README.md +++ b/translations/bn/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি **অ্যাটেনশন মেকানিজম** RNN-এর আউটপুট প্রেডিকশনে প্রতিটি ইনপুট ভেক্টরের প্রসঙ্গগত প্রভাবকে ওজন করার একটি উপায় প্রদান করে। এটি বাস্তবায়িত হয় ইনপুট RNN এবং আউটপুট RNN-এর মধ্যবর্তী স্টেটগুলোর মধ্যে শর্টকাট তৈরি করে। এইভাবে, আউটপুট প্রতীক yt তৈরি করার সময়, আমরা সমস্ত ইনপুট হিডেন স্টেট hi বিবেচনা করব, বিভিন্ন ওজন সহগ αt,i সহ। -![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bn.png) +![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)-এ অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ এনকোডার-ডিকোডার মডেল, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে উদ্ধৃত। অ্যাটেনশন ম্যাট্রিক্স {αi,j} একটি আউটপুট সিকোয়েন্সের একটি নির্দিষ্ট শব্দ তৈরিতে ইনপুট শব্দগুলো কীভাবে ভূমিকা রাখে তা উপস্থাপন করবে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হয়েছে: -![Bahdanau - arviz.org থেকে নেওয়া RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bn.png) +![Bahdanau - arviz.org থেকে নেওয়া RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে চিত্র (Fig.3) @@ -66,7 +66,7 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি এরপর, আমাদের সিকোয়েন্সের মধ্যে কিছু প্যাটার্ন ধরতে হবে। এটি করতে, ট্রান্সফর্মারগুলো **সেলফ-অ্যাটেনশন** মেকানিজম ব্যবহার করে, যা মূলত ইনপুট এবং আউটপুট হিসেবে একই সিকোয়েন্সে অ্যাটেনশন প্রয়োগ। সেলফ-অ্যাটেনশন প্রয়োগ করে আমরা বাক্যের প্রসঙ্গ বিবেচনা করতে পারি এবং কোন শব্দগুলো আন্তঃসম্পর্কিত তা দেখতে পারি। উদাহরণস্বরূপ, এটি আমাদের *it* দ্বারা উল্লেখিত শব্দগুলো দেখতে এবং প্রসঙ্গ বিবেচনা করতে সাহায্য করে: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.bn.png) +![](../../../../../translated_images/bn/CoreferenceResolution.861924d6d384a7d6.png) > [গুগলের ব্লগ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) থেকে চিত্র। @@ -91,7 +91,7 @@ RNN ব্যবহার করে সিকোয়েন্স-টু-সি **BERT** (Bidirectional Encoder Representations from Transformers) একটি খুব বড় মাল্টি লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base*-এর জন্য 12টি লেয়ার এবং *BERT-large*-এর জন্য 24টি লেয়ার রয়েছে। মডেলটি প্রথমে একটি বড় টেক্সট ডেটা কর্পাস (WikiPedia + বই) ব্যবহার করে আনসুপারভাইজড প্রশিক্ষণের মাধ্যমে প্রি-ট্রেইন করা হয় (একটি বাক্যে মাস্ক করা শব্দগুলো প্রেডিক্ট করা)। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষা বোঝার স্তর অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াকে **ট্রান্সফার লার্নিং** বলা হয়। -![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bn.png) +![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > [উৎস](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index e62b8156..04236077 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/bn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**অ্যাটেনশন মেকানিজম** একটি পদ্ধতি প্রদান করে, যা প্রতিটি ইনপুট ভেক্টরের প্রাসঙ্গিক প্রভাবকে RNN-এর প্রতিটি আউটপুট প্রেডিকশনে ওজন দেয়। এটি বাস্তবায়িত হয় ইনপুট RNN-এর মধ্যবর্তী স্টেট এবং আউটপুট RNN-এর মধ্যে শর্টকাট তৈরি করে। এই পদ্ধতিতে, যখন আউটপুট প্রতীক $y_t$ তৈরি করা হয়, তখন আমরা সব ইনপুট হিডেন স্টেট $h_i$-কে বিভিন্ন ওজন সহগ $\\alpha_{t,i}$ সহ বিবেচনা করব।\n", "\n", - "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bn.png)\n", + "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে উদ্ধৃত, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে নেওয়া এনকোডার-ডিকোডার মডেল অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ]*\n", "\n", "অ্যাটেনশন ম্যাট্রিক্স $\\{\\alpha_{i,j}\\}$ নির্দেশ করে যে নির্দিষ্ট ইনপুট শব্দগুলো আউটপুট সিকোয়েন্সের একটি নির্দিষ্ট শব্দ তৈরিতে কতটা ভূমিকা রাখে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হলো:\n", "\n", - "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bn.png)\n", + "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে নেওয়া (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) একটি খুব বড় মাল্টি-লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base*-এর জন্য ১২টি লেয়ার এবং *BERT-large*-এর জন্য ২৪টি লেয়ার রয়েছে। মডেলটি প্রথমে একটি বড় টেক্সট ডেটাসেট (উইকিপিডিয়া + বই) ব্যবহার করে আনসুপারভাইজড প্রশিক্ষণের মাধ্যমে প্রি-ট্রেইন করা হয় (একটি বাক্যে মাস্ক করা শব্দগুলো প্রেডিক্ট করা)। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষাগত বোঝাপড়া অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াটিকে **ট্রান্সফার লার্নিং** বলা হয়।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ থেকে নেওয়া ছবি](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bn.png)\n", + "![http://jalammar.github.io/illustrated-bert/ থেকে নেওয়া ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ট্রান্সফর্মার আর্কিটেকচারের অনেক ভেরিয়েশন রয়েছে, যেমন BERT, DistilBERT, BigBird, OpenGPT3 এবং আরও অনেক, যেগুলো ফাইন টিউন করা যায়। [HuggingFace প্যাকেজ](https://github.com/huggingface/) PyTorch ব্যবহার করে এই আর্কিটেকচারগুলোর অনেকগুলোর প্রশিক্ষণের জন্য একটি রিপোজিটরি প্রদান করে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 29ce6ce5..3b23e1b3 100644 --- a/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/bn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**অ্যাটেনশন মেকানিজম** RNN-এর প্রতিটি আউটপুট প্রেডিকশনে প্রতিটি ইনপুট ভেক্টরের প্রাসঙ্গিক প্রভাবকে ওজন দেওয়ার একটি উপায় প্রদান করে। এটি বাস্তবায়িত হয় ইনপুট RNN-এর মধ্যবর্তী স্টেট এবং আউটপুট RNN-এর মধ্যে শর্টকাট তৈরি করে। এই পদ্ধতিতে, যখন আউটপুট প্রতীক $y_t$ তৈরি করা হয়, তখন আমরা বিভিন্ন ওজন সহগ $\\alpha_{t,i}$ সহ সমস্ত ইনপুট হিডেন স্টেট $h_i$ বিবেচনায় নেব। \n", "\n", - "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.bn.png)\n", + "![এনকোডার/ডিকোডার মডেল একটি অ্যাডিটিভ অ্যাটেনশন লেয়ার সহ](../../../../../translated_images/bn/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে উদ্ধৃত, [এই ব্লগ পোস্ট](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) থেকে নেওয়া এনকোডার-ডিকোডার মডেল অ্যাডিটিভ অ্যাটেনশন মেকানিজম সহ*\n", "\n", "অ্যাটেনশন ম্যাট্রিক্স $\\{\\alpha_{i,j}\\}$ একটি নির্দিষ্ট আউটপুট সিকোয়েন্সের একটি শব্দ তৈরিতে কোন ইনপুট শব্দগুলো কতটা ভূমিকা রাখছে তা উপস্থাপন করে। নিচে এমন একটি ম্যাট্রিক্সের উদাহরণ দেওয়া হলো:\n", "\n", - "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.bn.png)\n", + "![RNNsearch-50 দ্বারা পাওয়া একটি নমুনা অ্যালাইনমেন্ট, Bahdanau - arviz.org থেকে নেওয়া](../../../../../translated_images/bn/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) থেকে নেওয়া (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) একটি অত্যন্ত বড় মাল্টি-লেয়ার ট্রান্সফর্মার নেটওয়ার্ক, যেখানে *BERT-base* এর জন্য ১২টি স্তর এবং *BERT-large* এর জন্য ২৪টি স্তর রয়েছে। এই মডেলটি প্রথমে বিশাল পরিমাণ টেক্সট ডেটা (উইকিপিডিয়া + বই) ব্যবহার করে অ-পর্যবেক্ষণমূলক প্রশিক্ষণের মাধ্যমে (একটি বাক্যে মাস্ক করা শব্দ অনুমান করা) প্রি-ট্রেইন করা হয়। প্রি-ট্রেইনিংয়ের সময় মডেলটি উল্লেখযোগ্য ভাষাগত বোঝাপড়া অর্জন করে, যা পরে অন্যান্য ডেটাসেটের সাথে ফাইন টিউনিংয়ের মাধ্যমে ব্যবহার করা যায়। এই প্রক্রিয়াকে **ট্রান্সফার লার্নিং** বলা হয়।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.bn.png)\n", + "![http://jalammar.github.io/illustrated-bert/ থেকে ছবি](../../../../../translated_images/bn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ট্রান্সফর্মার আর্কিটেকচারের অনেক বৈচিত্র্য রয়েছে, যেমন BERT, DistilBERT, BigBird, OpenGPT3 এবং আরও অনেক কিছু, যেগুলো ফাইন টিউন করা যেতে পারে।\n", "\n", diff --git a/translations/bn/lessons/5-NLP/19-NER/README.md b/translations/bn/lessons/5-NLP/19-NER/README.md index 9ce5ebe4..053d7238 100644 --- a/translations/bn/lessons/5-NLP/19-NER/README.md +++ b/translations/bn/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O যেহেতু আমাদের টোকেন এবং শ্রেণীগুলোর মধ্যে এক-একটি সম্পর্ক তৈরি করতে হবে, আমরা এই চিত্র থেকে একটি ডানদিকের **অনেক-থেকে-অনেক** নিউরাল নেটওয়ার্ক মডেল প্রশিক্ষণ দিতে পারি: -![সাধারণ পুনরাবৃত্ত নিউরাল নেটওয়ার্ক প্যাটার্ন দেখানো চিত্র।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.bn.jpg) +![সাধারণ পুনরাবৃত্ত নিউরাল নেটওয়ার্ক প্যাটার্ন দেখানো চিত্র।](../../../../../translated_images/bn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *[এই ব্লগ পোস্ট](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) থেকে চিত্র, লেখক [Andrej Karpathy](http://karpathy.github.io/)। NER টোকেন শ্রেণীবিন্যাস মডেল এই চিত্রের ডানদিকের নেটওয়ার্ক আর্কিটেকচারের সাথে মিলে যায়।* diff --git a/translations/bn/lessons/5-NLP/README.md b/translations/bn/lessons/5-NLP/README.md index 381038e0..325fc92b 100644 --- a/translations/bn/lessons/5-NLP/README.md +++ b/translations/bn/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # প্রাকৃতিক ভাষা প্রক্রিয়াকরণ -![NLP কাজগুলোর সারাংশ একটি ডুডলে](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.bn.png) +![NLP কাজগুলোর সারাংশ একটি ডুডলে](../../../../translated_images/bn/ai-nlp.b22dcb8ca4707cea.png) এই অংশে, আমরা **প্রাকৃতিক ভাষা প্রক্রিয়াকরণ (NLP)** সম্পর্কিত কাজগুলো পরিচালনা করতে নিউরাল নেটওয়ার্ক ব্যবহার করার উপর মনোযোগ দেব। অনেক ধরনের NLP সমস্যা রয়েছে যা আমরা চাই কম্পিউটার সমাধান করতে সক্ষম হোক: diff --git a/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md b/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md index 76a78737..ef46b642 100644 --- a/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/bn/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo-এর একটি চমৎকার দিক হলো এটি এ মডেলটি খোলার পরে, আপনি NetLogo-এর প্রধান স্ক্রিনে নিয়ে যাওয়া হবে। এখানে একটি নমুনা মডেল রয়েছে যা সীমিত সম্পদ (ঘাস) দেওয়া হলে নেকড়ে এবং ভেড়ার জনসংখ্যা বর্ণনা করে। -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.bn.png) +![NetLogo Main Screen](../../../../../translated_images/bn/NetLogo-Main.32653711ec1a01b3.png) > দিমিত্রি সশনিকভের স্ক্রিনশট diff --git a/translations/bn/lessons/README.md b/translations/bn/lessons/README.md index 6635ec5d..ce2610e4 100644 --- a/translations/bn/lessons/README.md +++ b/translations/bn/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # সংক্ষিপ্ত বিবরণ -![ডুডলে সংক্ষিপ্ত বিবরণ](../../../translated_images/ai-overview.0857791951d19500.bn.png) +![ডুডলে সংক্ষিপ্ত বিবরণ](../../../translated_images/bn/ai-overview.0857791951d19500.png) > স্কেচনোট করেছেন [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/bn/lessons/X-Extras/X1-MultiModal/README.md b/translations/bn/lessons/X-Extras/X1-MultiModal/README.md index ecf2410f..ad18f694 100644 --- a/translations/bn/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/bn/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP-এর মূল ধারণা হলো টেক্সট প্রম্পটের সাথে একটি ইমেজ তুলনা করা এবং নির্ধারণ করা যে ইমেজটি প্রম্পটের সাথে কতটা সঙ্গতিপূর্ণ। -![CLIP আর্কিটেকচার](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.bn.png) +![CLIP আর্কিটেকচার](../../../../../translated_images/bn/clip-arch.b3dbf20b4e8ed8be.png) > *ছবি [এই ব্লগ পোস্ট](https://openai.com/blog/clip/) থেকে নেওয়া হয়েছে* @@ -31,7 +31,7 @@ CLIP মডেল/লাইব্রেরি [OpenAI GitHub](https://github.com ধরা যাক আমাদের ইমেজগুলোকে বিড়াল, কুকুর এবং মানুষের মধ্যে শ্রেণীবদ্ধ করতে হবে। এই ক্ষেত্রে, আমরা মডেলটিকে একটি ইমেজ এবং একটি সিরিজ টেক্সট প্রম্পট দিতে পারি: "*একটি বিড়ালের ছবি*", "*একটি কুকুরের ছবি*", "*একটি মানুষের ছবি*"। ফলাফল হিসেবে প্রাপ্ত ৩টি সম্ভাবনার ভেক্টরে আমরা সর্বোচ্চ মানের ইনডেক্সটি নির্বাচন করব। -![ইমেজ ক্লাসিফিকেশনের জন্য CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.bn.png) +![ইমেজ ক্লাসিফিকেশনের জন্য CLIP](../../../../../translated_images/bn/clip-class.3af42ef0b2b19369.png) > *ছবি [এই ব্লগ পোস্ট](https://openai.com/blog/clip/) থেকে নেওয়া হয়েছে* @@ -55,13 +55,13 @@ VQGAN সম্পর্কে আরও জানতে [Taming Transformers](h VQGAN এবং সাধারণ GAN-এর মধ্যে একটি গুরুত্বপূর্ণ পার্থক্য হলো, সাধারণ GAN যেকোনো ইনপুট ভেক্টর থেকে একটি ভালো ইমেজ তৈরি করতে পারে, কিন্তু VQGAN সম্ভবত একটি অসঙ্গতিপূর্ণ ইমেজ তৈরি করবে। তাই, ইমেজ তৈরির প্রক্রিয়াকে আরও নির্দেশনা দিতে হবে, এবং এটি CLIP ব্যবহার করে করা যেতে পারে। -![VQGAN+CLIP আর্কিটেকচার](../../../../../translated_images/vqgan.5027fe05051dfa31.bn.png) +![VQGAN+CLIP আর্কিটেকচার](../../../../../translated_images/bn/vqgan.5027fe05051dfa31.png) টেক্সট প্রম্পটের সাথে সঙ্গতিপূর্ণ একটি ইমেজ তৈরি করতে, আমরা কিছু র্যান্ডম এনকোডিং ভেক্টর দিয়ে শুরু করি যা VQGAN-এর মাধ্যমে একটি ইমেজ তৈরি করে। তারপর CLIP ব্যবহার করে একটি লস ফাংশন তৈরি করা হয় যা দেখায় ইমেজটি টেক্সট প্রম্পটের সাথে কতটা সঙ্গতিপূর্ণ। এরপর লক্ষ্য হলো এই লসকে কমানো, ব্যাক প্রোপাগেশন ব্যবহার করে ইনপুট ভেক্টর প্যারামিটারগুলো সামঞ্জস্য করা। VQGAN+CLIP বাস্তবায়নের জন্য একটি চমৎকার লাইব্রেরি হলো [Pixray](http://github.com/pixray/pixray) -![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.bn.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.bn.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.bn.png) +![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- প্রম্পট থেকে তৈরি ছবি *একটি বই সহ তরুণ পুরুষ সাহিত্য শিক্ষকের একটি ক্লোজআপ জলরঙের প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি কম্পিউটার সহ তরুণ নারী কম্পিউটার বিজ্ঞান শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি ব্ল্যাকবোর্ডের সামনে বৃদ্ধ পুরুষ গণিত শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* @@ -77,7 +77,7 @@ CLIP-এর বিপরীতে, DALL-E টেক্সট এবং ইমে DALL-E 1 এবং 2-এর প্রধান পার্থক্য হলো, এটি আরও বাস্তবসম্মত ইমেজ এবং শিল্পকর্ম তৈরি করে। DALL-E দিয়ে তৈরি ইমেজের উদাহরণ: -![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.bn.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.bn.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.bn.png) +![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray দ্বারা তৈরি ছবি](../../../../../translated_images/bn/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- প্রম্পট থেকে তৈরি ছবি *একটি বই সহ তরুণ পুরুষ সাহিত্য শিক্ষকের একটি ক্লোজআপ জলরঙের প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি কম্পিউটার সহ তরুণ নারী কম্পিউটার বিজ্ঞান শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* | প্রম্পট থেকে তৈরি ছবি *একটি ব্ল্যাকবোর্ডের সামনে বৃদ্ধ পুরুষ গণিত শিক্ষকের একটি ক্লোজআপ তেল প্রতিকৃতি* diff --git a/translations/br/README.md b/translations/br/README.md index b5e67988..2b9aeaaf 100644 --- a/translations/br/README.md +++ b/translations/br/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligência Artificial para Iniciantes - Um Currículo -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.br.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/br/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote por [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/br/lessons/1-Intro/README.md b/translations/br/lessons/1-Intro/README.md index ddfb281a..10e919fd 100644 --- a/translations/br/lessons/1-Intro/README.md +++ b/translations/br/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à IA -![Resumo do conteúdo de Introdução à IA em um doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c.br.png) +![Resumo do conteúdo de Introdução à IA em um doodle](../../../../translated_images/br/ai-intro.bf28d1ac4235881c.png) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originalmente, os computadores foram inventados por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar com números seguindo um procedimento bem definido - um algoritmo. Os computadores modernos, embora significativamente mais avançados que o modelo original proposto no século XIX, ainda seguem a mesma ideia de cálculos controlados. Assim, é possível programar um computador para fazer algo se soubermos a sequência exata de passos necessários para alcançar o objetivo. -![Foto de uma pessoa](../../../../translated_images/dsh_age.d212a30d4e54fb5f.br.png) +![Foto de uma pessoa](../../../../translated_images/br/dsh_age.d212a30d4e54fb5f.png) > Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para mais informações, consulte **[Inteligência Artificial Geral](https://en. Um dos problemas ao lidar com o termo **[Inteligência](https://en.wikipedia.org/wiki/Intelligence)** é que não há uma definição clara para esse termo. Pode-se argumentar que inteligência está conectada ao **pensamento abstrato** ou à **autoconsciência**, mas não conseguimos defini-la adequadamente. -![Foto de um gato](../../../../translated_images/photo-cat.8c8e8fb760ffe457.br.jpg) +![Foto de um gato](../../../../translated_images/br/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) do Unsplash @@ -98,13 +98,13 @@ Alternativamente, podemos tentar modelar os elementos mais simples dentro de nos > | E o ML? | | > |--------------|-----------| -> | Parte da Inteligência Artificial que se baseia no aprendizado do computador para resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.br.png) | +> | Parte da Inteligência Artificial que se baseia no aprendizado do computador para resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/br/ml-for-beginners.9e4fed176fd5817d.png) | ## Um Breve Histórico da IA A Inteligência Artificial começou como um campo no meio do século XX. Inicialmente, o raciocínio simbólico era a abordagem predominante, e isso levou a uma série de sucessos importantes, como sistemas especialistas – programas de computador capazes de atuar como especialistas em alguns domínios de problemas limitados. No entanto, logo ficou claro que essa abordagem não escala bem. Extrair o conhecimento de um especialista, representá-lo em um computador e manter essa base de conhecimento precisa acaba sendo uma tarefa muito complexa e cara demais para ser prática em muitos casos. Isso levou ao chamado [Inverno da IA](https://en.wikipedia.org/wiki/AI_winter) na década de 1970. -Breve Histórico da IA +Breve Histórico da IA > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Da mesma forma, podemos ver como a abordagem para criar “programas que falam * Assistentes modernos, como Cortana, Siri ou Google Assistant, são todos sistemas híbridos que usam redes neurais para converter fala em texto e reconhecer nossa intenção, e então empregam algum raciocínio ou algoritmos explícitos para realizar as ações necessárias. * No futuro, podemos esperar um modelo completamente baseado em redes neurais para lidar com diálogos por conta própria. As recentes redes neurais da família GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostram grande sucesso nisso. -a evolução do teste de Turing +a evolução do teste de Turing > Imagem de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) por [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Pesquisas Recentes em IA diff --git a/translations/br/lessons/2-Symbolic/Animals.ipynb b/translations/br/lessons/2-Symbolic/Animals.ipynb index 905fa12e..6688d5b7 100644 --- a/translations/br/lessons/2-Symbolic/Animals.ipynb +++ b/translations/br/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Neste exemplo, vamos implementar um sistema simples baseado em conhecimento para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore completa, podemos facilmente adicionar mais regras):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.br.png)\n" + "![](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/br/lessons/2-Symbolic/README.md b/translations/br/lessons/2-Symbolic/README.md index 90872653..ca30e8cc 100644 --- a/translations/br/lessons/2-Symbolic/README.md +++ b/translations/br/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representação de Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/ai-symbolic.715a30cb610411a6.br.png) +![Resumo do conteúdo de IA Simbólica](../../../../translated_images/br/ai-symbolic.715a30cb610411a6.png) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Na maioria das vezes, não definimos estritamente o conhecimento, mas o alinhamo Assim, o problema da **representação de conhecimento** é encontrar uma maneira eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/knowledge-spectrum.b60df631852c0217.br.png) +![Espectro de representação de conhecimento](../../../../translated_images/br/knowledge-spectrum.b60df631852c0217.png) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxe de Bloco | Indentação | | | Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para agir como especialistas em um domínio de problema limitado. Eles eram baseados em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.br.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.br.png) +![Arquitetura Humana](../../../../translated_images/br/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/br/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento @@ -106,7 +106,7 @@ Os sistemas especialistas são construídos como o sistema de raciocínio humano Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base em suas características físicas: -![Árvore AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.br.png) +![Árvore AND-OR](../../../../translated_images/br/AND-OR-Tree.5592d2c70187f283.png) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index f8258849..30e19e92 100644 --- a/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/br/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "No caso de termos mais de 2 classes, o softmax irá normalizar as probabilidades entre todas elas. Aqui está um diagrama da arquitetura de rede que realiza a classificação de dígitos do MNIST:\n", "\n", - "![Classificador MNIST](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.br.png)\n" + "![Classificador MNIST](../../../../../translated_images/br/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1256,7 +1256,7 @@ "* Baixa perda de treinamento - o modelo consegue aproximar bem os dados de treinamento, pois tem poder expressivo suficiente.\n", "* A perda de validação pode ser muito maior do que a perda de treinamento e pode começar a aumentar durante o treinamento - isso ocorre porque o modelo \"memoriza\" os pontos de treinamento e perde a \"visão geral\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.br.png)\n", + "![Overfitting](../../../../../translated_images/br/overfit.a0bd57f717c15769.png)\n", "\n", "> Nesta imagem, `x` representa os dados de treinamento, e `o` os dados de validação. À esquerda - modelo linear (uma camada), que aproxima bem a natureza dos dados. À direita - modelo com overfitting, que aproxima perfeitamente os dados de treinamento, mas deixa de fazer sentido com qualquer outro dado (o erro de validação é muito alto).\n" ] diff --git a/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md index 69a63341..d86eafa9 100644 --- a/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/br/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting é um conceito extremamente importante em aprendizado de máquina, e Considere o seguinte problema de aproximar 5 pontos (representados por `x` nos gráficos abaixo): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.br.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.br.jpg) +![linear](../../../../../translated_images/br/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/br/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Modelo linear, 2 parâmetros** | **Modelo não-linear, 7 parâmetros** Erro de treinamento = 5.3 | Erro de treinamento = 0 @@ -79,7 +79,7 @@ Erro de validação = 5.1 | Erro de validação = 20 Como você pode ver no gráfico acima, o overfitting pode ser detectado por um erro de treinamento muito baixo e um erro de validação alto. Normalmente, durante o treinamento, veremos tanto o erro de treinamento quanto o de validação começarem a diminuir, e então, em algum momento, o erro de validação pode parar de diminuir e começar a aumentar. Isso será um sinal de overfitting e um indicador de que provavelmente devemos parar o treinamento nesse ponto (ou pelo menos salvar um snapshot do modelo). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.br.png) +![overfitting](../../../../../translated_images/br/Overfitting.408ad91cd90b4371.png) ## Como prevenir o overfitting diff --git a/translations/br/lessons/3-NeuralNetworks/README.md b/translations/br/lessons/3-NeuralNetworks/README.md index db01996b..54ed22c4 100644 --- a/translations/br/lessons/3-NeuralNetworks/README.md +++ b/translations/br/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução às Redes Neurais -![Resumo do conteúdo de Introdução às Redes Neurais em um desenho](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.br.png) +![Resumo do conteúdo de Introdução às Redes Neurais em um desenho](../../../../translated_images/br/ai-neuralnetworks.1c687ae40bc86e83.png) Como discutimos na introdução, uma das formas de alcançar inteligência é treinar um **modelo computacional** ou um **cérebro artificial**. Desde meados do século XX, pesquisadores experimentaram diferentes modelos matemáticos, até que, nos últimos anos, essa abordagem se mostrou extremamente bem-sucedida. Esses modelos matemáticos do cérebro são chamados de **redes neurais**. @@ -36,13 +36,13 @@ Neste currículo, focaremos apenas em modelos de redes neurais. Na biologia, sabemos que nosso cérebro é composto por células neurais (neurônios), cada uma delas tendo múltiplas "entradas" (dendritos) e uma única "saída" (axônio). Tanto os dendritos quanto os axônios podem conduzir sinais elétricos, e as conexões entre eles — conhecidas como sinapses — podem apresentar diferentes graus de condutividade, que são regulados por neurotransmissores. -![Modelo de um Neurônio](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.br.jpg) | ![Modelo de um Neurônio](../../../../translated_images/artneuron.1a5daa88d20ebe6f.br.png) +![Modelo de um Neurônio](../../../../translated_images/br/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo de um Neurônio](../../../../translated_images/br/artneuron.1a5daa88d20ebe6f.png) ----|---- Neurônio Real *([Imagem](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipedia)* | Neurônio Artificial *(Imagem do Autor)* Assim, o modelo matemático mais simples de um neurônio contém várias entradas X1, ..., XN e uma saída Y, e uma série de pesos W1, ..., WN. A saída é calculada como: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) onde f é alguma **função de ativação** não linear. diff --git a/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md index d766e9b2..7343159e 100644 --- a/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/br/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Em nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis * **Pré-processamento de uma fotografia de um livro em Braille**. Focamos em como podemos usar limiarização, detecção de características, transformação de perspectiva e manipulações NumPy para separar símbolos individuais em Braille para posterior classificação por uma rede neural. -![Imagem Braille](../../../../../translated_images/braille.341962ff76b1bd70.br.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/braille-result.46530fea020b03c7.br.png) | ![Símbolos Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.br.png) +![Imagem Braille](../../../../../translated_images/br/braille.341962ff76b1bd70.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/br/braille-result.46530fea020b03c7.png) | ![Símbolos Braille](../../../../../translated_images/br/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Imagem de [OpenCV.ipynb](OpenCV.ipynb) * **Detectando movimento em vídeo usando diferença de quadros**. Se a câmera estiver fixa, os quadros do feed da câmera devem ser bastante semelhantes entre si. Como os quadros são representados como arrays, apenas subtraindo esses arrays de dois quadros subsequentes obteremos a diferença de pixels, que deve ser baixa para quadros estáticos e se tornar maior quando houver movimento substancial na imagem. -![Imagem de quadros de vídeo e diferenças de quadros](../../../../../translated_images/frame-difference.706f805491a0883c.br.png) +![Imagem de quadros de vídeo e diferenças de quadros](../../../../../translated_images/br/frame-difference.706f805491a0883c.png) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Em nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis - **Fluxo Óptico Denso** calcula o campo vetorial que mostra para cada pixel onde ele está se movendo. - **Fluxo Óptico Esparso** é baseado em pegar algumas características distintivas na imagem (por exemplo, bordas) e construir sua trajetória de quadro a quadro. -![Imagem de Fluxo Óptico](../../../../../translated_images/optical.1f4a94464579a83a.br.png) +![Imagem de Fluxo Óptico](../../../../../translated_images/br/optical.1f4a94464579a83a.png) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index fcb166ba..49471e64 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 é uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014. Ela possui a seguinte estrutura de camadas: -![Camadas do ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.br.jpg) +![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.jpg) Como você pode ver, a VGG segue uma arquitetura tradicional em forma de pirâmide, que é uma sequência de camadas de convolução e pooling. -![Pirâmide do ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.br.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 54544b21..b7bf2d0e 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque à medida que os padrões se tornam mais avançados, há mais combinações interessantes possíveis que precisamos buscar.\n", "\n", - "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.br.png)\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" ] diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 8bc4646b..b315ddb6 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque, à medida que os padrões se tornam mais avançados, há mais combinações interessantes que precisamos procurar.\n", "\n", - "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.br.png)\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/br/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" ] diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md index c885db75..474a726d 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Na vida real, queremos ser capazes de reconhecer objetos em uma imagem independe Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Como você sabe, uma imagem é representada por uma matriz 2D ou um tensor 3D com profundidade de cor. Aplicar um filtro significa que pegamos uma matriz relativamente pequena chamada **kernel do filtro**, e para cada pixel na imagem original calculamos a média ponderada com os pontos vizinhos. Podemos imaginar isso como uma pequena janela deslizando sobre toda a imagem e calculando a média de todos os pixels de acordo com os pesos na matriz do kernel do filtro. -![Filtro de Borda Vertical](../../../../../translated_images/filter-vert.b7148390ca0bc356.br.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.br.png) +![Filtro de Borda Vertical](../../../../../translated_images/br/filter-vert.b7148390ca0bc356.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/br/filter-horiz.59b80ed4feb946ef.png) ----|---- > Imagem por Dmitry Soshnikov @@ -38,7 +38,7 @@ O funcionamento das CNNs é baseado nas seguintes ideias importantes: * Podemos projetar a rede de forma que os filtros sejam treinados automaticamente * Podemos usar a mesma abordagem para encontrar padrões em características de alto nível, não apenas na imagem original. Assim, a extração de características pelas CNNs funciona em uma hierarquia de características, começando com combinações de pixels de baixo nível até combinações de alto nível de partes da imagem. -![Extração Hierárquica de Características](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.br.png) +![Extração Hierárquica de Características](../../../../../translated_images/br/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Imagem de [um artigo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseado em [sua pesquisa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A maioria das CNNs usadas para processamento de imagens segue a chamada arquitet Como exemplo, vejamos a arquitetura do VGG-16, uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014: -![Camadas do ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.br.jpg) +![Camadas do ImageNet](../../../../../translated_images/br/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Pirâmide do ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.br.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/br/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md index c06c85ef..9fa971b5 100644 --- a/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/br/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Você precisa treinar uma rede neural convolucional para classificar diferentes Usaremos o [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), que contém imagens de 37 diferentes raças de cães e gatos. -![Conjunto de dados com o qual trabalharemos](../../../../../../translated_images/data.50b2a9d5484bdbf0.br.png) +![Conjunto de dados com o qual trabalharemos](../../../../../../translated_images/br/data.50b2a9d5484bdbf0.png) Para baixar o conjunto de dados, use este trecho de código: diff --git a/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 19859ff6..b04e1887 100644 --- a/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/br/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Para visualizar o gato ideal, começaremos com uma imagem de ruído aleatório e tentaremos usar a técnica de otimização por descida de gradiente para ajustar a imagem de forma que a rede reconheça um gato.\n", "\n", - "![Loop de Otimização](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.br.png)\n", + "![Loop de Otimização](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Aqui está nossa imagem inicial:\n" ] diff --git a/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md index 5b643e0f..373e2fff 100644 --- a/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/br/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tanto o Keras quanto o PyTorch possuem funções para carregar facilmente pesos Aqui estão características extraídas de uma imagem de um gato pela rede VGG-16: -![Características extraídas pelo VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.br.png) +![Características extraídas pelo VGG-16](../../../../../translated_images/br/features.6291f9c7ba3a0b95.png) ## Conjunto de Dados de Gatos vs. Cachorros @@ -48,19 +48,19 @@ Uma rede neural pré-treinada contém diferentes padrões em seu *cérebro*, inc Uma abordagem que podemos adotar é começar com uma imagem aleatória e, em seguida, tentar usar a técnica de **otimização por descida de gradiente** para ajustar essa imagem de forma que a rede comece a pensar que é um gato. -![Loop de Otimização de Imagem](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.br.png) +![Loop de Otimização de Imagem](../../../../../translated_images/br/ideal-cat-loop.999fbb8ff306e044.png) No entanto, se fizermos isso, receberemos algo muito semelhante a um ruído aleatório. Isso ocorre porque *existem muitas maneiras de fazer a rede pensar que a imagem de entrada é um gato*, incluindo algumas que não fazem sentido visualmente. Embora essas imagens contenham muitos padrões típicos de um gato, não há nada que as restrinja a serem visualmente distintas. Para melhorar o resultado, podemos adicionar outro termo à função de perda, chamado de **perda de variação**. É uma métrica que mostra quão semelhantes são os pixels vizinhos da imagem. Minimizar a perda de variação torna a imagem mais suave e elimina o ruído, revelando padrões mais visualmente atraentes. Aqui está um exemplo de tais imagens "ideais", classificadas como gato e zebra com alta probabilidade: -![Gato Ideal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.br.png) | ![Zebra Ideal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.br.png) +![Gato Ideal](../../../../../translated_images/br/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/br/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Gato Ideal* | *Zebra Ideal* Uma abordagem semelhante pode ser usada para realizar os chamados **ataques adversariais** em uma rede neural. Suponha que queremos enganar uma rede neural e fazer um cachorro parecer um gato. Se pegarmos a imagem de um cachorro, que é reconhecida pela rede como um cachorro, podemos ajustá-la um pouco usando otimização por descida de gradiente até que a rede comece a classificá-la como um gato: -![Imagem de um Cachorro](../../../../../translated_images/original-dog.8f68a67d2fe0911f.br.png) | ![Imagem de um cachorro classificada como gato](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.br.png) +![Imagem de um Cachorro](../../../../../translated_images/br/original-dog.8f68a67d2fe0911f.png) | ![Imagem de um cachorro classificada como gato](../../../../../translated_images/br/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Imagem original de um cachorro* | *Imagem de um cachorro classificada como gato* diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index f851b6fd..016f4116 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Como estamos treinando o autoencoder para capturar o máximo de informações da imagem original possível para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para capturar o significado.\n", "\n", - "![Diagrama do AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.br.jpg)\n", + "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 01635f5a..700622d1 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Como estamos treinando o autoencoder para capturar o máximo de informações possíveis da imagem original para uma reconstrução precisa, a rede tenta encontrar o melhor **embedding** das imagens de entrada para capturar o significado.\n", "\n", - "![Diagrama do AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.br.jpg)\n", + "![Diagrama do AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md index 758429a3..6c8fdf70 100644 --- a/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/br/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ No entanto, podemos querer usar dados brutos (não rotulados) para treinar extra Como estamos treinando um autoencoder para capturar o máximo de informações da imagem original possível para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para capturar seu significado. -![Diagrama de AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.br.jpg) +![Diagrama de AutoEncoder](../../../../../translated_images/br/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Imagem do [blog do Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md index fd417ea6..e86e1eaa 100644 --- a/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/br/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Os modelos de classificação de imagens que abordamos até agora tomavam uma im ## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detecção de Objetos](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.br.png) +![Detecção de Objetos](../../../../../translated_images/br/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Imagem do [site YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Suponha que queremos encontrar um gato em uma imagem. Uma abordagem muito ingên 2. Executar a classificação de imagem em cada bloco. 3. Os blocos que resultarem em uma ativação suficientemente alta podem ser considerados como contendo o objeto em questão. -![Detecção Ingênua de Objetos](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.br.png) +![Detecção Ingênua de Objetos](../../../../../translated_images/br/naive-detection.e7f1ba220ccd08c6.png) > *Imagem do [Notebook de Exercícios](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Você pode encontrar os seguintes conjuntos de dados para essa tarefa: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Objetos Comuns em Contexto. 80 classes, caixas delimitadoras e máscaras de segmentação -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.br.jpg) +![COCO](../../../../../translated_images/br/coco-examples.71bc60380fa6cceb.jpg) ## Métricas de Detecção de Objetos @@ -50,7 +50,7 @@ Você pode encontrar os seguintes conjuntos de dados para essa tarefa: Enquanto na classificação de imagens é fácil medir o desempenho do algoritmo, na detecção de objetos precisamos medir tanto a correção da classe quanto a precisão da localização inferida da caixa delimitadora. Para este último, usamos a chamada **Interseção sobre União** (IoU), que mede o quão bem duas caixas (ou duas áreas arbitrárias) se sobrepõem. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.br.png) +![IoU](../../../../../translated_images/br/iou_equation.9a4751d40fff4e11.png) > *Figura 2 de [este excelente post sobre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existem duas grandes classes de algoritmos de detecção de objetos: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) usa [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) para gerar uma estrutura hierárquica de regiões ROI, que são então passadas por extratores de características CNN e classificadores SVM para determinar a classe do objeto, e regressão linear para determinar as coordenadas da *caixa delimitadora*. [Artigo Oficial](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.br.png) +![RCNN](../../../../../translated_images/br/rcnn1.cae407020dfb1d1f.png) > *Imagem de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.br.png) +![RCNN-1](../../../../../translated_images/br/rcnn2.2d9530bb83516484.png) > *Imagens de [este blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existem duas grandes classes de algoritmos de detecção de objetos: Essa abordagem é semelhante à R-CNN, mas as regiões são definidas após as camadas de convolução terem sido aplicadas. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.br.png) +![FRCNN](../../../../../translated_images/br/f-rcnn.3cda6d9bb4188875.png) > Imagem do [Artigo Oficial](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Essa abordagem é semelhante à R-CNN, mas as regiões são definidas após as c A ideia principal dessa abordagem é usar uma rede neural para prever ROIs - a chamada *Rede de Proposta de Região*. [Artigo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.br.png) +![FasterRCNN](../../../../../translated_images/br/faster-rcnn.8d46c099b87ef30a.png) > Imagem do [Artigo Oficial](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Este algoritmo é ainda mais rápido que o Faster R-CNN. A ideia principal é a 1. As características são processadas por **Position-Sensitive Score Map**. Cada objeto de $C$ classes é dividido em regiões $k\times k$, e treinamos para prever partes dos objetos. 1. Para cada parte das regiões $k\times k$, todas as redes votam pelas classes de objetos, e a classe de objeto com o maior número de votos é selecionada. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.br.png) +![r-fcn image](../../../../../translated_images/br/r-fcn.13eb88158b99a3da.png) > Imagem do [Artigo Oficial](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO é um algoritmo de uma única passagem em tempo real. A ideia principal é * A imagem é dividida em regiões $S\times S$. * Para cada região, **CNN** prevê $n$ objetos possíveis, coordenadas da *caixa delimitadora* e *confiança*=*probabilidade* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.br.png) + ![YOLO](../../../../../translated_images/br/yolo.a2648ec82ee8bb4e.png) > Imagem do [Artigo Oficial](https://arxiv.org/abs/1506.02640) diff --git a/translations/br/lessons/4-ComputerVision/README.md b/translations/br/lessons/4-ComputerVision/README.md index d5d39fde..e03be1dd 100644 --- a/translations/br/lessons/4-ComputerVision/README.md +++ b/translations/br/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visão Computacional -![Resumo do conteúdo de Visão Computacional em um desenho](../../../../translated_images/ai-computervision.6506ebebac3fbf76.br.png) +![Resumo do conteúdo de Visão Computacional em um desenho](../../../../translated_images/br/ai-computervision.6506ebebac3fbf76.png) Nesta seção, vamos aprender sobre: diff --git a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 9622a277..c9613cda 100644 --- a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "A representação vetorial **Bag of Words** (BoW) é a representação vetorial tradicional mais comumente usada. Cada palavra está vinculada a um índice do vetor, e o elemento do vetor contém o número de ocorrências de uma palavra em um determinado documento.\n", "\n", - "![Imagem mostrando como uma representação vetorial Bag of Words é representada na memória.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.br.png) \n", + "![Imagem mostrando como uma representação vetorial Bag of Words é representada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Você também pode pensar no BoW como a soma de todos os vetores one-hot-encoded para palavras individuais no texto.\n", "\n", diff --git a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 50608eb4..8d3d8d47 100644 --- a/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/br/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "A representação vetorial **Bag-of-words** (BoW) é a forma tradicional mais simples de entender uma representação vetorial. Cada palavra é vinculada a um índice no vetor, e um elemento do vetor contém o número de ocorrências de cada palavra em um determinado documento.\n", "\n", - "![Imagem mostrando como uma representação vetorial Bag-of-words é armazenada na memória.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.br.png) \n", + "![Imagem mostrando como uma representação vetorial Bag-of-words é armazenada na memória.](../../../../../translated_images/br/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Você também pode pensar no BoW como a soma de todos os vetores one-hot codificados para as palavras individuais no texto.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 8ae0e05d..714e2242 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ao usar a camada de embedding como a primeira camada em nossa rede, podemos mudar do modelo bag-of-words para o modelo **embedding bag**, onde primeiro convertemos cada palavra em nosso texto no embedding correspondente e, em seguida, calculamos alguma função de agregação sobre todos esses embeddings, como `sum`, `average` ou `max`.\n", "\n", - "![Imagem mostrando um classificador de embedding para cinco palavras em sequência.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.br.png)\n", + "![Imagem mostrando um classificador de embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Nossa rede neural classificadora começará com uma camada de embedding, seguida por uma camada de agregação e, por fim, um classificador linear no topo:\n" ] @@ -176,7 +176,7 @@ "\n", "Na arquitetura anterior, precisávamos preencher todas as sequências para que tivessem o mesmo comprimento, a fim de ajustá-las em um minibatch. Essa não é a maneira mais eficiente de representar sequências de comprimento variável - outra abordagem seria usar um vetor de **offset**, que armazenaria os deslocamentos de todas as sequências em um único vetor grande.\n", "\n", - "![Imagem mostrando uma representação de sequência com offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.br.png)\n", + "![Imagem mostrando uma representação de sequência com offset](../../../../../translated_images/br/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Na imagem acima, mostramos uma sequência de caracteres, mas em nosso exemplo estamos trabalhando com sequências de palavras. No entanto, o princípio geral de representar sequências com um vetor de offset permanece o mesmo.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes.\n", "\n", - "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.br.png)\n", + "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Para experimentar com embeddings word2vec pré-treinados no conjunto de dados Google News, podemos usar a biblioteca **gensim**. Abaixo, encontramos as palavras mais semelhantes a 'neural'.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 0ab63d1d..20fdfd17 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/br/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ao usar uma camada de embedding como a primeira camada da nossa rede, podemos mudar de um modelo de bag-of-words para um modelo de **embedding bag**, onde primeiro convertemos cada palavra do nosso texto no embedding correspondente e, em seguida, calculamos alguma função de agregação sobre todos esses embeddings, como `sum`, `average` ou `max`.\n", "\n", - "![Imagem mostrando um classificador com embedding para cinco palavras em sequência.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.br.png)\n", + "![Imagem mostrando um classificador com embedding para cinco palavras em sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Nossa rede neural classificadora consiste nas seguintes camadas:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW é mais rápido, enquanto o skip-gram, embora mais lento, faz um trabalho melhor ao representar palavras menos frequentes.\n", "\n", - "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.br.png)\n", + "![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Para experimentar o embedding Word2Vec pré-treinado no conjunto de dados do Google News, podemos usar a biblioteca **gensim**. Abaixo, encontramos as palavras mais semelhantes a 'neural'.\n", "\n", diff --git a/translations/br/lessons/5-NLP/14-Embeddings/README.md b/translations/br/lessons/5-NLP/14-Embeddings/README.md index 62a552e3..6e720d2f 100644 --- a/translations/br/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/br/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Assim, a camada de embedding receberia uma palavra como entrada e produziria um Ao usar uma camada de embedding como a primeira camada em nossa rede de classificação, podemos mudar de um modelo bag-of-words para um modelo **embedding bag**, onde primeiro convertemos cada palavra em nosso texto no embedding correspondente e, em seguida, calculamos alguma função agregada sobre todos esses embeddings, como `sum`, `average` ou `max`. -![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.br.png) +![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/br/embedding-classifier-example.b77f021a7ee67eee.png) > Imagem do autor @@ -40,7 +40,7 @@ Para isso, precisamos pré-treinar nosso modelo de embedding em uma grande cole CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes. -![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.br.png) +![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/br/lessons/5-NLP/15-LanguageModeling/README.md b/translations/br/lessons/5-NLP/15-LanguageModeling/README.md index 11f492ff..35b44ad8 100644 --- a/translations/br/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/br/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nos nossos exemplos anteriores, usamos embeddings semânticos pré-treinados, ma * **Continuous Bag-of-Words** (CBoW), onde prevemos o token do meio $W_0$ em uma sequência de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, onde prevemos um conjunto de tokens vizinhos {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partir do token do meio $W_0$. -![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.br.png) +![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/br/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/br/lessons/5-NLP/16-RNN/README.md b/translations/br/lessons/5-NLP/16-RNN/README.md index 5344a60a..53a674a4 100644 --- a/translations/br/lessons/5-NLP/16-RNN/README.md +++ b/translations/br/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nas seções anteriores, utilizamos representações semânticas ricas de texto Para capturar o significado de uma sequência de texto, precisamos usar outra arquitetura de rede neural, chamada de **rede neural recorrente**, ou RNN. Na RNN, passamos nossa frase pela rede um símbolo de cada vez, e a rede produz algum **estado**, que então passamos novamente para a rede junto com o próximo símbolo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.br.png) +![RNN](../../../../../translated_images/br/rnn.27f5c29c53d727b5.png) > Imagem do autor @@ -61,7 +61,7 @@ Discutimos redes recorrentes que operam em uma direção, do início de uma sequ Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los em um vetor de estado ou passá-los para a saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível superior e construir a partir dos padrões de baixo nível extraídos pela primeira camada. Isso nos leva à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada. -![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.br.jpg) +![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Imagem retirada [deste post maravilhoso](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 376101a1..849d0812 100644 --- a/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los no vetor de estado ou passá-los para a saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível mais alto, construídos a partir de padrões de baixo nível extraídos pela primeira camada. Isso nos leva à noção de **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada.\n", "\n", - "![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.br.jpg)\n", + "![Imagem mostrando uma RNN LSTM multicamada](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Imagem retirada [deste post maravilhoso](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) por Fernando López*\n", "\n", diff --git a/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb index 850c536f..af3ea88d 100644 --- a/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/br/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Para capturar o significado de uma sequência de texto, usaremos uma arquitetura de rede neural chamada **rede neural recorrente**, ou RNN. Ao usar uma RNN, passamos nossa sentença pela rede um token de cada vez, e a rede produz algum **estado**, que então passamos novamente para a rede junto com o próximo token.\n", "\n", - "![Imagem mostrando um exemplo de geração de rede neural recorrente.](../../../../../translated_images/rnn.27f5c29c53d727b5.br.png)\n", + "![Imagem mostrando um exemplo de geração de rede neural recorrente.](../../../../../translated_images/br/rnn.27f5c29c53d727b5.png)\n", "\n", "Dada a sequência de entrada de tokens $X_0,\\dots,X_n$, a RNN cria uma sequência de blocos de rede neural e treina essa sequência de ponta a ponta usando retropropagação. Cada bloco de rede recebe um par $(X_i,S_i)$ como entrada e produz $S_{i+1}$ como resultado. O estado final $S_n$ ou a saída $Y_n$ é enviado para um classificador linear para produzir o resultado. Todos os blocos de rede compartilham os mesmos pesos e são treinados de ponta a ponta usando uma única passagem de retropropagação.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Redes recorrentes, sejam unidirecionais ou bidirecionais, capturam padrões dentro de uma sequência e os armazenam em vetores de estado ou os retornam como saída. Assim como nas redes convolucionais, podemos construir outra camada recorrente após a primeira para capturar padrões de nível mais alto, construídos a partir de padrões de nível mais baixo extraídos pela primeira camada. Isso nos leva à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada.\n", "\n", - "![Imagem mostrando uma RNN multicamada de memória de longo e curto prazo](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.br.jpg)\n", + "![Imagem mostrando uma RNN multicamada de memória de longo e curto prazo](../../../../../translated_images/br/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Imagem retirada [deste post incrível](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) por Fernando López.*\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 987b800b..43381200 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "A maneira como treinaremos a RNN para gerar texto é a seguinte. A cada etapa, pegaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo caractere de saída para cada caractere de entrada:\n", "\n", - "![Imagem mostrando um exemplo de geração de RNN com a palavra 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.br.png)\n", + "![Imagem mostrando um exemplo de geração de RNN com a palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Dependendo do cenário real, também podemos querer incluir alguns caracteres especiais, como *fim de sequência* ``. No nosso caso, queremos apenas treinar a rede para geração contínua de texto, então fixaremos o tamanho de cada sequência para ser igual a `nchars` tokens. Consequentemente, cada exemplo de treinamento consistirá em `nchars` entradas e `nchars` saídas (que são a sequência de entrada deslocada um símbolo para a esquerda). O minibatch consistirá de várias dessas sequências.\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index a840d0df..3c23a2a8 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "A maneira como treinaremos a RNN para gerar títulos de notícias é a seguinte. A cada etapa, pegaremos um título, que será alimentado em uma RNN, e para cada caractere de entrada pediremos à rede que gere o próximo caractere de saída:\n", "\n", - "![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.br.png)\n", + "![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Para o último caractere da nossa sequência, pediremos à rede que gere o token ``.\n", "\n", diff --git a/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md index cccc1d9f..4d4ce5c9 100644 --- a/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/br/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Na arquitetura de RNN que discutimos na unidade anterior, cada unidade RNN produ Isso permite diferentes arquiteturas neurais, como mostrado na imagem abaixo: -![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.br.jpg) +![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Imagem do post no blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Nesta unidade, focaremos em modelos generativos simples que nos ajudam a gerar t Treinaremos esta RNN para gerar texto passo a passo. Em cada etapa, pegaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo caractere de saída para cada caractere de entrada: -![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.br.png) +![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/br/rnn-generate.56c54afb52f9781d.png) Ao gerar texto (durante a inferência), começamos com algum **prompt**, que é passado pelas células RNN para gerar seu estado intermediário, e então a geração começa a partir desse estado. Geramos um caractere por vez e passamos o estado e o caractere gerado para outra célula RNN para gerar o próximo, até que tenhamos gerado caracteres suficientes. diff --git a/translations/br/lessons/5-NLP/18-Transformers/README.md b/translations/br/lessons/5-NLP/18-Transformers/README.md index 90c33099..28ef6ef9 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/README.md +++ b/translations/br/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Com RNNs, a tarefa de sequência para sequência é implementada por duas redes Os **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre os estados intermediários da RNN de entrada e a RNN de saída. Dessa forma, ao gerar o símbolo de saída yt, levamos em conta todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.br.png) +![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png) > O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post no blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada influenciam a geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.br.png) +![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ O resultado que obtemos com o embutimento posicional incorpora tanto o token ori Em seguida, precisamos capturar alguns padrões dentro de nossa sequência. Para isso, os transformers usam um mecanismo de **autoatenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. Aplicar autoatenção nos permite levar em conta o **contexto** dentro da sentença e ver quais palavras estão inter-relacionadas. Por exemplo, isso nos permite identificar quais palavras são referidas por correferências, como *it* (ele/ela), e também considerar o contexto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.br.png) +![](../../../../../translated_images/br/CoreferenceResolution.861924d6d384a7d6.png) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Bidirectional Encoder Representations from Transformers) é uma rede transformer muito grande com várias camadas: 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados textuais (Wikipedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Esse processo é chamado de **aprendizado por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.br.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 54518a89..d747cd95 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/br/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre estados intermediários da RNN de entrada e da RNN de saída. Dessa forma, ao gerar o símbolo de saída $y_t$, levaremos em conta todos os estados ocultos de entrada $h_i$, com diferentes coeficientes de peso $\\alpha_{t,i}$.\n", "\n", - "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.br.png)\n", + "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png)\n", "*O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A matriz de atenção $\\{\\alpha_{i,j}\\}$ representaria o grau em que certas palavras de entrada influenciam a geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz:\n", "\n", - "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.br.png)\n", + "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura retirada de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Representações de Codificador Bidirecional de Transformadores) é uma rede transformadora muito grande e multilayer com 12 camadas para *BERT-base* e 24 para *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados de texto (WikiPedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma frase). Durante o pré-treinamento, o modelo absorve um nível significativo de compreensão da linguagem, que pode ser aproveitado com outros conjuntos de dados usando ajuste fino. Esse processo é chamado de **aprendizado por transferência**.\n", "\n", - "![Imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.br.png)\n", + "![Imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existem muitas variações de arquiteturas de transformadores, incluindo BERT, DistilBERT, BigBird, OpenGPT3 e outras que podem ser ajustadas. O pacote [HuggingFace](https://github.com/huggingface/) fornece um repositório para treinar muitas dessas arquiteturas com PyTorch.\n", "\n", diff --git a/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3905c84d..59b5da37 100644 --- a/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/br/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isso é implementado criando atalhos entre os estados intermediários da RNN de entrada e a RNN de saída. Dessa forma, ao gerar o símbolo de saída $y_t$, levaremos em conta todos os estados ocultos de entrada $h_i$, com diferentes coeficientes de peso $\\alpha_{t,i}$.\n", "\n", - "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.br.png)\n", + "![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/br/encoder-decoder-attention.7a726296894fb567.png)\n", "*O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A matriz de atenção $\\{\\alpha_{i,j}\\}$ representaria o grau em que certas palavras de entrada influenciam a geração de uma determinada palavra na sequência de saída. Abaixo está um exemplo de tal matriz:\n", "\n", - "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.br.png)\n", + "![Imagem mostrando um alinhamento de exemplo encontrado pelo RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/br/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura retirada de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Representações de Codificadores Bidirecionais de Transformers) é uma rede transformer muito grande e multilayer, com 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é inicialmente pré-treinado em um grande corpus de dados textuais (WikiPedia + livros) usando treinamento não supervisionado (prevendo palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo adquire um nível significativo de compreensão da linguagem, que pode ser aproveitado com outros conjuntos de dados por meio de ajuste fino. Esse processo é chamado de **aprendizado por transferência**.\n", "\n", - "![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.br.png)\n", + "![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/br/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existem muitas variações de arquiteturas Transformer, incluindo BERT, DistilBERT, BigBird, OpenGPT3 e outras, que podem ser ajustadas.\n", "\n", diff --git a/translations/br/lessons/5-NLP/19-NER/README.md b/translations/br/lessons/5-NLP/19-NER/README.md index 3e503f4d..5e32d72e 100644 --- a/translations/br/lessons/5-NLP/19-NER/README.md +++ b/translations/br/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Como precisamos construir uma correspondência um-para-um entre tokens e classes, podemos treinar um modelo neural **muitos-para-muitos** da seguinte forma: -![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.br.jpg) +![Imagem mostrando padrões comuns de redes neurais recorrentes.](../../../../../translated_images/br/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Imagem do [post no blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/). Os modelos de classificação de tokens NER correspondem à arquitetura de rede mais à direita nesta imagem.* diff --git a/translations/br/lessons/5-NLP/README.md b/translations/br/lessons/5-NLP/README.md index c538a66c..3b597ba3 100644 --- a/translations/br/lessons/5-NLP/README.md +++ b/translations/br/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Processamento de Linguagem Natural -![Resumo das tarefas de PLN em um desenho](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.br.png) +![Resumo das tarefas de PLN em um desenho](../../../../translated_images/br/ai-nlp.b22dcb8ca4707cea.png) Nesta seção, vamos focar no uso de Redes Neurais para lidar com tarefas relacionadas ao **Processamento de Linguagem Natural (PLN)**. Existem muitos problemas de PLN que queremos que os computadores sejam capazes de resolver: diff --git a/translations/br/lessons/6-Other/23-MultiagentSystems/README.md b/translations/br/lessons/6-Other/23-MultiagentSystems/README.md index a9527792..7e40ef3e 100644 --- a/translations/br/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/br/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Você pode abrir um dos modelos, por exemplo **Biology → Flocking**. Após abrir o modelo, você será levado à tela principal do NetLogo. Aqui está um modelo de exemplo que descreve a população de lobos e ovelhas, considerando recursos finitos (grama). -![Tela Principal do NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.br.png) +![Tela Principal do NetLogo](../../../../../translated_images/br/NetLogo-Main.32653711ec1a01b3.png) > Captura de tela por Dmitry Soshnikov diff --git a/translations/br/lessons/README.md b/translations/br/lessons/README.md index e7adc7c2..7f2c8d84 100644 --- a/translations/br/lessons/README.md +++ b/translations/br/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visão Geral -![Visão Geral em um rabisco](../../../translated_images/ai-overview.0857791951d19500.br.png) +![Visão Geral em um rabisco](../../../translated_images/br/ai-overview.0857791951d19500.png) > Rabisco por [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/br/lessons/X-Extras/X1-MultiModal/README.md b/translations/br/lessons/X-Extras/X1-MultiModal/README.md index 34656523..58c3ccae 100644 --- a/translations/br/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/br/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Após o sucesso dos modelos transformers na resolução de tarefas de PLN, as me A ideia principal do CLIP é ser capaz de comparar descrições textuais com uma imagem e determinar o quão bem a imagem corresponde à descrição. -![Arquitetura do CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.br.png) +![Arquitetura do CLIP](../../../../../translated_images/br/clip-arch.b3dbf20b4e8ed8be.png) > *Imagem retirada [deste post no blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Uma vez que este modelo é pré-treinado, podemos fornecer a ele um lote de imag Suponha que precisamos classificar imagens entre, por exemplo, gatos, cachorros e humanos. Nesse caso, podemos fornecer ao modelo uma imagem e uma série de descrições textuais: "*uma foto de um gato*", "*uma foto de um cachorro*", "*uma foto de um humano*". No vetor resultante de 3 probabilidades, basta selecionar o índice com o maior valor. -![CLIP para Classificação de Imagens](../../../../../translated_images/clip-class.3af42ef0b2b19369.br.png) +![CLIP para Classificação de Imagens](../../../../../translated_images/br/clip-class.3af42ef0b2b19369.png) > *Imagem retirada [deste post no blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Saiba mais sobre o VQGAN no site [Taming Transformers](https://compvis.github.io Uma das diferenças importantes entre o VQGAN e um GAN tradicional é que o último pode produzir uma imagem decente a partir de qualquer vetor de entrada, enquanto o VQGAN provavelmente produzirá uma imagem incoerente. Assim, precisamos orientar ainda mais o processo de criação da imagem, e isso pode ser feito usando o CLIP. -![Arquitetura VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.br.png) +![Arquitetura VQGAN+CLIP](../../../../../translated_images/br/vqgan.5027fe05051dfa31.png) Para gerar uma imagem correspondente a uma descrição textual, começamos com algum vetor de codificação aleatório que é passado pelo VQGAN para produzir uma imagem. Em seguida, o CLIP é usado para produzir uma função de perda que mostra o quão bem a imagem corresponde à descrição textual. O objetivo, então, é minimizar essa perda, usando retropropagação para ajustar os parâmetros do vetor de entrada. Uma ótima biblioteca que implementa o VQGAN+CLIP é o [Pixray](http://github.com/pixray/pixray). -![Imagem gerada pelo Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.br.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.br.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.br.png) +![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Imagem gerada pelo Pixray](../../../../../translated_images/br/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Imagem gerada a partir da descrição *um retrato em aquarela de um jovem professor de literatura com um livro* | Imagem gerada a partir da descrição *um retrato a óleo de uma jovem professora de ciência da computação com um computador* | Imagem gerada a partir da descrição *um retrato a óleo de um velho professor de matemática em frente a um quadro-negro* @@ -75,7 +75,7 @@ Diferentemente do CLIP, o DALL-E recebe tanto texto quanto imagem como um único A principal diferença entre o DALL-E 1 e o 2 é que o último gera imagens e artes mais realistas. Exemplos de imagens geradas com o DALL-E: -![Imagem gerada pelo DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.br.png) | ![Imagem gerada pelo DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.br.png) | ![Imagem gerada pelo DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.br.png) +![Imagem gerada pelo DALL-E](../../../../../translated_images/br/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Imagem gerada pelo DALL-E](../../../../../translated_images/br/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Imagem gerada pelo DALL-E](../../../../../translated_images/br/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Imagem gerada a partir da descrição *um retrato em aquarela de um jovem professor de literatura com um livro* | Imagem gerada a partir da descrição *um retrato a óleo de uma jovem professora de ciência da computação com um computador* | Imagem gerada a partir da descrição *um retrato a óleo de um velho professor de matemática em frente a um quadro-negro* diff --git a/translations/cs/README.md b/translations/cs/README.md index efc97a83..06b2f274 100644 --- a/translations/cs/README.md +++ b/translations/cs/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umělá inteligence pro začátečníky - Kurz -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.cs.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/cs/ai-overview.0857791951d19500.png)| |:---:| | Umělá inteligence pro začátečníky - _sketchnote od [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/cs/lessons/1-Intro/README.md b/translations/cs/lessons/1-Intro/README.md index e8b4a54e..839c54c9 100644 --- a/translations/cs/lessons/1-Intro/README.md +++ b/translations/cs/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do AI -![Shrnutí obsahu Úvodu do AI ve formě kresby](../../../../translated_images/ai-intro.bf28d1ac4235881c.cs.png) +![Shrnutí obsahu Úvodu do AI ve formě kresby](../../../../translated_images/cs/ai-intro.bf28d1ac4235881c.png) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Původně byly počítače vynalezeny [Charlesem Babbagem](https://en.wikipedia.org/wiki/Charles_Babbage) k práci s čísly podle přesně definovaného postupu – algoritmu. Moderní počítače, i když jsou mnohem pokročilejší než původní model navržený v 19. století, stále vycházejí ze stejné myšlenky řízených výpočtů. Proto je možné naprogramovat počítač, aby něco vykonal, pokud známe přesnou posloupnost kroků, které je třeba provést k dosažení cíle. -![Fotografie osoby](../../../../translated_images/dsh_age.d212a30d4e54fb5f.cs.png) +![Fotografie osoby](../../../../translated_images/cs/dsh_age.d212a30d4e54fb5f.png) > Foto od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Pro více informací se podívejte na **[Obecná umělá inteligence](https://en Jedním z problémů při práci s termínem **[inteligence](https://en.wikipedia.org/wiki/Intelligence)** je, že neexistuje jasná definice tohoto pojmu. Lze argumentovat, že inteligence souvisí s **abstraktním myšlením** nebo **sebeuvědoměním**, ale nemůžeme ji přesně definovat. -![Fotografie kočky](../../../../translated_images/photo-cat.8c8e8fb760ffe457.cs.jpg) +![Fotografie kočky](../../../../translated_images/cs/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternativně můžeme zkusit modelovat nejjednodušší prvky uvnitř našeho m > | Co je ML? | | > |--------------|-----------| -> | Část umělé inteligence, která je založena na tom, že se počítač učí řešit problém na základě určitých dat, se nazývá **strojové učení**. V tomto kurzu se nebudeme zabývat klasickým strojovým učením – odkazujeme vás na samostatný [kurikulum Strojové učení pro začátečníky](http://aka.ms/ml-beginners). | ![ML pro začátečníky](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.cs.png) | +> | Část umělé inteligence, která je založena na tom, že se počítač učí řešit problém na základě určitých dat, se nazývá **strojové učení**. V tomto kurzu se nebudeme zabývat klasickým strojovým učením – odkazujeme vás na samostatný [kurikulum Strojové učení pro začátečníky](http://aka.ms/ml-beginners). | ![ML pro začátečníky](../../../../translated_images/cs/ml-for-beginners.9e4fed176fd5817d.png) | ## Stručná historie AI Umělá inteligence vznikla jako obor v polovině 20. století. Zpočátku byl převládajícím přístupem symbolický přístup, který vedl k řadě důležitých úspěchů, jako byly expertní systémy – počítačové programy, které dokázaly fungovat jako odborník v některých omezených oblastech problémů. Brzy se však ukázalo, že tento přístup není dobře škálovatelný. Extrakce znalostí od experta, jejich reprezentace v počítači a udržování této znalostní báze přesné se ukázalo být velmi složitým úkolem a v mnoha případech příliš nákladným na to, aby bylo praktické. To vedlo k takzvané [AI zimě](https://en.wikipedia.org/wiki/AI_winter) v 70. letech. -Stručná historie AI +Stručná historie AI > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobně můžeme vidět, jak se přístup k vytváření „mluvících program * Moderní asistenti, jako Cortana, Siri nebo Google Assistant, jsou všechny hybridní systémy, které používají neuronové sítě k převodu řeči na text a rozpoznání našeho záměru, a poté využívají určité uvažování nebo explicitní algoritmy k provedení požadovaných akcí. * V budoucnu můžeme očekávat kompletní model založený na neuronových sítích, který bude sám zvládat dialog. Nedávné GPT a [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rodiny neuronových sítí ukazují velký úspěch v tomto směru. -evoluce Turingova testu +evoluce Turingova testu > Obrázek od Dmitry Soshnikov, [fotografie](https://unsplash.com/photos/r8LmVbUKgns) od [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedávný výzkum v oblasti AI diff --git a/translations/cs/lessons/2-Symbolic/Animals.ipynb b/translations/cs/lessons/2-Symbolic/Animals.ipynb index ab7c05d7..307a9289 100644 --- a/translations/cs/lessons/2-Symbolic/Animals.ipynb +++ b/translations/cs/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "V tomto příkladu implementujeme jednoduchý systém založený na znalostech, který určí zvíře na základě některých fyzických charakteristik. Systém může být reprezentován následujícím AND-OR stromem (toto je část celého stromu, pravidla lze snadno rozšířit):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.cs.png)\n" + "![](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/cs/lessons/2-Symbolic/README.md b/translations/cs/lessons/2-Symbolic/README.md index eb691c34..d112748e 100644 --- a/translations/cs/lessons/2-Symbolic/README.md +++ b/translations/cs/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentace znalostí a expertní systémy -![Shrnutí obsahu Symbolické AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.cs.png) +![Shrnutí obsahu Symbolické AI](../../../../translated_images/cs/ai-symbolic.715a30cb610411a6.png) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Znalosti často nedefinujeme striktně, ale porovnáváme je s jinými souvisej Problém **reprezentace znalostí** tedy spočívá v nalezení efektivního způsobu, jak reprezentovat znalosti uvnitř počítače ve formě dat, aby byly automaticky použitelné. To lze chápat jako spektrum: -![Spektrum reprezentace znalostí](../../../../translated_images/knowledge-spectrum.b60df631852c0217.cs.png) +![Spektrum reprezentace znalostí](../../../../translated_images/cs/knowledge-spectrum.b60df631852c0217.png) > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Bloková syntaxe | Odsazení | | | Jedním z raných úspěchů symbolické AI byly tzv. **expertní systémy** - počítačové systémy navržené tak, aby fungovaly jako expert v omezené oblasti problémů. Byly založeny na **bázi znalostí** získané od jednoho nebo více lidských expertů a obsahovaly **inferenční stroj**, který na ní prováděl usuzování. -![Lidská architektura](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.cs.png) | ![Systém založený na znalostech](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.cs.png) +![Lidská architektura](../../../../translated_images/cs/arch-human.5d4d35f1bba3ab1c.png) | ![Systém založený na znalostech](../../../../translated_images/cs/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Zjednodušená struktura lidského nervového systému | Architektura systému založeného na znalostech @@ -106,7 +106,7 @@ Expertní systémy jsou postaveny podobně jako lidský systém usuzování, kte Jako příklad si vezměme následující expertní systém určování zvířete na základě jeho fyzických charakteristik: -![AND-OR strom](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.cs.png) +![AND-OR strom](../../../../translated_images/cs/AND-OR-Tree.5592d2c70187f283.png) > Obrázek od [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 3412a5c3..b86b4095 100644 --- a/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/cs/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Pokud máme více než 2 třídy, softmax normalizuje pravděpodobnosti napříč všemi z nich. Zde je diagram architektury sítě, která provádí klasifikaci číslic MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.cs.png)\n" + "![MNIST Classifier](../../../../../translated_images/cs/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Nízká trénovací ztráta – model dokáže dobře aproximovat trénovací data, protože má dostatečnou vyjadřovací schopnost.\n", "* Validační ztráta může být mnohem vyšší než trénovací ztráta a během trénování může začít růst – to je způsobeno tím, že model „si pamatuje“ trénovací body a ztrácí „celkový přehled“.\n", "\n", - "![Přeučení](../../../../../translated_images/overfit.a0bd57f717c15769.cs.png)\n", + "![Přeučení](../../../../../translated_images/cs/overfit.a0bd57f717c15769.png)\n", "\n", "> Na tomto obrázku `x` označuje trénovací data, `o` validační data. Vlevo – lineární model (jednovrstvý), který přibližně odpovídá povaze dat. Vpravo – přeučený model, který dokonale aproximuje trénovací data, ale přestává dávat smysl pro jakákoli jiná data (validační chyba je velmi vysoká).\n" ] diff --git a/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md index edc9f030..80d028a6 100644 --- a/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/cs/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Přeučení je extrémně důležitý koncept v strojovém učení a je velmi d Zvažte následující problém aproximace 5 bodů (reprezentovaných `x` na grafech níže): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.cs.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.cs.jpg) +![linear](../../../../../translated_images/cs/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/cs/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineární model, 2 parametry** | **Nelineární model, 7 parametrů** Chyba trénování = 5.3 | Chyba trénování = 0 @@ -79,7 +79,7 @@ Je velmi důležité najít správnou rovnováhu mezi složitostí modelu (počt Jak můžete vidět z grafu výše, přeučení lze detekovat velmi nízkou chybou trénování a vysokou chybou validace. Během trénování obvykle vidíme, že chyby trénování i validace začínají klesat, a poté v určitém bodě může chyba validace přestat klesat a začít stoupat. To bude znakem přeučení a indikátorem, že bychom pravděpodobně měli v tomto bodě zastavit trénování (nebo alespoň vytvořit snímek modelu). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.cs.png) +![overfitting](../../../../../translated_images/cs/Overfitting.408ad91cd90b4371.png) ## Jak zabránit přeučení diff --git a/translations/cs/lessons/3-NeuralNetworks/README.md b/translations/cs/lessons/3-NeuralNetworks/README.md index 93a86eed..39f926ec 100644 --- a/translations/cs/lessons/3-NeuralNetworks/README.md +++ b/translations/cs/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do neuronových sítí -![Shrnutí obsahu Úvodu do neuronových sítí v kresbě](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.cs.png) +![Shrnutí obsahu Úvodu do neuronových sítí v kresbě](../../../../translated_images/cs/ai-neuralnetworks.1c687ae40bc86e83.png) Jak jsme si řekli v úvodu, jedním ze způsobů, jak dosáhnout inteligence, je trénovat **počítačový model** nebo **umělý mozek**. Od poloviny 20. století vědci zkoušeli různé matematické modely, až se v posledních letech ukázalo, že tento směr je velmi úspěšný. Tyto matematické modely mozku se nazývají **neuronové sítě**. @@ -36,13 +36,13 @@ V tomto kurzu se zaměříme pouze na modely neuronových sítí. Z biologie víme, že náš mozek se skládá z nervových buněk (neuronů), z nichž každá má několik "vstupů" (dendritů) a jeden "výstup" (axon). Dendrity i axony mohou vést elektrické signály a spojení mezi nimi — známá jako synapse — mohou vykazovat různé stupně vodivosti, které jsou regulovány neurotransmitery. -![Model neuronu](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.cs.jpg) | ![Model neuronu](../../../../translated_images/artneuron.1a5daa88d20ebe6f.cs.png) +![Model neuronu](../../../../translated_images/cs/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neuronu](../../../../translated_images/cs/artneuron.1a5daa88d20ebe6f.png) ----|---- Skutečný neuron *([Obrázek](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipedie)* | Umělý neuron *(Obrázek od autora)* Nejjednodušší matematický model neuronu tedy obsahuje několik vstupů X1, ..., XN a jeden výstup Y, a řadu vah W1, ..., WN. Výstup se vypočítá jako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kde f je nějaká nelineární **aktivační funkce**. diff --git a/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md index 527a7cfc..428c286d 100644 --- a/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/cs/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našem [OpenCV Notebook](OpenCV.ipynb) uvádíme některé příklady, kdy lze * **Předzpracování fotografie Braillovy knihy**. Zaměřujeme se na to, jak můžeme použít prahování, detekci prvků, perspektivní transformaci a manipulace s NumPy k oddělení jednotlivých Braillových symbolů pro další klasifikaci neuronovou sítí. -![Braillův obrázek](../../../../../translated_images/braille.341962ff76b1bd70.cs.jpeg) | ![Předzpracovaný Braillův obrázek](../../../../../translated_images/braille-result.46530fea020b03c7.cs.png) | ![Braillovy symboly](../../../../../translated_images/braille-symbols.0159185ab69d5339.cs.png) +![Braillův obrázek](../../../../../translated_images/cs/braille.341962ff76b1bd70.jpeg) | ![Předzpracovaný Braillův obrázek](../../../../../translated_images/cs/braille-result.46530fea020b03c7.png) | ![Braillovy symboly](../../../../../translated_images/cs/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) * **Detekce pohybu ve videu pomocí rozdílu snímků**. Pokud je kamera pevná, pak by snímky z kamerového záznamu měly být velmi podobné. Protože snímky jsou reprezentovány jako pole, pouhým odečtením těchto polí pro dva po sobě jdoucí snímky získáme rozdíl pixelů, který by měl být nízký pro statické snímky a stoupat, jakmile dojde k výraznému pohybu na obrázku. -![Obrázek video snímků a rozdílů snímků](../../../../../translated_images/frame-difference.706f805491a0883c.cs.png) +![Obrázek video snímků a rozdílů snímků](../../../../../translated_images/cs/frame-difference.706f805491a0883c.png) > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našem [OpenCV Notebook](OpenCV.ipynb) uvádíme některé příklady, kdy lze - **Hustý optický tok** počítá vektorové pole, které ukazuje, kam se každý pixel pohybuje. - **Řídký optický tok** je založen na výběru některých výrazných prvků na obrázku (např. hran) a sestavení jejich trajektorie snímek po snímku. -![Obrázek optického toku](../../../../../translated_images/optical.1f4a94464579a83a.cs.png) +![Obrázek optického toku](../../../../../translated_images/cs/optical.1f4a94464579a83a.png) > Obrázek z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 0d34f2f1..8f6642d0 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je síť, která dosáhla 92,7% přesnosti v top-5 klasifikaci ImageNet v roce 2014. Má následující strukturu vrstev: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.cs.jpg) +![ImageNet Layers](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.jpg) Jak můžete vidět, VGG sleduje tradiční pyramidovou architekturu, což je sekvence vrstev konvoluce a pooling. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.cs.jpg) +![ImageNet Pyramid](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.jpg) > Obrázek z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index cb6a0c9f..4fba326c 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "V typické CNN tedy existuje několik konvolučních vrstev, mezi nimiž jsou pooling vrstvy, které snižují rozměry obrazu. Zároveň zvyšujeme počet filtrů, protože jak se vzory stávají složitějšími, existuje více možných zajímavých kombinací, které je třeba hledat.\n", "\n", - "![Obrázek zobrazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.cs.png)\n", + "![Obrázek zobrazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kvůli snižování prostorových rozměrů a zvyšování rozměrů vlastností/filtrů se tato architektura také nazývá **pyramidová architektura**.\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 28d92404..1b43992d 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "V typické CNN tedy najdeme několik konvolučních vrstev, mezi nimiž jsou pooling vrstvy, které snižují rozměry obrazu. Zároveň zvyšujeme počet filtrů, protože jak se vzory stávají složitějšími, existuje více možných zajímavých kombinací, které je třeba hledat.\n", "\n", - "![Obrázek ukazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.cs.png)\n", + "![Obrázek ukazující několik konvolučních vrstev s pooling vrstvami.](../../../../../translated_images/cs/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kvůli snižování prostorových rozměrů a zvyšování rozměrů funkcí/filtrů se tato architektura také nazývá **pyramidová architektura**.\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md index 41dfbca2..2e0b87f4 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V reálném životě chceme být schopni rozpoznat objekty na obrázku bez ohled K extrakci vzorů použijeme koncept **konvolučních filtrů**. Jak víte, obrázek je reprezentován jako 2D-matice nebo 3D-tensor s barevnou hloubkou. Aplikace filtru znamená, že vezmeme relativně malou matici **filtračního jádra** a pro každý pixel v původním obrázku vypočítáme vážený průměr s okolními body. Můžeme si to představit jako malé okno, které se posouvá po celém obrázku a průměruje všechny pixely podle vah v matici filtračního jádra. -![Vertikální filtr hran](../../../../../translated_images/filter-vert.b7148390ca0bc356.cs.png) | ![Horizontální filtr hran](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.cs.png) +![Vertikální filtr hran](../../../../../translated_images/cs/filter-vert.b7148390ca0bc356.png) | ![Horizontální filtr hran](../../../../../translated_images/cs/filter-horiz.59b80ed4feb946ef.png) ----|---- > Obrázek od Dmitry Soshnikov @@ -38,7 +38,7 @@ Fungování CNN je založeno na následujících důležitých principech: * Síť můžeme navrhnout tak, aby se filtry učily automaticky * Stejný přístup můžeme použít k hledání vzorů ve vysokoúrovňových rysech, nejen v původním obrázku. Extrakce rysů pomocí CNN tedy funguje na hierarchii rysů, počínaje nízkoúrovňovými kombinacemi pixelů až po vysokoúrovňové kombinace částí obrázku. -![Hierarchická extrakce rysů](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.cs.png) +![Hierarchická extrakce rysů](../../../../../translated_images/cs/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Obrázek z [práce Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), založené na [jejich výzkumu](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Většina CNN používaných pro zpracování obrázků následuje tzv. pyramido Jako příklad se podívejme na architekturu VGG-16, sítě, která dosáhla 92,7% přesnosti v top-5 klasifikaci ImageNetu v roce 2014: -![Vrstvy ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.cs.jpg) +![Vrstvy ImageNet](../../../../../translated_images/cs/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Pyramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.cs.jpg) +![Pyramida ImageNet](../../../../../translated_images/cs/vgg-16-arch.64ff2137f50dd49f.jpg) > Obrázek z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ae5d4a81..5dd9bbdc 100644 --- a/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/cs/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaším úkolem je natrénovat konvoluční neuronovou síť, která bude klasif Použijeme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), který obsahuje obrázky 37 různých plemen psů a koček. -![Dataset, se kterým budeme pracovat](../../../../../../translated_images/data.50b2a9d5484bdbf0.cs.png) +![Dataset, se kterým budeme pracovat](../../../../../../translated_images/cs/data.50b2a9d5484bdbf0.png) Pro stažení datasetu použijte tento kód: diff --git a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 150943d9..62a3ce91 100644 --- a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Abychom si představili ideální kočku, začneme s obrázkem náhodného šumu a pokusíme se použít optimalizační techniku gradientního sestupu k úpravě obrázku tak, aby síť rozpoznala kočku.\n", "\n", - "![Optimalizační smyčka](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.cs.png)\n", + "![Optimalizační smyčka](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Zde je náš výchozí obrázek:\n" ] diff --git a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md index feff0a36..e681c0c4 100644 --- a/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/cs/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras i PyTorch obsahují funkce pro snadné načtení předtrénovaných vah ne Zde jsou ukázkové rysy extrahované z obrázku kočky pomocí sítě VGG-16: -![Rysy extrahované sítí VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.cs.png) +![Rysy extrahované sítí VGG-16](../../../../../translated_images/cs/features.6291f9c7ba3a0b95.png) ## Dataset Kočky vs. Psi @@ -48,19 +48,19 @@ Předtrénovaná neuronová síť obsahuje různé vzory uvnitř svého *mozku*, Jedním z přístupů, které můžeme použít, je začít s náhodným obrázkem a poté se pokusit pomocí techniky **optimalizace gradientního sestupu** upravit tento obrázek tak, aby si síť začala myslet, že je to kočka. -![Optimalizační smyčka obrázku](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.cs.png) +![Optimalizační smyčka obrázku](../../../../../translated_images/cs/ideal-cat-loop.999fbb8ff306e044.png) Pokud to však uděláme, obdržíme něco velmi podobného náhodnému šumu. To je proto, že *existuje mnoho způsobů, jak síť přimět myslet si, že vstupní obrázek je kočka*, včetně některých, které vizuálně nedávají smysl. Zatímco tyto obrázky obsahují mnoho vzorů typických pro kočku, nic je neomezuje, aby byly vizuálně rozlišitelné. Pro zlepšení výsledku můžeme do ztrátové funkce přidat další člen, který se nazývá **variation loss**. Je to metrika, která ukazuje, jak podobné jsou sousední pixely obrázku. Minimalizace variation loss činí obrázek hladším a zbavuje se šumu – tím odhaluje vizuálně přitažlivější vzory. Zde je příklad takových "ideálních" obrázků, které jsou klasifikovány jako kočka a jako zebra s vysokou pravděpodobností: -![Ideální kočka](../../../../../translated_images/ideal-cat.203dd4597643d6b0.cs.png) | ![Ideální zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.cs.png) +![Ideální kočka](../../../../../translated_images/cs/ideal-cat.203dd4597643d6b0.png) | ![Ideální zebra](../../../../../translated_images/cs/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideální kočka* | *Ideální zebra* Podobný přístup lze použít k provádění tzv. **adversarial útoků** na neuronovou síť. Představme si, že chceme oklamat neuronovou síť a přimět ji, aby psa považovala za kočku. Pokud vezmeme obrázek psa, který je sítí rozpoznán jako pes, můžeme jej trochu upravit pomocí optimalizace gradientního sestupu, dokud síť nezačne klasifikovat obrázek jako kočku: -![Obrázek psa](../../../../../translated_images/original-dog.8f68a67d2fe0911f.cs.png) | ![Obrázek psa klasifikovaný jako kočka](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.cs.png) +![Obrázek psa](../../../../../translated_images/cs/original-dog.8f68a67d2fe0911f.png) | ![Obrázek psa klasifikovaný jako kočka](../../../../../translated_images/cs/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Původní obrázek psa* | *Obrázek psa klasifikovaný jako kočka* diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 7abd420a..c385a1f5 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Protože trénujeme autoencoder, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam.\n", "\n", - "![Schéma AutoEncoderu](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.cs.jpg)\n", + "![Schéma AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Obrázek z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9ea924fa..67db20fc 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Protože trénujeme autoencoder, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam.\n", "\n", - "![Diagram AutoEncoderu](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.cs.jpg)\n", + "![Diagram AutoEncoderu](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Obrázek z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md index 1dfa12a9..4e2e5345 100644 --- a/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/cs/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Nicméně bychom mohli chtít použít surová (neoznačená) data pro trénová Protože trénujeme autoenkodér, aby zachytil co nejvíce informací z původního obrázku pro přesnou rekonstrukci, síť se snaží najít nejlepší **embedding** vstupních obrázků, aby zachytila jejich význam. -![Schéma Autoenkodéru](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.cs.jpg) +![Schéma Autoenkodéru](../../../../../translated_images/cs/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Obrázek z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md index 30d200ff..a159ceea 100644 --- a/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/cs/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modely pro klasifikaci obrázků, se kterými jsme se dosud zabývali, přijíma ## [Kvíz před lekcí](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekce objektů](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.cs.png) +![Detekce objektů](../../../../../translated_images/cs/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Obrázek z [webu YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Předpokládejme, že chceme najít kočku na obrázku. Velmi naivní přístup 2. Proveďte klasifikaci obrázků na každé dlaždici. 3. Dlaždice, které vykazují dostatečně vysokou aktivaci, lze považovat za obsahující hledaný objekt. -![Naivní detekce objektů](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.cs.png) +![Naivní detekce objektů](../../../../../translated_images/cs/naive-detection.e7f1ba220ccd08c6.png) > *Obrázek z [cvičebního notebooku](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Můžete narazit na následující datové sady pro tento úkol: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 tříd * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 tříd, ohraničující rámečky a segmentační masky -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.cs.jpg) +![COCO](../../../../../translated_images/cs/coco-examples.71bc60380fa6cceb.jpg) ## Metriky pro detekci objektů @@ -50,7 +50,7 @@ Můžete narazit na následující datové sady pro tento úkol: Zatímco u klasifikace obrázků je snadné měřit, jak dobře algoritmus funguje, u detekce objektů musíme měřit jak správnost třídy, tak přesnost určené polohy ohraničujícího rámečku. Pro druhé zmíněné používáme tzv. **Průnik přes sjednocení** (IoU), který měří, jak dobře se dva rámečky (nebo dvě libovolné oblasti) překrývají. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.cs.png) +![IoU](../../../../../translated_images/cs/iou_equation.9a4751d40fff4e11.png) > *Obrázek 2 z [tohoto skvělého blogového příspěvku o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existují dvě široké kategorie algoritmů pro detekci objektů: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) používá [Selektivní vyhledávání](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) k vytvoření hierarchické struktury oblastí ROI, které jsou následně zpracovány extraktory funkcí CNN a klasifikátory SVM k určení třídy objektu, a lineární regresí k určení souřadnic *ohraničujícího rámečku*. [Oficiální článek](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.cs.png) +![RCNN](../../../../../translated_images/cs/rcnn1.cae407020dfb1d1f.png) > *Obrázek od van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.cs.png) +![RCNN-1](../../../../../translated_images/cs/rcnn2.2d9530bb83516484.png) > *Obrázky z [tohoto blogu](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existují dvě široké kategorie algoritmů pro detekci objektů: Tento přístup je podobný R-CNN, ale oblasti jsou definovány po aplikaci konvolučních vrstev. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.cs.png) +![FRCNN](../../../../../translated_images/cs/f-rcnn.3cda6d9bb4188875.png) > Obrázek z [oficiálního článku](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tento přístup je podobný R-CNN, ale oblasti jsou definovány po aplikaci konv Hlavní myšlenkou tohoto přístupu je použití neuronové sítě k předpovědi ROI – tzv. *Region Proposal Network*. [Článek](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.cs.png) +![FasterRCNN](../../../../../translated_images/cs/faster-rcnn.8d46c099b87ef30a.png) > Obrázek z [oficiálního článku](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tento algoritmus je ještě rychlejší než Faster R-CNN. Hlavní myšlenka je 2. Funkce jsou zpracovány pomocí **Position-Sensitive Score Map**. Každý objekt z $C$ tříd je rozdělen na $k\times k$ oblasti a trénujeme na předpověď částí objektů. 3. Pro každou část z $k\times k$ oblastí všechny sítě hlasují pro třídy objektů a třída objektu s maximálním počtem hlasů je vybrána. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.cs.png) +![r-fcn image](../../../../../translated_images/cs/r-fcn.13eb88158b99a3da.png) > Obrázek z [oficiálního článku](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritmus pro detekci v reálném čase s jedním průchodem. Hlavní m * Obrázek je rozdělen na $S\times S$ oblasti. * Pro každou oblast **CNN** předpovídá $n$ možných objektů, souřadnice *ohraničujícího rámečku* a *důvěru*=*pravděpodobnost* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.cs.png) + ![YOLO](../../../../../translated_images/cs/yolo.a2648ec82ee8bb4e.png) > Obrázek z [oficiálního článku](https://arxiv.org/abs/1506.02640) diff --git a/translations/cs/lessons/4-ComputerVision/README.md b/translations/cs/lessons/4-ComputerVision/README.md index a000a6bf..6f9f421a 100644 --- a/translations/cs/lessons/4-ComputerVision/README.md +++ b/translations/cs/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Počítačové vidění -![Shrnutí obsahu Počítačového vidění ve formě kresby](../../../../translated_images/ai-computervision.6506ebebac3fbf76.cs.png) +![Shrnutí obsahu Počítačového vidění ve formě kresby](../../../../translated_images/cs/ai-computervision.6506ebebac3fbf76.png) V této sekci se naučíme: diff --git a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index e21f9d53..fbd04708 100644 --- a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je nejčastěji používaná tradiční vektorová reprezentace. Každé slovo je spojeno s indexem vektoru, prvek vektoru obsahuje počet výskytů daného slova v konkrétním dokumentu.\n", "\n", - "![Obrázek ukazující, jak je v paměti reprezentována vektorová reprezentace Bag of Words.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.cs.png) \n", + "![Obrázek ukazující, jak je v paměti reprezentována vektorová reprezentace Bag of Words.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Na BoW můžete také nahlížet jako na součet všech vektorů zakódovaných metodou one-hot pro jednotlivá slova v textu.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c9a17c8e..e500f47e 100644 --- a/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/cs/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Reprezentace vektoru **Bag-of-words** (BoW) je nejjednodušší tradiční vektorová reprezentace na pochopení. Každé slovo je spojeno s indexem vektoru a prvek vektoru obsahuje počet výskytů každého slova v daném dokumentu.\n", "\n", - "![Obrázek ukazující, jak je reprezentace Bag-of-words uložena v paměti.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.cs.png) \n", + "![Obrázek ukazující, jak je reprezentace Bag-of-words uložena v paměti.](../../../../../translated_images/cs/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Na BoW můžete také nahlížet jako na součet všech one-hot-encoded vektorů pro jednotlivá slova v textu.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 65120ef3..741b2ce6 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Použitím embeddingové vrstvy jako první vrstvy v naší síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v našem textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci přes všechny tyto embeddingy, například `sum`, `average` nebo `max`.\n", "\n", - "![Obrázek ukazující klasifikátor embeddingu pro pět slov v sekvenci.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.cs.png)\n", + "![Obrázek ukazující klasifikátor embeddingu pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naše klasifikační neuronová síť začne embeddingovou vrstvou, poté agregační vrstvou a na vrcholu bude lineární klasifikátor:\n" ] @@ -176,7 +176,7 @@ "\n", "V předchozí architektuře jsme museli všechny sekvence doplnit na stejnou délku, aby se vešly do minibatch. To není nejefektivnější způsob, jak reprezentovat sekvence s proměnnou délkou – jiný přístup by byl použití **offsetového** vektoru, který by obsahoval offsety všech sekvencí uložených v jednom velkém vektoru.\n", "\n", - "![Obrázek znázorňující reprezentaci sekvencí pomocí offsetů](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.cs.png)\n", + "![Obrázek znázorňující reprezentaci sekvencí pomocí offsetů](../../../../../translated_images/cs/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Na obrázku výše je znázorněna sekvence znaků, ale v našem příkladu pracujeme se sekvencemi slov. Nicméně obecný princip reprezentace sekvencí pomocí offsetového vektoru zůstává stejný.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova.\n", "\n", - "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.cs.png)\n", + "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Pro experimentování s Word2Vec vektory předem natrénovanými na datasetu Google News můžeme použít knihovnu **gensim**. Níže najdeme slova nejpodobnější slovu 'neural'.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 1328c003..7b7473f1 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/cs/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Použitím embedding vrstvy jako první vrstvy v naší síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v našem textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci nad všemi těmito embeddingy, například `sum`, `average` nebo `max`.\n", "\n", - "![Obrázek ukazující embedding klasifikátor pro pět slov v sekvenci.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.cs.png)\n", + "![Obrázek ukazující embedding klasifikátor pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naše neuronová síť klasifikátoru se skládá z následujících vrstev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova.\n", "\n", - "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.cs.png)\n", + "![Obrázek ukazující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Pro experimentování s Word2Vec vektorizací předtrénovanou na datasetu Google News můžeme použít knihovnu **gensim**. Níže najdeme slova nejpodobnější slovu 'neural'.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/14-Embeddings/README.md b/translations/cs/lessons/5-NLP/14-Embeddings/README.md index 606e92c5..a7dd5cb1 100644 --- a/translations/cs/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/cs/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vrstva embedding tedy přijme slovo jako vstup a vytvoří výstupní vektor o s Použitím vrstvy embedding jako první vrstvy v naší klasifikační síti můžeme přejít od modelu bag-of-words k modelu **embedding bag**, kde nejprve převedeme každé slovo v textu na odpovídající embedding a poté vypočítáme nějakou agregační funkci nad všemi těmito embeddingy, například `sum`, `average` nebo `max`. -![Obrázek znázorňující klasifikátor s embeddingy pro pět slov v sekvenci.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.cs.png) +![Obrázek znázorňující klasifikátor s embeddingy pro pět slov v sekvenci.](../../../../../translated_images/cs/embedding-classifier-example.b77f021a7ee67eee.png) > Obrázek od autora @@ -40,7 +40,7 @@ K tomu je třeba předtrénovat model embedding na velké kolekci textů specifi CBoW je rychlejší, zatímco skip-gram je pomalejší, ale lépe reprezentuje méně častá slova. -![Obrázek znázorňující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.cs.png) +![Obrázek znázorňující algoritmy CBoW a Skip-Gram pro převod slov na vektory.](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obrázek z [tohoto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md b/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md index 82fba364..b94ea9e0 100644 --- a/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/cs/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V našich předchozích příkladech jsme používali předtrénované sémantic * **Continuous Bag-of-Words** (CBoW), kdy předpovídáme prostřední token $W_0$ v sekvenci tokenů $W_{-N}$, ..., $W_N$. * **Skip-gram**, kde předpovídáme sadu sousedních tokenů {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} na základě prostředního tokenu $W_0$. -![obrázek z článku o převodu slov na vektory](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.cs.png) +![obrázek z článku o převodu slov na vektory](../../../../../translated_images/cs/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obrázek z [tohoto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/cs/lessons/5-NLP/16-RNN/README.md b/translations/cs/lessons/5-NLP/16-RNN/README.md index 507efba0..aea81fc4 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/README.md +++ b/translations/cs/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V předchozích sekcích jsme používali bohaté sémantické reprezentace text Abychom zachytili význam textové sekvence, musíme použít jinou architekturu neuronové sítě, která se nazývá **rekurentní neuronová síť** (RNN). V RNN prochází věta sítí jeden symbol po druhém a síť produkuje nějaký **stav**, který se poté předává síti spolu s dalším symbolem. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.cs.png) +![RNN](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.png) > Obrázek od autora @@ -61,7 +61,7 @@ Diskutovali jsme o rekurentních sítích, které fungují jedním směrem, od z Rekurentní síť, ať už jednosměrná nebo bidirekcionální, zachycuje určité vzory v sekvenci a může je uložit do stavového vektoru nebo předat do výstupu. Stejně jako u konvolučních sítí můžeme na první vrstvu postavit další rekurentní vrstvu, která zachytí vzory na vyšší úrovni a vytvoří vzory na nižší úrovni extrahované první vrstvou. To nás vede k pojmu **vícevrstvá RNN**, která se skládá ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán další vrstvě jako vstup. -![Obrázek ukazující vícevrstvou LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.cs.jpg) +![Obrázek ukazující vícevrstvou LSTM RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Obrázek z [tohoto skvělého příspěvku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze* diff --git a/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index c4842590..7d9a6e9f 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentní síť, ať už jednosměrná nebo obousměrná, zachycuje určité vzory v rámci sekvence a může je uložit do stavu nebo předat do výstupu. Stejně jako u konvolučních sítí můžeme na první vrstvu postavit další rekurentní vrstvu, která zachytí vzory na vyšší úrovni, vytvořené z nízkoúrovňových vzorů extrahovaných první vrstvou. To nás přivádí k pojmu **vícevrstvé RNN**, která se skládá ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán jako vstup do následující vrstvy.\n", "\n", - "![Obrázek zobrazující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.cs.jpg)\n", + "![Obrázek zobrazující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Obrázek z [tohoto skvělého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze*\n", "\n", diff --git a/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb index 17bc0170..093fff5a 100644 --- a/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/cs/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Abychom zachytili význam textové sekvence, použijeme architekturu neuronové sítě nazývanou **rekurentní neuronová síť** (RNN). Při použití RNN procházíme větou sítí po jednom tokenu a síť produkuje určitý **stav**, který poté předáváme síti spolu s dalším tokenem.\n", "\n", - "![Obrázek ukazující příklad generování rekurentní neuronové sítě.](../../../../../translated_images/rnn.27f5c29c53d727b5.cs.png)\n", + "![Obrázek ukazující příklad generování rekurentní neuronové sítě.](../../../../../translated_images/cs/rnn.27f5c29c53d727b5.png)\n", "\n", "Při dané vstupní sekvenci tokenů $X_0,\\dots,X_n$ RNN vytváří sekvenci bloků neuronové sítě a trénuje tuto sekvenci end-to-end pomocí zpětné propagace. Každý blok sítě přijímá dvojici $(X_i,S_i)$ jako vstup a produkuje $S_{i+1}$ jako výsledek. Konečný stav $S_n$ nebo výstup $Y_n$ se předává do lineárního klasifikátoru, aby se vytvořil výsledek. Všechny bloky sítě sdílejí stejné váhy a jsou trénovány end-to-end jedním průchodem zpětné propagace.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentní sítě, ať už jednosměrné nebo obousměrné, zachycují vzory v rámci sekvence a ukládají je do stavových vektorů nebo je vracejí jako výstup. Stejně jako u konvolučních sítí můžeme vytvořit další rekurentní vrstvu, která následuje po první, aby zachytila vzory na vyšší úrovni, vytvořené z nižších úrovní vzorů extrahovaných první vrstvou. To nás přivádí k pojmu **vícevrstvé RNN**, které se skládají ze dvou nebo více rekurentních sítí, kde výstup předchozí vrstvy je předán jako vstup další vrstvě.\n", "\n", - "![Obrázek znázorňující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.cs.jpg)\n", + "![Obrázek znázorňující vícevrstvou dlouhodobou krátkodobou paměťovou RNN](../../../../../translated_images/cs/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Obrázek z [tohoto skvělého příspěvku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeze.*\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 76023f19..e688fa62 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Způsob, jakým budeme trénovat RNN pro generování textu, je následující. Při každém kroku vezmeme sekvenci znaků o délce `nchars` a požádáme síť, aby pro každý vstupní znak vygenerovala následující výstupní znak:\n", "\n", - "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.cs.png)\n", + "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png)\n", "\n", "V závislosti na konkrétním scénáři můžeme také chtít zahrnout některé speciální znaky, jako například *konec sekvence* ``. V našem případě chceme síť trénovat pouze pro nekonečné generování textu, a proto nastavíme velikost každé sekvence na `nchars` tokenů. Každý tréninkový příklad tedy bude sestávat z `nchars` vstupů a `nchars` výstupů (což je vstupní sekvence posunutá o jeden symbol doleva). Minibatch bude obsahovat několik takových sekvencí.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index edcfa14b..52eb8e0c 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Způsob, jakým budeme trénovat RNN na generování nadpisů zpráv, je následující. V každém kroku vezmeme jeden nadpis, který bude předán do RNN, a pro každý vstupní znak požádáme síť, aby vygenerovala následující výstupní znak:\n", "\n", - "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.cs.png)\n", + "![Obrázek ukazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Pro poslední znak naší sekvence požádáme síť, aby vygenerovala token ``.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md index 05a4398f..74f84d8e 100644 --- a/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/cs/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V architektuře RNN, kterou jsme probírali v předchozí kapitole, každá jedn To umožňuje různé neuronové architektury, jak je znázorněno na obrázku níže: -![Obrázek zobrazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.cs.jpg) +![Obrázek zobrazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Obrázek z blogového příspěvku [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreje Karpatyho](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V této kapitole se zaměříme na jednoduché generativní modely, které nám Tuto RNN budeme trénovat na generování textu krok za krokem. Na každém kroku vezmeme sekvenci znaků o délce `nchars` a požádáme síť, aby pro každý vstupní znak vygenerovala další výstupní znak: -![Obrázek zobrazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.cs.png) +![Obrázek zobrazující příklad generování slova 'HELLO' pomocí RNN.](../../../../../translated_images/cs/rnn-generate.56c54afb52f9781d.png) Při generování textu (během inference) začínáme s nějakým **podnětem**, který je předán přes RNN buňky pro vytvoření mezistavu, a poté začíná samotné generování. Generujeme jeden znak po druhém a předáváme stav a vygenerovaný znak další RNN buňce, aby vygenerovala další znak, dokud nevygenerujeme dostatek znaků. diff --git a/translations/cs/lessons/5-NLP/18-Transformers/README.md b/translations/cs/lessons/5-NLP/18-Transformers/README.md index 1cbf338e..1fe9460d 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/README.md +++ b/translations/cs/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ U RNN je sekvence na sekvenci implementována dvěma rekurentními sítěmi, kde **Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv každého vstupního vektoru na každou výstupní predikci RNN. Implementuje se to vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem při generování výstupního symbolu yt zohledníme všechny skryté stavy vstupu hi, s různými váhovými koeficienty αt,i. -![Obrázek zobrazující model enkodér/dekodér s vrstvou aditivní pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.cs.png) +![Obrázek zobrazující model enkodér/dekodér s vrstvou aditivní pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png) > Model enkodér-dekodér s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matice pozornosti {αi,j} by reprezentovala míru, jakou určitá vstupní slova ovlivňují generování daného slova ve výstupní sekvenci. Níže je příklad takové matice: -![Obrázek zobrazující vzorové zarovnání nalezené RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.cs.png) +![Obrázek zobrazující vzorové zarovnání nalezené RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png) > Obrázek z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3) @@ -66,7 +66,7 @@ Výsledek, který získáme s pozičním embeddingem, zahrnuje jak původní tok Dále potřebujeme zachytit určité vzorce v rámci naší sekvence. K tomu transformery používají mechanismus **vlastní pozornosti**, což je v podstatě pozornost aplikovaná na stejnou sekvenci jako vstup a výstup. Aplikace vlastní pozornosti nám umožňuje zohlednit **kontext** v rámci věty a vidět, která slova jsou vzájemně propojená. Například nám umožňuje vidět, na která slova odkazují koreference, jako *to*, a také zohlednit kontext: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.cs.png) +![](../../../../../translated_images/cs/CoreferenceResolution.861924d6d384a7d6.png) > Obrázek z [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Protože každá vstupní pozice je mapována nezávisle na každou výstupní p **BERT** (Bidirectional Encoder Representations from Transformers) je velmi velká vícevstvá síť transformeru s 12 vrstvami pro *BERT-base* a 24 pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí nesupervizovaného tréninku (predikce maskovaných slov ve větě). Během předtrénování model absorbuje významné úrovně porozumění jazyku, které lze následně využít s jinými datovými sadami pomocí jemného ladění. Tento proces se nazývá **transfer learning**. -![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.cs.png) +![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Obrázek [zdroj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 1b630e51..794d66f7 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/cs/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv jednotlivých vstupních vektorů na každou výstupní predikci RNN. To se implementuje vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem, při generování výstupního symbolu $y_t$, bereme v úvahu všechny skryté stavy vstupu $h_i$ s různými váhovými koeficienty $\\alpha_{t,i}$.\n", "\n", - "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.cs.png) \n", + "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png) \n", "*Model encoder-decoder s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matice pozornosti $\\{\\alpha_{i,j}\\}$ reprezentuje míru, do jaké určitá vstupní slova ovlivňují generování konkrétního slova ve výstupní sekvenci. Níže je příklad takové matice:\n", "\n", - "![Obrázek zobrazující příklad zarovnání nalezeného RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.cs.png) \n", + "![Obrázek zobrazující příklad zarovnání nalezeného RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png) \n", "\n", "*Obrázek převzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr. 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je velmi velká vícevrstvá síť Transformer s 12 vrstvami pro *BERT-base* a 24 pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí neřízeného učení (predikce maskovaných slov ve větě). Během předtrénování model absorbuje významnou úroveň porozumění jazyku, kterou lze následně využít s jinými datovými sadami pomocí doladění. Tento proces se nazývá **transfer learning**.\n", "\n", - "![Obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.cs.png)\n", + "![Obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existuje mnoho variant architektur Transformer, včetně BERT, DistilBERT, BigBird, OpenGPT3 a dalších, které lze doladit. Balíček [HuggingFace](https://github.com/huggingface/) poskytuje repozitář pro trénování mnoha těchto architektur pomocí PyTorch.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 1ebac137..5492d92a 100644 --- a/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/cs/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanismy pozornosti** poskytují způsob, jak vážit kontextuální vliv jednotlivých vstupních vektorů na každou výstupní predikci RNN. Toho je dosaženo vytvořením zkratek mezi mezistavy vstupní RNN a výstupní RNN. Tímto způsobem, při generování výstupního symbolu $y_t$, bereme v úvahu všechny skryté stavy vstupu $h_i$ s různými váhovými koeficienty $\\alpha_{t,i}$. \n", "\n", - "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.cs.png)\n", + "![Obrázek zobrazující model encoder/decoder s aditivní vrstvou pozornosti](../../../../../translated_images/cs/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder s mechanismem aditivní pozornosti podle [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citováno z [tohoto blogového příspěvku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matice pozornosti $\\{\\alpha_{i,j}\\}$ reprezentuje míru, do jaké určitá vstupní slova ovlivňují generování konkrétního slova ve výstupní sekvenci. Níže je příklad takové matice:\n", "\n", - "![Obrázek zobrazující ukázkové zarovnání nalezené modelem RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.cs.png)\n", + "![Obrázek zobrazující ukázkové zarovnání nalezené modelem RNNsearch-50, převzato z Bahdanau - arviz.org](../../../../../translated_images/cs/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Obrázek převzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr. 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je velmi rozsáhlá vícevrstvá transformátorová síť s 12 vrstvami pro *BERT-base* a 24 vrstvami pro *BERT-large*. Model je nejprve předtrénován na velkém korpusu textových dat (WikiPedia + knihy) pomocí nesupervizovaného učení (předpovídání maskovaných slov ve větě). Během předtrénování model získává významnou úroveň porozumění jazyku, kterou lze následně využít s jinými datovými sadami pomocí jemného ladění. Tento proces se nazývá **transferové učení**.\n", "\n", - "![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.cs.png)\n", + "![obrázek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/cs/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existuje mnoho variant architektur Transformerů, včetně BERT, DistilBERT, BigBird, OpenGPT3 a dalších, které lze jemně doladit.\n", "\n", diff --git a/translations/cs/lessons/5-NLP/19-NER/README.md b/translations/cs/lessons/5-NLP/19-NER/README.md index 2f235675..c66d3439 100644 --- a/translations/cs/lessons/5-NLP/19-NER/README.md +++ b/translations/cs/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorozence | O Protože potřebujeme vytvořit jednoznačnou korespondenci mezi tokeny a třídami, můžeme trénovat pravostranný **many-to-many** model neuronové sítě podle tohoto obrázku: -![Obrázek ukazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.cs.jpg) +![Obrázek ukazující běžné vzory rekurentních neuronových sítí.](../../../../../translated_images/cs/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Obrázek z [tohoto blogového příspěvku](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreje Karpathyho](http://karpathy.github.io/). Modely pro klasifikaci tokenů v NER odpovídají pravostranné architektuře na tomto obrázku.* diff --git a/translations/cs/lessons/5-NLP/README.md b/translations/cs/lessons/5-NLP/README.md index 7a2ff6ae..54723db9 100644 --- a/translations/cs/lessons/5-NLP/README.md +++ b/translations/cs/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Zpracování přirozeného jazyka -![Shrnutí úkolů NLP na kresbě](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.cs.png) +![Shrnutí úkolů NLP na kresbě](../../../../translated_images/cs/ai-nlp.b22dcb8ca4707cea.png) V této sekci se zaměříme na použití neuronových sítí k řešení úkolů spojených se **zpracováním přirozeného jazyka (NLP)**. Existuje mnoho problémů v oblasti NLP, které bychom chtěli, aby počítače dokázaly vyřešit: diff --git a/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md b/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md index 0be21733..49a35624 100644 --- a/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/cs/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Můžete otevřít jeden z modelů, například **Biology → Flocking* Po otevření modelu se dostanete na hlavní obrazovku NetLogo. Zde je ukázkový model, který popisuje populaci vlků a ovcí, vzhledem k omezeným zdrojům (tráva). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.cs.png) +![NetLogo Main Screen](../../../../../translated_images/cs/NetLogo-Main.32653711ec1a01b3.png) > Screenshot od Dmitry Soshnikov diff --git a/translations/cs/lessons/README.md b/translations/cs/lessons/README.md index 61cb4ef5..ac665c77 100644 --- a/translations/cs/lessons/README.md +++ b/translations/cs/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Přehled -![Přehled v kresbě](../../../translated_images/ai-overview.0857791951d19500.cs.png) +![Přehled v kresbě](../../../translated_images/cs/ai-overview.0857791951d19500.png) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/cs/lessons/X-Extras/X1-MultiModal/README.md b/translations/cs/lessons/X-Extras/X1-MultiModal/README.md index b32acafb..36435456 100644 --- a/translations/cs/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/cs/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po úspěchu modelů transformerů při řešení úloh NLP byly stejné nebo po Hlavní myšlenkou CLIP je schopnost porovnávat textové výzvy s obrázkem a určit, jak dobře obrázek odpovídá dané výzvě. -![CLIP Architektura](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.cs.png) +![CLIP Architektura](../../../../../translated_images/cs/clip-arch.b3dbf20b4e8ed8be.png) > *Obrázek z [tohoto blogového příspěvku](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Jakmile je model předtrénován, můžeme mu předložit dávku obrázků a tex Předpokládejme, že potřebujeme klasifikovat obrázky například na kočky, psy a lidi. V tomto případě můžeme modelu předložit obrázek a sérii textových výzev: "*obrázek kočky*", "*obrázek psa*", "*obrázek člověka*". Ve výsledném vektoru s 3 pravděpodobnostmi stačí vybrat index s nejvyšší hodnotou. -![CLIP pro Klasifikaci Obrázků](../../../../../translated_images/clip-class.3af42ef0b2b19369.cs.png) +![CLIP pro Klasifikaci Obrázků](../../../../../translated_images/cs/clip-class.3af42ef0b2b19369.png) > *Obrázek z [tohoto blogového příspěvku](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Více o VQGAN se dozvíte na webu [Taming Transformers](https://compvis.github.i Jedním z důležitých rozdílů mezi VQGAN a tradičním GAN je, že tradiční GAN může vytvořit slušný obrázek z jakéhokoli vstupního vektoru, zatímco VQGAN pravděpodobně vytvoří obrázek, který nebude koherentní. Proto je třeba dále řídit proces tvorby obrázku, což lze provést pomocí CLIP. -![VQGAN+CLIP Architektura](../../../../../translated_images/vqgan.5027fe05051dfa31.cs.png) +![VQGAN+CLIP Architektura](../../../../../translated_images/cs/vqgan.5027fe05051dfa31.png) Pro generování obrázku odpovídajícího textové výzvě začneme s nějakým náhodným kódovacím vektorem, který je předán přes VQGAN k vytvoření obrázku. Poté je použit CLIP k vytvoření ztrátové funkce, která ukazuje, jak dobře obrázek odpovídá textové výzvě. Cílem je minimalizovat tuto ztrátu pomocí zpětné propagace k úpravě parametrů vstupního vektoru. Skvělá knihovna, která implementuje VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Obrázek vytvořený Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.cs.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.cs.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.cs.png) +![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Obrázek vytvořený na základě výzvy *detailní akvarelový portrét mladého učitele literatury s knihou* | Obrázek vytvořený na základě výzvy *detailní olejový portrét mladé učitelky informatiky s počítačem* | Obrázek vytvořený na základě výzvy *detailní olejový portrét starého učitele matematiky před tabulí* @@ -75,7 +75,7 @@ Na rozdíl od CLIP přijímá DALL-E text i obrázek jako jeden tok tokenů pro Hlavní rozdíl mezi DALL.E 1 a 2 je, že DALL.E 2 generuje realističtější obrázky a umění. Příklady generování obrázků pomocí DALL-E: -![Obrázek vytvořený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.cs.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.cs.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.cs.png) +![Obrázek vytvořený Pixray](../../../../../translated_images/cs/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Obrázek vytvořený Pixray](../../../../../translated_images/cs/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Obrázek vytvořený na základě výzvy *detailní akvarelový portrét mladého učitele literatury s knihou* | Obrázek vytvořený na základě výzvy *detailní olejový portrét mladé učitelky informatiky s počítačem* | Obrázek vytvořený na základě výzvy *detailní olejový portrét starého učitele matematiky před tabulí* diff --git a/translations/da/README.md b/translations/da/README.md index 1727f8be..a6b4c346 100644 --- a/translations/da/README.md +++ b/translations/da/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstig intelligens for begyndere - Et pensum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.da.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/da/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote af [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/da/lessons/1-Intro/README.md b/translations/da/lessons/1-Intro/README.md index 516c8dc6..b9225132 100644 --- a/translations/da/lessons/1-Intro/README.md +++ b/translations/da/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion til AI -![Oversigt over introduktion til AI-indhold i en doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c.da.png) +![Oversigt over introduktion til AI-indhold i en doodle](../../../../translated_images/da/ai-intro.bf28d1ac4235881c.png) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Oprindeligt blev computere opfundet af [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) til at arbejde med tal ved at følge en veldefineret procedure - en algoritme. Moderne computere, selvom de er betydeligt mere avancerede end den oprindelige model foreslået i det 19. århundrede, følger stadig den samme idé om kontrollerede beregninger. Derfor er det muligt at programmere en computer til at udføre noget, hvis vi kender den præcise rækkefølge af trin, der skal til for at nå målet. -![Foto af en person](../../../../translated_images/dsh_age.d212a30d4e54fb5f.da.png) +![Foto af en person](../../../../translated_images/da/dsh_age.d212a30d4e54fb5f.png) > Foto af [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ For mere information henvises til **[Artificial General Intelligence](https://en Et af problemerne ved at arbejde med begrebet **[Intelligens](https://en.wikipedia.org/wiki/Intelligence)** er, at der ikke findes en klar definition af dette begreb. Man kan argumentere for, at intelligens er forbundet med **abstrakt tænkning** eller **selvbevidsthed**, men vi kan ikke definere det præcist. -![Foto af en kat](../../../../translated_images/photo-cat.8c8e8fb760ffe457.da.jpg) +![Foto af en kat](../../../../translated_images/da/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) af [Amber Kipp](https://unsplash.com/@sadmax) fra Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi forsøge at modellere de simpleste elementer i vores hjerne > | Hvad med ML? | | > |--------------|-----------| -> | En del af Kunstig Intelligens, der er baseret på, at computeren lærer at løse et problem baseret på nogle data, kaldes **Machine Learning**. Vi vil ikke overveje klassisk machine learning i dette kursus - vi henviser dig til en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) læseplan. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.da.png) | +> | En del af Kunstig Intelligens, der er baseret på, at computeren lærer at løse et problem baseret på nogle data, kaldes **Machine Learning**. Vi vil ikke overveje klassisk machine learning i dette kursus - vi henviser dig til en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) læseplan. | ![ML for Beginners](../../../../translated_images/da/ml-for-beginners.9e4fed176fd5817d.png) | ## En Kort Historie om AI Kunstig Intelligens blev startet som et felt i midten af det tyvende århundrede. Oprindeligt var symbolsk ræsonnement en fremherskende tilgang, og det førte til en række vigtige succeser, såsom ekspertsystemer – computerprogrammer, der kunne fungere som en ekspert inden for nogle begrænsede problemområder. Det blev dog hurtigt klart, at en sådan tilgang ikke skalerer godt. At udtrække viden fra en ekspert, repræsentere det i en computer og holde denne vidensbase nøjagtig viser sig at være en meget kompleks opgave og for dyr til at være praktisk i mange tilfælde. Dette førte til den såkaldte [AI-vinter](https://en.wikipedia.org/wiki/AI_winter) i 1970'erne. -Kort historie om AI +Kort historie om AI > Billede af [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samme måde kan vi se, hvordan tilgangen til at skabe "talende programmer" ( * Moderne assistenter, såsom Cortana, Siri eller Google Assistant, er alle hybride systemer, der bruger neurale netværk til at konvertere tale til tekst og genkende vores intention, og derefter anvender noget ræsonnement eller eksplicitte algoritmer til at udføre de nødvendige handlinger. * I fremtiden kan vi forvente en komplet neural-baseret model til at håndtere dialoger selvstændigt. De seneste GPT- og [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) familier af neurale netværk viser stor succes i dette. -Turing-testens udvikling +Turing-testens udvikling > Billede af Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) af [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nyere AI-forskning diff --git a/translations/da/lessons/2-Symbolic/Animals.ipynb b/translations/da/lessons/2-Symbolic/Animals.ipynb index 36a844ea..6f0ff074 100644 --- a/translations/da/lessons/2-Symbolic/Animals.ipynb +++ b/translations/da/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I dette eksempel vil vi implementere et simpelt vidensbaseret system til at bestemme et dyr baseret på nogle fysiske egenskaber. Systemet kan repræsenteres ved følgende OG-ELLER-træ (dette er en del af hele træet, vi kan nemt tilføje flere regler):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.da.png)\n" + "![](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/da/lessons/2-Symbolic/README.md b/translations/da/lessons/2-Symbolic/README.md index e0c8b42d..3be0a09b 100644 --- a/translations/da/lessons/2-Symbolic/README.md +++ b/translations/da/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Videnrepræsentation og Ekspertsystemer -![Oversigt over Symbolsk AI-indhold](../../../../translated_images/ai-symbolic.715a30cb610411a6.da.png) +![Oversigt over Symbolsk AI-indhold](../../../../translated_images/da/ai-symbolic.715a30cb610411a6.png) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Ofte definerer vi ikke viden strengt, men vi relaterer det til andre begreber ve Problemet med **videnrepræsentation** er derfor at finde en effektiv måde at repræsentere viden i en computer i form af data, så den kan bruges automatisk. Dette kan ses som et spektrum: -![Spektrum for videnrepræsentation](../../../../translated_images/knowledge-spectrum.b60df631852c0217.da.png) +![Spektrum for videnrepræsentation](../../../../translated_images/da/knowledge-spectrum.b60df631852c0217.png) > Billede af [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-syntaks | Indrykning | | | En af de tidlige succeser inden for symbolsk AI var de såkaldte **ekspertsystemer** - computersystemer designet til at fungere som en ekspert inden for et begrænset problemområde. De var baseret på en **vidensbase** udtrukket fra en eller flere menneskelige eksperter og indeholdt en **slutningsmotor**, der udførte ræsonnement ovenpå den. -![Menneskelig Arkitektur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.da.png) | ![Videnbaseret System](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.da.png) +![Menneskelig Arkitektur](../../../../translated_images/da/arch-human.5d4d35f1bba3ab1c.png) | ![Videnbaseret System](../../../../translated_images/da/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Forenklet struktur af et menneskeligt neuralt system | Arkitektur af et videnbaseret system @@ -106,7 +106,7 @@ Ekspertsystemer er bygget som det menneskelige ræsonnementsystem, der indeholde Som et eksempel kan vi overveje følgende ekspertsystem til at bestemme et dyr baseret på dets fysiske egenskaber: -![AND-OR Træ](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.da.png) +![AND-OR Træ](../../../../translated_images/da/AND-OR-Tree.5592d2c70187f283.png) > Billede af [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 750d4518..467c5a9e 100644 --- a/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/da/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Hvis vi har mere end 2 klasser, vil softmax normalisere sandsynlighederne på tværs af dem alle. Her er et diagram over netværksarkitekturen, der udfører MNIST-cifferklassifikation:\n", "\n", - "![MNIST Klassifikator](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.da.png)\n" + "![MNIST Klassifikator](../../../../../translated_images/da/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1257,7 +1257,7 @@ "* Lav træningstab - modellen kan tilpasse sig træningsdata godt, fordi den har tilstrækkelig udtrykskraft.\n", "* Valideringstab kan være meget højere end træningstab og kan begynde at stige under træningen - dette skyldes, at modellen \"husker\" træningspunkterne og mister det \"overordnede billede\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.da.png)\n", + "![Overfitting](../../../../../translated_images/da/overfit.a0bd57f717c15769.png)\n", "\n", "> På dette billede står `x` for træningsdata, `o` for valideringsdata. Til venstre - lineær model (en-lags), den afspejler dataenes natur ret godt. Til højre - overfitted model, modellen tilpasser sig træningsdata perfekt, men giver ingen mening med andre data (valideringsfejlen er meget høj).\n" ] diff --git a/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md index 6c61732a..e2a22ef4 100644 --- a/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/da/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting er et ekstremt vigtigt begreb inden for maskinlæring, og det er meg Overvej følgende problem med at approximere 5 punkter (repræsenteret ved `x` på graferne nedenfor): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.da.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.da.jpg) +![linear](../../../../../translated_images/da/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/da/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineær model, 2 parametre** | **Ikke-lineær model, 7 parametre** Træningsfejl = 5.3 | Træningsfejl = 0 @@ -79,7 +79,7 @@ Det er meget vigtigt at finde den rette balance mellem modellens kompleksitet (a Som du kan se på grafen ovenfor, kan overfitting opdages ved en meget lav træningsfejl og en høj valideringsfejl. Normalt under træning vil vi se både trænings- og valideringsfejl begynde at falde, og på et tidspunkt kan valideringsfejlen stoppe med at falde og begynde at stige. Dette vil være et tegn på overfitting og en indikator for, at vi sandsynligvis bør stoppe træningen på dette tidspunkt (eller i det mindste tage et snapshot af modellen). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.da.png) +![overfitting](../../../../../translated_images/da/Overfitting.408ad91cd90b4371.png) ## Hvordan man forhindrer overfitting diff --git a/translations/da/lessons/3-NeuralNetworks/README.md b/translations/da/lessons/3-NeuralNetworks/README.md index d6f310c6..8b683007 100644 --- a/translations/da/lessons/3-NeuralNetworks/README.md +++ b/translations/da/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion til Neurale Netværk -![Oversigt over indholdet i Intro Neural Networks i en doodle](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.da.png) +![Oversigt over indholdet i Intro Neural Networks i en doodle](../../../../translated_images/da/ai-neuralnetworks.1c687ae40bc86e83.png) Som vi diskuterede i introduktionen, er en af måderne at opnå intelligens på at træne en **computermodel** eller en **kunstig hjerne**. Siden midten af det 20. århundrede har forskere prøvet forskellige matematiske modeller, indtil denne tilgang i de seneste år har vist sig at være enormt succesfuld. Sådanne matematiske modeller af hjernen kaldes **neurale netværk**. @@ -36,13 +36,13 @@ I dette pensum vil vi kun fokusere på neurale netværksmodeller. Fra biologien ved vi, at vores hjerne består af nerveceller (neuroner), som hver har flere "inputs" (dendritter) og en enkelt "output" (axon). Både dendritter og axoner kan lede elektriske signaler, og forbindelserne mellem dem — kendt som synapser — kan udvise varierende grader af ledningsevne, som reguleres af neurotransmittere. -![Model af en neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.da.jpg) | ![Model af en neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.da.png) +![Model af en neuron](../../../../translated_images/da/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model af en neuron](../../../../translated_images/da/artneuron.1a5daa88d20ebe6f.png) ----|---- Ægte neuron *([Billede](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) fra Wikipedia)* | Kunstig neuron *(Billede af forfatteren)* Den simpleste matematiske model af en neuron indeholder således flere inputs X1, ..., XN og et output Y samt en række vægte W1, ..., WN. Et output beregnes som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) hvor f er en ikke-lineær **aktiveringsfunktion**. diff --git a/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md index 355ec049..da8ea11b 100644 --- a/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/da/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vores [OpenCV Notebook](OpenCV.ipynb) giver vi nogle eksempler på, hvornår c * **Forbehandling af et fotografi af en Braille-bog**. Vi fokuserer på, hvordan vi kan bruge thresholding, feature detection, perspektivtransformation og NumPy-manipulationer til at adskille individuelle Braille-symboler til videre klassifikation af et neuralt netværk. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.da.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.da.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.da.png) +![Braille Image](../../../../../translated_images/da/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/da/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/da/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Billede fra [OpenCV.ipynb](OpenCV.ipynb) * **Detektion af bevægelse i video ved hjælp af frame difference**. Hvis kameraet er fast, bør frames fra kameraets feed være ret ens. Da frames er repræsenteret som arrays, vil vi ved blot at trække disse arrays fra hinanden for to efterfølgende frames få pixel-forskellen, som bør være lav for statiske frames og blive højere, når der er betydelig bevægelse i billedet. -![Billede af video frames og frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.da.png) +![Billede af video frames og frame differences](../../../../../translated_images/da/frame-difference.706f805491a0883c.png) > Billede fra [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vores [OpenCV Notebook](OpenCV.ipynb) giver vi nogle eksempler på, hvornår c - **Dense Optical Flow** beregner vektorfeltet, der viser, hvor hver pixel bevæger sig hen. - **Sparse Optical Flow** er baseret på at tage nogle karakteristiske træk i billedet (f.eks. kanter) og bygge deres bane fra frame til frame. -![Billede af optisk flow](../../../../../translated_images/optical.1f4a94464579a83a.da.png) +![Billede af optisk flow](../../../../../translated_images/da/optical.1f4a94464579a83a.png) > Billede fra [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 76ce0d47..aab95b63 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 er et netværk, der opnåede 92,7% nøjagtighed i ImageNet top-5 klassifikation i 2014. Det har følgende lagstruktur: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.da.jpg) +![ImageNet Layers](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.jpg) Som du kan se, følger VGG en traditionel pyramidearkitektur, som er en sekvens af konvolutions- og pooling-lag. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.da.jpg) +![ImageNet Pyramid](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.jpg) > Billede fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 7e9e7767..1c407fe6 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Således vil der i en typisk CNN være flere konvolutionslag med pooling-lag imellem dem for at reducere dimensionerne af billedet. Vi vil også øge antallet af filtre, fordi mønstrene bliver mere avancerede – der er flere mulige interessante kombinationer, vi skal kigge efter.\n", "\n", - "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.da.png)\n", + "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grund af de reducerede rumlige dimensioner og de øgede feature-/filterdimensioner kaldes denne arkitektur også **pyramidearkitektur**.\n" ] diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index a38b1077..30694ba2 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Således vil en typisk CNN have flere konvolutionslag, med pooling-lag imellem dem for at reducere dimensionerne af billedet. Vi vil også øge antallet af filtre, fordi når mønstrene bliver mere avancerede, er der flere mulige interessante kombinationer, vi skal lede efter.\n", "\n", - "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.da.png)\n", + "![Et billede, der viser flere konvolutionslag med pooling-lag.](../../../../../translated_images/da/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grund af de faldende rumlige dimensioner og stigende feature/filtre-dimensioner kaldes denne arkitektur også **pyramidearkitektur**.\n" ] diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md index 829750d5..36f86f15 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I virkeligheden ønsker vi at kunne genkende objekter på et billede, uanset der For at udtrække mønstre vil vi bruge begrebet **konvolutionelle filtre**. Som du ved, repræsenteres et billede af en 2D-matrix eller en 3D-tensor med farvedybde. At anvende et filter betyder, at vi tager en relativt lille **filterkerne**-matrix, og for hver pixel i det oprindelige billede beregner vi det vægtede gennemsnit med nabopunkterne. Vi kan se dette som et lille vindue, der glider hen over hele billedet og udjævner alle pixels i henhold til vægtene i filterkernematrixen. -![Vertikalt Kantfilter](../../../../../translated_images/filter-vert.b7148390ca0bc356.da.png) | ![Horisontalt Kantfilter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.da.png) +![Vertikalt Kantfilter](../../../../../translated_images/da/filter-vert.b7148390ca0bc356.png) | ![Horisontalt Kantfilter](../../../../../translated_images/da/filter-horiz.59b80ed4feb946ef.png) ----|---- > Billede af Dmitry Soshnikov @@ -38,7 +38,7 @@ Måden CNN'er fungerer på, er baseret på følgende vigtige idéer: * Vi kan designe netværket, så filtrene trænes automatisk * Vi kan bruge den samme tilgang til at finde mønstre i højere niveauer af funktioner, ikke kun i det oprindelige billede. CNN's funktionsekstraktion arbejder således på en hierarki af funktioner, der starter fra lavniveau-pixelkombinationer og går op til højere niveau-kombinationer af billeddele. -![Hierarkisk Funktionsekstraktion](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.da.png) +![Hierarkisk Funktionsekstraktion](../../../../../translated_images/da/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Billede fra [en artikel af Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseret på [deres forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De fleste CNN'er, der bruges til billedbehandling, følger en såkaldt pyramidea Som et eksempel kan vi se på arkitekturen af VGG-16, et netværk der opnåede 92,7% nøjagtighed i ImageNet's top-5 klassifikation i 2014: -![ImageNet Lag](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.da.jpg) +![ImageNet Lag](../../../../../translated_images/da/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramide](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.da.jpg) +![ImageNet Pyramide](../../../../../translated_images/da/vgg-16-arch.64ff2137f50dd49f.jpg) > Billede fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 20fd738f..976d41eb 100644 --- a/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/da/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du skal træne et konvolutionelt neuralt netværk til at klassificere forskellig Vi vil bruge [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som indeholder billeder af 37 forskellige racer af hunde og katte. -![Datasæt vi skal arbejde med](../../../../../../translated_images/data.50b2a9d5484bdbf0.da.png) +![Datasæt vi skal arbejde med](../../../../../../translated_images/da/data.50b2a9d5484bdbf0.png) For at downloade datasættet, brug denne kode: diff --git a/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 261adf7d..e5d29c90 100644 --- a/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/da/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "For at visualisere den ideelle kat, starter vi med et tilfældigt støjbillede og forsøger at bruge gradient descent-optimeringsteknikken til at justere billedet, så netværket genkender en kat.\n", "\n", - "![Optimeringsloop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.da.png)\n", + "![Optimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Her er vores startbillede:\n" ] diff --git a/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md index 7c00d589..4d0f874c 100644 --- a/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/da/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras og PyTorch indeholder funktioner til nemt at indlæse forudtrænede Her er eksempler på funktioner udtrukket fra et billede af en kat ved hjælp af VGG-16-netværket: -![Funktioner udtrukket af VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.da.png) +![Funktioner udtrukket af VGG-16](../../../../../translated_images/da/features.6291f9c7ba3a0b95.png) ## Datasæt: Katte vs. Hunde @@ -48,19 +48,19 @@ Et forudtrænet neuralt netværk indeholder forskellige mønstre i sin *hjerne*, En tilgang, vi kan tage, er at starte med et tilfældigt billede og derefter bruge **gradient descent-optimering** til at justere billedet på en sådan måde, at netværket begynder at tro, at det er en kat. -![Billedoptimeringsloop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.da.png) +![Billedoptimeringsloop](../../../../../translated_images/da/ideal-cat-loop.999fbb8ff306e044.png) Hvis vi gør dette, vil vi dog få noget, der minder meget om tilfældig støj. Dette skyldes, at *der er mange måder at få netværket til at tro, at inputbilledet er en kat*, herunder nogle, der ikke giver mening visuelt. Selvom disse billeder indeholder mange mønstre, der er typiske for en kat, er der intet, der tvinger dem til at være visuelt genkendelige. For at forbedre resultatet kan vi tilføje et andet led til tab-funktionen, som kaldes **variationstab**. Det er en måling, der viser, hvor ens nabopixels i billedet er. Ved at minimere variationstabet bliver billedet glattere og fjerner støj – hvilket afslører mere visuelt tiltalende mønstre. Her er et eksempel på sådanne "ideelle" billeder, der klassificeres som henholdsvis kat og zebra med høj sandsynlighed: -![Ideel Kat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.da.png) | ![Ideel Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.da.png) +![Ideel Kat](../../../../../translated_images/da/ideal-cat.203dd4597643d6b0.png) | ![Ideel Zebra](../../../../../translated_images/da/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideel Kat* | *Ideel Zebra* En lignende tilgang kan bruges til at udføre såkaldte **adversarielle angreb** på et neuralt netværk. Antag, at vi vil narre et neuralt netværk og få en hund til at ligne en kat. Hvis vi tager et billede af en hund, som netværket genkender som en hund, kan vi justere det lidt ved hjælp af gradient descent-optimering, indtil netværket begynder at klassificere det som en kat: -![Billede af en Hund](../../../../../translated_images/original-dog.8f68a67d2fe0911f.da.png) | ![Billede af en hund klassificeret som en kat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.da.png) +![Billede af en Hund](../../../../../translated_images/da/original-dog.8f68a67d2fe0911f.png) | ![Billede af en hund klassificeret som en kat](../../../../../translated_images/da/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originalt billede af en hund* | *Billede af en hund klassificeret som en kat* diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 2de2bf55..9d72e2a0 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Da vi træner autoencoderen til at fange så meget information som muligt fra det originale billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **indlejring** af inputbilleder for at fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.da.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 6b5a9da6..840c84a3 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Da vi træner autoencoderen til at fange så meget information som muligt fra det originale billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **embedding** af inputbillederne for at fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.da.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md index 88a29767..e1161324 100644 --- a/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/da/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vi kan dog ønske at bruge rå (umærkede) data til at træne CNN-featureekstrak Da vi træner en autoencoder til at fange så meget information som muligt fra det oprindelige billede for at opnå en præcis rekonstruktion, forsøger netværket at finde den bedste **indlejring** af inputbilleder for at fange meningen. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.da.jpg) +![AutoEncoder Diagram](../../../../../translated_images/da/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Billede fra [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md index 08cd6bf9..928d9ac2 100644 --- a/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/da/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De billedklassifikationsmodeller, vi hidtil har arbejdet med, tog et billede og ## [Quiz før lektionen](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektgenkendelse](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.da.png) +![Objektgenkendelse](../../../../../translated_images/da/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Billede fra [YOLO v2 hjemmeside](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Hvis vi antager, at vi vil finde en kat på et billede, kunne en meget naiv tilg 2. Kør billedklassifikation på hver flise. 3. De fliser, der resulterer i tilstrækkelig høj aktivering, kan betragtes som indeholdende det ønskede objekt. -![Naiv objektgenkendelse](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.da.png) +![Naiv objektgenkendelse](../../../../../translated_images/da/naive-detection.e7f1ba220ccd08c6.png) > *Billede fra [Øvelsesnotebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan støde på følgende datasæt til denne opgave: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klasser * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klasser, afgrænsningsbokse og segmenteringsmasker -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.da.jpg) +![COCO](../../../../../translated_images/da/coco-examples.71bc60380fa6cceb.jpg) ## Metrikker for objektgenkendelse @@ -50,7 +50,7 @@ Du kan støde på følgende datasæt til denne opgave: Mens det er nemt at måle, hvor godt en algoritme klarer sig i billedklassifikation, skal vi i objektgenkendelse måle både korrektheden af klassen og præcisionen af den forudsagte placering af afgrænsningsboksen. Til det sidste bruger vi den såkaldte **Intersection over Union** (IoU), som måler, hvor godt to bokse (eller to vilkårlige områder) overlapper. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.da.png) +![IoU](../../../../../translated_images/da/iou_equation.9a4751d40fff4e11.png) > *Figur 2 fra [dette fremragende blogindlæg om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Der er to brede klasser af algoritmer til objektgenkendelse: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) bruger [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) til at generere en hierarkisk struktur af ROI-regioner, som derefter sendes gennem CNN-featureekstraktorer og SVM-klassifikatorer for at bestemme objektklassen og lineær regression for at bestemme *afgrænsningsboksens* koordinater. [Officiel artikel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.da.png) +![RCNN](../../../../../translated_images/da/rcnn1.cae407020dfb1d1f.png) > *Billede fra van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.da.png) +![RCNN-1](../../../../../translated_images/da/rcnn2.2d9530bb83516484.png) > *Billeder fra [denne blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Der er to brede klasser af algoritmer til objektgenkendelse: Denne tilgang ligner R-CNN, men regioner defineres efter, at konvolutionslagene er blevet anvendt. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.da.png) +![FRCNN](../../../../../translated_images/da/f-rcnn.3cda6d9bb4188875.png) > Billede fra [den officielle artikel](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denne tilgang ligner R-CNN, men regioner defineres efter, at konvolutionslagene Hovedideen med denne tilgang er at bruge et neuralt netværk til at forudsige ROIs – det såkaldte *Region Proposal Network*. [Artikel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.da.png) +![FasterRCNN](../../../../../translated_images/da/faster-rcnn.8d46c099b87ef30a.png) > Billede fra [den officielle artikel](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denne algoritme er endnu hurtigere end Faster R-CNN. Hovedideen er følgende: 2. Features behandles af **Position-Sensitive Score Map**. Hvert objekt fra $C$ klasser opdeles i $k\times k$ regioner, og vi træner til at forudsige dele af objekter. 3. For hver del fra $k\times k$ regioner stemmer alle netværk for objektklasser, og den objektklasse med flest stemmer vælges. -![r-fcn billede](../../../../../translated_images/r-fcn.13eb88158b99a3da.da.png) +![r-fcn billede](../../../../../translated_images/da/r-fcn.13eb88158b99a3da.png) > Billede fra [officiel artikel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO er en realtids one-pass algoritme. Hovedideen er følgende: * Billedet opdeles i $S\times S$ regioner. * For hver region forudsiger **CNN** $n$ mulige objekter, *afgrænsningsboksens* koordinater og *tillid*=*sandsynlighed* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.da.png) + ![YOLO](../../../../../translated_images/da/yolo.a2648ec82ee8bb4e.png) > Billede fra [officiel artikel](https://arxiv.org/abs/1506.02640) diff --git a/translations/da/lessons/4-ComputerVision/README.md b/translations/da/lessons/4-ComputerVision/README.md index a55bd683..20577cd9 100644 --- a/translations/da/lessons/4-ComputerVision/README.md +++ b/translations/da/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Oversigt over Computer Vision-indhold i en doodle](../../../../translated_images/ai-computervision.6506ebebac3fbf76.da.png) +![Oversigt over Computer Vision-indhold i en doodle](../../../../translated_images/da/ai-computervision.6506ebebac3fbf76.png) I denne sektion vil vi lære om: diff --git a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 5aaca369..7e54d0a4 100644 --- a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) vektorrepræsentation er den mest almindeligt anvendte traditionelle vektorrepræsentation. Hvert ord er knyttet til en vektorindeks, og vektorelementet indeholder antallet af forekomster af et ord i et givet dokument.\n", "\n", - "![Billede, der viser, hvordan en bag of words-vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.da.png) \n", + "![Billede, der viser, hvordan en bag of words-vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Du kan også tænke på BoW som en sum af alle one-hot-kodede vektorer for de enkelte ord i teksten.\n", "\n", diff --git a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index b3654978..55e8554f 100644 --- a/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/da/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepræsentation er den mest simple og letforståelige traditionelle vektorrepræsentation. Hvert ord er knyttet til en vektorindeks, og et vektorelement indeholder antallet af forekomster af hvert ord i et givent dokument.\n", "\n", - "![Billede, der viser, hvordan en bag-of-words vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.da.png) \n", + "![Billede, der viser, hvordan en bag-of-words vektorrepræsentation er repræsenteret i hukommelsen.](../../../../../translated_images/da/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Du kan også tænke på BoW som en sum af alle one-hot-enkodede vektorer for de enkelte ord i teksten.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 76033c1d..c0f25d9e 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ved at bruge embedding-laget som det første lag i vores netværk kan vi skifte fra bag-of-words til en **embedding bag**-model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende embedding og derefter beregner en eller anden aggregeringsfunktion over alle disse embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.da.png)\n", + "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vores klassifikator-neurale netværk vil starte med et embedding-lag, derefter et aggregeringslag og en lineær klassifikator ovenpå:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidligere arkitektur var vi nødt til at udfylde alle sekvenser til samme længde for at passe dem ind i en minibatch. Dette er ikke den mest effektive måde at repræsentere sekvenser med variabel længde på - en anden tilgang ville være at bruge en **offset**-vektor, som indeholder offsets for alle sekvenser, der er gemt i én stor vektor.\n", "\n", - "![Billede, der viser en offset-sekvensrepræsentation](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.da.png)\n", + "![Billede, der viser en offset-sekvensrepræsentation](../../../../../translated_images/da/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: På billedet ovenfor viser vi en sekvens af tegn, men i vores eksempel arbejder vi med sekvenser af ord. Dog forbliver det generelle princip om at repræsentere sekvenser med en offset-vektor det samme.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW er hurtigere, mens skip-gram er langsommere, men gør et bedre stykke arbejde med at repræsentere sjældne ord.\n", "\n", - "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.da.png)\n", + "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "For at eksperimentere med word2vec embedding fortrænet på Google News-datasættet kan vi bruge **gensim**-biblioteket. Nedenfor finder vi de ord, der minder mest om 'neural'.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 3d4a8e29..cee8f9ad 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/da/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ved at bruge et embedding-lag som det første lag i vores netværk kan vi skifte fra bag-of-words til en **embedding bag**-model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende embedding og derefter beregner en aggregeringsfunktion over alle disse embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.da.png)\n", + "![Billede, der viser en embedding-klassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vores klassifikator-neurale netværk består af følgende lag:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW er hurtigere, og selvom skip-gram er langsommere, er det bedre til at repræsentere sjældne ord.\n", "\n", - "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.da.png)\n", + "![Billede, der viser både CBoW- og Skip-Gram-algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "For at eksperimentere med Word2Vec-indlejringen, der er fortrænet på Google News-datasættet, kan vi bruge **gensim**-biblioteket. Nedenfor finder vi de ord, der minder mest om 'neural'.\n", "\n", diff --git a/translations/da/lessons/5-NLP/14-Embeddings/README.md b/translations/da/lessons/5-NLP/14-Embeddings/README.md index b40d6c1a..61692f2d 100644 --- a/translations/da/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/da/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så indlejringslaget vil tage et ord som input og producere en outputvektor med Ved at bruge et indlejringslag som det første lag i vores klassifikationsnetværk kan vi skifte fra en bag-of-words til en **embedding bag** model, hvor vi først konverterer hvert ord i vores tekst til den tilsvarende indlejring og derefter beregner en aggregeringsfunktion over alle disse indlejringer, såsom `sum`, `average` eller `max`. -![Billede, der viser en indlejringsklassifikator for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.da.png) +![Billede, der viser en indlejringsklassifikator for fem sekvensord.](../../../../../translated_images/da/embedding-classifier-example.b77f021a7ee67eee.png) > Billede af forfatteren @@ -40,7 +40,7 @@ For at gøre dette skal vi fortræne vores indlejringsmodel på en stor samling CBoW er hurtigere, mens skip-gram er langsommere, men gør et bedre arbejde med at repræsentere sjældne ord. -![Billede, der viser både CBoW og Skip-Gram algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.da.png) +![Billede, der viser både CBoW og Skip-Gram algoritmer til at konvertere ord til vektorer.](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Billede fra [denne artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/da/lessons/5-NLP/15-LanguageModeling/README.md b/translations/da/lessons/5-NLP/15-LanguageModeling/README.md index cbd6730a..36a5dfa3 100644 --- a/translations/da/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/da/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I vores tidligere eksempler brugte vi fortrænede semantiske indlejringer, men d * **Continuous Bag-of-Words** (CBoW), hvor vi forudsiger det midterste token $W_0$ i en token-sekvens $W_{-N}$, ..., $W_N$. * **Skip-gram**, hvor vi forudsiger et sæt af nabotokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ud fra det midterste token $W_0$. -![billede fra artikel om konvertering af ord til vektorer](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.da.png) +![billede fra artikel om konvertering af ord til vektorer](../../../../../translated_images/da/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Billede fra [denne artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/da/lessons/5-NLP/16-RNN/README.md b/translations/da/lessons/5-NLP/16-RNN/README.md index 64a351d7..0f2f84e0 100644 --- a/translations/da/lessons/5-NLP/16-RNN/README.md +++ b/translations/da/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I de tidligere afsnit har vi brugt rige semantiske repræsentationer af tekst og For at fange betydningen af tekstsekvenser skal vi bruge en anden neural netværksarkitektur, som kaldes et **rekurrent neuralt netværk**, eller RNN. I RNN sender vi vores sætning gennem netværket én symbol ad gangen, og netværket producerer en **tilstand**, som vi derefter sender tilbage til netværket sammen med det næste symbol. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.da.png) +![RNN](../../../../../translated_images/da/rnn.27f5c29c53d727b5.png) > Billede af forfatteren @@ -61,7 +61,7 @@ Vi har diskuteret rekurrente netværk, der opererer i én retning, fra begyndels Et rekurrent netværk, enten én-retnings eller bidirektionelt, fanger visse mønstre inden for en sekvens og kan gemme dem i en tilstandsvektor eller sende dem til output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag oven på det første for at fange højere niveau mønstre og bygge videre på lav-niveau mønstre, der er udtrukket af det første lag. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor output fra det foregående lag sendes til det næste lag som input. -![Billede der viser et flerlags long-short-term-memory RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.da.jpg) +![Billede der viser et flerlags long-short-term-memory RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Billede fra [denne vidunderlige artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López* diff --git a/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index cd215030..2bdf3039 100644 --- a/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Et rekurrent netværk, enten én-retnings eller bidirektionelt, fanger visse mønstre inden for en sekvens og kan gemme dem i en tilstandsvektor eller sende dem videre til output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag oven på det første for at fange mønstre på et højere niveau, bygget fra lav-niveau mønstre, som det første lag har udtrukket. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor output fra det foregående lag sendes til det næste lag som input.\n", "\n", - "![Billede der viser en flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.da.jpg)\n", + "![Billede der viser en flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Billede fra [denne fantastiske artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López*\n", "\n", diff --git a/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb index 95b2a618..be9d0bf4 100644 --- a/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/da/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "For at fange betydningen af en tekstsekvens vil vi bruge en neural netværksarkitektur kaldet **rekurrente neurale netværk**, eller RNN. Når vi bruger en RNN, sender vi vores sætning gennem netværket én token ad gangen, og netværket producerer en **tilstand**, som vi derefter sender videre til netværket sammen med den næste token.\n", "\n", - "![Billede, der viser et eksempel på generering med rekurrente neurale netværk.](../../../../../translated_images/rnn.27f5c29c53d727b5.da.png)\n", + "![Billede, der viser et eksempel på generering med rekurrente neurale netværk.](../../../../../translated_images/da/rnn.27f5c29c53d727b5.png)\n", "\n", "Givet inputsekvensen af tokens $X_0,\\dots,X_n$, skaber RNN en sekvens af neurale netværksblokke og træner denne sekvens ende-til-ende ved hjælp af backpropagation. Hver netværksblok tager et par $(X_i,S_i)$ som input og producerer $S_{i+1}$ som resultat. Den endelige tilstand $S_n$ eller output $Y_n$ går ind i en lineær klassifikator for at producere resultatet. Alle netværksblokke deler de samme vægte og trænes ende-til-ende ved hjælp af én backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrente netværk, enten unidirektionelle eller bidirektionelle, fanger mønstre inden for en sekvens og gemmer dem i tilstandsvektorer eller returnerer dem som output. Ligesom med konvolutionelle netværk kan vi bygge et andet rekurrent lag efter det første for at fange mønstre på et højere niveau, bygget fra mønstre på lavere niveau, som det første lag har udtrukket. Dette fører os til begrebet **flerlags RNN**, som består af to eller flere rekurrente netværk, hvor outputtet fra det foregående lag gives videre til det næste lag som input.\n", "\n", - "![Billede, der viser et flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.da.jpg)\n", + "![Billede, der viser et flerlags lang-kort-tids-hukommelses-RNN](../../../../../translated_images/da/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Billede fra [denne fantastiske artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) af Fernando López.*\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b3c72453..e773bc20 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Måden, vi vil træne en RNN til at generere tekst, er som følger. Ved hvert trin tager vi en sekvens af tegn med længden `nchars` og beder netværket om at generere det næste outputtegn for hvert inputtegn:\n", "\n", - "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.da.png)\n", + "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Afhængigt af det konkrete scenarie kan vi også ønske at inkludere nogle specialtegn, såsom *end-of-sequence* ``. I vores tilfælde ønsker vi blot at træne netværket til uendelig tekstgenerering, så vi vil fastsætte størrelsen af hver sekvens til at være lig med `nchars` tokens. Derfor vil hvert træningseksempel bestå af `nchars` input og `nchars` output (hvilket er inputsekvensen forskudt med ét symbol til venstre). En minibatch vil bestå af flere sådanne sekvenser.\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 4a109243..4018f8ce 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Måden, vi vil træne RNN til at generere nyhedstitler, er som følger. Ved hvert trin tager vi en titel, som bliver fodret ind i en RNN, og for hvert inputtegn beder vi netværket om at generere det næste outputtegn:\n", "\n", - "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.da.png)\n", + "![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png)\n", "\n", "For det sidste tegn i vores sekvens beder vi netværket om at generere ``-token.\n", "\n", diff --git a/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md index 7e772efe..346f3e8e 100644 --- a/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/da/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen, som vi diskuterede i den forrige enhed, producerede hver RNN Dette muliggør forskellige neurale arkitekturer, som vist på billedet nedenfor: -![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.da.jpg) +![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Billede fra blogindlægget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) af [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denne enhed vil vi fokusere på simple generative modeller, der hjælper os me Vi vil træne denne RNN til at generere tekst trin for trin. Ved hvert trin tager vi en sekvens af tegn med længden `nchars` og beder netværket om at generere det næste outputtegn for hvert inputtegn: -![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.da.png) +![Billede, der viser et eksempel på RNN-generering af ordet 'HELLO'.](../../../../../translated_images/da/rnn-generate.56c54afb52f9781d.png) Når vi genererer tekst (under inferens), starter vi med en **prompt**, som sendes gennem RNN-celler for at generere dens mellemliggende tilstand, og derefter starter genereringen fra denne tilstand. Vi genererer ét tegn ad gangen og sender tilstanden og det genererede tegn til en anden RNN-celle for at generere det næste, indtil vi har genereret nok tegn. diff --git a/translations/da/lessons/5-NLP/18-Transformers/README.md b/translations/da/lessons/5-NLP/18-Transformers/README.md index 6c7c6993..b662e775 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/README.md +++ b/translations/da/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNN'er implementeres sekvens-til-sekvens med to rekurrente netværk, hvor de **Attention Mekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse i RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbol yt, tager vi alle input skjulte tilstande hi i betragtning med forskellige vægtkoefficienter αt,i. -![Billede, der viser en encoder/decoder-model med et additivt attention-lag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.da.png) +![Billede, der viser en encoder/decoder-model med et additivt attention-lag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png) > Encoder-decoder modellen med additiv attention mekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Attention-matrixen {αi,j} repræsenterer graden af, hvor meget visse inputord spiller en rolle i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix: -![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.da.png) +![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png) > Figur fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet, vi får med positionskodning, embedder både det originale token og Dernæst skal vi fange nogle mønstre inden for vores sekvens. For at gøre dette bruger transformers en **self-attention** mekanisme, som i bund og grund er attention anvendt på den samme sekvens som input og output. Ved at anvende self-attention kan vi tage **kontekst** inden for sætningen i betragtning og se, hvilke ord der er relaterede. For eksempel giver det os mulighed for at se, hvilke ord der refereres til af coreferencer, såsom *det*, og også tage konteksten i betragtning: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.da.png) +![](../../../../../translated_images/da/CoreferenceResolution.861924d6d384a7d6.png) > Billede fra [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Da hver inputposition uafhængigt kortlægges til hver outputposition, kan trans **BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort multi-lags transformer-netværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen trænes først på en stor tekstkorpus (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under pre-træning absorberer modellen betydelige niveauer af sprogforståelse, som derefter kan udnyttes med andre datasæt ved hjælp af finjustering. Denne proces kaldes **transfer learning**. -![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.da.png) +![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Billede [kilde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 5ca82ef1..6d3f0e4d 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/da/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Opmærksomhedsmekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse i RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbolet $y_t$, tager vi højde for alle skjulte inputtilstande $h_i$ med forskellige vægtkoefficienter $\\alpha_{t,i}$. \n", "\n", - "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.da.png)\n", + "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv opmærksomhedsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Opmærksomhedsmatricen $\\{\\alpha_{i,j}\\}$ repræsenterer graden, hvormed visse inputord spiller en rolle i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix:\n", "\n", - "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.da.png)\n", + "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur taget fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort flerlagstransformernetværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen fortrænes først på en stor tekstkorpus (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under fortræningen absorberer modellen et betydeligt niveau af sprogforståelse, som derefter kan udnyttes med andre datasæt ved hjælp af finjustering. Denne proces kaldes **transfer learning**. \n", "\n", - "![Billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.da.png)\n", + "![Billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Der findes mange variationer af Transformer-arkitekturer, herunder BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres. [HuggingFace-pakken](https://github.com/huggingface/) giver et bibliotek til træning af mange af disse arkitekturer med PyTorch. \n", "\n", diff --git a/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3e94ba66..e475a00f 100644 --- a/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/da/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Opmærksomhedsmekanismer** giver en metode til at vægte den kontekstuelle indflydelse af hver inputvektor på hver outputforudsigelse af RNN. Dette implementeres ved at skabe genveje mellem de mellemliggende tilstande i input-RNN og output-RNN. På denne måde, når vi genererer outputsymbolet $y_t$, tager vi højde for alle skjulte inputtilstande $h_i$ med forskellige vægtkoefficienter $\\alpha_{t,i}$.\n", "\n", - "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.da.png)\n", + "![Billede, der viser en encoder/decoder-model med et additivt opmærksomhedslag](../../../../../translated_images/da/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv opmærksomhedsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citeret fra [denne blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Opmærksomhedsmatrixen $\\{\\alpha_{i,j}\\}$ repræsenterer graden, som visse inputord spiller i genereringen af et givet ord i outputsekvensen. Nedenfor er et eksempel på en sådan matrix:\n", "\n", - "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.da.png)\n", + "![Billede, der viser en prøvejustering fundet af RNNsearch-50, taget fra Bahdanau - arviz.org](../../../../../translated_images/da/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur taget fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et meget stort multi-lags transformer-netværk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen bliver først forudtrænet på en stor mængde tekstdata (Wikipedia + bøger) ved hjælp af usuperviseret træning (forudsige maskerede ord i en sætning). Under forudtræningen opnår modellen en betydelig forståelse af sproget, som derefter kan udnyttes med andre datasæt gennem finjustering. Denne proces kaldes **transfer learning**.\n", "\n", - "![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.da.png)\n", + "![billede fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/da/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Der findes mange variationer af Transformer-arkitekturer, herunder BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres.\n", "\n", diff --git a/translations/da/lessons/5-NLP/19-NER/README.md b/translations/da/lessons/5-NLP/19-NER/README.md index 4bdea640..b3205a5d 100644 --- a/translations/da/lessons/5-NLP/19-NER/README.md +++ b/translations/da/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ spædbarn | O Da vi skal opbygge en én-til-én-korrespondance mellem tokens og klasser, kan vi træne en højreorienteret **mange-til-mange** neuralt netværksmodel fra dette billede: -![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.da.jpg) +![Billede, der viser almindelige mønstre for rekurrente neurale netværk.](../../../../../translated_images/da/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Billede fra [denne blogpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) af [Andrej Karpathy](http://karpathy.github.io/). NER-tokenklassifikationsmodeller svarer til den højreorienterede netværksarkitektur på dette billede.* diff --git a/translations/da/lessons/5-NLP/README.md b/translations/da/lessons/5-NLP/README.md index bbe7fd4a..8fc959e8 100644 --- a/translations/da/lessons/5-NLP/README.md +++ b/translations/da/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Sprogbehandling -![Oversigt over NLP-opgaver i en doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.da.png) +![Oversigt over NLP-opgaver i en doodle](../../../../translated_images/da/ai-nlp.b22dcb8ca4707cea.png) I denne sektion vil vi fokusere på at bruge neurale netværk til at håndtere opgaver relateret til **naturlig sprogbehandling (NLP)**. Der er mange NLP-problemer, som vi ønsker, at computere skal kunne løse: diff --git a/translations/da/lessons/6-Other/23-MultiagentSystems/README.md b/translations/da/lessons/6-Other/23-MultiagentSystems/README.md index b600ac47..5496e9ee 100644 --- a/translations/da/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/da/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan åbne en af modellerne, for eksempel **Biology → Flocking**. Efter åbning af modellen kommer du til NetLogos hovedskærm. Her er en eksempelmodel, der beskriver populationen af ulve og får, givet begrænsede ressourcer (græs). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.da.png) +![NetLogo Main Screen](../../../../../translated_images/da/NetLogo-Main.32653711ec1a01b3.png) > Skærmbillede af Dmitry Soshnikov diff --git a/translations/da/lessons/README.md b/translations/da/lessons/README.md index 4af2a904..cd3c29bc 100644 --- a/translations/da/lessons/README.md +++ b/translations/da/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Oversigt -![Oversigt i en doodle](../../../translated_images/ai-overview.0857791951d19500.da.png) +![Oversigt i en doodle](../../../translated_images/da/ai-overview.0857791951d19500.png) > Sketchnote af [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/da/lessons/X-Extras/X1-MultiModal/README.md b/translations/da/lessons/X-Extras/X1-MultiModal/README.md index bef9ae60..0d6b36cf 100644 --- a/translations/da/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/da/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Efter succesen med transformer-modeller til løsning af NLP-opgaver, er de samme Hovedideen med CLIP er at kunne sammenligne tekstprompter med et billede og afgøre, hvor godt billedet svarer til prompten. -![CLIP Arkitektur](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.da.png) +![CLIP Arkitektur](../../../../../translated_images/da/clip-arch.b3dbf20b4e8ed8be.png) > *Billede fra [denne blogpost](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Når denne model er fortrænet, kan vi give den en batch af billeder og en batch Antag, at vi skal klassificere billeder mellem f.eks. katte, hunde og mennesker. I dette tilfælde kan vi give modellen et billede og en række tekstprompter: "*et billede af en kat*", "*et billede af en hund*", "*et billede af et menneske*". I den resulterende vektor med 3 sandsynligheder skal vi blot vælge det indeks med den højeste værdi. -![CLIP til Billedklassifikation](../../../../../translated_images/clip-class.3af42ef0b2b19369.da.png) +![CLIP til Billedklassifikation](../../../../../translated_images/da/clip-class.3af42ef0b2b19369.png) > *Billede fra [denne blogpost](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lær mere om VQGAN på [Taming Transformers](https://compvis.github.io/taming-tr En af de vigtige forskelle mellem VQGAN og traditionelle GAN er, at sidstnævnte kan producere et anstændigt billede fra enhver inputvektor, mens VQGAN sandsynligvis vil producere et billede, der ikke er sammenhængende. Derfor skal vi yderligere vejlede billedskabelsesprocessen, og det kan gøres ved hjælp af CLIP. -![VQGAN+CLIP Arkitektur](../../../../../translated_images/vqgan.5027fe05051dfa31.da.png) +![VQGAN+CLIP Arkitektur](../../../../../translated_images/da/vqgan.5027fe05051dfa31.png) For at generere et billede, der svarer til en tekstprompt, starter vi med en tilfældig kodningsvektor, der sendes gennem VQGAN for at producere et billede. Derefter bruges CLIP til at producere en tab-funktion, der viser, hvor godt billedet svarer til tekstprompten. Målet er derefter at minimere denne tab ved hjælp af backpropagation for at justere inputvektorens parametre. Et fantastisk bibliotek, der implementerer VQGAN+CLIP, er [Pixray](http://github.com/pixray/pixray). -![Billede produceret af Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.da.png) | ![Billede produceret af Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.da.png) | ![Billede produceret af Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.da.png) +![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Billede genereret fra prompten *et nærbillede akvarelportræt af en ung mandlig litteraturlærer med en bog* | Billede genereret fra prompten *et nærbillede oliemaleri af en ung kvindelig lærer i datalogi med en computer* | Billede genereret fra prompten *et nærbillede oliemaleri af en ældre mandlig matematiklærer foran en tavle* @@ -75,7 +75,7 @@ I modsætning til CLIP modtager DALL-E både tekst og billede som en enkelt str Den største forskel mellem DALL-E 1 og 2 er, at den genererer mere realistiske billeder og kunst. Eksempler på billedgenerering med DALL-E: -![Billede produceret af Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.da.png) | ![Billede produceret af Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.da.png) | ![Billede produceret af Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.da.png) +![Billede produceret af Pixray](../../../../../translated_images/da/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Billede produceret af Pixray](../../../../../translated_images/da/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Billede genereret fra prompten *et nærbillede akvarelportræt af en ung mandlig litteraturlærer med en bog* | Billede genereret fra prompten *et nærbillede oliemaleri af en ung kvindelig lærer i datalogi med en computer* | Billede genereret fra prompten *et nærbillede oliemaleri af en ældre mandlig matematiklærer foran en tavle* diff --git a/translations/de/README.md b/translations/de/README.md index 10f87154..3f2dd232 100644 --- a/translations/de/README.md +++ b/translations/de/README.md @@ -1,8 +1,8 @@ -[Arabisch](../ar/README.md) | [Bengalisch](../bn/README.md) | [Bulgarisch](../bg/README.md) | [Birmanisch (Myanmar)](../my/README.md) | [Chinesisch (vereinfacht)](../zh/README.md) | [Chinesisch (traditionell, Hongkong)](../hk/README.md) | [Chinesisch (traditionell, Macau)](../mo/README.md) | [Chinesisch (traditionell, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tschechisch](../cs/README.md) | [Dänisch](../da/README.md) | [Niederländisch](../nl/README.md) | [Estnisch](../et/README.md) | [Finnisch](../fi/README.md) | [Französisch](../fr/README.md) | [Deutsch](./README.md) | [Griechisch](../el/README.md) | [Hebräisch](../he/README.md) | [Hindi](../hi/README.md) | [Ungarisch](../hu/README.md) | [Indonesisch](../id/README.md) | [Italienisch](../it/README.md) | [Japanisch](../ja/README.md) | [Kannada](../kn/README.md) | [Koreanisch](../ko/README.md) | [Litauisch](../lt/README.md) | [Malaiisch](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerianisches Pidgin](../pcm/README.md) | [Norwegisch](../no/README.md) | [Persisch (Farsi)](../fa/README.md) | [Polnisch](../pl/README.md) | [Portugiesisch (Brasilien)](../br/README.md) | [Portugiesisch (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumänisch](../ro/README.md) | [Russisch](../ru/README.md) | [Serbisch (Kyrillisch)](../sr/README.md) | [Slowakisch](../sk/README.md) | [Slowenisch](../sl/README.md) | [Spanisch](../es/README.md) | [Swahili](../sw/README.md) | [Schwedisch](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thailändisch](../th/README.md) | [Türkisch](../tr/README.md) | [Ukrainisch](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisch](../vi/README.md) +[Arabisch](../ar/README.md) | [Bengalisch](../bn/README.md) | [Bulgarisch](../bg/README.md) | [Birmanisch (Myanmar)](../my/README.md) | [Chinesisch (Vereinfacht)](../zh/README.md) | [Chinesisch (Traditionell, Hongkong)](../hk/README.md) | [Chinesisch (Traditionell, Macau)](../mo/README.md) | [Chinesisch (Traditionell, Taiwan)](../tw/README.md) | [Kroatisch](../hr/README.md) | [Tschechisch](../cs/README.md) | [Dänisch](../da/README.md) | [Niederländisch](../nl/README.md) | [Estnisch](../et/README.md) | [Finnisch](../fi/README.md) | [Französisch](../fr/README.md) | [Deutsch](./README.md) | [Griechisch](../el/README.md) | [Hebräisch](../he/README.md) | [Hindi](../hi/README.md) | [Ungarisch](../hu/README.md) | [Indonesisch](../id/README.md) | [Italienisch](../it/README.md) | [Japanisch](../ja/README.md) | [Kannada](../kn/README.md) | [Koreanisch](../ko/README.md) | [Litauisch](../lt/README.md) | [Malaiisch](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalesisch](../ne/README.md) | [Nigerianisches Pidgin](../pcm/README.md) | [Norwegisch](../no/README.md) | [Persisch (Farsi)](../fa/README.md) | [Polnisch](../pl/README.md) | [Portugiesisch (Brasilien)](../br/README.md) | [Portugiesisch (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumänisch](../ro/README.md) | [Russisch](../ru/README.md) | [Serbisch (Kyrillisch)](../sr/README.md) | [Slowakisch](../sk/README.md) | [Slowenisch](../sl/README.md) | [Spanisch](../es/README.md) | [Swahili](../sw/README.md) | [Schwedisch](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thailändisch](../th/README.md) | [Türkisch](../tr/README.md) | [Ukrainisch](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamesisch](../vi/README.md) -> **Bevorzugen Sie das lokale Klonen?** +> **Möchten Sie lokal klonen?** -> Dieses Repository enthält über 50 Sprachübersetzungen, was die Download-Größe erheblich erhöht. Um ohne Übersetzungen zu klonen, verwenden Sie Sparse Checkout: +> Dieses Repository enthält über 50 Sprachübersetzungen, was die Downloadgröße erheblich erhöht. Um ohne Übersetzungen zu klonen, verwenden Sie Sparse Checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Das gibt Ihnen alles, was Sie zum Absolvieren des Kurses benötigen, mit einem deutlich schnelleren Download. +> Das gibt Ihnen alles, was Sie zur Teilnahme am Kurs benötigen, mit einem viel schnelleren Download. -**Wenn Sie weitere unterstützte Übersetzungssprachen wünschen, sind diese [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) aufgeführt.** +**Falls Sie weitere unterstützte Übersetzungssprachen wünschen, finden Sie diese [hier](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** -## Tritt der Community bei +## Werden Sie Teil der Community [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Was Sie lernen werden -**[Gedankenkarte des Kurses](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mindmap des Kurses](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** In diesem Lehrplan lernen Sie: -* Verschiedene Ansätze zur Künstlichen Intelligenz, einschließlich des "guten alten" symbolischen Ansatzes mit **Wissensrepräsentation** und Schlussfolgerungen ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neuronale Netze** und **Deep Learning**, die das Herzstück der modernen KI bilden. Wir veranschaulichen die Konzepte hinter diesen wichtigen Themen anhand von Code in zwei der beliebtesten Frameworks – [TensorFlow](http://Tensorflow.org) und [PyTorch](http://pytorch.org). -* **Neuronale Architekturen** für die Arbeit mit Bildern und Texten. Wir behandeln aktuelle Modelle, können jedoch etwas hinter dem neuesten Stand zurückbleiben. -* Weniger populäre KI-Ansätze wie **Genetische Algorithmen** und **Multi-Agenten-Systeme**. +* Verschiedene Ansätze der Künstlichen Intelligenz, einschließlich des „guten alten“ symbolischen Ansatzes mit **Wissensrepräsentation** und Schlussfolgerungen ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neuronale Netzwerke** und **Deep Learning**, die im Kern der modernen KI stehen. Wir werden die Konzepte hinter diesen wichtigen Themen anhand von Code in zwei der beliebtesten Frameworks veranschaulichen - [TensorFlow](http://Tensorflow.org) und [PyTorch](http://pytorch.org). +* **Neuronale Architekturen** für die Arbeit mit Bildern und Text. Wir behandeln aktuelle Modelle, könnten aber leicht hinter dem neuesten Stand sein. +* Weniger populäre KI-Ansätze, wie **Genetische Algorithmen** und **Multiagentensysteme**. Was wir in diesem Lehrplan nicht behandeln: -> [Finden Sie alle zusätzlichen Ressourcen für diesen Kurs in unserer Microsoft Learn Sammlung](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Finden Sie alle zusätzlichen Ressourcen zu diesem Kurs in unserer Microsoft Learn Sammlung](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Geschäftsanwendungen von **KI im Business**. Ziehen Sie den Lernpfad [Einführung in KI für Geschäftsbenutzer](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) auf Microsoft Learn oder die [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) in Zusammenarbeit mit [INSEAD](https://www.insead.edu/) in Betracht. -* **Klassisches Machine Learning**, das in unserem [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) gut beschrieben ist. -* Praktische KI-Anwendungen, die mit **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** erstellt wurden. Dafür empfehlen wir den Start mit Microsoft Learn Modulen für [Vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natürliche Sprachverarbeitung](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative KI mit Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** und andere. -* Spezifische ML **Cloud-Frameworks**, wie [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) oder [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Nutzen Sie die Lernpfade [Machine Learning-Lösungen mit Azure Machine Learning entwickeln und betreiben](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) und [Machine Learning-Lösungen mit Azure Databricks entwickeln und betreiben](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Konversations-KI** und **Chatbots**. Es gibt einen eigenen Lernpfad [Konversations-KI-Lösungen erstellen](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), und Sie können sich auch diesen [Blogbeitrag](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) für weitere Details ansehen. -* **Tiefe Mathematik** hinter Deep Learning. Dafür empfehlen wir [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) von Ian Goodfellow, Yoshua Bengio und Aaron Courville, das auch online unter [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) verfügbar ist. +* Geschäftsfälle für den Einsatz von **KI im Geschäftsleben**. Ziehen Sie in Erwägung, den Lernpfad [Einführung in KI für Geschäftsanwender](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) bei Microsoft Learn oder die [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), entwickelt in Zusammenarbeit mit [INSEAD](https://www.insead.edu/), zu besuchen. +* **Klassisches Machine Learning**, das in unserem [Lehrplan für Machine Learning für Anfänger](http://github.com/Microsoft/ML-for-Beginners) gut beschrieben ist. +* Praktische KI-Anwendungen, die mit **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** erstellt wurden. Dafür empfehlen wir Ihnen, mit den Modulen bei Microsoft Learn für [Vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [natürliche Sprachverarbeitung](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI mit Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** und andere zu beginnen. +* Spezifische ML-**Cloud-Frameworks**, wie [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) oder [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Ziehen Sie die Lernpfade [Maschinelles Lernen mit Azure Machine Learning entwickeln und betreiben](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) und [Maschinelles Lernen mit Azure Databricks entwickeln und betreiben](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) in Betracht. +* **Konversationelle KI** und **Chatbots**. Es gibt einen separaten Lernpfad [Konversationelle KI-Lösungen erstellen](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), und Sie können auch [diesen Blogbeitrag](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) für weitere Details lesen. +* **Tiefgehende Mathematik** hinter Deep Learning. Dafür empfehlen wir [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) von Ian Goodfellow, Yoshua Bengio und Aaron Courville, das auch online unter [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) verfügbar ist. -Für eine sanfte Einführung in _KI in der Cloud_ Themen können Sie den Lernpfad [Erste Schritte mit künstlicher Intelligenz auf Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) absolvieren. +Für eine sanfte Einführung in _KI in der Cloud_-Themen können Sie den Lernpfad [Erste Schritte mit Künstlicher Intelligenz auf Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) in Betracht ziehen. # Inhalt -| | Lektion Link | PyTorch/Keras/TensorFlow | Labor | +| | Link zur Lektion | PyTorch/Keras/TensorFlow | Labor | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Kurs Einrichtung](./lessons/0-course-setup/setup.md) | [Richten Sie Ihre Entwicklungsumgebung ein](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Kurs-Einrichtung](./lessons/0-course-setup/setup.md) | [Einrichten Ihrer Entwicklungsumgebung](./lessons/0-course-setup/how-to-run.md) | | | I | [**Einführung in KI**](./lessons/1-Intro/README.md) | | | | 01 | [Einführung und Geschichte der KI](./lessons/1-Intro/README.md) | - | - | | II | **Symbolische KI** | -| 02 | [Wissensrepräsentation und Expertensysteme](./lessons/2-Symbolic/README.md) | [Expertensysteme](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Begriffsgraf](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Einführung in Neuronale Netze**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perzeptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Labor](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Mehrschichtiger Perzeptron und Erstellung unseres eigenen Frameworks](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Labor](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Einführung in Frameworks (PyTorch/TensorFlow) und Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Labor](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Entdecken Sie Computer Vision auf Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Einführung in Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Labor](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architekturen](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Labor](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Vortrainierte Netzwerke und Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) und [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Labor](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 02 | [Wissensrepräsentation und Expertensysteme](./lessons/2-Symbolic/README.md) | [Expertensysteme](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Konzeptgraph](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Einführung in Neuronale Netzwerke**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [Perzeptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Mehrschichtiger Perzeptron und Erstellung unseres eigenen Frameworks](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Einführung in Frameworks (PyTorch/TensorFlow) und Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Erkunden Sie Computer Vision auf Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Einführung in Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN-Architekturen](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Vortrainierte Netzwerke und Transferlernen](./lessons/4-ComputerVision/08-TransferLearning/README.md) und [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencoder und VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | | 10 | [Generative Adversarial Networks & Künstlerischer Stiltransfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Objekterkennung](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Labor](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 11 | [Objekterkennung](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantische Segmentierung. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Natürliche Sprachverarbeitung**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Entdecken Sie Natural Language Processing auf Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Textdarstellung. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Semantische Wort-Embeddings. Word2Vec und GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Sprachmodellierung. Eigene Embeddings trainieren](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Labor](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Rekurrente neuronale Netzwerke](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Generative rekurrente Netzwerke](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Labor](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| V | [**Natural Language Processing**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Erkunden Sie Natural Language Processing auf Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Textrepräsentation. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Semantische Wort-Einbettungen. Word2Vec und GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Sprachmodellierung. Training eigener Einbettungen](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Rekurrente Neuronale Netze](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [Generative Rekurrente Netze](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Benannte Entitätserkennung](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Labor](./lessons/5-NLP/19-NER/lab/README.md) | +| 19 | [Erkennung benannter Entitäten](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | | 20 | [Große Sprachmodelle, Prompt-Programmierung und Few-Shot-Aufgaben](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Andere KI-Techniken** || | | 21 | [Genetische Algorithmen](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Labor](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 22 | [Tiefes Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Multi-Agenten-Systeme](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **KI-Ethik** | | | -| 24 | [KI-Ethik und verantwortungsbewusste KI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Prinzipien verantwortungsvoller KI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [KI-Ethik und verantwortungsvolle KI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Prinzipien verantwortungsvoller KI](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Extras** | | | | 25 | [Multimodale Netzwerke, CLIP und VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Jede Lektion enthält -* Vorlesestoff -* Ausführbare Jupyter Notebooks, die oft frameworkspezifisch sind (**PyTorch** oder **TensorFlow**). Das ausführbare Notebook enthält auch viel theoretisches Material, sodass Sie zum Verständnis des Themas mindestens eine Version des Notebooks durchgehen sollten (entweder PyTorch oder TensorFlow). -* **Labore**, die zu einigen Themen verfügbar sind und Ihnen die Möglichkeit geben, das Gelernte bei einer bestimmten Aufgabe anzuwenden. -* Einige Abschnitte enthalten Links zu [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)-Modulen, die verwandte Themen abdecken. +* Vorbereitendes Lesematerial +* Ausführbare Jupyter-Notebooks, die häufig framework-spezifisch sind (**PyTorch** oder **TensorFlow**). Das ausführbare Notebook enthält auch viel theoretisches Material, daher ist zum Verständnis des Themas mindestens eine Version des Notebooks durchzugehen (entweder PyTorch oder TensorFlow). +* **Labs** zu einigen Themen, die dir die Möglichkeit geben, das Gelernte an einem konkreten Problem anzuwenden. +* Einige Abschnitte enthalten Links zu [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) Modulen, die verwandte Themen abdecken. ## Erste Schritte -### 🎯 Neu in KI? Hier anfangen! +### 🎯 Neu in KI? Hier starten! -Wenn Sie völlig neu in der KI sind und schnelle, praktische Beispiele suchen, sehen Sie sich unsere [**Anfängerfreundlichen Beispiele**](./examples/README.md) an! Diese umfassen: +Wenn du völlig neu in KI bist und schnelle, praxisorientierte Beispiele suchst, sieh dir unsere [**Anfängergerechten Beispiele**](./examples/README.md) an! Diese umfassen: -- 🌟 **Hallo KI Welt** – Ihr erstes KI-Programm (Mustererkennung) -- 🧠 **Einfaches neuronales Netzwerk** – Erstellen Sie ein neuronales Netzwerk von Grund auf -- 🖼️ **Bildklassifikator** – Bilder mit detaillierten Kommentaren klassifizieren -- 💬 **Textstimmung** - Analysiere positiven/negativen Text +- 🌟 **Hello AI World** - Dein erstes KI-Programm (Mustererkennung) +- 🧠 **Einfaches neuronales Netzwerk** - Erstellen eines neuronalen Netzwerks von Grund auf +- 🖼️ **Bildklassifikator** - Bilder klassifizieren mit detaillierten Kommentaren +- 💬 **Textsentiment** - Analysiere positiven/negativen Text -Diese Beispiele sollen Ihnen helfen, KI-Konzepte zu verstehen, bevor Sie in das vollständige Curriculum eintauchen. +Diese Beispiele sollen dir helfen, KI-Konzepte zu verstehen, bevor du in den vollständigen Lehrplan eintauchst. -### 📚 Komplette Curriculum-Einrichtung +### 📚 Einrichten des vollständigen Lehrplans -- Wir haben eine [Einrichtungslektion](./lessons/0-course-setup/setup.md) erstellt, um Ihnen bei der Einrichtung Ihrer Entwicklungsumgebung zu helfen. - Für Lehrende haben wir ebenfalls eine [Curricula-Einrichtungslektion](./lessons/0-course-setup/for-teachers.md) erstellt! -- Wie man den Code in VSCode oder Codepace [ausführt](./lessons/0-course-setup/how-to-run.md) +- Wir haben eine [Setup-Lektion](./lessons/0-course-setup/setup.md) erstellt, um dir bei der Einrichtung deiner Entwicklungsumgebung zu helfen. - Für Lehrkräfte haben wir ebenfalls eine [Lehrplan-Einrichtungslektion](./lessons/0-course-setup/for-teachers.md) erstellt! +- Wie man den Code in VSCode oder einem Codespace [ausführt](./lessons/0-course-setup/how-to-run.md) -Folgen Sie diesen Schritten: +Folge diesen Schritten: -Repository forken: Klicken Sie auf die Schaltfläche „Fork“ oben rechts auf dieser Seite. +Forke das Repository: Klicke auf die Schaltfläche „Fork“ oben rechts auf dieser Seite. -Repository klonen: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Klonen des Repositorys: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Vergessen Sie nicht, dieses Repo mit einem Stern (🌟) zu markieren, um es später leichter zu finden. +Vergiss nicht, dieses Repo mit einem Stern (🌟) zu markieren, damit du es später leichter findest. -## Treffen Sie andere Lernende +## Andere Lernende treffen -Treten Sie unserem [offiziellen AI Discord-Server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) bei, um andere Lernende, die diesen Kurs absolvieren, zu treffen, sich zu vernetzen und Unterstützung zu erhalten. +Tritt unserem [offiziellen AI Discord-Server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) bei, um andere Lernende, die diesen Kurs machen, zu treffen und dich zu vernetzen und Unterstützung zu erhalten. -Wenn Sie Produktfeedback oder Fragen während des Erstellens haben, besuchen Sie unser [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Wenn du Produkt-Feedback oder Fragen beim Entwickeln hast, besuche unser [Azure AI Foundry Entwicklerforum](https://aka.ms/foundry/forum) ## Quizze -> **Ein Hinweis zu Quizzen**: Alle Quizze befinden sich im Ordner Quiz-app in etc\quiz-app oder [Online hier](https://ff-quizzes.netlify.app/). Sie sind aus den Lektionen verlinkt, die Quiz-App kann lokal ausgeführt oder zu Azure bereitgestellt werden; folgen Sie den Anweisungen im `quiz-app`-Ordner. Sie werden nach und nach lokalisiert. +> **Eine Anmerkung zu den Quizzen**: Alle Quizze befinden sich im Quiz-App-Ordner in etc\quiz-app oder [online hier](https://ff-quizzes.netlify.app/) Sie sind aus den Lektionen verlinkt. Die Quiz-App kann lokal ausgeführt oder auf Azure bereitgestellt werden; folge den Anweisungen im `quiz-app` Ordner. Sie werden schrittweise lokalisiert. -## Hilfe Gesucht +## Hilfe gesucht -Haben Sie Vorschläge oder Fehler in Rechtschreibung oder Code gefunden? Eröffnen Sie ein Issue oder erstellen Sie einen Pull Request. +Hast du Vorschläge oder Rechtschreib- oder Codefehler gefunden? Erstelle ein Issue oder einen Pull Request. ## Besonderer Dank * **✍️ Hauptautor:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🔥 Herausgeber:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Sketchnote-Illustratorin:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Quiz-Erstellerin:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Kernmitwirkende:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 Hauptbeitragende:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Andere Curricula +## Andere Lehrpläne -Unser Team erstellt weitere Curricula! Schauen Sie sich an: +Unser Team entwickelt weitere Lehrpläne! Schau mal rein: ### LangChain @@ -183,12 +184,12 @@ Unser Team erstellt weitere Curricula! Schauen Sie sich an: ### Azure / Edge / MCP / Agents [![AZD für Anfänger](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge KI für Anfänger](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI für Anfänger](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) [![MCP für Anfänger](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![KI-Agenten für Anfänger](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents für Anfänger](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Generative KI-Serie [![Generative KI für Anfänger](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative KI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) @@ -196,10 +197,10 @@ Unser Team erstellt weitere Curricula! Schauen Sie sich an: [![Generative KI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - -### Kernwissen Lernen + +### Kernlernen [![ML für Anfänger](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science für Anfänger](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![Datenwissenschaft für Anfänger](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![KI für Anfänger](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) [![Cybersicherheit für Anfänger](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) [![Webentwicklung für Anfänger](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) @@ -207,26 +208,26 @@ Unser Team erstellt weitere Curricula! Schauen Sie sich an: [![XR-Entwicklung für Anfänger](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Copilot-Serie -[![Copilot für AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot für KI-Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot für C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot-Abenteuer](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Hilfe erhalten -Wenn Sie nicht weiterkommen oder Fragen zur Entwicklung von KI-Anwendungen haben, treten Sie Gleichgesinnten und erfahrenen Entwicklern bei, um über MCP zu diskutieren. Es ist eine unterstützende Gemeinschaft, in der Fragen willkommen sind und Wissen frei geteilt wird. +Wenn du feststeckst oder Fragen zum Erstellen von KI-Apps hast. Tritt anderen Lernenden und erfahrenen Entwicklern bei Diskussionen über MCP bei. Es ist eine unterstützende Gemeinschaft, in der Fragen willkommen sind und Wissen frei geteilt wird. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Wenn Sie Produktfeedback oder Fehler beim Erstellen haben, besuchen Sie: +Wenn du Produkt-Feedback oder Fehler beim Entwickeln hast, besuche: -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) +[![Microsoft Foundry Entwicklerforum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Haftungsausschluss**: -Dieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, sollten Sie beachten, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Ursprungssprache ist als maßgebliche Quelle zu betrachten. Für wichtige Informationen empfehlen wir eine professionelle menschliche Übersetzung. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die durch die Verwendung dieser Übersetzung entstehen. +**Haftungsausschluss**: +Dieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir auf Genauigkeit achten, sind automatisierte Übersetzungen möglicherweise fehlerhaft oder ungenau. Das Originaldokument in seiner Ursprungssprache ist die maßgebliche Quelle. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die durch die Nutzung dieser Übersetzung entstehen. \ No newline at end of file diff --git a/translations/de/lessons/0-course-setup/how-to-run.md b/translations/de/lessons/0-course-setup/how-to-run.md index e6bcc713..c3457638 100644 --- a/translations/de/lessons/0-course-setup/how-to-run.md +++ b/translations/de/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ -# So führen Sie den Code aus +# Wie man den Code ausführt -Dieses Curriculum enthält viele ausführbare Beispiele und Übungen, die Sie ausprobieren möchten. Um dies zu tun, benötigen Sie die Möglichkeit, Python-Code in den Jupyter-Notebooks auszuführen, die Teil dieses Curriculums sind. Es gibt mehrere Möglichkeiten, den Code auszuführen: +Dieses Curriculum enthält viele ausführbare Beispiele und Labs, die Sie ausführen möchten. Um dies zu tun, benötigen Sie die Möglichkeit, Python-Code in Jupyter Notebooks auszuführen, die als Teil dieses Curriculums bereitgestellt werden. Sie haben mehrere Optionen, um den Code auszuführen: ## Lokal auf Ihrem Computer ausführen -Um den Code lokal auf Ihrem Computer auszuführen, benötigen Sie eine installierte Python-Version. Ich empfehle persönlich die Installation von **[miniconda](https://conda.io/en/latest/miniconda.html)** – eine schlanke Installation, die den `conda`-Paketmanager für verschiedene Python-**virtuelle Umgebungen** unterstützt. +Um den Code lokal auf Ihrem Computer auszuführen, ist eine Python-Installation erforderlich. Eine Empfehlung ist die Installation von **[miniconda](https://conda.io/en/latest/miniconda.html)** – es ist eine eher leichtgewichtige Installation, die den `conda` Paketmanager für verschiedene Python-**virtuelle Umgebungen** unterstützt. -Nachdem Sie Miniconda installiert haben, müssen Sie das Repository klonen und eine virtuelle Umgebung für diesen Kurs erstellen: +Nachdem Sie miniconda installiert haben, klonen Sie das Repository und erstellen eine virtuelle Umgebung, die für diesen Kurs verwendet wird: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,51 +26,55 @@ conda activate ai4beg ### Verwendung von Visual Studio Code mit Python-Erweiterung -Die wahrscheinlich beste Möglichkeit, das Curriculum zu nutzen, besteht darin, es in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) mit der [Python-Erweiterung](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) zu öffnen. +Dieses Curriculum wird am besten verwendet, wenn es in [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) mit der [Python-Erweiterung](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) geöffnet wird. -> **Hinweis**: Sobald Sie das Verzeichnis in VS Code klonen und öffnen, wird Ihnen automatisch vorgeschlagen, die Python-Erweiterungen zu installieren. Sie müssen auch Miniconda wie oben beschrieben installieren. +> **Hinweis**: Sobald Sie das Verzeichnis klonen und in VS Code öffnen, wird automatisch vorgeschlagen, Python-Erweiterungen zu installieren. Sie müssen auch miniconda wie oben beschrieben installieren. -> **Hinweis**: Wenn VS Code Ihnen vorschlägt, das Repository in einem Container zu öffnen, sollten Sie dies ablehnen, um die lokale Python-Installation zu verwenden. +> **Hinweis**: Wenn VS Code vorschlägt, das Repository in einem Container erneut zu öffnen, sollten Sie dies ablehnen, um die lokale Python-Installation zu verwenden. ### Verwendung von Jupyter im Browser -Sie können die Jupyter-Umgebung auch direkt im Browser auf Ihrem eigenen Computer nutzen. Tatsächlich bieten sowohl das klassische Jupyter als auch Jupyter Hub eine recht komfortable Entwicklungsumgebung mit Autovervollständigung, Syntaxhervorhebung usw. +Sie können auch eine Jupyter-Umgebung aus dem Browser auf Ihrem eigenen Computer verwenden. Sowohl klassisches Jupyter als auch JupyterHub bieten eine bequeme Entwicklungsumgebung mit Autovervollständigung, Code-Hervorhebung usw. -Um Jupyter lokal zu starten, gehen Sie in das Verzeichnis des Kurses und führen Sie aus: +Um Jupyter lokal zu starten, wechseln Sie in das Verzeichnis des Kurses und führen aus: ```bash jupyter notebook -``` -oder +``` +oder ```bash jupyterhub -``` -Anschließend können Sie zu einer der `.ipynb`-Dateien navigieren, diese öffnen und mit der Arbeit beginnen. +``` +Sie können dann zu einer beliebigen `.ipynb`-Datei navigieren, sie öffnen und mit der Arbeit beginnen. -### Ausführung in einem Container +### Ausführen in einem Container -Eine Alternative zur Python-Installation besteht darin, den Code in einem Container auszuführen. Da unser Repository einen speziellen `.devcontainer`-Ordner enthält, der Anweisungen zum Erstellen eines Containers für dieses Repository bereitstellt, wird Ihnen VS Code anbieten, den Code in einem Container zu öffnen. Dies erfordert die Installation von Docker und ist auch etwas komplexer, daher empfehlen wir dies eher erfahrenen Nutzern. +Eine Alternative zur Python-Installation wäre das Ausführen des Codes in einem Container. Da unser Repository einen speziellen `.devcontainer`-Ordner bereitstellt, der beschreibt, wie ein Container für dieses Repo gebaut wird, bietet VS Code die Möglichkeit, den Code in einem Container erneut zu öffnen. Dies erfordert die Installation von Docker und wäre auch komplexer, daher empfehlen wir dies eher erfahrenen Nutzern. ## Ausführung in der Cloud -Wenn Sie Python nicht lokal installieren möchten und Zugriff auf Cloud-Ressourcen haben, ist eine gute Alternative, den Code in der Cloud auszuführen. Es gibt mehrere Möglichkeiten, dies zu tun: +Wenn Sie Python nicht lokal installieren möchten und Zugriff auf Cloud-Ressourcen haben – eine gute Alternative ist es, den Code in der Cloud auszuführen. Es gibt mehrere Möglichkeiten, dies zu tun: -* Verwendung von **[GitHub Codespaces](https://github.com/features/codespaces)**, einer virtuellen Umgebung, die für Sie auf GitHub erstellt wird und über die Browseroberfläche von VS Code zugänglich ist. Wenn Sie Zugriff auf Codespaces haben, können Sie einfach auf die Schaltfläche **Code** im Repository klicken, einen Codespace starten und sofort loslegen. -* Verwendung von **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) bietet kostenlose Rechenressourcen in der Cloud, mit denen Sie Code auf GitHub testen können. Auf der Startseite gibt es eine Schaltfläche, um das Repository in Binder zu öffnen – dies sollte Sie schnell zur Binder-Seite führen, die den zugrunde liegenden Container erstellt und nahtlos die Jupyter-Weboberfläche startet. +* Verwendung von **[GitHub Codespaces](https://github.com/features/codespaces)**, einer virtuellen Umgebung, die für Sie auf GitHub erstellt wird und über eine VS Code-Browseroberfläche zugänglich ist. Wenn Sie Zugriff auf Codespaces haben, können Sie einfach auf die **Code**-Schaltfläche im Repo klicken, einen Codespace starten und sofort loslegen. +* Verwendung von **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) bietet kostenfreie Cloud-Computing-Ressourcen für Personen wie Sie, um Code auf GitHub auszuprobieren. Es gibt eine Schaltfläche auf der Startseite, um das Repository in Binder zu öffnen – dies sollte Sie schnell auf die Binder-Seite bringen, die einen zugrundeliegenden Container baut und nahtlos eine Jupyter-Weboberfläche für Sie startet. -> **Hinweis**: Um Missbrauch zu verhindern, hat Binder den Zugriff auf einige Webressourcen blockiert. Dies kann dazu führen, dass bestimmter Code, der Modelle und/oder Datensätze aus dem öffentlichen Internet abruft, nicht funktioniert. Sie müssen möglicherweise Workarounds finden. Außerdem sind die von Binder bereitgestellten Rechenressourcen recht begrenzt, sodass das Training, insbesondere in späteren komplexeren Lektionen, langsam sein wird. +> **Hinweis**: Zur Vermeidung von Missbrauch hat Binder den Zugriff auf einige Webressourcen blockiert. Dies kann verhindern, dass ein Teil des Codes funktioniert, der Modelle und/oder Datensätze aus dem öffentlichen Internet lädt. Sie müssen möglicherweise einige Umgehungen finden. Außerdem sind die von Binder bereitgestellten Rechenressourcen eher grundlegend, sodass das Training langsam sein wird, besonders in späteren, komplexeren Lektionen. ## Ausführung in der Cloud mit GPU -Einige der späteren Lektionen in diesem Curriculum profitieren stark von GPU-Unterstützung, da das Training sonst extrem langsam wäre. Es gibt einige Optionen, die Sie nutzen können, insbesondere wenn Sie über [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) oder Ihre Institution Zugang zur Cloud haben: +Einige der späteren Lektionen in diesem Curriculum würden erheblich von GPU-Unterstützung profitieren. Das Modelltraining kann sonst sehr langsam sein. Es gibt einige Möglichkeiten, die Sie nutzen können, besonders wenn Sie über [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) oder über Ihre Institution Zugang zur Cloud haben: -* Erstellen Sie eine [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) und verbinden Sie sich über Jupyter mit ihr. Sie können das Repository dann direkt auf die Maschine klonen und mit dem Lernen beginnen. NC-Serien-VMs unterstützen GPUs. +* Erstellen Sie eine [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) und verbinden Sie sich über Jupyter mit ihr. Sie können dann das Repo direkt auf die Maschine klonen und mit dem Lernen beginnen. NC-Serien VMs unterstützen GPU. -> **Hinweis**: Einige Abonnements, einschließlich Azure for Students, bieten standardmäßig keine GPU-Unterstützung. Sie müssen möglicherweise zusätzliche GPU-Kerne über eine technische Supportanfrage anfordern. +> **Hinweis**: Einige Abonnements, einschließlich Azure for Students, bieten nicht standardmäßig GPU-Unterstützung. Möglicherweise müssen Sie zusätzliche GPU-Kerne per technischem Supportantrag anfordern. -* Erstellen Sie einen [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) und nutzen Sie dort die Notebook-Funktion. [Dieses Video](https://azure-for-academics.github.io/quickstart/azureml-papers/) zeigt, wie Sie ein Repository in ein Azure ML-Notebook klonen und es verwenden können. +* Erstellen Sie einen [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) und verwenden Sie dort die Notizbuchfunktion. [Dieses Video](https://azure-for-academics.github.io/quickstart/azureml-papers/) zeigt, wie man ein Repository in ein Azure ML-Notizbuch klont und es verwendet. -Sie können auch Google Colab verwenden, das einige kostenlose GPU-Ressourcen bietet, und Jupyter-Notebooks hochladen, um sie dort Schritt für Schritt auszuführen. +Sie können auch Google Colab verwenden, das über eine kostenlose GPU-Unterstützung verfügt, und Jupyter Notebooks dort hochladen, um sie einzeln auszuführen. +--- + + **Haftungsausschluss**: -Dieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben. \ No newline at end of file +Dieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir bemüht sind, eine genaue Übersetzung zu gewährleisten, sollten Sie beachten, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Ursprungssprache gilt als verbindliche Quelle. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Nutzung dieser Übersetzung entstehen. + \ No newline at end of file diff --git a/translations/de/lessons/1-Intro/README.md b/translations/de/lessons/1-Intro/README.md index 6e4e8c02..76dc84f6 100644 --- a/translations/de/lessons/1-Intro/README.md +++ b/translations/de/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Einführung in KI -![Zusammenfassung der Einführung in KI in einer Skizze](../../../../translated_images/ai-intro.bf28d1ac4235881c.de.png) +![Zusammenfassung der Einführung in KI in einer Skizze](../../../../translated_images/de/ai-intro.bf28d1ac4235881c.webp) > Sketchnote von [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ursprünglich wurden Computer von [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) erfunden, um Zahlen nach einem klar definierten Verfahren – einem Algorithmus – zu verarbeiten. Moderne Computer, obwohl sie wesentlich fortschrittlicher sind als das ursprüngliche Modell aus dem 19. Jahrhundert, folgen immer noch derselben Idee kontrollierter Berechnungen. Daher ist es möglich, einen Computer so zu programmieren, dass er etwas tut, wenn wir die genaue Abfolge der Schritte kennen, die wir ausführen müssen, um das Ziel zu erreichen. -![Foto einer Person](../../../../translated_images/dsh_age.d212a30d4e54fb5f.de.png) +![Foto einer Person](../../../../translated_images/de/dsh_age.d212a30d4e54fb5f.webp) > Foto von [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Weitere Informationen finden Sie unter **[Allgemeine Künstliche Intelligenz](ht Eines der Probleme im Umgang mit dem Begriff **[Intelligenz](https://en.wikipedia.org/wiki/Intelligence)** ist, dass es keine klare Definition dieses Begriffs gibt. Man könnte argumentieren, dass Intelligenz mit **abstraktem Denken** oder **Selbstbewusstsein** verbunden ist, aber wir können sie nicht eindeutig definieren. -![Foto einer Katze](../../../../translated_images/photo-cat.8c8e8fb760ffe457.de.jpg) +![Foto einer Katze](../../../../translated_images/de/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) von [Amber Kipp](https://unsplash.com/@sadmax) auf Unsplash @@ -98,13 +98,13 @@ Alternativ können wir versuchen, die einfachsten Elemente in unserem Gehirn zu > | Was ist mit ML? | | > |--------------|-----------| -> | Ein Teil der Künstlichen Intelligenz, der darauf basiert, dass Computer lernen, ein Problem anhand von Daten zu lösen, wird als **Maschinelles Lernen** bezeichnet. Wir werden klassisches maschinelles Lernen in diesem Kurs nicht behandeln – wir verweisen Sie auf den separaten [Maschinelles Lernen für Anfänger](http://aka.ms/ml-beginners)-Lehrplan. | ![ML für Anfänger](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.de.png) | +> | Ein Teil der Künstlichen Intelligenz, der darauf basiert, dass Computer lernen, ein Problem anhand von Daten zu lösen, wird als **Maschinelles Lernen** bezeichnet. Wir werden klassisches maschinelles Lernen in diesem Kurs nicht behandeln – wir verweisen Sie auf den separaten [Maschinelles Lernen für Anfänger](http://aka.ms/ml-beginners)-Lehrplan. | ![ML für Anfänger](../../../../translated_images/de/ml-for-beginners.9e4fed176fd5817d.webp) | ## Ein kurzer Überblick über die Geschichte der KI Künstliche Intelligenz wurde Mitte des 20. Jahrhunderts als Forschungsfeld begründet. Anfangs war symbolisches Denken der vorherrschende Ansatz, und es führte zu einer Reihe wichtiger Erfolge, wie z. B. Expertensysteme – Computerprogramme, die in der Lage waren, in begrenzten Problembereichen als Experte zu agieren. Es wurde jedoch bald klar, dass ein solcher Ansatz nicht gut skalierbar ist. Wissen von einem Experten zu extrahieren, es im Computer darzustellen und diese Wissensbasis aktuell zu halten, stellte sich als sehr komplexe Aufgabe heraus und war in vielen Fällen zu teuer, um praktikabel zu sein. Dies führte in den 1970er Jahren zum sogenannten [KI-Winter](https://en.wikipedia.org/wiki/AI_winter). -Kurze Geschichte der KI +Kurze Geschichte der KI > Bild von [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Wir können beobachten, wie sich die Ansätze geändert haben, z. B. bei der Ent * Moderne Assistenten wie Cortana, Siri oder Google Assistant sind alle Hybridsysteme, die neuronale Netzwerke verwenden, um Sprache in Text umzuwandeln und unsere Absicht zu erkennen, und dann einige Schlussfolgerungen oder explizite Algorithmen anwenden, um die erforderlichen Aktionen auszuführen. * In der Zukunft können wir erwarten, dass ein vollständiges neuronales Modell den Dialog eigenständig handhabt. Die jüngsten GPT- und [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)-Familien von neuronalen Netzwerken zeigen große Erfolge in diesem Bereich. -Die Entwicklung des Turing-Tests +Die Entwicklung des Turing-Tests > Bild von Dmitry Soshnikov, [Foto](https://unsplash.com/photos/r8LmVbUKgns) von [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Aktuelle KI-Forschung diff --git a/translations/de/lessons/2-Symbolic/Animals.ipynb b/translations/de/lessons/2-Symbolic/Animals.ipynb index 556d020b..e9313157 100644 --- a/translations/de/lessons/2-Symbolic/Animals.ipynb +++ b/translations/de/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Implementierung eines Tier-Expertensystems\n", + "# Implementierung eines Experten­systems für Tiere\n", "\n", "Ein Beispiel aus dem [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "In diesem Beispiel werden wir ein einfaches wissensbasiertes System implementieren, um ein Tier anhand einiger physischer Merkmale zu bestimmen. Das System kann durch den folgenden UND-ODER-Baum dargestellt werden (dies ist ein Teil des gesamten Baums, wir können leicht weitere Regeln hinzufügen):\n", + "In diesem Beispiel werden wir ein einfaches wissensbasiertes System implementieren, um ein Tier anhand einiger körperlicher Merkmale zu bestimmen. Das System kann durch den folgenden AND-OR-Baum dargestellt werden (dies ist ein Teil des gesamten Baums, wir können problemlos weitere Regeln hinzufügen):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/de/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Unsere eigene Expertensystem-Shell mit Rückwärtsinferenzen\n", + "## Unser eigenes Expertensystem-Shell mit Rückwärtsverkettung\n", "\n", - "Versuchen wir, eine einfache Sprache zur Wissensrepräsentation basierend auf Produktionsregeln zu definieren. Wir werden Python-Klassen als Schlüsselwörter verwenden, um Regeln zu definieren. Es gibt im Wesentlichen drei Arten von Klassen:\n", - "* `Ask` repräsentiert eine Frage, die dem Benutzer gestellt werden muss. Sie enthält die Menge der möglichen Antworten.\n", - "* `If` repräsentiert eine Regel und ist lediglich syntaktischer Zucker, um den Inhalt der Regel zu speichern.\n", - "* `AND`/`OR` sind Klassen, die AND/OR-Verzweigungen des Baums darstellen. Sie speichern einfach die Liste der Argumente. Um den Code zu vereinfachen, wird die gesamte Funktionalität in der Elternklasse `Content` definiert.\n" + "Lassen Sie uns versuchen, eine einfache Sprache für die Wissensrepräsentation basierend auf Produktionsregeln zu definieren. Wir verwenden Python-Klassen als Schlüsselwörter, um Regeln zu definieren. Es gibt im Wesentlichen 3 Arten von Klassen:\n", + "* `Ask` repräsentiert eine Frage, die dem Benutzer gestellt werden muss. Es enthält die Menge möglicher Antworten.\n", + "* `If` repräsentiert eine Regel und ist nur ein syntaktischer Zucker, um den Inhalt der Regel zu speichern.\n", + "* `AND`/`OR` sind Klassen, um AND/OR-Verzweigungen des Baums darzustellen. Sie speichern nur die Liste der Argumente darin. Zur Vereinfachung des Codes ist die gesamte Funktionalität in der Oberklasse `Content` definiert.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In unserem System würde der Arbeitsspeicher die Liste von **Fakten** als **Attribut-Wert-Paare** enthalten. Die Wissensdatenbank kann als ein großes Wörterbuch definiert werden, das Aktionen (neue Fakten, die in den Arbeitsspeicher eingefügt werden sollen) auf Bedingungen abbildet, ausgedrückt als UND-ODER-Ausdrücke. Außerdem können einige Fakten `gefragt` werden.\n" + "In unserem System würde das Arbeitsgedächtnis die Liste der **Fakten** als **Attribut-Wert-Paare** enthalten. Die Wissensbasis kann als ein großes Wörterbuch definiert werden, das Aktionen (neue Fakten, die in das Arbeitsgedächtnis eingefügt werden sollen) auf Bedingungen abbildet, die als UND-ODER-Ausdrücke dargestellt sind. Außerdem können einige Fakten `Ask`-ed werden.\n" ] }, { @@ -100,12 +100,12 @@ "metadata": {}, "source": [ "Um die Rückwärtsinferenz durchzuführen, definieren wir die Klasse `Knowledgebase`. Sie wird enthalten:\n", - "* Funktionierender `Speicher` - ein Wörterbuch, das Attribute mit Werten verknüpft\n", - "* `Regeln` der Wissensbasis im oben definierten Format\n", + "* Arbeitsspeicher `memory` – ein Wörterbuch, das Attribute auf Werte abbildet\n", + "* Knowledgebase-`rules` im oben definierten Format\n", "\n", "Zwei Hauptmethoden sind:\n", - "* `get`, um den Wert eines Attributs zu erhalten und bei Bedarf eine Inferenz durchzuführen. Zum Beispiel würde `get('color')` den Wert eines Farbfeldes abrufen (es wird bei Bedarf nachfragen und den Wert für die spätere Verwendung im Arbeitsspeicher speichern). Wenn wir `get('color:blue')` abfragen, wird nach einer Farbe gefragt und dann ein `y`/`n`-Wert zurückgegeben, abhängig von der Farbe.\n", - "* `eval` führt die eigentliche Inferenz durch, d.h. durchläuft den UND/ODER-Baum, bewertet Unterziele usw.\n" + "* `get`, um den Wert eines Attributs zu erhalten, wobei bei Bedarf eine Inferenz durchgeführt wird. Zum Beispiel würde `get('color')` den Wert eines Farb-Slots abfragen (es wird bei Bedarf gefragt und der Wert für die spätere Verwendung im Arbeitsspeicher gespeichert). Wenn wir `get('color:blue')` abfragen, wird nach einer Farbe gefragt und dann abhängig von der Farbe ein `y`/`n`-Wert zurückgegeben.\n", + "* `eval` führt die eigentliche Inferenz durch, d.h. durchläuft den AND/OR-Baum, bewertet Teilziele usw.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Nun lassen Sie uns unsere Tier-Wissensdatenbank definieren und die Beratung durchführen. Beachten Sie, dass dieser Aufruf Ihnen Fragen stellen wird. Sie können mit `y`/`n` für Ja-Nein-Fragen antworten oder eine Zahl (0..N) für Fragen mit längeren Multiple-Choice-Antworten angeben.\n" + "Definieren wir nun unsere Tierwissensdatenbank und führen die Beratung durch. Beachten Sie, dass Ihnen bei diesem Aufruf Fragen gestellt werden. Sie können mit `y`/`n` für Ja-Nein-Fragen antworten oder eine Zahl (0..N) für Fragen mit längeren Mehrfachauswahlantworten angeben.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Verwendung von PyKnow für Vorwärtsinferenz\n", + "## Verwendung von Experta für Vorwärts-Schlussfolgerungen\n", "\n", - "Im nächsten Beispiel werden wir versuchen, Vorwärtsinferenz mit einer der Bibliotheken für Wissensrepräsentation, [PyKnow](https://github.com/buguroo/pyknow/), zu implementieren. **PyKnow** ist eine Bibliothek zur Erstellung von Vorwärtsinferenzsystemen in Python, die so konzipiert ist, dass sie dem klassischen alten System [CLIPS](http://www.clipsrules.net/index.html) ähnelt.\n", + "Im nächsten Beispiel werden wir versuchen, Vorwärts-Schlussfolgerungen mit einer der Bibliotheken für Wissensrepräsentation, [Experta](https://github.com/nilp0inter/experta), zu implementieren. **Experta** ist eine Bibliothek zur Erstellung von Vorwärts-Schlusssystemen in Python, die so gestaltet ist, dass sie dem klassischen alten System [CLIPS](http://www.clipsrules.net/index.html) ähnelt.\n", "\n", - "Wir hätten die Vorwärtsverkettung auch selbst ohne größere Probleme implementieren können, aber naive Implementierungen sind in der Regel nicht sehr effizient. Für eine effektivere Regelanpassung wird ein spezieller Algorithmus namens [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) verwendet.\n" + "Wir hätten die Vorwärtsverkettung auch selbst ohne große Probleme implementieren können, aber naive Implementierungen sind normalerweise nicht sehr effizient. Für ein effektiveres Regelmatching wird ein spezieller Algorithmus namens [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) verwendet.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Wir werden unser System als eine Klasse definieren, die `KnowledgeEngine` unterklassen. Jede Regel wird durch eine separate Funktion mit der `@Rule`-Annotation definiert, die angibt, wann die Regel ausgelöst werden soll. Innerhalb der Regel können wir neue Fakten mit der Funktion `declare` hinzufügen, und das Hinzufügen dieser Fakten führt dazu, dass einige weitere Regeln vom Vorwärts-Inferenzmotor aufgerufen werden.\n" + "Wir definieren unser System als eine Klasse, die von `KnowledgeEngine` erbt. Jede Regel wird durch eine separate Funktion mit der `@Rule`-Annotation definiert, die angibt, wann die Regel ausgelöst werden soll. Innerhalb der Regel können wir mit der Funktion `declare` neue Fakten hinzufügen, und das Hinzufügen dieser Fakten führt dazu, dass weitere Regeln durch die vorwärtsgerichtete Inferenzmaschine aufgerufen werden.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sobald wir eine Wissensbasis definiert haben, füllen wir unser Arbeitsgedächtnis mit einigen anfänglichen Fakten und rufen dann die Methode `run()` auf, um die Schlussfolgerung durchzuführen. Als Ergebnis können Sie sehen, dass neue abgeleitete Fakten dem Arbeitsgedächtnis hinzugefügt werden, einschließlich der endgültigen Tatsache über das Tier (wenn wir alle anfänglichen Fakten korrekt eingerichtet haben).\n" + "Sobald wir eine Wissensbasis definiert haben, füllen wir unser Arbeitsgedächtnis mit einigen Anfangsfakten und rufen dann die `run()`-Methode auf, um die Inferenz durchzuführen. Sie können als Ergebnis sehen, dass neue abgeleitete Fakten dem Arbeitsgedächtnis hinzugefügt werden, einschließlich der endgültigen Tatsache über das Tier (wenn wir alle anfänglichen Fakten korrekt eingerichtet haben).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Haftungsausschluss**: \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die sich aus der Nutzung dieser Übersetzung ergeben.\n" + "---\n\n\n**Haftungsausschluss**: \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, beachten Sie bitte, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Ursprungssprache gilt als maßgebliche Quelle. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Nutzung dieser Übersetzung entstehen.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T16:26:30+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:38:45+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "de" } diff --git a/translations/de/lessons/2-Symbolic/README.md b/translations/de/lessons/2-Symbolic/README.md index 334a00fe..127fa435 100644 --- a/translations/de/lessons/2-Symbolic/README.md +++ b/translations/de/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Wissensrepräsentation und Expertensysteme -![Zusammenfassung des Symbolischen KI-Inhalts](../../../../translated_images/ai-symbolic.715a30cb610411a6.de.png) +![Zusammenfassung des symbolischen KI-Inhalts](../../../../../../translated_images/de/ai-symbolic.715a30cb610411a6.webp) > Sketchnote von [Tomomi Imura](https://twitter.com/girlie_mac) -Die Suche nach künstlicher Intelligenz basiert auf dem Streben nach Wissen, um die Welt ähnlich wie Menschen zu verstehen. Aber wie kann man das erreichen? +Die Suche nach künstlicher Intelligenz basiert auf der Suche nach Wissen, um die Welt ähnlich wie Menschen zu verstehen. Aber wie kann man das angehen? -## [Quiz vor der Vorlesung](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Vorlesungsquiz](https://ff-quizzes.netlify.app/en/ai/quiz/3) -In den frühen Tagen der KI war der Top-Down-Ansatz zur Erstellung intelligenter Systeme (im vorherigen Kapitel besprochen) beliebt. Die Idee war, das Wissen von Menschen in eine maschinenlesbare Form zu extrahieren und es dann zu nutzen, um Probleme automatisch zu lösen. Dieser Ansatz basierte auf zwei großen Konzepten: +In den frühen Tagen der KI war der Top-Down-Ansatz zur Erstellung intelligenter Systeme (im vorherigen Kapitel besprochen) beliebt. Die Idee war, Wissen von Menschen in eine maschinenlesbare Form zu extrahieren und es dann automatisch zur Problemlösung zu verwenden. Dieser Ansatz basierte auf zwei großen Ideen: * Wissensrepräsentation * Schlussfolgerung ## Wissensrepräsentation -Eines der wichtigen Konzepte in der symbolischen KI ist **Wissen**. Es ist wichtig, Wissen von *Information* oder *Daten* zu unterscheiden. Zum Beispiel könnte man sagen, dass Bücher Wissen enthalten, weil man durch das Studium von Büchern ein Experte werden kann. Tatsächlich enthalten Bücher jedoch *Daten*, und durch das Lesen und Integrieren dieser Daten in unser Weltmodell verwandeln wir diese Daten in Wissen. +Eines der wichtigen Konzepte in der symbolischen KI ist **Wissen**. Es ist wichtig, Wissen von *Information* oder *Daten* abzugrenzen. Zum Beispiel kann man sagen, dass Bücher Wissen enthalten, weil man Bücher studieren und Experte werden kann. Aber was Bücher tatsächlich enthalten, nennt man *Daten*, und durch das Lesen von Büchern und die Integration dieser Daten in unser Weltmodell wandeln wir diese Daten in Wissen um. -> ✅ **Wissen** ist etwas, das in unserem Kopf enthalten ist und unsere Vorstellung von der Welt repräsentiert. Es wird durch einen aktiven **Lernprozess** gewonnen, der die erhaltenen Informationen in unser aktives Weltmodell integriert. +> ✅ **Wissen** ist etwas, das in unserem Kopf enthalten ist und unser Verständnis der Welt repräsentiert. Es wird durch einen aktiven **Lernprozess** gewonnen, der erhaltene Informationsstücke in unser aktives Weltmodell integriert. -Meistens definieren wir Wissen nicht streng, sondern ordnen es anderen verwandten Konzepten zu, wie sie in der [DIKW-Pyramide](https://en.wikipedia.org/wiki/DIKW_pyramid) dargestellt sind. Sie enthält die folgenden Konzepte: +Meistens definieren wir Wissen nicht strikt, sondern ordnen es anderen verwandten Konzepten mithilfe der [DIKW-Pyramide](https://de.wikipedia.org/wiki/DIKW-Pyramide) zu. Sie beinhaltet folgende Begriffe: -* **Daten** sind etwas, das in physischer Form dargestellt wird, wie geschriebener Text oder gesprochene Worte. Daten existieren unabhängig von Menschen und können zwischen ihnen weitergegeben werden. -* **Information** ist, wie wir Daten in unserem Kopf interpretieren. Zum Beispiel haben wir eine Vorstellung davon, was ein *Computer* ist, wenn wir das Wort hören. -* **Wissen** ist Information, die in unser Weltmodell integriert wird. Sobald wir lernen, was ein Computer ist, entwickeln wir Ideen darüber, wie er funktioniert, wie viel er kostet und wofür er verwendet werden kann. Dieses Netzwerk von miteinander verbundenen Konzepten bildet unser Wissen. -* **Weisheit** ist eine weitere Ebene unseres Weltverständnisses und repräsentiert *Meta-Wissen*, z. B. eine Vorstellung davon, wie und wann Wissen verwendet werden sollte. +* **Daten** sind etwas, das in physischen Medien dargestellt wird, wie geschriebener Text oder gesprochene Worte. Daten existieren unabhängig von Menschen und können zwischen Menschen weitergegeben werden. +* **Information** ist, wie wir Daten in unserem Kopf interpretieren. Zum Beispiel haben wir beim Hören des Wortes *Computer* eine Vorstellung davon, was es ist. +* **Wissen** ist Information, die in unser Weltmodell integriert wird. Wenn wir beispielsweise lernen, was ein Computer ist, haben wir Vorstellungen darüber, wie er funktioniert, wie viel er kostet und wofür er verwendet werden kann. Dieses Netz von miteinander verbundenen Konzepten bildet unser Wissen. +* **Weisheit** ist eine weitere Ebene unseres Weltverständnisses und repräsentiert *Metawissen*, z.B. eine Vorstellung darüber, wie und wann Wissen angewendet werden sollte. - + -*Bild [von Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Eigenes Werk, CC BY-SA 4.0* +*Bild [von Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Von Longlivetheux - Eigenes Werk, CC BY-SA 4.0* -Das Problem der **Wissensrepräsentation** besteht also darin, eine effektive Methode zu finden, Wissen in Form von Daten in einem Computer darzustellen, um es automatisch nutzbar zu machen. Dies kann als Spektrum betrachtet werden: +Somit besteht das Problem der **Wissensrepräsentation** darin, eine effektive Methode zu finden, Wissen innerhalb eines Computers in Form von Daten so darzustellen, dass es automatisch nutzbar ist. Dies kann als Spektrum gesehen werden: -![Spektrum der Wissensrepräsentation](../../../../translated_images/knowledge-spectrum.b60df631852c0217.de.png) +![Spektrum der Wissensrepräsentation](../../../../../../translated_images/de/knowledge-spectrum.b60df631852c0217.webp) > Bild von [Dmitry Soshnikov](http://soshnikov.com) -* Links gibt es sehr einfache Arten der Wissensrepräsentation, die effektiv von Computern genutzt werden können. Die einfachste ist die algorithmische Darstellung, bei der Wissen durch ein Computerprogramm repräsentiert wird. Dies ist jedoch keine flexible Methode, da Wissen in unserem Kopf oft nicht algorithmisch ist. -* Rechts gibt es Darstellungen wie natürlichen Text. Diese sind am mächtigsten, können jedoch nicht für automatisches Schließen verwendet werden. +* Links sind sehr einfache Arten von Wissensrepräsentationen, die von Computern effektiv genutzt werden können. Die einfachste ist die algorithmische, bei der Wissen durch ein Computerprogramm dargestellt wird. Dies ist jedoch nicht die beste Form der Wissensrepräsentation, weil sie nicht flexibel ist. Wissen in unserem Kopf ist oft nicht-algorithmisch. +* Rechts sind Repräsentationen wie natürliche Texte. Sie sind am mächtigsten, können aber nicht für automatische Schlussfolgerungen verwendet werden. -> ✅ Überlege einen Moment, wie du Wissen in deinem Kopf darstellst und in Notizen umwandelst. Gibt es ein bestimmtes Format, das dir hilft, Informationen besser zu behalten? +> ✅ Denken Sie einen Moment darüber nach, wie Sie Wissen in Ihrem Kopf repräsentieren und in Notizen umwandeln. Gibt es ein bestimmtes Format, das für Sie gut funktioniert, um das Behalten zu unterstützen? -## Klassifizierung von Wissensrepräsentationen in Computern +## Klassifikation von Computer-Wissensrepräsentationen -Wir können verschiedene Methoden der Wissensrepräsentation in Computern in folgende Kategorien einteilen: +Wir können verschiedene Computer-Wissensrepräsentationsmethoden in folgende Kategorien einordnen: -* **Netzwerkrepräsentationen** basieren auf der Tatsache, dass wir ein Netzwerk von miteinander verbundenen Konzepten in unserem Kopf haben. Wir können versuchen, dieselben Netzwerke als Graphen in einem Computer nachzubilden – ein sogenanntes **semantisches Netzwerk**. +* **Netzwerkrepräsentationen** basieren auf der Tatsache, dass wir ein Netzwerk miteinander verbundener Konzepte in unserem Kopf haben. Wir können versuchen, dieselben Netzwerke als Graphen in einem Computer nachzubilden – ein sogenanntes **semantisches Netzwerk**. -1. **Objekt-Attribut-Wert-Tripel** oder **Attribut-Wert-Paare**. Da ein Graph in einem Computer als Liste von Knoten und Kanten dargestellt werden kann, können wir ein semantisches Netzwerk durch eine Liste von Tripeln repräsentieren, die Objekte, Attribute und Werte enthalten. Zum Beispiel erstellen wir die folgenden Tripel über Programmiersprachen: +1. **Objekt-Attribut-Wert-Tripel** oder **Attribut-Wert-Paare**. Da ein Graph im Computer als Liste von Knoten und Kanten dargestellt werden kann, können wir ein semantisches Netzwerk durch eine Liste von Tripeln repräsentieren, die Objekte, Attribute und Werte enthalten. Zum Beispiel erstellen wir folgende Tripel über Programmiersprachen: -Objekt | Attribut | Wert --------|----------|----- -Python | ist | Untyped-Language -Python | erfunden-von | Guido van Rossum -Python | Block-Syntax | Einrückung -Untyped-Language | hat nicht | Typdefinitionen +Objekt | Attribut | Wert +-------|-----------|------ +Python | ist | Untyped-Language +Python | erfunden von | Guido van Rossum +Python | Blocksyntax | Einrückung +Untyped-Language | hat nicht | Typdefinitionen -> ✅ Überlege, wie Tripel verwendet werden können, um andere Arten von Wissen darzustellen. +> ✅ Überlegen Sie, wie Tripel verwendet werden können, um andere Wissensarten darzustellen. -2. **Hierarchische Repräsentationen** betonen die Tatsache, dass wir oft eine Hierarchie von Objekten in unserem Kopf erstellen. Zum Beispiel wissen wir, dass ein Kanarienvogel ein Vogel ist und alle Vögel Flügel haben. Wir haben auch eine Vorstellung davon, welche Farbe ein Kanarienvogel normalerweise hat und wie schnell er fliegen kann. +2. **Hierarchische Repräsentationen** betonen die Tatsache, dass wir oft eine Hierarchie von Objekten in unserem Kopf anlegen. Zum Beispiel wissen wir, dass eine Kanarienvogel ein Vogel ist, und alle Vögel Flügel haben. Wir haben auch eine Vorstellung davon, welche Farbe ein Kanarienvogel gewöhnlich hat und wie schnell er fliegt. - - **Frame-Repräsentation** basiert darauf, jedes Objekt oder jede Klasse von Objekten als **Frame** darzustellen, der **Slots** enthält. Slots haben mögliche Standardwerte, Wertbeschränkungen oder gespeicherte Prozeduren, die aufgerufen werden können, um den Wert eines Slots zu erhalten. Alle Frames bilden eine Hierarchie, ähnlich wie eine Objekt-Hierarchie in objektorientierten Programmiersprachen. - - **Szenarien** sind spezielle Arten von Frames, die komplexe Situationen darstellen, die sich im Laufe der Zeit entwickeln können. + - **Frame-Repräsentation** basiert darauf, jedes Objekt oder jede Objektklasse als **Frame** darzustellen, der **Slots** enthält. Slots haben mögliche Standardwerte, Wertbeschränkungen oder gespeicherte Prozeduren, die aufgerufen werden können, um den Wert eines Slots zu erhalten. Alle Frames bilden eine Hierarchie ähnlich der Objekt-Hierarchie in objektorientierten Programmiersprachen. + - **Szenarien** sind eine spezielle Art von Frames, die komplexe Situationen darstellen, die sich im Zeitverlauf entfalten können. **Python** -Slot | Wert | Standardwert | Intervall | ------|------|--------------|----------| -Name | Python | | | -Ist-Ein | Untyped-Language | | | -Variablen-Schreibweise | | CamelCase | | -Programmlänge | | | 5-5000 Zeilen | -Block-Syntax | Einrückung | | | +Slot | Wert | Standardwert | Intervall +-----|-------|---------------|---------- +Name | Python | | | +Ist-ein | Untyped-Language | | | +VariablenSchreibweise | | CamelCase | | +Programmlänge | | | 5-5000 Zeilen | +Blocksyntax | Einrückung | | | -3. **Prozedurale Repräsentationen** basieren darauf, Wissen durch eine Liste von Aktionen darzustellen, die ausgeführt werden können, wenn eine bestimmte Bedingung eintritt. - - Produktionsregeln sind Wenn-Dann-Aussagen, die es uns ermöglichen, Schlussfolgerungen zu ziehen. Zum Beispiel könnte ein Arzt eine Regel haben, die besagt: **WENN** ein Patient hohes Fieber **ODER** einen hohen C-reaktiven Proteinspiegel im Bluttest hat, **DANN** hat er eine Entzündung. Sobald wir eine der Bedingungen feststellen, können wir eine Schlussfolgerung über die Entzündung ziehen und diese dann für weitere Überlegungen verwenden. - - Algorithmen können als eine andere Form der prozeduralen Repräsentation betrachtet werden, obwohl sie fast nie direkt in wissensbasierten Systemen verwendet werden. +3. **Prozedurale Repräsentationen** basieren darauf, Wissen durch eine Liste von auszuführenden Aktionen darzustellen, wenn eine bestimmte Bedingung eintritt. + - Produktionsregeln sind Wenn-Dann-Anweisungen, die uns erlauben Schlussfolgerungen zu ziehen. Zum Beispiel kann ein Arzt eine Regel haben, die besagt, dass **WENN** ein Patient hohes Fieber **ODER** einen hohen C-reaktiven Proteinspiegel im Bluttest hat, **DANN** hat er eine Entzündung. Wenn eine der Bedingungen erfüllt wird, können wir eine Schlussfolgerung über die Entzündung ziehen und diese dann im weiteren Schlussfolgern verwenden. + - Algorithmen können als eine weitere Form prozeduraler Repräsentation betrachtet werden, obwohl sie in wissensbasierten Systemen fast nie direkt verwendet werden. -4. **Logik** wurde ursprünglich von Aristoteles als Methode vorgeschlagen, universelles menschliches Wissen darzustellen. - - Prädikatenlogik als mathematische Theorie ist zu reichhaltig, um berechenbar zu sein, daher wird normalerweise ein Teil davon verwendet, wie Horn-Klauseln in Prolog. - - Beschreibungslogik ist eine Familie von logischen Systemen, die verwendet werden, um Hierarchien von Objekten und verteilte Wissensrepräsentationen wie das *semantische Web* darzustellen und zu verarbeiten. +4. **Logik** wurde ursprünglich von Aristoteles als Möglichkeit vorgeschlagen, universelles menschliches Wissen darzustellen. + - Prädikatenlogik als mathematische Theorie ist zu reichhaltig, um berechenbar zu sein, daher wird normalerweise ein Teil davon verwendet, z.B. Horn-Klauseln, wie sie in Prolog verwendet werden. + - Beschreibende Logik ist eine Familie von logischen Systemen, die verwendet werden, um über Hierarchien von Objekten in verteilten Wissensrepräsentationen wie dem *Semantic Web* zu repräsentieren und zu schlussfolgern. ## Expertensysteme -Einer der frühen Erfolge der symbolischen KI waren sogenannte **Expertensysteme** – Computersysteme, die so konzipiert waren, dass sie in einem begrenzten Problembereich als Experte agieren. Sie basierten auf einer **Wissensbasis**, die von einem oder mehreren menschlichen Experten extrahiert wurde, und enthielten eine **Schlussfolgerungsmaschine**, die darauf basierende Überlegungen anstellte. +Einer der frühen Erfolge der symbolischen KI waren sogenannte **Expertensysteme** – Computersysteme, die entwickelt wurden, um als Experte in einem begrenzten Problembereich zu wirken. Sie basierten auf einer **Wissensbasis**, die von einem oder mehreren menschlichen Experten extrahiert wurde, und enthielten eine **Inferenzmaschine**, die darauf basierend Schlussfolgerungen zog. -![Menschliche Architektur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.de.png) | ![Wissensbasiertes System](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.de.png) ---------------------------------------------------|----------------------------------------------- -Vereinfachte Struktur des menschlichen Nervensystems | Architektur eines wissensbasierten Systems +![Menschliche Architektur](../../../../../../translated_images/de/arch-human.5d4d35f1bba3ab1c.webp) | ![Wissensbasiertes System](../../../../../../translated_images/de/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +Vereinfachte Struktur des menschlichen neuronalen Systems | Architektur eines wissensbasierten Systems -Expertensysteme sind ähnlich aufgebaut wie das menschliche Denksystem, das **Kurzzeitgedächtnis** und **Langzeitgedächtnis** enthält. Ebenso unterscheiden wir in wissensbasierten Systemen die folgenden Komponenten: +Expertensysteme sind ähnlich wie das menschliche Schlusssystem aufgebaut, das **Kurzzeitgedächtnis** und **Langzeitgedächtnis** enthält. Ebenso unterscheiden wir in wissensbasierten Systemen folgende Komponenten: -* **Problemspeicher**: enthält das Wissen über das aktuell zu lösende Problem, z. B. die Temperatur oder den Blutdruck eines Patienten, ob er eine Entzündung hat oder nicht usw. Dieses Wissen wird auch als **statisches Wissen** bezeichnet, da es eine Momentaufnahme dessen enthält, was wir derzeit über das Problem wissen – den sogenannten *Problemzustand*. -* **Wissensbasis**: repräsentiert langfristiges Wissen über einen Problembereich. Es wird manuell von menschlichen Experten extrahiert und ändert sich nicht von einer Konsultation zur nächsten. Da es uns ermöglicht, von einem Problemzustand zum anderen zu navigieren, wird es auch als **dynamisches Wissen** bezeichnet. -* **Schlussfolgerungsmaschine**: orchestriert den gesamten Prozess der Suche im Problemzustandsraum und stellt dem Benutzer bei Bedarf Fragen. Sie ist auch dafür verantwortlich, die richtigen Regeln für jeden Zustand zu finden. +* **Problemspeicher**: enthält Wissen über das gerade zu lösende Problem, z.B. die Temperatur oder den Blutdruck eines Patienten, ob er eine Entzündung hat oder nicht. Dieses Wissen wird auch als **statisches Wissen** bezeichnet, weil es einen Schnappschuss dessen darstellt, was wir aktuell über das Problem wissen – den sogenannten *Problemzustand*. +* **Wissensbasis**: repräsentiert langfristiges Wissen über ein Problemgebiet. Es wird manuell von menschlichen Experten extrahiert und ändert sich nicht von Konsultation zu Konsultation. Da es erlaubt, von einem Problemzustand zum anderen zu navigieren, wird es auch als **dynamisches Wissen** bezeichnet. +* **Inferenzmaschine**: steuert den gesamten Prozess der Suche im Problemzustandsraum und stellt bei Bedarf Fragen an den Benutzer. Sie ist auch verantwortlich dafür, die richtigen Regeln auszuwählen, die auf jeden Zustand angewendet werden. -Als Beispiel betrachten wir das folgende Expertensystem zur Bestimmung eines Tieres basierend auf seinen physischen Merkmalen: +Als Beispiel betrachten wir folgendes Expertensystem zur Bestimmung eines Tieres anhand seiner physischen Eigenschaften: -![AND-OR-Baum](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.de.png) +![AND-OR-Baum](../../../../../../translated_images/de/AND-OR-Tree.5592d2c70187f283.webp) > Bild von [Dmitry Soshnikov](http://soshnikov.com) -Dieses Diagramm wird als **AND-OR-Baum** bezeichnet und ist eine grafische Darstellung einer Reihe von Produktionsregeln. Das Zeichnen eines Baums ist nützlich zu Beginn der Wissensextraktion vom Experten. Um das Wissen im Computer darzustellen, ist es jedoch praktischer, Regeln zu verwenden: +Dieses Diagramm wird als **AND-OR-Baum** bezeichnet und ist eine grafische Darstellung eines Satzes von Produktionsregeln. Das Zeichnen eines Baums ist zu Beginn der Wissensextraktion vom Experten nützlich. Um das Wissen im Computer darzustellen, ist es praktischer, Regeln zu verwenden: ``` IF the animal eats meat @@ -120,79 +120,79 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` + +Man kann erkennen, dass jede Bedingung auf der linken Seite der Regel und die Aktion im Wesentlichen Objekt-Attribut-Wert-(OAV-)Tripel sind. Das **Arbeitsgedächtnis** enthält den Satz von OAV-Tripeln, die dem aktuell zu lösenden Problem entsprechen. Eine **Regelmaschine** sucht nach Regeln, deren Bedingung erfüllt ist, und wendet diese an, indem sie ein weiteres Tripel zum Arbeitsgedächtnis hinzufügt. -Man kann erkennen, dass jede Bedingung auf der linken Seite der Regel und die Aktion im Wesentlichen Objekt-Attribut-Wert (OAV)-Tripel sind. **Arbeitsgedächtnis** enthält die Menge von OAV-Tripeln, die dem aktuell zu lösenden Problem entsprechen. Eine **Regelmaschine** sucht nach Regeln, deren Bedingungen erfüllt sind, und wendet sie an, indem sie ein weiteres Tripel zum Arbeitsgedächtnis hinzufügt. +> ✅ Erstellen Sie Ihren eigenen AND-OR-Baum zu einem Thema, das Ihnen gefällt! -> ✅ Erstelle deinen eigenen AND-OR-Baum zu einem Thema, das dir gefällt! +### Vorwärts- vs. Rückwärtsschlussfolgerung -### Vorwärts- vs. Rückwärts-Schlussfolgerung +Der oben beschriebene Prozess wird **Vorwärtsschlussfolgerung** genannt. Er beginnt mit einigen Anfangsdaten über das Problem, die im Arbeitsgedächtnis vorhanden sind, und führt dann die folgende Schlussfolgerungsschleife aus: -Der oben beschriebene Prozess wird als **Vorwärts-Schlussfolgerung** bezeichnet. Er beginnt mit einigen Anfangsdaten über das Problem, die im Arbeitsgedächtnis verfügbar sind, und führt dann die folgende Schlussfolgerungsschleife aus: - -1. Wenn das Zielattribut im Arbeitsgedächtnis vorhanden ist – stoppe und gib das Ergebnis aus. -2. Suche nach allen Regeln, deren Bedingungen derzeit erfüllt sind – erhalte den **Konfliktset** von Regeln. -3. Führe eine **Konfliktlösung** durch – wähle eine Regel aus, die in diesem Schritt ausgeführt wird. Es gibt verschiedene Strategien zur Konfliktlösung: - - Wähle die erste anwendbare Regel in der Wissensbasis. - - Wähle eine zufällige Regel. - - Wähle eine *spezifischere* Regel, d. h. diejenige, die die meisten Bedingungen auf der "linken Seite" (LHS) erfüllt. -4. Wende die ausgewählte Regel an und füge ein neues Wissenselement zum Problemzustand hinzu. +1. Wenn das Zielfeld im Arbeitsgedächtnis vorhanden ist – stoppe und gib das Ergebnis aus +2. Suche alle Regeln, deren Bedingung derzeit erfüllt ist – erhalte eine **Konfliktmenge** von Regeln +3. Führe die **Konfliktauflösung** durch – wähle eine Regel aus, die in diesem Schritt ausgeführt wird. Es kann verschiedene Strategien der Konfliktauflösung geben: + - Wähle die erste anwendbare Regel in der Wissensbasis + - Wähle eine zufällige Regel + - Wähle eine *spezifischere* Regel, d.h. diejenige, die die meisten Bedingungen auf der linken Seite (LHS) erfüllt +4. Wende die ausgewählte Regel an und füge ein neues Wissenselement in den Problemzustand ein 5. Wiederhole ab Schritt 1. -In einigen Fällen möchten wir jedoch mit einem leeren Wissen über das Problem beginnen und Fragen stellen, die uns helfen, zu einer Schlussfolgerung zu gelangen. Zum Beispiel führen wir bei einer medizinischen Diagnose normalerweise nicht alle medizinischen Analysen im Voraus durch, bevor wir mit der Diagnose des Patienten beginnen. Stattdessen möchten wir Analysen durchführen, wenn eine Entscheidung getroffen werden muss. +In manchen Fällen möchten wir jedoch mit leerem Wissen über das Problem starten und Fragen stellen, die uns zur Schlussfolgerung führen. Zum Beispiel führen wir bei einer medizinischen Diagnose normalerweise nicht alle Analysen im Voraus durch, bevor wir mit der Diagnose beginnen. Stattdessen möchten wir Analysen durchführen, wenn eine Entscheidung getroffen werden muss. -Dieser Prozess kann mit **Rückwärts-Schlussfolgerung** modelliert werden. Er wird durch das **Ziel** gesteuert – den Attributwert, den wir finden möchten: +Dieser Prozess kann mit **Rückwärtsschlussfolgerung** modelliert werden. Er wird vom **Ziel** angetrieben – dem Attributwert, den wir finden wollen: -1. Wähle alle Regeln aus, die uns den Wert eines Ziels geben können (d. h. mit dem Ziel auf der RHS ("rechte Seite")) – ein Konfliktset. -1. Wenn es keine Regeln für dieses Attribut gibt oder eine Regel besagt, dass wir den Wert vom Benutzer erfragen sollen – frage danach, andernfalls: -1. Verwende eine Konfliktlösungsstrategie, um eine Regel auszuwählen, die wir als *Hypothese* verwenden – wir versuchen, sie zu beweisen. -1. Wiederhole den Prozess rekursiv für alle Attribute auf der LHS der Regel und versuche, sie als Ziele zu beweisen. -1. Wenn der Prozess zu irgendeinem Zeitpunkt fehlschlägt – verwende eine andere Regel in Schritt 3. +1. Wähle alle Regeln aus, die uns den Wert des Ziels geben können (d.h. mit dem Ziel auf der rechten Seite (RHS)) – eine Konfliktmenge +1. Wenn es keine Regeln für dieses Attribut gibt oder eine Regel besagt, dass wir den Wert vom Benutzer erfragen sollen – frage danach, sonst: +1. Verwende eine Konfliktauflösungsstrategie, um eine Regel auszuwählen, die wir als *Hypothese* verwenden – wir versuchen, sie zu beweisen +1. Wiederhole rekursiv den Prozess für alle Attribute auf der linken Seite (LHS) der Regel, um sie als Ziele zu beweisen +1. Wenn der Prozess an irgendeiner Stelle fehlschlägt – verwende eine andere Regel bei Schritt 3. -> ✅ In welchen Situationen ist die Vorwärts-Schlussfolgerung besser geeignet? Und wie sieht es mit der Rückwärts-Schlussfolgerung aus? +> ✅ In welchen Situationen ist Vorwärtsschlussfolgerung besser geeignet? Wie sieht es mit Rückwärtsschlussfolgerung aus? ### Implementierung von Expertensystemen Expertensysteme können mit verschiedenen Werkzeugen implementiert werden: -* Direkte Programmierung in einer Hochsprache. Dies ist keine optimale Lösung, da der Hauptvorteil eines wissensbasierten Systems darin besteht, dass Wissen von der Schlussfolgerung getrennt ist und ein Fachexperte potenziell in der Lage sein sollte, Regeln zu schreiben, ohne die Details des Schlussfolgerungsprozesses zu verstehen. -* Verwendung eines **Expertensystem-Shells**, d. h. eines Systems, das speziell dafür entwickelt wurde, mit Wissen in einer Wissensrepräsentationssprache gefüllt zu werden. +* Direkte Programmierung in einer höheren Programmiersprache. Das ist keine gute Idee, da der Hauptvorteil eines wissensbasierten Systems darin liegt, dass Wissen von der Schlussfolgerung getrennt ist und potenziell ein Experte des Fachgebiets Regeln schreiben können sollte, ohne die Details des Inferenzprozesses verstehen zu müssen. +* Verwendung einer **Expertensystem-Shell**, d.h. eines Systems, das speziell dafür ausgelegt ist, mit Wissen unter Verwendung einer Wissensrepräsentationssprache befüllt zu werden. -## ✍️ Übung: Tier-Schlussfolgerung +## ✍️ Übung: Tierische Schlussfolgerung -Siehe [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) für ein Beispiel zur Implementierung eines Expertensystems mit Vorwärts- und Rückwärts-Schlussfolgerung. +Siehe [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) für ein Beispiel zur Implementierung eines Forward- und Backward-Inferenz-Expertensystems. -> **Hinweis**: Dieses Beispiel ist recht einfach und gibt nur eine Vorstellung davon, wie ein Expertensystem aussieht. Sobald du anfängst, ein solches System zu erstellen, wirst du erst ab einer bestimmten Anzahl von Regeln, etwa 200+, ein *intelligentes* Verhalten bemerken. Ab einem bestimmten Punkt werden die Regeln zu komplex, um alle im Kopf zu behalten, und du wirst dich möglicherweise fragen, warum das System bestimmte Entscheidungen trifft. Ein wichtiger Vorteil von wissensbasierten Systemen ist jedoch, dass du immer genau erklären kannst, wie jede Entscheidung getroffen wurde. +> **Hinweis**: Dieses Beispiel ist recht einfach und vermittelt nur die Idee, wie ein Expertensystem aussieht. Erst wenn Sie ein solches System mit einer bestimmten Anzahl an Regeln (etwa 200+) erstellen, werden Sie ein gewisses *intelligentes* Verhalten bemerken. Irgendwann werden Regeln zu komplex, um sie alle im Kopf zu behalten, und Sie fragen sich vielleicht, warum ein System bestimmte Entscheidungen trifft. Doch die wichtige Eigenschaft von wissensbasierten Systemen ist, dass Sie jederzeit *erklären* können, wie eine Entscheidung zustande kam. -## Ontologien und das semantische Web +## Ontologien und das Semantic Web -Ende des 20. Jahrhunderts gab es eine Initiative, Wissensrepräsentation zu nutzen, um Internetressourcen zu annotieren, sodass es möglich wäre, Ressourcen zu finden, die sehr spezifischen Anfragen entsprechen. Diese Bewegung wurde **Semantisches Web** genannt und basierte auf mehreren Konzepten: +Ende des 20. Jahrhunderts gab es die Initiative, Wissensrepräsentation zu verwenden, um Internet-Ressourcen zu annotieren, sodass es möglich wäre, Ressourcen zu finden, die sehr spezifischen Anfragen entsprechen. Diese Bewegung wurde **Semantic Web** genannt und basierte auf mehreren Konzepten: -- Eine spezielle Wissensrepräsentation basierend auf **[Beschreibungslogiken](https://en.wikipedia.org/wiki/Description_logic)** (DL). Sie ähnelt der Frame-Wissensrepräsentation, da sie eine Hierarchie von Objekten mit Eigenschaften aufbaut, aber sie hat formale logische Semantik und Schlussfolgerung. Es gibt eine ganze Familie von DLs, die zwischen Ausdruckskraft und algorithmischer Komplexität der Schlussfolgerung balancieren. -- Verteilte Wissensrepräsentation, bei der alle Konzepte durch einen globalen URI-Identifikator repräsentiert werden, was es ermöglicht, Wissenshierarchien zu erstellen, die das Internet umfassen. +- Eine spezielle Wissensrepräsentation basierend auf **[beschreibender Logik](https://de.wikipedia.org/wiki/Beschreibende_Logik)** (Description Logic, DL). Sie ähnelt der Frame-Wissensrepräsentation, weil sie eine Hierarchie von Objekten mit Eigenschaften aufbaut, hat aber formale logische Semantik und Inferenz. Es gibt eine ganze Familie von DLs, die einen Ausgleich zwischen Ausdrucksstärke und der algorithmischen Komplexität der Inferenz bilden. +- Verteilte Wissensrepräsentation, bei der alle Konzepte durch eine globale URI kennzeichnet sind, was es ermöglicht, Wissenshierarchien zu erschaffen, die sich über das Internet erstrecken. - Eine Familie von XML-basierten Sprachen zur Wissensbeschreibung: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Ein Kernkonzept im Semantic Web ist das Konzept der **Ontologie**. Es bezeichnet eine explizite Spezifikation eines Problemfeldes mithilfe einer formalen Wissensrepräsentation. Die einfachste Ontologie kann einfach eine Hierarchie von Objekten in einem Problemfeld sein, aber komplexere Ontologien enthalten Regeln, die für Schlussfolgerungen verwendet werden können. +Ein Kernkonzept im Semantic Web ist das Konzept der **Ontologie**. Es bezieht sich auf eine explizite Spezifikation eines Problemfeldes mithilfe einer formalen Wissensrepräsentation. Die einfachste Ontologie kann nur eine Hierarchie von Objekten im Problemfeld sein, aber komplexere Ontologien schließen Regeln ein, die für Schlussfolgerungen verwendet werden können. -Im Semantic Web basieren alle Darstellungen auf Triplets. Jedes Objekt und jede Beziehung werden eindeutig durch eine URI identifiziert. Zum Beispiel, wenn wir die Tatsache ausdrücken möchten, dass dieses AI Curriculum von Dmitry Soshnikov am 1. Januar 2022 entwickelt wurde, könnten wir die folgenden Triplets verwenden: +Im Semantic Web basieren alle Repräsentationen auf Tripeln. Jedes Objekt und jede Relation wird eindeutig durch eine URI identifiziert. Zum Beispiel, wenn wir die Tatsache angeben wollen, dass dieser AI Curriculum von Dmitry Soshnikov am 1. Januar 2022 entwickelt wurde – hier sind die Tripel, die wir verwenden können: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Hier sind `http://www.example.com/terms/creation-date` und `http://purl.org/dc/elements/1.1/creator` einige bekannte und allgemein akzeptierte URIs, um die Konzepte *Ersteller* und *Erstellungsdatum* auszudrücken. +> ✅ Hier sind `http://www.example.com/terms/creation-date` und `http://purl.org/dc/elements/1.1/creator` einige bekannte und universell akzeptierte URIs, um die Konzepte *Ersteller* und *Erstellungsdatum* auszudrücken. -In einem komplexeren Fall, wenn wir eine Liste von Erstellern definieren möchten, können wir einige in RDF definierte Datenstrukturen verwenden. +In einem komplexeren Fall, wenn wir eine Liste von Erstellern definieren wollen, können wir einige Datenstrukturen verwenden, die in RDF definiert sind. - + -> Diagramme oben von [Dmitry Soshnikov](http://soshnikov.com) +> Obige Diagramme von [Dmitry Soshnikov](http://soshnikov.com) -Der Fortschritt beim Aufbau des Semantic Web wurde durch den Erfolg von Suchmaschinen und Techniken der natürlichen Sprachverarbeitung etwas verlangsamt, die es ermöglichen, strukturierte Daten aus Text zu extrahieren. Dennoch gibt es in einigen Bereichen weiterhin bedeutende Bemühungen, Ontologien und Wissensbasen zu pflegen. Einige bemerkenswerte Projekte: +Der Fortschritt beim Aufbau des Semantic Web wurde durch den Erfolg von Suchmaschinen und Verfahren der natürlichen Sprachverarbeitung, die strukturierte Daten aus Text extrahieren können, etwas verlangsamt. Dennoch gibt es in einigen Bereichen weiterhin erhebliche Anstrengungen, Ontologien und Wissensbasen zu pflegen. Einige bemerkenswerte Projekte: -* [WikiData](https://wikidata.org/) ist eine Sammlung maschinenlesbarer Wissensbasen, die mit Wikipedia verbunden sind. Die meisten Daten werden aus den Wikipedia *InfoBoxes* extrahiert, strukturierten Inhalten innerhalb der Wikipedia-Seiten. Sie können [WikiData abfragen](https://query.wikidata.org/) mit SPARQL, einer speziellen Abfragesprache für das Semantic Web. Hier ist eine Beispielabfrage, die die beliebtesten Augenfarben unter Menschen anzeigt: +* [WikiData](https://wikidata.org/) ist eine Sammlung maschinenlesbarer Wissensbasen, die mit Wikipedia verbunden sind. Die meisten Daten werden aus Wikipedia *InfoBoxes* gewonnen, strukturierten Inhaltsfragmenten innerhalb von Wikipedia-Seiten. Sie können Wikidata in SPARQL, einer speziellen Abfragesprache für das Semantic Web, [abfragen](https://query.wikidata.org/). Hier ist eine Beispielabfrage, die die beliebtesten Augenfarben bei Menschen anzeigt: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) ist ein weiteres Projekt, das ähnlich wie WikiData funktioniert. +* [DBpedia](https://www.dbpedia.org/) ist ein weiteres ähnliches Projekt wie WikiData. -> ✅ Wenn Sie mit dem Aufbau eigener Ontologien oder dem Öffnen bestehender experimentieren möchten, gibt es einen großartigen visuellen Ontologie-Editor namens [Protégé](https://protege.stanford.edu/). Laden Sie ihn herunter oder nutzen Sie ihn online. +> ✅ Wenn Sie mit dem Erstellen eigener Ontologien experimentieren oder bestehende öffnen möchten, gibt es einen großartigen visuellen Ontologie-Editor namens [Protégé](https://protege.stanford.edu/). Laden Sie ihn herunter oder nutzen Sie ihn online. - + -*Web Protégé Editor geöffnet mit der Romanov-Familienontologie. Screenshot von Dmitry Soshnikov* +*Web-Protégé-Editor geöffnet mit der Romanov-Familienontologie. Screenshot von Dmitry Soshnikov* ## ✍️ Übung: Eine Familienontologie -Sehen Sie sich [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) an, um ein Beispiel für die Verwendung von Semantic-Web-Techniken zur Analyse von Familienbeziehungen zu sehen. Wir werden einen Familienstammbaum im üblichen GEDCOM-Format und eine Ontologie von Familienbeziehungen verwenden, um einen Graphen aller Familienbeziehungen für eine gegebene Gruppe von Individuen zu erstellen. +Siehe [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) für ein Beispiel zur Verwendung von Semantic Web-Techniken, um über familiäre Beziehungen zu schlussfolgern. Wir nehmen einen Familienstammbaum, dargestellt im üblichen GEDCOM-Format, und eine Ontologie familiärer Beziehungen und bauen daraus einen Graphen aller familiären Beziehungen für eine gegebene Menge von Individuen. ## Microsoft Concept Graph -In den meisten Fällen werden Ontologien sorgfältig von Hand erstellt. Es ist jedoch auch möglich, Ontologien aus unstrukturierten Daten zu **extrahieren**, beispielsweise aus Texten in natürlicher Sprache. +In den meisten Fällen werden Ontologien sorgfältig von Hand erstellt. Es ist jedoch auch möglich, Ontologien aus unstrukturierten Daten, zum Beispiel aus natürlichen Sprachtexten, zu **extrahieren**. Ein solcher Versuch wurde von Microsoft Research unternommen und führte zum [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Es handelt sich um eine große Sammlung von Entitäten, die mithilfe der `is-a`-Vererbungsbeziehung gruppiert sind. Damit können Fragen wie "Was ist Microsoft?" beantwortet werden – die Antwort könnte etwa lauten: "Ein Unternehmen mit einer Wahrscheinlichkeit von 0,87 und eine Marke mit einer Wahrscheinlichkeit von 0,75". +Es handelt sich um eine große Sammlung von Entitäten, die mit der `ist-ein`-Vererbungsbeziehung gruppiert sind. Es erlaubt Fragen zu beantworten wie "Was ist Microsoft?" – mit der Antwort in etwa: "ein Unternehmen mit Wahrscheinlichkeit 0,87 und eine Marke mit Wahrscheinlichkeit 0,75." -Der Graph ist entweder als REST-API oder als große herunterladbare Textdatei verfügbar, die alle Entitätspaare auflistet. +Der Graph ist entweder als REST-API verfügbar oder als große herunterladbare Textdatei, die alle Entitätenpaare auflistet. -## ✍️ Übung: Ein Concept Graph +## ✍️ Übung: Ein Konzeptgraph -Probieren Sie das Notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) aus, um zu sehen, wie wir den Microsoft Concept Graph verwenden können, um Nachrichtenartikel in verschiedene Kategorien zu gruppieren. +Probieren Sie das Notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) aus, um zu sehen, wie wir den Microsoft Concept Graph nutzen können, um Nachrichtenartikel in verschiedene Kategorien zu gruppieren. ## Fazit -Heutzutage wird KI oft als Synonym für *Maschinelles Lernen* oder *Neuronale Netze* betrachtet. Ein Mensch zeigt jedoch auch explizites Denken, etwas, das derzeit von neuronalen Netzen nicht behandelt wird. In realen Projekten wird explizites Denken weiterhin verwendet, um Aufgaben zu erfüllen, die Erklärungen erfordern oder die Fähigkeit, das Verhalten des Systems kontrolliert zu ändern. +Heutzutage wird KI oft als Synonym für *Machine Learning* oder *Neuronale Netze* betrachtet. Ein menschliches Wesen zeigt jedoch auch explizites Schlussfolgern, was derzeit von neuronalen Netzen nicht behandelt wird. In realen Projekten wird explizites Schlussfolgern weiterhin eingesetzt, um Aufgaben zu erfüllen, die Erklärungen erfordern oder die Fähigkeit, das Verhalten des Systems kontrolliert zu modifizieren. ## 🚀 Herausforderung -Im Family Ontology Notebook, das mit dieser Lektion verbunden ist, gibt es die Möglichkeit, mit anderen Familienbeziehungen zu experimentieren. Versuchen Sie, neue Verbindungen zwischen Personen im Familienstammbaum zu entdecken. +Im Family Ontology-Notebook zu dieser Lektion gibt es die Möglichkeit, mit anderen familiären Beziehungen zu experimentieren. Versuchen Sie, neue Verbindungen zwischen Personen im Familienstammbaum zu entdecken. ## [Quiz nach der Vorlesung](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Überprüfung & Selbststudium +## Wiederholung & Selbststudium -Recherchieren Sie im Internet, um Bereiche zu entdecken, in denen Menschen versucht haben, Wissen zu quantifizieren und zu kodifizieren. Schauen Sie sich Bloom's Taxonomy an und gehen Sie in der Geschichte zurück, um zu erfahren, wie Menschen versucht haben, ihre Welt zu verstehen. Erkunden Sie die Arbeit von Linnaeus zur Erstellung einer Taxonomie von Organismen und beobachten Sie, wie Dmitri Mendeleev eine Methode zur Beschreibung und Gruppierung chemischer Elemente entwickelt hat. Welche anderen interessanten Beispiele können Sie finden? +Recherchieren Sie im Internet, um Bereiche zu entdecken, in denen Menschen versucht haben, Wissen zu quantifizieren und zu kodifizieren. Schauen Sie sich Blooms Taxonomie an und gehen Sie zurück in die Geschichte, um zu lernen, wie Menschen versucht haben, ihre Welt zu verstehen. Erkunden Sie die Arbeit von Linnaeus zur Erstellung einer Taxonomie von Organismen und beobachten Sie, wie Dmitri Mendelejew eine Methode zur Beschreibung und Gruppierung chemischer Elemente schuf. Welche anderen interessanten Beispiele können Sie finden? **Aufgabe**: [Erstellen Sie eine Ontologie](assignment.md) --- + +**Haftungsausschluss**: +Dieses Dokument wurde mithilfe des KI-Übersetzungsdienstes [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, bitten wir zu beachten, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner Ursprungssprache gilt als maßgebliche Quelle. Für wichtige Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die durch die Nutzung dieser Übersetzung entstehen. + \ No newline at end of file diff --git a/translations/de/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/de/lessons/3-NeuralNetworks/05-Frameworks/README.md index b9e0f50d..4945e419 100644 --- a/translations/de/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/de/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting ist ein äußerst wichtiges Konzept im maschinellen Lernen, und es i Betrachten Sie das folgende Problem der Annäherung an 5 Punkte (dargestellt durch `x` in den untenstehenden Diagrammen): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.de.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.de.jpg) +![linear](../../../../../translated_images/de/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/de/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Lineares Modell, 2 Parameter** | **Nicht-lineares Modell, 7 Parameter** Trainingsfehler = 5.3 | Trainingsfehler = 0 @@ -79,7 +79,7 @@ Es ist sehr wichtig, ein korrektes Gleichgewicht zwischen der Komplexität des M Wie Sie aus dem obigen Diagramm sehen können, kann Overfitting durch einen sehr niedrigen Trainingsfehler und einen hohen Validierungsfehler erkannt werden. Normalerweise sehen wir während des Trainings, dass sowohl der Trainings- als auch der Validierungsfehler abnehmen. An einem bestimmten Punkt könnte der Validierungsfehler jedoch aufhören zu sinken und anfangen zu steigen. Dies ist ein Zeichen für Overfitting und ein Hinweis darauf, dass wir das Training an diesem Punkt wahrscheinlich stoppen sollten (oder zumindest einen Schnappschuss des Modells machen sollten). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.de.png) +![overfitting](../../../../../translated_images/de/Overfitting.408ad91cd90b4371.webp) ## Wie verhindert man Overfitting? diff --git a/translations/de/lessons/3-NeuralNetworks/README.md b/translations/de/lessons/3-NeuralNetworks/README.md index 75d562c7..c0885544 100644 --- a/translations/de/lessons/3-NeuralNetworks/README.md +++ b/translations/de/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Einführung in Neuronale Netzwerke -![Zusammenfassung des Inhalts zu neuronalen Netzwerken in einer Skizze](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.de.png) +![Zusammenfassung des Inhalts zu neuronalen Netzwerken in einer Skizze](../../../../translated_images/de/ai-neuralnetworks.1c687ae40bc86e83.webp) Wie wir in der Einführung besprochen haben, besteht eine Möglichkeit, Intelligenz zu erreichen, darin, ein **Computermodell** oder ein **künstliches Gehirn** zu trainieren. Seit Mitte des 20. Jahrhunderts haben Forscher verschiedene mathematische Modelle ausprobiert, bis sich in den letzten Jahren dieser Ansatz als äußerst erfolgreich erwiesen hat. Solche mathematischen Modelle des Gehirns werden als **neuronale Netzwerke** bezeichnet. @@ -36,13 +36,13 @@ In diesem Lehrplan konzentrieren wir uns ausschließlich auf Modelle neuronaler Aus der Biologie wissen wir, dass unser Gehirn aus Nervenzellen (Neuronen) besteht, von denen jede mehrere "Eingänge" (Dendriten) und einen einzigen "Ausgang" (Axon) hat. Sowohl Dendriten als auch Axone können elektrische Signale leiten, und die Verbindungen zwischen ihnen — bekannt als Synapsen — können unterschiedliche Grade der Leitfähigkeit aufweisen, die durch Neurotransmitter reguliert werden. -![Modell eines Neurons](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.de.jpg) | ![Modell eines Neurons](../../../../translated_images/artneuron.1a5daa88d20ebe6f.de.png) +![Modell eines Neurons](../../../../translated_images/de/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modell eines Neurons](../../../../translated_images/de/artneuron.1a5daa88d20ebe6f.webp) ----|---- Echtes Neuron *([Bild](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) von Wikipedia)* | Künstliches Neuron *(Bild vom Autor)* Das einfachste mathematische Modell eines Neurons enthält daher mehrere Eingänge X1, ..., XN und einen Ausgang Y sowie eine Reihe von Gewichten W1, ..., WN. Der Ausgang wird berechnet als: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) wobei **f** eine nichtlineare **Aktivierungsfunktion** ist. diff --git a/translations/de/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/de/lessons/4-ComputerVision/06-IntroCV/README.md index ad06db94..e69872f1 100644 --- a/translations/de/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/de/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ In unserem [OpenCV Notebook](OpenCV.ipynb) geben wir einige Beispiele, wann Comp * **Vorverarbeitung eines Fotos eines Braille-Buchs**. Wir konzentrieren uns darauf, wie wir Thresholding, Merkmalserkennung, perspektivische Transformation und NumPy-Manipulationen verwenden können, um einzelne Braille-Symbole für die weitere Klassifikation durch ein neuronales Netzwerk zu trennen. -![Braille Bild](../../../../../translated_images/braille.341962ff76b1bd70.de.jpeg) | ![Braille Bild vorverarbeitet](../../../../../translated_images/braille-result.46530fea020b03c7.de.png) | ![Braille Symbole](../../../../../translated_images/braille-symbols.0159185ab69d5339.de.png) +![Braille Bild](../../../../../translated_images/de/braille.341962ff76b1bd70.webp) | ![Braille Bild vorverarbeitet](../../../../../translated_images/de/braille-result.46530fea020b03c7.webp) | ![Braille Symbole](../../../../../translated_images/de/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Bild aus [OpenCV.ipynb](OpenCV.ipynb) * **Bewegungserkennung in Videos durch Frame-Differenz**. Wenn die Kamera fixiert ist, sollten die Frames des Kamerafeeds ziemlich ähnlich zueinander sein. Da Frames als Arrays dargestellt werden, erhalten wir durch das Subtrahieren dieser Arrays für zwei aufeinanderfolgende Frames die Pixelunterschiede, die bei statischen Frames gering sein sollten und bei erheblicher Bewegung im Bild höher werden. -![Bild von Video-Frames und Frame-Differenzen](../../../../../translated_images/frame-difference.706f805491a0883c.de.png) +![Bild von Video-Frames und Frame-Differenzen](../../../../../translated_images/de/frame-difference.706f805491a0883c.webp) > Bild aus [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ In unserem [OpenCV Notebook](OpenCV.ipynb) geben wir einige Beispiele, wann Comp - **Dichter Optischer Fluss** berechnet das Vektorfeld, das zeigt, wohin sich jeder Pixel bewegt. - **Spärlicher Optischer Fluss** basiert darauf, einige markante Merkmale im Bild (z. B. Kanten) zu nehmen und deren Trajektorie von Frame zu Frame zu erstellen. -![Bild des Optischen Flusses](../../../../../translated_images/optical.1f4a94464579a83a.de.png) +![Bild des Optischen Flusses](../../../../../translated_images/de/optical.1f4a94464579a83a.webp) > Bild aus [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/de/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/de/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 7bf4e53f..0d831b95 100644 --- a/translations/de/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/de/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ist ein Netzwerk, das 2014 eine Genauigkeit von 92,7 % bei der ImageNet-Top-5-Klassifikation erreichte. Es hat die folgende Schichtstruktur: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.de.jpg) +![ImageNet Layers](../../../../../translated_images/de/vgg-16-arch1.d901a5583b3a51ba.webp) Wie man sehen kann, folgt VGG einer traditionellen Pyramidenarchitektur, die aus einer Abfolge von Convolution-Pooling-Schichten besteht. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.de.jpg) +![ImageNet Pyramid](../../../../../translated_images/de/vgg-16-arch.64ff2137f50dd49f.webp) > Bild von [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/de/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/de/lessons/4-ComputerVision/07-ConvNets/README.md index 2a6e20a5..713a0641 100644 --- a/translations/de/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/de/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Im echten Leben möchten wir Objekte auf einem Bild unabhängig von ihrer genaue Um Muster zu extrahieren, verwenden wir den Begriff der **Faltung (Convolutional Filters)**. Wie du weißt, wird ein Bild durch eine 2D-Matrix oder einen 3D-Tensor mit Farbtiefe dargestellt. Das Anwenden eines Filters bedeutet, dass wir eine relativ kleine **Filterkern**-Matrix nehmen und für jedes Pixel im Originalbild den gewichteten Durchschnitt mit benachbarten Punkten berechnen. Man kann sich das wie ein kleines Fenster vorstellen, das über das gesamte Bild gleitet und alle Pixel gemäß den Gewichten in der Filterkern-Matrix mittelt. -![Vertikaler Kantenfilter](../../../../../translated_images/filter-vert.b7148390ca0bc356.de.png) | ![Horizontaler Kantenfilter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.de.png) +![Vertikaler Kantenfilter](../../../../../translated_images/de/filter-vert.b7148390ca0bc356.webp) | ![Horizontaler Kantenfilter](../../../../../translated_images/de/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Bild von Dmitry Soshnikov @@ -38,7 +38,7 @@ Die Funktionsweise von CNNs basiert auf den folgenden wichtigen Konzepten: * Wir können das Netzwerk so gestalten, dass die Filter automatisch trainiert werden. * Wir können denselben Ansatz verwenden, um Muster in hochrangigen Merkmalen zu finden, nicht nur im Originalbild. Die Merkmalsextraktion in CNNs arbeitet also mit einer Hierarchie von Merkmalen, beginnend mit einfachen Pixelkombinationen bis hin zu komplexeren Kombinationen von Bildteilen. -![Hierarchische Merkmalsextraktion](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.de.png) +![Hierarchische Merkmalsextraktion](../../../../../translated_images/de/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Bild aus [einem Paper von Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basierend auf [ihrer Forschung](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Die meisten CNNs, die für die Bildverarbeitung verwendet werden, folgen einer s Als Beispiel betrachten wir die Architektur von VGG-16, einem Netzwerk, das 2014 eine Genauigkeit von 92,7 % in der Top-5-Klassifikation von ImageNet erreichte: -![ImageNet-Schichten](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.de.jpg) +![ImageNet-Schichten](../../../../../translated_images/de/vgg-16-arch1.d901a5583b3a51ba.webp) -![ImageNet-Pyramide](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.de.jpg) +![ImageNet-Pyramide](../../../../../translated_images/de/vgg-16-arch.64ff2137f50dd49f.webp) > Bild von [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/de/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/de/lessons/4-ComputerVision/07-ConvNets/lab/README.md index d8da7623..d0746fa7 100644 --- a/translations/de/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/de/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Sie müssen ein konvolutionales neuronales Netzwerk trainieren, um verschiedene Wir verwenden den [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), der Bilder von 37 verschiedenen Hunde- und Katzenrassen enthält. -![Datensatz, mit dem wir arbeiten werden](../../../../../../translated_images/data.50b2a9d5484bdbf0.de.png) +![Datensatz, mit dem wir arbeiten werden](../../../../../../translated_images/de/data.50b2a9d5484bdbf0.webp) Um den Datensatz herunterzuladen, verwenden Sie diesen Code-Schnipsel: diff --git a/translations/de/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/de/lessons/4-ComputerVision/08-TransferLearning/README.md index f7a88a1b..4d014280 100644 --- a/translations/de/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/de/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Sowohl Keras als auch PyTorch enthalten Funktionen, um vortrainierte neuronale N Hier sind Beispielmerkmale, die von einem Bild einer Katze durch das VGG-16-Netzwerk extrahiert wurden: -![Merkmale extrahiert von VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.de.png) +![Merkmale extrahiert von VGG-16](../../../../../translated_images/de/features.6291f9c7ba3a0b95.webp) ## Cats vs. Dogs Datensatz @@ -48,19 +48,19 @@ Ein vortrainiertes neuronales Netzwerk enthält verschiedene Muster in seinem *G Ein Ansatz, den wir verfolgen können, besteht darin, mit einem zufälligen Bild zu beginnen und dann die Technik der **Gradientenabstiegsoptimierung** zu verwenden, um dieses Bild so anzupassen, dass das Netzwerk denkt, es sei eine Katze. -![Bildoptimierungsschleife](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.de.png) +![Bildoptimierungsschleife](../../../../../translated_images/de/ideal-cat-loop.999fbb8ff306e044.webp) Wenn wir dies jedoch tun, erhalten wir etwas, das einem zufälligen Rauschen sehr ähnlich ist. Dies liegt daran, dass *es viele Möglichkeiten gibt, das Netzwerk glauben zu lassen, dass das Eingabebild eine Katze ist*, einschließlich solcher, die visuell keinen Sinn ergeben. Während diese Bilder viele für eine Katze typische Muster enthalten, gibt es nichts, das sie visuell unterscheidbar macht. Um das Ergebnis zu verbessern, können wir einen weiteren Term in die Verlustfunktion einfügen, der als **Variationsverlust** bezeichnet wird. Dies ist eine Metrik, die zeigt, wie ähnlich benachbarte Pixel des Bildes sind. Die Minimierung des Variationsverlusts macht das Bild glatter und beseitigt Rauschen – wodurch visuell ansprechendere Muster sichtbar werden. Hier ist ein Beispiel für solche "idealen" Bilder, die mit hoher Wahrscheinlichkeit als Katze bzw. Zebra klassifiziert werden: -![Ideale Katze](../../../../../translated_images/ideal-cat.203dd4597643d6b0.de.png) | ![Ideales Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.de.png) +![Ideale Katze](../../../../../translated_images/de/ideal-cat.203dd4597643d6b0.webp) | ![Ideales Zebra](../../../../../translated_images/de/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Ideale Katze* | *Ideales Zebra* Ein ähnlicher Ansatz kann verwendet werden, um sogenannte **adversariale Angriffe** auf ein neuronales Netzwerk durchzuführen. Angenommen, wir möchten ein neuronales Netzwerk täuschen und einen Hund wie eine Katze aussehen lassen. Wenn wir das Bild eines Hundes nehmen, das vom Netzwerk als Hund erkannt wird, können wir es mit Hilfe der Gradientenabstiegsoptimierung so lange leicht anpassen, bis das Netzwerk es als Katze klassifiziert: -![Bild eines Hundes](../../../../../translated_images/original-dog.8f68a67d2fe0911f.de.png) | ![Bild eines Hundes, der als Katze klassifiziert wird](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.de.png) +![Bild eines Hundes](../../../../../translated_images/de/original-dog.8f68a67d2fe0911f.webp) | ![Bild eines Hundes, der als Katze klassifiziert wird](../../../../../translated_images/de/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Originalbild eines Hundes* | *Bild eines Hundes, der als Katze klassifiziert wird* diff --git a/translations/de/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/de/lessons/4-ComputerVision/09-Autoencoders/README.md index cfa34482..19b449f7 100644 --- a/translations/de/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/de/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Es kann jedoch sinnvoll sein, rohe (nicht gelabelte) Daten für das Training von Da wir einen Autoencoder trainieren, um so viele Informationen wie möglich aus dem ursprünglichen Bild zu erfassen, um eine genaue Rekonstruktion zu ermöglichen, versucht das Netzwerk, die beste **Einbettung** der Eingabebilder zu finden, um deren Bedeutung zu erfassen. -![AutoEncoder Diagramm](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.de.jpg) +![AutoEncoder Diagramm](../../../../../translated_images/de/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Bild von [Keras Blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/de/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/de/lessons/4-ComputerVision/11-ObjectDetection/README.md index b50fa645..b4d67baa 100644 --- a/translations/de/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/de/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Die Bildklassifizierungsmodelle, die wir bisher behandelt haben, nahmen ein Bild ## [Quiz vor der Vorlesung](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objekterkennung](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.de.png) +![Objekterkennung](../../../../../translated_images/de/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Bild von der [YOLO v2 Webseite](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Angenommen, wir wollten eine Katze auf einem Bild finden, dann wäre ein sehr na 2. Führe eine Bildklassifikation auf jeder Kachel durch. 3. Die Kacheln, die eine ausreichend hohe Aktivierung zeigen, können als die Kacheln betrachtet werden, die das gesuchte Objekt enthalten. -![Naive Objekterkennung](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.de.png) +![Naive Objekterkennung](../../../../../translated_images/de/naive-detection.e7f1ba220ccd08c6.webp) > *Bild aus dem [Übungsnotebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Für diese Aufgabe könnten Sie auf die folgenden Datensätze stoßen: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 Klassen * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 Klassen, Begrenzungsboxen und Segmentierungsmasken -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.de.jpg) +![COCO](../../../../../translated_images/de/coco-examples.71bc60380fa6cceb.webp) ## Metriken für Objekterkennung @@ -50,7 +50,7 @@ Für diese Aufgabe könnten Sie auf die folgenden Datensätze stoßen: Während es bei der Bildklassifikation einfach ist, die Leistung des Algorithmus zu messen, müssen wir bei der Objekterkennung sowohl die Richtigkeit der Klasse als auch die Genauigkeit der vorhergesagten Position der Begrenzungsbox messen. Für Letzteres verwenden wir die sogenannte **Intersection over Union** (IoU), die misst, wie gut sich zwei Boxen (oder zwei beliebige Bereiche) überlappen. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.de.png) +![IoU](../../../../../translated_images/de/iou_equation.9a4751d40fff4e11.webp) > *Abbildung 2 aus [diesem ausgezeichneten Blogbeitrag über IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Es gibt zwei Hauptklassen von Objekterkennungsalgorithmen: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) verwendet [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), um eine hierarchische Struktur von ROI-Regionen zu generieren, die dann durch CNN-Feature-Extraktoren und SVM-Klassifikatoren geleitet werden, um die Objektklasse zu bestimmen, sowie durch lineare Regression, um die *Koordinaten der Begrenzungsbox* zu bestimmen. [Offizielles Paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.de.png) +![RCNN](../../../../../translated_images/de/rcnn1.cae407020dfb1d1f.webp) > *Bild von van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.de.png) +![RCNN-1](../../../../../translated_images/de/rcnn2.2d9530bb83516484.webp) > *Bilder aus [diesem Blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Es gibt zwei Hauptklassen von Objekterkennungsalgorithmen: Dieser Ansatz ähnelt R-CNN, aber die Regionen werden definiert, nachdem die Convolution-Schichten angewendet wurden. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.de.png) +![FRCNN](../../../../../translated_images/de/f-rcnn.3cda6d9bb4188875.webp) > Bild aus [dem offiziellen Paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Dieser Ansatz ähnelt R-CNN, aber die Regionen werden definiert, nachdem die Con Die Hauptidee dieses Ansatzes ist die Verwendung eines neuronalen Netzwerks zur Vorhersage von ROIs – des sogenannten *Region Proposal Network*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.de.png) +![FasterRCNN](../../../../../translated_images/de/faster-rcnn.8d46c099b87ef30a.webp) > Bild aus [dem offiziellen Paper](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Dieser Algorithmus ist sogar schneller als Faster R-CNN. Die Hauptidee ist folge 2. Die Features werden durch **Position-Sensitive Score Map** verarbeitet. Jedes Objekt aus $C$ Klassen wird in $k\times k$ Regionen unterteilt, und wir trainieren, um Teile von Objekten vorherzusagen. 3. Für jeden Teil aus den $k\times k$ Regionen stimmen alle Netzwerke für Objektklassen ab, und die Objektklasse mit der maximalen Stimmenanzahl wird ausgewählt. -![r-fcn Bild](../../../../../translated_images/r-fcn.13eb88158b99a3da.de.png) +![r-fcn Bild](../../../../../translated_images/de/r-fcn.13eb88158b99a3da.webp) > Bild aus [dem offiziellen Paper](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO ist ein Echtzeit-One-Pass-Algorithmus. Die Hauptidee ist folgende: * Das Bild wird in $S\times S$ Regionen unterteilt. * Für jede Region sagt **CNN** $n$ mögliche Objekte, *Koordinaten der Begrenzungsbox* und *Confidence*=*Wahrscheinlichkeit* * IoU voraus. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.de.png) + ![YOLO](../../../../../translated_images/de/yolo.a2648ec82ee8bb4e.webp) > Bild aus [dem offiziellen Paper](https://arxiv.org/abs/1506.02640) diff --git a/translations/de/lessons/5-NLP/14-Embeddings/README.md b/translations/de/lessons/5-NLP/14-Embeddings/README.md index 2b100206..3647f5ff 100644 --- a/translations/de/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/de/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Die Einbettungsschicht würde also ein Wort als Eingabe nehmen und einen Ausgabe Durch die Verwendung einer Einbettungsschicht als erste Schicht in unserem Klassifikator-Netzwerk können wir von einem Bag-of-Words-Modell zu einem **Embedding-Bag-Modell** wechseln, bei dem wir zunächst jedes Wort in unserem Text in die entsprechende Einbettung umwandeln und dann eine Aggregatfunktion über alle diese Einbettungen berechnen, wie z. B. `sum`, `average` oder `max`. -![Bild zeigt einen Einbettungsklassifikator für fünf Sequenzwörter.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.de.png) +![Bild zeigt einen Einbettungsklassifikator für fünf Sequenzwörter.](../../../../../translated_images/de/embedding-classifier-example.b77f021a7ee67eee.webp) > Bild vom Autor @@ -40,7 +40,7 @@ Um dies zu erreichen, müssen wir unser Einbettungsmodell auf einer großen Text CBoW ist schneller, während Skip-Gram langsamer ist, aber eine bessere Darstellung von seltenen Wörtern liefert. -![Bild zeigt sowohl CBoW- als auch Skip-Gram-Algorithmen zur Umwandlung von Wörtern in Vektoren.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.de.png) +![Bild zeigt sowohl CBoW- als auch Skip-Gram-Algorithmen zur Umwandlung von Wörtern in Vektoren.](../../../../../translated_images/de/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bild aus [diesem Paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/de/lessons/5-NLP/15-LanguageModeling/README.md b/translations/de/lessons/5-NLP/15-LanguageModeling/README.md index 08732e49..24649cf5 100644 --- a/translations/de/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/de/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ In unseren vorherigen Beispielen haben wir vortrainierte semantische Einbettunge * **Continuous Bag-of-Words** (CBoW), bei der wir das mittlere Token $W_0$ in einer Token-Sequenz $W_{-N}$, ..., $W_N$ vorhersagen. * **Skip-Gramm**, bei dem wir eine Menge benachbarter Tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} aus dem mittleren Token $W_0$ vorhersagen. -![Bild aus einer Arbeit über die Umwandlung von Wörtern in Vektoren](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.de.png) +![Bild aus einer Arbeit über die Umwandlung von Wörtern in Vektoren](../../../../../translated_images/de/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Bild aus [dieser Arbeit](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/de/lessons/5-NLP/16-RNN/README.md b/translations/de/lessons/5-NLP/16-RNN/README.md index e329bd8d..91a9e1ac 100644 --- a/translations/de/lessons/5-NLP/16-RNN/README.md +++ b/translations/de/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ In den vorherigen Abschnitten haben wir reichhaltige semantische Repräsentation Um die Bedeutung einer Textsequenz zu erfassen, müssen wir eine andere Architektur für neuronale Netze verwenden, die als **rekurrentes neuronales Netz** oder RNN bezeichnet wird. Im RNN geben wir unseren Satz ein Symbol nach dem anderen durch das Netzwerk, und das Netzwerk erzeugt einen **Zustand**, den wir dann mit dem nächsten Symbol erneut in das Netzwerk einspeisen. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.de.png) +![RNN](../../../../../translated_images/de/rnn.27f5c29c53d727b5.webp) > Bild vom Autor @@ -61,7 +61,7 @@ Wir haben rekurrente Netzwerke besprochen, die in eine Richtung arbeiten, vom An Ein rekurrentes Netzwerk, sei es eindirektional oder bidirektional, erfasst bestimmte Muster innerhalb einer Sequenz und kann sie in einem Zustandsvektor speichern oder in die Ausgabe weitergeben. Wie bei konvolutionalen Netzwerken können wir eine weitere rekurrente Schicht auf die erste aufbauen, um höherstufige Muster zu erfassen und aus den niedrigstufigen Mustern zu bauen, die von der ersten Schicht extrahiert wurden. Dies führt uns zum Konzept eines **mehrschichtigen RNN**, das aus zwei oder mehr rekurrenten Netzwerken besteht, wobei die Ausgabe der vorherigen Schicht als Eingabe an die nächste Schicht weitergegeben wird. -![Bild eines mehrschichtigen Long Short Term Memory-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.de.jpg) +![Bild eines mehrschichtigen Long Short Term Memory-RNN](../../../../../translated_images/de/multi-layer-lstm.dd975e29bb2a59fe.webp) *Bild aus [diesem wunderbaren Beitrag](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) von Fernando López* diff --git a/translations/de/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/de/lessons/5-NLP/17-GenerativeNetworks/README.md index 72d28e87..26170669 100644 --- a/translations/de/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/de/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ In der RNN-Architektur, die wir in der vorherigen Einheit besprochen haben, erze Dies ermöglicht verschiedene neuronale Architekturen, die im folgenden Bild dargestellt sind: -![Bild zeigt gängige Muster von rekurrenten neuronalen Netzwerken.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.de.jpg) +![Bild zeigt gängige Muster von rekurrenten neuronalen Netzwerken.](../../../../../translated_images/de/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Bild aus dem Blogpost [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) von [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In dieser Einheit konzentrieren wir uns auf einfache generative Modelle, die uns Wir werden dieses RNN trainieren, um Text Schritt für Schritt zu generieren. Bei jedem Schritt nehmen wir eine Zeichenfolge der Länge `nchars` und bitten das Netzwerk, das nächste Ausgabesymbol für jedes Eingabesymbol zu generieren: -![Bild zeigt ein Beispiel für die RNN-Generierung des Wortes 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.de.png) +![Bild zeigt ein Beispiel für die RNN-Generierung des Wortes 'HELLO'.](../../../../../translated_images/de/rnn-generate.56c54afb52f9781d.webp) Bei der Textgenerierung (während der Inferenz) beginnen wir mit einem **Prompt**, der durch die RNN-Zellen geleitet wird, um seinen Zwischenzustand zu erzeugen, und dann beginnt die Generierung aus diesem Zustand. Wir generieren ein Zeichen nach dem anderen und übergeben den Zustand und das generierte Zeichen an eine andere RNN-Zelle, um das nächste zu generieren, bis wir genügend Zeichen erzeugt haben. diff --git a/translations/de/lessons/5-NLP/18-Transformers/README.md b/translations/de/lessons/5-NLP/18-Transformers/README.md index 832191d4..ac09b3ff 100644 --- a/translations/de/lessons/5-NLP/18-Transformers/README.md +++ b/translations/de/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Mit RNNs wird Sequence-to-Sequence durch zwei rekurrente Netzwerke implementiert **Aufmerksamkeitsmechanismen** bieten eine Möglichkeit, den kontextuellen Einfluss jedes Eingabevektors auf jede Ausgabewahrscheinlichkeit des RNN zu gewichten. Dies wird durch die Erstellung von Abkürzungen zwischen den Zwischenzuständen des Eingabe-RNN und des Ausgabe-RNN umgesetzt. Auf diese Weise berücksichtigen wir beim Generieren des Ausgabesymbols yt alle versteckten Eingabezustände hi, mit unterschiedlichen Gewichtungskoeffizienten αt,i. -![Bild zeigt ein Encoder/Decoder-Modell mit einer additiven Aufmerksamkeits-Schicht](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.de.png) +![Bild zeigt ein Encoder/Decoder-Modell mit einer additiven Aufmerksamkeits-Schicht](../../../../../translated_images/de/encoder-decoder-attention.7a726296894fb567.webp) > Das Encoder-Decoder-Modell mit additivem Aufmerksamkeitsmechanismus aus [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), zitiert aus [diesem Blogbeitrag](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Die Aufmerksamkeitsmatrix {αi,j} repräsentiert den Grad, in dem bestimmte Eingabewörter bei der Generierung eines bestimmten Wortes in der Ausgabesequenz eine Rolle spielen. Unten ist ein Beispiel für eine solche Matrix: -![Bild zeigt eine Beispielausrichtung, gefunden von RNNsearch-50, entnommen aus Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.de.png) +![Bild zeigt eine Beispielausrichtung, gefunden von RNNsearch-50, entnommen aus Bahdanau - arviz.org](../../../../../translated_images/de/bahdanau-fig3.09ba2d37f202a6af.webp) > Abbildung aus [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Das Ergebnis, das wir mit Positionskodierung erhalten, bettet sowohl das ursprü Als Nächstes müssen wir einige Muster innerhalb unserer Sequenz erfassen. Um dies zu tun, verwenden Transformer einen **Self-Attention**-Mechanismus, der im Wesentlichen Aufmerksamkeit ist, die auf dieselbe Sequenz als Eingabe und Ausgabe angewendet wird. Die Anwendung von Self-Attention ermöglicht es uns, den **Kontext** innerhalb des Satzes zu berücksichtigen und zu sehen, welche Wörter miteinander in Beziehung stehen. Zum Beispiel ermöglicht es uns zu sehen, auf welche Wörter durch Koreferenzen wie *es* verwiesen wird, und auch den Kontext zu berücksichtigen: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.de.png) +![](../../../../../translated_images/de/CoreferenceResolution.861924d6d384a7d6.webp) > Bild aus dem [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Da jede Eingabeposition unabhängig von jeder Ausgabeposition abgebildet wird, k **BERT** (Bidirectional Encoder Representations from Transformers) ist ein sehr großes mehrschichtiges Transformer-Netzwerk mit 12 Schichten für *BERT-base* und 24 für *BERT-large*. Das Modell wird zunächst auf einem großen Textkorpus (Wikipedia + Bücher) mit unüberwachtem Training (Vorhersage maskierter Wörter in einem Satz) vortrainiert. Während des Vortrainings nimmt das Modell ein erhebliches Maß an Sprachverständnis auf, das dann mit anderen Datensätzen durch Feintuning genutzt werden kann. Dieser Prozess wird als **Transfer Learning** bezeichnet. -![Bild von http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.de.png) +![Bild von http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/de/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Bild [Quelle](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/de/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/de/lessons/5-NLP/18-Transformers/READMEtransformers.md index 8b92fa1e..21501ab3 100644 --- a/translations/de/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/de/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Bei RNNs wird Sequenz-zu-Sequenz durch zwei rekursive Netzwerke implementiert, w **Aufmerksamkeitsmechanismen** bieten ein Mittel, um den kontextuellen Einfluss jedes Eingangsvektors auf jede Vorhersageausgabe des RNN zu gewichten. Die Implementierung erfolgt durch die Schaffung von Abkürzungen zwischen den Zwischenzuständen des Eingangs-RNN und dem Ausgangs-RNN. Auf diese Weise berücksichtigen wir beim Generieren des Ausgabesymbols yt alle Eingangsversteckzustände hi mit unterschiedlichen Gewichtungskoeffizienten αt,i. -![Bild, das ein Encoder/Decoder-Modell mit einer additiven Aufmerksamkeitschicht zeigt](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.de.png) +![Bild, das ein Encoder/Decoder-Modell mit einer additiven Aufmerksamkeitschicht zeigt](../../../../../translated_images/de/encoder-decoder-attention.7a726296894fb567.webp) > Das Encoder-Decoder-Modell mit dem additiven Aufmerksamkeitsmechanismus in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), zitiert aus [diesem Blogbeitrag](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Die Aufmerksamkeitsmatrix {αi,j} würde den Grad darstellen, in dem bestimmte Eingabewörter an der Generierung eines bestimmten Wortes in der Ausgabesequenz beteiligt sind. Im Folgenden finden Sie ein Beispiel für eine solche Matrix: -![Bild, das eine Beispielausrichtung zeigt, die von RNNsearch-50 gefunden wurde, entnommen von Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.de.png) +![Bild, das eine Beispielausrichtung zeigt, die von RNNsearch-50 gefunden wurde, entnommen von Bahdanau - arviz.org](../../../../../translated_images/de/bahdanau-fig3.09ba2d37f202a6af.webp) > Abbildung aus [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Das Ergebnis, das wir mit der Positions-Einbettung erhalten, bettet sowohl das u Als Nächstes müssen wir einige Muster innerhalb unserer Sequenz erfassen. Dazu verwenden Transformer einen **Selbstaufmerksamkeits**mechanismus, der im Wesentlichen Aufmerksamkeit auf die gleiche Sequenz anwendet, die als Eingabe und Ausgabe dient. Die Anwendung von Selbstaufmerksamkeit ermöglicht es uns, den **Kontext** innerhalb des Satzes zu berücksichtigen und zu sehen, welche Wörter miteinander in Beziehung stehen. Zum Beispiel ermöglicht es uns zu sehen, welche Wörter durch Kofe referenziert werden, wie *es*, und auch den Kontext zu berücksichtigen: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.de.png) +![](../../../../../translated_images/de/CoreferenceResolution.861924d6d384a7d6.webp) > Bild aus dem [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Da jede Eingabeposition unabhängig auf jede Ausgabeposition abgebildet wird, k **BERT** (Bidirektionale Encoder-Darstellungen von Transformern) ist ein sehr großes, mehrschichtiges Transformernetzwerk mit 12 Schichten für *BERT-base* und 24 für *BERT-large*. Das Modell wird zunächst auf einem großen Korpus von Textdaten (Wikipedia + Bücher) mit unüberwachtem Training (Vorhersage maskierter Wörter in einem Satz) vortrainiert. Während des Vortrainings absorbiert das Modell erhebliche Niveaus des Sprachverständnisses, die dann mit anderen Datensätzen durch Feinabstimmung genutzt werden können. Dieser Prozess wird als **Transferlernen** bezeichnet. -![Bild von http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.de.png) +![Bild von http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/de/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Bild [Quelle](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/de/lessons/5-NLP/19-NER/README.md b/translations/de/lessons/5-NLP/19-NER/README.md index e7b7c4e3..afa4006b 100644 --- a/translations/de/lessons/5-NLP/19-NER/README.md +++ b/translations/de/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ Säugling | O Da wir eine Eins-zu-Eins-Korrespondenz zwischen Tokens und Klassen herstellen müssen, können wir ein rechtsbasiertes **Many-to-Many**-Neuralnetzwerkmodell aus diesem Bild trainieren: -![Bild zeigt gängige Muster von rekurrenten neuronalen Netzwerken.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.de.jpg) +![Bild zeigt gängige Muster von rekurrenten neuronalen Netzwerken.](../../../../../translated_images/de/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Bild aus [diesem Blogbeitrag](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) von [Andrej Karpathy](http://karpathy.github.io/). NER-Token-Klassifikationsmodelle entsprechen der rechtsbasierten Netzwerkarchitektur auf diesem Bild.* diff --git a/translations/de/lessons/6-Other/23-MultiagentSystems/README.md b/translations/de/lessons/6-Other/23-MultiagentSystems/README.md index 12f49225..dae57470 100644 --- a/translations/de/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/de/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Sie können eines der Modelle öffnen, beispielsweise **Biologie → Fl Nach dem Öffnen des Modells gelangen Sie zum Hauptbildschirm von NetLogo. Hier ist ein Beispielmodell, das die Population von Wölfen und Schafen beschreibt, basierend auf begrenzten Ressourcen (Gras). -![NetLogo Hauptbildschirm](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.de.png) +![NetLogo Hauptbildschirm](../../../../../translated_images/de/NetLogo-Main.32653711ec1a01b3.webp) > Screenshot von Dmitry Soshnikov diff --git a/translations/el/README.md b/translations/el/README.md index 0821218a..8341986c 100644 --- a/translations/el/README.md +++ b/translations/el/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Τεχνητή Νοημοσύνη για Αρχάριους - Ένα Αναλυτικό Πρόγραμμα -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.el.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/el/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Σημείωση σχεδίασης από [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/el/lessons/1-Intro/README.md b/translations/el/lessons/1-Intro/README.md index db65aa96..fd527dfe 100644 --- a/translations/el/lessons/1-Intro/README.md +++ b/translations/el/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Εισαγωγή στην Τεχνητή Νοημοσύνη -![Περίληψη του περιεχομένου της Εισαγωγής στην Τεχνητή Νοημοσύνη σε ένα σκίτσο](../../../../translated_images/ai-intro.bf28d1ac4235881c.el.png) +![Περίληψη του περιεχομένου της Εισαγωγής στην Τεχνητή Νοημοσύνη σε ένα σκίτσο](../../../../translated_images/el/ai-intro.bf28d1ac4235881c.png) > Σκίτσο από την [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Αρχικά, οι υπολογιστές εφευρέθηκαν από τον [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) για να λειτουργούν με αριθμούς ακολουθώντας μια καλά καθορισμένη διαδικασία - έναν αλγόριθμο. Οι σύγχρονοι υπολογιστές, αν και σημαντικά πιο προηγμένοι από το αρχικό μοντέλο που προτάθηκε τον 19ο αιώνα, εξακολουθούν να ακολουθούν την ίδια ιδέα των ελεγχόμενων υπολογισμών. Έτσι, είναι δυνατόν να προγραμματίσουμε έναν υπολογιστή να κάνει κάτι, αν γνωρίζουμε την ακριβή ακολουθία βημάτων που πρέπει να ακολουθήσουμε για να επιτύχουμε τον στόχο. -![Φωτογραφία ενός ατόμου](../../../../translated_images/dsh_age.d212a30d4e54fb5f.el.png) +![Φωτογραφία ενός ατόμου](../../../../translated_images/el/dsh_age.d212a30d4e54fb5f.png) > Φωτογραφία από την [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Ένα από τα προβλήματα όταν ασχολούμαστε με τον όρο **[Νοημοσύνη](https://en.wikipedia.org/wiki/Intelligence)** είναι ότι δεν υπάρχει σαφής ορισμός αυτού του όρου. Κάποιος μπορεί να υποστηρίξει ότι η νοημοσύνη συνδέεται με την **αφηρημένη σκέψη** ή με την **αυτογνωσία**, αλλά δεν μπορούμε να την ορίσουμε σωστά. -![Φωτογραφία μιας γάτας](../../../../translated_images/photo-cat.8c8e8fb760ffe457.el.jpg) +![Φωτογραφία μιας γάτας](../../../../translated_images/el/photo-cat.8c8e8fb760ffe457.jpg) > [Φωτογραφία](https://unsplash.com/photos/75715CVEJhI) από την [Amber Kipp](https://unsplash.com/@sadmax) στο Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | Τι γίνεται με τη Μηχανική Μάθηση; | | > |--------------|-----------| -> | Μέρος της Τεχνητής Νοημοσύνης που βασίζεται στο να μαθαίνει ο υπολογιστής να λύνει ένα πρόβλημα με βάση κάποια δεδομένα ονομάζεται **Μηχανική Μάθηση**. Δεν θα εξετάσουμε την κλασική μηχανική μάθηση σε αυτό το μάθημα - σας παραπέμπουμε σε ένα ξεχωριστό πρόγραμμα σπουδών [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.el.png) | +> | Μέρος της Τεχνητής Νοημοσύνης που βασίζεται στο να μαθαίνει ο υπολογιστής να λύνει ένα πρόβλημα με βάση κάποια δεδομένα ονομάζεται **Μηχανική Μάθηση**. Δεν θα εξετάσουμε την κλασική μηχανική μάθηση σε αυτό το μάθημα - σας παραπέμπουμε σε ένα ξεχωριστό πρόγραμμα σπουδών [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/el/ml-for-beginners.9e4fed176fd5817d.png) | ## Μια Σύντομη Ιστορία της ΤΝ Η Τεχνητή Νοημοσύνη ξεκίνησε ως πεδίο στα μέσα του εικοστού αιώνα. Αρχικά, η συμβολική συλλογιστική ήταν η κυρίαρχη προσέγγιση, και οδήγησε σε μια σειρά από σημαντικές επιτυχίες, όπως τα συστήματα ειδικών – προγράμματα υπολογιστών που μπορούσαν να λειτουργούν ως ειδικοί σε ορισμένους περιορισμένους τομείς προβλημάτων. Ωστόσο, σύντομα έγινε σαφές ότι αυτή η προσέγγιση δεν κλιμακώνεται καλά. Η εξαγωγή γνώσης από έναν ειδικό, η αναπαράστασή της σε έναν υπολογιστή και η διατήρηση αυτής της βάσης γνώσης ακριβούς αποδείχθηκε μια πολύπλοκη εργασία και πολύ δαπανηρή για να είναι πρακτική σε πολλές περιπτώσεις. Αυτό οδήγησε στον λεγόμενο [Χειμώνα της ΤΝ](https://en.wikipedia.org/wiki/AI_winter) τη δεκαετία του 1970. -Σύντομη Ιστορία της ΤΝ +Σύντομη Ιστορία της ΤΝ > Εικόνα από τον [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/el/lessons/2-Symbolic/Animals.ipynb b/translations/el/lessons/2-Symbolic/Animals.ipynb index 6f6288f9..bb22b3ea 100644 --- a/translations/el/lessons/2-Symbolic/Animals.ipynb +++ b/translations/el/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Σε αυτό το παράδειγμα, θα υλοποιήσουμε ένα απλό σύστημα βασισμένο στη γνώση για να προσδιορίσουμε ένα ζώο με βάση ορισμένα φυσικά χαρακτηριστικά. Το σύστημα μπορεί να αναπαρασταθεί από το παρακάτω δέντρο AND-OR (αυτό είναι ένα μέρος του συνολικού δέντρου, μπορούμε εύκολα να προσθέσουμε περισσότερους κανόνες):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.el.png)\n" + "![](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/el/lessons/2-Symbolic/README.md b/translations/el/lessons/2-Symbolic/README.md index 3ed21e83..9c668c35 100644 --- a/translations/el/lessons/2-Symbolic/README.md +++ b/translations/el/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Αναπαράσταση Γνώσης και Συστήματα Ειδικών -![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../translated_images/ai-symbolic.715a30cb610411a6.el.png) +![Περίληψη περιεχομένου Συμβολικής Τεχνητής Νοημοσύνης](../../../../translated_images/el/ai-symbolic.715a30cb610411a6.png) > Σχεδιαστικό σημείωμα από [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Έτσι, το πρόβλημα της **αναπαράστασης γνώσης** είναι να βρεθεί ένας αποτελεσματικός τρόπος να αναπαρασταθεί η γνώση μέσα σε έναν υπολογιστή με τη μορφή δεδομένων, ώστε να είναι αυτόματα χρησιμοποιήσιμη. Αυτό μπορεί να θεωρηθεί ως ένα φάσμα: -![Φάσμα αναπαράστασης γνώσης](../../../../translated_images/knowledge-spectrum.b60df631852c0217.el.png) +![Φάσμα αναπαράστασης γνώσης](../../../../translated_images/el/knowledge-spectrum.b60df631852c0217.png) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | σύνταξη μπλοκ | εσοχή Μία από τις πρώτες επιτυχίες της συμβολικής Τεχνητής Νοημοσύνης ήταν τα λεγόμενα **συστήματα ειδικών** - υπολογιστικά συστήματα που σχεδιάστηκαν για να λειτουργούν ως ειδικός σε κάποιο περιορισμένο πεδίο προβλημάτων. Βασίζονταν σε μια **βάση γνώσης** που εξαγόταν από έναν ή περισσότερους ανθρώπινους ειδικούς και περιείχαν μια **μηχανή συλλογιστικής** που εκτελούσε κάποια συλλογιστική πάνω σε αυτήν. -![Αρχιτεκτονική Ανθρώπου](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.el.png) | ![Αρχιτεκτονική Συστήματος Βασισμένου στη Γνώση](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.el.png) +![Αρχιτεκτονική Ανθρώπου](../../../../translated_images/el/arch-human.5d4d35f1bba3ab1c.png) | ![Αρχιτεκτονική Συστήματος Βασισμένου στη Γνώση](../../../../translated_images/el/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Απλοποιημένη δομή του ανθρώπινου νευρικού συστήματος | Αρχιτεκτονική ενός συστήματος βασισμένου στη γνώση @@ -106,7 +106,7 @@ Python | σύνταξη μπλοκ | εσοχή Ως παράδειγμα, ας εξετάσουμε το εξής σύστημα ειδικών για τον προσδιορισμό ενός ζώου βάσει των φυσικών του χαρακτηριστικών: -![Δέντρο AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.el.png) +![Δέντρο AND-OR](../../../../translated_images/el/AND-OR-Tree.5592d2c70187f283.png) > Εικόνα από [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 6551c325..11dfdea4 100644 --- a/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/el/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -489,7 +489,7 @@ "\n", "Σε περίπτωση που έχουμε περισσότερες από 2 κατηγορίες, η softmax θα κανονικοποιήσει τις πιθανότητες για όλες. Εδώ είναι ένα διάγραμμα της αρχιτεκτονικής δικτύου που εκτελεί ταξινόμηση ψηφίων MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.el.png)\n" + "![MNIST Classifier](../../../../../translated_images/el/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1261,7 +1261,7 @@ "* Χαμηλή απώλεια εκπαίδευσης - το μοντέλο μπορεί να προσεγγίσει καλά τα δεδομένα εκπαίδευσης, επειδή έχει αρκετή εκφραστική ισχύ.\n", "* Η απώλεια επικύρωσης μπορεί να είναι πολύ υψηλότερη από την απώλεια εκπαίδευσης και να αρχίσει να αυξάνεται κατά τη διάρκεια της εκπαίδευσης - αυτό συμβαίνει επειδή το μοντέλο \"απομνημονεύει\" τα σημεία εκπαίδευσης και χάνει τη \"γενική εικόνα\".\n", "\n", - "![Υπερπροσαρμογή](../../../../../translated_images/overfit.a0bd57f717c15769.el.png)\n", + "![Υπερπροσαρμογή](../../../../../translated_images/el/overfit.a0bd57f717c15769.png)\n", "\n", "> Στην εικόνα αυτή, το `x` αντιπροσωπεύει δεδομένα εκπαίδευσης, το `o` - δεδομένα επικύρωσης. Αριστερά - γραμμικό μοντέλο (μονοεπίπεδο), προσεγγίζει τη φύση των δεδομένων αρκετά καλά. Δεξιά - υπερπροσαρμοσμένο μοντέλο, το μοντέλο προσεγγίζει τέλεια τα δεδομένα εκπαίδευσης, αλλά χάνει τη λογική με οποιαδήποτε άλλα δεδομένα (το σφάλμα επικύρωσης είναι πολύ υψηλό).\n" ] diff --git a/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md index 0d07642b..85bc4df5 100644 --- a/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/el/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* Ας εξετάσουμε το παρακάτω πρόβλημα προσέγγισης 5 σημείων (που αναπαρίστανται από `x` στα γραφήματα παρακάτω): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.el.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.el.jpg) +![linear](../../../../../translated_images/el/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/el/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Γραμμικό μοντέλο, 2 παράμετροι** | **Μη γραμμικό μοντέλο, 7 παράμετροι** Σφάλμα εκπαίδευσης = 5.3 | Σφάλμα εκπαίδευσης = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* Όπως μπορείτε να δείτε από το παραπάνω γράφημα, η υπερπροσαρμογή μπορεί να ανιχνευθεί από ένα πολύ χαμηλό σφάλμα εκπαίδευσης και ένα υψηλό σφάλμα επικύρωσης. Κανονικά κατά την εκπαίδευση θα βλέπουμε τόσο το σφάλμα εκπαίδευσης όσο και το σφάλμα επικύρωσης να αρχίζουν να μειώνονται, και στη συνέχεια σε κάποιο σημείο το σφάλμα επικύρωσης μπορεί να σταματήσει να μειώνεται και να αρχίσει να αυξάνεται. Αυτό θα είναι ένα σημάδι υπερπροσαρμογής και η ένδειξη ότι πιθανώς πρέπει να σταματήσουμε την εκπαίδευση σε αυτό το σημείο (ή τουλάχιστον να κάνουμε ένα στιγμιότυπο του μοντέλου). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.el.png) +![overfitting](../../../../../translated_images/el/Overfitting.408ad91cd90b4371.png) ## Πώς να αποτρέψετε την υπερπροσαρμογή diff --git a/translations/el/lessons/3-NeuralNetworks/README.md b/translations/el/lessons/3-NeuralNetworks/README.md index 81ed3e8b..2eac35dc 100644 --- a/translations/el/lessons/3-NeuralNetworks/README.md +++ b/translations/el/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Εισαγωγή στα Νευρωνικά Δίκτυα -![Περίληψη του περιεχομένου της Εισαγωγής στα Νευρωνικά Δίκτυα σε ένα σκίτσο](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.el.png) +![Περίληψη του περιεχομένου της Εισαγωγής στα Νευρωνικά Δίκτυα σε ένα σκίτσο](../../../../translated_images/el/ai-neuralnetworks.1c687ae40bc86e83.png) Όπως συζητήσαμε στην εισαγωγή, ένας από τους τρόπους για να επιτευχθεί η νοημοσύνη είναι η εκπαίδευση ενός **μοντέλου υπολογιστή** ή ενός **τεχνητού εγκεφάλου**. Από τα μέσα του 20ού αιώνα, οι ερευνητές δοκίμασαν διάφορα μαθηματικά μοντέλα, μέχρι που τα τελευταία χρόνια αυτή η κατεύθυνση αποδείχθηκε εξαιρετικά επιτυχημένη. Αυτά τα μαθηματικά μοντέλα του εγκεφάλου ονομάζονται **νευρωνικά δίκτυα**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: Από τη βιολογία, γνωρίζουμε ότι ο εγκέφαλός μας αποτελείται από νευρικά κύτταρα (νευρώνες), καθένα από τα οποία έχει πολλαπλές "εισόδους" (δενδρίτες) και μία "έξοδο" (άξονα). Τόσο οι δενδρίτες όσο και οι άξονες μπορούν να μεταφέρουν ηλεκτρικά σήματα, και οι συνδέσεις μεταξύ τους — γνωστές ως συνάψεις — μπορούν να παρουσιάζουν διαφορετικούς βαθμούς αγωγιμότητας, οι οποίοι ρυθμίζονται από νευροδιαβιβαστές. -![Μοντέλο Νευρώνα](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.el.jpg) | ![Μοντέλο Νευρώνα](../../../../translated_images/artneuron.1a5daa88d20ebe6f.el.png) +![Μοντέλο Νευρώνα](../../../../translated_images/el/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Μοντέλο Νευρώνα](../../../../translated_images/el/artneuron.1a5daa88d20ebe6f.png) ----|---- Πραγματικός Νευρώνας *([Εικόνα](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) από τη Wikipedia)* | Τεχνητός Νευρώνας *(Εικόνα από τον Συγγραφέα)* Έτσι, το απλούστερο μαθηματικό μοντέλο ενός νευρώνα περιέχει αρκετές εισόδους X1, ..., XN και μία έξοδο Y, καθώς και μια σειρά από βάρη W1, ..., WN. Η έξοδος υπολογίζεται ως: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) όπου f είναι κάποια μη γραμμική **συνάρτηση ενεργοποίησης**. diff --git a/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md index 843e1f52..5a80b8fe 100644 --- a/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/el/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Προεπεξεργασία μιας φωτογραφίας ενός βιβλίου Braille**. Εστιάζουμε στο πώς μπορούμε να χρησιμοποιήσουμε την κατωφλίωση, την ανίχνευση χαρακτηριστικών, τον μετασχηματισμό προοπτικής και τους χειρισμούς NumPy για να διαχωρίσουμε μεμονωμένα σύμβολα Braille για περαιτέρω ταξινόμηση από ένα νευρωνικό δίκτυο. -![Εικόνα Braille](../../../../../translated_images/braille.341962ff76b1bd70.el.jpeg) | ![Προεπεξεργασμένη Εικόνα Braille](../../../../../translated_images/braille-result.46530fea020b03c7.el.png) | ![Σύμβολα Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.el.png) +![Εικόνα Braille](../../../../../translated_images/el/braille.341962ff76b1bd70.jpeg) | ![Προεπεξεργασμένη Εικόνα Braille](../../../../../translated_images/el/braille-result.46530fea020b03c7.png) | ![Σύμβολα Braille](../../../../../translated_images/el/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) * **Ανίχνευση κίνησης σε βίντεο χρησιμοποιώντας διαφορά καρέ**. Αν η κάμερα είναι σταθερή, τότε τα καρέ από τη ροή της κάμερας θα πρέπει να είναι αρκετά παρόμοια μεταξύ τους. Επειδή τα καρέ αναπαρίστανται ως πίνακες, απλώς αφαιρώντας αυτούς τους πίνακες για δύο διαδοχικά καρέ θα πάρουμε τη διαφορά των pixels, η οποία θα πρέπει να είναι χαμηλή για στατικά καρέ και να αυξάνεται όταν υπάρχει σημαντική κίνηση στην εικόνα. -![Εικόνα καρέ βίντεο και διαφορές καρέ](../../../../../translated_images/frame-difference.706f805491a0883c.el.png) +![Εικόνα καρέ βίντεο και διαφορές καρέ](../../../../../translated_images/el/frame-difference.706f805491a0883c.png) > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Πυκνή Οπτική Ροή** υπολογίζει το πεδίο διανυσμάτων που δείχνει για κάθε pixel πού κινείται - **Αραιή Οπτική Ροή** βασίζεται στη λήψη κάποιων χαρακτηριστικών στην εικόνα (π.χ. άκρες) και στην κατασκευή της τροχιάς τους από καρέ σε καρέ. -![Εικόνα Οπτικής Ροής](../../../../../translated_images/optical.1f4a94464579a83a.el.png) +![Εικόνα Οπτικής Ροής](../../../../../translated_images/el/optical.1f4a94464579a83a.png) > Εικόνα από [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index fd3dd452..d1b13dda 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: Το VGG-16 είναι ένα δίκτυο που πέτυχε ακρίβεια 92.7% στην ταξινόμηση top-5 του ImageNet το 2014. Έχει την εξής δομή στρώσεων: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.el.jpg) +![ImageNet Layers](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.jpg) Όπως μπορείτε να δείτε, το VGG ακολουθεί μια παραδοσιακή πυραμιδική αρχιτεκτονική, η οποία είναι μια ακολουθία από στρώσεις συνελικτικής και pooling. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.el.jpg) +![ImageNet Pyramid](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.jpg) > Εικόνα από [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index de7d70ec..6d353abc 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Έτσι, σε ένα τυπικό CNN θα υπάρχουν αρκετά επίπεδα συνέλιξης, με επίπεδα pooling ανάμεσά τους για να μειώσουν τις διαστάσεις της εικόνας. Θα αυξήσουμε επίσης τον αριθμό των φίλτρων, επειδή καθώς τα μοτίβα γίνονται πιο σύνθετα - υπάρχουν περισσότερες πιθανές ενδιαφέρουσες συνδυαστικές μορφές που πρέπει να αναζητήσουμε.\n", "\n", - "![Μια εικόνα που δείχνει αρκετά επίπεδα συνέλιξης με επίπεδα pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.el.png)\n", + "![Μια εικόνα που δείχνει αρκετά επίπεδα συνέλιξης με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Λόγω της μείωσης των χωρικών διαστάσεων και της αύξησης των χαρακτηριστικών/διαστάσεων φίλτρων, αυτή η αρχιτεκτονική ονομάζεται επίσης **αρχιτεκτονική πυραμίδας**.\n" ] diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index aed04a3a..962a5c17 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Έτσι, σε ένα τυπικό CNN υπάρχουν αρκετά συνελικτικά επίπεδα, με επίπεδα pooling ανάμεσά τους για να μειώσουν τις διαστάσεις της εικόνας. Παράλληλα, αυξάνουμε τον αριθμό των φίλτρων, επειδή καθώς τα μοτίβα γίνονται πιο σύνθετα, υπάρχουν περισσότεροι πιθανοί ενδιαφέροντες συνδυασμοί που πρέπει να αναζητήσουμε.\n", "\n", - "![Μια εικόνα που δείχνει αρκετά συνελικτικά επίπεδα με επίπεδα pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.el.png)\n", + "![Μια εικόνα που δείχνει αρκετά συνελικτικά επίπεδα με επίπεδα pooling.](../../../../../translated_images/el/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Λόγω της μείωσης των χωρικών διαστάσεων και της αύξησης των διαστάσεων χαρακτηριστικών/φίλτρων, αυτή η αρχιτεκτονική ονομάζεται επίσης **αρχιτεκτονική πυραμίδας**.\n" ] diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md index 092ad177..bb90b32a 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Για να εξάγουμε μοτίβα, θα χρησιμοποιήσουμε την έννοια των **συνελικτικών φίλτρων**. Όπως γνωρίζετε, μια εικόνα αναπαρίσταται από έναν δισδιάστατο πίνακα ή έναν τρισδιάστατο τανυστή με βάθος χρώματος. Η εφαρμογή ενός φίλτρου σημαίνει ότι παίρνουμε έναν σχετικά μικρό πίνακα **πυρήνα φίλτρου**, και για κάθε pixel στην αρχική εικόνα υπολογίζουμε τον σταθμισμένο μέσο όρο με τα γειτονικά σημεία. Μπορούμε να το δούμε σαν ένα μικρό παράθυρο που γλιστρά πάνω από ολόκληρη την εικόνα, και εξομαλύνει όλα τα pixel σύμφωνα με τα βάρη στον πίνακα πυρήνα φίλτρου. -![Φίλτρο Κάθετης Άκρης](../../../../../translated_images/filter-vert.b7148390ca0bc356.el.png) | ![Φίλτρο Οριζόντιας Άκρης](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.el.png) +![Φίλτρο Κάθετης Άκρης](../../../../../translated_images/el/filter-vert.b7148390ca0bc356.png) | ![Φίλτρο Οριζόντιας Άκρης](../../../../../translated_images/el/filter-horiz.59b80ed4feb946ef.png) ----|---- > Εικόνα από τον Dmitry Soshnikov @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Μπορούμε να σχεδιάσουμε το δίκτυο με τέτοιο τρόπο ώστε τα φίλτρα να εκπαιδεύονται αυτόματα * Μπορούμε να χρησιμοποιήσουμε την ίδια προσέγγιση για να βρούμε μοτίβα σε χαρακτηριστικά υψηλού επιπέδου, όχι μόνο στην αρχική εικόνα. Έτσι, η εξαγωγή χαρακτηριστικών από τα CNN λειτουργεί σε μια ιεραρχία χαρακτηριστικών, ξεκινώντας από συνδυασμούς pixel χαμηλού επιπέδου, μέχρι συνδυασμούς υψηλότερου επιπέδου τμημάτων της εικόνας. -![Ιεραρχική Εξαγωγή Χαρακτηριστικών](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.el.png) +![Ιεραρχική Εξαγωγή Χαρακτηριστικών](../../../../../translated_images/el/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Εικόνα από [μια εργασία των Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), βασισμένη στην [έρευνά τους](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Ως παράδειγμα, ας δούμε την αρχιτεκτονική του VGG-16, ενός δικτύου που πέτυχε ακρίβεια 92.7% στην ταξινόμηση top-5 του ImageNet το 2014: -![Στρώσεις ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.el.jpg) +![Στρώσεις ImageNet](../../../../../translated_images/el/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Πυραμίδα ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.el.jpg) +![Πυραμίδα ImageNet](../../../../../translated_images/el/vgg-16-arch.64ff2137f50dd49f.jpg) > Εικόνα από [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 767d0bec..b68c2305 100644 --- a/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/el/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Θα χρησιμοποιήσουμε το [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), το οποίο περιέχει εικόνες από 37 διαφορετικές φυλές σκύλων και γατών. -![Το σύνολο δεδομένων που θα χρησιμοποιήσουμε](../../../../../../translated_images/data.50b2a9d5484bdbf0.el.png) +![Το σύνολο δεδομένων που θα χρησιμοποιήσουμε](../../../../../../translated_images/el/data.50b2a9d5484bdbf0.png) Για να κατεβάσετε το σύνολο δεδομένων, χρησιμοποιήστε αυτό το απόσπασμα κώδικα: diff --git a/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 0bfba605..1287e249 100644 --- a/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/el/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Για να οπτικοποιήσουμε την ιδανική γάτα, θα ξεκινήσουμε με μια τυχαία εικόνα θορύβου και θα προσπαθήσουμε να χρησιμοποιήσουμε την τεχνική βελτιστοποίησης με καθοδική κλίση για να προσαρμόσουμε την εικόνα ώστε ένα δίκτυο να αναγνωρίσει μια γάτα.\n", "\n", - "![Βρόχος Βελτιστοποίησης](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.el.png)\n", + "![Βρόχος Βελτιστοποίησης](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Εδώ είναι η αρχική μας εικόνα:\n" ] diff --git a/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md index 9d8f2392..033981d4 100644 --- a/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/el/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ακολουθούν δείγματα χαρακτηριστικών που εξήχθησαν από μια εικόνα γάτας από το δίκτυο VGG-16: -![Χαρακτηριστικά που εξήχθησαν από το VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.el.png) +![Χαρακτηριστικά που εξήχθησαν από το VGG-16](../../../../../translated_images/el/features.6291f9c7ba3a0b95.png) ## Σύνολο Δεδομένων Γάτες vs. Σκύλοι @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Μια προσέγγιση που μπορούμε να ακολουθήσουμε είναι να ξεκινήσουμε με μια τυχαία εικόνα και στη συνέχεια να χρησιμοποιήσουμε την τεχνική **βελτιστοποίησης καθοδικής κλίσης** για να προσαρμόσουμε αυτή την εικόνα με τέτοιο τρόπο ώστε το δίκτυο να αρχίσει να πιστεύει ότι είναι γάτα. -![Βρόχος Βελτιστοποίησης Εικόνας](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.el.png) +![Βρόχος Βελτιστοποίησης Εικόνας](../../../../../translated_images/el/ideal-cat-loop.999fbb8ff306e044.png) Ωστόσο, αν το κάνουμε αυτό, θα λάβουμε κάτι που μοιάζει πολύ με τυχαίο θόρυβο. Αυτό συμβαίνει επειδή *υπάρχουν πολλοί τρόποι να κάνουμε το δίκτυο να πιστέψει ότι η είσοδος είναι γάτα*, συμπεριλαμβανομένων κάποιων που δεν έχουν οπτική λογική. Ενώ αυτές οι εικόνες περιέχουν πολλά μοτίβα τυπικά για μια γάτα, δεν υπάρχει τίποτα που να τις περιορίζει να είναι οπτικά διακριτές. Για να βελτιώσουμε το αποτέλεσμα, μπορούμε να προσθέσουμε έναν άλλο όρο στη συνάρτηση απώλειας, που ονομάζεται **απώλεια παραλλαγής**. Είναι μια μετρική που δείχνει πόσο παρόμοια είναι τα γειτονικά εικονοστοιχεία της εικόνας. Ελαχιστοποιώντας την απώλεια παραλλαγής, η εικόνα γίνεται πιο ομαλή και απαλλάσσεται από τον θόρυβο - αποκαλύπτοντας έτσι πιο οπτικά ελκυστικά μοτίβα. Εδώ είναι ένα παράδειγμα τέτοιων "ιδανικών" εικόνων, που ταξινομούνται ως γάτα και ως ζέβρα με υψηλή πιθανότητα: -![Ιδανική Γάτα](../../../../../translated_images/ideal-cat.203dd4597643d6b0.el.png) | ![Ιδανική Ζέβρα](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.el.png) +![Ιδανική Γάτα](../../../../../translated_images/el/ideal-cat.203dd4597643d6b0.png) | ![Ιδανική Ζέβρα](../../../../../translated_images/el/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ιδανική Γάτα* | *Ιδανική Ζέβρα* Παρόμοια προσέγγιση μπορεί να χρησιμοποιηθεί για την εκτέλεση των λεγόμενων **αντιφατικών επιθέσεων** σε ένα νευρωνικό δίκτυο. Ας υποθέσουμε ότι θέλουμε να ξεγελάσουμε ένα νευρωνικό δίκτυο και να κάνουμε έναν σκύλο να μοιάζει με γάτα. Αν πάρουμε την εικόνα ενός σκύλου, που αναγνωρίζεται από το δίκτυο ως σκύλος, μπορούμε στη συνέχεια να την τροποποιήσουμε λίγο χρησιμοποιώντας τη βελτιστοποίηση καθοδικής κλίσης, μέχρι το δίκτυο να αρχίσει να την ταξινομεί ως γάτα: -![Εικόνα Σκύλου](../../../../../translated_images/original-dog.8f68a67d2fe0911f.el.png) | ![Εικόνα σκύλου που ταξινομείται ως γάτα](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.el.png) +![Εικόνα Σκύλου](../../../../../translated_images/el/original-dog.8f68a67d2fe0911f.png) | ![Εικόνα σκύλου που ταξινομείται ως γάτα](../../../../../translated_images/el/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Αρχική εικόνα σκύλου* | *Εικόνα σκύλου που ταξινομείται ως γάτα* diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 0ddcaa03..97a6545e 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Επειδή εκπαιδεύουμε το autoencoder να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου ώστε να αποτυπώσει το νόημα.\n", "\n", - "![Διάγραμμα AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.el.jpg)\n", + "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Εικόνα από [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 5d6a9114..bafb86ab 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Επειδή εκπαιδεύουμε το autoencoder να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου ώστε να συλλάβει το νόημα.\n", "\n", - "![Διάγραμμα AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.el.jpg)\n", + "![Διάγραμμα AutoEncoder](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Εικόνα από [το blog του Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md index f630a4c6..42ca0a39 100644 --- a/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/el/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Καθώς εκπαιδεύουμε έναν αυτόματο κωδικοποιητή για να συλλάβει όσο το δυνατόν περισσότερες πληροφορίες από την αρχική εικόνα για ακριβή ανακατασκευή, το δίκτυο προσπαθεί να βρει την καλύτερη **ενσωμάτωση** των εικόνων εισόδου για να αποτυπώσει το νόημα. -![Διάγραμμα Αυτόματου Κωδικοποιητή](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.el.jpg) +![Διάγραμμα Αυτόματου Κωδικοποιητή](../../../../../translated_images/el/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Εικόνα από το [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md index c5dc2b13..30661734 100644 --- a/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/el/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Προ-μάθημα κουίζ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Ανίχνευση Αντικειμένων](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.el.png) +![Ανίχνευση Αντικειμένων](../../../../../translated_images/el/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Εικόνα από [ιστοσελίδα YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Εκτελέστε ταξινόμηση εικόνας σε κάθε πλακίδιο. 3. Τα πλακίδια που δίνουν αρκετά υψηλή ενεργοποίηση μπορούν να θεωρηθούν ότι περιέχουν το αντικείμενο που αναζητούμε. -![Αφελής Ανίχνευση Αντικειμένων](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.el.png) +![Αφελής Ανίχνευση Αντικειμένων](../../../../../translated_images/el/naive-detection.e7f1ba220ccd08c6.png) > *Εικόνα από [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 κατηγορίες * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 κατηγορίες, πλαίσια και μάσκες τμηματοποίησης -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.el.jpg) +![COCO](../../../../../translated_images/el/coco-examples.71bc60380fa6cceb.jpg) ## Μετρικές Ανίχνευσης Αντικειμένων @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Ενώ για την ταξινόμηση εικόνων είναι εύκολο να μετρήσουμε πόσο καλά αποδίδει ο αλγόριθμος, για την ανίχνευση αντικειμένων πρέπει να μετρήσουμε τόσο την ορθότητα της κατηγορίας όσο και την ακρίβεια της προβλεπόμενης θέσης του πλαισίου. Για το τελευταίο, χρησιμοποιούμε τη λεγόμενη **Intersection over Union** (IoU), η οποία μετρά πόσο καλά δύο πλαίσια (ή δύο αυθαίρετες περιοχές) επικαλύπτονται. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.el.png) +![IoU](../../../../../translated_images/el/iou_equation.9a4751d40fff4e11.png) > *Εικόνα 2 από [αυτό το εξαιρετικό άρθρο για το IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ Το [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) χρησιμοποιεί [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) για να δημιουργήσει ιεραρχική δομή περιοχών ROI, οι οποίες στη συνέχεια περνούν από εξαγωγείς χαρακτηριστικών CNN και ταξινομητές SVM για να προσδιοριστεί η κατηγορία του αντικειμένου, και γραμμική παλινδρόμηση για να προσδιοριστούν οι συντεταγμένες του *bounding box*. [Επίσημο Άρθρο](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.el.png) +![RCNN](../../../../../translated_images/el/rcnn1.cae407020dfb1d1f.png) > *Εικόνα από van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.el.png) +![RCNN-1](../../../../../translated_images/el/rcnn2.2d9530bb83516484.png) > *Εικόνες από [αυτό το άρθρο](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Αυτή η προσέγγιση είναι παρόμοια με το R-CNN, αλλά οι περιοχές ορίζονται αφού έχουν εφαρμοστεί τα επίπεδα συνελικτικής. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.el.png) +![FRCNN](../../../../../translated_images/el/f-rcnn.3cda6d9bb4188875.png) > Εικόνα από [το Επίσημο Άρθρο](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Η βασική ιδέα αυτής της προσέγγισης είναι να χρησιμοποιηθεί ένα νευρωνικό δίκτυο για να προβλέψει τα ROI - το λεγόμενο *Region Proposal Network*. [Άρθρο](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.el.png) +![FasterRCNN](../../../../../translated_images/el/faster-rcnn.8d46c099b87ef30a.png) > Εικόνα από [το επίσημο άρθρο](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 1. Τα χαρακτηριστικά επεξεργάζονται από **Position-Sensitive Score Map**. Κάθε αντικείμενο από $C$ κατηγορίες διαιρείται σε $k\times k$ περιοχές, και εκπαιδεύουμε το δίκτυο να προβλέπει μέρη αντικειμένων. 1. Για κάθε μέρος από τις $k\times k$ περιοχές, όλα τα δίκτυα ψηφίζουν για τις κατηγορίες αντικειμένων, και η κατηγορία αντικειμένου με τη μέγιστη ψήφο επιλέγεται. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.el.png) +![r-fcn image](../../../../../translated_images/el/r-fcn.13eb88158b99a3da.png) > Εικόνα από [επίσημο άρθρο](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ $$ * Η εικόνα διαιρείται σε $S\times S$ περιοχές. * Για κάθε περιοχή, **CNN** προβλέπει $n$ πιθανά αντικείμενα, τις συντεταγμένες του *bounding box* και την *confidence*=*πιθανότητα* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.el.png) + ![YOLO](../../../../../translated_images/el/yolo.a2648ec82ee8bb4e.png) > Εικόνα από [επίσημο άρθρο](https://arxiv.org/abs/1506.02640) diff --git a/translations/el/lessons/4-ComputerVision/README.md b/translations/el/lessons/4-ComputerVision/README.md index e6c46cff..5c344a6b 100644 --- a/translations/el/lessons/4-ComputerVision/README.md +++ b/translations/el/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Υπολογιστική Όραση -![Περίληψη του περιεχομένου της Υπολογιστικής Όρασης σε ένα σκίτσο](../../../../translated_images/ai-computervision.6506ebebac3fbf76.el.png) +![Περίληψη του περιεχομένου της Υπολογιστικής Όρασης σε ένα σκίτσο](../../../../translated_images/el/ai-computervision.6506ebebac3fbf76.png) Σε αυτήν την ενότητα θα μάθουμε για: diff --git a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index d0e6b40b..cfc70f64 100644 --- a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Η **αναπαράσταση Bag of Words** (BoW) είναι η πιο συχνά χρησιμοποιούμενη παραδοσιακή μέθοδος αναπαράστασης με διανύσματα. Κάθε λέξη συνδέεται με έναν δείκτη διανύσματος, και το στοιχείο του διανύσματος περιέχει τον αριθμό εμφανίσεων μιας λέξης σε ένα συγκεκριμένο έγγραφο.\n", "\n", - "![Εικόνα που δείχνει πώς η αναπαράσταση διανύσματος Bag of Words αποθηκεύεται στη μνήμη.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.el.png) \n", + "![Εικόνα που δείχνει πώς η αναπαράσταση διανύσματος Bag of Words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Μπορείτε επίσης να σκεφτείτε το BoW ως το άθροισμα όλων των διανυσμάτων one-hot-encoded για τις μεμονωμένες λέξεις του κειμένου.\n", "\n", diff --git a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 5f3a6014..818d83f3 100644 --- a/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/el/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Η **αναπαράσταση Bag-of-words** (BoW) είναι η πιο απλή και κατανοητή παραδοσιακή μέθοδος αναπαράστασης με διανύσματα. Κάθε λέξη συνδέεται με έναν δείκτη διανύσματος, και κάθε στοιχείο του διανύσματος περιέχει τον αριθμό εμφανίσεων κάθε λέξης σε ένα συγκεκριμένο έγγραφο.\n", "\n", - "![Εικόνα που δείχνει πώς η αναπαράσταση Bag-of-words αποθηκεύεται στη μνήμη.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.el.png) \n", + "![Εικόνα που δείχνει πώς η αναπαράσταση Bag-of-words αποθηκεύεται στη μνήμη.](../../../../../translated_images/el/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Μπορείτε επίσης να σκεφτείτε το BoW ως το άθροισμα όλων των διανυσμάτων one-hot-encoding για τις μεμονωμένες λέξεις του κειμένου.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 92f3c7bd..7bb8c655 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Χρησιμοποιώντας τη στρώση ενσωμάτωσης ως την πρώτη στρώση στο δίκτυό μας, μπορούμε να μεταβούμε από το μοντέλο bag-of-words στο μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στην αντίστοιχη ενσωμάτωσή της και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλες αυτές τις ενσωματώσεις, όπως `sum`, `average` ή `max`.\n", "\n", - "![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.el.png)\n", + "![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Το νευρωνικό δίκτυο ταξινομητή μας θα ξεκινά με τη στρώση ενσωμάτωσης, στη συνέχεια τη στρώση συσσωμάτωσης και έναν γραμμικό ταξινομητή στην κορυφή:\n" ] @@ -176,7 +176,7 @@ "\n", "Στην προηγούμενη αρχιτεκτονική, έπρεπε να συμπληρώσουμε όλες τις ακολουθίες ώστε να έχουν το ίδιο μήκος για να τις εντάξουμε σε ένα minibatch. Αυτός δεν είναι ο πιο αποδοτικός τρόπος για να αναπαραστήσουμε ακολουθίες μεταβλητού μήκους - μια άλλη προσέγγιση θα ήταν να χρησιμοποιήσουμε έναν **διάνυσμα μετατοπίσεων (offset)**, το οποίο θα περιείχε τις μετατοπίσεις όλων των ακολουθιών που αποθηκεύονται σε ένα μεγάλο διάνυσμα.\n", "\n", - "![Εικόνα που δείχνει την αναπαράσταση ακολουθιών με μετατοπίσεις](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.el.png)\n", + "![Εικόνα που δείχνει την αναπαράσταση ακολουθιών με μετατοπίσεις](../../../../../translated_images/el/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Σημείωση**: Στην παραπάνω εικόνα, δείχνουμε μια ακολουθία χαρακτήρων, αλλά στο παράδειγμά μας δουλεύουμε με ακολουθίες λέξεων. Ωστόσο, η γενική αρχή της αναπαράστασης ακολουθιών με διάνυσμα μετατοπίσεων παραμένει η ίδια.\n", "\n", @@ -311,7 +311,7 @@ "\n", "Το CBoW είναι ταχύτερο, ενώ το skip-gram είναι πιο αργό, αλλά αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων.\n", "\n", - "![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.el.png)\n", + "![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Για να πειραματιστούμε με την ενσωμάτωση word2vec που έχει προεκπαιδευτεί στο σύνολο δεδομένων Google News, μπορούμε να χρησιμοποιήσουμε τη βιβλιοθήκη **gensim**. Παρακάτω βρίσκουμε τις λέξεις που είναι πιο παρόμοιες με τη λέξη 'neural'.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a1e48a9d..02b58efd 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/el/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Χρησιμοποιώντας ένα embedding layer ως το πρώτο layer στο δίκτυό μας, μπορούμε να μεταβούμε από το μοντέλο bag-of-words σε ένα μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στο αντίστοιχο embedding και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλα αυτά τα embeddings, όπως `sum`, `average` ή `max`.\n", "\n", - "![Εικόνα που δείχνει έναν ταξινομητή embedding για πέντε λέξεις ακολουθίας.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.el.png)\n", + "![Εικόνα που δείχνει έναν ταξινομητή embedding για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Το νευρωνικό δίκτυο ταξινομητή μας αποτελείται από τα εξής layers:\n", "\n", @@ -283,7 +283,7 @@ "\n", "Το CBoW είναι πιο γρήγορο, ενώ το skip-gram, αν και πιο αργό, αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων.\n", "\n", - "![Εικόνα που δείχνει τους αλγόριθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.el.png)\n", + "![Εικόνα που δείχνει τους αλγόριθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Για να πειραματιστούμε με την ενσωμάτωση Word2Vec που έχει προεκπαιδευτεί στο σύνολο δεδομένων Google News, μπορούμε να χρησιμοποιήσουμε τη βιβλιοθήκη **gensim**. Παρακάτω βρίσκουμε τις λέξεις που είναι πιο παρόμοιες με τη λέξη 'neural'.\n", "\n", diff --git a/translations/el/lessons/5-NLP/14-Embeddings/README.md b/translations/el/lessons/5-NLP/14-Embeddings/README.md index 26c258e9..d8d1fc6a 100644 --- a/translations/el/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/el/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Χρησιμοποιώντας ένα επίπεδο ενσωμάτωσης ως το πρώτο επίπεδο στο δίκτυο ταξινόμησής μας, μπορούμε να μεταβούμε από ένα μοντέλο bag-of-words σε ένα μοντέλο **embedding bag**, όπου πρώτα μετατρέπουμε κάθε λέξη στο κείμενό μας στην αντίστοιχη ενσωμάτωσή της και στη συνέχεια υπολογίζουμε κάποια συνάρτηση συσσωμάτωσης πάνω σε όλες αυτές τις ενσωματώσεις, όπως `sum`, `average` ή `max`. -![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.el.png) +![Εικόνα που δείχνει έναν ταξινομητή ενσωμάτωσης για πέντε λέξεις ακολουθίας.](../../../../../translated_images/el/embedding-classifier-example.b77f021a7ee67eee.png) > Εικόνα από τον συγγραφέα @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: Το CBoW είναι ταχύτερο, ενώ το skip-gram είναι πιο αργό, αλλά αποδίδει καλύτερα στην αναπαράσταση σπάνιων λέξεων. -![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.el.png) +![Εικόνα που δείχνει τους αλγορίθμους CBoW και Skip-Gram για τη μετατροπή λέξεων σε διανύσματα.](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Εικόνα από [αυτό το άρθρο](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/el/lessons/5-NLP/15-LanguageModeling/README.md b/translations/el/lessons/5-NLP/15-LanguageModeling/README.md index 5b8c1f55..46b00a5b 100644 --- a/translations/el/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/el/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW), όπου προβλέπουμε το μεσαίο σύμβολο $W_0$ σε μια ακολουθία συμβόλων $W_{-N}$, ..., $W_N$. * **Skip-gram**, όπου προβλέπουμε ένα σύνολο γειτονικών συμβόλων {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} από το μεσαίο σύμβολο $W_0$. -![εικόνα από άρθρο για τη μετατροπή λέξεων σε διανύσματα](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.el.png) +![εικόνα από άρθρο για τη μετατροπή λέξεων σε διανύσματα](../../../../../translated_images/el/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Εικόνα από [αυτό το άρθρο](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/el/lessons/5-NLP/16-RNN/README.md b/translations/el/lessons/5-NLP/16-RNN/README.md index 8fad2539..18246f99 100644 --- a/translations/el/lessons/5-NLP/16-RNN/README.md +++ b/translations/el/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Για να κατανοήσουμε τη σημασία μιας ακολουθίας κειμένου, πρέπει να χρησιμοποιήσουμε μια άλλη αρχιτεκτονική νευρωνικού δικτύου, που ονομάζεται **επαναλαμβανόμενο νευρωνικό δίκτυο** ή RNN. Στο RNN, περνάμε την πρότασή μας μέσα από το δίκτυο ένα σύμβολο τη φορά, και το δίκτυο παράγει μια **κατάσταση**, την οποία στη συνέχεια περνάμε ξανά στο δίκτυο μαζί με το επόμενο σύμβολο. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.el.png) +![RNN](../../../../../translated_images/el/rnn.27f5c29c53d727b5.png) > Εικόνα από τον συγγραφέα @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: Ένα επαναλαμβανόμενο δίκτυο, είτε μονοκατευθυντικό είτε διπλής κατεύθυνσης, καταγράφει ορισμένα μοτίβα μέσα σε μια ακολουθία και μπορεί να τα αποθηκεύσει σε ένα διάνυσμα κατάστασης ή να τα περάσει στην έξοδο. Όπως και με τα συνελικτικά δίκτυα, μπορούμε να κατασκευάσουμε ένα άλλο επαναλαμβανόμενο επίπεδο πάνω από το πρώτο για να καταγράψουμε μοτίβα υψηλότερου επιπέδου και να χτίσουμε από τα μοτίβα χαμηλού επιπέδου που εξάγονται από το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια ενός **πολυεπίπεδου RNN**, που αποτελείται από δύο ή περισσότερα επαναλαμβανόμενα δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνά στο επόμενο επίπεδο ως είσοδος. -![Εικόνα που δείχνει ένα Πολυεπίπεδο LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.el.jpg) +![Εικόνα που δείχνει ένα Πολυεπίπεδο LSTM RNN](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Εικόνα από [αυτή την υπέροχη ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López* diff --git a/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 2c3debac..fbf5304f 100644 --- a/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Το επαναλαμβανόμενο δίκτυο, είτε μονής κατεύθυνσης είτε διπλής κατεύθυνσης, καταγράφει ορισμένα μοτίβα μέσα σε μια ακολουθία και μπορεί να τα αποθηκεύσει σε ένα διάνυσμα κατάστασης ή να τα περάσει στην έξοδο. Όπως συμβαίνει με τα συνελικτικά δίκτυα, μπορούμε να χτίσουμε ένα άλλο επαναλαμβανόμενο επίπεδο πάνω από το πρώτο για να καταγράψουμε μοτίβα υψηλότερου επιπέδου, χτισμένα από μοτίβα χαμηλότερου επιπέδου που εξάγονται από το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια του **πολυεπίπεδου RNN**, το οποίο αποτελείται από δύο ή περισσότερα επαναλαμβανόμενα δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνά στο επόμενο επίπεδο ως είσοδος.\n", "\n", - "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με μακροχρόνια-βραχυχρόνια μνήμη](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.el.jpg)\n", + "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με μακροχρόνια-βραχυχρόνια μνήμη](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Εικόνα από [αυτή την υπέροχη ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López*\n", "\n", diff --git a/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb index 9060de02..2ee54f57 100644 --- a/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/el/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Για να αποτυπώσουμε τη σημασία μιας ακολουθίας κειμένου, θα χρησιμοποιήσουμε μια αρχιτεκτονική νευρωνικού δικτύου που ονομάζεται **επαναλαμβανόμενο νευρωνικό δίκτυο** (recurrent neural network ή RNN). Όταν χρησιμοποιούμε ένα RNN, περνάμε την πρότασή μας μέσα από το δίκτυο μία λέξη τη φορά, και το δίκτυο παράγει μια **κατάσταση**, την οποία στη συνέχεια περνάμε ξανά στο δίκτυο μαζί με την επόμενη λέξη.\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας επαναλαμβανόμενου νευρωνικού δικτύου.](../../../../../translated_images/rnn.27f5c29c53d727b5.el.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας επαναλαμβανόμενου νευρωνικού δικτύου.](../../../../../translated_images/el/rnn.27f5c29c53d727b5.png)\n", "\n", "Δεδομένης της εισόδου μιας ακολουθίας λέξεων $X_0,\\dots,X_n$, το RNN δημιουργεί μια ακολουθία μπλοκ νευρωνικού δικτύου και εκπαιδεύει αυτή την ακολουθία από άκρη σε άκρη χρησιμοποιώντας οπισθοδιάδοση. Κάθε μπλοκ δικτύου λαμβάνει ένα ζεύγος $(X_i,S_i)$ ως είσοδο και παράγει το $S_{i+1}$ ως αποτέλεσμα. Η τελική κατάσταση $S_n$ ή η έξοδος $Y_n$ περνάει σε έναν γραμμικό ταξινομητή για να παραχθεί το αποτέλεσμα. Όλα τα μπλοκ του δικτύου μοιράζονται τα ίδια βάρη και εκπαιδεύονται από άκρη σε άκρη με μία διαδικασία οπισθοδιάδοσης.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Τα αναδρομικά δίκτυα, είτε μονής είτε διπλής κατεύθυνσης, εντοπίζουν μοτίβα μέσα σε μια ακολουθία και τα αποθηκεύουν σε διανύσματα κατάστασης ή τα επιστρέφουν ως έξοδο. Όπως και με τα συνελικτικά δίκτυα, μπορούμε να κατασκευάσουμε ένα ακόμη αναδρομικό επίπεδο μετά το πρώτο, για να εντοπίσουμε μοτίβα υψηλότερου επιπέδου, τα οποία προκύπτουν από μοτίβα χαμηλότερου επιπέδου που εξήγαγε το πρώτο επίπεδο. Αυτό μας οδηγεί στην έννοια του **πολυεπίπεδου RNN**, το οποίο αποτελείται από δύο ή περισσότερα αναδρομικά δίκτυα, όπου η έξοδος του προηγούμενου επιπέδου περνάει ως είσοδος στο επόμενο επίπεδο.\n", "\n", - "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.el.jpg)\n", + "![Εικόνα που δείχνει ένα πολυεπίπεδο RNN με LSTM](../../../../../translated_images/el/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Εικόνα από [αυτή την εξαιρετική ανάρτηση](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) του Fernando López.*\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 56497ad3..f117b6f1 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Ο τρόπος με τον οποίο θα εκπαιδεύσουμε το RNN για να δημιουργεί κείμενο είναι ο εξής. Σε κάθε βήμα, θα παίρνουμε μια ακολουθία χαρακτήρων μήκους `nchars` και θα ζητάμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου για κάθε χαρακτήρα εισόδου:\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.el.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Ανάλογα με το συγκεκριμένο σενάριο, μπορεί να θέλουμε να συμπεριλάβουμε και ειδικούς χαρακτήρες, όπως το *τέλος ακολουθίας* ``. Στην περίπτωσή μας, θέλουμε απλώς να εκπαιδεύσουμε το δίκτυο για ατελείωτη δημιουργία κειμένου, επομένως θα ορίσουμε το μέγεθος κάθε ακολουθίας να είναι ίσο με `nchars` tokens. Συνεπώς, κάθε παράδειγμα εκπαίδευσης θα αποτελείται από `nchars` εισόδους και `nchars` εξόδους (που είναι η ακολουθία εισόδου μετατοπισμένη κατά ένα σύμβολο προς τα αριστερά). Το minibatch θα αποτελείται από αρκετές τέτοιες ακολουθίες.\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index d7eb7ef9..84858860 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Ο τρόπος με τον οποίο θα εκπαιδεύσουμε το RNN για να δημιουργεί τίτλους ειδήσεων είναι ο εξής. Σε κάθε βήμα, θα παίρνουμε έναν τίτλο, ο οποίος θα εισάγεται σε ένα RNN, και για κάθε χαρακτήρα εισόδου θα ζητάμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου:\n", "\n", - "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.el.png)\n", + "![Εικόνα που δείχνει ένα παράδειγμα δημιουργίας της λέξης 'HELLO' από RNN.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Για τον τελευταίο χαρακτήρα της ακολουθίας μας, θα ζητάμε από το δίκτυο να παράγει το token ``.\n", "\n", diff --git a/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md index 195f2beb..17486709 100644 --- a/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/el/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Αυτό επιτρέπει διαφορετικές νευρωνικές αρχιτεκτονικές, όπως φαίνεται στην παρακάτω εικόνα: -![Εικόνα που δείχνει κοινά μοτίβα επαναληπτικών νευρωνικών δικτύων.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.el.jpg) +![Εικόνα που δείχνει κοινά μοτίβα επαναληπτικών νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Εικόνα από το blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) του [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Θα εκπαιδεύσουμε αυτό το RNN να δημιουργεί κείμενο βήμα προς βήμα. Σε κάθε βήμα, θα πάρουμε μια ακολουθία χαρακτήρων μήκους `nchars` και θα ζητήσουμε από το δίκτυο να παράγει τον επόμενο χαρακτήρα εξόδου για κάθε χαρακτήρα εισόδου: -![Εικόνα που δείχνει ένα παράδειγμα RNN που δημιουργεί τη λέξη 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.el.png) +![Εικόνα που δείχνει ένα παράδειγμα RNN που δημιουργεί τη λέξη 'HELLO'.](../../../../../translated_images/el/rnn-generate.56c54afb52f9781d.png) Κατά τη δημιουργία κειμένου (κατά την πρόβλεψη), ξεκινάμε με κάποιο **προτροπή**, η οποία περνά μέσα από τα RNN cells για να δημιουργήσει την ενδιάμεση κατάσταση, και στη συνέχεια από αυτή την κατάσταση ξεκινά η δημιουργία. Παράγουμε έναν χαρακτήρα τη φορά και περνάμε την κατάσταση και τον παραγόμενο χαρακτήρα σε άλλο RNN cell για να δημιουργήσουμε τον επόμενο, μέχρι να δημιουργήσουμε αρκετούς χαρακτήρες. diff --git a/translations/el/lessons/5-NLP/18-Transformers/README.md b/translations/el/lessons/5-NLP/18-Transformers/README.md index e61451ec..1a4220d9 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/README.md +++ b/translations/el/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: Οι **Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισαγωγικού διανύσματος στο κάθε αποτέλεσμα πρόβλεψης του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του εισαγωγικού RNN και του εξαγωγικού RNN. Με αυτόν τον τρόπο, κατά τη δημιουργία του εξαγωγικού συμβόλου yt, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου hi, με διαφορετικούς συντελεστές βάρους αt,i. -![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.el.png) +![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png) > Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτό το blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ο πίνακας προσοχής {αi,j} αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου συμβάλλουν στη δημιουργία μιας δεδομένης λέξης στην εξαγωγική ακολουθία. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα: -![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.el.png) +![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png) > Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Στη συνέχεια, πρέπει να ανιχνεύσουμε κάποια μοτίβα μέσα στην ακολουθία μας. Για να το κάνουμε αυτό, οι transformers χρησιμοποιούν έναν μηχανισμό **αυτοπροσοχής**, που είναι ουσιαστικά προσοχή εφαρμοσμένη στην ίδια ακολουθία ως είσοδος και έξοδος. Η εφαρμογή αυτοπροσοχής μας επιτρέπει να λαμβάνουμε υπόψη το **πλαίσιο** μέσα στην πρόταση και να βλέπουμε ποιες λέξεις σχετίζονται μεταξύ τους. Για παράδειγμα, μας επιτρέπει να δούμε ποιες λέξεις αναφέρονται από συνυποδηλώσεις, όπως *αυτό*, και να λαμβάνουμε υπόψη το πλαίσιο: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.el.png) +![](../../../../../translated_images/el/CoreferenceResolution.861924d6d384a7d6.png) > Εικόνα από το [Blog της Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: Το **BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο transformer με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε ένα μεγάλο σώμα δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο απορροφά σημαντικά επίπεδα κατανόησης της γλώσσας, τα οποία μπορούν στη συνέχεια να αξιοποιηθούν με άλλα σύνολα δεδομένων μέσω της προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**. -![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.el.png) +![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Εικόνα [πηγή](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 17a975c1..616786e7 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/el/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Οι Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισόδου στην πρόβλεψη κάθε εξόδου του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του εισόδου RNN και του εξόδου RNN. Με αυτόν τον τρόπο, κατά τη δημιουργία του συμβόλου εξόδου $y_t$, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου $h_i$, με διαφορετικούς συντελεστές βάρους $\\alpha_{t,i}$.\n", "\n", - "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο στρώμα προσοχής](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.el.png)\n", + "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο στρώμα προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png)\n", "*Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτή την ανάρτηση](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ο πίνακας προσοχής $\\{\\alpha_{i,j}\\}$ αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου επηρεάζουν τη δημιουργία μιας δεδομένης λέξης στην ακολουθία εξόδου. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα:\n", "\n", - "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από το Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.el.png)\n", + "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης που βρέθηκε από το RNNsearch-50, από το Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο μετασχηματιστών με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε μεγάλο σώμα δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο απορροφά σημαντικό επίπεδο κατανόησης της γλώσσας, το οποίο μπορεί στη συνέχεια να αξιοποιηθεί με άλλα σύνολα δεδομένων μέσω λεπτομερούς προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**.\n", "\n", - "![Εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.el.png)\n", + "![Εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Υπάρχουν πολλές παραλλαγές αρχιτεκτονικών μετασχηματιστών, όπως BERT, DistilBERT, BigBird, OpenGPT3 και άλλες, που μπορούν να προσαρμοστούν. Το [πακέτο HuggingFace](https://github.com/huggingface/) παρέχει αποθετήριο για την εκπαίδευση πολλών από αυτές τις αρχιτεκτονικές με PyTorch.\n", "\n", diff --git a/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index df687253..658532a1 100644 --- a/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/el/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Οι Μηχανισμοί Προσοχής** παρέχουν έναν τρόπο να δίνεται βάρος στην επίδραση κάθε εισόδου στο κάθε αποτέλεσμα του RNN. Αυτό υλοποιείται δημιουργώντας συντομεύσεις μεταξύ των ενδιάμεσων καταστάσεων του RNN εισόδου και του RNN εξόδου. Με αυτόν τον τρόπο, κατά τη δημιουργία του συμβόλου εξόδου $y_t$, λαμβάνουμε υπόψη όλες τις κρυφές καταστάσεις εισόδου $h_i$, με διαφορετικούς συντελεστές βάρους $\\alpha_{t,i}$.\n", "\n", - "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.el.png)\n", + "![Εικόνα που δείχνει ένα μοντέλο encoder/decoder με ένα πρόσθετο επίπεδο προσοχής](../../../../../translated_images/el/encoder-decoder-attention.7a726296894fb567.png)\n", "*Το μοντέλο encoder-decoder με μηχανισμό πρόσθετης προσοχής στο [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), από [αυτή την ανάρτηση](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ο πίνακας προσοχής $\\{\\alpha_{i,j}\\}$ αντιπροσωπεύει τον βαθμό στον οποίο συγκεκριμένες λέξεις εισόδου συμβάλλουν στη δημιουργία μιας δεδομένης λέξης στην ακολουθία εξόδου. Παρακάτω είναι ένα παράδειγμα ενός τέτοιου πίνακα:\n", "\n", - "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.el.png)\n", + "![Εικόνα που δείχνει ένα δείγμα ευθυγράμμισης από το RNNsearch-50, από Bahdanau - arviz.org](../../../../../translated_images/el/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Εικόνα από [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) είναι ένα πολύ μεγάλο πολυεπίπεδο δίκτυο transformer με 12 επίπεδα για το *BERT-base* και 24 για το *BERT-large*. Το μοντέλο εκπαιδεύεται αρχικά σε ένα μεγάλο σύνολο δεδομένων κειμένου (WikiPedia + βιβλία) χρησιμοποιώντας μη επιβλεπόμενη εκπαίδευση (πρόβλεψη λέξεων που έχουν καλυφθεί σε μια πρόταση). Κατά τη διάρκεια της αρχικής εκπαίδευσης, το μοντέλο αποκτά σημαντικό επίπεδο κατανόησης της γλώσσας, το οποίο μπορεί στη συνέχεια να αξιοποιηθεί με άλλα σύνολα δεδομένων μέσω της διαδικασίας της λεπτομερούς προσαρμογής. Αυτή η διαδικασία ονομάζεται **μεταφορά μάθησης**.\n", "\n", - "![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.el.png)\n", + "![εικόνα από http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/el/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Υπάρχουν πολλές παραλλαγές αρχιτεκτονικών Transformer, όπως BERT, DistilBERT, BigBird, OpenGPT3 και άλλες, που μπορούν να προσαρμοστούν περαιτέρω.\n", "\n", diff --git a/translations/el/lessons/5-NLP/19-NER/README.md b/translations/el/lessons/5-NLP/19-NER/README.md index 12ef99c6..34be8c83 100644 --- a/translations/el/lessons/5-NLP/19-NER/README.md +++ b/translations/el/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Επειδή πρέπει να δημιουργήσουμε μια αντιστοιχία ένα προς ένα μεταξύ συμβόλων και κατηγοριών, μπορούμε να εκπαιδεύσουμε ένα δεξιότερο **πολλά-προς-πολλά** μοντέλο νευρωνικού δικτύου από αυτή την εικόνα: -![Εικόνα που δείχνει κοινά μοτίβα επαναλαμβανόμενων νευρωνικών δικτύων.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.el.jpg) +![Εικόνα που δείχνει κοινά μοτίβα επαναλαμβανόμενων νευρωνικών δικτύων.](../../../../../translated_images/el/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Εικόνα από [αυτό το άρθρο](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) του [Andrej Karpathy](http://karpathy.github.io/). Τα μοντέλα ταξινόμησης συμβόλων NER αντιστοιχούν στην αρχιτεκτονική του δικτύου που βρίσκεται δεξιά στην εικόνα.* diff --git a/translations/el/lessons/5-NLP/README.md b/translations/el/lessons/5-NLP/README.md index a5c9b49b..9166dfa8 100644 --- a/translations/el/lessons/5-NLP/README.md +++ b/translations/el/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Επεξεργασία Φυσικής Γλώσσας -![Περίληψη των εργασιών NLP σε ένα σκίτσο](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.el.png) +![Περίληψη των εργασιών NLP σε ένα σκίτσο](../../../../translated_images/el/ai-nlp.b22dcb8ca4707cea.png) Σε αυτή την ενότητα, θα επικεντρωθούμε στη χρήση Νευρωνικών Δικτύων για την αντιμετώπιση εργασιών που σχετίζονται με την **Επεξεργασία Φυσικής Γλώσσας (NLP)**. Υπάρχουν πολλά προβλήματα NLP που θέλουμε οι υπολογιστές να μπορούν να λύσουν: diff --git a/translations/el/lessons/6-Other/23-MultiagentSystems/README.md b/translations/el/lessons/6-Other/23-MultiagentSystems/README.md index e7ea346a..da00726a 100644 --- a/translations/el/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/el/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Αφού ανοίξετε το μοντέλο, μεταφέρεστε στην κύρια οθόνη του NetLogo. Εδώ είναι ένα δείγμα μοντέλου που περιγράφει τον πληθυσμό λύκων και προβάτων, δεδομένων πεπερασμένων πόρων (γρασίδι). -![Κύρια Οθόνη NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.el.png) +![Κύρια Οθόνη NetLogo](../../../../../translated_images/el/NetLogo-Main.32653711ec1a01b3.png) > Στιγμιότυπο οθόνης από τον Dmitry Soshnikov diff --git a/translations/el/lessons/README.md b/translations/el/lessons/README.md index 25989e0e..bd1b8abb 100644 --- a/translations/el/lessons/README.md +++ b/translations/el/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Επισκόπηση -![Επισκόπηση σε ένα σκίτσο](../../../translated_images/ai-overview.0857791951d19500.el.png) +![Επισκόπηση σε ένα σκίτσο](../../../translated_images/el/ai-overview.0857791951d19500.png) > Σκίτσο από την [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/el/lessons/X-Extras/X1-MultiModal/README.md b/translations/el/lessons/X-Extras/X1-MultiModal/README.md index c157e79f..250a4c70 100644 --- a/translations/el/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/el/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Η βασική ιδέα του CLIP είναι να μπορεί να συγκρίνει κείμενα με εικόνες και να καθορίζει πόσο καλά η εικόνα αντιστοιχεί στο κείμενο. -![Αρχιτεκτονική CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.el.png) +![Αρχιτεκτονική CLIP](../../../../../translated_images/el/clip-arch.b3dbf20b4e8ed8be.png) > *Εικόνα από [αυτήν την ανάρτηση στο blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ας υποθέσουμε ότι πρέπει να ταξινομήσουμε εικόνες μεταξύ, για παράδειγμα, γάτες, σκύλους και ανθρώπους. Σε αυτήν την περίπτωση, μπορούμε να δώσουμε στο μοντέλο μια εικόνα και μια σειρά από κείμενα: "*μια εικόνα μιας γάτας*", "*μια εικόνα ενός σκύλου*", "*μια εικόνα ενός ανθρώπου*". Στον προκύπτοντα διανυσματικό πίνακα με 3 πιθανότητες, απλώς πρέπει να επιλέξουμε τον δείκτη με τη μεγαλύτερη τιμή. -![CLIP για Ταξινόμηση Εικόνων](../../../../../translated_images/clip-class.3af42ef0b2b19369.el.png) +![CLIP για Ταξινόμηση Εικόνων](../../../../../translated_images/el/clip-class.3af42ef0b2b19369.png) > *Εικόνα από [αυτήν την ανάρτηση στο blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CO_OP_TRANSLATOR_METADATA: Μία από τις σημαντικές διαφορές μεταξύ VQGAN και παραδοσιακού GAN είναι ότι το τελευταίο μπορεί να παράγει μια αξιοπρεπή εικόνα από οποιοδήποτε διανυσματικό εισόδου, ενώ το VQGAN είναι πιθανό να παράγει μια εικόνα που δεν είναι συνεκτική. Επομένως, πρέπει να καθοδηγήσουμε περαιτέρω τη διαδικασία δημιουργίας εικόνας, και αυτό μπορεί να γίνει χρησιμοποιώντας το CLIP. -![Αρχιτεκτονική VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.el.png) +![Αρχιτεκτονική VQGAN+CLIP](../../../../../translated_images/el/vqgan.5027fe05051dfa31.png) Για να δημιουργήσουμε μια εικόνα που αντιστοιχεί σε ένα κείμενο, ξεκινάμε με κάποιο τυχαίο διανυσματικό κωδικοποίησης που περνάει μέσω του VQGAN για να παράγει μια εικόνα. Στη συνέχεια, το CLIP χρησιμοποιείται για να παράγει μια συνάρτηση απώλειας που δείχνει πόσο καλά η εικόνα αντιστοιχεί στο κείμενο. Ο στόχος είναι να ελαχιστοποιήσουμε αυτήν την απώλεια, χρησιμοποιώντας back propagation για να προσαρμόσουμε τις παραμέτρους του διανυσματικού εισόδου. Μια εξαιρετική βιβλιοθήκη που υλοποιεί το VQGAN+CLIP είναι το [Pixray](http://github.com/pixray/pixray). -![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.el.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.el.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.el.png) +![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ακουαρέλα πορτραίτο ενός νεαρού άνδρα δασκάλου λογοτεχνίας με ένα βιβλίο* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο μιας νεαρής γυναίκας δασκάλας πληροφορικής με έναν υπολογιστή* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο ενός ηλικιωμένου άνδρα δασκάλου μαθηματικών μπροστά από έναν πίνακα* @@ -75,7 +75,7 @@ CO_OP_TRANSLATOR_METADATA: Η κύρια διαφορά μεταξύ DALL-E 1 και 2 είναι ότι το δεύτερο δημιουργεί πιο ρεαλιστικές εικόνες και τέχνη. Παραδείγματα δημιουργίας εικόνων με το DALL-E: -![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.el.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.el.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.el.png) +![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Εικόνα που δημιουργήθηκε από το Pixray](../../../../../translated_images/el/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ακουαρέλα πορτραίτο ενός νεαρού άνδρα δασκάλου λογοτεχνίας με ένα βιβλίο* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο μιας νεαρής γυναίκας δασκάλας πληροφορικής με έναν υπολογιστή* | Εικόνα που δημιουργήθηκε από το κείμενο *μια κοντινή ελαιογραφία πορτραίτο ενός ηλικιωμένου άνδρα δασκάλου μαθηματικών μπροστά από έναν πίνακα* diff --git a/translations/en/README.md b/translations/en/README.md index c3d6fa8d..20b3102c 100644 --- a/translations/en/README.md +++ b/translations/en/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificial Intelligence for Beginners - A Curriculum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.en.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/en/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.png)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/en/lessons/1-Intro/README.md b/translations/en/lessons/1-Intro/README.md index cb8a1447..941ec363 100644 --- a/translations/en/lessons/1-Intro/README.md +++ b/translations/en/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduction to AI -![Summary of Introduction of AI content in a doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.en.png) +![Summary of Introduction of AI content in a doodle](../../../../translated_images/en/ai-intro.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originally, computers were invented by [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) to process numbers using a well-defined procedure—an algorithm. Modern computers, while far more advanced than Babbage's 19th-century model, still operate on the same principle of controlled computations. This means we can program a computer to perform a task if we know the exact sequence of steps required to achieve the goal. -![Photo of a person](../../../../translated_images/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.en.png) +![Photo of a person](../../../../translated_images/en/dsh_age.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.png) > Photo by [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ For more information, refer to **[Artificial General Intelligence](https://en.wi One challenge in discussing **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** is the lack of a clear definition. Some argue intelligence is linked to **abstract thinking** or **self-awareness**, but it remains difficult to define. -![Photo of a Cat](../../../../translated_images/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.en.jpg) +![Photo of a Cat](../../../../translated_images/en/photo-cat.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.jpg) > [Photo](https://unsplash.com/photos/75715CVEJhI) by [Amber Kipp](https://unsplash.com/@sadmax) from Unsplash @@ -98,13 +98,13 @@ Alternatively, we can model the simplest elements of the human brain—a neuron. > | What about ML? | | > |--------------|-----------| -> | Part of Artificial Intelligence that involves computers learning to solve problems using data is called **Machine Learning**. This course does not cover classical machine learning—we recommend the separate [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.en.png) | +> | Part of Artificial Intelligence that involves computers learning to solve problems using data is called **Machine Learning**. This course does not cover classical machine learning—we recommend the separate [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML for Beginners](../../../../translated_images/en/ml-for-beginners.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.png) | ## A Brief History of AI Artificial Intelligence emerged as a field in the mid-20th century. Initially, symbolic reasoning was the dominant approach, leading to significant achievements like expert systems—programs capable of acting as experts in specific domains. However, this approach proved difficult to scale. Extracting knowledge from experts, representing it in a computer, and maintaining an accurate knowledge base became too complex and costly for many applications, leading to the [AI Winter](https://en.wikipedia.org/wiki/AI_winter) in the 1970s. -Brief History of AI +Brief History of AI > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Similarly, the evolution of "talking programs" (potentially passing the Turing T * Modern assistants like Cortana, Siri, and Google Assistant are hybrid systems, using neural networks to convert speech to text, recognize intent, and employ reasoning or algorithms to perform tasks. * Future systems may rely entirely on neural models to handle dialogue. Recent neural networks like GPT and [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) have shown remarkable progress. -the Turing test's evolution +the Turing test's evolution > Image by Dmitry Soshnikov, [photo](https://unsplash.com/photos/r8LmVbUKgns) by [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Recent AI Research diff --git a/translations/en/lessons/2-Symbolic/Animals.ipynb b/translations/en/lessons/2-Symbolic/Animals.ipynb index e76c6555..c99d3043 100644 --- a/translations/en/lessons/2-Symbolic/Animals.ipynb +++ b/translations/en/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "In this example, we will create a simple knowledge-based system to identify an animal based on certain physical characteristics. The system can be visualized using the following AND-OR tree (this is just a portion of the complete tree, and we can easily add more rules):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.en.png)\n" + "![](../../../../translated_images/en/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.png)\n" ] }, { diff --git a/translations/en/lessons/2-Symbolic/README.md b/translations/en/lessons/2-Symbolic/README.md index 73aaa4c6..c4042689 100644 --- a/translations/en/lessons/2-Symbolic/README.md +++ b/translations/en/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Knowledge Representation and Expert Systems -![Summary of Symbolic AI content](../../../../translated_images/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.en.png) +![Summary of Symbolic AI content](../../../../translated_images/en/ai-symbolic.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Typically, knowledge isn’t strictly defined but is aligned with related concep The challenge of **knowledge representation** is finding an effective way to encode knowledge in a computer as data, making it usable automatically. This can be visualized as a spectrum: -![Knowledge representation spectrum](../../../../translated_images/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.en.png) +![Knowledge representation spectrum](../../../../translated_images/en/knowledge-spectrum.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | One of the early achievements of symbolic AI was the development of **expert systems**—computer systems designed to act as experts in specific problem domains. These systems relied on a **knowledge base** extracted from human experts and an **inference engine** to perform reasoning. -![Human Architecture](../../../../translated_images/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.en.png) | ![Knowledge-Based System](../../../../translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.en.png) +![Human Architecture](../../../../translated_images/en/arch-human.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.png) | ![Knowledge-Based System](../../../../translated_images/en/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.png) ---------------------------------------------|------------------------------------------------ Simplified structure of a human neural system | Architecture of a knowledge-based system @@ -106,7 +106,7 @@ Expert systems are modeled after human reasoning, which involves **short-term me For example, consider an expert system designed to identify animals based on physical characteristics: -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.en.png) +![AND-OR Tree](../../../../translated_images/en/AND-OR-Tree.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/en/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/en/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index d64cdbd6..d12bc36e 100644 --- a/translations/en/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/en/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "If we have more than 2 classes, softmax will normalize the probabilities across all of them. Below is a diagram of the network architecture used for MNIST digit classification:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.en.png)\n" + "![MNIST Classifier](../../../../../translated_images/en/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1250,7 +1250,7 @@ "* Low training loss – the model can fit the training data well because it has sufficient expressive power.\n", "* Validation loss can be much higher than training loss and may start increasing during training – this happens because the model \"memorizes\" the training data points and loses the \"big picture.\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.en.png)\n", + "![Overfitting](../../../../../translated_images/en/overfit.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.png)\n", "\n", "> In this image, `x` represents training data, and `o` represents validation data. On the left is a linear model (single-layer), which captures the true nature of the data fairly well. On the right is an overfitted model, which fits the training data perfectly but fails to make sense of other data (validation error is very high).\n" ] diff --git a/translations/en/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/en/lessons/3-NeuralNetworks/05-Frameworks/README.md index 4dfb43ce..51ca5eda 100644 --- a/translations/en/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/en/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting is a crucial concept in machine learning, and it's essential to unde Consider the problem of approximating 5 data points (represented by `x` in the graphs below): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.en.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.en.jpg) +![linear](../../../../../translated_images/en/overfit1.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.jpg) | ![overfit](../../../../../translated_images/en/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.jpg) -------------------------|-------------------------- **Linear model, 2 parameters** | **Non-linear model, 7 parameters** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Striking the right balance between model complexity (number of parameters) and t As shown in the graph above, overfitting can be identified by a very low training error and a high validation error. During training, both training and validation errors typically decrease initially. However, at some point, the validation error may stop decreasing and start increasing. This indicates overfitting and suggests that training should be stopped (or a snapshot of the model should be saved). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.en.png) +![overfitting](../../../../../translated_images/en/Overfitting.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.png) ## How to prevent overfitting diff --git a/translations/en/lessons/3-NeuralNetworks/README.md b/translations/en/lessons/3-NeuralNetworks/README.md index 735ae39e..84df5187 100644 --- a/translations/en/lessons/3-NeuralNetworks/README.md +++ b/translations/en/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduction to Neural Networks -![Summary of Intro Neural Networks content in a doodle](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.en.png) +![Summary of Intro Neural Networks content in a doodle](../../../../translated_images/en/ai-neuralnetworks.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.png) As discussed in the introduction, one way to achieve intelligence is by training a **computer model** or an **artificial brain**. Since the mid-20th century, researchers have experimented with various mathematical models, and in recent years, this approach has proven to be highly successful. These mathematical models of the brain are known as **neural networks**. @@ -36,13 +36,13 @@ This curriculum focuses exclusively on neural network models. Biologically, we know that the brain is composed of neural cells (neurons), each with multiple "inputs" (dendrites) and a single "output" (axon). Both dendrites and axons transmit electrical signals, and the connections between them — called synapses — can vary in conductivity, regulated by neurotransmitters. -![Model of a Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.en.jpg) | ![Model of a Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.en.png) +![Model of a Neuron](../../../../translated_images/en/synapse-wikipedia.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.jpg) | ![Model of a Neuron](../../../../translated_images/en/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.png) ----|---- Real Neuron *([Image](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) from Wikipedia)* | Artificial Neuron *(Image by Author)* The simplest mathematical model of a neuron includes several inputs X1, ..., XN, an output Y, and a set of weights W1, ..., WN. The output is calculated as: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) where **f** is a non-linear **activation function**. diff --git a/translations/en/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/en/lessons/4-ComputerVision/06-IntroCV/README.md index ed1d72d0..5c92a012 100644 --- a/translations/en/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/en/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ In our [OpenCV Notebook](OpenCV.ipynb), we provide examples of how computer visi * **Preprocessing a photograph of a Braille book**. This example demonstrates how thresholding, feature detection, perspective transformation, and NumPy manipulations can be used to isolate individual Braille symbols for further classification by a neural network. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.en.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.en.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.en.png) +![Braille Image](../../../../../translated_images/en/braille.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/en/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.png) | ![Braille Symbols](../../../../../translated_images/en/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.png) ----|-----|----- > Image from [OpenCV.ipynb](OpenCV.ipynb) * **Detecting motion in video using frame difference**. If the camera is stationary, frames from the camera feed should be quite similar. Since frames are represented as arrays, subtracting the arrays of two consecutive frames will reveal pixel differences. These differences will be minimal for static frames and more pronounced when there is significant motion in the image. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.en.png) +![Image of video frames and frame differences](../../../../../translated_images/en/frame-difference.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.png) > Image from [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ In our [OpenCV Notebook](OpenCV.ipynb), we provide examples of how computer visi - **Dense Optical Flow** calculates a vector field showing the movement of each pixel. - **Sparse Optical Flow** focuses on distinctive features in the image (e.g., edges) and tracks their movement across frames. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.en.png) +![Image of Optical Flow](../../../../../translated_images/en/optical.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.png) > Image from [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/en/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/en/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d81f46cd..60376d44 100644 --- a/translations/en/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/en/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 is a network that achieved 92.7% accuracy in ImageNet top-5 classification in 2014. It has the following layer structure: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.en.jpg) +![ImageNet Layers](../../../../../translated_images/en/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.jpg) As you can see, VGG follows a traditional pyramid architecture, which is a sequence of convolution-pooling layers. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.en.jpg) +![ImageNet Pyramid](../../../../../translated_images/en/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.jpg) > Image from [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 70f77531..128a0f6b 100644 --- a/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "In a typical CNN, there are multiple convolutional layers, with pooling layers placed between them to reduce the image's dimensions. Additionally, the number of filters is increased as the patterns become more complex, requiring us to search for a greater variety of meaningful combinations.\n", "\n", - "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.en.png)\n", + "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/en/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.png)\n", "\n", "Due to the reduction in spatial dimensions and the increase in feature/filter dimensions, this architecture is often referred to as a **pyramid architecture**.\n" ] diff --git a/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 3a16fb5f..f30955b6 100644 --- a/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/en/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "In a typical CNN, there are usually several convolutional layers, with pooling layers in between to reduce the image dimensions. At the same time, we increase the number of filters, as more advanced patterns require us to look for a greater variety of meaningful combinations.\n", "\n", - "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.en.png)\n", + "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/en/cnn-pyramid.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.png)\n", "\n", "Due to the reduction in spatial dimensions and the increase in feature/filter dimensions, this architecture is often referred to as a **pyramid architecture**.\n" ] diff --git a/translations/en/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/en/lessons/4-ComputerVision/07-ConvNets/README.md index 9d3608a2..52af9b5e 100644 --- a/translations/en/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/en/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ In real-world scenarios, we want to be able to recognize objects in an image reg To extract these patterns, we use **convolutional filters**. As you know, an image is represented as a 2D matrix or a 3D tensor with color depth. Applying a filter involves using a relatively small **filter kernel** matrix and calculating the weighted average with neighboring points for each pixel in the original image. This process can be visualized as a small window sliding across the entire image, averaging out all pixels based on the weights in the filter kernel matrix. -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.en.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.en.png) +![Vertical Edge Filter](../../../../../translated_images/en/filter-vert.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.png) | ![Horizontal Edge Filter](../../../../../translated_images/en/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.png) ----|---- > Image by Dmitry Soshnikov @@ -38,7 +38,7 @@ The functionality of CNNs is based on the following key concepts: * Networks can be designed to train filters automatically. * The same approach can be used to identify patterns in high-level features, not just in the original image. CNNs extract features hierarchically, starting with low-level pixel combinations and progressing to higher-level combinations of image components. -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.en.png) +![Hierarchical Feature Extraction](../../../../../translated_images/en/FeatureExtractionCNN.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.png) > Image from [a paper by Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), based on [their research](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Most CNNs used for image processing follow a pyramid architecture. The first con For example, consider the architecture of VGG-16, a network that achieved 92.7% accuracy in ImageNet's top-5 classification in 2014: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.en.jpg) +![ImageNet Layers](../../../../../translated_images/en/vgg-16-arch1.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.en.jpg) +![ImageNet Pyramid](../../../../../translated_images/en/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.jpg) > Image from [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/en/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/en/lessons/4-ComputerVision/07-ConvNets/lab/README.md index cda10e00..87e54a1c 100644 --- a/translations/en/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/en/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Your task is to train a convolutional neural network to classify different breed We will use the [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), which contains images of 37 different breeds of dogs and cats. -![Dataset we will deal with](../../../../../../translated_images/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.en.png) +![Dataset we will deal with](../../../../../../translated_images/en/data.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.png) To download the dataset, use this code snippet: diff --git a/translations/en/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/en/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index c080f859..3603b542 100644 --- a/translations/en/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/en/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "To visualize the perfect cat, we will begin with a random noise image and use the gradient descent optimization method to modify the image so that the network identifies it as a cat.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.en.png)\n", + "![Optimization Loop](../../../../../translated_images/en/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.png)\n", "\n", "This is our initial image:\n" ] diff --git a/translations/en/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/en/lessons/4-ComputerVision/08-TransferLearning/README.md index 5aa56a0a..e4d0d35a 100644 --- a/translations/en/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/en/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Both Keras and PyTorch provide functions to easily load pre-trained neural netwo Below are sample features extracted from a picture of a cat using the VGG-16 network: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.en.png) +![Features extracted by VGG-16](../../../../../translated_images/en/features.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.png) ## Cats vs. Dogs Dataset @@ -48,19 +48,19 @@ A pre-trained neural network contains various patterns within its *brain*, inclu One approach is to start with a random image and use **gradient descent optimization** to adjust the image so that the network perceives it as a cat. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.en.png) +![Image Optimization Loop](../../../../../translated_images/en/ideal-cat-loop.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.png) However, this process often results in images resembling random noise. This happens because *there are many ways to make the network think an input image is a cat*, including ways that are visually nonsensical. While these images contain patterns typical of a cat, they lack constraints to make them visually distinct. To improve the result, we can add another term to the loss function called **variation loss**. This metric measures how similar neighboring pixels in the image are. Minimizing variation loss smooths the image and reduces noise, revealing more visually appealing patterns. Below are examples of "ideal" images classified as a cat and a zebra with high confidence: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.en.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.en.png) +![Ideal Cat](../../../../../translated_images/en/ideal-cat.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.png) | ![Ideal Zebra](../../../../../translated_images/en/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.png) -----|----- *Ideal Cat* | *Ideal Zebra* A similar approach can be used for **adversarial attacks** on a neural network. For instance, if we want to trick a neural network into classifying a dog as a cat, we can start with an image of a dog (recognized as a dog by the network) and tweak it slightly using gradient descent optimization until the network classifies it as a cat: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.en.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.en.png) +![Picture of a Dog](../../../../../translated_images/en/original-dog.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/en/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.png) -----|----- *Original picture of a dog* | *Picture of a dog classified as a cat* diff --git a/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 752dfc25..63ae8457 100644 --- a/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "By training the autoencoder to retain as much information as possible from the original image for accurate reconstruction, the network learns to find the best **embedding** of the input images to capture their essence.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.en.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/en/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.jpg)\n", "\n", "> Image from [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index a1cd2f7f..1550a25d 100644 --- a/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/en/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "By training the autoencoder to retain as much information as possible from the original image for accurate reconstruction, the network learns to find the best **embedding** of the input images to capture their essence.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.en.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/en/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.jpg)\n", "\n", "*Image from [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/en/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/en/lessons/4-ComputerVision/09-Autoencoders/README.md index 1eb637de..960da52d 100644 --- a/translations/en/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/en/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ However, we might want to use raw (unlabeled) data to train CNN feature extracto By training an autoencoder to capture as much information as possible from the original image for accurate reconstruction, the network attempts to find the best **embedding** of the input images to represent their meaning. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.en.jpg) +![AutoEncoder Diagram](../../../../../translated_images/en/autoencoder_schema.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.jpg) > Image from [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/en/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/en/lessons/4-ComputerVision/11-ObjectDetection/README.md index ab30b524..a778a47d 100644 --- a/translations/en/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/en/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ The image classification models we've explored so far take an image as input and ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.en.png) +![Object Detection](../../../../../translated_images/en/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.png) > Image from [YOLO v2 website](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Imagine we want to locate a cat in an image. A very simplistic approach to objec 2. Perform image classification on each tile. 3. Identify tiles with sufficiently high activation as containing the object of interest. -![Naive Object Detection](../../../../../translated_images/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.en.png) +![Naive Object Detection](../../../../../translated_images/en/naive-detection.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.png) > *Image from [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Here are some commonly used datasets for object detection: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Common Objects in Context. Includes 80 classes, bounding boxes, and segmentation masks. -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.en.jpg) +![COCO](../../../../../translated_images/en/coco-examples.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.jpg) ## Object Detection Metrics @@ -50,7 +50,7 @@ Here are some commonly used datasets for object detection: While evaluating image classification models is straightforward, object detection requires assessing both the accuracy of the predicted class and the precision of the bounding box location. For the latter, we use **Intersection over Union** (IoU), which measures the overlap between two bounding boxes (or areas). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.en.png) +![IoU](../../../../../translated_images/en/iou_equation.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.png) > *Figure 2 from [this excellent blog post on IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ Object detection algorithms can be broadly categorized into two types: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) uses [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) to generate a hierarchical structure of ROIs. These ROIs are passed through CNN feature extractors and SVM classifiers to determine object classes, while linear regression predicts the *bounding box* coordinates. [Official Paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.en.png) +![RCNN](../../../../../translated_images/en/rcnn1.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.png) > *Image from van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.en.png) +![RCNN-1](../../../../../translated_images/en/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.png) > *Images from [this blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ Object detection algorithms can be broadly categorized into two types: This method is similar to R-CNN, but the regions are defined after applying convolutional layers. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.en.png) +![FRCNN](../../../../../translated_images/en/f-rcnn.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.png) > Image from [the Official Paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ This method is similar to R-CNN, but the regions are defined after applying conv This approach introduces a neural network to predict ROIs, known as the *Region Proposal Network*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.en.png) +![FasterRCNN](../../../../../translated_images/en/faster-rcnn.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.png) > Image from [the official paper](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ This algorithm is faster than Faster R-CNN. The key idea is: 2. Process features with a **Position-Sensitive Score Map**. Each object from $C$ classes is divided into $k\times k$ regions, and the network predicts parts of objects. 3. For each part of the $k\times k$ regions, the networks vote for object classes, and the class with the highest vote is selected. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.en.png) +![r-fcn image](../../../../../translated_images/en/r-fcn.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.png) > Image from [official paper](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO is a real-time, one-pass algorithm. The main idea is: * Divide the image into $S\times S$ regions. * For each region, the **CNN** predicts $n$ possible objects, *bounding box* coordinates, and *confidence* = *probability* × IoU. -![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.en.png) +![YOLO](../../../../../translated_images/en/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.png) > Image from [official paper](https://arxiv.org/abs/1506.02640) diff --git a/translations/en/lessons/4-ComputerVision/README.md b/translations/en/lessons/4-ComputerVision/README.md index 7630026b..7a8480bf 100644 --- a/translations/en/lessons/4-ComputerVision/README.md +++ b/translations/en/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Summary of Computer Vision content in a doodle](../../../../translated_images/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.en.png) +![Summary of Computer Vision content in a doodle](../../../../translated_images/en/ai-computervision.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.png) In this section, we will explore: diff --git a/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 926a5df7..8d4347c2 100644 --- a/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "The **Bag of Words** (BoW) vector representation is the most widely used traditional method for representing text as vectors. Each word is assigned to a specific index in the vector, and the corresponding vector element indicates the number of times that word appears in a given document.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.en.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/en/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.png) \n", "\n", "> **Note**: You can also think of BoW as the sum of all one-hot-encoded vectors for the individual words in the text.\n", "\n", diff --git a/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 36c27b5c..0f814ff5 100644 --- a/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/en/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vector representation is the simplest and most intuitive traditional vector representation. Each word is assigned to a specific index in the vector, and the corresponding vector element indicates the frequency of that word in a given document.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.en.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/en/bag-of-words-example.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.png) \n", "\n", "> **Note**: You can also think of BoW as the sum of all one-hot-encoded vectors for the individual words in the text.\n", "\n", diff --git a/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index beeaa52c..0b7a4c50 100644 --- a/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "By using an embedding layer as the first layer in our network, we can transition from a bag-of-words model to an **embedding bag** model. In this approach, each word in the text is first converted into its corresponding embedding, and then an aggregate function—such as `sum`, `average`, or `max`—is applied to all those embeddings.\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.en.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/en/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.png)\n", "\n", "Our neural network classifier will begin with an embedding layer, followed by an aggregation layer, and finally a linear classifier on top:\n" ] @@ -176,7 +176,7 @@ "\n", "In the previous architecture, we had to pad all sequences to the same length to fit them into a minibatch. This is not the most efficient way to represent variable-length sequences. An alternative approach is to use an **offset** vector, which stores the starting positions of all sequences within a single large vector.\n", "\n", - "![Image showing an offset sequence representation](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.en.png)\n", + "![Image showing an offset sequence representation](../../../../../translated_images/en/offset-sequence-representation.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.png)\n", "\n", "> **Note**: In the image above, we illustrate a sequence of characters, but in our example, we are working with sequences of words. However, the general principle of representing sequences with an offset vector remains the same.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW is faster, while skip-gram is slower but performs better at representing infrequent words.\n", "\n", - "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.en.png)\n", + "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/en/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.png)\n", "\n", "To experiment with Word2Vec embeddings pre-trained on the Google News dataset, we can use the **gensim** library. Below, we find the words most similar to 'neural.'\n", "\n", diff --git a/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 9cd25187..16e24faa 100644 --- a/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/en/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "By using an embedding layer as the first layer in our network, we can transition from a bag-of-words model to an **embedding bag** model. In this approach, we first convert each word in the text into its corresponding embedding and then apply an aggregate function over all those embeddings, such as `sum`, `average`, or `max`.\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.en.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/en/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.png)\n", "\n", "Our neural network classifier is composed of the following layers:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW is faster, but skip-gram, while slower, performs better at representing rare words.\n", "\n", - "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.en.png)\n", + "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/en/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.png)\n", "\n", "To experiment with the Word2Vec embedding pretrained on the Google News dataset, we can use the **gensim** library. Below, we find the words most similar to 'neural'.\n", "\n", diff --git a/translations/en/lessons/5-NLP/14-Embeddings/README.md b/translations/en/lessons/5-NLP/14-Embeddings/README.md index c15bf61a..2b96bb01 100644 --- a/translations/en/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/en/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ An embedding layer takes a word as input and produces an output vector of a spec By using an embedding layer as the first layer in our classifier network, we can transition from a bag-of-words model to an **embedding bag** model. Here, each word in the text is converted into its corresponding embedding, and an aggregate function, such as `sum`, `average`, or `max`, is applied to all those embeddings. -![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.en.png) +![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/en/embedding-classifier-example.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.png) > Image by the author @@ -40,7 +40,7 @@ To achieve this, we need to pre-train our embedding model on a large text corpus CBoW is faster, while skip-gram is slower but performs better at representing infrequent words. -![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.en.png) +![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/en/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.png) > Image from [this paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/en/lessons/5-NLP/15-LanguageModeling/README.md b/translations/en/lessons/5-NLP/15-LanguageModeling/README.md index 2ef7c623..a6ac1390 100644 --- a/translations/en/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/en/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ In previous examples, we used pre-trained semantic embeddings, but it’s intere * **Continuous Bag-of-Words** (CBoW), where the middle token $W_0$ in a sequence $W_{-N}$, ..., $W_N$ is predicted. * **Skip-gram**, where a set of neighboring tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} is predicted from the middle token $W_0$. -![image from paper on converting words to vectors](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.en.png) +![image from paper on converting words to vectors](../../../../../translated_images/en/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.png) > Image from [this paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/en/lessons/5-NLP/16-RNN/README.md b/translations/en/lessons/5-NLP/16-RNN/README.md index 55c38c94..b65c71bf 100644 --- a/translations/en/lessons/5-NLP/16-RNN/README.md +++ b/translations/en/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ In previous sections, we used rich semantic representations of text combined wit To understand the meaning of text sequences, we need a different neural network architecture called a **recurrent neural network** (RNN). In an RNN, we process a sentence one symbol at a time through the network, which produces a **state** that is then passed back into the network along with the next symbol. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.en.png) +![RNN](../../../../../translated_images/en/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.png) > Image by the author @@ -61,7 +61,7 @@ So far, we’ve discussed recurrent networks that process sequences in one direc A recurrent network, whether unidirectional or bidirectional, captures patterns within a sequence and stores them in a state vector or passes them to the output. Similar to convolutional networks, we can stack another recurrent layer on top of the first to capture higher-level patterns based on the low-level patterns extracted by the first layer. This leads to the concept of a **multi-layer RNN**, which consists of two or more recurrent networks, where the output of one layer serves as the input to the next. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.en.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/en/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.jpg) *Picture from [this wonderful post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) by Fernando López* diff --git a/translations/en/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/en/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 03fcacc9..36b10a4f 100644 --- a/translations/en/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/en/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -416,7 +416,7 @@ "\n", "A recurrent network, whether one-directional or bidirectional, captures certain patterns within a sequence and can store them in the state vector or pass them to the output. Similar to convolutional networks, we can stack another recurrent layer on top of the first one to capture higher-level patterns, built from the low-level patterns extracted by the first layer. This brings us to the concept of a **multi-layer RNN**, which consists of two or more recurrent networks, where the output of one layer is passed as input to the next layer.\n", "\n", - "![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.en.jpg)\n", + "![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/en/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.jpg)\n", "\n", "*Image from [this excellent post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) by Fernando López*\n", "\n", diff --git a/translations/en/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/en/lessons/5-NLP/16-RNN/RNNTF.ipynb index f4f3f75d..7c259d30 100644 --- a/translations/en/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/en/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "To understand the meaning of a text sequence, we'll use a neural network architecture called **recurrent neural network**, or RNN. With an RNN, we process a sentence through the network one token at a time, and the network generates a **state**, which is then passed back into the network along with the next token.\n", "\n", - "![Image showing an example recurrent neural network generation.](../../../../../translated_images/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.en.png)\n", + "![Image showing an example recurrent neural network generation.](../../../../../translated_images/en/rnn.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.png)\n", "\n", "Given the input sequence of tokens $X_0,\\dots,X_n$, the RNN constructs a sequence of neural network blocks and trains this sequence end-to-end using backpropagation. Each network block takes a pair $(X_i,S_i)$ as input and produces $S_{i+1}$ as output. The final state $S_n$ or output $Y_n$ is passed into a linear classifier to generate the result. All network blocks share the same weights and are trained end-to-end in a single backpropagation pass.\n", "\n", @@ -371,7 +371,7 @@ "\n", "Recurrent networks, whether unidirectional or bidirectional, identify patterns within a sequence and store them in state vectors or return them as output. Similar to convolutional networks, we can stack another recurrent layer on top of the first one to capture higher-level patterns derived from the lower-level patterns identified by the initial layer. This concept is known as a **multi-layer RNN**, which consists of two or more recurrent networks, where the output of one layer serves as the input for the next.\n", "\n", - "![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.en.jpg)\n", + "![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/en/multi-layer-lstm.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.jpg)\n", "\n", "*Image sourced from [this excellent post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) by Fernando López.*\n", "\n", diff --git a/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 45885f9a..09a102f4 100644 --- a/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "The method we will use to train the RNN to generate text works as follows. At each step, we will take a sequence of characters of length `nchars` and ask the network to predict the next output character for each input character:\n", "\n", - "![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.en.png)\n", + "![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/en/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.png)\n", "\n", "Depending on the specific use case, we might also want to include special characters, such as *end-of-sequence* ``. In our case, we aim to train the network for continuous text generation, so we will set the size of each sequence to a fixed length of `nchars` tokens. As a result, each training example will consist of `nchars` inputs and `nchars` outputs (where the output sequence is the input sequence shifted one character to the left). A minibatch will contain several such sequences.\n", "\n", diff --git a/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index c8dc867f..288e1a4a 100644 --- a/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/en/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "The method we will use to train an RNN to generate news titles is as follows. At each step, we will take one title, feed it into the RNN, and for each input character, we will ask the network to generate the next output character:\n", "\n", - "![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.en.png)\n", + "![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/en/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.png)\n", "\n", "For the last character in our sequence, we will ask the network to generate the `` token.\n", "\n", diff --git a/translations/en/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/en/lessons/5-NLP/17-GenerativeNetworks/README.md index 1cec10eb..a1aaeff0 100644 --- a/translations/en/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/en/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ In the RNN architecture discussed in the previous unit, each RNN unit produced t This enables different neural architectures, as shown in the image below: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.en.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/en/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.jpg) > Image from the blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) by [Andrej Karpathy](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In this unit, we will focus on simple generative models for text generation. For We will train an RNN to generate text step by step. At each step, we will take a sequence of characters of length `nchars` and ask the network to generate the next output character for each input character: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.en.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/en/rnn-generate.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.png) When generating text (during inference), we start with a **prompt**, which is passed through RNN cells to generate its intermediate state. From this state, the generation begins. We generate one character at a time, passing the state and the generated character to another RNN cell to generate the next one, until we have generated enough characters. diff --git a/translations/en/lessons/5-NLP/18-Transformers/README.md b/translations/en/lessons/5-NLP/18-Transformers/README.md index 24e69ef3..ee13ebe5 100644 --- a/translations/en/lessons/5-NLP/18-Transformers/README.md +++ b/translations/en/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ With RNNs, sequence-to-sequence tasks are implemented using two recurrent networ **Attention Mechanisms** address these issues by assigning different weights to the contextual impact of each input vector on each output prediction of the RNN. This is achieved by creating shortcuts between intermediate states of the input RNN and the output RNN. When generating output symbol yt, all input hidden states hi are considered, with varying weight coefficients αt,i. -![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.en.png) +![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/en/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.png) > The encoder-decoder model with additive attention mechanism in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cited from [this blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) The attention matrix {αi,j} represents the extent to which specific input words contribute to the generation of a particular word in the output sequence. Below is an example of such a matrix: -![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.en.png) +![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/en/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.png) > Figure from [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ The result of positional embedding combines both the original token and its posi Next, we need to identify patterns within the sequence. Transformers achieve this using a **self-attention** mechanism, which applies attention to the same sequence for both input and output. Self-attention allows the model to consider **context** within the sentence and identify interrelated words. For example, it can determine which words are referenced by pronouns like *it* and incorporate the surrounding context: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.en.png) +![](../../../../../translated_images/en/CoreferenceResolution.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.png) > Image from the [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Since each input position is mapped independently to each output position, trans **BERT** (Bidirectional Encoder Representations from Transformers) is a large multi-layer transformer network with 12 layers for *BERT-base* and 24 layers for *BERT-large*. The model is first pre-trained on a large corpus of text data (Wikipedia + books) using unsupervised training (predicting masked words in a sentence). During pre-training, the model acquires significant language understanding, which can then be leveraged for other datasets through fine-tuning. This process is known as **transfer learning**. -![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.en.png) +![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/en/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.png) > Image [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/en/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/en/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index f6863594..85e85ae9 100644 --- a/translations/en/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/en/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Attention Mechanisms** offer a way to assign varying levels of importance to each input vector when predicting each output of the RNN. This is achieved by creating shortcuts between the intermediate states of the input RNN and the output RNN. In this way, when generating the output symbol $y_t$, all input hidden states $h_i$ are considered, each with a different weight coefficient $\\alpha_{t,i}$.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.en.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/en/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.png)\n", "*The encoder-decoder model with additive attention mechanism in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cited from [this blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "The attention matrix $\\{\\alpha_{i,j}\\}$ represents the extent to which specific input words contribute to the generation of a particular word in the output sequence. Below is an example of such a matrix:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.en.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/en/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.png)\n", "\n", "*Figure taken from [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is a large multi-layer transformer network with 12 layers for *BERT-base* and 24 layers for *BERT-large*. The model is first pre-trained on a large corpus of text data (Wikipedia + books) using unsupervised training (predicting masked words in a sentence). During pre-training, the model acquires a significant level of language understanding, which can then be leveraged with other datasets through fine-tuning. This process is known as **transfer learning**.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.en.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/en/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.png)\n", "\n", "There are many variations of Transformer architectures, including BERT, DistilBERT, BigBird, OpenGPT3, and others, which can be fine-tuned. The [HuggingFace package](https://github.com/huggingface/) provides a repository for training many of these architectures using PyTorch.\n", "\n", diff --git a/translations/en/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/en/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 4404a706..9c30fda6 100644 --- a/translations/en/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/en/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Attention Mechanisms** offer a way to assign different levels of importance to each input vector when predicting each output of the RNN. This is achieved by creating shortcuts between the intermediate states of the input RNN and the output RNN. In this way, when generating the output symbol $y_t$, all input hidden states $h_i$ are considered, but with varying weight coefficients $\\alpha_{t,i}$.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.en.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/en/encoder-decoder-attention.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.png)\n", "*The encoder-decoder model with additive attention mechanism in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cited from [this blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "The attention matrix $\\{\\alpha_{i,j}\\}$ represents the extent to which specific input words contribute to the generation of a particular word in the output sequence. Below is an example of such a matrix:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.en.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/en/bahdanau-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.png)\n", "\n", "*Figure taken from [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -223,7 +223,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is a very large multi-layer transformer network with 12 layers for *BERT-base* and 24 for *BERT-large*. The model is initially pre-trained on a large corpus of text data (Wikipedia + books) using unsupervised training (predicting masked words in a sentence). During pre-training, the model gains a significant level of language understanding, which can then be applied to other datasets through fine-tuning. This process is known as **transfer learning**.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.en.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/en/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.png)\n", "\n", "There are many variations of Transformer architectures, including BERT, DistilBERT, BigBird, OpenGPT3, and more, which can be fine-tuned.\n", "\n", diff --git a/translations/en/lessons/5-NLP/19-NER/README.md b/translations/en/lessons/5-NLP/19-NER/README.md index 64ec5afa..2941bb16 100644 --- a/translations/en/lessons/5-NLP/19-NER/README.md +++ b/translations/en/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O To establish a one-to-one correspondence between tokens and classes, we can train a **many-to-many** neural network model, as shown in the following diagram: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.en.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/en/unreasonable-effectiveness-of-rnn.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.jpg) > *Image from [this blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) by [Andrej Karpathy](http://karpathy.github.io/). NER token classification models correspond to the right-most network architecture in this diagram.* diff --git a/translations/en/lessons/5-NLP/README.md b/translations/en/lessons/5-NLP/README.md index f1916890..c5f4e5b7 100644 --- a/translations/en/lessons/5-NLP/README.md +++ b/translations/en/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natural Language Processing -![Summary of NLP tasks in a doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.en.png) +![Summary of NLP tasks in a doodle](../../../../translated_images/en/ai-nlp.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.png) In this section, we will explore how Neural Networks can be used to tackle tasks related to **Natural Language Processing (NLP)**. There are numerous NLP challenges we aim for computers to solve: diff --git a/translations/en/lessons/6-Other/23-MultiagentSystems/README.md b/translations/en/lessons/6-Other/23-MultiagentSystems/README.md index 9adde189..917b3869 100644 --- a/translations/en/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/en/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ You can open one of the models, such as **Biology → Flocking**. After opening a model, you’ll see the main NetLogo screen. Here’s an example model that simulates the population of wolves and sheep, given finite resources (grass). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.en.png) +![NetLogo Main Screen](../../../../../translated_images/en/NetLogo-Main.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.png) > Screenshot by Dmitry Soshnikov diff --git a/translations/en/lessons/README.md b/translations/en/lessons/README.md index cb08ec8a..8ae58aaf 100644 --- a/translations/en/lessons/README.md +++ b/translations/en/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Overview -![Overview in a doodle](../../../translated_images/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.en.png) +![Overview in a doodle](../../../translated_images/en/ai-overview.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/en/lessons/X-Extras/X1-MultiModal/README.md b/translations/en/lessons/X-Extras/X1-MultiModal/README.md index 496700ed..dabf1778 100644 --- a/translations/en/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/en/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ After the success of transformer models in solving NLP tasks, similar architectu The main idea behind CLIP is to compare text prompts with an image and determine how well the image matches the prompt. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.en.png) +![CLIP Architecture](../../../../../translated_images/en/clip-arch.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.png) > *Image from [this blog post](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Once the model is pre-trained, it can process a batch of images and text prompts For example, if we need to classify images into categories like cats, dogs, and humans, we can provide the model with an image and a series of text prompts: "*a picture of a cat*", "*a picture of a dog*", "*a picture of a human*". From the resulting vector of three probabilities, we select the index with the highest value. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.en.png) +![CLIP for Image Classification](../../../../../translated_images/en/clip-class.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.png) > *Image from [this blog post](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Learn more about VQGAN on the [Taming Transformers](https://compvis.github.io/ta A significant difference between VQGAN and traditional GANs is that the latter can produce a coherent image from any input vector, while VQGAN may generate an incoherent image. Therefore, the image creation process needs additional guidance, which can be provided using CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.en.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/en/vqgan.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.png) To generate an image based on a text prompt, we start with a random encoding vector that is passed through VQGAN to produce an image. CLIP is then used to calculate a loss function that measures how well the image matches the text prompt. The goal is to minimize this loss by using backpropagation to adjust the input vector parameters. A great library that implements VQGAN+CLIP is [Pixray](http://github.com/pixray/pixray). -![Picture produced by Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.en.png) | ![Picture produced by pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.en.png) | ![Picture produced by Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.en.png) +![Picture produced by Pixray](../../../../../translated_images/en/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.png) | ![Picture produced by pixray](../../../../../translated_images/en/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.png) | ![Picture produced by Pixray](../../../../../translated_images/en/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.png) ----|----|---- Image generated from prompt *a closeup watercolor portrait of young male teacher of literature with a book* | Image generated from prompt *a closeup oil portrait of young female teacher of computer science with a computer* | Image generated from prompt *a closeup oil portrait of old male teacher of mathematics in front of blackboard* @@ -75,7 +75,7 @@ Unlike CLIP, DALL-E processes both text and image as a single stream of tokens f The main difference between DALL-E 1 and DALL-E 2 is that the latter generates more realistic images and artwork. Examples of image generation with DALL-E: -![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.en.png) | ![Picture produced by pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.en.png) | ![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.en.png) +![Picture produced by Pixray](../../../../../translated_images/en/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Picture produced by pixray](../../../../../translated_images/en/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Picture produced by Pixray](../../../../../translated_images/en/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Image generated from prompt *a closeup watercolor portrait of young male teacher of literature with a book* | Image generated from prompt *a closeup oil portrait of young female teacher of computer science with a computer* | Image generated from prompt *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/es/README.md b/translations/es/README.md index 39fc675a..d36ee0e1 100644 --- a/translations/es/README.md +++ b/translations/es/README.md @@ -1,8 +1,8 @@ -[Árabe](../ar/README.md) | [Bengalí](../bn/README.md) | [Búlgaro](../bg/README.md) | [Birmano (Myanmar)](../my/README.md) | [Chino (Simplificado)](../zh/README.md) | [Chino (Tradicional, Hong Kong)](../hk/README.md) | [Chino (Tradicional, Macau)](../mo/README.md) | [Chino (Tradicional, Taiwán)](../tw/README.md) | [Croata](../hr/README.md) | [Checo](../cs/README.md) | [Danés](../da/README.md) | [Neerlandés](../nl/README.md) | [Estonio](../et/README.md) | [Finlandés](../fi/README.md) | [Francés](../fr/README.md) | [Alemán](../de/README.md) | [Griego](../el/README.md) | [Hebreo](../he/README.md) | [Hindi](../hi/README.md) | [Húngaro](../hu/README.md) | [Indonesio](../id/README.md) | [Italiano](../it/README.md) | [Japonés](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malayo](../ms/README.md) | [Malayalam](../ml/README.md) | [Maratí](../mr/README.md) | [Nepalí](../ne/README.md) | [Pidgin Nigeriano](../pcm/README.md) | [Noruego](../no/README.md) | [Persa (Farsi)](../fa/README.md) | [Polaco](../pl/README.md) | [Portugués (Brasil)](../br/README.md) | [Portugués (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Rumano](../ro/README.md) | [Ruso](../ru/README.md) | [Serbio (Cirílico)](../sr/README.md) | [Eslovaco](../sk/README.md) | [Esloveno](../sl/README.md) | [Español](./README.md) | [Swahili](../sw/README.md) | [Sueco](../sv/README.md) | [Tagalo (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Tailandés](../th/README.md) | [Turco](../tr/README.md) | [Ucraniano](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](./README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **¿Prefieres Clonar Localmente?** -> Este repositorio incluye más de 50 traducciones, lo cual incrementa significativamente el tamaño de la descarga. Para clonar sin traducciones, usa sparse checkout: +> Este repositorio incluye más de 50 traducciones que aumentan significativamente el tamaño de descarga. Para clonar sin traducciones, usa sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Esto te brinda todo lo que necesitas para completar el curso con una descarga mucho más rápida. +> Esto te da todo lo que necesitas para completar el curso con una descarga mucho más rápida. -**Si deseas que se soporten más idiomas de traducción, están listados [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Si deseas que se agreguen más idiomas de traducción, están listados [aquí](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Únete a la Comunidad [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Qué aprenderás +## Lo que aprenderás **[Mapa mental del curso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -En este plan de estudios aprenderás: +En este currículo aprenderás: -* Diferentes enfoques de Inteligencia Artificial, incluyendo el "buen y viejo" enfoque simbólico con **Representación del Conocimiento** y razonamiento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Redes Neuronales** y **Aprendizaje Profundo**, que están en el núcleo de la IA moderna. Ilustraremos los conceptos detrás de estos temas importantes usando código en dos de los frameworks más populares: [TensorFlow](http://Tensorflow.org) y [PyTorch](http://pytorch.org). -* **Arquitecturas Neuronales** para trabajar con imágenes y texto. Cubriremos modelos recientes aunque puede que falten algunos de última generación. +* Diferentes enfoques de la Inteligencia Artificial, incluyendo el "buen y viejo" enfoque simbólico con **Representación del Conocimiento** y razonamiento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Redes Neuronales** y **Aprendizaje Profundo**, que están en el núcleo de la IA moderna. Ilustraremos los conceptos detrás de estos temas importantes usando código en dos de los frameworks más populares - [TensorFlow](http://Tensorflow.org) y [PyTorch](http://pytorch.org). +* **Arquitecturas Neuronales** para trabajar con imágenes y texto. Cubriremos modelos recientes pero puede que falte un poco del estado del arte. * Enfoques menos populares de IA, como **Algoritmos Genéticos** y **Sistemas Multiagente**. -Lo que no cubriremos en este plan de estudios: +Lo que no cubriremos en este currículo: > [Encuentra todos los recursos adicionales para este curso en nuestra colección de Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Casos de negocio de uso de **IA en los Negocios**. Considera tomar la ruta de aprendizaje [Introducción a la IA para usuarios de negocio](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) en Microsoft Learn, o [Escuela de Negocios de IA](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desarrollada en cooperación con [INSEAD](https://www.insead.edu/). -* **Aprendizaje Automático Clásico**, que está bien descrito en nuestro [Plan de Estudios para Principiantes en Machine Learning](http://github.com/Microsoft/ML-for-Beginners). -* Aplicaciones prácticas de IA construidas usando **[Servicios Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para ello, recomendamos comenzar con los módulos de Microsoft Learn para [visión](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [procesamiento de lenguaje natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa con Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** y otros. -* Frameworks específicos de ML en la **Nube**, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), o [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considera usar las rutas de aprendizaje [Construye y opera soluciones de machine learning con Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) y [Construye y opera soluciones de machine learning con Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA Conversacional** y **Chat Bots**. Hay una ruta de aprendizaje separada [Crea soluciones de IA conversacionales](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), y también puedes consultar [este blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para más detalles. -* **Matemáticas profundas** detrás del aprendizaje profundo. Para esto, recomendamos [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio y Aaron Courville, que también está disponible en línea en [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Casos de negocio usando **IA en Negocios**. Considera tomar la ruta de aprendizaje [Introducción a la IA para usuarios de negocio](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) en Microsoft Learn, o [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desarrollado en cooperación con [INSEAD](https://www.insead.edu/). +* **Aprendizaje Automático Clásico**, que está bien descrito en nuestro [Currículo de Aprendizaje automático para principiantes](http://github.com/Microsoft/ML-for-Beginners). +* Aplicaciones prácticas de IA construidas usando **[Servicios Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para esto, recomendamos comenzar con los módulos de Microsoft Learn sobre [visión](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [procesamiento de lenguaje natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa con Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**, y otros. +* **Frameworks específicos en la nube** para ML, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), o [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considera usar las rutas de aprendizaje [Construye y opera soluciones de aprendizaje automático con Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) y [Construye y opera soluciones de aprendizaje automático con Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA conversacional** y **Chat Bots**. Hay una ruta de aprendizaje separada [Crea soluciones de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), y también puedes consultar [esta publicación del blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para más detalles. +* **Matemáticas profundas** detrás del aprendizaje profundo. Para esto, recomendamos [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) por Ian Goodfellow, Yoshua Bengio y Aaron Courville, que también está disponible en línea en [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Para una introducción suave a los temas de _IA en la Nube_ puedes considerar tomar la ruta de aprendizaje [Comienza con inteligencia artificial en Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Para una introducción suave a temas de _IA en la nube_ puedes considerar tomar la Ruta de Aprendizaje [Comienza con inteligencia artificial en Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Contenido -| | Enlace de la Lección | PyTorch/Keras/TensorFlow | Laboratorio | +| | Enlace a la Lección | PyTorch/Keras/TensorFlow | Lab | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Configuración del Curso](./lessons/0-course-setup/setup.md) | [Configura tu entorno de desarrollo](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Configuración del Curso](./lessons/0-course-setup/setup.md) | [Configura tu Entorno de Desarrollo](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introducción a la IA**](./lessons/1-Intro/README.md) | | | | 01 | [Introducción e Historia de la IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Simbólica** | -| 02 | [Representación del Conocimiento y Sistemas Expertos](./lessons/2-Symbolic/README.md) | [Sistemas Expertos](./lessons/2-Symbolic/Animals.ipynb) / [Ontología](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo Conceptual](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Representación del Conocimiento y Sistemas Expertos](./lessons/2-Symbolic/README.md) | [Sistemas Expertos](./lessons/2-Symbolic/Animals.ipynb) / [Ontología](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo de Conceptos](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introducción a las Redes Neuronales**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Perceptrón](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptrón multicapa y creación de nuestro propio framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Introducción a frameworks (PyTorch/TensorFlow) y sobreajuste](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 03 | [Perceptrón](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Cuaderno](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Perceptrón Multicapa y Creando nuestro propio Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Cuaderno](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introducción a Frameworks (PyTorch/TensorFlow) y Sobreajuste](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Visión por Computadora**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explora Visión por Computadora en Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Introducción a Visión por Computadora. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Redes Neuronales Convolucionales](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquitecturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Redes preentrenadas y aprendizaje por transferencia](./lessons/4-ComputerVision/08-TransferLearning/README.md) y [Trucos de entrenamiento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 06 | [Introducción a Visión por Computadora. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Cuaderno](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorio](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Redes Neuronales Convolucionales](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquitecturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Laboratorio](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Redes Preentrenadas y Aprendizaje por Transferencia](./lessons/4-ComputerVision/08-TransferLearning/README.md) y [Trucos de Entrenamiento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Laboratorio](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencoders y VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Redes Generativas Antagónicas y Transferencia de estilo artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Detección de objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Segmentación semántica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Procesamiento del Lenguaje Natural**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explora Procesamiento del Lenguaje Natural en Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Representación de texto. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Incrustaciones de palabras semánticas. Word2Vec y GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Modelado de lenguaje. Entrenando tus propias incrustaciones](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 10 | [Redes Generativas Antagónicas y Transferencia de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Detección de Objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Laboratorio](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Segmentación Semántica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**Procesamiento de Lenguaje Natural**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explora el Procesamiento de Lenguaje Natural en Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Representación de Texto. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Embeddings semánticos de palabras. Word2Vec y GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modelado de Lenguaje. Entrenando tus propios embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Laboratorio](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Redes Neuronales Recurrentes](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Redes Recurrentes Generativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 17 | [Redes Recurrentes Generativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Laboratorio](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Reconocimiento de Entidades Nombradas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Modelos de Lenguaje Grandes, Programación por indicios y Tareas Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 19 | [Reconocimiento de Entidades Nombradas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Laboratorio](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Modelos de Lenguaje Grandes, Programación de Prompts y Tareas con Pocas Muestras](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Otras Técnicas de IA** || | -| 21 | [Algoritmos Genéticos](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Aprendizaje por Refuerzo Profundo](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Sistemas Multi-Agente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 21 | [Algoritmos Genéticos](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Cuaderno](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Aprendizaje Profundo por Refuerzo](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Laboratorio](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Sistemas Multiagente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Ética en IA** | | | -| 24 | [Ética en IA y IA Responsable](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principios de IA Responsable](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Ética en IA e IA Responsable](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principios de IA Responsable](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Extras** | | | -| 25 | [Redes multimodales, CLIP y VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Redes Multimodales, CLIP y VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Cuaderno](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Cada lección contiene -* Material de lectura previa -* Jupyter Notebooks ejecutables, que a menudo son específicos para el framework (**PyTorch** o **TensorFlow**). El notebook ejecutable también contiene mucho material teórico, por lo que para entender el tema es necesario repasar al menos una versión del notebook (ya sea PyTorch o TensorFlow). -* **Laboratorios** disponibles para algunos temas, que te dan la oportunidad de aplicar el material aprendido a un problema específico. +* Material de prelectura +* Cuadernos Jupyter ejecutables, que a menudo son específicos para el framework (**PyTorch** o **TensorFlow**). El cuaderno ejecutable también contiene mucho material teórico, por lo que para entender el tema necesitas revisar al menos una versión del cuaderno (ya sea PyTorch o TensorFlow). +* **Laboratorios** disponibles para algunos temas, que te brindan la oportunidad de probar aplicar el material aprendido a un problema específico. * Algunas secciones contienen enlaces a módulos de [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que cubren temas relacionados. -## Comenzando +## Empezando -### 🎯 ¿Nuevo en IA? ¡Empieza aquí! +### 🎯 ¿Nuevo en IA? ¡Comienza Aquí! -Si eres completamente nuevo en IA y quieres ejemplos rápidos y prácticos, ¡consulta nuestros [**Ejemplos amigables para principiantes**](./examples/README.md)! Estos incluyen: +Si eres completamente nuevo en IA y quieres ejemplos rápidos y prácticos, echa un vistazo a nuestros [**Ejemplos para Principiantes**](./examples/README.md). Estos incluyen: -- 🌟 **Hola Mundo de IA** - Tu primer programa de IA (reconocimiento de patrones) +- 🌟 **Hola Mundo IA** - Tu primer programa de IA (reconocimiento de patrones) - 🧠 **Red Neuronal Simple** - Construye una red neuronal desde cero -- 🖼️ **Clasificador de imágenes** - Clasifica imágenes con comentarios detallados -- 💬 **Sentimiento del Texto** - Analiza texto positivo/negativo +- 🖼️ **Clasificador de Imágenes** - Clasifica imágenes con comentarios detallados +- 💬 **Sentimiento de Texto** - Analiza texto positivo/negativo -Estos ejemplos están diseñados para ayudarte a entender conceptos de IA antes de profundizar en el plan completo de estudios. +Estos ejemplos están diseñados para ayudarte a entender conceptos de IA antes de sumergirte en el plan de estudios completo. -### 📚 Configuración del plan completo +### 📚 Configuración del Plan de Estudios Completo -- Hemos creado una [lección de configuración](./lessons/0-course-setup/setup.md) para ayudarte a configurar tu entorno de desarrollo. - ¡Para educadores, también hemos creado una [lección de configuración del plan de estudios](./lessons/0-course-setup/for-teachers.md)! -- Cómo [Ejecutar el código en VSCode o Codepace](./lessons/0-course-setup/how-to-run.md) +- Hemos creado una [lección de configuración](./lessons/0-course-setup/setup.md) para ayudarte a configurar tu entorno de desarrollo. - Para educadores, también hemos creado una [lección de configuración del plan de estudios](./lessons/0-course-setup/for-teachers.md) para ustedes. +- Cómo [Ejecutar el código en VSCode o en un Codespace](./lessons/0-course-setup/how-to-run.md) Sigue estos pasos: -Bifurca el repositorio: Haz clic en el botón "Fork" en la esquina superior derecha de esta página. +Haz un Fork del Repositorio: Haz clic en el botón "Fork" en la esquina superior derecha de esta página. -Clona el repositorio: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Clona el Repositorio: `git clone https://github.com/microsoft/AI-For-Beginners.git` -No olvides darle una estrella (🌟) a este repositorio para encontrarlo más fácilmente después. +No olvides agregar una estrella (🌟) a este repositorio para encontrarlo más fácilmente después. -## Conoce a otros estudiantes +## Conoce a otros Estudiantes -Únete a nuestro [servidor oficial de Discord de IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conocer y conectar con otros estudiantes que toman este curso y obtener apoyo. +Únete a nuestro [servidor oficial de Discord de IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conocer y conectar con otros estudiantes que están tomando este curso y obtener soporte. -Si tienes comentarios sobre el producto o preguntas mientras construyes, visita nuestro [Foro de Desarrolladores de Azure AI Foundry](https://aka.ms/foundry/forum) +Si tienes comentarios sobre el producto o preguntas mientras construyes visita nuestro [Foro de Desarrolladores Azure AI Foundry](https://aka.ms/foundry/forum) -## Cuestionarios +## Cuestionarios -> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la carpeta Quiz-app en etc\quiz-app, o [En línea Aquí](https://ff-quizzes.netlify.app/). Están vinculados desde dentro de las lecciones; la aplicación de cuestionarios puede ejecutarse localmente o desplegarse en Azure; sigue las instrucciones en la carpeta `quiz-app`. Están siendo localizados gradualmente. +> **Una nota sobre los cuestionarios**: Todos los cuestionarios están contenidos en la carpeta Quiz-app en etc\quiz-app, o [En línea Aquí](https://ff-quizzes.netlify.app/). Están enlazados desde dentro de las lecciones, la app de cuestionario puede ejecutarse localmente o desplegarse en Azure; sigue las instrucciones en la carpeta `quiz-app`. Se están localizando gradualmente. -## Se requiere ayuda +## Se Busca Ayuda -¿Tienes sugerencias o encontraste errores ortográficos o en el código? Abre un issue o crea un pull request. +¿Tienes sugerencias o encontraste errores de ortografía o código? Abre un issue o crea un pull request. -## Agradecimientos especiales +## Agradecimientos Especiales -* **✍️ Autor principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **✍️ Autor Principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Ilustradora de Sketchnotes:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Creadora de cuestionarios:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Contribuidores principales:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✅ Creador de Cuestionarios:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Colaboradores Principales:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Otros planes de estudios +## Otros Planes de Estudio -¡Nuestro equipo produce otros planes de estudios! Echa un vistazo: +Nuestro equipo produce otros planes de estudio. ¡Mira: ### LangChain -[![LangChain4j para principiantes](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js para principiantes](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j para Principiantes](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js para Principiantes](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Agentes -[![AZD para principiantes](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI para principiantes](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP para principiantes](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Agentes de IA para principiantes](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD para Principiantes](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI para Principiantes](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP para Principiantes](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Agentes de IA para Principiantes](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- -### Serie de IA generativa -[![IA generativa para principiantes](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![IA generativa (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![IA generativa (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![IA generativa (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +### Serie de IA Generativa +[![IA Generativa para Principiantes](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- -### Aprendizaje principal -[![ML para principiantes](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Ciencia de datos para principiantes](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![IA para principiantes](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Ciberseguridad para principiantes](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Desarrollo web para principiantes](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT para principiantes](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![Desarrollo XR para principiantes](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +### Aprendizaje Fundamental +[![ML para Principiantes](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Ciencia de Datos para Principiantes](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![IA para Principiantes](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Ciberseguridad para Principiantes](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Desarrollo Web para Principiantes](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT para Principiantes](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Desarrollo XR para Principiantes](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- ### Serie Copilot -[![Copilot para programación en pareja con IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot para Programación en Pareja con IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot para C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) [![Aventura Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Obtener ayuda +## Obtener Ayuda -Si te atascas o tienes alguna pregunta sobre cómo crear aplicaciones de IA. Únete a otros estudiantes y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. +Si te quedas atascado o tienes alguna pregunta sobre cómo construir aplicaciones de IA, únete a otros estudiantes y desarrolladores experimentados en discusiones sobre MCP. Es una comunidad de apoyo donde las preguntas son bienvenidas y el conocimiento se comparte libremente. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si tienes comentarios sobre el producto o errores durante la creación, visita: +Si tienes comentarios sobre el producto o errores mientras construyes, visita: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- -**Descargo de responsabilidad**: -Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas derivadas del uso de esta traducción. +**Aviso legal**: +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda la traducción profesional humana. No nos hacemos responsables por malentendidos o interpretaciones erróneas derivadas del uso de esta traducción. \ No newline at end of file diff --git a/translations/es/lessons/0-course-setup/how-to-run.md b/translations/es/lessons/0-course-setup/how-to-run.md index 72bedd8f..892ebfb2 100644 --- a/translations/es/lessons/0-course-setup/how-to-run.md +++ b/translations/es/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Cómo Ejecutar el Código -Este curso contiene muchos ejemplos ejecutables y laboratorios que querrás ejecutar. Para hacerlo, necesitas la capacidad de ejecutar código Python en Jupyter Notebooks proporcionados como parte de este curso. Tienes varias opciones para ejecutar el código: +Este plan de estudios contiene muchos ejemplos ejecutables y laboratorios que querrás ejecutar. Para hacerlo, necesitas la capacidad de ejecutar código Python en los Jupyter Notebooks proporcionados como parte de este plan de estudios. Tienes varias opciones para ejecutar el código: ## Ejecutar localmente en tu computadora -Para ejecutar el código localmente en tu computadora, necesitas tener alguna versión de Python instalada. Personalmente, recomiendo instalar **[miniconda](https://conda.io/en/latest/miniconda.html)**: es una instalación bastante ligera que soporta el gestor de paquetes `conda` para diferentes **entornos virtuales** de Python. +Para ejecutar el código localmente en tu computadora, se necesita una instalación de Python. Una recomendación es instalar **[miniconda](https://conda.io/en/latest/miniconda.html)**: es una instalación bastante ligera que soporta el gestor de paquetes `conda` para diferentes **entornos virtuales** de Python. -Después de instalar miniconda, necesitas clonar el repositorio y crear un entorno virtual para usar en este curso: +Después de instalar miniconda, clona el repositorio y crea un entorno virtual que será usado para este curso: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Usar Visual Studio Code con la Extensión de Python +### Usando Visual Studio Code con la Extensión de Python -Probablemente la mejor manera de usar el curso es abrirlo en [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) con la [Extensión de Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Este plan de estudios se usa mejor cuando se abre en [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) con la [Extensión de Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Nota**: Una vez que clones y abras el directorio en VS Code, automáticamente te sugerirá instalar las extensiones de Python. También tendrás que instalar miniconda como se describió anteriormente. +> **Nota**: Una vez que clones y abras el directorio en VS Code, automáticamente te sugerirá instalar las extensiones de Python. También tendrás que instalar miniconda como se describió arriba. -> **Nota**: Si VS Code te sugiere reabrir el repositorio en un contenedor, necesitas rechazar esta opción para usar la instalación local de Python. +> **Nota**: Si VS Code te sugiere volver a abrir el repositorio en un contenedor, deberías rechazar esto para usar la instalación local de Python. -### Usar Jupyter en el Navegador +### Usando Jupyter en el Navegador -También puedes usar el entorno de Jupyter directamente desde el navegador en tu propia computadora. De hecho, tanto Jupyter clásico como Jupyter Hub ofrecen un entorno de desarrollo bastante conveniente con autocompletado, resaltado de código, etc. +También puedes usar un entorno Jupyter desde el navegador en tu propia computadora. Tanto Jupyter clásico como JupyterHub proveen un entorno de desarrollo conveniente con autocompletado, resaltado de código, etc. -Para iniciar Jupyter localmente, ve al directorio del curso y ejecuta: +Para iniciar Jupyter localmente, ve al directorio del curso, y ejecuta: ```bash jupyter notebook @@ -47,30 +47,34 @@ jupyterhub ``` Luego puedes navegar a cualquiera de los archivos `.ipynb`, abrirlos y comenzar a trabajar. -### Ejecutar en un contenedor +### Ejecutando en un contenedor -Una alternativa a la instalación de Python sería ejecutar el código en un contenedor. Dado que nuestro repositorio contiene una carpeta especial `.devcontainer` que indica cómo construir un contenedor para este repositorio, VS Code te ofrecerá reabrir el código en un contenedor. Esto requerirá la instalación de Docker y será más complejo, por lo que recomendamos esta opción para usuarios más experimentados. +Una alternativa a la instalación de Python sería ejecutar el código en un contenedor. Debido a que nuestro repositorio proporciona una carpeta especial `.devcontainer` que indica cómo construir un contenedor para este repositorio, VS Code ofrece la opción de volver a abrir el código en un contenedor. Esto requerirá la instalación de Docker y además sería más complejo, por lo que recomendamos esto a usuarios con más experiencia. ## Ejecutar en la Nube -Si no deseas instalar Python localmente y tienes acceso a algunos recursos en la nube, una buena alternativa sería ejecutar el código en la nube. Hay varias maneras de hacerlo: +Si no quieres instalar Python localmente y tienes acceso a algunos recursos en la nube, una buena alternativa sería ejecutar el código en la nube. Hay varias maneras de hacerlo: -* Usar **[GitHub Codespaces](https://github.com/features/codespaces)**, que es un entorno virtual creado para ti en GitHub, accesible a través de la interfaz del navegador de VS Code. Si tienes acceso a Codespaces, simplemente puedes hacer clic en el botón **Code** en el repositorio, iniciar un codespace y comenzar a trabajar rápidamente. -* Usar **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) proporciona recursos de computación gratuitos en la nube para personas como tú que quieren probar código en GitHub. Hay un botón en la página principal para abrir el repositorio en Binder, lo que debería llevarte rápidamente al sitio de Binder, que construirá el contenedor subyacente y comenzará la interfaz web de Jupyter de manera fluida. +* Usando **[GitHub Codespaces](https://github.com/features/codespaces)**, que es un entorno virtual creado para ti en GitHub, accesible a través de una interfaz de navegador de VS Code. Si tienes acceso a Codespaces, solo tienes que hacer clic en el botón **Code** en el repositorio, iniciar un codespace y comenzar a trabajar en muy poco tiempo. +* Usando **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) ofrece recursos de cómputo gratuitos proporcionados en la nube para personas como tú que quieren probar código en GitHub. Hay un botón en la página principal para abrir el repositorio en Binder; esto debería llevarte rápidamente al sitio de Binder, que construirá un contenedor subyacente e iniciará una interfaz web de Jupyter para ti sin inconvenientes. -> **Nota**: Para prevenir el mal uso, Binder tiene acceso bloqueado a algunos recursos web. Esto puede impedir que funcione parte del código que descarga modelos y/o conjuntos de datos desde Internet público. Es posible que necesites buscar algunas soluciones alternativas. Además, los recursos de computación proporcionados por Binder son bastante básicos, por lo que el entrenamiento será lento, especialmente en lecciones más complejas. +> **Nota**: Para prevenir uso indebido, Binder tiene acceso bloqueado a algunos recursos web. Esto puede prevenir que parte del código funcione si descarga modelos y/o conjuntos de datos de Internet público. Puede que necesites encontrar algunas soluciones alternativas. Además, los recursos de cómputo proporcionados por Binder son bastante básicos, por lo que el entrenamiento será lento, especialmente en lecciones posteriores y más complejas. ## Ejecutar en la Nube con GPU -Algunas de las lecciones más avanzadas de este curso se beneficiarían enormemente del soporte de GPU, ya que de lo contrario el entrenamiento será extremadamente lento. Hay algunas opciones que puedes seguir, especialmente si tienes acceso a la nube, ya sea a través de [Azure para Estudiantes](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) o a través de tu institución: +Algunas de las lecciones posteriores en este plan de estudios se beneficiarían mucho del soporte de GPU. El entrenamiento de modelos, por ejemplo, puede ser dolorosamente lento de otra forma. Hay algunas opciones que puedes seguir, especialmente si tienes acceso a la nube ya sea mediante [Azure para Estudiantes](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) o a través de tu institución: -* Crear una [Máquina Virtual de Ciencia de Datos](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) y conectarte a ella a través de Jupyter. Luego puedes clonar el repositorio directamente en la máquina y comenzar a aprender. Las máquinas virtuales de la serie NC tienen soporte para GPU. +* Crear una [Máquina Virtual para Ciencia de Datos](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) y conectarte a ella mediante Jupyter. Luego puedes clonar el repositorio directamente en la máquina y comenzar a aprender. Las máquinas virtuales de la serie NC tienen soporte para GPU. -> **Nota**: Algunas suscripciones, incluyendo Azure para Estudiantes, no proporcionan soporte para GPU de manera predeterminada. Es posible que necesites solicitar núcleos de GPU adicionales a través de una solicitud de soporte técnico. +> **Nota**: Algunas suscripciones, incluidas Azure para Estudiantes, no proporcionan soporte para GPU por defecto. Puede que necesites solicitar núcleos GPU adicionales mediante una solicitud de soporte técnico. -* Crear un [Espacio de Trabajo de Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) y luego usar la función de Notebook allí. [Este video](https://azure-for-academics.github.io/quickstart/azureml-papers/) muestra cómo clonar un repositorio en el notebook de Azure ML y comenzar a usarlo. +* Crear un [Espacio de Trabajo Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) y luego usar la función de Notebook allí. [Este video](https://azure-for-academics.github.io/quickstart/azureml-papers/) muestra cómo clonar un repositorio en un notebook de Azure ML y comenzar a usarlo. -También puedes usar Google Colab, que incluye soporte gratuito para GPU, y subir los Jupyter Notebooks allí para ejecutarlos uno por uno. +También puedes usar Google Colab, que viene con soporte gratuito de GPU y subir allí Jupyter Notebooks para ejecutarlos uno por uno. -**Descargo de responsabilidad**: -Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción. \ No newline at end of file +--- + + +**Aviso legal**: +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automatizadas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional humana. No nos hacemos responsables por malentendidos o interpretaciones erróneas derivadas del uso de esta traducción. + \ No newline at end of file diff --git a/translations/es/lessons/1-Intro/README.md b/translations/es/lessons/1-Intro/README.md index dcf967f9..6b6d82eb 100644 --- a/translations/es/lessons/1-Intro/README.md +++ b/translations/es/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducción a la IA -![Resumen del contenido de Introducción a la IA en un dibujo](../../../../translated_images/ai-intro.bf28d1ac4235881c.es.png) +![Resumen del contenido de Introducción a la IA en un dibujo](../../../../translated_images/es/ai-intro.bf28d1ac4235881c.webp) > Dibujo por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originalmente, las computadoras fueron inventadas por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar con números siguiendo un procedimiento bien definido: un algoritmo. Las computadoras modernas, aunque significativamente más avanzadas que el modelo original propuesto en el siglo XIX, aún siguen la misma idea de cálculos controlados. Por lo tanto, es posible programar una computadora para hacer algo si conocemos la secuencia exacta de pasos necesarios para lograr el objetivo. -![Foto de una persona](../../../../translated_images/dsh_age.d212a30d4e54fb5f.es.png) +![Foto de una persona](../../../../translated_images/es/dsh_age.d212a30d4e54fb5f.webp) > Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para más información, consulta **[Inteligencia Artificial General](https://en. Uno de los problemas al tratar con el término **[Inteligencia](https://en.wikipedia.org/wiki/Intelligence)** es que no hay una definición clara de este término. Se puede argumentar que la inteligencia está conectada con el **pensamiento abstracto** o con la **autoconciencia**, pero no podemos definirlo adecuadamente. -![Foto de un gato](../../../../translated_images/photo-cat.8c8e8fb760ffe457.es.jpg) +![Foto de un gato](../../../../translated_images/es/photo-cat.8c8e8fb760ffe457.webp) > [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) de Unsplash @@ -98,13 +98,13 @@ Alternativamente, podemos intentar modelar los elementos más simples dentro de > | ¿Qué hay de ML? | | > |--------------|-----------| -> | Parte de la Inteligencia Artificial que se basa en que la computadora aprenda a resolver un problema basado en algunos datos se llama **Aprendizaje Automático**. No consideraremos el aprendizaje automático clásico en este curso; te remitimos a un currículo separado de [Machine Learning para Principiantes](http://aka.ms/ml-beginners). | ![ML para Principiantes](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.es.png) | +> | Parte de la Inteligencia Artificial que se basa en que la computadora aprenda a resolver un problema basado en algunos datos se llama **Aprendizaje Automático**. No consideraremos el aprendizaje automático clásico en este curso; te remitimos a un currículo separado de [Machine Learning para Principiantes](http://aka.ms/ml-beginners). | ![ML para Principiantes](../../../../translated_images/es/ml-for-beginners.9e4fed176fd5817d.webp) | ## Breve Historia de la IA La Inteligencia Artificial comenzó como un campo a mediados del siglo XX. Inicialmente, el razonamiento simbólico era el enfoque predominante, y condujo a una serie de éxitos importantes, como los sistemas expertos: programas informáticos que podían actuar como expertos en algunos dominios de problemas limitados. Sin embargo, pronto quedó claro que este enfoque no escala bien. Extraer el conocimiento de un experto, representarlo en una computadora y mantener esa base de conocimiento precisa resulta ser una tarea muy compleja y demasiado costosa para ser práctica en muchos casos. Esto llevó al llamado [Invierno de la IA](https://en.wikipedia.org/wiki/AI_winter) en la década de 1970. -Breve Historia de la IA +Breve Historia de la IA > Imagen por [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ De manera similar, podemos ver cómo cambió el enfoque hacia la creación de "p * Los asistentes modernos, como Cortana, Siri o Google Assistant, son todos sistemas híbridos que utilizan redes neuronales para convertir el habla en texto y reconocer nuestra intención, y luego emplean algún razonamiento o algoritmos explícitos para realizar las acciones requeridas. * En el futuro, podemos esperar un modelo completamente basado en redes neuronales para manejar el diálogo por sí mismo. Las recientes redes neuronales de la familia GPT y [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) muestran un gran éxito en esto. -la evolución de la prueba de Turing +la evolución de la prueba de Turing > Imagen de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) por [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Investigación reciente en IA diff --git a/translations/es/lessons/2-Symbolic/Animals.ipynb b/translations/es/lessons/2-Symbolic/Animals.ipynb index 5e955cad..d8a882b6 100644 --- a/translations/es/lessons/2-Symbolic/Animals.ipynb +++ b/translations/es/lessons/2-Symbolic/Animals.ipynb @@ -8,23 +8,23 @@ "source": [ "# Implementación de un Sistema Experto de Animales\n", "\n", - "Un ejemplo del [Currículo de IA para Principiantes](http://github.com/microsoft/ai-for-beginners).\n", + "Un ejemplo de [Currículo de IA para Principiantes](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "En este ejemplo, implementaremos un sistema basado en conocimiento sencillo para determinar un animal en función de algunas características físicas. El sistema puede representarse mediante el siguiente árbol AND-OR (esto es una parte del árbol completo, podemos añadir más reglas fácilmente):\n", + "En este ejemplo, implementaremos un sistema simple basado en conocimiento para determinar un animal basado en algunas características físicas. El sistema puede ser representado por el siguiente árbol AND-OR (esta es una parte del árbol completo, podemos agregar fácilmente algunas reglas más):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/es/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Nuestro propio sistema experto con inferencia hacia atrás\n", + "## Nuestra propia shell de sistemas expertos con inferencia hacia atrás\n", "\n", - "Intentemos definir un lenguaje simple para la representación del conocimiento basado en reglas de producción. Usaremos clases de Python como palabras clave para definir reglas. Básicamente, habrá 3 tipos de clases:\n", - "* `Ask` representa una pregunta que necesita ser hecha al usuario. Contiene el conjunto de posibles respuestas.\n", - "* `If` representa una regla, y es simplemente una forma sintáctica de almacenar el contenido de la regla.\n", - "* `AND`/`OR` son clases para representar las ramas AND/OR del árbol. Solo almacenan la lista de argumentos dentro. Para simplificar el código, toda la funcionalidad se define en la clase padre `Content`.\n" + "Intentemos definir un lenguaje simple para la representación del conocimiento basado en reglas de producción. Usaremos clases de Python como palabras clave para definir reglas. Básicamente habría 3 tipos de clases: \n", + "* `Ask` representa una pregunta que debe hacerse al usuario. Contiene el conjunto de posibles respuestas. \n", + "* `If` representa una regla, y es simplemente un azúcar sintáctico para almacenar el contenido de la regla \n", + "* `AND`/`OR` son clases para representar ramas AND/OR del árbol. Simplemente almacenan la lista de argumentos dentro. Para simplificar el código, toda la funcionalidad se define en la clase padre `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "En nuestro sistema, la memoria de trabajo contendría la lista de **hechos** como **pares atributo-valor**. La base de conocimiento puede definirse como un gran diccionario que asigna acciones (nuevos hechos que deben insertarse en la memoria de trabajo) a condiciones, expresadas como expresiones AND-OR. Además, algunos hechos pueden ser `Preguntados`.\n" + "En nuestro sistema, la memoria de trabajo contendría la lista de **hechos** como **pares atributo-valor**. La base de conocimiento puede definirse como un gran diccionario que asigna acciones (nuevos hechos que deberían insertarse en la memoria de trabajo) a condiciones, expresadas como expresiones AND-OR. Además, algunos hechos pueden ser `Ask`.\n" ] }, { @@ -100,12 +100,12 @@ "metadata": {}, "source": [ "Para realizar la inferencia hacia atrás, definiremos la clase `Knowledgebase`. Esta contendrá:\n", - "* `memory` de trabajo: un diccionario que asigna atributos a valores.\n", - "* `rules` de la base de conocimiento en el formato definido anteriormente.\n", + "* Memoria de trabajo `memory` - un diccionario que mapea atributos a valores\n", + "* Reglas de la base de conocimiento `rules` en el formato definido anteriormente\n", "\n", - "Los dos métodos principales son:\n", - "* `get` para obtener el valor de un atributo, realizando la inferencia si es necesario. Por ejemplo, `get('color')` obtendría el valor de un espacio de color (preguntará si es necesario y almacenará el valor para uso posterior en la memoria de trabajo). Si preguntamos `get('color:blue')`, pedirá un color y luego devolverá un valor de `y`/`n` dependiendo del color.\n", - "* `eval` realiza la inferencia propiamente dicha, es decir, recorre el árbol AND/OR, evalúa subobjetivos, etc.\n" + "Dos métodos principales son:\n", + "* `get` para obtener el valor de un atributo, realizando inferencia si es necesario. Por ejemplo, `get('color')` obtendría el valor de una casilla de color (preguntará si es necesario, y almacenará el valor para uso posterior en la memoria de trabajo). Si preguntamos `get('color:blue')`, preguntará por un color y luego devolverá un valor `y`/`n` dependiendo del color.\n", + "* `eval` realiza la inferencia real, es decir, recorre el árbol AND/OR, evalúa subobjetivos, etc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ahora definamos nuestra base de conocimientos sobre animales y realicemos la consulta. Ten en cuenta que esta llamada te hará preguntas. Puedes responder escribiendo `y`/`n` para preguntas de sí o no, o especificando un número (0..N) para preguntas con respuestas de opción múltiple más largas.\n" + "Ahora definamos nuestra base de conocimiento sobre animales y realicemos la consulta. Ten en cuenta que esta llamada te hará preguntas. Puedes responder escribiendo `y`/`n` para preguntas de sí o no, o especificando un número (0..N) para preguntas con respuestas de opción múltiple más largas.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Usando PyKnow para Inferencia Hacia Adelante\n", + "## Uso de Experta para Inferencia Hacia Adelante\n", "\n", - "En el siguiente ejemplo, intentaremos implementar inferencia hacia adelante utilizando una de las bibliotecas para representación del conocimiento, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** es una biblioteca para crear sistemas de inferencia hacia adelante en Python, diseñada para ser similar al clásico sistema antiguo [CLIPS](http://www.clipsrules.net/index.html).\n", + "En el siguiente ejemplo, intentaremos implementar inferencia hacia adelante utilizando una de las bibliotecas para representación de conocimiento, [Experta](https://github.com/nilp0inter/experta). **Experta** es una biblioteca para crear sistemas de inferencia hacia adelante en Python, que está diseñada para ser similar al sistema clásico antiguo [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "También podríamos haber implementado el encadenamiento hacia adelante nosotros mismos sin muchos problemas, pero las implementaciones ingenuas generalmente no son muy eficientes. Para un emparejamiento de reglas más efectivo, se utiliza un algoritmo especial llamado [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "También podríamos haber implementado encadenamiento hacia adelante nosotros mismos sin muchos problemas, pero las implementaciones ingenuas generalmente no son muy eficientes. Para una coincidencia más efectiva de reglas se utiliza un algoritmo especial [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Definiremos nuestro sistema como una clase que hereda de `KnowledgeEngine`. Cada regla se define mediante una función separada con la anotación `@Rule`, que especifica cuándo debe activarse la regla. Dentro de la regla, podemos agregar nuevos hechos utilizando la función `declare`, y al agregar esos hechos, se activarán más reglas mediante el motor de inferencia hacia adelante.\n" + "Definiremos nuestro sistema como una clase que hereda de `KnowledgeEngine`. Cada regla se define mediante una función separada con la anotación `@Rule`, que especifica cuándo debe activarse la regla. Dentro de la regla, podemos agregar nuevos hechos usando la función `declare`, y agregar esos hechos hará que se llamen más reglas mediante el motor de inferencia hacia adelante.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Una vez que hemos definido una base de conocimientos, llenamos nuestra memoria de trabajo con algunos hechos iniciales y luego llamamos al método `run()` para realizar la inferencia. Puedes ver como resultado que nuevos hechos inferidos se añaden a la memoria de trabajo, incluyendo el hecho final sobre el animal (si configuramos todos los hechos iniciales correctamente).\n" + "Una vez que hemos definido una base de conocimientos, llenamos nuestra memoria de trabajo con algunos hechos iniciales y luego llamamos al método `run()` para realizar la inferencia. Como resultado, se puede ver que se añaden nuevos hechos inferidos a la memoria de trabajo, incluyendo el hecho final sobre el animal (si configuramos todos los hechos iniciales correctamente).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Descargo de responsabilidad**: \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Si bien nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n" + "---\n\n\n**Descargo de responsabilidad**:\nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de ningún malentendido o interpretación errónea derivada del uso de esta traducción.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T16:26:55+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:38:20+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "es" } diff --git a/translations/es/lessons/2-Symbolic/README.md b/translations/es/lessons/2-Symbolic/README.md index ce183a20..23bc066e 100644 --- a/translations/es/lessons/2-Symbolic/README.md +++ b/translations/es/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Representación del Conocimiento y Sistemas Expertos -![Resumen del contenido de IA Simbólica](../../../../translated_images/ai-symbolic.715a30cb610411a6.es.png) +![Resumen del contenido de IA Simbólica](../../../../../../translated_images/es/ai-symbolic.715a30cb610411a6.webp) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) -La búsqueda de la inteligencia artificial se basa en la búsqueda de conocimiento, para entender el mundo de manera similar a como lo hacen los humanos. Pero, ¿cómo se puede lograr esto? +La búsqueda de la inteligencia artificial se basa en una búsqueda de conocimiento, para comprender el mundo de manera similar a como lo hacen los humanos. Pero, ¿cómo puedes lograr esto? -## [Cuestionario previo a la lección](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Cuestionario previo a la clase](https://ff-quizzes.netlify.app/en/ai/quiz/3) -En los primeros días de la IA, el enfoque de arriba hacia abajo para crear sistemas inteligentes (discutido en la lección anterior) era popular. La idea era extraer el conocimiento de las personas en una forma legible por máquinas y luego usarlo para resolver problemas automáticamente. Este enfoque se basaba en dos grandes ideas: +En los primeros días de la IA, el enfoque de arriba hacia abajo para crear sistemas inteligentes (discutido en la lección anterior) era popular. La idea era extraer el conocimiento de las personas a alguna forma legible por máquina, y luego usarlo para resolver problemas automáticamente. Este enfoque se basaba en dos grandes ideas: * Representación del Conocimiento * Razonamiento ## Representación del Conocimiento -Uno de los conceptos importantes en la IA Simbólica es el **conocimiento**. Es importante diferenciar el conocimiento de la *información* o los *datos*. Por ejemplo, se puede decir que los libros contienen conocimiento, porque uno puede estudiarlos y convertirse en un experto. Sin embargo, lo que los libros contienen en realidad se llama *datos*, y al leer libros e integrar estos datos en nuestro modelo del mundo, convertimos estos datos en conocimiento. +Uno de los conceptos importantes en la IA Simbólica es el **conocimiento**. Es importante diferenciar el conocimiento de la *información* o los *datos*. Por ejemplo, se puede decir que los libros contienen conocimiento, porque uno puede estudiar libros y volverse un experto. Sin embargo, lo que los libros en realidad contienen se llama *datos*, y al leer libros e integrar estos datos en nuestro modelo del mundo convertimos estos datos en conocimiento. -> ✅ **Conocimiento** es algo que está contenido en nuestra mente y representa nuestra comprensión del mundo. Se obtiene mediante un proceso activo de **aprendizaje**, que integra las piezas de información que recibimos en nuestro modelo activo del mundo. +> ✅ **Conocimiento** es algo que está contenido en nuestra mente y representa nuestra comprensión del mundo. Se obtiene mediante un proceso activo de **aprendizaje**, que integra fragmentos de información que recibimos en nuestro modelo activo del mundo. -A menudo, no definimos estrictamente el conocimiento, pero lo alineamos con otros conceptos relacionados utilizando la [Pirámide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Contiene los siguientes conceptos: +Generalmente, no definimos estrictamente el conocimiento, sino que lo alineamos con otros conceptos relacionados usando la [Pirámide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Contiene los siguientes conceptos: -* **Datos**: algo representado en medios físicos, como texto escrito o palabras habladas. Los datos existen independientemente de los seres humanos y pueden ser transmitidos entre personas. -* **Información**: cómo interpretamos los datos en nuestra mente. Por ejemplo, cuando escuchamos la palabra *computadora*, tenemos cierta comprensión de lo que es. -* **Conocimiento**: la información integrada en nuestro modelo del mundo. Por ejemplo, una vez que aprendemos qué es una computadora, comenzamos a tener ideas sobre cómo funciona, cuánto cuesta y para qué se puede usar. Esta red de conceptos interrelacionados forma nuestro conocimiento. -* **Sabiduría**: un nivel más de nuestra comprensión del mundo, que representa el *meta-conocimiento*, es decir, una noción sobre cómo y cuándo debe usarse el conocimiento. +* **Datos** son algo representado en medios físicos, como texto escrito o palabras habladas. Los datos existen independientemente de los humanos y pueden ser transmitidos entre personas. +* **Información** es cómo interpretamos los datos en nuestra mente. Por ejemplo, cuando escuchamos la palabra *computadora*, tenemos alguna comprensión de qué es. +* **Conocimiento** es la información integrada en nuestro modelo del mundo. Por ejemplo, una vez que aprendemos qué es una computadora, empezamos a tener algunas ideas sobre cómo funciona, cuánto cuesta y para qué puede usarse. Esta red de conceptos interrelacionados forma nuestro conocimiento. +* **Sabiduría** es un nivel más de nuestra comprensión del mundo, y representa *meta-conocimiento*, por ejemplo, alguna noción de cómo y cuándo debe usarse el conocimiento. - + *Imagen [de Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Por Longlivetheux - Trabajo propio, CC BY-SA 4.0* -Por lo tanto, el problema de la **representación del conocimiento** es encontrar una forma efectiva de representar el conocimiento dentro de una computadora en forma de datos, para que sea automáticamente utilizable. Esto puede verse como un espectro: +Así, el problema de la **representación del conocimiento** es hallar alguna forma efectiva de representar el conocimiento dentro de una computadora en forma de datos, para hacerlo automáticamente utilizable. Esto puede verse como un espectro: -![Espectro de representación del conocimiento](../../../../translated_images/knowledge-spectrum.b60df631852c0217.es.png) +![Espectro de representación del conocimiento](../../../../../../translated_images/es/knowledge-spectrum.b60df631852c0217.webp) > Imagen por [Dmitry Soshnikov](http://soshnikov.com) -* A la izquierda, hay tipos muy simples de representaciones de conocimiento que pueden ser utilizados de manera efectiva por las computadoras. El más simple es el algorítmico, donde el conocimiento se representa mediante un programa de computadora. Sin embargo, esta no es la mejor manera de representar el conocimiento, porque no es flexible. El conocimiento en nuestra mente a menudo no es algorítmico. -* A la derecha, hay representaciones como el texto natural. Es la más poderosa, pero no puede ser utilizada para el razonamiento automático. +* A la izquierda, hay tipos muy simples de representaciones del conocimiento que pueden ser efectivamente usados por computadoras. La más simple es la algorítmica, cuando el conocimiento es representado por un programa de computadora. Esto, sin embargo, no es la mejor forma de representar el conocimiento, porque no es flexible. El conocimiento dentro de nuestra cabeza a menudo no es algorítmico. +* A la derecha, hay representaciones como el texto natural. Es la más poderosa, pero no puede usarse para razonamiento automático. -> ✅ Piensa por un momento en cómo representas el conocimiento en tu mente y lo conviertes en notas. ¿Hay algún formato en particular que funcione bien para ti y te ayude a retenerlo? +> ✅ Piensa por un minuto cómo representas el conocimiento en tu mente y lo conviertes en notas. ¿Hay algún formato particular que funcione bien para ayudarte a retener la información? ## Clasificación de Representaciones de Conocimiento en Computadoras -Podemos clasificar diferentes métodos de representación del conocimiento en computadoras en las siguientes categorías: +Podemos clasificar diferentes métodos de representación del conocimiento computacional en las siguientes categorías: -* **Representaciones en red**: se basan en el hecho de que tenemos una red de conceptos interrelacionados en nuestra mente. Podemos intentar reproducir las mismas redes como un grafo dentro de una computadora, una llamada **red semántica**. +* Las **representaciones en red** se basan en el hecho de que tenemos una red de conceptos interrelacionados dentro de nuestra mente. Podemos intentar reproducir esas mismas redes como un grafo dentro de una computadora: una llamada **red semántica**. -1. **Tríadas Objeto-Atributo-Valor** o **pares atributo-valor**. Dado que un grafo puede representarse dentro de una computadora como una lista de nodos y aristas, podemos representar una red semántica mediante una lista de tríadas que contienen objetos, atributos y valores. Por ejemplo, construimos las siguientes tríadas sobre lenguajes de programación: +1. **Tripletas Objeto-Atributo-Valor** o **pares atributo-valor**. Puesto que un grafo puede ser representado dentro de una computadora como una lista de nodos y aristas, podemos representar una red semántica mediante una lista de tripletas que contienen objetos, atributos y valores. Por ejemplo, construimos las siguientes tripletas sobre lenguajes de programación: Objeto | Atributo | Valor --------|----------|------ -Python | es | Lenguaje-No-Tipado +-------|----------|------- +Python | es | Lenguaje no tipado Python | inventado-por | Guido van Rossum -Python | sintaxis-de-bloque | indentación -Lenguaje-No-Tipado | no tiene | definiciones de tipo +Python | sintaxis-de-bloques | indentación +Lenguaje no tipado | no tiene | definiciones de tipo -> ✅ Piensa cómo las tríadas pueden ser utilizadas para representar otros tipos de conocimiento. +> ✅ Piensa cómo las tripletas pueden usarse para representar otros tipos de conocimiento. -2. **Representaciones jerárquicas**: enfatizan el hecho de que a menudo creamos una jerarquía de objetos en nuestra mente. Por ejemplo, sabemos que un canario es un pájaro, y que todos los pájaros tienen alas. También tenemos una idea de qué color suele ser un canario y cuál es su velocidad de vuelo. +2. Las **representaciones jerárquicas** enfatizan el hecho de que a menudo creamos una jerarquía de objetos en nuestra mente. Por ejemplo, sabemos que el canario es un pájaro, y todos los pájaros tienen alas. También tenemos alguna idea de qué color suele ser un canario y cuál es su velocidad de vuelo. - - **Representación mediante marcos**: se basa en representar cada objeto o clase de objetos como un **marco** que contiene **ranuras**. Las ranuras tienen posibles valores predeterminados, restricciones de valor o procedimientos almacenados que pueden ser llamados para obtener el valor de una ranura. Todos los marcos forman una jerarquía similar a una jerarquía de objetos en lenguajes de programación orientados a objetos. - - **Escenarios**: son un tipo especial de marcos que representan situaciones complejas que pueden desarrollarse en el tiempo. + - La **representación por marcos** se basa en representar cada objeto o clase de objetos como un **marco** que contiene **espacios (slots)**. Los espacios tienen posibles valores por defecto, restricciones de valor o procedimientos almacenados que pueden llamarse para obtener el valor de un espacio. Todos los marcos forman una jerarquía similar a una jerarquía de objetos en lenguajes de programación orientados a objetos. + - **Escenarios** son un tipo especial de marcos que representan situaciones complejas que pueden desarrollarse en el tiempo. **Python** -Ranura | Valor | Valor predeterminado | Intervalo | --------|-------|----------------------|-----------| +Espacio | Valor | Valor por defecto | Intervalo | +--------|-------|-------------------|-----------| Nombre | Python | | | -Es-Un | Lenguaje-No-Tipado | | | -Caso de Variables | | CamelCase | | -Longitud del Programa | | | 5-5000 líneas | -Sintaxis de Bloque | Indentación | | | +Es-Un | Lenguaje no tipado | | | +Estilo de variable | | CamelCase | | +Longitud del programa | | | 5-5000 líneas | +Sintaxis de bloques | Indentación | | | -3. **Representaciones procedimentales**: se basan en representar el conocimiento mediante una lista de acciones que pueden ejecutarse cuando ocurre una cierta condición. - - Las reglas de producción son declaraciones del tipo si-entonces que nos permiten sacar conclusiones. Por ejemplo, un médico puede tener una regla que diga que **SI** un paciente tiene fiebre alta **O** un nivel alto de proteína C reactiva en un análisis de sangre, **ENTONCES** tiene una inflamación. Una vez que encontramos una de las condiciones, podemos sacar una conclusión sobre la inflamación y luego usarla en razonamientos posteriores. - - Los algoritmos pueden considerarse otra forma de representación procedimental, aunque casi nunca se usan directamente en sistemas basados en conocimiento. +3. Las **representaciones procedurales** se basan en representar el conocimiento mediante una lista de acciones que pueden ejecutarse cuando ocurre cierta condición. + - Las reglas de producción son sentencias si-entonces que nos permiten sacar conclusiones. Por ejemplo, un médico puede tener una regla que dice que **SI** un paciente tiene fiebre alta **O** un nivel alto de proteína C reactiva en análisis de sangre **ENTONCES** tiene una inflamación. Una vez que encontramos una de las condiciones, podemos concluir inflamación y usarla en razonamientos posteriores. + - Los algoritmos pueden considerarse otra forma de representación procedural, aunque casi nunca se usan directamente en sistemas basados en conocimiento. -4. **Lógica**: fue propuesta originalmente por Aristóteles como una forma de representar el conocimiento humano universal. - - La Lógica de Predicados como teoría matemática es demasiado rica para ser computable, por lo que normalmente se utiliza algún subconjunto de ella, como las cláusulas de Horn utilizadas en Prolog. - - La Lógica Descriptiva es una familia de sistemas lógicos utilizados para representar y razonar sobre jerarquías de objetos y representaciones de conocimiento distribuidas como la *web semántica*. +4. La **lógica** fue originalmente propuesta por Aristóteles como una forma de representar el conocimiento humano universal. + - La Lógica de Predicados como teoría matemática es demasiado compleja para ser calculable, por lo que normalmente se usa algún subconjunto, como las cláusulas de Horn usadas en Prolog. + - La Lógica Descriptiva es una familia de sistemas lógicos usados para representar y razonar sobre jerarquías de objetos distribuidos en representaciones del conocimiento como la *web semántica*. ## Sistemas Expertos -Uno de los primeros éxitos de la IA simbólica fueron los llamados **sistemas expertos**: sistemas informáticos diseñados para actuar como un experto en un dominio de problemas limitado. Se basaban en una **base de conocimiento** extraída de uno o más expertos humanos, y contenían un **motor de inferencia** que realizaba razonamientos sobre ella. +Uno de los primeros éxitos de la IA simbólica fueron los llamados **sistemas expertos**: sistemas computacionales diseñados para actuar como un experto en un dominio problemático limitado. Se basaban en una **base de conocimiento** extraída de uno o más expertos humanos, y contenían un **motor de inferencia** que realizaba razonamientos sobre ella. -![Arquitectura Humana](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.es.png) | ![Sistema Basado en Conocimiento](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.es.png) +![Arquitectura humana](../../../../../../translated_images/es/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema basado en conocimiento](../../../../../../translated_images/es/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ Estructura simplificada del sistema neural humano | Arquitectura de un sistema basado en conocimiento -Los sistemas expertos están construidos como el sistema de razonamiento humano, que contiene **memoria a corto plazo** y **memoria a largo plazo**. De manera similar, en los sistemas basados en conocimiento distinguimos los siguientes componentes: +Los sistemas expertos están construidos como el sistema de razonamiento humano, que contiene **memoria a corto plazo** y **memoria a largo plazo**. De forma similar, en sistemas basados en conocimiento distinguimos los siguientes componentes: -* **Memoria del problema**: contiene el conocimiento sobre el problema que se está resolviendo actualmente, es decir, la temperatura o presión arterial de un paciente, si tiene inflamación o no, etc. Este conocimiento también se llama **conocimiento estático**, porque contiene una instantánea de lo que sabemos actualmente sobre el problema, el llamado *estado del problema*. -* **Base de conocimiento**: representa el conocimiento a largo plazo sobre un dominio de problemas. Se extrae manualmente de expertos humanos y no cambia de una consulta a otra. Debido a que nos permite navegar de un estado del problema a otro, también se llama **conocimiento dinámico**. -* **Motor de inferencia**: orquesta todo el proceso de búsqueda en el espacio de estados del problema, haciendo preguntas al usuario cuando sea necesario. También es responsable de encontrar las reglas correctas para aplicar en cada estado. +* **Memoria del problema**: contiene el conocimiento sobre el problema que se está resolviendo actualmente, es decir, la temperatura o presión sanguínea de un paciente, si tiene inflamación o no, etc. Este conocimiento también se llama **conocimiento estático**, porque contiene una instantánea de lo que actualmente sabemos sobre el problema - el llamado *estado del problema*. +* **Base de conocimiento**: representa el conocimiento a largo plazo sobre un dominio problemático. Se extrae manualmente de expertos humanos y no cambia entre consultas. Debido a que nos permite navegar de un estado a otro, también se llama **conocimiento dinámico**. +* **Motor de inferencia**: orquesta todo el proceso de búsqueda en el espacio de estados del problema, realizando preguntas al usuario cuando es necesario. También es responsable de encontrar las reglas correctas para aplicar en cada estado. -Como ejemplo, consideremos el siguiente sistema experto para determinar un animal basado en sus características físicas: +Como ejemplo, consideremos el siguiente sistema experto para determinar un animal según sus características físicas: -![Árbol AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.es.png) +![Árbol AND-OR](../../../../../../translated_images/es/AND-OR-Tree.5592d2c70187f283.webp) > Imagen por [Dmitry Soshnikov](http://soshnikov.com) -Este diagrama se llama un **árbol AND-OR**, y es una representación gráfica de un conjunto de reglas de producción. Dibujar un árbol es útil al comienzo de la extracción de conocimiento del experto. Para representar el conocimiento dentro de la computadora, es más conveniente usar reglas: +Este diagrama se llama **árbol AND-OR**, y es una representación gráfica de un conjunto de reglas de producción. Dibujar un árbol es útil al inicio de la extracción del conocimiento del experto. Para representar el conocimiento dentro de la computadora es más conveniente usar reglas: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Puedes notar que cada condición en el lado izquierdo de la regla y la acción son esencialmente tríadas Objeto-Atributo-Valor (OAV). La **memoria de trabajo** contiene el conjunto de tríadas OAV que corresponden al problema que se está resolviendo actualmente. Un **motor de reglas** busca reglas cuyas condiciones estén satisfechas y las aplica, agregando otra tríada a la memoria de trabajo. +Puedes notar que cada condición a la izquierda de la regla y la acción son esencialmente tripletas objeto-atributo-valor (OAV). La **memoria de trabajo** contiene el conjunto de tripletas OAV que corresponden al problema que se está resolviendo actualmente. Un **motor de reglas** busca reglas cuyas condiciones se satisfacen y las aplica, añadiendo otra tripleta a la memoria de trabajo. > ✅ ¡Escribe tu propio árbol AND-OR sobre un tema que te guste! -### Inferencia hacia adelante vs. Inferencia hacia atrás +### Inferencia hacia adelante vs. hacia atrás -El proceso descrito anteriormente se llama **inferencia hacia adelante**. Comienza con algunos datos iniciales sobre el problema disponibles en la memoria de trabajo y luego ejecuta el siguiente bucle de razonamiento: +El proceso descrito arriba se llama **inferencia hacia adelante**. Empieza con algunos datos iniciales sobre el problema disponibles en la memoria de trabajo, y luego ejecuta el siguiente ciclo de razonamiento: -1. Si el atributo objetivo está presente en la memoria de trabajo, detente y da el resultado. -2. Busca todas las reglas cuyas condiciones estén actualmente satisfechas: obtén el **conjunto de conflicto** de reglas. -3. Realiza la **resolución de conflictos**: selecciona una regla que se ejecutará en este paso. Podrían haber diferentes estrategias de resolución de conflictos: - - Seleccionar la primera regla aplicable en la base de conocimiento. - - Seleccionar una regla al azar. - - Seleccionar una regla *más específica*, es decir, la que cumpla con más condiciones en el lado izquierdo (LHS). -4. Aplica la regla seleccionada e inserta un nuevo conocimiento en el estado del problema. -5. Repite desde el paso 1. +1. Si el atributo objetivo está presente en la memoria de trabajo, detenerse y dar el resultado +2. Buscar todas las reglas cuyas condiciones se satisfacen actualmente - obtener el **conjunto de conflicto** de reglas. +3. Realizar **resolución de conflicto** - seleccionar una regla que será ejecutada en este paso. Puede haber diferentes estrategias de resolución de conflicto: + - Seleccionar la primera regla aplicable en la base de conocimiento + - Seleccionar una regla aleatoria + - Seleccionar una regla *más específica*, es decir, la que cumple con más condiciones en el "lado izquierdo" (LHS) +4. Aplicar la regla seleccionada e insertar nueva pieza de conocimiento en el estado del problema +5. Repetir desde el paso 1. -Sin embargo, en algunos casos podríamos querer comenzar con un conocimiento vacío sobre el problema y hacer preguntas que nos ayuden a llegar a la conclusión. Por ejemplo, al realizar un diagnóstico médico, generalmente no realizamos todos los análisis médicos de antemano antes de comenzar a diagnosticar al paciente. Más bien, queremos realizar análisis cuando sea necesario tomar una decisión. +Sin embargo, en algunos casos podríamos querer empezar con conocimiento vacío sobre el problema y hacer preguntas que nos ayuden a llegar a la conclusión. Por ejemplo, al hacer un diagnóstico médico, normalmente no realizamos todos los análisis médicos de antemano antes de empezar a diagnosticar al paciente. Más bien queremos hacer análisis cuando sea necesario. -Este proceso puede modelarse utilizando **inferencia hacia atrás**. Está impulsado por el **objetivo**, el valor del atributo que buscamos encontrar: +Este proceso puede modelarse usando **inferencia hacia atrás**. Está dirigido por el **objetivo**, es decir, el valor del atributo que queremos encontrar: -1. Selecciona todas las reglas que puedan darnos el valor de un objetivo (es decir, con el objetivo en el lado derecho (RHS)): un conjunto de conflicto. -2. Si no hay reglas para este atributo, o hay una regla que dice que debemos preguntar el valor al usuario, pregúntalo; de lo contrario: -3. Usa una estrategia de resolución de conflictos para seleccionar una regla que usaremos como *hipótesis*: intentaremos probarla. -4. Repite recursivamente el proceso para todos los atributos en el LHS de la regla, intentando probarlos como objetivos. -5. Si en algún momento el proceso falla, usa otra regla en el paso 3. +1. Seleccionar todas las reglas que nos pueden dar el valor de un objetivo (es decir, con el objetivo en el Lado Derecho ("right-hand-side")) - un conjunto de conflicto +1. Si no hay reglas para este atributo, o hay una regla que dice que debemos consultar el valor al usuario, preguntarlo; de lo contrario: +1. Usar la estrategia de resolución de conflicto para seleccionar una regla que usaremos como *hipótesis* - trataremos de probarla +1. Repetir recursivamente el proceso para todos los atributos en el Lado Izquierdo (LHS) de la regla, tratando de probarlos como objetivos +1. Si en algún momento el proceso falla, usar otra regla en el paso 3. > ✅ ¿En qué situaciones es más apropiada la inferencia hacia adelante? ¿Y la inferencia hacia atrás? ### Implementación de Sistemas Expertos -Los sistemas expertos pueden implementarse utilizando diferentes herramientas: +Los sistemas expertos pueden implementarse usando diferentes herramientas: -* Programándolos directamente en algún lenguaje de programación de alto nivel. Esto no es la mejor idea, porque la principal ventaja de un sistema basado en conocimiento es que el conocimiento está separado de la inferencia, y potencialmente un experto en el dominio del problema debería poder escribir reglas sin entender los detalles del proceso de inferencia. -* Usando un **shell de sistemas expertos**, es decir, un sistema diseñado específicamente para ser poblado con conocimiento utilizando algún lenguaje de representación del conocimiento. +* Programándolos directamente en algún lenguaje de alto nivel. Esto no es la mejor idea, porque la ventaja principal de un sistema basado en conocimiento es que el conocimiento está separado de la inferencia, y potencialmente un experto en el dominio problemático debería ser capaz de escribir reglas sin entender los detalles del proceso de inferencia +* Usando un **entorno de desarrollo para sistemas expertos**, es decir, un sistema diseñado específicamente para ser poblado con conocimiento usando algún lenguaje de representación del conocimiento. ## ✍️ Ejercicio: Inferencia de Animales -Consulta [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para un ejemplo de implementación de un sistema experto con inferencia hacia adelante y hacia atrás. +Consulta [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para un ejemplo de implementación de un sistema experto de inferencia hacia adelante y hacia atrás. -> **Nota**: Este ejemplo es bastante simple y solo da una idea de cómo se ve un sistema experto. Una vez que comiences a crear un sistema de este tipo, solo notarás un comportamiento *inteligente* cuando alcances un cierto número de reglas, alrededor de 200 o más. En algún momento, las reglas se vuelven demasiado complejas para mantenerlas todas en mente, y en ese punto podrías comenzar a preguntarte por qué el sistema toma ciertas decisiones. Sin embargo, una característica importante de los sistemas basados en conocimiento es que siempre puedes *explicar* exactamente cómo se tomaron las decisiones. +> **Nota**: Este ejemplo es bastante simple y solo da una idea de cómo es un sistema experto. Cuando empieces a crear un sistema así, notarás un comportamiento *inteligente* solo una vez alcances cierto número de reglas, alrededor de 200+. En algún punto, las reglas se vuelven demasiado complejas para recordarlas todas, y entonces puedes empezar a preguntarte por qué un sistema toma ciertas decisiones. Sin embargo, la característica importante de los sistemas basados en conocimiento es que siempre puedes *explicar* exactamente cómo se tomó cualquier decisión. ## Ontologías y la Web Semántica -A finales del siglo XX, hubo una iniciativa para usar la representación del conocimiento para anotar recursos de Internet, de modo que fuera posible encontrar recursos que correspondieran a consultas muy específicas. Este movimiento se llamó **Web Semántica**, y se basó en varios conceptos: +A finales del siglo XX hubo una iniciativa para usar la representación del conocimiento para anotar recursos de Internet, de modo que fuera posible encontrar recursos que correspondan a consultas muy específicas. Este movimiento se llamó **Web Semántica**, y se basó en varios conceptos: -- Una representación especial del conocimiento basada en **[lógicas descriptivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). Es similar a la representación mediante marcos, porque construye una jerarquía de objetos con propiedades, pero tiene semántica lógica formal e inferencia. Existe toda una familia de DLs que equilibran entre expresividad y complejidad algorítmica de la inferencia. -- Representación distribuida del conocimiento, donde todos los conceptos están representados por un identificador URI global, lo que hace posible crear jerarquías de conocimiento que abarcan Internet. +- Una representación especial del conocimiento basada en **[lógicas descriptivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). Es similar a la representación por marcos, porque construye una jerarquía de objetos con propiedades, pero tiene semántica formal lógica e inferencia. Existe una familia de DLs que balancean la expresividad y la complejidad algorítmica de la inferencia. +- Representación distribuida del conocimiento, donde todos los conceptos están representados por un identificador URI global, haciendo posible crear jerarquías de conocimiento a lo largo de internet. - Una familia de lenguajes basados en XML para la descripción del conocimiento: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Un concepto central en la Web Semántica es el de **Ontología**. Se refiere a una especificación explícita de un dominio de problema utilizando alguna representación formal del conocimiento. La ontología más simple puede ser solo una jerarquía de objetos en un dominio de problema, pero las ontologías más complejas incluirán reglas que pueden usarse para realizar inferencias. +Un concepto central en la Web Semántica es el concepto de **Ontología**. Se refiere a una especificación explícita de un dominio de problema utilizando alguna representación formal del conocimiento. La ontología más simple puede ser solo una jerarquía de objetos en un dominio de problema, pero ontologías más complejas incluirán reglas que pueden usarse para inferencia. -En la web semántica, todas las representaciones se basan en tríadas. Cada objeto y cada relación están identificados de manera única por un URI. Por ejemplo, si queremos expresar el hecho de que este Currículum de IA fue desarrollado por Dmitry Soshnikov el 1 de enero de 2022, aquí están las tríadas que podemos usar: +En la web semántica, todas las representaciones se basan en tripletas. Cada objeto y cada relación se identifican de manera única mediante la URI. Por ejemplo, si queremos afirmar el hecho de que este Curriculum de IA fue desarrollado por Dmitry Soshnikov el 1 de enero de 2022, aquí están las tripletas que podemos usar: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Aquí `http://www.example.com/terms/creation-date` y `http://purl.org/dc/elements/1.1/creator` son algunos URIs bien conocidos y universalmente aceptados para expresar los conceptos de *creador* y *fecha de creación*. +> ✅ Aquí `http://www.example.com/terms/creation-date` y `http://purl.org/dc/elements/1.1/creator` son algunas URIs bien conocidas y universalmente aceptadas para expresar los conceptos de *creador* y *fecha de creación*. En un caso más complejo, si queremos definir una lista de creadores, podemos usar algunas estructuras de datos definidas en RDF. - + -> Diagramas anteriores por [Dmitry Soshnikov](http://soshnikov.com) +> Diagramas arriba por [Dmitry Soshnikov](http://soshnikov.com) -El progreso en la construcción de la Web Semántica se vio de alguna manera ralentizado por el éxito de los motores de búsqueda y las técnicas de procesamiento de lenguaje natural, que permiten extraer datos estructurados de texto. Sin embargo, en algunas áreas todavía hay esfuerzos significativos para mantener ontologías y bases de conocimiento. Algunos proyectos que vale la pena destacar: +El progreso de construir la Web Semántica fue de alguna manera ralentizado por el éxito de los motores de búsqueda y las técnicas de procesamiento del lenguaje natural, que permiten extraer datos estructurados del texto. Sin embargo, en algunas áreas todavía existen esfuerzos significativos para mantener ontologías y bases de conocimiento. Algunos proyectos destacables: -* [WikiData](https://wikidata.org/) es una colección de bases de conocimiento legibles por máquinas asociadas con Wikipedia. La mayoría de los datos se extraen de las *InfoBoxes* de Wikipedia, piezas de contenido estructurado dentro de las páginas de Wikipedia. Puedes [consultar](https://query.wikidata.org/) WikiData en SPARQL, un lenguaje de consulta especial para la Web Semántica. Aquí hay una consulta de ejemplo que muestra los colores de ojos más populares entre los humanos: +* [WikiData](https://wikidata.org/) es una colección de bases de conocimiento legibles por máquina asociadas con Wikipedia. La mayor parte de los datos se extraen de los *InfoBoxes* de Wikipedia, fragmentos de contenido estructurado dentro de las páginas de Wikipedia. Puedes [consultar](https://query.wikidata.org/) wikidata en SPARQL, un lenguaje de consulta especial para la Web Semántica. Aquí hay una consulta de ejemplo que muestra los colores de ojos más populares entre los humanos: ```sparql #defaultView:BubbleChart @@ -208,45 +208,49 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) es otro esfuerzo similar a WikiData. -> ✅ Si deseas experimentar con la construcción de tus propias ontologías o abrir ontologías existentes, hay un excelente editor visual de ontologías llamado [Protégé](https://protege.stanford.edu/). Descárgalo o úsalo en línea. +> ✅ Si quieres experimentar con la construcción de tus propias ontologías, o abrir ontologías existentes, hay un gran editor visual de ontologías llamado [Protégé](https://protege.stanford.edu/). Descárgalo o úsalo en línea. - + -*Editor Web Protégé abierto con la ontología de la Familia Romanov. Captura de pantalla por Dmitry Soshnikov* +*Editor Web Protégé abierto con la ontología de la familia Romanov. Captura de pantalla por Dmitry Soshnikov* ## ✍️ Ejercicio: Una Ontología Familiar -Consulta [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para un ejemplo de uso de técnicas de la Web Semántica para razonar sobre relaciones familiares. Tomaremos un árbol genealógico representado en el formato común GEDCOM y una ontología de relaciones familiares para construir un gráfico de todas las relaciones familiares para un conjunto dado de individuos. +Consulta [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para un ejemplo de uso de técnicas de la Web Semántica para razonar sobre las relaciones familiares. Tomaremos un árbol genealógico representado en el formato común GEDCOM y una ontología de relaciones familiares para construir un grafo de todas las relaciones familiares para un conjunto dado de individuos. ## Microsoft Concept Graph -En la mayoría de los casos, las ontologías se crean cuidadosamente a mano. Sin embargo, también es posible **extraer** ontologías de datos no estructurados, por ejemplo, de textos en lenguaje natural. +En la mayoría de los casos, las ontologías son cuidadosamente creadas a mano. Sin embargo, también es posible **minar** ontologías a partir de datos no estructurados, por ejemplo, de textos en lenguaje natural. -Un intento de este tipo fue realizado por Microsoft Research, y resultó en [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Un intento así fue realizado por Microsoft Research, y resultó en [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Es una gran colección de entidades agrupadas utilizando la relación de herencia `is-a`. Permite responder preguntas como "¿Qué es Microsoft?" - la respuesta sería algo como "una empresa con probabilidad 0.87, y una marca con probabilidad 0.75". +Es una gran colección de entidades agrupadas usando la relación de herencia `is-a`. Permite responder preguntas como "¿Qué es Microsoft?" — la respuesta siendo algo como "una empresa con probabilidad 0.87, y una marca con probabilidad 0.75". -El gráfico está disponible como una API REST o como un gran archivo de texto descargable que enumera todos los pares de entidades. +El Grafo está disponible tanto como API REST, así como un gran archivo de texto descargable que lista todos los pares de entidades. -## ✍️ Ejercicio: Un Concept Graph +## ✍️ Ejercicio: Un Grafo de Conceptos Prueba el cuaderno [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) para ver cómo podemos usar Microsoft Concept Graph para agrupar artículos de noticias en varias categorías. ## Conclusión -Hoy en día, la IA a menudo se considera sinónimo de *aprendizaje automático* o *redes neuronales*. Sin embargo, un ser humano también exhibe razonamiento explícito, algo que actualmente no es manejado por las redes neuronales. En proyectos del mundo real, el razonamiento explícito todavía se utiliza para realizar tareas que requieren explicaciones o la capacidad de modificar el comportamiento del sistema de manera controlada. +Hoy en día, la IA a menudo se considera sinónimo de *Aprendizaje Automático* o *Redes Neuronales*. Sin embargo, un ser humano también exhibe razonamiento explícito, que es algo que actualmente no manejan las redes neuronales. En proyectos del mundo real, el razonamiento explícito todavía se usa para realizar tareas que requieren explicaciones, o que permiten modificar el comportamiento del sistema de una manera controlada. ## 🚀 Desafío -En el cuaderno de Ontología Familiar asociado a esta lección, hay una oportunidad para experimentar con otras relaciones familiares. Intenta descubrir nuevas conexiones entre las personas en el árbol genealógico. +En el cuaderno Ontología Familiar asociado a esta lección, hay una oportunidad para experimentar con otras relaciones familiares. Intenta descubrir nuevas conexiones entre personas en el árbol genealógico. -## [Cuestionario posterior a la lección](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Cuestionario posterior a la conferencia](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Revisión y Autoestudio -Investiga en internet para descubrir áreas donde los humanos han intentado cuantificar y codificar el conocimiento. Echa un vistazo a la Taxonomía de Bloom y retrocede en la historia para aprender cómo los humanos intentaron comprender su mundo. Explora el trabajo de Linneo para crear una taxonomía de organismos y observa cómo Dmitri Mendeléyev creó una forma de describir y agrupar los elementos químicos. ¿Qué otros ejemplos interesantes puedes encontrar? +Haz una investigación en internet para descubrir áreas donde los humanos han intentado cuantificar y codificar el conocimiento. Echa un vistazo a la Taxonomía de Bloom y regresa en la historia para aprender cómo los humanos intentaron comprender su mundo. Explora el trabajo de Linneo para crear una taxonomía de organismos, y observa la forma en que Dmitri Mendeléyev creó una manera para describir y agrupar elementos químicos. ¿Qué otros ejemplos interesantes puedes encontrar? **Tarea**: [Construir una Ontología](assignment.md) --- + +**Aviso legal**: +Este documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o inexactitudes. El documento original en su idioma nativo debe considerarse la fuente autorizada. Para información crítica, se recomienda la traducción profesional realizada por humanos. No nos responsabilizamos por cualquier malentendido o interpretación errónea derivada del uso de esta traducción. + \ No newline at end of file diff --git a/translations/es/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/es/lessons/3-NeuralNetworks/05-Frameworks/README.md index 89b89e75..f0b39a6f 100644 --- a/translations/es/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/es/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ El sobreajuste es un concepto extremadamente importante en el aprendizaje autom Considera el siguiente problema de aproximar 5 puntos (representados por `x` en los gráficos a continuación): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.es.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.es.jpg) +![linear](../../../../../translated_images/es/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/es/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modelo lineal, 2 parámetros** | **Modelo no lineal, 7 parámetros** Error de entrenamiento = 5.3 | Error de entrenamiento = 0 @@ -79,7 +79,7 @@ Es muy importante encontrar un equilibrio correcto entre la complejidad del mode Como puedes ver en el gráfico anterior, el sobreajuste puede detectarse por un error de entrenamiento muy bajo y un error de validación alto. Normalmente, durante el entrenamiento veremos que tanto el error de entrenamiento como el de validación comienzan a disminuir, y luego, en algún punto, el error de validación podría dejar de disminuir y empezar a aumentar. Esto será una señal de sobreajuste y un indicador de que probablemente deberíamos detener el entrenamiento en ese punto (o al menos hacer una instantánea del modelo). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.es.png) +![overfitting](../../../../../translated_images/es/Overfitting.408ad91cd90b4371.webp) ## Cómo prevenir el sobreajuste diff --git a/translations/es/lessons/3-NeuralNetworks/README.md b/translations/es/lessons/3-NeuralNetworks/README.md index 1fa00127..2d46c2de 100644 --- a/translations/es/lessons/3-NeuralNetworks/README.md +++ b/translations/es/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducción a las Redes Neuronales -![Resumen del contenido de Introducción a Redes Neuronales en un dibujo](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.es.png) +![Resumen del contenido de Introducción a Redes Neuronales en un dibujo](../../../../translated_images/es/ai-neuralnetworks.1c687ae40bc86e83.webp) Como discutimos en la introducción, una de las formas de lograr inteligencia es entrenar un **modelo computacional** o un **cerebro artificial**. Desde mediados del siglo XX, los investigadores han probado diferentes modelos matemáticos, hasta que en los últimos años esta dirección demostró ser enormemente exitosa. Estos modelos matemáticos del cerebro se llaman **redes neuronales**. @@ -36,13 +36,13 @@ En este plan de estudios, nos centraremos únicamente en modelos de redes neuron De la biología, sabemos que nuestro cerebro está compuesto por células neuronales (neuronas), cada una de las cuales tiene múltiples "entradas" (dendritas) y una única "salida" (axón). Tanto las dendritas como los axones pueden conducir señales eléctricas, y las conexiones entre ellos — conocidas como sinapsis — pueden mostrar diferentes grados de conductividad, que son regulados por neurotransmisores. -![Modelo de una Neurona](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.es.jpg) | ![Modelo de una Neurona](../../../../translated_images/artneuron.1a5daa88d20ebe6f.es.png) +![Modelo de una Neurona](../../../../translated_images/es/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modelo de una Neurona](../../../../translated_images/es/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neurona Real *([Imagen](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) de Wikipedia)* | Neurona Artificial *(Imagen del Autor)* Por lo tanto, el modelo matemático más simple de una neurona contiene varias entradas X1, ..., XN y una salida Y, junto con una serie de pesos W1, ..., WN. La salida se calcula como: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) donde f es alguna **función de activación** no lineal. diff --git a/translations/es/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/es/lessons/4-ComputerVision/06-IntroCV/README.md index 98c001a9..543d31b2 100644 --- a/translations/es/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/es/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ En nuestro [OpenCV Notebook](OpenCV.ipynb), damos algunos ejemplos de cuándo la * **Preprocesar una fotografía de un libro en Braille**. Nos enfocamos en cómo podemos usar umbralización, detección de características, transformación de perspectiva y manipulaciones de NumPy para separar símbolos individuales de Braille para su posterior clasificación por una red neuronal. -![Imagen Braille](../../../../../translated_images/braille.341962ff76b1bd70.es.jpeg) | ![Imagen Braille Preprocesada](../../../../../translated_images/braille-result.46530fea020b03c7.es.png) | ![Símbolos Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.es.png) +![Imagen Braille](../../../../../translated_images/es/braille.341962ff76b1bd70.webp) | ![Imagen Braille Preprocesada](../../../../../translated_images/es/braille-result.46530fea020b03c7.webp) | ![Símbolos Braille](../../../../../translated_images/es/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Imagen de [OpenCV.ipynb](OpenCV.ipynb) * **Detectar movimiento en video usando diferencia de fotogramas**. Si la cámara está fija, los fotogramas del feed de la cámara deberían ser bastante similares entre sí. Dado que los fotogramas se representan como arreglos, simplemente al restar esos arreglos de dos fotogramas consecutivos obtendremos la diferencia de píxeles, que debería ser baja para fotogramas estáticos y aumentar cuando haya un movimiento sustancial en la imagen. -![Imagen de fotogramas de video y diferencias de fotogramas](../../../../../translated_images/frame-difference.706f805491a0883c.es.png) +![Imagen de fotogramas de video y diferencias de fotogramas](../../../../../translated_images/es/frame-difference.706f805491a0883c.webp) > Imagen de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ En nuestro [OpenCV Notebook](OpenCV.ipynb), damos algunos ejemplos de cuándo la - **Flujo Óptico Denso** calcula el campo vectorial que muestra para cada píxel hacia dónde se está moviendo. - **Flujo Óptico Escaso** se basa en tomar algunas características distintivas en la imagen (por ejemplo, bordes) y construir su trayectoria de fotograma a fotograma. -![Imagen de Flujo Óptico](../../../../../translated_images/optical.1f4a94464579a83a.es.png) +![Imagen de Flujo Óptico](../../../../../translated_images/es/optical.1f4a94464579a83a.webp) > Imagen de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/es/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/es/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index e0a4c89f..336e7836 100644 --- a/translations/es/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/es/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 es una red que alcanzó un 92.7% de precisión en la clasificación top-5 de ImageNet en 2014. Tiene la siguiente estructura de capas: -![Capas de ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.es.jpg) +![Capas de ImageNet](../../../../../translated_images/es/vgg-16-arch1.d901a5583b3a51ba.webp) Como puedes ver, VGG sigue una arquitectura piramidal tradicional, que es una secuencia de capas de convolución y agrupamiento. -![Pirámide de ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.es.jpg) +![Pirámide de ImageNet](../../../../../translated_images/es/vgg-16-arch.64ff2137f50dd49f.webp) > Imagen de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/es/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/es/lessons/4-ComputerVision/07-ConvNets/README.md index e8c7c4d6..f9a7d9f6 100644 --- a/translations/es/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/es/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ En la vida real, queremos ser capaces de reconocer objetos en una imagen sin imp Para extraer patrones, utilizaremos el concepto de **filtros convolucionales**. Como sabes, una imagen se representa mediante una matriz 2D o un tensor 3D con profundidad de color. Aplicar un filtro significa que tomamos una matriz relativamente pequeña llamada **kernel del filtro**, y para cada píxel de la imagen original calculamos el promedio ponderado con los puntos vecinos. Podemos imaginar esto como una pequeña ventana que se desliza sobre toda la imagen, promediando todos los píxeles según los pesos en la matriz del kernel del filtro. -![Filtro de borde vertical](../../../../../translated_images/filter-vert.b7148390ca0bc356.es.png) | ![Filtro de borde horizontal](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.es.png) +![Filtro de borde vertical](../../../../../translated_images/es/filter-vert.b7148390ca0bc356.webp) | ![Filtro de borde horizontal](../../../../../translated_images/es/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Imagen por Dmitry Soshnikov @@ -38,7 +38,7 @@ El funcionamiento de las CNN se basa en las siguientes ideas importantes: * Podemos diseñar la red de manera que los filtros se entrenen automáticamente. * Podemos usar el mismo enfoque para encontrar patrones en características de alto nivel, no solo en la imagen original. Así, la extracción de características en las CNN funciona en una jerarquía de características, comenzando con combinaciones de píxeles de bajo nivel, hasta combinaciones de alto nivel de partes de la imagen. -![Extracción jerárquica de características](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.es.png) +![Extracción jerárquica de características](../../../../../translated_images/es/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Imagen de [un artículo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basado en [su investigación](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ La mayoría de las CNN utilizadas para el procesamiento de imágenes siguen una Como ejemplo, veamos la arquitectura de VGG-16, una red que logró un 92.7% de precisión en la clasificación top-5 de ImageNet en 2014: -![Capas de ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.es.jpg) +![Capas de ImageNet](../../../../../translated_images/es/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pirámide de ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.es.jpg) +![Pirámide de ImageNet](../../../../../translated_images/es/vgg-16-arch.64ff2137f50dd49f.webp) > Imagen de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/es/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/es/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ab6dcfad..6be838e7 100644 --- a/translations/es/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/es/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Necesitas entrenar una red neuronal convolucional para clasificar diferentes raz Usaremos el [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), que contiene imágenes de 37 razas diferentes de perros y gatos. -![Conjunto de datos con el que trabajaremos](../../../../../../translated_images/data.50b2a9d5484bdbf0.es.png) +![Conjunto de datos con el que trabajaremos](../../../../../../translated_images/es/data.50b2a9d5484bdbf0.webp) Para descargar el conjunto de datos, utiliza este fragmento de código: diff --git a/translations/es/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/es/lessons/4-ComputerVision/08-TransferLearning/README.md index 6eafcebf..bb31012d 100644 --- a/translations/es/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/es/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tanto Keras como PyTorch contienen funciones para cargar fácilmente pesos de re Aquí hay características extraídas de una imagen de un gato por la red VGG-16: -![Características extraídas por VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.es.png) +![Características extraídas por VGG-16](../../../../../translated_images/es/features.6291f9c7ba3a0b95.webp) ## Conjunto de Datos de Gatos vs. Perros @@ -48,19 +48,19 @@ Una red neuronal pre-entrenada contiene diferentes patrones en su *cerebro*, inc Un enfoque que podemos tomar es comenzar con una imagen aleatoria y luego usar la técnica de **optimización por descenso de gradiente** para ajustar esa imagen de tal manera que la red comience a pensar que es un gato. -![Bucle de Optimización de Imagen](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.es.png) +![Bucle de Optimización de Imagen](../../../../../translated_images/es/ideal-cat-loop.999fbb8ff306e044.webp) Sin embargo, si hacemos esto, obtendremos algo muy similar a un ruido aleatorio. Esto se debe a que *hay muchas formas de hacer que la red piense que la imagen de entrada es un gato*, incluyendo algunas que no tienen sentido visualmente. Aunque esas imágenes contienen muchos patrones típicos de un gato, no hay nada que las restrinja a ser visualmente distintivas. Para mejorar el resultado, podemos agregar otro término a la función de pérdida, llamado **pérdida de variación**. Es una métrica que muestra cuán similares son los píxeles vecinos de la imagen. Minimizar la pérdida de variación hace que la imagen sea más suave y elimina el ruido, revelando patrones más atractivos visualmente. Aquí hay un ejemplo de imágenes "ideales" que son clasificadas como gato y como cebra con alta probabilidad: -![Gato Ideal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.es.png) | ![Cebra Ideal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.es.png) +![Gato Ideal](../../../../../translated_images/es/ideal-cat.203dd4597643d6b0.webp) | ![Cebra Ideal](../../../../../translated_images/es/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Gato Ideal* | *Cebra Ideal* Un enfoque similar puede usarse para realizar los llamados **ataques adversariales** en una red neuronal. Supongamos que queremos engañar a una red neuronal y hacer que un perro parezca un gato. Si tomamos la imagen de un perro, que es reconocida por la red como un perro, podemos ajustarla un poco usando optimización por descenso de gradiente hasta que la red comience a clasificarla como un gato: -![Imagen de un Perro](../../../../../translated_images/original-dog.8f68a67d2fe0911f.es.png) | ![Imagen de un perro clasificada como un gato](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.es.png) +![Imagen de un Perro](../../../../../translated_images/es/original-dog.8f68a67d2fe0911f.webp) | ![Imagen de un perro clasificada como un gato](../../../../../translated_images/es/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Imagen original de un perro* | *Imagen de un perro clasificada como un gato* diff --git a/translations/es/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/es/lessons/4-ComputerVision/09-Autoencoders/README.md index d4de0b31..4ad30a8c 100644 --- a/translations/es/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/es/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Sin embargo, podríamos querer usar datos sin procesar (no etiquetados) para ent Dado que estamos entrenando un autoencoder para capturar la mayor cantidad de información posible de la imagen original para una reconstrucción precisa, la red intenta encontrar la mejor **representación** de las imágenes de entrada para capturar su significado. -![Diagrama de AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.es.jpg) +![Diagrama de AutoEncoder](../../../../../translated_images/es/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Imagen de [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/es/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/es/lessons/4-ComputerVision/11-ObjectDetection/README.md index 8e8b6586..6584d9be 100644 --- a/translations/es/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/es/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Los modelos de clasificación de imágenes que hemos tratado hasta ahora toman u ## [Cuestionario previo a la lección](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detección de Objetos](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.es.png) +![Detección de Objetos](../../../../../translated_images/es/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Imagen de [sitio web de YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supongamos que queremos encontrar un gato en una imagen. Un enfoque muy ingenuo 2. Ejecutar clasificación de imágenes en cada sección. 3. Las secciones que resulten en una activación suficientemente alta pueden considerarse que contienen el objeto en cuestión. -![Detección Ingenua de Objetos](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.es.png) +![Detección Ingenua de Objetos](../../../../../translated_images/es/naive-detection.e7f1ba220ccd08c6.webp) > *Imagen del [Cuaderno de Ejercicios](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Es posible que encuentres los siguientes conjuntos de datos para esta tarea: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 clases * [COCO](http://cocodataset.org/#home) - Objetos Comunes en Contexto. 80 clases, cuadros delimitadores y máscaras de segmentación. -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.es.jpg) +![COCO](../../../../../translated_images/es/coco-examples.71bc60380fa6cceb.webp) ## Métricas de Detección de Objetos @@ -50,7 +50,7 @@ Es posible que encuentres los siguientes conjuntos de datos para esta tarea: Mientras que para la clasificación de imágenes es fácil medir qué tan bien funciona el algoritmo, para la detección de objetos necesitamos medir tanto la corrección de la clase como la precisión de la ubicación inferida del cuadro delimitador. Para esto último, usamos la llamada **Intersección sobre Unión** (IoU), que mide qué tan bien se superponen dos cuadros (o dos áreas arbitrarias). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.es.png) +![IoU](../../../../../translated_images/es/iou_equation.9a4751d40fff4e11.webp) > *Figura 2 de [este excelente artículo sobre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existen dos grandes clases de algoritmos de detección de objetos: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utiliza [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) para generar una estructura jerárquica de regiones ROI, que luego se pasan por extractores de características CNN y clasificadores SVM para determinar la clase del objeto, y regresión lineal para determinar las coordenadas del *cuadro delimitador*. [Artículo Oficial](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.es.png) +![RCNN](../../../../../translated_images/es/rcnn1.cae407020dfb1d1f.webp) > *Imagen de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.es.png) +![RCNN-1](../../../../../translated_images/es/rcnn2.2d9530bb83516484.webp) > *Imágenes de [este artículo](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existen dos grandes clases de algoritmos de detección de objetos: Este enfoque es similar a R-CNN, pero las regiones se definen después de que se han aplicado las capas de convolución. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.es.png) +![FRCNN](../../../../../translated_images/es/f-rcnn.3cda6d9bb4188875.webp) > Imagen del [Artículo Oficial](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Este enfoque es similar a R-CNN, pero las regiones se definen después de que se La idea principal de este enfoque es usar una red neuronal para predecir las ROI, llamada *Red de Propuesta de Regiones*. [Artículo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.es.png) +![FasterRCNN](../../../../../translated_images/es/faster-rcnn.8d46c099b87ef30a.webp) > Imagen del [Artículo Oficial](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Este algoritmo es incluso más rápido que Faster R-CNN. La idea principal es la 1. Las características se procesan mediante **Position-Sensitive Score Map**. Cada objeto de $C$ clases se divide en regiones de $k\times k$, y entrenamos para predecir partes de objetos. 1. Para cada parte de las regiones de $k\times k$, todas las redes votan por las clases de objetos, y se selecciona la clase de objeto con el voto máximo. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.es.png) +![r-fcn image](../../../../../translated_images/es/r-fcn.13eb88158b99a3da.webp) > Imagen del [Artículo Oficial](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO es un algoritmo de una sola pasada en tiempo real. La idea principal es la * La imagen se divide en regiones de $S\times S$. * Para cada región, **CNN** predice $n$ posibles objetos, las coordenadas del *cuadro delimitador* y la *confianza*=*probabilidad* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.es.png) + ![YOLO](../../../../../translated_images/es/yolo.a2648ec82ee8bb4e.webp) > Imagen del [Artículo Oficial](https://arxiv.org/abs/1506.02640) diff --git a/translations/es/lessons/5-NLP/14-Embeddings/README.md b/translations/es/lessons/5-NLP/14-Embeddings/README.md index ffe7a627..f56ca266 100644 --- a/translations/es/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/es/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Entonces, la capa de embedding tomaría una palabra como entrada y produciría u Al usar una capa de embedding como la primera capa en nuestra red clasificadora, podemos cambiar de un modelo de bolsa de palabras a un modelo de **embedding bag**, donde primero convertimos cada palabra en nuestro texto en su correspondiente embedding, y luego calculamos alguna función de agregación sobre todos esos embeddings, como `sum`, `average` o `max`. -![Imagen que muestra un clasificador con embeddings para cinco palabras de una secuencia.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.es.png) +![Imagen que muestra un clasificador con embeddings para cinco palabras de una secuencia.](../../../../../translated_images/es/embedding-classifier-example.b77f021a7ee67eee.webp) > Imagen por el autor @@ -40,7 +40,7 @@ Para lograr esto, necesitamos preentrenar nuestro modelo de embedding en una gra CBoW es más rápido, mientras que skip-gram es más lento, pero hace un mejor trabajo representando palabras poco frecuentes. -![Imagen que muestra los algoritmos CBoW y Skip-Gram para convertir palabras en vectores.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.es.png) +![Imagen que muestra los algoritmos CBoW y Skip-Gram para convertir palabras en vectores.](../../../../../translated_images/es/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagen tomada de [este artículo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/es/lessons/5-NLP/15-LanguageModeling/README.md b/translations/es/lessons/5-NLP/15-LanguageModeling/README.md index 46992f59..76c3d30d 100644 --- a/translations/es/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/es/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ En nuestros ejemplos anteriores, utilizamos incrustaciones semánticas preentren * **Continuous Bag-of-Words** (CBoW), donde predecimos el token central $W_0$ en una secuencia de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, donde predecimos un conjunto de tokens vecinos {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partir del token central $W_0$. -![imagen del artículo sobre convertir palabras en vectores](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.es.png) +![imagen del artículo sobre convertir palabras en vectores](../../../../../translated_images/es/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Imagen tomada de [este artículo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/es/lessons/5-NLP/16-RNN/README.md b/translations/es/lessons/5-NLP/16-RNN/README.md index 7cb15052..4f064773 100644 --- a/translations/es/lessons/5-NLP/16-RNN/README.md +++ b/translations/es/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ En secciones anteriores, hemos estado utilizando representaciones semánticas en Para capturar el significado de una secuencia de texto, necesitamos usar otra arquitectura de red neuronal, llamada **red neuronal recurrente**, o RNN. En una RNN, pasamos nuestra oración a través de la red un símbolo a la vez, y la red produce un **estado**, que luego pasamos nuevamente a la red junto con el siguiente símbolo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.es.png) +![RNN](../../../../../translated_images/es/rnn.27f5c29c53d727b5.webp) > Imagen del autor @@ -61,7 +61,7 @@ Hemos discutido redes recurrentes que operan en una dirección, desde el inicio Una red recurrente, ya sea unidireccional o bidireccional, captura ciertos patrones dentro de una secuencia y puede almacenarlos en un vector de estado o pasarlos a la salida. Al igual que con las redes convolucionales, podemos construir otra capa recurrente sobre la primera para capturar patrones de nivel superior y construir a partir de los patrones de bajo nivel extraídos por la primera capa. Esto nos lleva a la noción de una **RNN multicapa**, que consiste en dos o más redes recurrentes, donde la salida de la capa anterior se pasa a la siguiente capa como entrada. -![Imagen mostrando una RNN multicapa de memoria a largo y corto plazo](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.es.jpg) +![Imagen mostrando una RNN multicapa de memoria a largo y corto plazo](../../../../../translated_images/es/multi-layer-lstm.dd975e29bb2a59fe.webp) *Imagen tomada de [este maravilloso artículo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/es/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/es/lessons/5-NLP/17-GenerativeNetworks/README.md index cd4400c7..3760d170 100644 --- a/translations/es/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/es/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ En la arquitectura de RNN que discutimos en la unidad anterior, cada unidad RNN Esto permite diferentes arquitecturas neuronales, como se muestra en la imagen a continuación: -![Imagen que muestra patrones comunes de redes neuronales recurrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.es.jpg) +![Imagen que muestra patrones comunes de redes neuronales recurrentes.](../../../../../translated_images/es/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Imagen del blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ En esta unidad, nos enfocaremos en modelos generativos simples que nos ayuden a Entrenaremos esta RNN para generar texto paso a paso. En cada paso, tomaremos una secuencia de caracteres de longitud `nchars` y pediremos a la red que genere el siguiente carácter de salida para cada carácter de entrada: -![Imagen que muestra un ejemplo de generación de la palabra 'HELLO' con una RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.es.png) +![Imagen que muestra un ejemplo de generación de la palabra 'HELLO' con una RNN.](../../../../../translated_images/es/rnn-generate.56c54afb52f9781d.webp) Cuando generamos texto (durante la inferencia), comenzamos con un **prompt**, que se pasa por las celdas RNN para generar su estado intermedio, y luego comienza la generación desde este estado. Generamos un carácter a la vez y pasamos el estado y el carácter generado a otra celda RNN para generar el siguiente, hasta que generemos suficientes caracteres. diff --git a/translations/es/lessons/5-NLP/18-Transformers/README.md b/translations/es/lessons/5-NLP/18-Transformers/README.md index 55e94901..0435dc8c 100644 --- a/translations/es/lessons/5-NLP/18-Transformers/README.md +++ b/translations/es/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Con las RNNs, la tarea de secuencia a secuencia se implementa mediante dos redes Los **mecanismos de atención** proporcionan una forma de ponderar el impacto contextual de cada vector de entrada en cada predicción de salida de la RNN. Esto se implementa creando atajos entre los estados intermedios de la RNN de entrada y la RNN de salida. De esta manera, al generar el símbolo de salida yt, tomaremos en cuenta todos los estados ocultos de entrada hi, con diferentes coeficientes de peso αt,i. -![Imagen que muestra un modelo codificador/decodificador con una capa de atención aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.es.png) +![Imagen que muestra un modelo codificador/decodificador con una capa de atención aditiva](../../../../../translated_images/es/encoder-decoder-attention.7a726296894fb567.webp) > El modelo codificador-decodificador con mecanismo de atención aditiva en [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado de [este blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matriz de atención {αi,j} representaría el grado en que ciertas palabras de entrada influyen en la generación de una palabra dada en la secuencia de salida. A continuación, se muestra un ejemplo de dicha matriz: -![Imagen que muestra un alineamiento de ejemplo encontrado por RNNsearch-50, tomada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.es.png) +![Imagen que muestra un alineamiento de ejemplo encontrado por RNNsearch-50, tomada de Bahdanau - arviz.org](../../../../../translated_images/es/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ El resultado que obtenemos con el embebido posicional incluye tanto el token ori A continuación, necesitamos capturar algunos patrones dentro de nuestra secuencia. Para ello, los transformadores utilizan un mecanismo de **auto-atención**, que es esencialmente atención aplicada a la misma secuencia como entrada y salida. Aplicar auto-atención nos permite tomar en cuenta el **contexto** dentro de la oración y ver qué palabras están interrelacionadas. Por ejemplo, nos permite ver qué palabras son referidas por correferencias, como *it*, y también considerar el contexto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.es.png) +![](../../../../../translated_images/es/CoreferenceResolution.861924d6d384a7d6.webp) > Imagen del [Blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Dado que cada posición de entrada se mapea independientemente a cada posición **BERT** (Representaciones de Codificador Bidireccional de Transformadores) es una red transformadora muy grande con múltiples capas: 12 capas para *BERT-base* y 24 para *BERT-large*. El modelo se preentrena primero en un gran corpus de datos de texto (Wikipedia + libros) utilizando entrenamiento no supervisado (predicción de palabras enmascaradas en una oración). Durante el preentrenamiento, el modelo absorbe niveles significativos de comprensión del lenguaje que luego pueden aprovecharse con otros conjuntos de datos mediante ajuste fino. Este proceso se llama **aprendizaje por transferencia**. -![imagen de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.es.png) +![imagen de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/es/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagen [fuente](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/es/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/es/lessons/5-NLP/18-Transformers/READMEtransformers.md index 2715fc93..578fe89d 100644 --- a/translations/es/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/es/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Con las RNN, la secuencia a secuencia se implementa mediante dos redes recurrent **Los Mecanismos de Atención** proporcionan un medio para ponderar el impacto contextual de cada vector de entrada en cada predicción de salida de la RNN. La forma en que se implementa es creando atajos entre los estados intermedios de la RNN de entrada y la RNN de salida. De esta manera, al generar el símbolo de salida yt, tomaremos en cuenta todos los estados ocultos de entrada hi, con diferentes coeficientes de peso αt,i. -![Imagen que muestra un modelo de codificador/decodificador con una capa de atención aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.es.png) +![Imagen que muestra un modelo de codificador/decodificador con una capa de atención aditiva](../../../../../translated_images/es/encoder-decoder-attention.7a726296894fb567.webp) > El modelo codificador-decodificador con mecanismo de atención aditiva en [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado de [esta publicación de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matriz de atención {αi,j} representaría el grado en que ciertas palabras de entrada juegan un papel en la generación de una palabra dada en la secuencia de salida. A continuación se muestra un ejemplo de tal matriz: -![Imagen que muestra una alineación de muestra encontrada por RNNsearch-50, tomada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.es.png) +![Imagen que muestra una alineación de muestra encontrada por RNNsearch-50, tomada de Bahdanau - arviz.org](../../../../../translated_images/es/bahdanau-fig3.09ba2d37f202a6af.webp) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ El resultado que obtenemos con el embebido posicional integra tanto el token ori A continuación, necesitamos capturar algunos patrones dentro de nuestra secuencia. Para hacer esto, los transformadores utilizan un mecanismo de **autoatención**, que es esencialmente atención aplicada a la misma secuencia como entrada y salida. Aplicar autoatención nos permite tener en cuenta el **contexto** dentro de la oración y ver qué palabras están interrelacionadas. Por ejemplo, nos permite ver qué palabras son referidas por co-referencias, como *ello*, y también tener en cuenta el contexto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.es.png) +![](../../../../../translated_images/es/CoreferenceResolution.861924d6d384a7d6.webp) > Imagen del [Blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Dado que cada posición de entrada se mapea independientemente a cada posición **BERT** (Representaciones de Codificador Bidireccional de Transformadores) es una red transformadora de múltiples capas muy grande con 12 capas para *BERT-base*, y 24 para *BERT-large*. El modelo se preentrena primero en un gran corpus de datos textuales (WikiPedia + libros) utilizando entrenamiento no supervisado (prediciendo palabras enmascaradas en una oración). Durante el preentrenamiento, el modelo absorbe niveles significativos de comprensión del lenguaje que luego pueden ser aprovechados con otros conjuntos de datos utilizando ajuste fino. Este proceso se llama **aprendizaje por transferencia**. -![imagen de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.es.png) +![imagen de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/es/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Imagen [fuente](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/es/lessons/5-NLP/19-NER/README.md b/translations/es/lessons/5-NLP/19-NER/README.md index b98f174e..c92511f6 100644 --- a/translations/es/lessons/5-NLP/19-NER/README.md +++ b/translations/es/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Dado que necesitamos construir una correspondencia uno a uno entre tokens y clases, podemos entrenar un modelo neuronal **muchos a muchos** como el que se muestra en esta imagen: -![Imagen que muestra patrones comunes de redes neuronales recurrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.es.jpg) +![Imagen que muestra patrones comunes de redes neuronales recurrentes.](../../../../../translated_images/es/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Imagen tomada de [este artículo](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/). Los modelos de clasificación de tokens NER corresponden a la arquitectura de red más a la derecha en esta imagen.* diff --git a/translations/es/lessons/6-Other/23-MultiagentSystems/README.md b/translations/es/lessons/6-Other/23-MultiagentSystems/README.md index 43372a6a..f14924ea 100644 --- a/translations/es/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/es/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Puedes abrir uno de los modelos, por ejemplo **Biology → Flocking**. Después de abrir el modelo, serás llevado a la pantalla principal de NetLogo. Aquí hay un modelo de ejemplo que describe la población de lobos y ovejas, dadas recursos finitos (hierba). -![Pantalla Principal de NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.es.png) +![Pantalla Principal de NetLogo](../../../../../translated_images/es/NetLogo-Main.32653711ec1a01b3.webp) > Captura de pantalla por Dmitry Soshnikov diff --git a/translations/et/README.md b/translations/et/README.md index c2bd1f0f..1f0ded16 100644 --- a/translations/et/README.md +++ b/translations/et/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Tehisintellekt algajatele - õppekava -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.et.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/et/ai-overview.0857791951d19500.png)| |:---:| | AI algajatele - _Sketchnote autor [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/et/lessons/1-Intro/README.md b/translations/et/lessons/1-Intro/README.md index 7f2d7400..cc199941 100644 --- a/translations/et/lessons/1-Intro/README.md +++ b/translations/et/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sissejuhatus tehisintellekti -![Sissejuhatuse kokkuvõte tehisintellekti teemal doodle'is](../../../../translated_images/ai-intro.bf28d1ac4235881c.et.png) +![Sissejuhatuse kokkuvõte tehisintellekti teemal doodle'is](../../../../translated_images/et/ai-intro.bf28d1ac4235881c.png) > Sketchnote autor: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Algselt leiutas [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) arvutid, et need töötleksid numbreid kindla protseduuri ehk algoritmi järgi. Kaasaegsed arvutid, kuigi palju arenenumad kui 19. sajandil välja pakutud mudel, järgivad endiselt sama ideed kontrollitud arvutustest. Seega on võimalik programmeerida arvutit midagi tegema, kui me teame täpset sammude jada, mida eesmärgi saavutamiseks vaja on. -![Foto inimesest](../../../../translated_images/dsh_age.d212a30d4e54fb5f.et.png) +![Foto inimesest](../../../../translated_images/et/dsh_age.d212a30d4e54fb5f.png) > Foto autor: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Lisateabe saamiseks vaata **[Üldine tehisintellekt](https://en.wikipedia.org/wi Üks probleem **[intelligentsuse](https://en.wikipedia.org/wiki/Intelligence)** mõistega tegelemisel on see, et sellel puudub selge määratlus. Võib väita, et intelligentsus on seotud **abstraktse mõtlemise** või **eneseteadvusega**, kuid me ei suuda seda korralikult defineerida. -![Foto kassist](../../../../translated_images/photo-cat.8c8e8fb760ffe457.et.jpg) +![Foto kassist](../../../../translated_images/et/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) autor: [Amber Kipp](https://unsplash.com/@sadmax) Unsplashist @@ -98,13 +98,13 @@ Teise võimalusena võime proovida modelleerida meie aju kõige lihtsamaid eleme > | Aga ML? | | > |--------------|-----------| -> | Tehisintellekti osa, mis põhineb arvuti õppimisel probleemi lahendamiseks andmete põhjal, nimetatakse **masinõppeks**. Me ei käsitle selles kursuses klassikalist masinõpet – soovitame tutvuda eraldi [Masinõppe algajatele](http://aka.ms/ml-beginners) õppekavaga. | ![ML algajatele](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.et.png) | +> | Tehisintellekti osa, mis põhineb arvuti õppimisel probleemi lahendamiseks andmete põhjal, nimetatakse **masinõppeks**. Me ei käsitle selles kursuses klassikalist masinõpet – soovitame tutvuda eraldi [Masinõppe algajatele](http://aka.ms/ml-beginners) õppekavaga. | ![ML algajatele](../../../../translated_images/et/ml-for-beginners.9e4fed176fd5817d.png) | ## Lühike ülevaade TI ajaloost Tehisintellekt kui valdkond sai alguse 20. sajandi keskel. Alguses oli sümboolne arutlemine valdav lähenemine ja see tõi kaasa mitmeid olulisi edusamme, näiteks ekspertsüsteemid – arvutiprogrammid, mis suutsid tegutseda eksperdina mõnes piiratud probleemivaldkonnas. Kuid peagi sai selgeks, et selline lähenemine ei ole hästi skaleeritav. Teadmiste eraldamine eksperdilt, nende esitamine arvutis ja teadmistebaasi täpsuse säilitamine osutus väga keeruliseks ja paljudel juhtudel liiga kulukaks. See viis nn [TI talveni](https://en.wikipedia.org/wiki/AI_winter) 1970. aastatel. -TI ajaloo lühikokkuvõte +TI ajaloo lühikokkuvõte > Pildi autor: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Samamoodi näeme, kuidas lähenemine "rääkivate programmide" (mis võiksid lä * Kaasaegsed assistendid, nagu Cortana, Siri või Google Assistant, on kõik hübriidsüsteemid, mis kasutavad närvivõrke kõne tekstiks teisendamiseks ja meie kavatsuste tuvastamiseks ning seejärel rakendavad mõningaid arutlusi või selgesõnalisi algoritme vajalike toimingute tegemiseks. * Tulevikus võime oodata täielikult närvivõrkudel põhinevat mudelit, mis suudab dialoogi iseseisvalt hallata. Hiljutised GPT ja [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) närvivõrkude perekonnad näitavad selles valdkonnas suurt edu. -Turingi testi areng +Turingi testi areng > Pilt Dmitry Soshnikovilt, [foto](https://unsplash.com/photos/r8LmVbUKgns) autoriks [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Viimased tehisintellekti uuringud diff --git a/translations/et/lessons/2-Symbolic/Animals.ipynb b/translations/et/lessons/2-Symbolic/Animals.ipynb index 56b83253..2a47b4fb 100644 --- a/translations/et/lessons/2-Symbolic/Animals.ipynb +++ b/translations/et/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Selles näites rakendame lihtsat teadmistepõhist süsteemi, et määrata loom füüsiliste omaduste põhjal. Süsteemi saab kujutada järgmise JA-VÕI puuna (see on osa kogu puust, reegleid saab hõlpsasti juurde lisada):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.et.png)\n" + "![](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/et/lessons/2-Symbolic/README.md b/translations/et/lessons/2-Symbolic/README.md index bc80b057..3a00bdbb 100644 --- a/translations/et/lessons/2-Symbolic/README.md +++ b/translations/et/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Teadmiste esitus ja ekspertsüsteemid -![Sümboolse AI sisu kokkuvõte](../../../../translated_images/ai-symbolic.715a30cb610411a6.et.png) +![Sümboolse AI sisu kokkuvõte](../../../../translated_images/et/ai-symbolic.715a30cb610411a6.png) > Sketchnote autorilt [Tomomi Imura](https://twitter.com/girlie_mac) @@ -35,13 +35,13 @@ Enamasti me ei defineeri teadmisi rangelt, vaid seostame neid teiste seotud mõi * **Teadmised** on informatsioon, mis on integreeritud meie maailmamudelisse. Näiteks, kui õpime, mis on arvuti, hakkame mõistma, kuidas see töötab, kui palju see maksab ja milleks seda saab kasutada. See omavahel seotud mõistete võrgustik moodustab meie teadmised. * **Tarkus** on veel üks tasand meie arusaamisest maailmast ja esindab *meta-teadmisi*, näiteks arusaama, kuidas ja millal teadmisi kasutada. - + *Pilt [Wikipedia-st](https://commons.wikimedia.org/w/index.php?curid=37705247), autor Longlivetheux - Oma töö, CC BY-SA 4.0* Seega on **teadmiste esitamise** probleem leida tõhus viis teadmiste esitamiseks arvutis andmete kujul, et neid automaatselt kasutada. Seda võib vaadelda spektrina: -![Teadmiste esitamise spekter](../../../../translated_images/knowledge-spectrum.b60df631852c0217.et.png) +![Teadmiste esitamise spekter](../../../../translated_images/et/knowledge-spectrum.b60df631852c0217.png) > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Ploki süntaks | Taanded | | | Sümboolse AI varajased edusammud olid nn **ekspertsüsteemid** - arvutisüsteemid, mis olid loodud tegutsema eksperdina mõnes piiratud probleemivaldkonnas. Need põhinesid **teadmistebaasil**, mis oli saadud ühelt või mitmelt inimeksperdilt, ja sisaldasid **järeldusmootorit**, mis tegi selle põhjal järeldusi. -![Inimese arhitektuur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.et.png) | ![Teadmistepõhise süsteemi arhitektuur](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.et.png) +![Inimese arhitektuur](../../../../translated_images/et/arch-human.5d4d35f1bba3ab1c.png) | ![Teadmistepõhise süsteemi arhitektuur](../../../../translated_images/et/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Lihtsustatud inimese närvisüsteemi struktuur | Teadmistepõhise süsteemi arhitektuur @@ -106,7 +106,7 @@ Ekspertsüsteemid on ehitatud nagu inimese järeldussüsteem, mis sisaldab **lü Näiteks vaatame järgmist ekspertsüsteemi, mis määrab looma füüsiliste omaduste põhjal: -![AND-OR puu](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.et.png) +![AND-OR puu](../../../../translated_images/et/AND-OR-Tree.5592d2c70187f283.png) > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) @@ -175,7 +175,7 @@ Semantilise veebi keskne mõiste on **ontoloogia**. See viitab probleemivaldkonn Semantilises veebis põhinevad kõik esitusviisid kolmikutel. Iga objekt ja iga seos on unikaalselt identifitseeritud URI abil. Näiteks, kui soovime väita, et see AI õppekava on koostanud Dmitry Soshnikov 1. jaanuaril 2022, siis siin on kolmikud, mida saame kasutada: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -186,7 +186,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre Keerukamal juhul, kui soovime määratleda loojate nimekirja, saame kasutada RDF-is määratletud andmestruktuure. - + > Ülaltoodud diagrammid: [Dmitry Soshnikov](http://soshnikov.com) @@ -210,7 +210,7 @@ GROUP BY ?eyeColorLabel > ✅ Kui soovite katsetada oma ontoloogiate loomist või olemasolevate avamist, on suurepärane visuaalne ontoloogia redaktor nimega [Protégé](https://protege.stanford.edu/). Laadige see alla või kasutage seda veebis. - + *Web Protégé redaktor avatud Romanovite perekonna ontoloogiaga. Ekraanipilt: Dmitry Soshnikov* diff --git a/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md index 096e82c2..79728ecd 100644 --- a/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/et/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | Mark 1 Perceptron| +|Frank Rosenblatt | Mark 1 Perceptron| > Pildid [Wikipediast](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) kus f on astmelise aktiveerimise funktsioon - + ## Perceptroni treenimine diff --git a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 4c787e5a..bfbf4b81 100644 --- a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "Oleme loonud andmekogu binaarse klassifikatsiooni probleemi jaoks. Kuid võtame seda algusest peale kui mitmeklassilist klassifikatsiooni, et saaksime hiljem oma koodi hõlpsalt mitmeklassiliseks klassifikatsiooniks ümber lülitada. Sel juhul on meie ühekihilise perceptroni arhitektuur järgmine:\n", "\n", - "\n", + "\n", "\n", "Võrgu kaks väljundit vastavad kahele klassile ning klass, mille väljundväärtus on kahe hulgast suurim, vastab õigele lahendusele.\n", "\n", @@ -489,7 +489,7 @@ "\n", "Kui meil on rohkem kui 2 klassi, normaliseerib softmax tõenäosused kõigi klasside vahel. Siin on diagramm võrgu arhitektuurist, mis teostab MNIST numbrite klassifitseerimist:\n", "\n", - "![MNIST Klassifikaator](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.et.png)\n" + "![MNIST Klassifikaator](../../../../../translated_images/et/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## Arvutusgraaf\n", "\n", - "\n", + "\n", "\n", "Siiani oleme defineerinud erinevad klassid võrgu erinevate kihtide jaoks. Nende kihtide koostist saab kujutada kui **arvutusgraafi**. Nüüd saame kaotuse arvutada antud treeningandmekogu (või selle osa) jaoks järgmiselt:\n" ] @@ -687,7 +687,7 @@ "source": [ "## Tagurpidi levik\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* See vastab muudatustele sõlmes $z$ väärtusega $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", "* Selle vea minimeerimiseks peame vastavalt kohandama parameetreid: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (ja sama kehtib $b$ kohta)\n", "\n", - "\n", + "\n", "\n", "See protsess algab kaotuse vea jaotamisega võrgu väljundist tagasi selle parameetriteni. Seetõttu nimetatakse seda protsessi **tagasilevikuks**.\n", "\n", @@ -1265,7 +1265,7 @@ "* Madal treeningkadu – mudel suudab treeningandmeid hästi jäljendada, kuna sellel on piisavalt väljendusvõimsust.\n", "* Valideerimiskadu võib olla palju suurem kui treeningkadu ja treeningu käigus hakata suurenema – see juhtub, kuna mudel \"mäletab\" treeningpunkte ja kaotab \"üldise pildi\".\n", "\n", - "![Üleõppimine](../../../../../translated_images/overfit.a0bd57f717c15769.et.png)\n", + "![Üleõppimine](../../../../../translated_images/et/overfit.a0bd57f717c15769.png)\n", "\n", "> Sellel pildil tähistab `x` treeningandmeid ja `o` valideerimisandmeid. Vasakul – lineaarne mudel (ühekihiline), mis jäljendab andmete olemust üsna hästi. Paremal – üleõppinud mudel, mis jäljendab treeningandmeid täiuslikult, kuid kaotab igasuguse mõtte teiste andmete puhul (valideerimisviga on väga suur).\n" ] diff --git a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md index ba736cad..2c973406 100644 --- a/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/et/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ Gradientlanguse algoritm jääb samaks, kuid gradientide arvutamine muutub keeru Pange tähele, et kõigi nende avaldiste vasakpoolne osa on sama, ja seega saame tuletised tõhusalt arvutada, alustades kaofunktsioonist ja liikudes "tagasi" läbi arvutusgraafi. Seetõttu nimetatakse mitmekihilise perceptroni treenimise meetodit **tagasilevikuks** ehk 'backprop'. -arvutusgraaf +arvutusgraaf > TODO: pildi viide diff --git a/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md index 24b20d43..703851cd 100644 --- a/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/et/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pärast raamistikest arusaamist vaatame üle üleliigse sobitamise (overfitting) Vaatleme järgmist probleemi, kus tuleb ligikaudselt määrata 5 punkti (graafikutel tähistatud `x`-ga): -![lineaarne](../../../../../translated_images/overfit1.f24b71c6f652e59e.et.jpg) | ![üleliigne sobitamine](../../../../../translated_images/overfit2.131f5800ae10ca5e.et.jpg) +![lineaarne](../../../../../translated_images/et/overfit1.f24b71c6f652e59e.jpg) | ![üleliigne sobitamine](../../../../../translated_images/et/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineaarne mudel, 2 parameetrit** | **Mitte-lineaarne mudel, 7 parameetrit** Treeningu viga = 5.3 | Treeningu viga = 0 @@ -79,7 +79,7 @@ On väga oluline leida õige tasakaal mudeli rikkuse (parameetrite arv) ja treen Nagu ülaltoodud graafikult näha, saab üleliigset sobitamist tuvastada väga madala treeningu vea ja kõrge valideerimise vea järgi. Tavaliselt näeme treenimise ajal, kuidas treeningu ja valideerimise vead hakkavad mõlemad vähenema, kuid mingil hetkel valideerimise viga võib lõpetada vähenemise ja hakata kasvama. See on märk üleliigsest sobitamisest ja indikaator, et treenimine tuleks tõenäoliselt lõpetada (või vähemalt mudelist hetkeseis salvestada). -![üleliigne sobitamine](../../../../../translated_images/Overfitting.408ad91cd90b4371.et.png) +![üleliigne sobitamine](../../../../../translated_images/et/Overfitting.408ad91cd90b4371.png) ## Kuidas vältida üleliigset sobitamist diff --git a/translations/et/lessons/3-NeuralNetworks/README.md b/translations/et/lessons/3-NeuralNetworks/README.md index 968de0be..5396a06f 100644 --- a/translations/et/lessons/3-NeuralNetworks/README.md +++ b/translations/et/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sissejuhatus tehisnärvivõrkudesse -![Kokkuvõte tehisnärvivõrkude sissejuhatuse sisust doodle'is](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.et.png) +![Kokkuvõte tehisnärvivõrkude sissejuhatuse sisust doodle'is](../../../../translated_images/et/ai-neuralnetworks.1c687ae40bc86e83.png) Nagu me arutasime sissejuhatuses, on üks viis intelligentsuse saavutamiseks treenida **arvutimudelit** või **tehisaju**. Alates 20. sajandi keskpaigast on teadlased katsetanud erinevaid matemaatilisi mudeleid, kuni viimastel aastatel osutus see suund väga edukaks. Selliseid aju matemaatilisi mudeleid nimetatakse **närvivõrkudeks**. @@ -36,13 +36,13 @@ Selles õppekavas keskendume ainult närvivõrkude mudelitele. Bioloogiast teame, et meie aju koosneb närvirakkudest (neuronitest), millest igaühel on mitu "sisendit" (dendriidid) ja üks "väljund" (akson). Nii dendriidid kui aksonid suudavad juhtida elektrilisi signaale ning nende vahelised ühendused — sünapsid — võivad näidata erinevat juhtivust, mida reguleerivad neurotransmitterid. -![Neuroni mudel](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.et.jpg) | ![Neuroni mudel](../../../../translated_images/artneuron.1a5daa88d20ebe6f.et.png) +![Neuroni mudel](../../../../translated_images/et/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuroni mudel](../../../../translated_images/et/artneuron.1a5daa88d20ebe6f.png) ----|---- Päris neuron *([Pilt](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediast)* | Tehisneuron *(Pilt autorilt)* Seega sisaldab neuroni lihtsaim matemaatiline mudel mitut sisendit X1, ..., XN ja ühte väljundit Y ning mitmeid kaale W1, ..., WN. Väljund arvutatakse järgmiselt: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kus f on mingi mittelineaarne **aktiveerimisfunktsioon**. diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md index a83d0711..f130ab09 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Meie [OpenCV Notebook](OpenCV.ipynb) näitab mõningaid näiteid, millal arvutin * **Pildi eeltöötlus Braille'i raamatu fotol**. Keskendume sellele, kuidas kasutada läve määramist, tunnuste tuvastamist, perspektiiviteisendust ja NumPy manipuleerimist, et eraldada individuaalsed Braille'i sümbolid edasiseks klassifitseerimiseks närvivõrgu abil. -![Braille'i pilt](../../../../../translated_images/braille.341962ff76b1bd70.et.jpeg) | ![Braille'i pilt eeltöödeldud](../../../../../translated_images/braille-result.46530fea020b03c7.et.png) | ![Braille'i sümbolid](../../../../../translated_images/braille-symbols.0159185ab69d5339.et.png) +![Braille'i pilt](../../../../../translated_images/et/braille.341962ff76b1bd70.jpeg) | ![Braille'i pilt eeltöödeldud](../../../../../translated_images/et/braille-result.46530fea020b03c7.png) | ![Braille'i sümbolid](../../../../../translated_images/et/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist * **Liikumise tuvastamine videos kaadrite erinevuse abil**. Kui kaamera on fikseeritud, peaksid kaadrid kaamera voos olema üksteisega üsna sarnased. Kuna kaadreid esitatakse massiividena, siis lihtsalt lahutades need massiivid kahe järjestikuse kaadri jaoks saame pikslite erinevuse, mis peaks olema madal staatiliste kaadrite puhul ja muutuma suuremaks, kui pildil toimub märkimisväärne liikumine. -![Kaadrite ja kaadrite erinevuste pilt](../../../../../translated_images/frame-difference.706f805491a0883c.et.png) +![Kaadrite ja kaadrite erinevuste pilt](../../../../../translated_images/et/frame-difference.706f805491a0883c.png) > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist @@ -89,7 +89,7 @@ Meie [OpenCV Notebook](OpenCV.ipynb) näitab mõningaid näiteid, millal arvutin - **Tihe optiline vool** arvutab vektorvälja, mis näitab iga piksli liikumissuunda. - **Hõre optiline vool** põhineb mõningate eristuvate tunnuste (nt servade) võtmisele pildil ja nende trajektoori ehitamisele kaadrist kaadrisse. -![Optilise voolu pilt](../../../../../translated_images/optical.1f4a94464579a83a.et.png) +![Optilise voolu pilt](../../../../../translated_images/et/optical.1f4a94464579a83a.png) > Pilt [OpenCV.ipynb](OpenCV.ipynb) failist @@ -115,7 +115,7 @@ Loe rohkem optilise voolu kohta [selles suurepärases juhendis](https://learnope Selles laboris teete video lihtsate žestidega ja teie eesmärk on optilise voolu abil tuvastada üles/alla/vasakule/paremale liikumised. -Käe liikumise kaader +Käe liikumise kaader --- diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 17b860c8..829b434c 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "Vaadake [seda videot](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), kus inimese peopesa liigub vasakule/paremale/üles/alla stabiilse tausta ees.\n", "\n", - "\"Peopesa\n", + "\"Peopesa\n", "\n", "**Teie eesmärk** on kasutada optilist voolu, et määrata, millised video osad sisaldavad üles/alla/vasakule/paremale liikumisi.\n", "\n", diff --git a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 83b7524e..460ff7d9 100644 --- a/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/et/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ Laboriülesanne [AI algajatele mõeldud õppekavast](https://aka.ms/ai-beginners Vaadake [seda videot](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), kus inimese peopesa liigub vasakule/paremale/üles/alla stabiilse tausta ees. -Peopesa liikumise kaader +Peopesa liikumise kaader **Teie eesmärk** on kasutada optilist voolu, et määrata, millised video osad sisaldavad üles/alla/vasakule/paremale liikumisi. diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 6505ae2e..52fe9f8d 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 on võrk, mis saavutas 2014. aastal ImageNet top-5 klassifikatsioonis 92,7% täpsuse. Sellel on järgmine kihistruktuur: -![ImageNet kihid](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.et.jpg) +![ImageNet kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.jpg) Nagu näha, järgib VGG traditsioonilist püramiidstruktuuri, mis koosneb järjestikustest konvolutsiooni- ja koondamiskihidest. -![ImageNet püramiid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.et.jpg) +![ImageNet püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.jpg) > Pilt [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) lehelt @@ -25,7 +25,7 @@ Nagu näha, järgib VGG traditsioonilist püramiidstruktuuri, mis koosneb järje ResNet on mudelite perekond, mille Microsoft Research esitas 2015. aastal. ResNeti peamine idee on kasutada **jääkblokke**: - + > Pilt [sellest artiklist](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +37,7 @@ Seda võrku võib mõelda ka kui võimet kohandada oma keerukust vastavalt andme Google Inception arhitektuur viib selle idee veelgi kaugemale ja ehitab iga võrgu kihi mitme erineva tee kombinatsioonina: - + > Pilt [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) lehelt diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index a6457cf8..21027af6 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Seega on tüüpilises CNN-is mitu konvolutsioonikihti, mille vahel on koondamiskihid, et pildi mõõtmeid vähendada. Samuti suurendame filtrite arvu, sest mustrid muutuvad keerukamaks – on rohkem huvitavaid kombinatsioone, mida tuleb otsida.\n", "\n", - "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.et.png)\n", + "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kuna ruumilised mõõtmed vähenevad ja tunnuste/filtrite mõõtmed suurenevad, nimetatakse seda arhitektuuri ka **püramiidi arhitektuuriks**.\n" ] diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 5899043b..045906f0 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -114,7 +114,7 @@ "\n", "Klassikalises arvutinägemises rakendati pildile mitmeid filtreid, et genereerida tunnuseid, mida seejärel kasutas masinõppe algoritm klassifikaatori loomiseks. Need filtrid on tegelikult sarnased närvisüsteemi struktuuridega, mis on olemas mõnede loomade nägemissüsteemis.\n", "\n", - "\n", + "\n", "\n", "Kuid süvaõppes konstrueerime võrgustikke, mis **õpivad** parimad konvolutsioonifiltrid klassifitseerimisprobleemi lahendamiseks. Selleks tutvustame **konvolutsioonikihte**.\n" ] @@ -360,7 +360,7 @@ "\n", "Seega on tüüpilises CNN-is mitu konvolutsioonikihti, mille vahel on koondamiskihid, et vähendada pildi dimensioone. Samuti suurendame filtrite arvu, sest mustrid muutuvad keerukamaks – on rohkem huvitavaid kombinatsioone, mida peame otsima.\n", "\n", - "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.et.png)\n", + "![Pilt, mis näitab mitut konvolutsioonikihti koos koondamiskihtidega.](../../../../../translated_images/et/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kuna ruumilised dimensioonid vähenevad ja tunnuste/filtrite dimensioonid suurenevad, nimetatakse seda arhitektuuri ka **püramiidi arhitektuuriks**.\n" ] diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md index b3ab1416..0d75b953 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ Päriselus tahame olla võimelised tuvastama objekte pildil sõltumata nende tä Mustrite leidmiseks kasutame **konvolutsioonifiltrite** mõistet. Nagu teate, on pilt esitatud 2D-maatriksina või 3D-tensorina koos värvisügavusega. Filtri rakendamine tähendab, et võtame suhteliselt väikese **filtrituuma** maatriksi ja arvutame iga originaalpildi pikseli jaoks kaalutud keskmise koos naaberpunktidega. Seda võib vaadelda kui väikest akent, mis libiseb üle kogu pildi ja keskmistab kõik pikslid vastavalt filtrituuma maatriksi kaaludele. -![Vertikaalse serva filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.et.png) | ![Horisontaalse serva filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.et.png) +![Vertikaalse serva filter](../../../../../translated_images/et/filter-vert.b7148390ca0bc356.png) | ![Horisontaalse serva filter](../../../../../translated_images/et/filter-horiz.59b80ed4feb946ef.png) ----|---- > Pilt: Dmitry Soshnikov Näiteks, kui rakendame MNIST numbritele 3x3 vertikaalse ja horisontaalse serva filtreid, saame esile tõsta (nt kõrged väärtused) kohad, kus originaalpildil on vertikaalsed ja horisontaalsed servad. Seega saab neid kahte filtrit kasutada servade "otsimiseks". Samamoodi saame kujundada erinevaid filtreid, et otsida teisi madala taseme mustreid: - + > Pilt: [Leung-Malik filtripank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) @@ -38,7 +38,7 @@ CNN-i töö põhineb järgmistel olulistel ideedel: * Võrgu saab kujundada nii, et filtrid treenitakse automaatselt * Sama lähenemist saab kasutada mustrite leidmiseks kõrgetasemelistes omadustes, mitte ainult originaalpildil. Seega töötab CNN-i omaduste tuvastamine hierarhias, alustades madala taseme pikslikombinatsioonidest kuni kõrgema taseme pildiosade kombinatsioonideni. -![Hierarhiline omaduste tuvastamine](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.et.png) +![Hierarhiline omaduste tuvastamine](../../../../../translated_images/et/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Pilt: [Hislop-Lynchi artikkel](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), põhineb [nende uurimusel](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Enamik pilttöötluseks kasutatavaid CNN-e järgib nn püramiidset arhitektuuri. Näiteks vaatame VGG-16 arhitektuuri, võrku, mis saavutas 2014. aastal ImageNeti top-5 klassifikatsioonis 92,7% täpsuse: -![ImageNeti kihid](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.et.jpg) +![ImageNeti kihid](../../../../../translated_images/et/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNeti püramiid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.et.jpg) +![ImageNeti püramiid](../../../../../translated_images/et/vgg-16-arch.64ff2137f50dd49f.jpg) > Pilt: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 0498cfc5..1a2e031c 100644 --- a/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/et/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Te peate treenima konvolutsioonilise närvivõrgu, et klassifitseerida erinevaid Me kasutame [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) andmestikku, mis sisaldab pilte 37 erinevast koerte ja kasside tõust. -![Andmestik, millega töötame](../../../../../../translated_images/data.50b2a9d5484bdbf0.et.png) +![Andmestik, millega töötame](../../../../../../translated_images/et/data.50b2a9d5484bdbf0.png) Andmestiku allalaadimiseks kasutage järgmist koodilõiku: diff --git a/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 25a6e847..f50e8db6 100644 --- a/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/et/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Et kujutada ette ideaalset kassi, alustame juhusliku müra pildiga ja proovime kasutada gradientse languse optimeerimistehnikat, et kohandada pilti nii, et võrk tunneks ära kassi.\n", "\n", - "![Optimeerimise tsükkel](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.et.png)\n", + "![Optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Siin on meie alguspilt:\n" ] diff --git a/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md index 30e71abf..54422d17 100644 --- a/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/et/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Nii Keras kui PyTorch sisaldavad funktsioone, mis võimaldavad hõlpsalt laadida Siin on näide omadustest, mille VGG-16 võrk kassipildilt tuvastas: -![VGG-16 tuvastatud omadused](../../../../../translated_images/features.6291f9c7ba3a0b95.et.png) +![VGG-16 tuvastatud omadused](../../../../../translated_images/et/features.6291f9c7ba3a0b95.png) ## Kasside ja koerte andmestik @@ -48,19 +48,19 @@ Eelnevalt treenitud närvivõrk sisaldab oma *ajus* erinevaid mustreid, sealhulg Üks lähenemine, mida saame kasutada, on alustada juhuslikust pildist ja seejärel proovida kasutada **gradientide optimeerimise** tehnikat, et kohandada seda pilti nii, et võrk hakkaks arvama, et see on kass. -![Pildi optimeerimise tsükkel](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.et.png) +![Pildi optimeerimise tsükkel](../../../../../translated_images/et/ideal-cat-loop.999fbb8ff306e044.png) Kui me seda teeme, saame tulemuseks midagi, mis on väga sarnane juhusliku müraga. See on tingitud sellest, et *on palju viise, kuidas panna võrk arvama, et sisendpilt on kass*, sealhulgas mõned, mis visuaalselt ei ole mõistlikud. Kuigi need pildid sisaldavad palju kassile tüüpilisi mustreid, pole midagi, mis sunniks neid olema visuaalselt eristatavad. Tulemuse parandamiseks saame lisada kaotuse funktsiooni teise termini, mida nimetatakse **variatsioonikaotuseks**. See on mõõdik, mis näitab, kui sarnased on pildi naaberpikslid. Variatsioonikaotuse minimeerimine muudab pildi sujuvamaks ja eemaldab müra – paljastades visuaalselt meeldivamad mustrid. Siin on näide sellistest "ideaalse" piltidest, mis klassifitseeritakse suure tõenäosusega kassiks ja sebraks: -![Ideaalne kass](../../../../../translated_images/ideal-cat.203dd4597643d6b0.et.png) | ![Ideaalne sebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.et.png) +![Ideaalne kass](../../../../../translated_images/et/ideal-cat.203dd4597643d6b0.png) | ![Ideaalne sebra](../../../../../translated_images/et/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideaalne kass* | *Ideaalne sebra* Sarnast lähenemist saab kasutada nn **adversariaalsete rünnakute** läbiviimiseks närvivõrgule. Oletame, et tahame petta närvivõrku ja panna koera välja nägema nagu kass. Kui võtame koera pildi, mida võrk tuvastab koerana, saame seda veidi kohandada, kasutades gradientide optimeerimist, kuni võrk hakkab seda klassifitseerima kassina: -![Koera pilt](../../../../../translated_images/original-dog.8f68a67d2fe0911f.et.png) | ![Koera pilt, mis klassifitseeritakse kassina](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.et.png) +![Koera pilt](../../../../../translated_images/et/original-dog.8f68a67d2fe0911f.png) | ![Koera pilt, mis klassifitseeritakse kassina](../../../../../translated_images/et/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originaalne koera pilt* | *Koera pilt, mis klassifitseeritakse kassina* diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index acfa92b0..2a94f2f2 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Kuna treenime autoenkoodrit, et see haaraks võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendust.\n", "\n", - "![Autoenkoodri skeem](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.et.jpg)\n", + "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", @@ -941,7 +941,7 @@ " * Valime juhusliku vektori `sample(z_val in code)` jaotusest $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$\n", " * Dekodeerija püüab dekodeerida algse pildi, kasutades `sample`-it sisendvektorina\n", "\n", - " \n", + " \n", "\n", " > Pilt [sellest blogipostitusest](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) autorilt Isaak Dykeman\n" ] @@ -1264,7 +1264,7 @@ "\n", "Selles lähenemises on meil **kolm kaotusfunktsiooni**: generaatori kaotus, diskrimineerija kaotus GAN-i puhul ja rekonstrueerimise kaotus VAE puhul.\n", "\n", - " \n", + " \n", "\n", " > Pilt [sellest blogipostitusest](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) autorilt Felipe Ducau\n" ] diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ca643110..21d9482e 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "Kuna treenime autoenkoodrit, et ta haaraks võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendust.\n", "\n", - "![Autoenkoodri skeem](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.et.jpg)\n", + "![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", @@ -888,7 +888,7 @@ " * Valime juhusliku vektori `sample` jaotusest $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$\n", " * Dekodeerija püüab dekodeerida algse pildi, kasutades `sample`-it sisendvektorina\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md index c6039b20..0d2b42ab 100644 --- a/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/et/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Siiski võime soovida kasutada toorandmeid (märgistamata) CNN-i funktsioonide e Kuna treenime autoenkoodrit, et haarata võimalikult palju teavet algsest pildist täpseks taastamiseks, püüab võrk leida parima **sisendpiltide representatsiooni**, et tabada nende tähendus. -![Autoenkoodri skeem](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.et.jpg) +![Autoenkoodri skeem](../../../../../translated_images/et/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Pilt [Kerase blogist](https://blog.keras.io/building-autoencoders-in-keras.html) @@ -46,7 +46,7 @@ Kokkuvõtteks: * Valime vektori `sample` jaotusest N(zmean,exp(zlog\_sigma)) * Dekooder püüab dekodeerida algset pilti, kasutades `sample` sisendvektorina - + > Pilt [sellest blogipostitusest](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) autorilt Isaak Dykeman @@ -57,13 +57,13 @@ Variatsioonilised autoenkoodrid kasutavad keerulist kaotusefunktsiooni, mis koos Üks oluline eelis VAE-de puhul on see, et need võimaldavad meil suhteliselt lihtsalt uusi pilte genereerida, kuna teame, millist jaotust latentvektorite valimiseks kasutada. Näiteks kui treenime VAE-d 2D latentvektoriga MNIST andmestikul, saame seejärel muuta latentvektori komponente, et saada erinevaid numbreid: -vaemnist +vaemnist > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) Vaadake, kuidas pildid sulanduvad üksteisesse, kui hakkame saama latentvektoreid latentparameetrite ruumi erinevatest osadest. Samuti saame visualiseerida seda ruumi 2D-s: -vaemnist cluster +vaemnist cluster > Pilt autorilt [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index edbe5531..58eb6391 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ " * **Generaator** võtab juhusliku vektori ja peaks sellest pildi genereerima.\n", " * **Diskrimineerija** on võrk, mis peaks eristama algset pilti (treeningandmestikust) generaatori loodud pildist.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> Pilt pärineb [sellest juhendist](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 2d4dc3d0..d79b1106 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ " * **Generaator** võtab juhusliku vektori ja peaks sellest pildi genereerima\n", " * **Diskrimineerija** on võrk, mis peaks eristama originaalpildi (treeningandmestikust) generaatori loodud pildist.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/et/lessons/4-ComputerVision/10-GANs/README.md b/translations/et/lessons/4-ComputerVision/10-GANs/README.md index 10cfa5aa..05add710 100644 --- a/translations/et/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/et/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ Kui aga proovime luua midagi tõeliselt tähenduslikku, näiteks maali mõistlik GAN-i peamine idee on kasutada kahte närvivõrku, mis treenivad üksteise vastu: - + > Pilt: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ Generaator on veidi keerulisem. Seda võib pidada pööratud diskrimineerijaks. > ✅ Kuna konvolutsioonikiht rakendatakse lineaarse filtrina, mis liigub üle pildi, on dekonvolutsioon sisuliselt sarnane konvolutsiooniga ja seda saab rakendada sama kihi loogikaga. - + > Pilt: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md index 00b8dd59..c1993e0c 100644 --- a/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/et/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Pildiklassifikatsiooni mudelid, millega oleme seni tegelenud, võtsid pildi ja a ## [Eelloengu viktoriin](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektide tuvastamine](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.et.png) +![Objektide tuvastamine](../../../../../translated_images/et/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Pilt [YOLO v2 veebilehelt](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Oletame, et tahame leida kassi pildilt. Väga naiivne lähenemine objektide tuva 2. Käivitame pildiklassifikatsiooni igal osal. 3. Need osad, mis annavad piisavalt kõrge aktiveerimise, võib pidada objektiks, mida otsime. -![Naivne objektide tuvastamine](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.et.png) +![Naivne objektide tuvastamine](../../../../../translated_images/et/naive-detection.e7f1ba220ccd08c6.png) > *Pilt [harjutuste märkmikust](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Selle ülesande jaoks võite kohata järgmisi andmekogumeid: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klassi * [COCO](http://cocodataset.org/#home) – Tavalised objektid kontekstis. 80 klassi, piiritlevad kastid ja segmentatsioonimaskid -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.et.jpg) +![COCO](../../../../../translated_images/et/coco-examples.71bc60380fa6cceb.jpg) ## Objektide tuvastamise mõõdikud @@ -50,7 +50,7 @@ Selle ülesande jaoks võite kohata järgmisi andmekogumeid: Kui pildiklassifikatsiooni puhul on lihtne mõõta, kui hästi algoritm töötab, siis objektide tuvastamise puhul peame mõõtma nii klassi õigsust kui ka tuvastatud piiritleva kasti asukoha täpsust. Viimase jaoks kasutame nn **ühisosa ja ühenduse suhet** (IoU), mis mõõdab, kui hästi kaks kasti (või kaks suvalist ala) kattuvad. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.et.png) +![IoU](../../../../../translated_images/et/iou_equation.9a4751d40fff4e11.png) > *Joonis 2 [sellest suurepärasest blogipostitusest IoU kohta](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Objektide tuvastamise algoritme on kahte laia klassi: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) kasutab [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), et genereerida ROI piirkondade hierarhiline struktuur, mis seejärel läbib CNN-i funktsioonide ekstraktorid ja SVM-klassi määrajad, et määrata objekti klass, ning lineaarse regressiooni, et määrata *piiritleva kasti* koordinaadid. [Ametlik artikkel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.et.png) +![RCNN](../../../../../translated_images/et/rcnn1.cae407020dfb1d1f.png) > *Pilt van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.et.png) +![RCNN-1](../../../../../translated_images/et/rcnn2.2d9530bb83516484.png) > *Pildid [sellest blogist](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Objektide tuvastamise algoritme on kahte laia klassi: See lähenemine on sarnane R-CNN-iga, kuid piirkonnad määratakse pärast konvolutsioonikihtide rakendamist. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.et.png) +![FRCNN](../../../../../translated_images/et/f-rcnn.3cda6d9bb4188875.png) > Pilt [ametlikust artiklist](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ See lähenemine on sarnane R-CNN-iga, kuid piirkonnad määratakse pärast konvo Selle lähenemise peamine idee on kasutada närvivõrku ROIside ennustamiseks – nn *piirkonna ettepanekuvõrk*. [Artikkel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.et.png) +![FasterRCNN](../../../../../translated_images/et/faster-rcnn.8d46c099b87ef30a.png) > Pilt [ametlikust artiklist](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ See algoritm on isegi kiirem kui Faster R-CNN. Peamine idee on järgmine: 2. Funktsioone töödeldakse **positsioonitundliku skoorikaardiga**. Iga objekt klassist $C$ jagatakse $k\times k$ piirkondadeks ja treenime ennustama objektide osi. 3. Iga osa $k\times k$ piirkondadest hääletavad kõik võrgud objektiklasside eest ja maksimaalse häälega objektiklass valitakse. -![r-fcn pilt](../../../../../translated_images/r-fcn.13eb88158b99a3da.et.png) +![r-fcn pilt](../../../../../translated_images/et/r-fcn.13eb88158b99a3da.png) > Pilt [ametlikust artiklist](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO on reaalajas ühe läbimise algoritm. Peamine idee on järgmine: * Pilt jagatakse $S\times S$ piirkondadeks. * Iga piirkonna jaoks ennustab **CNN** $n$ võimalikku objekti, *piiritleva kasti* koordinaate ja *usaldusväärsust*=*tõenäosust* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.et.png) + ![YOLO](../../../../../translated_images/et/yolo.a2648ec82ee8bb4e.png) > Pilt [ametlikust artiklist](https://arxiv.org/abs/1506.02640) diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md index d298ba94..f74a73e3 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ Segmenteerimist võib vaadelda kui **pikslite klassifikatsiooni**, kus **iga** p Instance segmenteerimise puhul on need lambad erinevad objektid, kuid semantilise segmenteerimise puhul esindavad kõik lambad ühte klassi. - + > Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +29,7 @@ Segmenteerimiseks on erinevaid närvivõrkude arhitektuure, kuid neil kõigil on * **Kodeerija** ekstraheerib sisendpildist omadused. * **Dekodeerija** teisendab need omadused **maskipildiks**, millel on sama suurus ja kanalite arv, mis vastab klasside arvule. - + > Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ Selles õppetükis näeme segmenteerimist tegevuses, treenides võrku inimeste n > ✅ See tehnika sobib eriti hästi sellist tüüpi meditsiiniliste piltide jaoks, kuid milliseid muid reaalse maailma rakendusi võiksite ette kujutada? -navi +navi > Pilt PH2 andmebaasist diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index dbd472b7..387e282f 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "Näiteks instantsi segmenteerimise puhul on 10 lammast erinevad objektid, semantilise segmenteerimise puhul esindavad kõik lambad ühte klassi.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n", "\n", @@ -26,7 +26,7 @@ "* **Kodeerija** eraldab sisendpildist omadused\n", "* **Dekodeerija** teisendab need omadused **maskipildiks**, mille suurus ja kanalite arv vastavad klasside arvule.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -252,7 +252,7 @@ "\n", "Lihtsaim kodeerija-dekodeerija arhitektuur kannab nime **SegNet**. See kasutab standardset CNN-i koos konvolutsioonide ja koondamistega kodeerijas ning dekodeerijas dekonvolutsioonilist CNN-i, mis sisaldab konvolutsioone ja ülesproovimisi. Samuti toetub see partiinormaliseerimisele, et edukalt treenida mitmekihilist võrku.\n", "\n", - "\n", + "\n", "\n", "> Pilt sellest artiklist: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: Sügav konvolutsiooniline kodeerija-dekodeerija arhitektuur pildisegmentatsiooniks](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -547,7 +547,7 @@ "\n", "Siin kasutame üsna lihtsat CNN arhitektuuri, kuid U-Net võib kasutada ka keerukamat kodeerijat omaduste eraldamiseks, näiteks ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> Pilt artiklist: Ronneberger, Olaf, Philipp Fischer ja Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index 27d84df0..f18c8c71 100644 --- a/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/et/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "Näiteks instantssegmentimisel on kümme autot **erinevad** objektid, semantilise segmentimise puhul on **kõik** autod üks klass.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest blogipostitusest](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50)\n", "\n", "Peaaegu kõigil arhitektuuridel on sama struktuur. Esimene osa on **kodeerija**, mis eraldab sisendpildist omadused, teine osa on **dekodeerija**, mis teisendab need omadused pildiks, millel on sama kõrgus ja laius ning teatud arv kanaleid, mis võib olla võrdne klasside arvuga.\n", "\n", - "\n", + "\n", "\n", "> Pilt [sellest publikatsioonist](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -210,7 +210,7 @@ "\n", "Lihtne kodeerija-dekodeerija arhitektuur, mis kasutab konvolutsioone ja koondamisi kodeerijas ning konvolutsioone ja ülesmõõdistamisi dekodeerijas.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: Sügav konvolutsiooniline kodeerija-dekodeerija arhitektuur pildisegmentatsiooniks](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -601,7 +601,7 @@ "\n", "U-Netil on tavaliselt vaikimisi kodeerija omaduste eraldamiseks, näiteks resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer ja Thomas Brox. [U-Net: Konvolutsioonivõrgud biomeditsiiniliste piltide segmentimiseks.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/et/lessons/4-ComputerVision/README.md b/translations/et/lessons/4-ComputerVision/README.md index 6fb39900..04e274bb 100644 --- a/translations/et/lessons/4-ComputerVision/README.md +++ b/translations/et/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Arvutinägemine -![Arvutinägemise sisu kokkuvõte visandina](../../../../translated_images/ai-computervision.6506ebebac3fbf76.et.png) +![Arvutinägemise sisu kokkuvõte visandina](../../../../translated_images/et/ai-computervision.6506ebebac3fbf76.png) Selles osas õpime: diff --git a/translations/et/lessons/5-NLP/13-TextRep/README.md b/translations/et/lessons/5-NLP/13-TextRep/README.md index 98ff6c74..6893006a 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/README.md +++ b/translations/et/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ Meie eesmärk on klassifitseerida uudisartikkel üheks kategooriaks, tuginedes t Kui tahame lahendada loomuliku keele töötlemise (NLP) ülesandeid närvivõrkudega, peame leidma viisi, kuidas teksti tensoritena esitada. Arvutid esindavad tekstimärke juba numbritena, mis kaardistuvad ekraanil olevate fontidega, kasutades kodeeringuid nagu ASCII või UTF-8. -Pilt, mis näitab skeemi, kuidas märk kaardistub ASCII ja binaarse esitusena +Pilt, mis näitab skeemi, kuidas märk kaardistub ASCII ja binaarse esitusena > [Pildi allikas](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ Mõnel juhul võime kaaluda ka tri-grammide - kolme sõna kombinatsioonide - kas Tekstiklassifikatsiooni ülesannete lahendamisel peame suutma esitada teksti ühe fikseeritud suurusega vektorina, mida kasutame lõpliku tiheda klassifikaatori sisendina. Üks lihtsamaid viise seda teha on kombineerida kõik üksikud sõnaesitused, näiteks neid liites. Kui liidame iga sõna ühe-kuuma kodeeringud, saame sagedusvektori, mis näitab, mitu korda iga sõna tekstis esineb. Sellist teksti esitust nimetatakse **sõnakotiks** (BoW). - + > Pilt autori poolt diff --git a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 58f6560e..ee2cded2 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Sõnade koti** (BoW) vektoriesitus on kõige sagedamini kasutatav traditsiooniline vektoriesitus. Iga sõna on seotud vektori indeksiga, vektori element sisaldab sõna esinemiste arvu antud dokumendis.\n", "\n", - "![Pilt, mis näitab, kuidas sõnade koti vektoriesitust mälus kujutatakse.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.et.png) \n", + "![Pilt, mis näitab, kuidas sõnade koti vektoriesitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-d võib mõelda ka kui kõigi teksti üksiksõnade ühekuumkodeeritud vektorite summat.\n", "\n", diff --git a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 117258af..28c4c964 100644 --- a/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/et/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Sõnade kogumi** (BoW) vektor-esitus on kõige lihtsamini mõistetav traditsiooniline vektor-esitus. Iga sõna on seotud vektori indeksiga ning vektori element sisaldab iga sõna esinemiskordade arvu antud dokumendis.\n", "\n", - "![Pilt, mis näitab, kuidas sõnade kogumi vektor-esitust mälus kujutatakse.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.et.png) \n", + "![Pilt, mis näitab, kuidas sõnade kogumi vektor-esitust mälus kujutatakse.](../../../../../translated_images/et/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-d võib mõelda ka kui kõigi teksti üksiksõnade ühekuumkooditud vektorite summana.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 8826081c..782029f2 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kasutades sisestuskihti meie võrgu esimesena kihina, saame liikuda sõnakoti mudelilt **sisestuskoti** mudelile, kus esmalt teisendame iga sõna tekstis vastavaks sisestuseks ja seejärel arvutame nende sisestuste üle mingi koondfunktsiooni, näiteks `sum`, `average` või `max`.\n", "\n", - "![Pilt, mis näitab viie sõna järjestuse sisestuse klassifikaatorit.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.et.png)\n", + "![Pilt, mis näitab viie sõna järjestuse sisestuse klassifikaatorit.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Meie klassifikaatori närvivõrk algab sisestuskihiga, millele järgneb koondamiskihiga ja lineaarne klassifikaator selle peal:\n" ] @@ -176,7 +176,7 @@ "\n", "Eelmises arhitektuuris pidime kõik järjestused täitma sama pikkuseni, et need minibatch'i sobiksid. See ei ole kõige tõhusam viis muutuva pikkusega järjestuste esitamiseks - teine lähenemine oleks kasutada **nihke** vektorit, mis sisaldaks kõigi järjestuste nihked, mis on salvestatud ühes suures vektoris.\n", "\n", - "![Pilt, mis näitab nihkevektori järjestuse esitust](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.et.png)\n", + "![Pilt, mis näitab nihkevektori järjestuse esitust](../../../../../translated_images/et/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Ülaloleval pildil on näidatud tähemärkide järjestus, kuid meie näites töötame sõnade järjestustega. Siiski jääb üldine põhimõte järjestuste esitamiseks nihkevektoriga samaks.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW on kiirem, samas kui vahegramm on aeglasem, kuid esindab haruldasi sõnu paremini.\n", "\n", - "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.et.png)\n", + "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Et katsetada Word2Vec-i sisestust, mis on eelnevalt treenitud Google News andmekogul, saame kasutada **gensim** teeki. Allpool otsime sõnu, mis on kõige sarnasemad sõnale 'neural'.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index e2a506b5..b5fd26e5 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/et/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kasutades embedding-kihti meie võrgu esimeseks kihiks, saame liikuda sõnakottide mudelilt **embedding-koti** mudelile, kus esmalt teisendame iga sõna tekstis vastavaks embedding'iks ja seejärel arvutame nende embedding'ite üle mingi koondfunktsiooni, näiteks `sum`, `average` või `max`.\n", "\n", - "![Pilt, mis näitab embedding-klassifikaatorit viie järjestikuse sõna jaoks.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.et.png)\n", + "![Pilt, mis näitab embedding-klassifikaatorit viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Meie klassifitseeriva närvivõrgu kihid on järgmised:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW on kiirem, samas kui vahelejätu-gramm on aeglasem, kuid esindab haruldasi sõnu paremini.\n", "\n", - "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.et.png)\n", + "![Pilt, mis näitab nii CBoW kui ka Skip-Gram algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Et katsetada Word2Vec sisendit, mis on eelnevalt treenitud Google News andmestiku peal, saame kasutada **gensim** teeki. Allpool otsime sõnu, mis on kõige sarnasemad sõnale 'neural'.\n", "\n", diff --git a/translations/et/lessons/5-NLP/14-Embeddings/README.md b/translations/et/lessons/5-NLP/14-Embeddings/README.md index 2e7ced25..7fc1dfe8 100644 --- a/translations/et/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/et/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Sisestuskihi ülesanne on võtta sõna sisendina ja anda väljundvektor kindlaks Kasutades sisestuskihti meie klassifitseerimisvõrgu esimese kihina, saame liikuda sõnakotilt **sisestuskoti** mudelile, kus esmalt teisendame iga sõna meie tekstis vastavaks sisestuseks ja seejärel arvutame nende sisestuste üle mingi koondfunktsiooni, nagu `sum`, `average` või `max`. -![Pilt, mis näitab sisestuskihi klassifitseerijat viie järjestikuse sõna jaoks.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.et.png) +![Pilt, mis näitab sisestuskihi klassifitseerijat viie järjestikuse sõna jaoks.](../../../../../translated_images/et/embedding-classifier-example.b77f021a7ee67eee.png) > Pilt autori poolt @@ -40,7 +40,7 @@ Selleks peame oma sisestusmudeli eelnevalt treenima suure tekstikogu peal spetsi CBoW on kiirem, samas kui hüppegramm on aeglasem, kuid teeb paremat tööd harvaesinevate sõnade esindamisel. -![Pilt, mis näitab nii CBoW kui ka hüppegrammi algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.et.png) +![Pilt, mis näitab nii CBoW kui ka hüppegrammi algoritme sõnade vektoriteks teisendamiseks.](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Pilt [sellest artiklist](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/et/lessons/5-NLP/15-LanguageModeling/README.md b/translations/et/lessons/5-NLP/15-LanguageModeling/README.md index 5bdf10a6..ad459761 100644 --- a/translations/et/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/et/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Eelnevates näidetes kasutasime eelnevalt treenitud semantilisi vektoreid, kuid * **Järjepidev sõnakott** (CBoW), kus ennustame keskmist tokenit $W_0$ tokenite järjestuses $W_{-N}$, ..., $W_N$. * **Skip-gramm**, kus ennustame keskmise tokeni $W_0$ põhjal naabertokenite komplekti {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}. -![pilt artiklist, mis käsitleb sõnade teisendamist vektoriteks](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.et.png) +![pilt artiklist, mis käsitleb sõnade teisendamist vektoriteks](../../../../../translated_images/et/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Pilt [sellest artiklist](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/et/lessons/5-NLP/16-RNN/README.md b/translations/et/lessons/5-NLP/16-RNN/README.md index 69f7b053..7bca4ea1 100644 --- a/translations/et/lessons/5-NLP/16-RNN/README.md +++ b/translations/et/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Eelnevates osades oleme kasutanud tekstide rikkalikke semantilisi esitusi ja lih Tekstijärjestuse tähenduse tabamiseks peame kasutama teistsugust neuraalvõrgu arhitektuuri, mida nimetatakse **korduvaks neuraalvõrguks** ehk RNN-iks. RNN-is edastame oma lause läbi võrgu ühe sümboli kaupa, ja võrk genereerib mingi **oleku**, mille edastame võrku uuesti koos järgmise sümboliga. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.et.png) +![RNN](../../../../../translated_images/et/rnn.27f5c29c53d727b5.png) > Pilt autori poolt @@ -31,7 +31,7 @@ Vaatame, kuidas lihtne RNN-rakk on üles ehitatud. See võtab sisendiks eelneva Lihtsal RNN-rakul on kaks kaalusid sisaldavat maatriksit: üks teisendab sisendsümbolit (nimetame seda W-ks) ja teine teisendab sisendolekut (H). Sellisel juhul arvutatakse võrgu väljund valemiga σ(W×Xi+H×Si-1+b), kus σ on aktivatsioonifunktsioon ja b on täiendav nihe. -RNN-raku anatoomia +RNN-raku anatoomia > Pilt autori poolt @@ -61,7 +61,7 @@ Oleme arutanud korduvaid võrke, mis töötavad ühes suunas, järjestuse alguse Korduv võrk, olgu see ühesuunaline või kahepoolne, tabab teatud mustreid järjestuses ja suudab neid salvestada olekuvektorisse või edastada väljundisse. Nagu konvolutsioonivõrkude puhul, saame ehitada teise korduva kihi esimese peale, et tabada kõrgema taseme mustreid ja ehitada madalama taseme mustritest, mida esimene kiht eraldas. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendiks. -![Pilt, mis näitab mitmekihilist pikaajalise lühimälu RNN-i](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.et.jpg) +![Pilt, mis näitab mitmekihilist pikaajalise lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorilt Fernando López* diff --git a/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index fcf4b025..83e4e8b7 100644 --- a/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijärjestuse tähenduse tabamiseks peame kasutama teistsugust närvivõrgu arhitektuuri, mida nimetatakse **korduvaks närvivõrguks** ehk RNN-iks. RNN-is edastame oma lause läbi võrgu ühe sümboli kaupa, ja võrk genereerib mingi **oleku**, mille edastame võrku uuesti koos järgmise sümboliga.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "Arvestades sisendjärjestust $X_0,\\dots,X_n$, loob RNN järjestuse närvivõrgu plokkidest ja treenib seda järjestust otsast lõpuni tagasileviku meetodil. Iga võrguplokk võtab sisendiks paari $(X_i,S_i)$ ja genereerib tulemuseks $S_{i+1}$. Lõplik olek $S_n$ või väljund $X_n$ suunatakse lineaarsele klassifikaatorile, et toota tulemus. Kõik võrguplokid jagavad samu kaalusid ja neid treenitakse otsast lõpuni ühe tagasileviku käigu abil.\n", "\n", @@ -428,7 +428,7 @@ "\n", "Korduv võrk, olgu see ühe- või kahepoolne, tuvastab teatud mustrid järjestuses ja suudab need salvestada oleku vektorisse või edastada väljundisse. Nagu konvolutsioonivõrkude puhul, saame ehitada esimese kihi peale teise korduva kihi, et tuvastada kõrgema taseme mustreid, mis on loodud esimese kihi poolt tuvastatud madalama taseme mustritest. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendina.\n", "\n", - "![Pilt, mis näitab mitmekihilist pika-lühiajalise-mälu RNN-i](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.et.jpg)\n", + "![Pilt, mis näitab mitmekihilist pika-lühiajalise-mälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorilt Fernando López*\n", "\n", diff --git a/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb index cb275d4f..146861c0 100644 --- a/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/et/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijada tähenduse tabamiseks kasutame närvivõrgu arhitektuuri, mida nimetatakse **korduvaks närvivõrguks** ehk RNN-iks. RNN-i kasutamisel edastame oma lause võrgu kaudu ühe tokeni korraga, ja võrk toodab mingi **seisundi**, mille edastame võrku uuesti koos järgmise tokeniga.\n", "\n", - "![Pilt, mis näitab korduva närvivõrgu genereerimise näidet.](../../../../../translated_images/rnn.27f5c29c53d727b5.et.png)\n", + "![Pilt, mis näitab korduva närvivõrgu genereerimise näidet.](../../../../../translated_images/et/rnn.27f5c29c53d727b5.png)\n", "\n", "Arvestades sisendjada tokenitest $X_0,\\dots,X_n$, loob RNN närvivõrgu plokkide jada ja treenib seda jada otsast lõpuni tagasileviku abil. Iga võrguplokk võtab sisendiks paari $(X_i,S_i)$ ja annab tulemuseks $S_{i+1}$. Lõplik seisund $S_n$ või väljund $Y_n$ suunatakse lineaarse klassifikaatori kaudu tulemuse saamiseks. Kõik võrguplokid jagavad samu kaale ja neid treenitakse otsast lõpuni ühe tagasileviku käigu abil.\n", "\n", @@ -371,7 +371,7 @@ "\n", "Korduvad närvivõrgud, olgu need ühe- või kaksuunalised, püüavad kinni mustreid järjestuses ja salvestavad need olekuvektoritesse või tagastavad need väljundina. Nii nagu konvolutsioonivõrkude puhul, saame esimesele kihile lisada teise korduva kihi, et püüda kinni kõrgema taseme mustreid, mis on loodud esimese kihi poolt tuvastatud madalama taseme mustritest. See viib meid **mitmekihilise RNN-i** mõisteni, mis koosneb kahest või enamast korduvast võrgust, kus eelmise kihi väljund edastatakse järgmisele kihile sisendina.\n", "\n", - "![Pilt, mis näitab mitmekihilist pika lühimälu RNN-i](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.et.jpg)\n", + "![Pilt, mis näitab mitmekihilist pika lühimälu RNN-i](../../../../../translated_images/et/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Pilt [sellest suurepärasest postitusest](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3), autor Fernando López.*\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 621d7315..702c0734 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN-i treenimiseks teksti genereerimiseks toimime järgmiselt. Igal sammul võtame `nchars` pikkuse tähemärkide jada ja palume võrgul genereerida iga sisendtähemärgi jaoks järgmine väljundtähemärk:\n", "\n", - "![Pilt, mis näitab näidet RNN-i genereerimisest sõnaga 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.et.png)\n", + "![Pilt, mis näitab näidet RNN-i genereerimisest sõnaga 'HELLO'.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Olenevalt konkreetsest stsenaariumist võime soovida lisada ka erimärke, näiteks *järjestuse lõpp* ``. Meie puhul tahame lihtsalt treenida võrku lõputu teksti genereerimiseks, seega määrame iga jada suuruseks `nchars` tokenit. Järelikult koosneb iga treeningnäide `nchars` sisendist ja `nchars` väljundist (mis on sisendjada, nihutatud ühe sümboli võrra vasakule). Minipartii koosneb mitmest sellisest jadast.\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index c93612ab..0a6b9405 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "RNN-i treenimine uudiste pealkirjade genereerimiseks toimub järgmiselt. Igal sammul võtame ühe pealkirja, mis sisestatakse RNN-i, ja iga sisendmärgi puhul palume võrgul genereerida järgmine väljundmärk:\n", "\n", - "![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.et.png)\n", + "![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Meie järjestuse viimase märgi puhul palume võrgul genereerida `` tokeni.\n", "\n", diff --git a/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md index 480583a3..0436ef78 100644 --- a/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/et/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN arhitektuuris, mida käsitlesime eelmises üksuses, genereeris iga RNN üksu See võimaldab erinevaid närvivõrgu arhitektuure, mida on näidatud alloleval pildil: -![Pilt, mis näitab korduvate närvivõrkude levinud mustreid.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.et.jpg) +![Pilt, mis näitab korduvate närvivõrkude levinud mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Pilt blogipostitusest [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorilt [Andrej Karpaty](http://karpathy.github.io/) @@ -32,11 +32,11 @@ Selles üksuses keskendume lihtsatele generatiivsetele mudelitele, mis aitavad m Treename selle RNN-i teksti genereerimiseks samm-sammult. Igal sammul võtame tähemärkide järjestuse pikkusega `nchars` ja palume võrgul genereerida järgmise väljundtähemärgi iga sisendtähemärgi jaoks: -![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.et.png) +![Pilt, mis näitab RNN-i näidet sõna 'HELLO' genereerimisel.](../../../../../translated_images/et/rnn-generate.56c54afb52f9781d.png) Teksti genereerimisel (järeldamisel) alustame mõne **alguspunktiga**, mis edastatakse RNN rakkude kaudu, et genereerida selle vaheolek, ja seejärel algab genereerimine sellest olekust. Genereerime ühe tähemärgi korraga ja edastame oleku ja genereeritud tähemärgi järgmisele RNN rakule, et genereerida järgmine, kuni oleme genereerinud piisavalt tähemärke. - + > Pilt autorilt diff --git a/translations/et/lessons/5-NLP/18-Transformers/README.md b/translations/et/lessons/5-NLP/18-Transformers/README.md index 18f33ca0..96d111c6 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/README.md +++ b/translations/et/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN-idega rakendatakse järjestusest-järjestusse meetodit kahe korduva võrgu a **Tähelepanu mehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Seda rakendatakse, luues otseteid sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereerime väljundisümbolit yt, võtame arvesse kõiki sisendvarjatud olekuid hi, erinevate kaalukoefitsientidega αt,i. -![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanu kihiga](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.et.png) +![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanu kihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png) > Kodeerija-dekodeerija mudel aditiivse tähelepanu mehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [sellest blogipostitusest](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Tähelepanu maatriks {αi,j} esindab, millises ulatuses teatud sisendsõnad mõjutavad antud sõna genereerimist väljundjärjestuses. Allpool on näide sellisest maatriksist: -![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.et.png) +![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png) > Joonis [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3) @@ -56,7 +56,7 @@ Positsioonilise kodeerimise idee on järgmine. * Treenitav embedimine, sarnane tokeni embedimisele. See on lähenemine, mida siin kaalume. Rakendame embedimise kihid nii tokenitele kui ka nende positsioonidele, mille tulemuseks on sama mõõtmetega embedimise vektorid, mille me seejärel kokku liidame. * Fikseeritud positsioonilise kodeerimise funktsioon, nagu on välja pakutud algses artiklis. - + > Pilt autorilt @@ -66,7 +66,7 @@ Positsioonilise embedimise tulemusena saame vektori, mis sisaldab nii algset tok Järgmine samm on mustrite tuvastamine järjestuses. Selleks kasutavad transformerid **isetähelepanu** mehhanismi, mis on sisuliselt tähelepanu rakendamine samale järjestusele sisendi ja väljundina. Isetähelepanu rakendamine võimaldab meil arvestada **konteksti** lauses ja näha, millised sõnad on omavahel seotud. Näiteks võimaldab see meil näha, millistele sõnadele viitavad kooreferentsid, nagu *see*, ja arvestada konteksti: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.et.png) +![](../../../../../translated_images/et/CoreferenceResolution.861924d6d384a7d6.png) > Pilt [Google'i blogist](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kuna iga sisendi positsioon kaardistatakse sõltumatult iga väljundi positsioon **BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformer võrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suure tekstikorpuse (Wikipedia + raamatud) peal kasutades juhendamata treeningut (ennustades maskeeritud sõnu lauses). Treeningu käigus omandab mudel märkimisväärsel tasemel keele mõistmist, mida saab seejärel kasutada teiste andmekogumitega peenhäälestamise abil. Seda protsessi nimetatakse **ülekandeõppeks**. -![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.et.png) +![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Pildi [allikas](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 4000814c..f4adea54 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/et/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Tähelepanumehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Seda rakendatakse, luues otseteid sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereeritakse väljundisümbolit $y_t$, arvestame kõiki sisendi varjatud olekuid $h_i$, erinevate kaalukoefitsientidega $\\alpha_{t,i}$.\n", "\n", - "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.et.png)\n", + "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png)\n", "*Kodeerija-dekodeerija mudel koos aditiivse tähelepanumehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [sellest blogipostitusest](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Tähelepanumaatriks $\\{\\alpha_{i,j}\\}$ esindab, millisel määral teatud sisendsõnad mõjutavad antud sõna genereerimist väljundjärjestuses. Allpool on näide sellisest maatriksist:\n", "\n", - "![Pilt, mis näitab näidisalineerimist, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.et.png)\n", + "![Pilt, mis näitab näidisalineerimist, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Joonis võetud [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformerivõrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suure tekstikorpuse (Wikipedia + raamatud) peal, kasutades juhendamata treeningut (maskeeritud sõnade ennustamine lauses). Treeningu käigus omandab mudel märkimisväärse taseme keele mõistmist, mida saab seejärel kasutada teiste andmekogumite peal peenhäälestamise abil. Seda protsessi nimetatakse **ülekandeõppeks**.\n", "\n", - "![Pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.et.png)\n", + "![Pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformeriarhitektuuridel, nagu BERT, DistilBERT, BigBird, OpenGPT3 ja teised, on palju variatsioone, mida saab peenhäälestada. [HuggingFace pakett](https://github.com/huggingface/) pakub repositooriumi paljude nende arhitektuuride treenimiseks PyTorchiga.\n", "\n", diff --git a/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 60f1d4f7..99f67010 100644 --- a/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/et/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Tähelepanumehhanismid** pakuvad võimalust kaaluda iga sisendvektori kontekstuaalset mõju RNN-i iga väljundprognoosi puhul. Selle rakendamine toimub lühenduste loomisega sisend-RNN-i vaheolekute ja väljund-RNN-i vahel. Sel viisil, kui genereerime väljundisümbolit $y_t$, võtame arvesse kõiki sisendi varjatud olekuid $h_i$, erinevate kaalukoefitsientidega $\\alpha_{t,i}$.\n", "\n", - "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.et.png)\n", + "![Pilt, mis näitab kodeerija/dekodeerija mudelit koos aditiivse tähelepanukihiga](../../../../../translated_images/et/encoder-decoder-attention.7a726296894fb567.png)\n", "*Kodeerija-dekodeerija mudel koos aditiivse tähelepanumehhanismiga [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), viidatud [selles blogipostituses](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Tähelepanumaatriks $\\{\\alpha_{i,j}\\}$ esindab, millisel määral teatud sisendsõnad osalevad antud sõna genereerimisel väljundjärjestuses. Allpool on näide sellisest maatriksist:\n", "\n", - "![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.et.png)\n", + "![Pilt, mis näitab näidisalini, mille leidis RNNsearch-50, võetud Bahdanau - arviz.org](../../../../../translated_images/et/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Joonis võetud [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Joonis 3)*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "See kiht koosneb kahest `Embedding` kihist: üks on mõeldud tokenite sisestamiseks (nagu me varem arutasime) ja teine tokenite positsioonide jaoks. Tokenite positsioonid luuakse naturaalarvude järjestusena vahemikus 0 kuni `maxlen`, kasutades `tf.range`, ning seejärel edastatakse need sisestuskihile. Kaks saadud sisestusvektorit liidetakse, mille tulemusena saadakse positsiooniliselt sisestatud sisendi esitus kujuga `maxlen`$\\times$`embed_dim`.\n", "\n", - "\n", + "\n", "\n", "Nüüd rakendame transformer ploki. See võtab sisendiks varem määratletud sisestuskihi väljundi:\n" ] @@ -134,7 +134,7 @@ "\n", "Selle kihi väljund edastatakse seejärel läbi `Dense` võrgu (meie puhul - kahekihi perceptron), ja tulemus lisatakse lõplikule väljundile (mis läbib uuesti normaliseerimise).\n", "\n", - "\n", + "\n", "\n", "Nüüd oleme valmis defineerima täieliku transformer mudeli:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on väga suur mitmekihiline transformer-võrk, millel on 12 kihti *BERT-base* jaoks ja 24 kihti *BERT-large* jaoks. Mudel treenitakse esmalt suurtel tekstikorpustel (WikiPedia + raamatud) kasutades juhendamata treenimist (ennustades lauses maskeeritud sõnu). Eeltreeningu käigus omandab mudel märkimisväärse taseme keele mõistmist, mida saab seejärel kasutada koos teiste andmekogumitega peenhäälestuse abil. Seda protsessi nimetatakse **ülekandeõppeks**.\n", "\n", - "![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.et.png)\n", + "![pilt aadressilt http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/et/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer-arhitektuuridel, sealhulgas BERT, DistilBERT, BigBird, OpenGPT3 ja teistel, on palju variatsioone, mida saab peenhäälestada.\n", "\n", diff --git a/translations/et/lessons/5-NLP/19-NER/README.md b/translations/et/lessons/5-NLP/19-NER/README.md index 4c33f072..5ac516fd 100644 --- a/translations/et/lessons/5-NLP/19-NER/README.md +++ b/translations/et/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ Siiani oleme peamiselt keskendunud ühele NLP ülesandele - klassifikatsioonile. Oletame, et soovite arendada loomuliku keele vestlusrobotit, sarnast Amazon Alexa või Google Assistantiga. Nutikad vestlusrobotid töötavad nii, et nad *mõistavad*, mida kasutaja tahab, tehes sisendlausele tekstiklassifikatsiooni. Selle klassifikatsiooni tulemus on nn **intent**, mis määrab, mida vestlusrobot peaks tegema. -Bot NER +Bot NER > Pilt autorilt @@ -54,7 +54,7 @@ vastsündinul | O Kuna peame looma üks-ühele vastavuse tokenite ja klasside vahel, saame treenida parempoolse **mitme-mitme** närvivõrgu mudeli sellest pildist: -![Pilt, mis näitab levinud korduvate närvivõrkude mustreid.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.et.jpg) +![Pilt, mis näitab levinud korduvate närvivõrkude mustreid.](../../../../../translated_images/et/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Pilt [sellest blogipostitusest](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorilt [Andrej Karpathy](http://karpathy.github.io/). NER tokenite klassifikatsioonimudelid vastavad parempoolsele võrgustiku arhitektuurile sellel pildil.* diff --git a/translations/et/lessons/5-NLP/README.md b/translations/et/lessons/5-NLP/README.md index d99df0f7..71d2910b 100644 --- a/translations/et/lessons/5-NLP/README.md +++ b/translations/et/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Loodusliku keele töötlemine -![NLP ülesannete kokkuvõte visandis](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.et.png) +![NLP ülesannete kokkuvõte visandis](../../../../translated_images/et/ai-nlp.b22dcb8ca4707cea.png) Selles osas keskendume **loodusliku keele töötlemise (NLP)** ülesannete lahendamisele, kasutades tehisnärvivõrke. On palju NLP probleeme, mida soovime, et arvutid suudaksid lahendada: diff --git a/translations/et/lessons/6-Other/22-DeepRL/README.md b/translations/et/lessons/6-Other/22-DeepRL/README.md index 8d0ef810..8560e4a1 100644 --- a/translations/et/lessons/6-Other/22-DeepRL/README.md +++ b/translations/et/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ Tõenäoliselt olete näinud kaasaegseid tasakaalustusseadmeid, nagu *Segway* v Lihtsustatud versioon tasakaalustamisest on tuntud kui **CartPole** probleem. CartPole maailmas on meil horisontaalne liugur, mis saab liikuda vasakule või paremale, ja eesmärk on tasakaalustada vertikaalne post liuguri peal, kui see liigub. -cartpole +cartpole Selle keskkonna loomiseks ja kasutamiseks on vaja paar rida Python koodi: diff --git a/translations/et/lessons/6-Other/22-DeepRL/lab/README.md b/translations/et/lessons/6-Other/22-DeepRL/lab/README.md index b551e5e5..042fab05 100644 --- a/translations/et/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/et/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ Laboriülesanne [AI algajatele õppekavast](https://github.com/microsoft/ai-for- Sinu eesmärk on treenida RL-agent juhtima [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) OpenAI keskkonnas. -Mountain Car +Mountain Car ## Keskkond diff --git a/translations/et/lessons/6-Other/23-MultiagentSystems/README.md b/translations/et/lessons/6-Other/23-MultiagentSystems/README.md index 5042d271..f23bebbc 100644 --- a/translations/et/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/et/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ NetLogo saate [alla laadida](https://ccl.northwestern.edu/netlogo/download.shtml NetLogo suurepärane omadus on see, et see sisaldab töötavate mudelite raamatukogu, mida saate proovida. Minge **File → Models Library**, ja teil on palju mudelikategooriaid, mille vahel valida. -NetLogo mudelite raamatukogu +NetLogo mudelite raamatukogu > Mudelite raamatukogu ekraanipilt Dmitry Soshnikovilt @@ -70,7 +70,7 @@ Saate avada ühe mudeli, näiteks **Biology → Flocking**. Pärast mudeli avamist jõuate NetLogo põhiekraanile. Siin on näidis, mis kirjeldab huntide ja lammaste populatsiooni piiratud ressursside (rohu) tingimustes. -![NetLogo põhiekraan](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.et.png) +![NetLogo põhiekraan](../../../../../translated_images/et/NetLogo-Main.32653711ec1a01b3.png) > Ekraanipilt Dmitry Soshnikovilt diff --git a/translations/et/lessons/README.md b/translations/et/lessons/README.md index bdd9554b..5b03c2a4 100644 --- a/translations/et/lessons/README.md +++ b/translations/et/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Ülevaade -![Ülevaade visandina](../../../translated_images/ai-overview.0857791951d19500.et.png) +![Ülevaade visandina](../../../translated_images/et/ai-overview.0857791951d19500.png) > Visandmärkmed autorilt [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/et/lessons/X-Extras/X1-MultiModal/README.md b/translations/et/lessons/X-Extras/X1-MultiModal/README.md index 98a0eaa8..cf6aa3de 100644 --- a/translations/et/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/et/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Pärast transformer-mudelite edu NLP-ülesannete lahendamisel on sama või sarna CLIP-i peamine idee on võrrelda tekstilisi juhiseid pildiga ja määrata, kui hästi pilt vastab juhisele. -![CLIP arhitektuur](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.et.png) +![CLIP arhitektuur](../../../../../translated_images/et/clip-arch.b3dbf20b4e8ed8be.png) > *Pilt [sellest blogipostitusest](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kui mudel on eelnevalt treenitud, saame anda sellele pildipartii ja tekstiliste Oletame, et peame klassifitseerima pilte näiteks kasside, koerte ja inimeste vahel. Sel juhul saame mudelile anda pildi ja rea tekstilisi juhiseid: "*kassi pilt*", "*koera pilt*", "*inimese pilt*". Kolme tõenäosuse vektoris peame lihtsalt valima indeksi, mille väärtus on kõige suurem. -![CLIP pildiklassifikatsiooniks](../../../../../translated_images/clip-class.3af42ef0b2b19369.et.png) +![CLIP pildiklassifikatsiooniks](../../../../../translated_images/et/clip-class.3af42ef0b2b19369.png) > *Pilt [sellest blogipostitusest](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lisateavet VQGAN-i kohta leiate [Taming Transformers](https://compvis.github.io/ Üks oluline erinevus VQGAN-i ja traditsioonilise GAN-i vahel on see, et viimane suudab genereerida korraliku pildi mis tahes sisendvektorist, samas kui VQGAN-i puhul on tõenäoline, et pilt ei ole koherentne. Seetõttu peame pildiloome protsessi täiendavalt suunama, mida saab teha CLIP-i abil. -![VQGAN+CLIP arhitektuur](../../../../../translated_images/vqgan.5027fe05051dfa31.et.png) +![VQGAN+CLIP arhitektuur](../../../../../translated_images/et/vqgan.5027fe05051dfa31.png) Tekstijuhisele vastava pildi genereerimiseks alustame juhusliku kodeerimisvektoriga, mis edastatakse VQGAN-ile, et luua pilt. Seejärel kasutatakse CLIP-i kaotusefunktsiooni loomiseks, mis näitab, kui hästi pilt vastab tekstilisele juhisele. Eesmärk on seejärel minimeerida kaotus, kasutades tagasipropageerimist sisendvektori parameetrite kohandamiseks. Suurepärane teek, mis rakendab VQGAN+CLIP-i, on [Pixray](http://github.com/pixray/pixray). -![Pixray loodud pilt](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.et.png) | ![Pixray loodud pilt](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.et.png) | ![Pixray loodud pilt](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.et.png) +![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray loodud pilt](../../../../../translated_images/et/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Pilt genereeritud juhisest *noore meesõpetaja lähivaade, akvarellportree, kirjanduse õpetaja, raamatuga* | Pilt genereeritud juhisest *noore naisõpetaja lähivaade, õliportree, arvutiteaduse õpetaja, arvutiga* | Pilt genereeritud juhisest *vana meesõpetaja lähivaade, õliportree, matemaatika õpetaja, tahvli ees* @@ -75,7 +75,7 @@ Erinevalt CLIP-ist võtab DALL-E vastu nii teksti kui pilti ühe tokenite voona. Peamine erinevus DALL.E 1 ja 2 vahel on see, et viimane genereerib realistlikumaid pilte ja kunsti. Näited DALL-E abil genereeritud piltidest: -![Pixray loodud pilt](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.et.png) | ![Pixray loodud pilt](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.et.png) | ![Pixray loodud pilt](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.et.png) +![Pixray loodud pilt](../../../../../translated_images/et/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray loodud pilt](../../../../../translated_images/et/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray loodud pilt](../../../../../translated_images/et/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Pilt genereeritud juhisest *noore meesõpetaja lähivaade, akvarellportree, kirjanduse õpetaja, raamatuga* | Pilt genereeritud juhisest *noore naisõpetaja lähivaade, õliportree, arvutiteaduse õpetaja, arvutiga* | Pilt genereeritud juhisest *vana meesõpetaja lähivaade, õliportree, matemaatika õpetaja, tahvli ees* diff --git a/translations/fa/README.md b/translations/fa/README.md index 214741e4..33a26a39 100644 --- a/translations/fa/README.md +++ b/translations/fa/README.md @@ -1,20 +1,20 @@ [![مجوز گیت‌هاب](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) -[![مشارکت‌کنندگان گیت‌هاب](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) +[![همکاران گیت‌هاب](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![مسائل گیت‌هاب](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) -[![درخواست‌های کشش گیت‌هاب](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) -[![خوش‌آمدگویی به PRها](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) +[![درخواست‌های pull گیت‌هاب](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) +[![درخواست‌های PR خوش‌آمدید](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![ناظران گیت‌هاب](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) -[![شعبات گیت‌هاب](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) +[![ناظرین گیت‌هاب](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) +[![انشعاب‌های گیت‌هاب](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) [![ستاره‌های گیت‌هاب](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) [![بایندر](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) [![گیتّر](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) @@ -23,123 +23,122 @@ CO_OP_TRANSLATOR_METADATA: # هوش مصنوعی برای مبتدیان - یک برنامه درسی -|![اسکچ‌نوت توسط @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.fa.png)| +|![سکت‌نوت از @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/fa/ai-overview.0857791951d19500.webp)| |:---:| -| هوش مصنوعی برای مبتدیان - _اسکچ‌نوت توسط [@girlie_mac](https://twitter.com/girlie_mac)_ | - -دنیای **هوش مصنوعی** (AI) را با برنامه درسی ۱۲ هفته‌ای و ۲۴ درس ما کاوش کنید! این برنامه شامل درس‌های عملی، آزمون‌ها و آزمایشگاه‌ها می‌شود. این برنامه برای مبتدیان مناسب است و ابزارهایی همچون TensorFlow و PyTorch، همچنین اخلاق در AI را پوشش می‌دهد. +| هوش مصنوعی برای مبتدیان - _سکت‌نوت توسط [@girlie_mac](https://twitter.com/girlie_mac)_ | +جهان **هوش مصنوعی** (AI) را با برنامه درسی ۱۲ هفته‌ای و ۲۴ درس ما کشف کنید! این شامل درس‌های عملی، آزمون‌ها و آزمایشگاه‌ها است. این برنامه برای مبتدیان مناسب است و ابزارهایی مانند TensorFlow و PyTorch و همچنین اخلاق در هوش مصنوعی را پوشش می‌دهد. ### 🌐 پشتیبانی چندزبانه -#### پشتیبانی شده از طریق اکشن گیت‌هاب (خودکار و همیشه به‌روز) +#### پشتیبانی شده از طریق GitHub Action (خودکار و همیشه به‌روز) -[عربی](../ar/README.md) | [بنگالی](../bn/README.md) | [بلغاری](../bg/README.md) | [برمه‌ای (میانمار)](../my/README.md) | [چینی (ساده شده)](../zh/README.md) | [چینی (سنتی، هنگ کنگ)](../hk/README.md) | [چینی (سنتی، ماکائو)](../mo/README.md) | [چینی (سنتی، تایوان)](../tw/README.md) | [کرواتی](../hr/README.md) | [چک](../cs/README.md) | [دانمارکی](../da/README.md) | [هلندی](../nl/README.md) | [اِستونی](../et/README.md) | [فنلاندی](../fi/README.md) | [فرانسوی](../fr/README.md) | [آلمانی](../de/README.md) | [یونانی](../el/README.md) | [عبری](../he/README.md) | [هندی](../hi/README.md) | [مجارستانی](../hu/README.md) | [اندونزیایی](../id/README.md) | [ایتالیایی](../it/README.md) | [ژاپنی](../ja/README.md) | [کانادا](../kn/README.md) | [کره‌ای](../ko/README.md) | [لیتوانیایی](../lt/README.md) | [مالایی](../ms/README.md) | [مالایالامی](../ml/README.md) | [مراتی](../mr/README.md) | [نپالی](../ne/README.md) | [پیدگین نیجریه](../pcm/README.md) | [نروژی](../no/README.md) | [فارسی](./README.md) | [لهستانی](../pl/README.md) | [پرتغالی (برزیل)](../br/README.md) | [پرتغالی (پرتغال)](../pt/README.md) | [پنجابی (گرموخی)](../pa/README.md) | [رومانیایی](../ro/README.md) | [روسی](../ru/README.md) | [صربی (سیریلیک)](../sr/README.md) | [اسلواک](../sk/README.md) | [اسلوونیایی](../sl/README.md) | [اسپانیایی](../es/README.md) | [سواحیلی](../sw/README.md) | [سوئدی](../sv/README.md) | [تاگالوگ (فیلیپینی)](../tl/README.md) | [تامیلی](../ta/README.md) | [تلگو](../te/README.md) | [تایلندی](../th/README.md) | [ترکی](../tr/README.md) | [اوکراینی](../uk/README.md) | [اردو](../ur/README.md) | [ویتنامی](../vi/README.md) +[عربی](../ar/README.md) | [بنگالی](../bn/README.md) | [بلغاری](../bg/README.md) | [برمه‌ای (میانمار)](../my/README.md) | [چینی (ساده شده)](../zh/README.md) | [چینی (سنتی، هنگ‌کنگ)](../hk/README.md) | [چینی (سنتی، ماکائو)](../mo/README.md) | [چینی (سنتی، تایوان)](../tw/README.md) | [کرواسی](../hr/README.md) | [چکی](../cs/README.md) | [دانمارکی](../da/README.md) | [هلندی](../nl/README.md) | [استونیایی](../et/README.md) | [فنلاندی](../fi/README.md) | [فرانسوی](../fr/README.md) | [آلمانی](../de/README.md) | [یونانی](../el/README.md) | [عبری](../he/README.md) | [هندی](../hi/README.md) | [مجارستانی](../hu/README.md) | [اندونزیایی](../id/README.md) | [ایتالیایی](../it/README.md) | [ژاپنی](../ja/README.md) | [کانادایی](../kn/README.md) | [کره‌ای](../ko/README.md) | [لیتوانیایی](../lt/README.md) | [مالایی](../ms/README.md) | [مالایالام](../ml/README.md) | [مراتی](../mr/README.md) | [نپالی](../ne/README.md) | [پیدگین نیجریه‌ای](../pcm/README.md) | [نروژی](../no/README.md) | [فارسی (پرشین)](./README.md) | [لهستانی](../pl/README.md) | [پرتغالی (برزیل)](../br/README.md) | [پرتغالی (پرتغال)](../pt/README.md) | [پنجابی (گورمخّی)](../pa/README.md) | [رومانیایی](../ro/README.md) | [روسی](../ru/README.md) | [صربی (سیریلیک)](../sr/README.md) | [اسلواکی](../sk/README.md) | [اسلوونیایی](../sl/README.md) | [اسپانیایی](../es/README.md) | [سواحیلی](../sw/README.md) | [سوئدی](../sv/README.md) | [تاگالوگ (فیلیپینی)](../tl/README.md) | [تامیلی](../ta/README.md) | [تلگو](../te/README.md) | [تایلندی](../th/README.md) | [ترکی](../tr/README.md) | [اوکراینی](../uk/README.md) | [اردو](../ur/README.md) | [ویتنامی](../vi/README.md) > **ترجیح می‌دهید به صورت محلی کلون کنید؟** -> این مخزن شامل ترجمه‌هایی به بیش از ۵۰ زبان است که اندازه دانلود را به طور قابل توجهی افزایش می‌دهد. برای کلون کردن بدون ترجمه‌ها، از sparse checkout استفاده کنید: +> این مخزن شامل بیش از ۵۰ ترجمه زبانی است که به طور قابل توجهی حجم دانلود را افزایش می‌دهد. برای کلون کردن بدون ترجمه‌ها، از sparse checkout استفاده کنید: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> این به شما همه چیز لازم برای تکمیل دوره را با دانلودی بسیار سریع‌تر می‌دهد. +> این به شما همه چیز لازم برای کامل کردن دوره را با دانلود بسیار سریع‌تر می‌دهد. -**اگر مایلید زبان‌های ترجمه اضافی پشتیبانی شوند، زبان‌ها در این [لینک](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) فهرست شده‌اند** +**اگر مایل به پشتیبانی از زبان‌های ترجمه اضافی هستید فهرست آنها در [اینجا](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) موجود است** ## به جامعه بپیوندید [![دیسکورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## آنچه یاد خواهید گرفت +## آنچه خواهید آموخت **[نقشه ذهنی دوره](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -در این برنامه درسی، شما یاد خواهید گرفت: +در این برنامه درسی، شما خواهید آموخت: -* رویکردهای مختلف به هوش مصنوعی، از جمله رویکرد نمادین "قدیمی" با **نمایش دانش** و استدلال ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **شبکه‌های عصبی** و **یادگیری عمیق** که در هسته هوش مصنوعی مدرن قرار دارند. مفاهیم پشت این موضوعات مهم را با استفاده از کد در دو چارچوب محبوب - [TensorFlow](http://Tensorflow.org) و [PyTorch](http://pytorch.org) نشان خواهیم داد. -* **معماری‌های عصبی** برای کار با تصاویر و متن. مدل‌های اخیر را پوشش خواهیم داد اما ممکن است در فناوری‌های به‌روز کمی ناقص باشیم. -* رویکردهای کمتر محبوب AI، مانند **الگوریتم‌های ژنتیکی** و **سیستم‌های چندعامله**. +* رویکردهای مختلف به هوش مصنوعی، از جمله روش نمادین «کلاسیک» با **نمایش دانش** و استدلال ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **شبکه‌های عصبی** و **یادگیری عمیق**، که هسته هوش مصنوعی مدرن هستند. ما مفاهیم پشت این موضوعات مهم را با استفاده از کد در دو چارچوب محبوب - [TensorFlow](http://Tensorflow.org) و [PyTorch](http://pytorch.org) نشان خواهیم داد. +* **معماری‌های عصبی** برای کار با تصاویر و متن. ما مدل‌های جدید را پوشش خواهیم داد اما ممکن است کمی از پیشرفته‌ترین مدل‌ها عقب باشیم. +* رویکردهای کمتر محبوب هوش مصنوعی، مانند **الگوریتم‌های ژنتیکی** و **سیستم‌های چندعاملی**. آنچه در این برنامه درسی پوشش داده نمی‌شود: -> [تمام منابع اضافی برای این دوره را در مجموعه Microsoft Learn ما پیدا کنید](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [تمام منابع اضافی این دوره را در مجموعه Microsoft Learn ما پیدا کنید](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* موارد کسب‌وکار استفاده از **AI در کسب‌وکار**. برای این منظور، مسیر یادگیری [مقدمه‌ای بر هوش مصنوعی برای کاربران کسب‌وکار](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) در Microsoft Learn یا [مدرسه کسب‌وکار AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) که با همکاری [INSEAD](https://www.insead.edu/) توسعه یافته است را در نظر بگیرید. -* **یادگیری ماشین کلاسیک** که به خوبی در [برنامه درسی یادگیری ماشین برای مبتدیان](http://github.com/Microsoft/ML-for-Beginners) توضیح داده شده است. -* برنامه‌های عملی AI ساخته شده با **[خدمات شناختی](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. برای این منظور، توصیه می‌کنیم با ماژول‌های Microsoft Learn برای [بینایی](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، [پردازش زبان طبیعی](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، **[هوش مولد با سرویس Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** و دیگران شروع کنید. -* چارچوب‌های خاص ابری ML، مانند [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)، یا [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). مسیرهای یادگیری [ساخت و اجرای راه‌حل‌های یادگیری ماشین با Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) و [ساخت و اجرای راه‌حل‌های یادگیری ماشین با Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) را در نظر بگیرید. -* **هوش مصنوعی مکالمه‌ای** و **چت‌بات‌ها**. مسیر یادگیری جداگانه [ایجاد راه‌حل‌های هوش مصنوعی مکالمه‌ای](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) وجود دارد و می‌توانید برای جزئیات بیشتر به [این پست وبلاگی](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) نیز مراجعه کنید. -* ریاضیات عمیق پشت یادگیری عمیق. برای این منظور کتاب [یادگیری عمیق](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) نوشته ایان گودفلو، یوشوا بنگیو و آرون کورویل را توصیه می‌کنیم که به‌صورت آنلاین نیز در [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) موجود است. +* موارد تجاری استفاده از **هوش مصنوعی در کسب و کار**. به یادگیری مسیر [مقدمه‌ای بر هوش مصنوعی برای کاربران کسب‌وکار](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) در Microsoft Learn، یا [مدرسه کسب‌وکار هوش مصنوعی](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) که با همکاری [INSEAD](https://www.insead.edu/) توسعه یافته است، فکر کنید. +* **یادگیری ماشین کلاسیک** که در برنامه درسی [یادگیری ماشین برای مبتدیان](http://github.com/Microsoft/ML-for-Beginners) ما به خوبی شرح داده شده است. +* کاربردهای عملی هوش مصنوعی ساخته شده با استفاده از **[سرویس‌های شناختی](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. برای این منظور، ما پیشنهاد می‌کنیم با ماژول‌های Microsoft Learn برای [بینایی](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)، [پردازش زبان طبیعی](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)، **[هوش مصنوعی مولد با سرویس Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** و دیگر موارد آغاز کنید. +* چارچوب‌های خاص ابری ML مانند [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)، [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) یا [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). استفاده از مسیرهای یادگیری [ساخت و اجرای راه‌حل‌های یادگیری ماشین با Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) و [ساخت و اجرای راه‌حل‌های یادگیری ماشین با Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) را در نظر داشته باشید. +* **هوش مصنوعی مکالمه‌ای** و **چت‌بات‌ها**. یک مسیر یادگیری جداگانه [ساخت راه‌حل‌های هوش مصنوعی مکالمه‌ای](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) وجود دارد، و همچنین می‌توانید به [این پست وبلاگ](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) برای جزئیات بیشتر مراجعه کنید. +* **ریاضیات عمیق** پشت یادگیری عمیق. برای این منظور، ما کتاب [یادگیری عمیق](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) نوشته ایان گودفلو، یوشوا بنجیو و آرون کورویل را توصیه می‌کنیم که به صورت آنلاین نیز در [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) موجود است. -برای مقدمه‌ای آرام بر مباحث _AI در ابر_ می‌توانید مسیر یادگیری [شروع با هوش مصنوعی در Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) را در نظر بگیرید. +برای معرفی ملایم به موضوعات _هوش مصنوعی ابری_ ممکن است مسیر یادگیری [شروع با هوش مصنوعی در Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) را دنبال کنید. # محتوا | | لینک درس | PyTorch/Keras/TensorFlow | آزمایشگاه | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [راه‌اندازی دوره](./lessons/0-course-setup/setup.md) | [راه‌اندازی محیط توسعه شما](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [شروع دوره](./lessons/0-course-setup/setup.md) | [راه‌اندازی محیط توسعه](./lessons/0-course-setup/how-to-run.md) | | | I | [**مقدمه‌ای بر هوش مصنوعی**](./lessons/1-Intro/README.md) | | | | 01 | [مقدمه و تاریخچه هوش مصنوعی](./lessons/1-Intro/README.md) | - | - | | II | **هوش مصنوعی نمادین** | -| 02 | [نمایش دانش و سیستم‌های خبره](./lessons/2-Symbolic/README.md) | [سیستم‌های خبره](./lessons/2-Symbolic/Animals.ipynb) / [آنتولوژی](./lessons/2-Symbolic/FamilyOntology.ipynb) /[گراف مفهومی](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [نمایش دانش و سیستم‌های خبره](./lessons/2-Symbolic/README.md) | [سیستم‌های خبره](./lessons/2-Symbolic/Animals.ipynb) / [انتولوژی](./lessons/2-Symbolic/FamilyOntology.ipynb) /[گراف مفهوم](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**مقدمه‌ای بر شبکه‌های عصبی**](./lessons/3-NeuralNetworks/README.md) ||| -| ۰۳ | [پرستروَن](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [دفترچه یادداشت](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [لاب](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| ۰۴ | [پرستروَن چندلایه و ساخت چارچوب خودمان](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [دفترچه یادداشت](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [لاب](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| ۰۵ | [مقدمه‌ای بر چارچوب‌ها (PyTorch/TensorFlow) و بیش‌برازش](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [پایتورچ](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [کراس](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [تنسورفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لاب](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**بینایی کامپیوتری**](./lessons/4-ComputerVision/README.md) | [پایتورچ](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [تنسورفلو](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [کشف بینایی کامپیوتری در مایکروسافت آژور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| ۰۶ | [مقدمه‌ای بر بینایی کامپیوتری. اوپن‌سی‌وی](./lessons/4-ComputerVision/06-IntroCV/README.md) | [دفترچه یادداشت](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [لاب](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| ۰۷ | [شبکه‌های عصبی کانولوشنی](./lessons/4-ComputerVision/07-ConvNets/README.md) & [معماری‌های CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [پایتورچ](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[تنسورفلو](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [لاب](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| ۰۸ | [شبکه‌های پیش‌آموزش دیده و یادگیری انتقالی](./lessons/4-ComputerVision/08-TransferLearning/README.md) و [ترفندهای آموزش](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [پایتورچ](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [تنسورفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [لاب](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| ۰۹ | [خودرمزگذارها و VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [پایتورچ](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [تنسورفلو](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| ۱۰ | [شبکه‌های مولد تخاصمی و انتقال سبک هنری](./lessons/4-ComputerVision/10-GANs/README.md) | [پایتورچ](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [تنسورفلو](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| ۱۱ | [تشخیص شیء](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [تنسورفلو](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [لاب](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| ۱۲ | [بخش‌بندی معنایی. یو-نت](./lessons/4-ComputerVision/12-Segmentation/README.md) | [پایتورچ](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [تنسورفلو](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**پردازش زبان طبیعی**](./lessons/5-NLP/README.md) | [پایتورچ](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[تنسورفلو](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [کشف پردازش زبان طبیعی در مایکروسافت آژور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| ۱۳ | [نمایش متن. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| ۱۴ | [تعبیه‌های معنایی کلمات. Word2Vec و GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| ۱۵ | [مدل‌سازی زبان. آموزش تعبیه‌های خود](./lessons/5-NLP/15-LanguageModeling/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [لاب](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| ۱۶ | [شبکه‌های عصبی مکرر](./lessons/5-NLP/16-RNN/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| ۱۷ | [شبکه‌های تولیدی مکرر](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [لاب](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | -| ۱۸ | [تبدیل‌کننده‌ها. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[تنسورفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| ۱۹ | [شناسایی نهاد نام‌دار](./lessons/5-NLP/19-NER/README.md) | [تنسورفلو](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [لاب](./lessons/5-NLP/19-NER/lab/README.md) | -| ۲۰ | [مدل‌های زبانی بزرگ، برنامه‌نویسی پِرامپت و وظایف چندنمونه‌ای](./lessons/5-NLP/20-LangModels/README.md) | [پایتورچ](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **سایر تکنیک‌های هوش مصنوعی** || | -| ۲۱ | [الگوریتم‌های ژنتیکی](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [دفترچه یادداشت](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| ۲۲ | [یادگیری تقویتی عمیق](./lessons/6-Other/22-DeepRL/README.md) | [پایتورچ](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[تنسورفلو](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [لاب](./lessons/6-Other/22-DeepRL/lab/README.md) | -| ۲۳ | [سامانه‌های چندعامل](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 03 | [پرسپترون](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [دفترچه نوت‌بوک](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [آزمایشگاه](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [پرسپترون چندلایه و ساخت فریمورک خودمان](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [دفترچه نوت‌بوک](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [آزمایشگاه](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [مقدمه‌ای بر فریمورک‌ها (PyTorch/TensorFlow) و اورفیتینگ](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [پایتورچ](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [کرَس](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [تنسرفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [آزمایشگاه](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**بینایی کامپیوتری**](./lessons/4-ComputerVision/README.md) | [پایتورچ](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [تنسرفلو](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [کاوش بینایی کامپیوتری در مایکروسافت آژور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [مقدمه‌ای بر بینایی کامپیوتری. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [دفترچه نوت‌بوک](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [آزمایشگاه](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [شبکه‌های عصبی پیچشی](./lessons/4-ComputerVision/07-ConvNets/README.md) و [معماری‌های CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [پایتورچ](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[تنسرفلو](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [آزمایشگاه](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [شبکه‌های پیش‌آموزش داده شده و یادگیری انتقالی](./lessons/4-ComputerVision/08-TransferLearning/README.md) و [ترفندهای آموزش](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [پایتورچ](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [تنسرفلو](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [آزمایشگاه](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [اتو انکدرها و VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [پایتورچ](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [تنسرفلو](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [شبکه‌های هم‌ترازی مولد و انتقال سبک هنری](./lessons/4-ComputerVision/10-GANs/README.md) | [پایتورچ](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [تنسرفلو](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [کشف اشیاء](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [تنسرفلو](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [آزمایشگاه](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [بخش‌بندی معنایی. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [پایتورچ](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [تنسرفلو](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**پردازش زبان طبیعی**](./lessons/5-NLP/README.md) | [پایتورچ](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[تنسرفلو](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [کاوش پردازش زبان طبیعی در مایکروسافت آژور](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [نمایش متن. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [جاسازی معنایی کلمات. Word2Vec و GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [مدلسازی زبان. آموزش جاسازی‌های خود](./lessons/5-NLP/15-LanguageModeling/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [آزمایشگاه](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [شبکه‌های عصبی بازگشتی](./lessons/5-NLP/16-RNN/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [شبکه‌های بازگشتی مولد](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [آزمایشگاه](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [ترانسفورمرها. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [پایتورچ](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[تنسرفلو](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [شناسایی موجودیت‌های نام‌دار](./lessons/5-NLP/19-NER/README.md) | [تنسرفلو](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [آزمایشگاه](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [مدل‌های زبان بزرگ، برنامه‌نویسی پرامپت و تسک‌های چندنمونه‌ای](./lessons/5-NLP/20-LangModels/README.md) | [پایتورچ](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **تکنیک‌های دیگر هوش مصنوعی** || | +| 21 | [الگوریتم‌های ژنتیک](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [دفترچه نوت‌بوک](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [یادگیری تقویتی عمیق](./lessons/6-Other/22-DeepRL/README.md) | [پایتورچ](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[تنسرفلو](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [آزمایشگاه](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [سامانه‌های چندعامله](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **اخلاق هوش مصنوعی** | | | -| ۲۴ | [اخلاق هوش مصنوعی و هوش مصنوعی مسئولانه](./lessons/7-Ethics/README.md) | [مایکروسافت لرن: اصول هوش مصنوعی مسئولانه](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [اخلاق هوش مصنوعی و هوش مصنوعی مسئولانه](./lessons/7-Ethics/README.md) | [مایکروسافت لرن: اصول هوش مصنوعی مسئولانه](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **موارد اضافی** | | | -| ۲۵ | [شبکه‌های چندرسانه‌ای، CLIP و VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [دفترچه یادداشت](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [شبکه‌های چندمودالی، CLIP و VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [دفترچه نوت‌بوک](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## هر درس شامل * مطالب پیش‌خوانی -* دفترچه‌های یادداشت اجرایی Jupyter که معمولاً خاص چارچوب هستند (**PyTorch** یا **TensorFlow**). دفترچه‌ی اجرایی همچنین شامل مطالب نظری زیادی است، بنابراین برای درک موضوع، باید حداقل یکی از نسخه‌های دفترچه (پایتورچ یا تنسورفلو) را مطالعه کنید. -* **لاب‌ها** برای برخی موضوعات موجود است که فرصتی برای شما فراهم می‌آورد تا مطالب یادگرفته شده را روی مسئله‌ای خاص آزمایش کنید. -* برخی بخش‌ها حاوی لینک به ماژول‌های [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) هستند که موضوعات مرتبط را پوشش می‌دهند. +* دفاتر جوی‌پایتر قابل اجرا، که اغلب مخصوص فریمورک (**پایتورچ** یا **تنسرفلو**) هستند. دفترچه قابل اجرا همچنین شامل مقدار زیادی مطلب نظری است، بنابراین برای درک موضوع باید حداقل یک نسخه از دفترچه را دنبال کنید (یا پایتورچ یا تنسرفلو). +* **آزمایشگاه‌ها** برای برخی موضوعات در دسترس هستند که به شما فرصت می‌دهند مواد یادگرفته شده را در یک مسئله خاص به کار ببرید. +* برخی بخش‌ها شامل لینک به ماژول‌های [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) هستند که موضوعات مرتبط را پوشش می‌دهند. -## شروع کنید +## شروع به کار -### 🎯 تازه‌وارد به هوش مصنوعی؟ همینجا شروع کنید! +### 🎯 تازه‌وارد به هوش مصنوعی؟ از اینجا شروع کنید! -اگر کاملاً تازه‌کار در هوش مصنوعی هستید و می‌خواهید با مثال‌های سریع و عملی شروع کنید، به [**مثال‌های مناسب برای مبتدیان**](./examples/README.md) ما نگاهی بیندازید! این‌ها شامل: +اگر کاملاً به هوش مصنوعی تازه‌وارد هستید و به دنبال مثال‌های سریع و عملی هستید، نمونه‌های [**مناسب مبتدی‌ها**](./examples/README.md) را بررسی کنید! این موارد شامل: -- 🌟 **سلام دنیای هوش مصنوعی** - اولین برنامه هوش مصنوعی شما (شناسایی الگو) -- 🧠 **شبکه عصبی ساده** - ساخت یک شبکه عصبی از ابتدا -- 🖼️ **دسته‌بند تصویر** - دسته‌بندی تصاویر با توضیحات مفصل -- 💬 **احساس متن** - تجزیه و تحلیل متن مثبت/منفی +- 🌟 **سلام جهان هوش مصنوعی** - اولین برنامه هوش مصنوعی شما (شناسایی الگو) +- 🧠 **شبکه عصبی ساده** - ساخت شبکه عصبی از ابتدا +- 🖼️ **دسته‌بند تصویر** - طبقه‌بندی تصاویر با توضیحات دقیق +- 💬 **احساس متن** - تحلیل متن مثبت/منفی -این مثال‌ها برای کمک به درک مفاهیم هوش مصنوعی قبل از ورود به دوره کامل طراحی شده‌اند. +این مثال‌ها برای کمک به شما در درک مفاهیم هوش مصنوعی قبل از ورود به برنامه درسی کامل طراحی شده‌اند. -### 📚 راه‌اندازی دوره کامل +### 📚 تنظیم برنامه درسی کامل -- ما یک [درس راه‌اندازی](./lessons/0-course-setup/setup.md) ایجاد کرده‌ایم تا به شما در راه‌اندازی محیط توسعه کمک کند. - برای مربیان نیز یک [درس راه‌اندازی برنامه درسی](./lessons/0-course-setup/for-teachers.md) تهیه کرده‌ایم! -- نحوه [اجرای کد در VSCode یا یک Codepace](./lessons/0-course-setup/how-to-run.md) +- ما یک [درس تنظیم](./lessons/0-course-setup/setup.md) ایجاد کرده‌ایم تا به شما در راه‌اندازی محیط توسعه کمک کند. - برای آموزش‌دهندگان، ما نیز یک [درس تنظیم برنامه درسی](./lessons/0-course-setup/for-teachers.md) ایجاد کرده‌ایم! +- چگونه [کد را در VSCode یا Codespace اجرا کنیم](./lessons/0-course-setup/how-to-run.md) این مراحل را دنبال کنید: @@ -147,33 +146,33 @@ CO_OP_TRANSLATOR_METADATA: مخزن را کلون کنید: `git clone https://github.com/microsoft/AI-For-Beginners.git` -فراموش نکنید که ستاره (🌟) به این مخزن بدهید تا بعداً راحت‌تر آن را پیدا کنید. +فراموش نکنید این مخزن را ستاره‌دار (🌟) کنید تا بعداً راحت‌تر پیدایش کنید. -## ملاقات با یادگیرندگان دیگر +## ملاقات با سایر یادگیرندگان -برای ملاقات و شبکه‌سازی با دیگر یادگیرندگانی که این دوره را می‌گذرانند و دریافت پشتیبانی، به [سرور رسمی دیسکورد هوش مصنوعی ما](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) بپیوندید. +به [سرور رسمی دیسکورد AI ما](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) بپیوندید تا با سایر یادگیرندگانی که این دوره را می‌گذرانند آشنا شوید و شبکه‌سازی کنید و پشتیبانی دریافت کنید. -اگر بازخورد محصول یا سوال دارید در حین ساخت به [انجمن توسعه‌دهندگان Azure AI Foundry](https://aka.ms/foundry/forum) مراجعه کنید. +اگر بازخورد محصول یا سوال در حین ساخت دارید به [انجمن توسعه‌دهندگان Azure AI Foundry](https://aka.ms/foundry/forum) مراجعه کنید. ## آزمون‌ها -> **نکته‌ای درباره آزمون‌ها**: تمام آزمون‌ها در پوشه Quiz-app در etc\quiz-app قرار دارند، یا به صورت [آنلاین اینجا](https://ff-quizzes.netlify.app/) موجود هستند. این‌ها از درون درس‌ها لینک شده‌اند. برنامه آزمون می‌تواند به صورت محلی اجرا شود یا در Azure مستقر شود؛ دستورالعمل‌های پوشه `quiz-app` را دنبال کنید. این آزمون‌ها به تدریج محلی‌سازی می‌شوند. +> **نکته‌ای درباره آزمون‌ها**: همه آزمون‌ها در پوشه Quiz-app در etc\quiz-app قرار دارند، یا [به صورت آنلاین اینجا](https://ff-quizzes.netlify.app/) قابل دسترسی هستند. این‌ها از داخل دروس لینک شده‌اند و اپلیکیشن آزمون می‌تواند به صورت محلی اجرا شود یا در Azure مستقر گردد؛ دستورالعمل‌ها را در پوشه `quiz-app` دنبال کنید. آن‌ها به تدریج بومی‌سازی می‌شوند. ## درخواست کمک -آیا پیشنهادی دارید یا خطاهای املایی یا برنامه‌ای پیدا کرده‌اید؟ یک issue مطرح کنید یا pull request ایجاد کنید. +آیا پیشنهاد یا خطای املایی یا کد پیدا کرده‌اید؟ یک issue باز کنید یا pull request بسازید. ## تشکر ویژه -* **✍️ نویسنده اصلی:** [دیمیتری سوشنیکوف](http://soshnikov.com)، دکترا -* **🔥 ویراستار:** [جن لوپر](https://twitter.com/jenlooper)، دکترا +* **✍️ نویسنده اصلی:** [دیمیتری سوشنیکوف](http://soshnikov.com)، دکترای علوم +* **🔥 ویراستار:** [جن لوپر](https://twitter.com/jenlooper)، دکترای علوم * **🎨 تصویرگر اسکچ‌نوت:** [تومومی ایمورا](https://twitter.com/girlie_mac) -* **✅ سازنده آزمون:** [لاتیفا بلو](https://github.com/CinnamonXI)، [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 مشارکت‌کنندگان اصلی:** [اِوگنی پیشچیک](https://github.com/Pe4enIks) +* **✅ سازنده آزمون:** [لتیفه بلا](https://github.com/CinnamonXI)، [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 مشارکت‌کنندگان اصلی:** [اوگنی پیچچیک](https://github.com/Pe4enIks) -## دوره‌های آموزشی دیگر +## برنامه‌های درسی دیگر -تیم ما دوره‌های دیگری نیز تولید می‌کند! بررسی کنید: +تیم ما برنامه‌های درسی دیگری تولید می‌کند! بررسی کنید: ### LangChain @@ -191,14 +190,14 @@ CO_OP_TRANSLATOR_METADATA: --- ### سری هوش مصنوعی مولد -[![Generative AI برای مبتدیان](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) +[![هوش مصنوعی مولد برای مبتدیان](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![هوش مصنوعی مولد (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![هوش مصنوعی مولد (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![هوش مصنوعی مولد (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- -### آموزش‌های پایه‌ای +### یادگیری هسته‌ای [![ML برای مبتدیان](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![علم داده برای مبتدیان](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![هوش مصنوعی برای مبتدیان](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -209,25 +208,25 @@ CO_OP_TRANSLATOR_METADATA: --- -### سری Copilot -[![Copilot برای برنامه‌نویسی جفتی AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot برای C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +### سری کپايلوت +[![کپايلوت برای برنامه‌نویسی جفتی AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![کپايلوت برای C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![ماجراجویی کپايلوت](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## دریافت کمک -اگر گیر کردید یا سوالی درباره توسعه برنامه‌های هوش مصنوعی دارید، به همراه سایر یادگیرندگان و توسعه‌دهندگان باتجربه در بحث‌های MCP بپیوندید. این یک جامعه حمایتی است که سوالات استقبال می‌شود و دانش به صورت رایگان به اشتراک گذاشته می‌شود. +اگر در ساخت برنامه‌های هوش مصنوعی به مشکل خوردید یا سوالی داشتید به همراه دیگر یادگیرندگان و توسعه‌دهندگان با تجربه در بحث‌های MCP شرکت کنید. این یک جامعه حمایت‌کننده است که سوالات پذیرفته می‌شوند و دانش به طور آزاد به اشتراک گذاشته می‌شود. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -اگر بازخورد محصول یا خطاهایی هنگام ساخت داشتید به: +اگر هنگام ساخت بازخورد محصول یا خطا داشتید مراجعه کنید به: -[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) مراجعه کنید. +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) --- **سلب مسئولیت**: -این سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً به این نکته توجه داشته باشید که ترجمه‌های خودکار ممکن است حاوی خطا یا نادرستی باشند. سند اصلی به زبان بومی خود باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، استفاده از ترجمه حرفه‌ای انسانی توصیه می‌شود. ما در قبال هرگونه سوءتفاهم یا تفسیر نادرست ناشی از استفاده از این ترجمه مسئولیتی نداریم. +این سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما در تلاش برای دقت هستیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است دارای خطا یا نقص‌هایی باشند. سند اصلی به زبان بومی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، استفاده از ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هرگونه سوءتفاهم یا تفسیر نادرست ناشی از استفاده از این ترجمه نیستیم. \ No newline at end of file diff --git a/translations/fa/lessons/0-course-setup/how-to-run.md b/translations/fa/lessons/0-course-setup/how-to-run.md index fb566080..ecad9c46 100644 --- a/translations/fa/lessons/0-course-setup/how-to-run.md +++ b/translations/fa/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # چگونه کد را اجرا کنیم -این دوره آموزشی شامل مثال‌ها و آزمایش‌های قابل اجرا زیادی است که ممکن است بخواهید آن‌ها را اجرا کنید. برای انجام این کار، باید توانایی اجرای کدهای Python در Jupyter Notebooks که به عنوان بخشی از این دوره ارائه شده‌اند را داشته باشید. چندین گزینه برای اجرای کد وجود دارد: +این برنامه آموزشی حاوی مثال‌ها و آزمایش‌های قابل اجرا زیادی است که احتمالاً می‌خواهید اجرا کنید. برای انجام این کار، باید توانایی اجرای کد پایتون در دفترچه‌های Jupyter ارائه شده به عنوان بخشی از این برنامه آموزشی را داشته باشید. چند گزینه برای اجرای کد دارید: -## اجرای محلی روی کامپیوتر شما +## اجرا به صورت محلی روی کامپیوتر خود -برای اجرای کد به صورت محلی روی کامپیوتر خود، باید نسخه‌ای از Python نصب شده باشد. من شخصاً توصیه می‌کنم **[miniconda](https://conda.io/en/latest/miniconda.html)** را نصب کنید - این یک نصب سبک است که از مدیر بسته `conda` برای ایجاد **محیط‌های مجازی** Python پشتیبانی می‌کند. +برای اجرای کد به صورت محلی روی کامپیوتر خود، نیاز به نصب پایتون دارید. یکی از توصیه‌ها نصب **[miniconda](https://conda.io/en/latest/miniconda.html)** است - این یک نصب نسبتاً سبک است که از مدیر بسته `conda` برای **محیط‌های مجازی** مختلف پایتون پشتیبانی می‌کند. -پس از نصب miniconda، باید مخزن را کلون کرده و یک محیط مجازی برای استفاده در این دوره ایجاد کنید: +پس از نصب miniconda، مخزن را کلون کنید و یک محیط مجازی برای استفاده در این دوره ایجاد کنید: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,53 +24,57 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### استفاده از Visual Studio Code با افزونه Python +### استفاده از Visual Studio Code با افزونه پایتون -احتمالاً بهترین روش برای استفاده از این دوره آموزشی این است که آن را در [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) با [افزونه Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) باز کنید. +این برنامه آموزشی بهترین بهره را زمانی دارد که در [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) با افزونه [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste) باز شود. -> **توجه**: پس از کلون کردن و باز کردن دایرکتوری در VS Code، به طور خودکار پیشنهاد نصب افزونه‌های Python را دریافت خواهید کرد. همچنین باید miniconda را همانطور که در بالا توضیح داده شد نصب کنید. +> **توجه**: پس از کلون کردن و باز کردن دایرکتوری در VS Code، به طور خودکار به شما پیشنهاد نصب افزونه‌های پایتون داده می‌شود. همچنین باید miniconda را طبق توضیحات بالا نصب کنید. -> **توجه**: اگر VS Code پیشنهاد باز کردن مخزن در کانتینر را داد، باید این پیشنهاد را رد کنید تا از نصب محلی Python استفاده کنید. +> **توجه**: اگر VS Code به شما پیشنهاد داد که مخزن را در یک کانتینر باز کنید، باید این پیشنهاد را رد کنید تا از نصب محلی پایتون استفاده کنید. ### استفاده از Jupyter در مرورگر -شما همچنین می‌توانید محیط Jupyter را مستقیماً از طریق مرورگر روی کامپیوتر خود استفاده کنید. در واقع، هم Jupyter کلاسیک و هم Jupyter Hub محیط توسعه بسیار راحتی با تکمیل خودکار، برجسته‌سازی کد و غیره ارائه می‌دهند. +شما همچنین می‌توانید از محیط Jupyter از طریق مرورگر روی کامپیوتر خود استفاده کنید. هم Jupyter کلاسیک و هم JupyterHub محیط توسعه راحتی با تکمیل خودکار، برجسته‌سازی کد و غیره ارائه می‌دهند. -برای شروع Jupyter به صورت محلی، به دایرکتوری دوره بروید و اجرا کنید: +برای شروع Jupyter به صورت محلی، به دایرکتوری دوره بروید و دستور زیر را اجرا کنید: ```bash jupyter notebook -``` -یا +``` +یا ```bash jupyterhub -``` +``` سپس می‌توانید به هر یک از فایل‌های `.ipynb` بروید، آن‌ها را باز کنید و شروع به کار کنید. -### اجرای در کانتینر +### اجرا در کانتینر -یک جایگزین برای نصب Python این است که کد را در کانتینر اجرا کنید. از آنجا که مخزن ما شامل پوشه خاص `.devcontainer` است که نحوه ساخت کانتینر برای این مخزن را مشخص می‌کند، VS Code به شما پیشنهاد می‌دهد کد را در کانتینر باز کنید. این نیاز به نصب Docker دارد و همچنین پیچیده‌تر است، بنابراین ما این روش را برای کاربران با تجربه‌تر توصیه می‌کنیم. +یکی از جایگزین‌های نصب پایتون، اجرای کد در یک کانتینر است. از آنجا که مخزن ما پوشه ویژه `.devcontainer` را فراهم می‌کند که نحوه ساخت کانتینر برای این مخزن را آموزش می‌دهد، VS Code این امکان را به شما می‌دهد که کد را در یک کانتینر باز کنید. این کار نیاز به نصب Docker دارد و همچنین پیچیده‌تر است، بنابراین این روش را به کاربران باتجربه‌تر توصیه می‌کنیم. -## اجرای در فضای ابری +## اجرا در فضای ابری -اگر نمی‌خواهید Python را به صورت محلی نصب کنید و به برخی منابع ابری دسترسی دارید، یک جایگزین خوب این است که کد را در فضای ابری اجرا کنید. چندین روش برای انجام این کار وجود دارد: +اگر نمی‌خواهید پایتون را به صورت محلی نصب کنید و به برخی منابع ابری دسترسی دارید، گزینه خوبی اجرای کد در فضای ابری است. چند روش برای انجام این کار وجود دارد: -* استفاده از **[GitHub Codespaces](https://github.com/features/codespaces)**، که یک محیط مجازی است که برای شما در GitHub ایجاد شده و از طریق رابط مرورگر VS Code قابل دسترسی است. اگر به Codespaces دسترسی دارید، می‌توانید فقط روی دکمه **Code** در مخزن کلیک کنید، یک Codespace را شروع کنید و به سرعت اجرا کنید. -* استفاده از **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) منابع محاسباتی رایگانی را در فضای ابری برای افرادی مانند شما فراهم می‌کند تا برخی کدها را در GitHub آزمایش کنند. یک دکمه در صفحه اصلی وجود دارد که مخزن را در Binder باز می‌کند - این باید شما را به سایت Binder هدایت کند، که کانتینر زیرین را می‌سازد و رابط وب Jupyter را برای شما به صورت یکپارچه شروع می‌کند. +* استفاده از **[GitHub Codespaces](https://github.com/features/codespaces)** که یک محیط مجازی است که برای شما در GitHub ایجاد می‌شود و از طریق رابط مرورگر VS Code قابل دسترسی است. اگر به Codespaces دسترسی دارید، فقط کافی است روی دکمه **Code** در مخزن کلیک کنید، یک codespace راه‌اندازی کنید و بدون اتلاف وقت شروع به کار کنید. +* استفاده از **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) منابع رایگان پردازشی در فضای ابری را برای افرادی مانند شما فراهم می‌کند تا کدهایی را که در گیت‌هاب هستند تست کنند. در صفحه اول دکمه‌ای برای باز کردن مخزن در Binder وجود دارد - این شما را سریع به سایت Binder منتقل می‌کند، جایی که کانتینر زیرساختی ساخته می‌شود و رابط وب Jupyter به‌طور بی‌وقفه شروع می‌شود. -> **توجه**: برای جلوگیری از سوءاستفاده، Binder دسترسی به برخی منابع وب را مسدود کرده است. این ممکن است مانع از اجرای برخی کدها شود که مدل‌ها و/یا مجموعه داده‌ها را از اینترنت عمومی دریافت می‌کنند. ممکن است نیاز به یافتن راه‌حل‌هایی داشته باشید. همچنین، منابع محاسباتی ارائه شده توسط Binder بسیار ابتدایی هستند، بنابراین آموزش کند خواهد بود، به ویژه در درس‌های پیچیده‌تر بعدی. +> **توجه**: برای جلوگیری از سوء استفاده، استفاده Binder از برخی منابع وب مسدود شده است. این ممکن است مانع اجرای برخی کدها شود که مدل‌ها و/یا داده‌ها را از اینترنت عمومی دریافت می‌کنند. ممکن است نیاز به راه‌حل‌های جایگزین داشته باشید. همچنین منابع محاسباتی ارائه شده توسط Binder بسیار پایه‌ای است، بنابراین آموزش به‌خصوص در درس‌های پیچیده‌تر و بعدی کند خواهد بود. -## اجرای در فضای ابری با GPU +## اجرا در فضای ابری با GPU -برخی از درس‌های بعدی در این دوره آموزشی به شدت از پشتیبانی GPU بهره‌مند خواهند شد، زیرا در غیر این صورت آموزش بسیار کند خواهد بود. چند گزینه وجود دارد که می‌توانید دنبال کنید، به ویژه اگر از طریق [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) یا از طریق مؤسسه خود به فضای ابری دسترسی دارید: +برخی از درس‌های بعدی این برنامه آموزشی به شدت از پشتیبانی GPU بهره‌مند می‌شوند. آموزش مدل، برای مثال، در غیر این صورت می‌تواند بسیار کند باشد. چند گزینه می‌توانید دنبال کنید، به ویژه اگر به فضای ابری از طریق [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) یا از طریق مؤسسه خود دسترسی دارید: -* ایجاد [ماشین مجازی علوم داده](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) و اتصال به آن از طریق Jupyter. سپس می‌توانید مخزن را مستقیماً روی ماشین کلون کنید و شروع به یادگیری کنید. ماشین‌های سری NC از پشتیبانی GPU برخوردارند. +* ایجاد [ماشین مجازی داده‌کاوی](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) و اتصال به آن از طریق Jupyter. می‌توانید سپس مخزن را مستقیماً روی ماشین کلون کنید و شروع به یادگیری کنید. ماشین‌های مجازی سری NC از GPU پشتیبانی می‌کنند. -> **توجه**: برخی اشتراک‌ها، از جمله Azure for Students، به طور پیش‌فرض پشتیبانی GPU ارائه نمی‌دهند. ممکن است نیاز به درخواست هسته‌های GPU اضافی از طریق درخواست پشتیبانی فنی داشته باشید. +> **توجه**: برخی اشتراک‌ها از جمله Azure for Students به طور پیش‌فرض پشتیبانی GPU ارائه نمی‌دهند. ممکن است نیاز به درخواست هسته‌های GPU اضافی از طریق پشتیبانی فنی داشته باشید. -* ایجاد [فضای کاری Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) و سپس استفاده از ویژگی Notebook در آنجا. [این ویدیو](https://azure-for-academics.github.io/quickstart/azureml-papers/) نشان می‌دهد که چگونه یک مخزن را به دفترچه Azure ML کلون کنید و شروع به استفاده از آن کنید. +* ایجاد [محیط کاری Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) و سپس استفاده از قابلیت Notebook در آنجا. [این ویدیو](https://azure-for-academics.github.io/quickstart/azureml-papers/) نحوه کلون کردن یک مخزن در نوت‌بوک Azure ML و شروع استفاده از آن را نشان می‌دهد. -شما همچنین می‌توانید از Google Colab استفاده کنید، که با برخی پشتیبانی رایگان GPU ارائه می‌شود، و Jupyter Notebooks را در آنجا آپلود کنید تا آن‌ها را یکی‌یکی اجرا کنید. +شما همچنین می‌توانید از Google Colab استفاده کنید که همراه با پشتیبانی رایگان GPU ارائه می‌شود و دفترچه‌های Jupyter را آنجا آپلود کرده و یکی‌یکی اجرا کنید. +--- + + **سلب مسئولیت**: -این سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌ها باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، توصیه می‌شود از ترجمه انسانی حرفه‌ای استفاده کنید. ما مسئولیتی در قبال سوء تفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم. \ No newline at end of file +این سند با استفاده از خدمات ترجمه ماشین (AI) [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش می‌کنیم، لطفاً آگاه باشید که ترجمه‌های خودکار ممکن است حاوی خطاها یا نواقصی باشند. سند اصلی به زبان بومی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هیچ گونه سوءتفاهم یا تفسیر نادرستی که از استفاده این ترجمه ناشی شود، نیستیم. + \ No newline at end of file diff --git a/translations/fa/lessons/1-Intro/README.md b/translations/fa/lessons/1-Intro/README.md index 5249c458..a6c2a52e 100644 --- a/translations/fa/lessons/1-Intro/README.md +++ b/translations/fa/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مقدمه‌ای بر هوش مصنوعی -![خلاصه‌ای از محتوای مقدمه هوش مصنوعی در یک طرح](../../../../translated_images/ai-intro.bf28d1ac4235881c.fa.png) +![خلاصه‌ای از محتوای مقدمه هوش مصنوعی در یک طرح](../../../../translated_images/fa/ai-intro.bf28d1ac4235881c.webp) > طرح توسط [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: در ابتدا، کامپیوترها توسط [چارلز ببیج](https://en.wikipedia.org/wiki/Charles_Babbage) برای کار با اعداد و اجرای یک فرآیند مشخص - یک الگوریتم - اختراع شدند. کامپیوترهای مدرن، با وجود پیشرفت‌های چشمگیر نسبت به مدل اولیه‌ای که در قرن نوزدهم پیشنهاد شد، همچنان بر اساس همان ایده محاسبات کنترل‌شده عمل می‌کنند. بنابراین، امکان برنامه‌ریزی کامپیوتر برای انجام کاری وجود دارد، اگر دقیقاً بدانیم که چه مراحل متوالی برای رسیدن به هدف لازم است. -![عکس یک فرد](../../../../translated_images/dsh_age.d212a30d4e54fb5f.fa.png) +![عکس یک فرد](../../../../translated_images/fa/dsh_age.d212a30d4e54fb5f.webp) > عکس توسط [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: یکی از مشکلات هنگام برخورد با اصطلاح **[هوش](https://en.wikipedia.org/wiki/Intelligence)** این است که تعریف واضحی از این اصطلاح وجود ندارد. می‌توان گفت که هوش به **تفکر انتزاعی** یا **خودآگاهی** مرتبط است، اما نمی‌توانیم آن را به‌طور دقیق تعریف کنیم. -![عکس یک گربه](../../../../translated_images/photo-cat.8c8e8fb760ffe457.fa.jpg) +![عکس یک گربه](../../../../translated_images/fa/photo-cat.8c8e8fb760ffe457.webp) > [عکس](https://unsplash.com/photos/75715CVEJhI) توسط [Amber Kipp](https://unsplash.com/@sadmax) از Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | درباره یادگیری ماشین؟ | | > |--------------|-----------| -> | بخشی از هوش مصنوعی که بر اساس یادگیری کامپیوتر برای حل یک مشکل بر اساس برخی داده‌ها است، **یادگیری ماشین** نامیده می‌شود. ما یادگیری ماشین کلاسیک را در این دوره بررسی نمی‌کنیم - شما را به برنامه درسی جداگانه [یادگیری ماشین برای مبتدیان](http://aka.ms/ml-beginners) ارجاع می‌دهیم. | ![یادگیری ماشین برای مبتدیان](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.fa.png) | +> | بخشی از هوش مصنوعی که بر اساس یادگیری کامپیوتر برای حل یک مشکل بر اساس برخی داده‌ها است، **یادگیری ماشین** نامیده می‌شود. ما یادگیری ماشین کلاسیک را در این دوره بررسی نمی‌کنیم - شما را به برنامه درسی جداگانه [یادگیری ماشین برای مبتدیان](http://aka.ms/ml-beginners) ارجاع می‌دهیم. | ![یادگیری ماشین برای مبتدیان](../../../../translated_images/fa/ml-for-beginners.9e4fed176fd5817d.webp) | ## تاریخچه مختصر هوش مصنوعی هوش مصنوعی به‌عنوان یک رشته در اواسط قرن بیستم آغاز شد. در ابتدا، استدلال نمادین یک رویکرد غالب بود و منجر به موفقیت‌های مهمی مانند سیستم‌های خبره - برنامه‌های کامپیوتری که قادر به عمل به‌عنوان یک متخصص در برخی حوزه‌های مشکل محدود بودند - شد. با این حال، به زودی مشخص شد که چنین رویکردی به خوبی مقیاس‌پذیر نیست. استخراج دانش از یک متخصص، نمایش آن در کامپیوتر، و حفظ پایگاه دانش دقیق، به یک وظیفه بسیار پیچیده و در بسیاری موارد بسیار پرهزینه تبدیل شد. این منجر به اصطلاح [زمستان هوش مصنوعی](https://en.wikipedia.org/wiki/AI_winter) در دهه ۱۹۷۰ شد. -تاریخچه مختصر هوش مصنوعی +تاریخچه مختصر هوش مصنوعی > تصویر توسط [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * دستیارهای مدرن، مانند Cortana، Siri یا Google Assistant، همگی سیستم‌های ترکیبی هستند که از شبکه‌های عصبی برای تبدیل گفتار به متن و تشخیص قصد ما استفاده می‌کنند، و سپس از برخی استدلال‌ها یا الگوریتم‌های صریح برای انجام اقدامات مورد نیاز استفاده می‌کنند. * در آینده، ممکن است انتظار داشته باشیم یک مدل کاملاً مبتنی بر شبکه عصبی بتواند به‌طور مستقل گفت‌وگو را مدیریت کند. خانواده اخیر GPT و [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) از شبکه‌های عصبی موفقیت‌های بزرگی در این زمینه نشان داده‌اند. -تکامل آزمون تورینگ +تکامل آزمون تورینگ > تصویر از Dmitry Soshnikov، [عکس](https://unsplash.com/photos/r8LmVbUKgns) توسط [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto)، Unsplash ## تحقیقات اخیر در زمینه هوش مصنوعی diff --git a/translations/fa/lessons/2-Symbolic/Animals.ipynb b/translations/fa/lessons/2-Symbolic/Animals.ipynb index 47f4ad84..68de0d6c 100644 --- a/translations/fa/lessons/2-Symbolic/Animals.ipynb +++ b/translations/fa/lessons/2-Symbolic/Animals.ipynb @@ -6,13 +6,13 @@ "collapsed": true }, "source": [ - "# پیاده‌سازی یک سیستم خبره حیوانات\n", + "# پیاده‌سازی سیستم خبره حیوانات\n", "\n", - "مثالی از [برنامه درسی هوش مصنوعی برای مبتدیان](http://github.com/microsoft/ai-for-beginners).\n", + "یک مثال از [دوره مقدماتی هوش مصنوعی](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "در این نمونه، ما یک سیستم ساده مبتنی بر دانش را پیاده‌سازی خواهیم کرد که بر اساس برخی ویژگی‌های فیزیکی، یک حیوان را شناسایی می‌کند. این سیستم می‌تواند با درخت AND-OR زیر نمایش داده شود (این فقط بخشی از کل درخت است و می‌توانیم به‌راحتی قوانین بیشتری اضافه کنیم):\n", + "در این نمونه، ما یک سیستم ساده مبتنی بر دانش را برای تعیین یک حیوان بر اساس برخی ویژگی‌های فیزیکی پیاده‌سازی خواهیم کرد. سیستم می‌تواند توسط درخت AND-OR زیر نمایش داده شود (این بخشی از کل درخت است، ما می‌توانیم به آسانی قوانین بیشتری اضافه کنیم):\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/fa/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { @@ -21,10 +21,10 @@ "source": [ "## پوسته سیستم‌های خبره خودمان با استنتاج معکوس\n", "\n", - "بیایید یک زبان ساده برای نمایش دانش بر اساس قوانین تولید تعریف کنیم. از کلاس‌های پایتون به عنوان کلمات کلیدی برای تعریف قوانین استفاده خواهیم کرد. اساساً سه نوع کلاس وجود خواهد داشت:\n", - "* `Ask` نمایانگر یک سوال است که باید از کاربر پرسیده شود. این کلاس شامل مجموعه‌ای از پاسخ‌های ممکن است.\n", - "* `If` نمایانگر یک قانون است و فقط یک شکر نحوی برای ذخیره محتوای قانون است.\n", - "* `AND`/`OR` کلاس‌هایی هستند که شاخه‌های AND/OR درخت را نمایش می‌دهند. این کلاس‌ها فقط لیست آرگومان‌ها را در داخل خود ذخیره می‌کنند. برای ساده‌سازی کد، تمام قابلیت‌ها در کلاس والد `Content` تعریف شده‌اند.\n" + "بیایید سعی کنیم یک زبان ساده برای نمایش دانش مبتنی بر قواعد تولید تعریف کنیم. از کلاس‌های پایتون به عنوان کلیدواژه برای تعریف قواعد استفاده خواهیم کرد. اساساً سه نوع کلاس وجود خواهد داشت:\n", + "* `Ask` نمایانگر سوالی است که باید از کاربر پرسیده شود. این کلاس مجموعه‌ای از پاسخ‌های ممکن را در بر دارد.\n", + "* `If` نمایانگر یک قاعده است و فقط یک شکرنگار نحوی برای ذخیره محتوای قاعده می‌باشد.\n", + "* `AND`/`OR` کلاس‌هایی برای نمایندگی شاخه‌های AND/OR در درخت هستند. آنها فقط لیست آرگومان‌های درونشان را ذخیره می‌کنند. برای ساده‌سازی کد، تمام عملکردها در کلاس والد `Content` تعریف شده‌اند.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "در سیستم ما، حافظه کاری شامل فهرستی از **حقایق** به صورت **جفت‌های ویژگی-مقدار** خواهد بود. پایگاه دانش می‌تواند به عنوان یک دیکشنری بزرگ تعریف شود که اقدامات (حقایق جدیدی که باید در حافظه کاری وارد شوند) را به شرایطی که به صورت عبارات AND-OR بیان شده‌اند، نگاشت می‌کند. همچنین، برخی حقایق می‌توانند `پرسیده شوند`.\n" + "در سیستم ما، حافظه کاری شامل لیستی از **حقایق** به صورت **جفت‌های ویژگی-مقدار** خواهد بود. پایگاه دانش را می‌توان به عنوان یک فرهنگ لغت بزرگ تعریف کرد که اعمال (حقایق جدیدی که باید در حافظه کاری وارد شوند) را به شرایطی که به صورت عبارات AND-OR بیان شده‌اند، نگاشت می‌کند. همچنین، برخی از حقایق می‌توانند `Ask` شوند.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "برای انجام استنتاج معکوس، ما کلاس `Knowledgebase` را تعریف خواهیم کرد. این کلاس شامل موارد زیر خواهد بود:\n", - "* `حافظه` کاری - یک دیکشنری که ویژگی‌ها را به مقادیر نگاشت می‌کند\n", - "* `قوانین` پایگاه دانش در قالبی که در بالا تعریف شده است\n", + "برای انجام استنتاج معکوس، کلاس `Knowledgebase` را تعریف خواهیم کرد. این کلاس شامل خواهد بود از:\n", + "* حافظه کاری `memory` - یک دیکشنری که ویژگی‌ها را به مقادیر نگاشت می‌کند\n", + "* قوانین پایگاه دانش `rules` به قالبی که در بالا تعریف شده است\n", "\n", - "دو متد اصلی عبارتند از:\n", - "* `get` برای به دست آوردن مقدار یک ویژگی، با انجام استنتاج در صورت لزوم. به عنوان مثال، `get('color')` مقدار یک اسلات رنگ را دریافت می‌کند (در صورت نیاز سؤال می‌پرسد و مقدار را برای استفاده‌های بعدی در حافظه کاری ذخیره می‌کند). اگر ما `get('color:blue')` را بپرسیم، ابتدا رنگ را می‌پرسد و سپس مقدار `y`/`n` را بسته به رنگ بازمی‌گرداند.\n", - "* `eval` استنتاج واقعی را انجام می‌دهد، یعنی درخت AND/OR را پیمایش می‌کند، اهداف فرعی را ارزیابی می‌کند و غیره.\n" + "دو روش اصلی عبارتند از:\n", + "* `get` برای به‌دست آوردن مقدار یک ویژگی، انجام استنتاج در صورت نیاز. برای مثال، `get('color')` مقدار یک جایگاه رنگ را به دست می‌آورد (در صورت نیاز سوال می‌پرسد و مقدار را برای استفاده‌های بعدی در حافظه کاری ذخیره می‌کند). اگر `get('color:blue')` را بپرسیم، برای رنگ سوال می‌پرسد و سپس مقدار `y`/`n` را بسته به رنگ برمی‌گرداند.\n", + "* `eval` استنتاج واقعی را انجام می‌دهد، یعنی درخت AND/OR را مرور می‌کند، زیرهدف‌ها را ارزیابی می‌کند و غیره.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "حالا بیایید پایگاه دانش حیوانات خود را تعریف کنیم و مشاوره را انجام دهیم. توجه داشته باشید که این تماس از شما سوالاتی خواهد پرسید. شما می‌توانید با تایپ کردن `y`/`n` برای سوالات بله-خیر پاسخ دهید، یا برای سوالات با پاسخ‌های چند گزینه‌ای طولانی‌تر، عددی (۰..N) مشخص کنید.\n" + "حال بیایید پایگاه دانش حیوانات خود را تعریف کنیم و مشاوره را انجام دهیم. توجه داشته باشید که این فراخوانی از شما سوال خواهد پرسید. شما می‌توانید با تایپ `y`/`n` برای سوالات بلی-خیر پاسخ دهید، یا با مشخص کردن عدد (۰..N) برای سوالات با پاسخ‌های چندگزینه‌ای طولانی‌تر پاسخ دهید.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## استفاده از PyKnow برای استنتاج پیشرو\n", + "## استفاده از Experta برای استنتاج پیشرو\n", "\n", - "در مثال بعدی، سعی می‌کنیم استنتاج پیشرو را با استفاده از یکی از کتابخانه‌های نمایش دانش، [PyKnow](https://github.com/buguroo/pyknow/) پیاده‌سازی کنیم. **PyKnow** یک کتابخانه برای ایجاد سیستم‌های استنتاج پیشرو در پایتون است که به گونه‌ای طراحی شده تا شبیه به سیستم کلاسیک قدیمی [CLIPS](http://www.clipsrules.net/index.html) باشد.\n", + "در مثال بعدی، سعی خواهیم کرد استنتاج پیشرو را با استفاده از یکی از کتابخانه‌های نمایش دانش، [Experta](https://github.com/nilp0inter/experta) پیاده‌سازی کنیم. **Experta** یک کتابخانه برای ایجاد سیستم‌های استنتاج پیشرو در پایتون است که طراحی شده تا مشابه سیستم کلاسیک قدیمی [CLIPS](http://www.clipsrules.net/index.html) باشد.\n", "\n", - "ما می‌توانستیم استنتاج پیشرو را خودمان نیز بدون مشکلات زیادی پیاده‌سازی کنیم، اما پیاده‌سازی‌های ساده معمولاً خیلی کارآمد نیستند. برای تطبیق قوانین به شکل مؤثرتر، از یک الگوریتم خاص به نام [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) استفاده می‌شود.\n" + "همچنین می‌توانستیم استنتاج زنجیره‌ای پیشرو را خودمان بدون مشکلات زیاد پیاده‌سازی کنیم، اما پیاده‌سازی‌های ساده معمولاً چندان بهینه نیستند. برای تطبیق موثرتر قوانین از الگوریتم ویژه‌ای به نام [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) استفاده می‌شود.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "ما سیستم خود را به عنوان یک کلاس که از `KnowledgeEngine` ارث‌بری می‌کند تعریف خواهیم کرد. هر قانون توسط یک تابع جداگانه با حاشیه‌نویسی `@Rule` تعریف می‌شود که مشخص می‌کند چه زمانی قانون باید اجرا شود. داخل قانون، می‌توانیم با استفاده از تابع `declare` حقایق جدیدی اضافه کنیم و افزودن این حقایق باعث می‌شود که قوانین بیشتری توسط موتور استنتاج پیشرو فراخوانی شوند.\n" + "ما سیستم خود را به صورت یک کلاس که زیرکلاسی از `KnowledgeEngine` است تعریف می‌کنیم. هر قاعده توسط یک تابع جداگانه با تزئین‌کننده `@Rule` تعریف می‌شود، که مشخص می‌کند قاعده کی باید فعال شود. درون قاعده، می‌توانیم حقایق جدید را با استفاده از تابع `declare` اضافه کنیم، و افزودن این حقایق منجر به فراخوانی برخی قواعد بیشتر توسط موتور استنتاج پیشرو خواهد شد.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "هنگامی که یک پایگاه دانش تعریف کردیم، حافظه کاری خود را با برخی حقایق اولیه پر می‌کنیم و سپس متد `run()` را فراخوانی می‌کنیم تا استنتاج انجام شود. می‌توانید مشاهده کنید که در نتیجه، حقایق استنتاج‌شده جدید به حافظه کاری اضافه می‌شوند، از جمله حقیقت نهایی درباره حیوان (اگر تمام حقایق اولیه را به درستی تنظیم کرده باشیم).\n" + "زمانی که یک پایگاه دانش تعریف کردیم، حافظه کاری خود را با چند حقیقت اولیه پر می‌کنیم، و سپس متد `run()` را برای انجام استنتاج فراخوانی می‌کنیم. شما می‌توانید ببینید که در نتیجه حقایق استنتاج شده جدید به حافظه کاری اضافه می‌شوند، از جمله حقیقت نهایی درباره حیوان (اگر تمام حقایق اولیه را به درستی تنظیم کرده باشیم).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**سلب مسئولیت**: \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما تلاش می‌کنیم دقت را حفظ کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است حاوی خطاها یا نادرستی‌هایی باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\n" + "---\n\n\n**سلب مسئولیت**: \nاین سند با استفاده از خدمات ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. با اینکه تلاش ما برای دقت است، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است شامل خطاها یا نادرستی‌هایی باشند. سند اصلی به زبان بومی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هیچگونه سوءتفاهم یا برداشت نادرستی که از استفاده این ترجمه ناشی شود، نیستیم.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T16:27:22+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:39:57+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "fa" } diff --git a/translations/fa/lessons/2-Symbolic/README.md b/translations/fa/lessons/2-Symbolic/README.md index 35525bac..ab519a12 100644 --- a/translations/fa/lessons/2-Symbolic/README.md +++ b/translations/fa/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # نمایش دانش و سیستم‌های خبره -![خلاصه‌ای از محتوای هوش مصنوعی نمادین](../../../../translated_images/ai-symbolic.715a30cb610411a6.fa.png) +![خلاصه محتوای هوش مصنوعی نمادین](../../../../../../translated_images/fa/ai-symbolic.715a30cb610411a6.webp) -> یادداشت تصویری توسط [Tomomi Imura](https://twitter.com/girlie_mac) +> اسکچ‌نوت توسط [Tomomi Imura](https://twitter.com/girlie_mac) -جستجوی هوش مصنوعی بر اساس یافتن دانش است، به منظور درک جهان به شیوه‌ای مشابه انسان‌ها. اما چگونه می‌توان این کار را انجام داد؟ +جستجو برای هوش مصنوعی بر اساس جستجوی دانش است، برای درک جهان مشابه به نحوی که انسان‌ها انجام می‌دهند. اما چگونه می‌توان این کار را انجام داد؟ -## [آزمون پیش از درس](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [آزمون پیش‌درس](https://ff-quizzes.netlify.app/en/ai/quiz/3) -در روزهای اولیه هوش مصنوعی، رویکرد بالا به پایین برای ایجاد سیستم‌های هوشمند (که در درس قبلی مورد بحث قرار گرفت) محبوب بود. ایده این بود که دانش را از افراد استخراج کرده و به شکلی قابل خواندن توسط ماشین تبدیل کنیم و سپس از آن برای حل خودکار مسائل استفاده کنیم. این رویکرد بر اساس دو ایده بزرگ بود: +در روزهای اولیه هوش مصنوعی، رویکرد از بالا به پایین برای ایجاد سیستم‌های هوشمند (که در درس قبلی بررسی شد) محبوب بود. ایده این بود که دانش را از افراد استخراج کرده و به فرم قابل خواندن توسط ماشین تبدیل کنیم و سپس به صورت خودکار برای حل مسائل استفاده کنیم. این رویکرد بر دو ایده بزرگ بنا شده بود: -* نمایش دانش -* استدلال +* نمایش دانش +* استدلال ## نمایش دانش -یکی از مفاهیم مهم در هوش مصنوعی نمادین، **دانش** است. مهم است که دانش را از *اطلاعات* یا *داده‌ها* متمایز کنیم. به عنوان مثال، می‌توان گفت که کتاب‌ها حاوی دانش هستند، زیرا می‌توان با مطالعه کتاب‌ها به یک متخصص تبدیل شد. با این حال، آنچه کتاب‌ها در واقع حاوی آن هستند *داده* نامیده می‌شود، و با خواندن کتاب‌ها و ادغام این داده‌ها در مدل جهانی خود، این داده‌ها را به دانش تبدیل می‌کنیم. +یکی از مفاهیم مهم در هوش مصنوعی نمادین، **دانش** است. مهم است که دانش را از *اطلاعات* یا *داده‌ها* متمایز کنیم. برای مثال، می‌توان گفت کتاب‌ها حاوی دانش هستند، زیرا می‌توان با مطالعه آنها متخصص شد. اما در واقع آنچه کتاب‌ها دارند را *داده* می‌نامند، و با خواندن کتاب‌ها و یکپارچه‌سازی این داده‌ها در مدل جهان خود، این داده‌ها تبدیل به دانش می‌شوند. -> ✅ **دانش** چیزی است که در ذهن ما وجود دارد و نمایانگر درک ما از جهان است. این دانش از طریق یک فرآیند فعال **یادگیری** به دست می‌آید که قطعات اطلاعاتی که دریافت می‌کنیم را در مدل فعال جهانی ما ادغام می‌کند. +> ✅ **دانش** چیزی است که در ذهن ما قراردارد و نمایانگر درک ما از جهان است. این دانش از فرایند فعال **یادگیری** حاصل می‌شود که قطعات اطلاعاتی که دریافت می‌کنیم را در مدل فعال ما از جهان ادغام می‌کند. -اغلب، ما دانش را به طور دقیق تعریف نمی‌کنیم، بلکه آن را با مفاهیم مرتبط دیگر با استفاده از [هرم DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid) هماهنگ می‌کنیم. این هرم شامل مفاهیم زیر است: +معمولاً دانش به طور دقیق تعریف نمی‌شود، اما با مفاهیم مرتبط دیگر با استفاده از [هرم DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid) هماهنگ می‌شود. این هرم شامل مفاهیم زیر است: * **داده** چیزی است که در رسانه‌های فیزیکی مانند متن نوشته شده یا کلمات گفته شده نمایش داده می‌شود. داده مستقل از انسان‌ها وجود دارد و می‌تواند بین افراد منتقل شود. -* **اطلاعات** نحوه تفسیر داده‌ها در ذهن ما است. به عنوان مثال، وقتی کلمه *کامپیوتر* را می‌شنویم، درک خاصی از آن داریم. -* **دانش** اطلاعاتی است که در مدل جهانی ما ادغام شده است. به عنوان مثال، وقتی یاد می‌گیریم کامپیوتر چیست، شروع به داشتن ایده‌هایی درباره نحوه کارکرد آن، هزینه آن و کاربردهای آن می‌کنیم. این شبکه از مفاهیم مرتبط، دانش ما را تشکیل می‌دهد. -* **حکمت** یک سطح بالاتر از درک ما از جهان است و نمایانگر *فرا-دانش* است، مانند مفهومی درباره نحوه و زمان استفاده از دانش. +* **اطلاعات** نحوه تفسیر داده‌ها در ذهن ما است. به عنوان مثال، هنگامی که کلمه *کامپیوتر* را می‌شنویم، برخی درک‌ها از آن داریم. +* **دانش** اطلاعاتی است که در مدل جهان ما ادغام شده است. برای مثال، وقتی یاد می‌گیریم کامپیوتر چیست، شروع به داشتن ایده‌هایی درباره نحوه کارکرد، هزینه آن و کاربردهای آن می‌کنیم. این شبکه‌ای از مفاهیم مرتبط، دانش ما را شکل می‌دهد. +* **حکمت** سطح بالاتری از درک ما از جهان است و نمایانگر *فرادانش* است، مثلا یک برداشت درباره چگونگی و زمان استفاده از دانش. - + -*تصویر [از ویکی‌پدیا](https://commons.wikimedia.org/w/index.php?curid=37705247)، توسط Longlivetheux - اثر خود، CC BY-SA 4.0* +*تصویر [از ویکی‌پدیا](https://commons.wikimedia.org/w/index.php?curid=37705247)، اثر Longlivetheux - کار خود، مجوز CC BY-SA 4.0* -بنابراین، مسئله **نمایش دانش** یافتن راهی مؤثر برای نمایش دانش در داخل یک کامپیوتر به شکل داده است، به طوری که بتوان از آن به صورت خودکار استفاده کرد. این مسئله را می‌توان به صورت یک طیف مشاهده کرد: +بنابراین، مسئله **نمایش دانش** یافتن روش موثری برای نمایش دانش درون رایانه به شکل داده است تا به صورت خودکار قابل استفاده باشد. این را می‌توان به صورت یک طیف دید: -![طیف نمایش دانش](../../../../translated_images/knowledge-spectrum.b60df631852c0217.fa.png) +![نمایش دانش طیف](../../../../../../translated_images/fa/knowledge-spectrum.b60df631852c0217.webp) > تصویر توسط [Dmitry Soshnikov](http://soshnikov.com) -* در سمت چپ، انواع بسیار ساده‌ای از نمایش دانش وجود دارد که می‌توان به طور مؤثر توسط کامپیوترها استفاده کرد. ساده‌ترین نوع آن الگوریتمی است، زمانی که دانش توسط یک برنامه کامپیوتری نمایش داده می‌شود. با این حال، این بهترین روش برای نمایش دانش نیست، زیرا انعطاف‌پذیر نیست. دانش در ذهن ما اغلب غیر الگوریتمی است. -* در سمت راست، نمایش‌هایی مانند متن طبیعی وجود دارد. این نوع نمایش قدرتمندترین است، اما نمی‌توان از آن برای استدلال خودکار استفاده کرد. +* سمت چپ، انواع ساده‌ای از نمایش دانش وجود دارد که می‌توانند به شکل موثری توسط رایانه‌ها استفاده شوند. ساده‌ترین آن الگوریتمی است، زمانی که دانش توسط یک برنامه کامپیوتری نمایش داده می‌شود. اما این بهترین روش نمایش دانش نیست چون انعطاف‌پذیر نیست. دانش در ذهن انسان اغلب غیرالگوریتمی است. +* سمت راست، نمایه‌هایی مانند متن طبیعی وجود دارند. این قوی‌ترین شکل است اما نمی‌توان از آن برای استدلال خودکار استفاده کرد. -> ✅ یک دقیقه فکر کنید که چگونه دانش را در ذهن خود نمایش می‌دهید و آن را به یادداشت تبدیل می‌کنید. آیا قالب خاصی وجود دارد که برای شما در حفظ اطلاعات بهتر عمل کند؟ +> ✅ به یک دقیقه فکر کنید که دانش را چگونه در ذهنتان نمایش می‌دهید و آن را به یادداشت تبدیل می‌کنید. آیا قالب خاصی برای کمک به حفظ آن برای شما خوب عمل می‌کند؟ -## دسته‌بندی نمایش‌های دانش کامپیوتری +## دسته‌بندی روش‌های نمایش دانش در کامپیوتر -ما می‌توانیم روش‌های مختلف نمایش دانش کامپیوتری را در دسته‌های زیر طبقه‌بندی کنیم: +می‌توانیم روش‌های مختلف نمایش دانش در کامپیوتر را در دسته‌های زیر طبقه‌بندی کنیم: -* **نمایش‌های شبکه‌ای** بر اساس این واقعیت هستند که ما یک شبکه از مفاهیم مرتبط در ذهن خود داریم. می‌توانیم تلاش کنیم همین شبکه‌ها را به صورت یک گراف در داخل کامپیوتر بازتولید کنیم - یک **شبکه معنایی**. +* **نمایش‌های شبکه‌ای** بر این واقعیت تکیه دارند که ما یک شبکه از مفاهیم مرتبط در ذهن داریم. می‌توانیم همان شبکه‌ها را به صورت گرافی در کامپیوتر بازتولید کنیم - شبکه معنایی یا همان **semantic network**. -1. **سه‌گانه‌های شیء-ویژگی-مقدار** یا **جفت‌های ویژگی-مقدار**. از آنجا که یک گراف می‌تواند در داخل کامپیوتر به صورت لیستی از گره‌ها و لبه‌ها نمایش داده شود، می‌توانیم یک شبکه معنایی را با لیستی از سه‌گانه‌ها نمایش دهیم که شامل اشیاء، ویژگی‌ها و مقادیر است. به عنوان مثال، سه‌گانه‌های زیر را درباره زبان‌های برنامه‌نویسی می‌سازیم: +1. **سه‌تایی‌های شی-ویژگی-مقدار** یا **جفت‌های ویژگی-مقدار**. چون یک گراف می‌تواند در کامپیوتر به صورت فهرستی از گره‌ها و یال‌ها نمایش داده شود، می‌توانیم شبکه معنایی را با لیستی از سه‌تایی‌ها نمایش دهیم که شامل اشیاء، ویژگی‌ها و مقادیر هستند. برای مثال، سه‌تایی‌هایی درباره زبان‌های برنامه‌نویسی ایجاد می‌کنیم: -شیء | ویژگی | مقدار -----|--------|------ -پایتون | است | زبان بدون نوع -پایتون | اختراع شده توسط | Guido van Rossum -پایتون | نحو بلوک | تورفتگی -زبان بدون نوع | ندارد | تعریف نوع +شیء | ویژگی | مقدار +-------|-----------|------ +Python | است | زبان بدون نوع +Python | اختراع شده توسط | گویدو وان روسوم +Python | نحو بلاک | تورفتگی +زبان بدون نوع | ندارد | تعریف نوع -> ✅ فکر کنید که چگونه می‌توان از سه‌گانه‌ها برای نمایش انواع دیگر دانش استفاده کرد. +> ✅ فکر کنید چگونه می‌توان از سه‌تایی‌ها برای نمایش سایر انواع دانش استفاده کرد. -2. **نمایش‌های سلسله‌مراتبی** بر این واقعیت تأکید دارند که ما اغلب یک سلسله‌مراتب از اشیاء در ذهن خود ایجاد می‌کنیم. به عنوان مثال، می‌دانیم که قناری یک پرنده است و همه پرندگان بال دارند. همچنین ایده‌ای درباره رنگ معمولی قناری و سرعت پرواز آن داریم. +2. **نمایش‌های سلسله‌مراتبی** بر این واقعیت تأکید دارند که اغلب در ذهن خود سلسله‌مراتبی از اشیاء ایجاد می‌کنیم. برای مثال، می‌دانیم که قناری یک پرنده است و همه پرندگان بال دارند. همچنین ایده‌ای درباره رنگ معمول یک قناری و سرعت پرواز آن داریم. - - **نمایش قاب** بر اساس نمایش هر شیء یا کلاس اشیاء به صورت یک **قاب** است که شامل **شکاف‌ها** می‌شود. شکاف‌ها ممکن است مقادیر پیش‌فرض، محدودیت‌های مقدار یا رویه‌های ذخیره شده داشته باشند که می‌توانند برای به دست آوردن مقدار یک شکاف فراخوانی شوند. همه قاب‌ها یک سلسله‌مراتب مشابه سلسله‌مراتب اشیاء در زبان‌های برنامه‌نویسی شیء‌گرا تشکیل می‌دهند. - - **سناریوها** نوع خاصی از قاب‌ها هستند که موقعیت‌های پیچیده‌ای را که ممکن است در طول زمان رخ دهند نمایش می‌دهند. + - **نمایش قاب** بر اساس نمایش هر شیء یا کلاس از اشیاء به عنوان **قاب** است که شامل **شکاف‌ها** (slots) است. شکاف‌ها مقادیر پیش‌فرض ممکن، محدودیت‌های مقداری یا رویه‌های ذخیره‌شده دارند که می‌توان برای دریافت مقدار شکاف فراخوانی کرد. همه قاب‌ها سلسله‌مراتبی مانند سلسله‌مراتب اشیاء در برنامه‌نویسی شیءگرا تشکیل می‌دهند. + - **سناریوها** نوع خاصی از قاب‌ها هستند که وضعیت‌های پیچیده‌ای را که می‌توانند در زمان رخ دهند، نمایش می‌دهند. -**پایتون** +**Python** -شکاف | مقدار | مقدار پیش‌فرض | بازه ------|-------|---------------|------ -نام | پایتون | | -است | زبان بدون نوع | | -حالت متغیر | | CamelCase | -طول برنامه | | | ۵-۵۰۰۰ خط -نحو بلوک | تورفتگی | | +شکاف | مقدار | مقدار پیش‌فرض | بازه | +-----|-------|---------------|----------| +نام | Python | | | +نوع | زبان بدون نوع | | | +حالت متغیر | | CamelCase | | +طول برنامه | | | ۵-۵۰۰۰ خط | +نحو بلاک | تورفتگی | | | -3. **نمایش‌های رویه‌ای** بر اساس نمایش دانش به صورت لیستی از اقدامات هستند که می‌توانند زمانی که یک شرط خاص رخ می‌دهد اجرا شوند. - - قوانین تولیدی، عبارات اگر-آنگاه هستند که به ما اجازه می‌دهند نتیجه‌گیری کنیم. به عنوان مثال، یک پزشک ممکن است قانونی داشته باشد که می‌گوید **اگر** بیمار تب بالا **یا** سطح بالای پروتئین C-reactive در آزمایش خون داشته باشد **آنگاه** او التهاب دارد. هنگامی که یکی از شرایط رخ دهد، می‌توانیم نتیجه‌گیری کنیم و سپس از آن در استدلال‌های بعدی استفاده کنیم. - - الگوریتم‌ها می‌توانند به عنوان نوع دیگری از نمایش رویه‌ای در نظر گرفته شوند، اگرچه تقریباً هرگز به طور مستقیم در سیستم‌های مبتنی بر دانش استفاده نمی‌شوند. +3. **نمایش‌های رویه‌ای** بر بازنمایی دانش به صورت فهرستی از اعمالی که می‌توانند هنگام وقوع شرایط خاص اجرا شوند، مبتنی‌اند. + - قوانین تولید، جملات شرط-آنگاه هستند که به ما اجازه می‌دهند به نتیجه برسیم. برای مثال، یک پزشک ممکن است قانونی داشته باشد که بگوید **اگر** بیمار تب بالا **یا** سطح بالای پروتئین واکنشی C در آزمایش خون دارد **آنگاه** او التهاب دارد. وقتی یکی از شرایط رخ دهد، می‌توانیم نتیجه‌گیری کنیم و سپس آن را در استدلال‌های بعدی استفاده کنیم. + - الگوریتم‌ها می‌توانند شکل دیگری از نمایش رویه‌ای باشند، گرچه تقریباً هرگز مستقیماً در سیستم‌های مبتنی بر دانش استفاده نمی‌شوند. -4. **منطق** در ابتدا توسط ارسطو به عنوان روشی برای نمایش دانش جهانی انسان پیشنهاد شد. - - منطق گزاره‌ای به عنوان یک نظریه ریاضی بسیار غنی است و قابل محاسبه نیست، بنابراین معمولاً از یک زیرمجموعه آن استفاده می‌شود، مانند بندهای Horn که در Prolog استفاده می‌شوند. - - منطق توصیفی خانواده‌ای از سیستم‌های منطقی است که برای نمایش و استدلال درباره سلسله‌مراتب اشیاء و نمایش‌های دانش توزیع شده مانند *وب معنایی* استفاده می‌شود. +4. **منطق** در اصل توسط ارسطو پیشنهاد شد به عنوان روشی برای نمایش دانش جهانی انسان. + - منطق گزاره‌ای به عنوان یک نظریه ریاضی بسیار غنی است و نمی‌تواند کاملاً محاسبه‌ای باشد، بنابراین زیرمجموعه‌ای از آن معمولاً استفاده می‌شود مانند قاعده‌های Horn که در Prolog استفاده می‌شوند. + - منطق توصیفی خانواده‌ای از سیستم‌های منطقی است که برای نمایش و استدلال درباره سلسله‌مراتب اشیاء در نمایش‌های دانش توزیع شده مانند *وب معنایی* استفاده می‌شود. ## سیستم‌های خبره -یکی از موفقیت‌های اولیه هوش مصنوعی نمادین، **سیستم‌های خبره** بود - سیستم‌های کامپیوتری که طراحی شده بودند تا به عنوان یک متخصص در یک حوزه محدود عمل کنند. این سیستم‌ها بر اساس یک **پایگاه دانش** استخراج شده از یک یا چند متخصص انسانی بودند و شامل یک **موتور استنتاج** بودند که بر اساس آن استدلال انجام می‌داد. +یکی از موفقیت‌های اولیه هوش مصنوعی نمادین، سیستم‌های خبره نام داشتند - سیستم‌های کامپیوتری که برای عمل کردن به عنوان کارشناس در حوزه‌های محدود طراحی شده بودند. آنها بر پایه **پایگاه دانش** استخراج شده از یک یا چند متخصص انسانی ساخته شده بودند و دارای **موتور استنتاج** بودند که روی آن مقداری استدلال انجام می‌داد. -![معماری انسان](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.fa.png) | ![سیستم مبتنی بر دانش](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.fa.png) ----------------------------------------------|------------------------------------------------ -ساختار ساده‌شده سیستم عصبی انسان | معماری یک سیستم مبتنی بر دانش +![معماری انسان](../../../../../../translated_images/fa/arch-human.5d4d35f1bba3ab1c.webp) | ![سیستم مبتنی بر دانش](../../../../../../translated_images/fa/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +ساختار ساده‌شده سیستم عصبی انسان | معماری سیستم مبتنی بر دانش -سیستم‌های خبره مشابه سیستم استدلال انسانی ساخته شده‌اند که شامل **حافظه کوتاه‌مدت** و **حافظه بلندمدت** است. به طور مشابه، در سیستم‌های مبتنی بر دانش، اجزای زیر را متمایز می‌کنیم: +سیستم‌های خبره مانند سیستم استدلال انسانی ساخته می‌شوند، که شامل **حافظه کوتاه‌مدت** و **حافظه بلندمدت** است. مشابه آن، در سیستم‌های مبتنی بر دانش اجزای زیر را تمایز می‌دهیم: -* **حافظه مسئله**: شامل دانش درباره مسئله‌ای است که در حال حاضر حل می‌شود، مانند دما یا فشار خون بیمار، اینکه آیا او التهاب دارد یا نه، و غیره. این دانش همچنین **دانش ایستا** نامیده می‌شود، زیرا شامل یک عکس فوری از آنچه در حال حاضر درباره مسئله می‌دانیم است - به اصطلاح *وضعیت مسئله*. -* **پایگاه دانش**: نمایانگر دانش بلندمدت درباره یک حوزه مسئله است. این دانش به صورت دستی از متخصصان انسانی استخراج می‌شود و از مشاوره‌ای به مشاوره دیگر تغییر نمی‌کند. از آنجا که به ما اجازه می‌دهد از یک وضعیت مسئله به وضعیت دیگر حرکت کنیم، همچنین **دانش پویا** نامیده می‌شود. -* **موتور استنتاج**: فرآیند جستجو در فضای وضعیت مسئله را هماهنگ می‌کند و در صورت لزوم از کاربر سوال می‌پرسد. همچنین مسئول یافتن قوانین مناسب برای اعمال در هر وضعیت است. +* **حافظه مسئله**: شامل دانش درباره مسئله‌ای است که در حال حاضر حل می‌شود، مانند دما یا فشار خون بیمار، آیا التهاب دارد یا خیر و غیره. این دانش همچنین به عنوان **دانش ایستا** خوانده می‌شود، زیرا نمایه‌ای از آنچه هم‌اکنون درباره مسئله می‌دانیم را در بر دارد - به اصطلاح *وضعیت مسئله*. +* **پایگاه دانش**: دانش بلندمدت درباره حوزه مسئله را نمایش می‌دهد. آن به صورت دستی از متخصصین انسانی استخراج شده و از مشاوره‌ای تا مشاوره‌ای تغییر نمی‌کند. چون به ما اجازه می‌دهد بین وضعیت‌های مسئله حرکت کنیم، به آن **دانش دینامیک** نیز گفته می‌شود. +* **موتور استنتاج**: کل فرایند جستجو در فضای وضعیت مسئله را هماهنگ می‌کند و در صورت لزوم از کاربر سوال می‌پرسد. همچنین مسئول یافتن قوانین درست برای اعمال روی هر وضعیت است. -به عنوان مثال، بیایید سیستم خبره‌ای را در نظر بگیریم که بر اساس ویژگی‌های فیزیکی یک حیوان را تعیین می‌کند: +به عنوان مثال، سیستم خبره زیر را در نظر بگیرید که حیوانی را بر اساس ویژگی‌های فیزیکی‌اش تعیین می‌کند: -![درخت AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.fa.png) +![درخت AND-OR](../../../../../../translated_images/fa/AND-OR-Tree.5592d2c70187f283.webp) > تصویر توسط [Dmitry Soshnikov](http://soshnikov.com) -این نمودار **درخت AND-OR** نامیده می‌شود و یک نمایش گرافیکی از مجموعه‌ای از قوانین تولیدی است. رسم یک درخت در ابتدای استخراج دانش از متخصص مفید است. برای نمایش دانش در داخل کامپیوتر، استفاده از قوانین راحت‌تر است: +این نمودار به نام **درخت AND-OR** شناخته می‌شود و نمایش گرافیکی مجموعه‌ای از قوانین تولید است. رسم درخت در ابتدای استخراج دانش از کارشناس مفید است. اما برای نمایش دانش در کامپیوتر، استفاده از قوانین راحت‌تر است: ``` IF the animal eats meat @@ -120,79 +120,79 @@ OR (animal has sharp teeth ) THEN the animal is a carnivore ``` - -می‌توانید متوجه شوید که هر شرط در سمت چپ قانون و عمل اساساً سه‌گانه‌های شیء-ویژگی-مقدار (OAV) هستند. **حافظه کاری** شامل مجموعه‌ای از سه‌گانه‌های OAV است که با مسئله‌ای که در حال حاضر حل می‌شود مطابقت دارند. **موتور قوانین** به دنبال قوانینی می‌گردد که شرط آن‌ها برآورده شده باشد و آن‌ها را اعمال می‌کند، و یک سه‌گانه جدید به حافظه کاری اضافه می‌کند. -> ✅ درخت AND-OR خود را درباره موضوعی که دوست دارید رسم کنید! +می‌توانید توجه کنید که هر شرط در سمت چپ قانون و عمل در اصل، سه‌تایی شی-ویژگی-مقدار (OAV) هستند. **حافظه کاری** شامل مجموعه سه‌تایی‌های OAV است که مربوط به مسئله‌ای که در حال حاضر حل می‌شود، می‌باشد. یک **موتور قوانین** به دنبال قوانینی می‌گردد که شرط آنها برقرار است و اعمالشان می‌کند و سه‌تایی جدیدی به حافظه کاری اضافه می‌کند. -### استنتاج پیشرو در مقابل استنتاج پسرو +> ✅ یک درخت AND-OR خودتان درباره موضوعی که دوست دارید رسم کنید! -فرآیند توصیف شده در بالا **استنتاج پیشرو** نامیده می‌شود. این فرآیند با برخی داده‌های اولیه درباره مسئله موجود در حافظه کاری شروع می‌شود و سپس حلقه استدلال زیر را اجرا می‌کند: +### استنتاج پیشرو در مقابل استنتاج بازگشتی -1. اگر ویژگی هدف در حافظه کاری موجود باشد - متوقف شوید و نتیجه را بدهید -2. به دنبال تمام قوانینی بگردید که شرط آن‌ها در حال حاضر برآورده شده است - مجموعه تضاد قوانین را به دست آورید -3. **حل تضاد** را انجام دهید - یک قانون را انتخاب کنید که در این مرحله اجرا شود. ممکن است استراتژی‌های مختلفی برای حل تضاد وجود داشته باشد: - - انتخاب اولین قانون قابل اجرا در پایگاه دانش - - انتخاب یک قانون تصادفی - - انتخاب یک قانون *خاص‌تر*، یعنی قانونی که بیشترین شرایط را در سمت چپ (LHS) برآورده کند -4. قانون انتخاب شده را اعمال کنید و یک قطعه جدید از دانش را به وضعیت مسئله اضافه کنید -5. از مرحله 1 تکرار کنید +فرایند توصیف‌شده در بالا، **استنتاج پیشرو** نامیده می‌شود. این فرایند با داده‌های اولیه موجود در حافظه کاری شروع می‌شود و سپس حلقه استدلال زیر را اجرا می‌کند: -با این حال، در برخی موارد ممکن است بخواهیم با دانش خالی درباره مسئله شروع کنیم و سوالاتی بپرسیم که به ما کمک کند به نتیجه برسیم. به عنوان مثال، هنگام انجام تشخیص پزشکی، معمولاً همه آزمایش‌های پزشکی را از قبل انجام نمی‌دهیم قبل از شروع تشخیص بیمار. بلکه می‌خواهیم آزمایش‌ها را زمانی انجام دهیم که نیاز به تصمیم‌گیری باشد. +1. اگر ویژگی هدف در حافظه کاری موجود باشد - متوقف شده و نتیجه را می‌دهد +2. به دنبال تمام قوانینی می‌گردد که شرط آنها در آن لحظه برقرار است - و **مجموعه تضاد** قوانین را به دست می‌آورد. +3. **حل تضاد** را انجام می‌دهد - قانونی را انتخاب می‌کند که در این مرحله اجرا شود. ممکن است استراتژی‌های مختلفی برای حل تضاد وجود داشته باشد: + - انتخاب اولین قانونی که در پایگاه دانش قابل اجراست + - انتخاب قانون به صورت تصادفی + - انتخاب قانونی *خاص‌تر*، یعنی قانونی که بیشترین شروط را در سمت چپ (LHS) برآورده می‌کند +4. قانون انتخاب شده را اعمال کرده و قطعه دانش جدیدی به وضعیت مسئله اضافه می‌کند +5. از مرحله 1 تکرار می‌کند. -این فرآیند را می‌توان با استفاده از **استنتاج پسرو** مدل‌سازی کرد. این فرآیند توسط **هدف** هدایت می‌شود - مقدار ویژگی که به دنبال یافتن آن هستیم: +اما در برخی موارد ممکن است بخواهیم با دانش خالی درباره مسئله شروع کنیم و سوالاتی بپرسیم که ما را به نتیجه برسانند. برای مثال، در انجام تشخیص پزشکی، معمولاً تمام آزمایشات پزشکی را از قبل انجام نمی‌دهیم. بلکه وقتی تصمیمی باید گرفته شود، آزمایش انجام می‌دهیم. -1. تمام قوانینی را انتخاب کنید که می‌توانند مقدار یک هدف را به ما بدهند (یعنی با هدف در سمت راست (RHS)) - مجموعه تضاد -1. اگر هیچ قانونی برای این ویژگی وجود نداشته باشد، یا قانونی وجود داشته باشد که می‌گوید باید مقدار را از کاربر بپرسیم - از کاربر بپرسید، در غیر این صورت: -1. از استراتژی حل تضاد برای انتخاب یک قانون که به عنوان *فرضیه* استفاده خواهد شد استفاده کنید - سعی می‌کنیم آن را اثبات کنیم -1. فرآیند را به صورت بازگشتی برای همه ویژگی‌های موجود در سمت چپ قانون تکرار کنید، سعی کنید آن‌ها را به عنوان اهداف اثبات کنید -1. اگر در هر نقطه فرآیند شکست خورد - از قانون دیگری در مرحله 3 استفاده کنید +این فرایند می‌تواند با استفاده از **استنتاج بازگشتی** مدل شود. این فرایند توسط **هدف** هدایت می‌شود - مقدار ویژگی که می‌خواهیم پیدا کنیم: -> ✅ در چه شرایطی استنتاج پیشرو مناسب‌تر است؟ استنتاج پسرو چطور؟ +1. همه قوانینی که می‌توانند مقدار هدف را به ما بدهند (یعنی با هدف در سمت راست قانون (RHS)) انتخاب می‌شوند - یک مجموعه تضاد +1. اگر هیچ قانونی برای این ویژگی وجود نداشته باشد، یا قانونی بیان کند که باید از کاربر پرسیده شود - از کاربر پرسیده می‌شود، در غیر این صورت: +1. با استفاده از استراتژی حل تضاد، یک قانون انتخاب می‌شود که به عنوان *فرضیه* استفاده شود - تلاش می‌شود اثبات شود +1. این فرایند به صورت بازگشتی برای همه ویژگی‌های در سمت چپ قانون (LHS) تکرار می‌شود تا آنها را به عنوان اهداف اثبات کند +1. اگر در هر مرحله فرایند شکست خورد - قانون دیگری در مرحله 3 انتخاب می‌شود. + +> ✅ در چه موقعیت‌هایی استنتاج پیشرو مناسب‌تر است؟ درباره استنتاج بازگشتی چطور؟ ### پیاده‌سازی سیستم‌های خبره -سیستم‌های خبره را می‌توان با استفاده از ابزارهای مختلف پیاده‌سازی کرد: +سیستم‌های خبره می‌توانند با ابزارهای مختلفی پیاده‌سازی شوند: -* برنامه‌نویسی مستقیم آن‌ها در یک زبان برنامه‌نویسی سطح بالا. این بهترین ایده نیست، زیرا مزیت اصلی یک سیستم مبتنی بر دانش این است که دانش از استنتاج جدا شده است و به طور بالقوه یک متخصص حوزه مسئله باید بتواند قوانین را بدون درک جزئیات فرآیند استنتاج بنویسد. -* استفاده از **پوسته سیستم‌های خبره**، یعنی سیستمی که به طور خاص طراحی شده است تا با استفاده از یک زبان نمایش دانش پر شود. +* برنامه‌نویسی مستقیم آنها در زبان‌های برنامه‌نویسی سطح بالا. این بهترین ایده نیست، زیرا اصلی‌ترین مزیت سیستم‌های مبتنی بر دانش این است که دانش از استنتاج جداست و احتمالاً کارشناس حوزه مسئله می‌تواند بدون درک جزئیات فرایند استنتاج قوانین را بنویسد. +* استفاده از **پوسته سیستم خبره**، یعنی سیستمی که به طور خاص برای جمع‌آوری دانش با استفاده از یک زبان نمایش دانش طراحی شده است. -## ✍️ تمرین: استنتاج حیوانات +## ✍️ تمرین: استنتاج حیوان -به [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) مراجعه کنید برای نمونه‌ای از پیاده‌سازی سیستم خبره استنتاج پیشرو و پسرو. +مثال پیاده‌سازی سیستم خبره استنتاج پیشرو و بازگشتی را در [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) ببینید. -> **توجه**: این مثال نسبتاً ساده است و فقط ایده‌ای از شکل یک سیستم خبره ارائه می‌دهد. هنگامی که شروع به ایجاد چنین سیستمی می‌کنید، تنها زمانی ممکن است برخی رفتارهای *هوشمندانه* از آن مشاهده کنید که به تعداد مشخصی از قوانین، حدود ۲۰۰+ برسید. در یک نقطه، قوانین بسیار پیچیده می‌شوند تا بتوان همه آن‌ها را در ذهن نگه داشت، و در این نقطه ممکن است شروع به تعجب کنید که چرا سیستم تصمیمات خاصی می‌گیرد. با این حال، ویژگی مهم سیستم‌های مبتنی بر دانش این است که شما همیشه می‌توانید *توضیح دهید* که چگونه هر یک از تصمیمات گرفته شده است. +> **توجه**: این مثال نسبتاً ساده است و فقط ایده کلی سیستم خبره را نشان می‌دهد. وقتی شروع به ساخت چنین سیستمی کنید، تنها پس از رسیدن به تعداد مشخصی قوانین، حدود ۲۰۰+، رفتار *هوشمند* آن را متوجه خواهید شد. در نقطه‌ای، قوانین آنقدر پیچیده می‌شوند که حفظ همه آنها دشوار است و در آن زمان ممکن است بپرسید چرا سیستم تصمیمات خاصی می‌گیرد. با این حال، ویژگی مهم سیستم‌های مبتنی بر دانش این است که همیشه می‌توانید *توضیح دهید* دقیقاً چگونه هر یک از تصمیمات گرفته شده است. ## هستی‌شناسی‌ها و وب معنایی -در پایان قرن بیستم، یک ابتکار برای استفاده از نمایش دانش برای توضیح منابع اینترنتی وجود داشت، به طوری که امکان یافتن منابعی که با پرسش‌های بسیار خاص مطابقت دارند فراهم شود. این حرکت **وب معنایی** نامیده شد و بر چند مفهوم متکی بود: +در پایان قرن بیستم، ابتکاری برای استفاده از نمایش دانش برای حاشیه‌نویسی منابع اینترنتی مطرح شد، تا بتوان منابع مرتبط با پرسش‌های بسیار خاص را یافت. این حرکت به نام **وب معنایی** شناخته شد و بر چند مفهوم تکیه داشت: -- یک نمایش دانش خاص بر اساس **[منطق توصیفی](https://en.wikipedia.org/wiki/Description_logic)** (DL). این نمایش مشابه نمایش دانش قاب است، زیرا یک سلسله‌مراتب از اشیاء با ویژگی‌ها ایجاد می‌کند، اما دارای معناشناسی منطقی رسمی و استنتاج است. یک خانواده کامل از DL‌ها وجود دارد که بین بیان‌پذیری و پیچیدگی الگوریتمی استنتاج تعادل برقرار می‌کنند. -- نمایش دانش توزیع شده، جایی که همه مفاهیم توسط یک شناسه URI جهانی نمایش داده می‌شوند، که امکان ایجاد سلسله‌مراتب دانش را که اینترنت را پوشش می‌دهد فراهم می‌کند. -- خانواده‌ای از زبان‌های مبتنی بر XML برای توصیف دانش: RDF (چارچوب توصیف منابع)، RDFS (طرح‌واره RDF)، OWL (زبان وب هستی‌شناسی). +- یک نمایش دانش خاص بر اساس **[منطق‌های توصیفی](https://en.wikipedia.org/wiki/Description_logic)** (DL). این نمایش مشابه نمایش قاب است، زیرا سلسله‌مراتبی از اشیاء با خواص می‌سازد، اما دارای معناشناسی منطقی رسمی و استنتاج است. خانواده‌ای از DL‌ها وجود دارد که تعادل بین بیان‌گری و پیچیدگی الگوریتمی استنتاج را برقرار می‌کنند. +- نمایش دانش توزیع شده، جایی که همه مفاهیم به وسیله شناسگر URI جهانی نمایش داده می‌شوند، که امکان ایجاد سلسله‌مراتب دانش در سراسر اینترنت را ممکن می‌سازد. +- خانواده‌ای از زبان‌های مبتنی بر XML برای توصیف دانش: RDF (چارچوب توصیف منابع)، RDFS (طرحواره RDF)، OWL (زبان معناشناسی وب). -یکی از مفاهیم اصلی در وب معنایی، مفهوم **هستی‌شناسی** است. هستی‌شناسی به مشخصات صریح یک حوزه مسئله با استفاده از نوعی نمایش دانش رسمی اشاره دارد. ساده‌ترین هستی‌شناسی می‌تواند فقط یک سلسله‌مراتب از اشیاء در یک حوزه مسئله باشد، اما هستی‌شناسی‌های پیچیده‌تر شامل قوانینی خواهند بود که می‌توانند برای استنتاج استفاده شوند. +یک مفهوم اساسی در وب معنایی، مفهوم **وجودشناسی** است. این اشاره به مشخصه صریح یک حوزه مسئله با استفاده از نمایشی رسمی از دانش دارد. ساده‌ترین وجودشناسی می‌تواند فقط یک سلسله‌مراتب از اشیاء در حوزه مسئله باشد، اما وجودشناسی‌های پیچیده‌تر شامل قوانینی هستند که می‌توان از آنها برای استنتاج استفاده کرد. -در وب معنایی، تمام نمایش‌ها بر اساس سه‌گانه‌ها هستند. هر شیء و هر رابطه به‌طور منحصربه‌فرد توسط URI شناسایی می‌شود. به‌عنوان مثال، اگر بخواهیم بیان کنیم که این برنامه درسی هوش مصنوعی توسط Dmitry Soshnikov در تاریخ ۱ ژانویه ۲۰۲۲ توسعه داده شده است، اینجا سه‌گانه‌هایی است که می‌توانیم استفاده کنیم: +در وب معنایی، همه نمایش‌ها بر اساس سه‌تایی‌ها هستند. هر شیء و هر رابطه به طور یکتا توسط URI شناخته می‌شوند. به عنوان مثال، اگر بخواهیم بیان کنیم که این برنامه درسی هوش مصنوعی توسط دیمیتری سوشنیکوف در ۱ ژانویه ۲۰۲۲ توسعه یافته است - سه‌تایی‌های زیر را می‌توانیم استفاده کنیم: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ در اینجا `http://www.example.com/terms/creation-date` و `http://purl.org/dc/elements/1.1/creator` برخی URI‌های شناخته‌شده و جهانی هستند که برای بیان مفاهیم *سازنده* و *تاریخ ایجاد* استفاده می‌شوند. +> ✅ در اینجا `http://www.example.com/terms/creation-date` و `http://purl.org/dc/elements/1.1/creator` برخی از URIهای شناخته شده و پذیرفته شده جهانی برای بیان مفاهیم *خالق* و *تاریخ ساخت* هستند. -در یک مورد پیچیده‌تر، اگر بخواهیم لیستی از سازندگان تعریف کنیم، می‌توانیم از برخی ساختارهای داده تعریف‌شده در RDF استفاده کنیم. +در مورد پیچیده‌تر، اگر بخواهیم لیستی از خالقان را تعریف کنیم، می‌توانیم از برخی ساختارهای داده‌ای تعریف شده در RDF استفاده کنیم. - + -> نمودارهای بالا توسط [Dmitry Soshnikov](http://soshnikov.com) +> نمودارهای بالا توسط [دیمیتری سوشنیکوف](http://soshnikov.com) -پیشرفت در ساخت وب معنایی تا حدی به دلیل موفقیت موتورهای جستجو و تکنیک‌های پردازش زبان طبیعی که امکان استخراج داده‌های ساختاریافته از متن را فراهم می‌کنند، کند شد. با این حال، در برخی حوزه‌ها هنوز تلاش‌های قابل‌توجهی برای حفظ هستی‌شناسی‌ها و پایگاه‌های دانش وجود دارد. چند پروژه قابل توجه: +پیشرفت ساخت وب معنایی تا حدودی توسط موفقیت موتورهای جستجو و تکنیک‌های پردازش زبان طبیعی که اجازه استخراج داده‌های ساخت‌یافته از متن را می‌دهند، کند شده است. با این حال، در برخی حوزه‌ها هنوز تلاش‌های قابل توجهی برای نگهداری وجودشناسی‌ها و پایگاه‌های دانش انجام می‌شود. چند پروژه قابل اشاره: -* [WikiData](https://wikidata.org/) مجموعه‌ای از پایگاه‌های دانش قابل خواندن توسط ماشین است که با ویکی‌پدیا مرتبط هستند. بیشتر داده‌ها از *InfoBoxes* ویکی‌پدیا استخراج می‌شوند، بخش‌هایی از محتوای ساختاریافته در صفحات ویکی‌پدیا. شما می‌توانید [WikiData را در SPARQL](https://query.wikidata.org/)، یک زبان پرس‌وجوی ویژه برای وب معنایی، جستجو کنید. اینجا یک نمونه پرس‌وجو است که محبوب‌ترین رنگ‌های چشم در میان انسان‌ها را نمایش می‌دهد: +* [WikiData](https://wikidata.org/) مجموعه‌ای از پایگاه‌های دانش قابل خواندن توسط ماشین متصل به ویکی‌پدیا است. بیشتر داده‌ها از *اطلاعات ساختیافته* داخل صفحات ویکی‌پدیا استخراج شده است. می‌توانید با زبان خاص پرسشگری وب معنایی به نام SPARQL [از ویکی‌دیتا پرسش کنید](https://query.wikidata.org/). در اینجا یک نمونه پرسش که رایج‌ترین رنگ‌های چشم انسان‌ها را نمایش می‌دهد: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) تلاش دیگری مشابه WikiData است. +* [DBpedia](https://www.dbpedia.org/) تلاشی مشابه ویکی‌دیتا است. -> ✅ اگر می‌خواهید با ساخت هستی‌شناسی‌های خودتان یا باز کردن هستی‌شناسی‌های موجود آزمایش کنید، یک ویرایشگر بصری عالی هستی‌شناسی به نام [Protégé](https://protege.stanford.edu/) وجود دارد. آن را دانلود کنید یا به‌صورت آنلاین استفاده کنید. +> ✅ اگر می‌خواهید در ساخت وجودشناسی‌های خود آزمایش کنید یا وجودشناسی‌های موجود را باز کنید، یک ویرایشگر وجودشناسی بصری عالی به نام [Protégé](https://protege.stanford.edu/) وجود دارد. آن را دانلود کنید یا به صورت آنلاین استفاده کنید. - + -*ویرایشگر وب Protégé باز شده با هستی‌شناسی خانواده رومانوف. تصویر توسط Dmitry Soshnikov* +*ویرایشگر وب Protégé با وجودشناسی خانواده رومانوف باز شده است. عکس از دیمیتری سوشنیکوف* -## ✍️ تمرین: هستی‌شناسی خانواده +## ✍️ تمرین: وجودشناسی خانواده -به [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) مراجعه کنید تا نمونه‌ای از استفاده از تکنیک‌های وب معنایی برای استنتاج روابط خانوادگی را ببینید. ما یک شجره‌نامه خانوادگی که در قالب رایج GEDCOM نمایش داده شده است و یک هستی‌شناسی از روابط خانوادگی را می‌گیریم و یک گراف از تمام روابط خانوادگی برای مجموعه‌ای از افراد مشخص می‌سازیم. +نمونه‌ای از استفاده از تکنیک‌های وب معنایی برای استدلال درباره روابط خانوادگی را در [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) ببینید. ما یک شجره‌نامه خانوادگی که در فرمت متداول GEDCOM نمایش داده شده و یک وجودشناسی از روابط خانوادگی را گرفته و گرافی از همه روابط خانوادگی برای مجموعه‌ای از افراد را می‌سازیم. -## گراف مفهومی مایکروسافت +## Microsoft Concept Graph -در بیشتر موارد، هستی‌شناسی‌ها با دقت توسط انسان ایجاد می‌شوند. با این حال، همچنین امکان **استخراج** هستی‌شناسی‌ها از داده‌های غیرساختاریافته، به‌عنوان مثال، از متون زبان طبیعی وجود دارد. +در بیشتر موارد، وجودشناسی‌ها به صورت دقیق و دستی ایجاد می‌شوند. با این حال، امکان **استخراج** وجودشناسی‌ها از داده‌های بدون ساختار، مثلاً متون زبان طبیعی، نیز وجود دارد. -یکی از این تلاش‌ها توسط Microsoft Research انجام شد و منجر به [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) شد. +یکی از این تلاش‌ها توسط تحقیقات مایکروسافت انجام شد که منجر به [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) گردید. -این گراف مجموعه بزرگی از موجودیت‌ها است که با استفاده از رابطه ارث‌بری `is-a` گروه‌بندی شده‌اند. این گراف امکان پاسخ به سوالاتی مانند "مایکروسافت چیست؟" را فراهم می‌کند - پاسخی مانند "یک شرکت با احتمال ۰.۸۷ و یک برند با احتمال ۰.۷۵". +این یک مجموعه بزرگ از موجودیت‌ها است که با استفاده از رابطه وراثت `is-a` گروه‌بندی شده‌اند. این امکان را می‌دهد که به سوالاتی مانند «مایکروسافت چیست؟» جواب دهید - پاسخ چیزی مانند «یک شرکت با احتمال ۰.۸۷ و یک برند با احتمال ۰.۷۵» است. -این گراف یا به‌صورت REST API در دسترس است یا به‌صورت یک فایل متنی بزرگ قابل دانلود که تمام جفت‌های موجودیت را فهرست می‌کند. +این گراف یا به صورت REST API در دسترس است و یا به صورت یک فایل متنی بزرگ قابل دانلود که شامل تمام جفت‌های موجودیت است. -## ✍️ تمرین: یک گراف مفهومی +## ✍️ تمرین: گراف مفهومی -دفترچه [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) را امتحان کنید تا ببینید چگونه می‌توانیم از گراف مفهومی مایکروسافت برای گروه‌بندی مقالات خبری در چند دسته استفاده کنیم. +دفترچه [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) را امتحان کنید تا ببینید چگونه می‌توان از Microsoft Concept Graph برای گروه‌بندی مقالات خبری در چند دسته استفاده کرد. ## نتیجه‌گیری -امروزه، هوش مصنوعی اغلب به‌عنوان مترادف *یادگیری ماشین* یا *شبکه‌های عصبی* در نظر گرفته می‌شود. با این حال، انسان‌ها همچنین استدلال صریح را نشان می‌دهند، چیزی که در حال حاضر توسط شبکه‌های عصبی مدیریت نمی‌شود. در پروژه‌های دنیای واقعی، استدلال صریح هنوز برای انجام وظایفی که نیاز به توضیحات دارند یا توانایی تغییر رفتار سیستم به‌صورت کنترل‌شده دارند، استفاده می‌شود. +امروزه، هوش مصنوعی اغلب مترادف با *یادگیری ماشین* یا *شبکه‌های عصبی* در نظر گرفته می‌شود. با این حال، انسان همچنین استدلال صریح را نشان می‌دهد، که چیزی است که در حال حاضر شبکه‌های عصبی به آن نمی‌پردازند. در پروژه‌های دنیای واقعی، استدلال صریح هنوز برای انجام کارهایی که نیاز به توضیح دارند یا توانایی اصلاح رفتار سیستم به صورت کنترل شده، استفاده می‌شود. ## 🚀 چالش -در دفترچه هستی‌شناسی خانواده مرتبط با این درس، فرصتی برای آزمایش با روابط دیگر خانوادگی وجود دارد. سعی کنید ارتباطات جدیدی بین افراد در شجره‌نامه خانوادگی کشف کنید. +در دفترچه وجودشناسی خانواده که همراه این درس است، فرصت تجربی با روابط دیگر خانوادگی وجود دارد. سعی کنید ارتباطات جدیدی بین افراد در شجره خانواده کشف کنید. ## [آزمون پس از درس](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## مرور و مطالعه خودآموز +## مرور و خودآموزی -تحقیقی در اینترنت انجام دهید تا حوزه‌هایی را کشف کنید که انسان‌ها تلاش کرده‌اند دانش را کمی‌سازی و کدگذاری کنند. به طبقه‌بندی بلوم نگاهی بیندازید و به تاریخ برگردید تا ببینید چگونه انسان‌ها تلاش کرده‌اند دنیای خود را درک کنند. کار Linnaeus را برای ایجاد یک طبقه‌بندی از موجودات بررسی کنید و نحوه‌ای که Dmitri Mendeleev راهی برای توصیف و گروه‌بندی عناصر شیمیایی ایجاد کرد را مشاهده کنید. چه مثال‌های جالب دیگری می‌توانید پیدا کنید؟ +کمی در اینترنت تحقیق کنید تا حوزه‌هایی را کشف کنید که انسان‌ها سعی کرده‌اند دانش را کمّی و کدگذاری کنند. نگاهی به طبقه‌بندی بلوم بیندازید، و به تاریخچه بازگردید تا ببینید چگونه انسان‌ها سعی کرده‌اند جهان خود را معنا کنند. آثار لینئوس برای ایجاد طبقه‌بندی موجودات را بررسی کنید، و مشاهده کنید چگونه دیمیتری مندلیف روشی برای توصیف و گروه‌بندی عناصر شیمیایی ایجاد کرد. چه نمونه‌های جالب دیگری می‌توانید بیابید؟ -**تکلیف**: [ساخت یک هستی‌شناسی](assignment.md) +**تکلیف**: [ساخت یک وجودشناسی](assignment.md) --- + +**سلب مسئولیت**: +این سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما در تلاش برای دقت هستیم، لطفاً به این نکته توجه داشته باشید که ترجمه‌های خودکار ممکن است حاوی خطا یا نادرستی باشند. سند اصلی به زبان بومی خود باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هیچ گونه سوءتفاهم یا تعبیر نادرست ناشی از استفاده از این ترجمه نیستیم. + \ No newline at end of file diff --git a/translations/fa/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/fa/lessons/3-NeuralNetworks/05-Frameworks/README.md index 242ddadb..1af0f44f 100644 --- a/translations/fa/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/fa/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ API سطح بالا | [Keras](IntroKeras.ipynb) | *PyTorch Lightning* به مشکل زیر در تقریب ۵ نقطه (نمایش داده شده با `x` در نمودارهای زیر) توجه کنید: -![خطی](../../../../../translated_images/overfit1.f24b71c6f652e59e.fa.jpg) | ![بیش‌برازش](../../../../../translated_images/overfit2.131f5800ae10ca5e.fa.jpg) +![خطی](../../../../../translated_images/fa/overfit1.f24b71c6f652e59e.webp) | ![بیش‌برازش](../../../../../translated_images/fa/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **مدل خطی، ۲ پارامتر** | **مدل غیرخطی، ۷ پارامتر** خطای آموزش = ۵.۳ | خطای آموزش = ۰ @@ -79,7 +79,7 @@ API سطح بالا | [Keras](IntroKeras.ipynb) | *PyTorch Lightning* همانطور که از نمودار بالا می‌بینید، بیش‌برازش را می‌توان با خطای آموزشی بسیار کم و خطای اعتبارسنجی بالا تشخیص داد. معمولاً در طول آموزش، هر دو خطای آموزشی و اعتبارسنجی شروع به کاهش می‌کنند و سپس در یک نقطه خطای اعتبارسنجی ممکن است کاهش را متوقف کند و شروع به افزایش کند. این نشانه‌ای از بیش‌برازش خواهد بود و نشان‌دهنده این است که احتمالاً باید در این نقطه آموزش را متوقف کنیم (یا حداقل یک نسخه از مدل را ذخیره کنیم). -![بیش‌برازش](../../../../../translated_images/Overfitting.408ad91cd90b4371.fa.png) +![بیش‌برازش](../../../../../translated_images/fa/Overfitting.408ad91cd90b4371.webp) ## چگونه از بیش‌برازش جلوگیری کنیم diff --git a/translations/fa/lessons/3-NeuralNetworks/README.md b/translations/fa/lessons/3-NeuralNetworks/README.md index cbfb394e..72357141 100644 --- a/translations/fa/lessons/3-NeuralNetworks/README.md +++ b/translations/fa/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مقدمه‌ای بر شبکه‌های عصبی -![خلاصه‌ای از محتوای مقدمه شبکه‌های عصبی در یک طرح](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.fa.png) +![خلاصه‌ای از محتوای مقدمه شبکه‌های عصبی در یک طرح](../../../../translated_images/fa/ai-neuralnetworks.1c687ae40bc86e83.webp) همان‌طور که در مقدمه بحث کردیم، یکی از راه‌های دستیابی به هوش، آموزش یک **مدل کامپیوتری** یا یک **مغز مصنوعی** است. از اواسط قرن بیستم، پژوهشگران مدل‌های ریاضی مختلفی را امتحان کردند تا اینکه در سال‌های اخیر این مسیر به موفقیت چشمگیری دست یافت. این مدل‌های ریاضی مغز را **شبکه‌های عصبی** می‌نامند. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: از زیست‌شناسی می‌دانیم که مغز ما از سلول‌های عصبی (نورون‌ها) تشکیل شده است، که هر کدام دارای چندین "ورودی" (دندریت‌ها) و یک "خروجی" (آکسون) هستند. هم دندریت‌ها و هم آکسون‌ها می‌توانند سیگنال‌های الکتریکی را منتقل کنند، و اتصالات بین آن‌ها — که به عنوان سیناپس شناخته می‌شوند — می‌توانند درجات مختلفی از رسانایی را نشان دهند که توسط انتقال‌دهنده‌های عصبی تنظیم می‌شوند. -![مدل یک نورون](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fa.jpg) | ![مدل یک نورون](../../../../translated_images/artneuron.1a5daa88d20ebe6f.fa.png) +![مدل یک نورون](../../../../translated_images/fa/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![مدل یک نورون](../../../../translated_images/fa/artneuron.1a5daa88d20ebe6f.webp) ----|---- نورون واقعی *([تصویر](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) از ویکی‌پدیا)* | نورون مصنوعی *(تصویر توسط نویسنده)* بنابراین، ساده‌ترین مدل ریاضی یک نورون شامل چندین ورودی X1, ..., XN و یک خروجی Y، و مجموعه‌ای از وزن‌ها W1, ..., WN است. خروجی به صورت زیر محاسبه می‌شود: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) که در آن f یک **تابع فعال‌سازی** غیرخطی است. diff --git a/translations/fa/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/fa/lessons/4-ComputerVision/06-IntroCV/README.md index 8b256d2b..07473c41 100644 --- a/translations/fa/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/fa/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **پیش‌پردازش عکس یک کتاب بریل**. ما بر روی استفاده از آستانه‌گذاری، تشخیص ویژگی، تبدیل پرسپکتیو و دستکاری‌های NumPy تمرکز می‌کنیم تا نمادهای بریل جداگانه را برای طبقه‌بندی بیشتر توسط یک شبکه عصبی جدا کنیم. -![تصویر بریل](../../../../../translated_images/braille.341962ff76b1bd70.fa.jpeg) | ![تصویر پیش‌پردازش شده بریل](../../../../../translated_images/braille-result.46530fea020b03c7.fa.png) | ![نمادهای بریل](../../../../../translated_images/braille-symbols.0159185ab69d5339.fa.png) +![تصویر بریل](../../../../../translated_images/fa/braille.341962ff76b1bd70.webp) | ![تصویر پیش‌پردازش شده بریل](../../../../../translated_images/fa/braille-result.46530fea020b03c7.webp) | ![نمادهای بریل](../../../../../translated_images/fa/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > تصویر از [OpenCV.ipynb](OpenCV.ipynb) * **تشخیص حرکت در ویدئو با استفاده از تفاوت فریم‌ها**. اگر دوربین ثابت باشد، فریم‌های ویدئو باید بسیار مشابه یکدیگر باشند. از آنجا که فریم‌ها به صورت آرایه نمایش داده می‌شوند، با کم کردن آرایه‌های دو فریم متوالی می‌توان تفاوت پیکسلی را به دست آورد، که برای فریم‌های ثابت کم است و با وجود حرکت در تصویر افزایش می‌یابد. -![تصویر فریم‌های ویدئو و تفاوت فریم‌ها](../../../../../translated_images/frame-difference.706f805491a0883c.fa.png) +![تصویر فریم‌های ویدئو و تفاوت فریم‌ها](../../../../../translated_images/fa/frame-difference.706f805491a0883c.webp) > تصویر از [OpenCV.ipynb](OpenCV.ipynb) @@ -91,7 +91,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **جریان نوری متراکم** میدان برداری را محاسبه می‌کند که نشان می‌دهد هر پیکسل به کجا حرکت می‌کند. - **جریان نوری پراکنده** بر اساس گرفتن برخی ویژگی‌های متمایز در تصویر (مانند لبه‌ها) و ساختن مسیر آن‌ها از فریم به فریم است. -![تصویر جریان نوری](../../../../../translated_images/optical.1f4a94464579a83a.fa.png) +![تصویر جریان نوری](../../../../../translated_images/fa/optical.1f4a94464579a83a.webp) > تصویر از [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/fa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/fa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index bf4f1be2..07fa24dc 100644 --- a/translations/fa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/fa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 یک شبکه است که در سال ۲۰۱۴ به دقت ۹۲.۷٪ در طبقه‌بندی ImageNet در پنج کلاس برتر دست یافت. ساختار لایه‌های آن به شکل زیر است: -![لایه‌های ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fa.jpg) +![لایه‌های ImageNet](../../../../../translated_images/fa/vgg-16-arch1.d901a5583b3a51ba.webp) همان‌طور که می‌بینید، VGG از یک معماری هرمی سنتی پیروی می‌کند که شامل توالی لایه‌های کانولوشن و پولینگ است. -![هرم ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fa.jpg) +![هرم ImageNet](../../../../../translated_images/fa/vgg-16-arch.64ff2137f50dd49f.webp) > تصویر از [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fa/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/fa/lessons/4-ComputerVision/07-ConvNets/README.md index 6b3276d3..2db32e9e 100644 --- a/translations/fa/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/fa/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: برای استخراج الگوها، از مفهوم **فیلترهای کانولوشنی** استفاده خواهیم کرد. همان‌طور که می‌دانید، یک تصویر به صورت یک ماتریس دو‌بعدی یا یک تنسور سه‌بعدی با عمق رنگ نمایش داده می‌شود. اعمال یک فیلتر به این معناست که یک ماتریس کوچک **هسته فیلتر** را می‌گیریم و برای هر پیکسل در تصویر اصلی میانگین وزنی را با نقاط همسایه محاسبه می‌کنیم. می‌توانیم این فرآیند را به صورت یک پنجره کوچک که روی کل تصویر حرکت می‌کند و تمام پیکسل‌ها را بر اساس وزن‌های موجود در ماتریس هسته فیلتر میانگین‌گیری می‌کند، تصور کنیم. -![فیلتر لبه عمودی](../../../../../translated_images/filter-vert.b7148390ca0bc356.fa.png) | ![فیلتر لبه افقی](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.fa.png) +![فیلتر لبه عمودی](../../../../../translated_images/fa/filter-vert.b7148390ca0bc356.webp) | ![فیلتر لبه افقی](../../../../../translated_images/fa/filter-horiz.59b80ed4feb946ef.webp) ----|---- > تصویر از دیمیتری سوشنیکوف @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * می‌توانیم شبکه را به گونه‌ای طراحی کنیم که فیلترها به صورت خودکار آموزش ببینند. * می‌توانیم از همین روش برای یافتن الگوها در ویژگی‌های سطح بالا، نه فقط در تصویر اصلی، استفاده کنیم. بنابراین استخراج ویژگی‌های CNN بر اساس سلسله مراتبی از ویژگی‌ها عمل می‌کند، از ترکیب‌های پیکسل سطح پایین تا ترکیب‌های سطح بالای بخش‌های تصویر. -![استخراج ویژگی سلسله‌مراتبی](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fa.png) +![استخراج ویژگی سلسله‌مراتبی](../../../../../translated_images/fa/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > تصویر از [مقاله‌ای توسط هیسلوپ-لینچ](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)، بر اساس [تحقیقات آن‌ها](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: به عنوان مثال، بیایید به معماری VGG-16 نگاه کنیم، شبکه‌ای که در سال ۲۰۱۴ به دقت ۹۲.۷٪ در طبقه‌بندی پنج‌تایی ImageNet دست یافت: -![لایه‌های ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fa.jpg) +![لایه‌های ImageNet](../../../../../translated_images/fa/vgg-16-arch1.d901a5583b3a51ba.webp) -![هرم ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fa.jpg) +![هرم ImageNet](../../../../../translated_images/fa/vgg-16-arch.64ff2137f50dd49f.webp) > تصویر از [ریسرچ‌گیت](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fa/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/fa/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 75a7581a..b95db1d1 100644 --- a/translations/fa/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/fa/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ما از [مجموعه داده حیوانات خانگی آکسفورد-IIIT](https://www.robots.ox.ac.uk/~vgg/data/pets/) استفاده خواهیم کرد که شامل تصاویر 37 نژاد مختلف از سگ‌ها و گربه‌ها است. -![مجموعه داده‌ای که با آن کار خواهیم کرد](../../../../../../translated_images/data.50b2a9d5484bdbf0.fa.png) +![مجموعه داده‌ای که با آن کار خواهیم کرد](../../../../../../translated_images/fa/data.50b2a9d5484bdbf0.webp) برای دانلود مجموعه داده، از این قطعه کد استفاده کنید: diff --git a/translations/fa/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/fa/lessons/4-ComputerVision/08-TransferLearning/README.md index ec18a546..7e84c46a 100644 --- a/translations/fa/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/fa/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: در اینجا نمونه‌ای از ویژگی‌هایی که توسط شبکه VGG-16 از یک تصویر گربه استخراج شده است آورده شده است: -![ویژگی‌های استخراج‌شده توسط VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.fa.png) +![ویژگی‌های استخراج‌شده توسط VGG-16](../../../../../translated_images/fa/features.6291f9c7ba3a0b95.webp) ## مجموعه داده گربه‌ها و سگ‌ها @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: یک رویکردی که می‌توانیم اتخاذ کنیم این است که با یک تصویر تصادفی شروع کنیم و سپس از تکنیک **بهینه‌سازی نزول گرادیان** استفاده کنیم تا آن تصویر را به گونه‌ای تنظیم کنیم که شبکه شروع به فکر کردن کند که این یک گربه است. -![حلقه بهینه‌سازی تصویر](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.fa.png) +![حلقه بهینه‌سازی تصویر](../../../../../translated_images/fa/ideal-cat-loop.999fbb8ff306e044.webp) با این حال، اگر این کار را انجام دهیم، چیزی بسیار شبیه به نویز تصادفی دریافت خواهیم کرد. این به این دلیل است که *راه‌های زیادی وجود دارد که شبکه فکر کند تصویر ورودی یک گربه است*، از جمله برخی که از نظر بصری منطقی نیستند. در حالی که این تصاویر شامل بسیاری از الگوهای معمول برای یک گربه هستند، هیچ چیزی آن‌ها را مجبور نمی‌کند که از نظر بصری متمایز باشند. برای بهبود نتیجه، می‌توانیم یک عبارت دیگر به تابع زیان اضافه کنیم که **زیان تغییرات** نامیده می‌شود. این یک معیار است که نشان می‌دهد پیکسل‌های مجاور تصویر چقدر مشابه هستند. کمینه‌سازی زیان تغییرات تصویر را صاف‌تر می‌کند و نویز را از بین می‌برد - بنابراین الگوهای بصری جذاب‌تری را آشکار می‌کند. در اینجا نمونه‌ای از چنین تصاویر "ایده‌آلی" آورده شده است که با احتمال بالا به عنوان گربه و گورخر طبقه‌بندی شده‌اند: -![گربه ایده‌آل](../../../../../translated_images/ideal-cat.203dd4597643d6b0.fa.png) | ![گورخر ایده‌آل](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.fa.png) +![گربه ایده‌آل](../../../../../translated_images/fa/ideal-cat.203dd4597643d6b0.webp) | ![گورخر ایده‌آل](../../../../../translated_images/fa/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *گربه ایده‌آل* | *گورخر ایده‌آل* رویکرد مشابهی می‌تواند برای انجام **حملات تقلبی** روی یک شبکه عصبی استفاده شود. فرض کنید می‌خواهیم یک شبکه عصبی را فریب دهیم و یک سگ را شبیه به گربه کنیم. اگر تصویر سگی را بگیریم که توسط شبکه به عنوان سگ شناخته شده است، می‌توانیم آن را کمی با استفاده از بهینه‌سازی نزول گرادیان تغییر دهیم تا زمانی که شبکه شروع به طبقه‌بندی آن به عنوان گربه کند: -![تصویر سگ](../../../../../translated_images/original-dog.8f68a67d2fe0911f.fa.png) | ![تصویر سگی که به عنوان گربه طبقه‌بندی شده است](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.fa.png) +![تصویر سگ](../../../../../translated_images/fa/original-dog.8f68a67d2fe0911f.webp) | ![تصویر سگی که به عنوان گربه طبقه‌بندی شده است](../../../../../translated_images/fa/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *تصویر اصلی سگ* | *تصویر سگی که به عنوان گربه طبقه‌بندی شده است* diff --git a/translations/fa/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/fa/lessons/4-ComputerVision/09-Autoencoders/README.md index f8ad4d37..0a00863c 100644 --- a/translations/fa/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/fa/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: از آنجا که ما اتوانکودر را برای گرفتن بیشترین اطلاعات از تصویر اصلی برای بازسازی دقیق آموزش می‌دهیم، شبکه تلاش می‌کند بهترین **تعبیر** از تصاویر ورودی را پیدا کند تا معنا را ثبت کند. -![نمودار اتوانکودر](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fa.jpg) +![نمودار اتوانکودر](../../../../../translated_images/fa/autoencoder_schema.5e6fc9ad98a5eb61.webp) > تصویر از [وبلاگ Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/fa/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/fa/lessons/4-ComputerVision/11-ObjectDetection/README.md index 7b8d962b..1775c0bb 100644 --- a/translations/fa/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/fa/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [پیش‌کوئیز](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![تشخیص اشیا](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fa.png) +![تشخیص اشیا](../../../../../translated_images/fa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > تصویر از [وب‌سایت YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. طبقه‌بندی تصویر را روی هر کاشی اجرا کنید. 3. کاشی‌هایی که منجر به فعال‌سازی کافی بالا می‌شوند، می‌توانند به عنوان حاوی شیء مورد نظر در نظر گرفته شوند. -![تشخیص ساده اشیا](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.fa.png) +![تشخیص ساده اشیا](../../../../../translated_images/fa/naive-detection.e7f1ba220ccd08c6.webp) > *تصویر از [دفترچه تمرین](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - شامل ۲۰ کلاس * [COCO](http://cocodataset.org/#home) - اشیای عمومی در زمینه. شامل ۸۰ کلاس، جعبه‌های محدودکننده و ماسک‌های تقسیم‌بندی -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.fa.jpg) +![COCO](../../../../../translated_images/fa/coco-examples.71bc60380fa6cceb.webp) ## معیارهای تشخیص اشیا @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: در حالی که برای طبقه‌بندی تصویر اندازه‌گیری عملکرد الگوریتم آسان است، برای تشخیص اشیا باید هم درستی کلاس و هم دقت مکان‌یابی جعبه محدودکننده استنباط شده را اندازه‌گیری کنیم. برای مورد دوم، از معیاری به نام **تقاطع بر اتحاد** (IoU) استفاده می‌کنیم که میزان همپوشانی دو جعبه (یا دو ناحیه دلخواه) را اندازه‌گیری می‌کند. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.fa.png) +![IoU](../../../../../translated_images/fa/iou_equation.9a4751d40fff4e11.webp) > *شکل ۲ از [این پست وبلاگ عالی درباره IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) از [جستجوی انتخابی](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) برای تولید ساختار سلسله‌مراتبی ناحیه‌های ROI استفاده می‌کند که سپس از طریق استخراج‌کننده‌های ویژگی CNN و طبقه‌بندی‌کننده‌های SVM عبور داده می‌شوند تا کلاس شیء تعیین شود، و از رگرسیون خطی برای تعیین مختصات *جعبه محدودکننده* استفاده می‌شود. [مقاله رسمی](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.fa.png) +![RCNN](../../../../../translated_images/fa/rcnn1.cae407020dfb1d1f.webp) > *تصویر از van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.fa.png) +![RCNN-1](../../../../../translated_images/fa/rcnn2.2d9530bb83516484.webp) > *تصاویر از [این وبلاگ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ این روش مشابه R-CNN است، اما ناحیه‌ها پس از اعمال لایه‌های کانولوشنی تعریف می‌شوند. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.fa.png) +![FRCNN](../../../../../translated_images/fa/f-rcnn.3cda6d9bb4188875.webp) > تصویر از [مقاله رسمی](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)، [arXiv](https://arxiv.org/pdf/1504.08083.pdf)، ۲۰۱۵ @@ -118,7 +118,7 @@ $$ ایده اصلی این روش استفاده از شبکه عصبی برای پیش‌بینی ROIها است - به اصطلاح *شبکه پیشنهاد ناحیه*. [مقاله](https://arxiv.org/pdf/1506.01497.pdf)، ۲۰۱۶ -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.fa.png) +![FasterRCNN](../../../../../translated_images/fa/faster-rcnn.8d46c099b87ef30a.webp) > تصویر از [مقاله رسمی](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. ویژگی‌ها توسط **نقشه امتیاز حساس به موقعیت** پردازش می‌شوند. هر شیء از کلاس‌های $C$ به ناحیه‌های $k\times k$ تقسیم می‌شود و ما برای پیش‌بینی بخش‌های اشیا آموزش می‌بینیم. 3. برای هر بخش از ناحیه‌های $k\times k$، تمام شبکه‌ها برای کلاس‌های اشیا رأی می‌دهند و کلاس شیء با بیشترین رأی انتخاب می‌شود. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.fa.png) +![r-fcn image](../../../../../translated_images/fa/r-fcn.13eb88158b99a3da.webp) > تصویر از [مقاله رسمی](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO یک الگوریتم یک‌مرحله‌ای بلادرنگ است. ای * تصویر به ناحیه‌های $S\times S$ تقسیم می‌شود. * برای هر ناحیه، **CNN** $n$ شیء ممکن، مختصات *جعبه محدودکننده* و *اعتماد* = *احتمال* * IoU را پیش‌بینی می‌کند. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.fa.png) + ![YOLO](../../../../../translated_images/fa/yolo.a2648ec82ee8bb4e.webp) > تصویر از [مقاله رسمی](https://arxiv.org/abs/1506.02640) diff --git a/translations/fa/lessons/5-NLP/14-Embeddings/README.md b/translations/fa/lessons/5-NLP/14-Embeddings/README.md index 480a1691..5c36b4c6 100644 --- a/translations/fa/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/fa/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: با استفاده از یک لایه تعبیه به‌عنوان اولین لایه در شبکه دسته‌بند خود، می‌توانیم از مدل کیسه کلمات به مدل **کیسه تعبیه‌ها** تغییر دهیم، جایی که ابتدا هر کلمه در متن خود را به تعبیه مربوطه تبدیل می‌کنیم و سپس یک تابع تجمعی مانند `sum`، `average` یا `max` را بر روی تمام این تعبیه‌ها محاسبه می‌کنیم. -![تصویری که یک دسته‌بند تعبیه برای پنج کلمه دنباله را نشان می‌دهد.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.fa.png) +![تصویری که یک دسته‌بند تعبیه برای پنج کلمه دنباله را نشان می‌دهد.](../../../../../translated_images/fa/embedding-classifier-example.b77f021a7ee67eee.webp) > تصویر توسط نویسنده @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW سریع‌تر است، در حالی که اسکیپ‌گرام کندتر است اما در نمایش کلمات نادر بهتر عمل می‌کند. -![تصویری که الگوریتم‌های CBoW و اسکیپ‌گرام را برای تبدیل کلمات به بردارها نشان می‌دهد.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fa.png) +![تصویری که الگوریتم‌های CBoW و اسکیپ‌گرام را برای تبدیل کلمات به بردارها نشان می‌دهد.](../../../../../translated_images/fa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > تصویر از [این مقاله](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fa/lessons/5-NLP/15-LanguageModeling/README.md b/translations/fa/lessons/5-NLP/15-LanguageModeling/README.md index ead1b449..3ad5a567 100644 --- a/translations/fa/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/fa/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **کیسه کلمات پیوسته** (CBoW)، که در آن توکن میانی $W_0$ را در یک دنباله توکن $W_{-N}$, ..., $W_N$ پیش‌بینی می‌کنیم. * **Skip-gram**، که در آن مجموعه‌ای از توکن‌های همسایه {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} را از توکن میانی $W_0$ پیش‌بینی می‌کنیم. -![تصویر از مقاله‌ای درباره تبدیل کلمات به بردارها](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fa.png) +![تصویر از مقاله‌ای درباره تبدیل کلمات به بردارها](../../../../../translated_images/fa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > تصویر از [این مقاله](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fa/lessons/5-NLP/16-RNN/README.md b/translations/fa/lessons/5-NLP/16-RNN/README.md index 991489f7..9116c695 100644 --- a/translations/fa/lessons/5-NLP/16-RNN/README.md +++ b/translations/fa/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: برای درک معنای دنباله‌های متنی، نیاز به استفاده از معماری دیگری از شبکه‌های عصبی داریم که به آن **شبکه عصبی بازگشتی** یا RNN گفته می‌شود. در RNN، جمله را به صورت نماد به نماد از طریق شبکه عبور می‌دهیم و شبکه یک **وضعیت** تولید می‌کند که سپس با نماد بعدی به شبکه بازگردانده می‌شود. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.fa.png) +![RNN](../../../../../translated_images/fa/rnn.27f5c29c53d727b5.webp) > تصویر از نویسنده @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: یک شبکه بازگشتی، چه یک‌طرفه و چه دوطرفه، الگوهای خاصی را در یک دنباله ضبط می‌کند و می‌تواند آنها را در یک بردار وضعیت ذخیره کند یا به خروجی منتقل کند. همانند شبکه‌های کانولوشنی، می‌توانیم یک لایه بازگشتی دیگر بر روی لایه اول بسازیم تا الگوهای سطح بالاتری را ضبط کنیم و از الگوهای سطح پایین استخراج‌شده توسط لایه اول بسازیم. این ما را به مفهوم **RNN چندلایه** می‌رساند که شامل دو یا چند شبکه بازگشتی است، جایی که خروجی لایه قبلی به عنوان ورودی به لایه بعدی منتقل می‌شود. -![تصویر یک RNN چندلایه حافظه طولانی-کوتاه مدت](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.fa.jpg) +![تصویر یک RNN چندلایه حافظه طولانی-کوتاه مدت](../../../../../translated_images/fa/multi-layer-lstm.dd975e29bb2a59fe.webp) *تصویر از [این پست فوق‌العاده](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) نوشته فرناندو لوپز* diff --git a/translations/fa/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/fa/lessons/5-NLP/17-GenerativeNetworks/README.md index b84e9d9a..25d8b3a4 100644 --- a/translations/fa/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/fa/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: این قابلیت امکان ایجاد معماری‌های عصبی مختلفی را فراهم می‌کند که در تصویر زیر نشان داده شده‌اند: -![تصویری که الگوهای رایج شبکه‌های عصبی بازگشتی را نشان می‌دهد.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fa.jpg) +![تصویری که الگوهای رایج شبکه‌های عصبی بازگشتی را نشان می‌دهد.](../../../../../translated_images/fa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > تصویر از پست وبلاگ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) نوشته‌ی [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: ما این RNN را آموزش خواهیم داد تا متن را مرحله به مرحله تولید کند. در هر مرحله، یک دنباله از کاراکترها با طول `nchars` می‌گیریم و از شبکه می‌خواهیم کاراکتر خروجی بعدی را برای هر کاراکتر ورودی تولید کند: -![تصویری که یک مثال از تولید کلمه‌ی 'HELLO' توسط RNN را نشان می‌دهد.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.fa.png) +![تصویری که یک مثال از تولید کلمه‌ی 'HELLO' توسط RNN را نشان می‌دهد.](../../../../../translated_images/fa/rnn-generate.56c54afb52f9781d.webp) هنگام تولید متن (در زمان استنتاج)، با یک **پیش‌زمینه** شروع می‌کنیم که از طریق سلول‌های RNN عبور داده می‌شود تا حالت میانی آن تولید شود، و سپس از این حالت تولید آغاز می‌شود. ما یک کاراکتر در هر زمان تولید می‌کنیم و حالت و کاراکتر تولید شده را به یک سلول RNN دیگر می‌دهیم تا کاراکتر بعدی را تولید کند، تا زمانی که تعداد کافی کاراکتر تولید کنیم. diff --git a/translations/fa/lessons/5-NLP/18-Transformers/README.md b/translations/fa/lessons/5-NLP/18-Transformers/README.md index 37b2648d..7f411400 100644 --- a/translations/fa/lessons/5-NLP/18-Transformers/README.md +++ b/translations/fa/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **مکانیزم‌های توجه** راهی برای وزن‌دهی تأثیر متنی هر بردار ورودی بر هر پیش‌بینی خروجی RNN فراهم می‌کنند. این روش با ایجاد میانبرهایی بین حالت‌های میانی RNN ورودی و RNN خروجی پیاده‌سازی می‌شود. به این ترتیب، هنگام تولید نماد خروجی yt، تمام حالت‌های مخفی ورودی hi را با ضرایب وزنی مختلف αt,i در نظر می‌گیریم. -![تصویری از مدل رمزگذار/رمزگشا با لایه توجه جمعی](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fa.png) +![تصویری از مدل رمزگذار/رمزگشا با لایه توجه جمعی](../../../../../translated_images/fa/encoder-decoder-attention.7a726296894fb567.webp) > مدل رمزگذار-رمزگشا با مکانیزم توجه جمعی در [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)، نقل شده از [این پست وبلاگ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ماتریس توجه {αi,j} نشان‌دهنده درجه‌ای است که کلمات خاص ورودی در تولید یک کلمه خاص در توالی خروجی نقش دارند. در زیر نمونه‌ای از چنین ماتریسی آورده شده است: -![تصویری از نمونه هم‌ترازی که توسط RNNsearch-50 پیدا شده است، گرفته شده از Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fa.png) +![تصویری از نمونه هم‌ترازی که توسط RNNsearch-50 پیدا شده است، گرفته شده از Bahdanau - arviz.org](../../../../../translated_images/fa/bahdanau-fig3.09ba2d37f202a6af.webp) > تصویر از [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (شکل 3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: سپس، باید برخی الگوها را در توالی خود شناسایی کنیم. برای این کار، ترانسفورمرها از مکانیزم **توجه به خود** استفاده می‌کنند، که اساساً توجهی است که به همان توالی به عنوان ورودی و خروجی اعمال می‌شود. اعمال توجه به خود به ما اجازه می‌دهد **زمینه** را در جمله در نظر بگیریم و ببینیم کدام کلمات به هم مرتبط هستند. به عنوان مثال، این امکان را فراهم می‌کند که ببینیم کدام کلمات توسط ارجاعات مانند *آن* اشاره شده‌اند و همچنین زمینه را در نظر بگیریم: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.fa.png) +![](../../../../../translated_images/fa/CoreferenceResolution.861924d6d384a7d6.webp) > تصویر از [وبلاگ گوگل](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (نمایش‌های رمزگذار دوطرفه از ترانسفورمرها) یک شبکه ترانسفورمر چندلایه بسیار بزرگ است که دارای 12 لایه برای *BERT-base* و 24 لایه برای *BERT-large* است. این مدل ابتدا بر روی یک مجموعه داده بزرگ متنی (ویکی‌پدیا + کتاب‌ها) با استفاده از آموزش بدون نظارت (پیش‌بینی کلمات ماسک‌شده در یک جمله) پیش‌آموزش داده می‌شود. در طول پیش‌آموزش، مدل سطوح قابل توجهی از درک زبان را جذب می‌کند که سپس می‌توان با مجموعه داده‌های دیگر از طریق تنظیم دقیق استفاده کرد. این فرآیند **یادگیری انتقالی** نامیده می‌شود. -![تصویر از http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fa.png) +![تصویر از http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > تصویر [منبع](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/fa/lessons/5-NLP/19-NER/README.md b/translations/fa/lessons/5-NLP/19-NER/README.md index f5dd25a3..76494372 100644 --- a/translations/fa/lessons/5-NLP/19-NER/README.md +++ b/translations/fa/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ CO_OP_TRANSLATOR_METADATA: از آنجا که باید یک تطابق یک‌به‌یک بین توکن‌ها و کلاس‌ها ایجاد کنیم، می‌توانیم یک مدل شبکه عصبی **چند-به-چند** از تصویر زیر آموزش دهیم: -![تصویری که الگوهای رایج شبکه عصبی بازگشتی را نشان می‌دهد.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fa.jpg) +![تصویری که الگوهای رایج شبکه عصبی بازگشتی را نشان می‌دهد.](../../../../../translated_images/fa/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *تصویر از [این پست وبلاگ](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) توسط [آندری کارپاتی](http://karpathy.github.io/). مدل‌های طبقه‌بندی توکن NER با معماری شبکه سمت راست در این تصویر مطابقت دارند.* diff --git a/translations/fa/lessons/6-Other/23-MultiagentSystems/README.md b/translations/fa/lessons/6-Other/23-MultiagentSystems/README.md index 5db58664..bb6ec120 100644 --- a/translations/fa/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/fa/lessons/6-Other/23-MultiagentSystems/README.md @@ -71,7 +71,7 @@ ask turtles [ پس از باز کردن مدل، به صفحه اصلی NetLogo منتقل می‌شوید. در اینجا یک مدل نمونه که جمعیت گرگ‌ها و گوسفندها را با منابع محدود (چمن) توصیف می‌کند، آورده شده است. -![صفحه اصلی NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.fa.png) +![صفحه اصلی NetLogo](../../../../../translated_images/fa/NetLogo-Main.32653711ec1a01b3.webp) > تصویر توسط دیمیتری سوشنیکوف diff --git a/translations/fi/README.md b/translations/fi/README.md index fa2f6d31..d8715a4d 100644 --- a/translations/fi/README.md +++ b/translations/fi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Keinotekoisen älykkyyden perusteet - Opetussuunnitelma -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.fi.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/fi/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/fi/lessons/1-Intro/README.md b/translations/fi/lessons/1-Intro/README.md index ac9d2376..7b46f67b 100644 --- a/translations/fi/lessons/1-Intro/README.md +++ b/translations/fi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Johdanto tekoälyyn -![Yhteenveto tekoälyn johdannon sisällöstä piirroksena](../../../../translated_images/ai-intro.bf28d1ac4235881c.fi.png) +![Yhteenveto tekoälyn johdannon sisällöstä piirroksena](../../../../translated_images/fi/ai-intro.bf28d1ac4235881c.png) > Piirros: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Alun perin tietokoneet keksittiin [Charles Babbagen](https://en.wikipedia.org/wiki/Charles_Babbage) toimesta käsittelemään numeroita ennalta määritellyn menettelyn - algoritmin - mukaisesti. Vaikka modernit tietokoneet ovat huomattavasti kehittyneempiä kuin 1800-luvulla ehdotettu alkuperäinen malli, ne noudattavat yhä samaa ohjattujen laskentojen periaatetta. Näin ollen tietokone voidaan ohjelmoida tekemään jotain, jos tiedämme tarkalleen, mitä vaiheita tavoitteen saavuttamiseksi tarvitaan. -![Henkilön valokuva](../../../../translated_images/dsh_age.d212a30d4e54fb5f.fi.png) +![Henkilön valokuva](../../../../translated_images/fi/dsh_age.d212a30d4e54fb5f.png) > Kuva: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Lisätietoja löytyy **[Artificial General Intelligence](https://en.wikipedia.or Yksi ongelma käsiteltäessä termiä **[älykkyys](https://en.wikipedia.org/wiki/Intelligence)** on, ettei sille ole selkeää määritelmää. Voidaan väittää, että älykkyys liittyy **abstraktiin ajatteluun** tai **itsetietoisuuteen**, mutta emme pysty määrittelemään sitä kunnolla. -![Kissan valokuva](../../../../translated_images/photo-cat.8c8e8fb760ffe457.fi.jpg) +![Kissan valokuva](../../../../translated_images/fi/photo-cat.8c8e8fb760ffe457.jpg) > [Kuva](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) Unsplashista @@ -98,13 +98,13 @@ Vaihtoehtoisesti voimme yrittää mallintaa aivojemme yksinkertaisimpia elementt > | Entä koneoppiminen? | | > |--------------|-----------| -> | Tekoälyn osa-alue, jossa tietokone oppii ratkaisemaan ongelman jonkin datan perusteella, kutsutaan **koneoppimiseksi**. Emme käsittele perinteistä koneoppimista tässä kurssissa – suosittelemme erillistä [Koneoppiminen aloittelijoille](http://aka.ms/ml-beginners) -opetussuunnitelmaa. | ![Koneoppiminen aloittelijoille](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.fi.png) | +> | Tekoälyn osa-alue, jossa tietokone oppii ratkaisemaan ongelman jonkin datan perusteella, kutsutaan **koneoppimiseksi**. Emme käsittele perinteistä koneoppimista tässä kurssissa – suosittelemme erillistä [Koneoppiminen aloittelijoille](http://aka.ms/ml-beginners) -opetussuunnitelmaa. | ![Koneoppiminen aloittelijoille](../../../../translated_images/fi/ml-for-beginners.9e4fed176fd5817d.png) | ## Lyhyt katsaus tekoälyn historiaan Tekoäly syntyi tieteenalana 1900-luvun puolivälissä. Aluksi symbolinen järkeily oli vallitseva lähestymistapa, ja se johti useisiin merkittäviin saavutuksiin, kuten asiantuntijajärjestelmiin – tietokoneohjelmiin, jotka pystyivät toimimaan asiantuntijana tietyillä rajatuilla ongelma-alueilla. Pian kuitenkin huomattiin, että tällainen lähestymistapa ei skaalaudu hyvin. Tiedon kerääminen asiantuntijalta, sen esittäminen tietokoneessa ja tietokannan pitäminen ajan tasalla osoittautui erittäin monimutkaiseksi ja liian kalliiksi monissa tapauksissa. Tämä johti niin sanottuun [tekoälytalveen](https://en.wikipedia.org/wiki/AI_winter) 1970-luvulla. -Tekoälyn historian lyhyt katsaus +Tekoälyn historian lyhyt katsaus > Kuva: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Samoin voimme nähdä, kuinka lähestymistapa "puhuvien ohjelmien" (jotka saatta * Modernit avustajat, kuten Cortana, Siri tai Google Assistant, ovat kaikki hybridijärjestelmiä, jotka käyttävät neuroverkkoja muuntamaan puheen tekstiksi ja tunnistamaan tarkoituksemme, ja sitten hyödyntävät järkeilyä tai eksplisiittisiä algoritmeja suorittaakseen tarvittavat toiminnot. * Tulevaisuudessa voimme odottaa täysin neuroverkkoihin perustuvaa mallia, joka käsittelee dialogia itsenäisesti. Viimeaikaiset GPT- ja [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) -neuroverkkojen perheet osoittavat suurta menestystä tässä. -Turingin testin kehitys +Turingin testin kehitys > Kuva: Dmitry Soshnikov, [valokuva](https://unsplash.com/photos/r8LmVbUKgns) [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Viimeaikainen tekoälytutkimus diff --git a/translations/fi/lessons/2-Symbolic/Animals.ipynb b/translations/fi/lessons/2-Symbolic/Animals.ipynb index 7be53e6e..3a753799 100644 --- a/translations/fi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/fi/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Tässä esimerkissä toteutamme yksinkertaisen tietopohjaisen järjestelmän, joka määrittää eläimen fyysisten ominaisuuksien perusteella. Järjestelmä voidaan esittää seuraavalla JA-TAI-puulla (tämä on osa koko puusta, sääntöjä voidaan helposti lisätä): \n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.fi.png)\n" + "![](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/fi/lessons/2-Symbolic/README.md b/translations/fi/lessons/2-Symbolic/README.md index aa0f6bfe..a80b20e3 100644 --- a/translations/fi/lessons/2-Symbolic/README.md +++ b/translations/fi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tiedon esittäminen ja asiantuntijajärjestelmät -![Symbolisen tekoälyn sisällön yhteenveto](../../../../translated_images/ai-symbolic.715a30cb610411a6.fi.png) +![Symbolisen tekoälyn sisällön yhteenveto](../../../../translated_images/fi/ai-symbolic.715a30cb610411a6.png) > Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Usein emme määrittele tietoa tarkasti, vaan yhdistämme sen muihin siihen liit Näin ollen **tiedon esittämisen** ongelma on löytää tehokas tapa esittää tieto tietokoneessa datan muodossa, jotta sitä voidaan käyttää automaattisesti. Tämä voidaan nähdä spektrinä: -![Tiedon esittämisen spektri](../../../../translated_images/knowledge-spectrum.b60df631852c0217.fi.png) +![Tiedon esittämisen spektri](../../../../translated_images/fi/knowledge-spectrum.b60df631852c0217.png) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Lohkorakenne | Sisennys | | | Symbolisen tekoälyn varhaisia menestyksiä olivat niin sanotut **asiantuntijajärjestelmät** - tietokonejärjestelmät, jotka suunniteltiin toimimaan asiantuntijana jollakin rajatulla ongelma-alueella. Ne perustuivat **tietokantaan**, joka oli kerätty yhdeltä tai useammalta ihmisasiantuntijalta, ja ne sisälsivät **päättelymoottorin**, joka suoritti päättelyä sen pohjalta. -![Ihmisen arkkitehtuuri](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.fi.png) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.fi.png) +![Ihmisen arkkitehtuuri](../../../../translated_images/fi/arch-human.5d4d35f1bba3ab1c.png) | ![Tietopohjaisen järjestelmän arkkitehtuuri](../../../../translated_images/fi/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Ihmisen hermojärjestelmän yksinkertaistettu rakenne | Tietopohjaisen järjestelmän arkkitehtuuri @@ -106,7 +106,7 @@ Asiantuntijajärjestelmät rakennetaan ihmisen päättelyjärjestelmän tapaan, Esimerkiksi tarkastellaan seuraavaa asiantuntijajärjestelmää, joka määrittää eläimen sen fyysisten ominaisuuksien perusteella: -![AND-OR-puu](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.fi.png) +![AND-OR-puu](../../../../translated_images/fi/AND-OR-Tree.5592d2c70187f283.png) > Kuva: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 93cca84b..9d8db9c1 100644 --- a/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/fi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Jos meillä on enemmän kuin 2 luokkaa, softmax normalisoi todennäköisyydet kaikkien luokkien välillä. Tässä on kaavio verkkoarkkitehtuurista, joka suorittaa MNIST-numeroiden luokittelun:\n", "\n", - "![MNIST-luokitin](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.fi.png)\n" + "![MNIST-luokitin](../../../../../translated_images/fi/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Matala harjoitusdatan häviö - malli pystyy lähentämään harjoitusdataa hyvin, koska sillä on tarpeeksi ilmaisukykyä.\n", "* Validointidatan häviö voi olla paljon korkeampi kuin harjoitusdatan häviö ja voi alkaa kasvaa harjoittelun aikana - tämä johtuu siitä, että malli \"muistaa\" harjoitusdatan pisteet ja menettää \"kokonaiskuvan\".\n", "\n", - "![Ylisovittaminen](../../../../../translated_images/overfit.a0bd57f717c15769.fi.png)\n", + "![Ylisovittaminen](../../../../../translated_images/fi/overfit.a0bd57f717c15769.png)\n", "\n", "> Tässä kuvassa `x` edustaa harjoitusdataa ja `o` validointidataa. Vasemmalla - lineaarinen malli (yksikerroksinen), se lähentää datan luonnetta melko hyvin. Oikealla - ylisovitettu malli, joka lähentää harjoitusdataa täydellisesti, mutta menettää merkityksensä muun datan kanssa (validointivirhe on erittäin korkea).\n" ] diff --git a/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md index d6301ac0..81f18bfe 100644 --- a/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/fi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Ylikoulutus on erittäin tärkeä käsite koneoppimisessa, ja on erittäin tärk Tarkastellaan seuraavaa ongelmaa, jossa pyritään approksimoimaan 5 pistettä (esitettynä `x`-merkeillä alla olevissa kaavioissa): -![lineaarinen](../../../../../translated_images/overfit1.f24b71c6f652e59e.fi.jpg) | ![ylikoulutus](../../../../../translated_images/overfit2.131f5800ae10ca5e.fi.jpg) +![lineaarinen](../../../../../translated_images/fi/overfit1.f24b71c6f652e59e.jpg) | ![ylikoulutus](../../../../../translated_images/fi/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineaarinen malli, 2 parametria** | **Ei-lineaarinen malli, 7 parametria** Koulutusvirhe = 5.3 | Koulutusvirhe = 0 @@ -79,7 +79,7 @@ On erittäin tärkeää löytää oikea tasapaino mallin monimutkaisuuden (param Kuten yllä olevasta kaaviosta näkyy, ylikoulutus voidaan havaita erittäin pienestä koulutusvirheestä ja suuresta validointivirheestä. Normaalisti koulutuksen aikana näemme sekä koulutus- että validointivirheiden alkavan pienentyä, ja jossain vaiheessa validointivirhe saattaa lakata pienentymästä ja alkaa kasvaa. Tämä on merkki ylikoulutuksesta ja indikaattori siitä, että koulutus pitäisi todennäköisesti lopettaa tässä vaiheessa (tai ainakin tehdä mallista tilannekuva). -![ylikoulutus](../../../../../translated_images/Overfitting.408ad91cd90b4371.fi.png) +![ylikoulutus](../../../../../translated_images/fi/Overfitting.408ad91cd90b4371.png) ## Miten ylikoulutusta estetään diff --git a/translations/fi/lessons/3-NeuralNetworks/README.md b/translations/fi/lessons/3-NeuralNetworks/README.md index 6ce6b398..29d75e3a 100644 --- a/translations/fi/lessons/3-NeuralNetworks/README.md +++ b/translations/fi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Johdanto neuroverkkoihin -![Yhteenveto neuroverkkojen sisällöstä piirroksena](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.fi.png) +![Yhteenveto neuroverkkojen sisällöstä piirroksena](../../../../translated_images/fi/ai-neuralnetworks.1c687ae40bc86e83.png) Kuten keskustelimme johdannossa, yksi tapa saavuttaa älykkyyttä on kouluttaa **tietokonemalli** tai **keinotekoinen aivot**. 1900-luvun puolivälistä lähtien tutkijat kokeilivat erilaisia matemaattisia malleja, kunnes viime vuosina tämä suunta osoittautui erittäin menestyksekkääksi. Näitä aivojen matemaattisia malleja kutsutaan **neuroverkoiksi**. @@ -36,13 +36,13 @@ Tässä oppimateriaalissa keskitymme vain neuroverkkopohjaisiin malleihin. Biologiasta tiedämme, että aivomme koostuvat hermosoluista (neuroneista), joilla jokaisella on useita "syötteitä" (dendriittejä) ja yksi "tulos" (aksoni). Sekä dendriitit että aksonit voivat johtaa sähköisiä signaaleja, ja niiden väliset yhteydet — synapsit — voivat osoittaa vaihtelevaa johtavuutta, jota säätelevät välittäjäaineet. -![Neuronin malli](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fi.jpg) | ![Neuronin malli](../../../../translated_images/artneuron.1a5daa88d20ebe6f.fi.png) +![Neuronin malli](../../../../translated_images/fi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuronin malli](../../../../translated_images/fi/artneuron.1a5daa88d20ebe6f.png) ----|---- Oikea neuroni *([Kuva](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediasta)* | Keinotekoinen neuroni *(Kuva tekijältä)* Näin ollen yksinkertaisin matemaattinen malli neuronista sisältää useita syötteitä X1, ..., XN ja yhden tuloksen Y sekä joukon painoja W1, ..., WN. Tulos lasketaan seuraavasti: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) missä f on jokin epälineaarinen **aktivointifunktio**. diff --git a/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md index 142ff003..08bde087 100644 --- a/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/fi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Ennen kuin kuva syötetään neuroverkkoon, voi olla hyödyllistä suorittaa use * **Pistekirjakuvan esikäsittely**. Keskitymme siihen, miten voimme käyttää kynnysarvoja, piirteiden tunnistusta, perspektiivimuunnoksia ja NumPy-manipulaatioita erottamaan yksittäiset pistekirjoitussymbolit, jotta ne voidaan luokitella neuroverkolla. -![Pistekirjakuvan esimerkki](../../../../../translated_images/braille.341962ff76b1bd70.fi.jpeg) | ![Esikäsitelty pistekirjakuva](../../../../../translated_images/braille-result.46530fea020b03c7.fi.png) | ![Pistekirjoitussymbolit](../../../../../translated_images/braille-symbols.0159185ab69d5339.fi.png) +![Pistekirjakuvan esimerkki](../../../../../translated_images/fi/braille.341962ff76b1bd70.jpeg) | ![Esikäsitelty pistekirjakuva](../../../../../translated_images/fi/braille-result.46530fea020b03c7.png) | ![Pistekirjoitussymbolit](../../../../../translated_images/fi/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Kuva [OpenCV.ipynb](OpenCV.ipynb) * **Liikkeen tunnistaminen videosta kehysten erotuksen avulla**. Jos kamera on kiinteä, kameran syötteen kehysten pitäisi olla melko samanlaisia. Koska kehykset esitetään taulukoina, kahden peräkkäisen kehyksen taulukoiden vähentämisellä saadaan pikseliero, joka on pieni staattisille kehyksille ja kasvaa merkittävästi, kun kuvassa on huomattavaa liikettä. -![Kuva videokehysten ja kehysten erojen analyysistä](../../../../../translated_images/frame-difference.706f805491a0883c.fi.png) +![Kuva videokehysten ja kehysten erojen analyysistä](../../../../../translated_images/fi/frame-difference.706f805491a0883c.png) > Kuva [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Ennen kuin kuva syötetään neuroverkkoon, voi olla hyödyllistä suorittaa use - **Tiheä optinen virtaus** laskee vektorikentän, joka näyttää jokaisen pikselin liikkeen suunnan. - **Harva optinen virtaus** perustuu tiettyjen erottuvien piirteiden (esim. reunojen) valintaan kuvassa ja niiden liikeradan rakentamiseen kehyksestä toiseen. -![Kuva optisesta virtauksesta](../../../../../translated_images/optical.1f4a94464579a83a.fi.png) +![Kuva optisesta virtauksesta](../../../../../translated_images/fi/optical.1f4a94464579a83a.png) > Kuva [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8c338f0e..76a0ea33 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 on verkko, joka saavutti 92,7 % tarkkuuden ImageNetin top-5-luokittelussa vuonna 2014. Sen kerrosrakenne on seuraava: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fi.jpg) +![ImageNet Layers](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.jpg) Kuten näet, VGG noudattaa perinteistä pyramidirakennetta, joka koostuu konvoluutio- ja pooling-kerrosten sarjasta. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fi.jpg) +![ImageNet Pyramid](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.jpg) > Kuva [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) -sivustolta diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index dbb93e34..0c0d88e6 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Näin ollen tyypillisessä CNN:ssä on useita konvoluutiokerroksia, joiden välissä on pooling-kerroksia kuvan ulottuvuuksien pienentämiseksi. Samalla lisäämme suotimien määrää, koska kuvioiden muuttuessa monimutkaisemmiksi on enemmän mahdollisia kiinnostavia yhdistelmiä, joita meidän täytyy etsiä.\n", "\n", - "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.fi.png)\n", + "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Koska tilalliset ulottuvuudet pienenevät ja piirre-/suodatinulottuvuudet kasvavat, tätä arkkitehtuuria kutsutaan myös **pyramidiarkkitehtuuriksi**.\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 3c00c5c4..8118c476 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Näin ollen tyypillisessä CNN:ssä on useita konvoluutiokerroksia, joiden välissä on pooling-kerroksia kuvan dimensioiden pienentämiseksi. Samalla lisäämme suodattimien määrää, koska kuvioiden muuttuessa monimutkaisemmiksi on enemmän mahdollisia kiinnostavia yhdistelmiä, joita meidän täytyy etsiä.\n", "\n", - "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.fi.png)\n", + "![Kuva, joka näyttää useita konvoluutiokerroksia ja pooling-kerroksia.](../../../../../translated_images/fi/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Koska tiladimensioita pienennetään ja piirre-/suodatin-dimensioita kasvatetaan, tätä arkkitehtuuria kutsutaan myös **pyramidiarkkitehtuuriksi**.\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md index 4440c618..a7e332b5 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Todellisessa elämässä haluamme pystyä tunnistamaan esineitä kuvasta niiden Kuvioiden tunnistamiseen käytämme **konvoluutiokertoimia**. Kuten tiedät, kuva esitetään 2D-matriisina tai 3D-tensorina värisyvyyden kanssa. Suodattimen soveltaminen tarkoittaa, että otamme suhteellisen pienen **suodatinytimen** matriisin, ja alkuperäisen kuvan jokaiselle pikselille laskemme painotetun keskiarvon naapuripisteiden kanssa. Voimme ajatella tämän olevan kuin pieni ikkuna, joka liukuu koko kuvan yli ja tasoittaa kaikki pikselit suodatinytimen matriisin painojen mukaan. -![Pystysuuntainen reunasuodatin](../../../../../translated_images/filter-vert.b7148390ca0bc356.fi.png) | ![Vaakasuuntainen reunasuodatin](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.fi.png) +![Pystysuuntainen reunasuodatin](../../../../../translated_images/fi/filter-vert.b7148390ca0bc356.png) | ![Vaakasuuntainen reunasuodatin](../../../../../translated_images/fi/filter-horiz.59b80ed4feb946ef.png) ----|---- > Kuva: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN:ien toiminta perustuu seuraaviin tärkeisiin ideoihin: * Voimme suunnitella verkon siten, että suodattimet koulutetaan automaattisesti * Voimme käyttää samaa lähestymistapaa löytääksemme kuvioita korkeamman tason ominaisuuksista, ei vain alkuperäisestä kuvasta. Näin CNN:n ominaisuuksien tunnistus toimii hierarkiana, alkaen matalan tason pikseliyhdistelmistä ja päätyen korkeamman tason kuvan osien yhdistelmiin. -![Hierarkkinen ominaisuuksien tunnistus](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fi.png) +![Hierarkkinen ominaisuuksien tunnistus](../../../../../translated_images/fi/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Kuva [Hislop-Lynchin artikkelista](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), perustuen [heidän tutkimukseensa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Useimmat kuvankäsittelyyn käytetyt CNN:t noudattavat niin sanottua pyramidirak Esimerkiksi tarkastellaan VGG-16-arkkitehtuuria, verkkoa, joka saavutti 92,7 % tarkkuuden ImageNetin top-5-luokittelussa vuonna 2014: -![ImageNet-kerrokset](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fi.jpg) +![ImageNet-kerrokset](../../../../../translated_images/fi/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet-pyramidi](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fi.jpg) +![ImageNet-pyramidi](../../../../../translated_images/fi/vgg-16-arch.64ff2137f50dd49f.jpg) > Kuva [Researchgate-sivustolta](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 32cb5d55..600a6869 100644 --- a/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/fi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Sinun tulee kouluttaa konvoluutio-neuroverkko luokittelemaan eri kissojen ja koi Käytämme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) -datakokonaisuutta, joka sisältää kuvia 37 eri koira- ja kissarodusta. -![Datakokonaisuus, jonka kanssa työskentelemme](../../../../../../translated_images/data.50b2a9d5484bdbf0.fi.png) +![Datakokonaisuus, jonka kanssa työskentelemme](../../../../../../translated_images/fi/data.50b2a9d5484bdbf0.png) Ladataksesi datakokonaisuuden, käytä tätä koodinpätkää: diff --git a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 235217da..0f3bd746 100644 --- a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Visualisoidaksemme ihanteellisen kissan aloitamme satunnaisesta kohinakuvaasta ja yritämme käyttää gradienttilaskeutumisoptimointitekniikkaa säätääksemme kuvaa niin, että verkko tunnistaa kissan.\n", "\n", - "![Optimointisilmukka](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.fi.png)\n", + "![Optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Tässä on aloituskuvamme:\n" ] diff --git a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md index c001df78..ad61f319 100644 --- a/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/fi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Sekä Keras että PyTorch sisältävät toimintoja, joilla voi helposti ladata e Tässä on esimerkki piirteistä, jotka VGG-16-verkko on tunnistanut kissan kuvasta: -![Piirteet, jotka VGG-16 tunnisti](../../../../../translated_images/features.6291f9c7ba3a0b95.fi.png) +![Piirteet, jotka VGG-16 tunnisti](../../../../../translated_images/fi/features.6291f9c7ba3a0b95.png) ## Kissojen ja koirien datasetti @@ -48,19 +48,19 @@ Esikoulutettu neuroverkko sisältää erilaisia kuvioita "aivoissaan", mukaan lu Yksi lähestymistapa on aloittaa satunnaisesta kuvasta ja yrittää käyttää **gradient descent -optimointitekniikkaa** säätämään kuvaa niin, että verkko alkaa ajatella sen olevan kissa. -![Kuvan optimointisilmukka](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.fi.png) +![Kuvan optimointisilmukka](../../../../../translated_images/fi/ideal-cat-loop.999fbb8ff306e044.png) Jos teemme näin, saamme jotain hyvin satunnaisen kohinan kaltaista. Tämä johtuu siitä, että *on monia tapoja saada verkko ajattelemaan, että syötekuva on kissa*, mukaan lukien sellaisia, jotka eivät ole visuaalisesti järkeviä. Vaikka nämä kuvat sisältävät paljon kissalle tyypillisiä kuvioita, mikään ei rajoita niitä olemaan visuaalisesti erottuvia. Tuloksen parantamiseksi voimme lisätä toisen termin häviöfunktioon, jota kutsutaan **variation loss** -termiksi. Se on mittari, joka osoittaa, kuinka samanlaisia kuvan vierekkäiset pikselit ovat. Variation lossin minimointi tekee kuvasta tasaisemman ja poistaa kohinaa - paljastaen visuaalisesti miellyttävämpiä kuvioita. Tässä esimerkki tällaisista "ihanteellisista" kuvista, jotka luokitellaan kissaksi ja seepraksi suurella todennäköisyydellä: -![Ihanteellinen kissa](../../../../../translated_images/ideal-cat.203dd4597643d6b0.fi.png) | ![Ihanteellinen seepra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.fi.png) +![Ihanteellinen kissa](../../../../../translated_images/fi/ideal-cat.203dd4597643d6b0.png) | ![Ihanteellinen seepra](../../../../../translated_images/fi/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ihanteellinen kissa* | *Ihanteellinen seepra* Samaa lähestymistapaa voidaan käyttää suorittamaan niin sanottuja **adversaarisia hyökkäyksiä** neuroverkkoon. Oletetaan, että haluamme huijata neuroverkkoa ja saada koiran näyttämään kissalta. Jos otamme koiran kuvan, jonka verkko tunnistaa koiraksi, voimme sitten säätää sitä hieman gradient descent -optimoinnin avulla, kunnes verkko alkaa luokitella sen kissaksi: -![Koiran kuva](../../../../../translated_images/original-dog.8f68a67d2fe0911f.fi.png) | ![Kuva koirasta, joka luokitellaan kissaksi](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.fi.png) +![Koiran kuva](../../../../../translated_images/fi/original-dog.8f68a67d2fe0911f.png) | ![Kuva koirasta, joka luokitellaan kissaksi](../../../../../translated_images/fi/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Alkuperäinen kuva koirasta* | *Kuva koirasta, joka luokitellaan kissaksi* diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index e3ab82ef..964accf8 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Koska koulutamme autokooderia vangitsemaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan mahdollisen **upotuksen** syötekuvien merkityksen vangitsemiseksi.\n", "\n", - "![Autokooderin kaavio](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fi.jpg)\n", + "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 7a34d33f..a2c23f90 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Koska koulutamme autokooderia vangitsemaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan mahdollisen **upotuksen** syötekuvien merkityksen vangitsemiseksi.\n", "\n", - "![Autokooderin kaavio](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fi.jpg)\n", + "![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md index d0f6d49f..d85e1a32 100644 --- a/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/fi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Voimme kuitenkin haluta käyttää raakadataa (merkitsemätöntä) CNN-ominaisuu Koska koulutamme autokooderia tallentamaan mahdollisimman paljon alkuperäisen kuvan informaatiota tarkkaa rekonstruointia varten, verkko pyrkii löytämään parhaan **upotuksen** syötekuville merkityksen tallentamiseksi. -![Autokooderin kaavio](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fi.jpg) +![Autokooderin kaavio](../../../../../translated_images/fi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Kuva [Keras-blogista](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md index fdd06eb5..c715cc51 100644 --- a/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/fi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Kuvien luokittelumallit, joita olemme tähän mennessä käsitelleet, ottavat ku ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektien tunnistus](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fi.png) +![Objektien tunnistus](../../../../../translated_images/fi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Kuva [YOLO v2 -verkkosivustolta](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Oletetaan, että haluaisimme löytää kissan kuvasta. Hyvin yksinkertainen läh 2. Suorita kuvien luokittelu jokaiselle ruudulle. 3. Ruudut, jotka tuottavat riittävän korkean aktivoinnin, voidaan katsoa sisältävän kyseisen objektin. -![Naiivi objektien tunnistus](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.fi.png) +![Naiivi objektien tunnistus](../../../../../translated_images/fi/naive-detection.e7f1ba220ccd08c6.png) > *Kuva [harjoitusmuistiosta](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Tässä tehtävässä saatat törmätä seuraaviin tietoaineistoihin: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 luokkaa * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 luokkaa, rajauslaatikot ja segmentointimaskit -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.fi.jpg) +![COCO](../../../../../translated_images/fi/coco-examples.71bc60380fa6cceb.jpg) ## Objektien tunnistuksen mittarit @@ -50,7 +50,7 @@ Tässä tehtävässä saatat törmätä seuraaviin tietoaineistoihin: Kuvien luokittelussa algoritmin suorituskyvyn mittaaminen on helppoa, mutta objektien tunnistuksessa meidän täytyy mitata sekä luokan oikeellisuus että ennustetun rajauslaatikon sijainnin tarkkuus. Jälkimmäistä varten käytämme niin kutsuttua **Intersection over Union** (IoU) -mittaria, joka mittaa, kuinka hyvin kaksi laatikkoa (tai kaksi satunnaista aluetta) menevät päällekkäin. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.fi.png) +![IoU](../../../../../translated_images/fi/iou_equation.9a4751d40fff4e11.png) > *Kuva 2 [tästä erinomaisesta IoU-blogikirjoituksesta](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Objektien tunnistusalgoritmit voidaan jakaa kahteen pääluokkaan: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) käyttää [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) -menetelmää luomaan hierarkkisen rakenteen ROI-alueista, jotka sitten syötetään CNN-ominaisuuksien erottimiin ja SVM-luokittelijoihin objektin luokan määrittämiseksi, sekä lineaariseen regressioon *rajauslaatikon* koordinaattien määrittämiseksi. [Virallinen artikkeli](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.fi.png) +![RCNN](../../../../../translated_images/fi/rcnn1.cae407020dfb1d1f.png) > *Kuva van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.fi.png) +![RCNN-1](../../../../../translated_images/fi/rcnn2.2d9530bb83516484.png) > *Kuvat [tästä blogista](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Objektien tunnistusalgoritmit voidaan jakaa kahteen pääluokkaan: Tämä lähestymistapa on samanlainen kuin R-CNN, mutta alueet määritellään vasta konvoluutiokerrosten soveltamisen jälkeen. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.fi.png) +![FRCNN](../../../../../translated_images/fi/f-rcnn.3cda6d9bb4188875.png) > Kuva [virallisesta artikkelista](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tämä lähestymistapa on samanlainen kuin R-CNN, mutta alueet määritellään Tämän lähestymistavan pääidea on käyttää neuroverkkoa ennustamaan ROI:t – niin kutsuttu *Region Proposal Network*. [Artikkeli](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.fi.png) +![FasterRCNN](../../../../../translated_images/fi/faster-rcnn.8d46c099b87ef30a.png) > Kuva [virallisesta artikkelista](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tämä algoritmi on jopa nopeampi kuin Faster R-CNN. Pääidea on seuraava: 2. Ominaisuudet käsitellään **Position-Sensitive Score Map** -kartalla. Jokainen objekti luokasta $C$ jaetaan $k\times k$ alueisiin, ja verkkoa koulutetaan ennustamaan objektien osia. 3. Jokaiselle osalle $k\times k$ alueista kaikki verkot äänestävät objektin luokista, ja eniten ääniä saanut luokka valitaan. -![r-fcn kuva](../../../../../translated_images/r-fcn.13eb88158b99a3da.fi.png) +![r-fcn kuva](../../../../../translated_images/fi/r-fcn.13eb88158b99a3da.png) > Kuva [virallisesta artikkelista](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO on reaaliaikainen yhden passin algoritmi. Pääidea on seuraava: * Kuva jaetaan $S\times S$ alueisiin. * Jokaiselle alueelle **CNN** ennustaa $n$ mahdollista objektia, *rajauslaatikon* koordinaatit ja *luottamus*=*todennäköisyys* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.fi.png) + ![YOLO](../../../../../translated_images/fi/yolo.a2648ec82ee8bb4e.png) > Kuva [virallisesta artikkelista](https://arxiv.org/abs/1506.02640) diff --git a/translations/fi/lessons/4-ComputerVision/README.md b/translations/fi/lessons/4-ComputerVision/README.md index a74dffe7..9c3d2b83 100644 --- a/translations/fi/lessons/4-ComputerVision/README.md +++ b/translations/fi/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tietokonenäkö -![Yhteenveto tietokonenäön sisällöstä piirroksena](../../../../translated_images/ai-computervision.6506ebebac3fbf76.fi.png) +![Yhteenveto tietokonenäön sisällöstä piirroksena](../../../../translated_images/fi/ai-computervision.6506ebebac3fbf76.png) Tässä osiossa opimme seuraavista aiheista: diff --git a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index ecf6e839..588b6bb6 100644 --- a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) -vektoriesitys on yleisimmin käytetty perinteinen vektoriesitys. Jokainen sana on yhdistetty vektorin indeksiin, ja vektorin elementti sisältää sanan esiintymiskertojen määrän tietyssä dokumentissa.\n", "\n", - "![Kuva, joka näyttää, miten bag of words -vektoriesitys tallennetaan muistiin.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.fi.png) \n", + "![Kuva, joka näyttää, miten bag of words -vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Voit myös ajatella BoW:n olevan summa kaikista yksittäisten sanojen yksi-hot-koodatuista vektoreista tekstissä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index e3e30654..f6489e80 100644 --- a/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/fi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) -vektoriesitys on perinteisistä vektoriesityksistä yksinkertaisin ymmärtää. Jokainen sana yhdistetään vektorin indeksiin, ja vektorin elementti sisältää kunkin sanan esiintymiskertojen määrän tietyssä dokumentissa.\n", "\n", - "![Kuva, joka näyttää, miten bag-of-words-vektoriesitys tallennetaan muistiin.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.fi.png) \n", + "![Kuva, joka näyttää, miten bag-of-words-vektoriesitys tallennetaan muistiin.](../../../../../translated_images/fi/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Voit myös ajatella BoW:n olevan summa kaikista yksittäisten sanojen yksi-kuuma-koodatuista vektoreista tekstissä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index f24d144a..17dd4751 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kun käytämme upotuskerrosta verkkomme ensimmäisenä kerroksena, voimme siirtyä bag-of-words-mallista **embedding bag** -malliin, jossa ensin muutamme jokaisen tekstimme sanan vastaavaksi upotukseksi ja sitten laskemme jonkin aggregaattifunktion kaikkien näiden upotusten yli, kuten `sum`, `average` tai `max`.\n", "\n", - "![Kuva, joka näyttää upotusluokittelijan viidelle sanajonolle.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.fi.png)\n", + "![Kuva, joka näyttää upotusluokittelijan viidelle sanajonolle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Luokittelijaneuroverkkomme alkaa upotuskerroksella, sitten aggregaatiokerroksella ja sen päällä lineaarisella luokittelijalla:\n" ] @@ -176,7 +176,7 @@ "\n", "Edellisessä arkkitehtuurissa meidän täytyi täyttää kaikki sekvenssit samanpituisiksi, jotta ne sopisivat minibatchiin. Tämä ei ole tehokkain tapa esittää muuttuvan pituisia sekvenssejä – toinen lähestymistapa olisi käyttää **offset**-vektoria, joka sisältää kaikkien yhteen suureen vektoriin tallennettujen sekvenssien offsetit.\n", "\n", - "![Kuva, joka näyttää offset-sekvenssiesityksen](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.fi.png)\n", + "![Kuva, joka näyttää offset-sekvenssiesityksen](../../../../../translated_images/fi/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Huom**: Yllä olevassa kuvassa esitetään merkkijonosekvenssi, mutta esimerkissämme työskentelemme sanasekvenssien kanssa. Yleinen periaate sekvenssien esittämisestä offset-vektorilla pysyy kuitenkin samana.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin.\n", "\n", - "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fi.png)\n", + "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Kokeillaksemme Word2Vec-upotusta, joka on esikoulutettu Google News -aineistolla, voimme käyttää **gensim**-kirjastoa. Alla etsimme sanoja, jotka ovat lähimpänä sanaa 'neural'.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 7fdb838e..b2372a16 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/fi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kun käytämme upotuskerrosta verkkomme ensimmäisenä kerroksena, voimme siirtyä sanojen pussi -mallista **upotuspussi**-malliin, jossa ensin muutamme tekstimme jokaisen sanan vastaavaksi upotukseksi ja sitten laskemme jonkin aggregaattifunktion näiden upotusten yli, kuten `sum`, `average` tai `max`.\n", "\n", - "![Kuva, joka näyttää upotusluokittelijan viidelle sekvenssisanalle.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.fi.png)\n", + "![Kuva, joka näyttää upotusluokittelijan viidelle sekvenssisanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Luokittelijaneuroverkkomme koostuu seuraavista kerroksista:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin.\n", "\n", - "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fi.png)\n", + "![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Kokeillaksemme Word2Vec-upotusta, joka on esikoulutettu Google News -aineistolla, voimme käyttää **gensim**-kirjastoa. Alla etsimme sanoja, jotka ovat lähimpänä sanaa 'neural'.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/14-Embeddings/README.md b/translations/fi/lessons/5-NLP/14-Embeddings/README.md index 3131e86d..30034ee8 100644 --- a/translations/fi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/fi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Upotuskerros ottaa sanan syötteenä ja tuottaa ulostulovektorin, jonka pituus o Käyttämällä upotuskerrosta luokittelijaverkkomme ensimmäisenä kerroksena voimme siirtyä bag-of-words-mallista **embedding bag** -malliin, jossa ensin muutamme tekstimme jokaisen sanan vastaavaksi upotukseksi ja laskemme sitten jonkin aggregaattifunktion näiden upotusten yli, kuten `sum`, `average` tai `max`. -![Kuva, joka näyttää upotusluokittelijan viidelle sanajonon sanalle.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.fi.png) +![Kuva, joka näyttää upotusluokittelijan viidelle sanajonon sanalle.](../../../../../translated_images/fi/embedding-classifier-example.b77f021a7ee67eee.png) > Kuva tekijältä @@ -40,7 +40,7 @@ Tämän saavuttamiseksi meidän täytyy esikouluttaa upotusmallimme suurella tek CBoW on nopeampi, kun taas skip-gram on hitaampi, mutta se edustaa harvinaisia sanoja paremmin. -![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fi.png) +![Kuva, joka näyttää sekä CBoW- että Skip-Gram-algoritmit sanojen muuntamiseksi vektoreiksi.](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Kuva [tästä artikkelista](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md index 84241cdc..f8adf102 100644 --- a/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/fi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Aiemmissa esimerkeissämme käytimme valmiiksi koulutettuja semanttisia upotuksi * **Continuous Bag-of-Words** (CBoW), jossa ennustamme keskimmäisen sanan $W_0$ sanajonossa $W_{-N}$, ..., $W_N$. * **Skip-gram**, jossa ennustamme joukon naapurisanoja {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} keskimmäisestä sanasta $W_0$. -![kuva paperista, jossa käsitellään sanojen muuntamista vektoreiksi](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fi.png) +![kuva paperista, jossa käsitellään sanojen muuntamista vektoreiksi](../../../../../translated_images/fi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Kuva [tästä paperista](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fi/lessons/5-NLP/16-RNN/README.md b/translations/fi/lessons/5-NLP/16-RNN/README.md index c3c5ba0d..646cc29f 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/README.md +++ b/translations/fi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Aiemmissa osioissa olemme käyttäneet tekstin semanttisia esityksiä ja yksinke Tekstijonon merkityksen vangitsemiseksi meidän on käytettävä toisenlaista neuronaaliverkkoarkkitehtuuria, jota kutsutaan **toistuvaksi neuronaaliverkoksi** eli RNN:ksi. RNN:ssä syötämme lauseen verkon läpi yksi symboli kerrallaan, ja verkko tuottaa jonkin **tilan**, jonka syötämme verkkoon uudelleen seuraavan symbolin kanssa. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.fi.png) +![RNN](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.png) > Kuva: kirjoittaja @@ -61,7 +61,7 @@ Olemme käsitelleet toistuvia verkkoja, jotka toimivat yhteen suuntaan, jaksosta Toistuva verkko, joko yksisuuntainen tai kaksisuuntainen, tunnistaa tiettyjä kuvioita jaksossa ja voi tallentaa ne tilavektoriin tai välittää ulostuloon. Kuten konvoluutiokerroksissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen päälle tunnistaaksemme korkeamman tason kuvioita ja rakentaaksemme matalan tason kuvioista, jotka ensimmäinen kerros on tunnistanut. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo syötetään seuraavaan kerrokseen. -![Kuva, joka esittää monikerroksisen pitkäkestoisen muistiyksikön RNN:n](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.fi.jpg) +![Kuva, joka esittää monikerroksisen pitkäkestoisen muistiyksikön RNN:n](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López* diff --git a/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 83955c8f..ddbf7aa0 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Toistuva verkko, olipa se yksisuuntainen tai kaksisuuntainen, tunnistaa tiettyjä kuvioita sekvenssissä ja voi tallentaa ne tilavektoriin tai välittää ne ulostuloon. Kuten konvoluutioneuroverkoissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen päälle tunnistamaan korkeamman tason kuvioita, jotka on muodostettu ensimmäisen kerroksen tunnistamista matalan tason kuvioista. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo välitetään seuraavan kerroksen syötteeksi.\n", "\n", - "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.fi.jpg)\n", + "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López*\n", "\n", diff --git a/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb index 27d47a82..37d15800 100644 --- a/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/fi/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Tekstijonon merkityksen tallentamiseksi käytämme neuroverkkoarkkitehtuuria nimeltä **toistuva neuroverkko** (recurrent neural network, RNN). RNN:ää käytettäessä syötämme lauseen verkon läpi yksi token kerrallaan, ja verkko tuottaa jonkin **tilan**, jonka syötämme verkkoon uudelleen seuraavan tokenin kanssa.\n", "\n", - "![Kuva, joka esittää esimerkin toistuvan neuroverkon generoinnista.](../../../../../translated_images/rnn.27f5c29c53d727b5.fi.png)\n", + "![Kuva, joka esittää esimerkin toistuvan neuroverkon generoinnista.](../../../../../translated_images/fi/rnn.27f5c29c53d727b5.png)\n", "\n", "Kun syötteenä on tokenien jono $X_0,\\dots,X_n$, RNN luo neuroverkkolohkojen sarjan ja kouluttaa tämän sarjan päästä päähän takaisinlevityksen avulla. Jokainen verkkolohko ottaa syötteenä parin $(X_i,S_i)$ ja tuottaa tuloksena $S_{i+1}$. Lopullinen tila $S_n$ tai tulos $Y_n$ syötetään lineaariseen luokittelijaan tuloksen tuottamiseksi. Kaikilla verkkolohkoilla on samat painot, ja ne koulutetaan päästä päähän yhden takaisinlevityskierroksen avulla.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Toistuvat verkot, olivatpa ne yksisuuntaisia tai kaksisuuntaisia, tunnistavat sekvenssin sisäisiä kuvioita ja tallentavat ne tilavektoreihin tai palauttavat ne ulostulona. Kuten konvoluutiokerroksissa, voimme rakentaa toisen toistuvan kerroksen ensimmäisen jälkeen tunnistamaan korkeammantason kuvioita, jotka on muodostettu ensimmäisen kerroksen tunnistamista alemman tason kuvioista. Tämä johtaa käsitteeseen **monikerroksinen RNN**, joka koostuu kahdesta tai useammasta toistuvasta verkosta, joissa edellisen kerroksen ulostulo välitetään seuraavalle kerrokselle syötteenä.\n", "\n", - "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.fi.jpg)\n", + "![Kuva, joka esittää monikerroksista long-short-term-memory-RNN:ää](../../../../../translated_images/fi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Kuva [tästä upeasta artikkelista](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) kirjoittanut Fernando López.*\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 126440ef..dcdb0a00 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Tapa, jolla koulutamme RNN:n tuottamaan tekstiä, on seuraava. Jokaisella askeleella otamme `nchars` merkin pituisen merkkijonon ja pyydämme verkkoa tuottamaan seuraavan ulostulomerkin jokaiselle syötemerkille:\n", "\n", - "![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.fi.png)\n", + "![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Riippuen todellisesta tilanteesta, saatamme haluta sisällyttää myös joitakin erikoismerkkejä, kuten *sekvenssin loppu* ``. Meidän tapauksessamme haluamme vain kouluttaa verkon loputtomaan tekstin tuottamiseen, joten kiinnitämme jokaisen sekvenssin koon `nchars`-merkkien pituiseksi. Näin ollen jokainen koulutusesimerkki koostuu `nchars` syötteistä ja `nchars` ulostuloista (jotka ovat syötesekvenssi siirrettynä yhden symbolin verran vasemmalle). Minibatch koostuu useista tällaisista sekvensseistä.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 3621e7a0..9c1a581c 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Tapa, jolla koulutamme RNN:n luomaan uutisotsikoita, on seuraava. Jokaisella askeleella otamme yhden otsikon, joka syötetään RNN:ään, ja jokaiselle syötehahmolle pyydämme verkkoa tuottamaan seuraavan ulostulohahmon:\n", "\n", - "![Kuva, joka näyttää esimerkin RNN:n sanan 'HELLO' generoinnista.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.fi.png)\n", + "![Kuva, joka näyttää esimerkin RNN:n sanan 'HELLO' generoinnista.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Sekvenssimme viimeisen hahmon kohdalla pyydämme verkkoa tuottamaan ``-tokenin.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md index b4a73492..d6c9edc8 100644 --- a/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/fi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN-arkkitehtuurissa, jota käsittelimme edellisessä osiossa, jokainen RNN-yksi Tämä mahdollistaa erilaiset neuroarkkitehtuurit, jotka näkyvät alla olevassa kuvassa: -![Kuva, joka näyttää yleisiä toistuvien neuroverkkojen malleja.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fi.jpg) +![Kuva, joka näyttää yleisiä toistuvien neuroverkkojen malleja.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Kuva blogikirjoituksesta [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) kirjoittanut [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Tässä osiossa keskitymme yksinkertaisiin generatiivisiin malleihin, jotka autt Koulutamme tämän RNN:n tuottamaan tekstiä askel askeleelta. Jokaisessa vaiheessa otamme `nchars`-pituisen merkkisekvenssin ja pyydämme verkkoa tuottamaan seuraavan ulostulomerkin jokaiselle syötemerkille: -![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.fi.png) +![Kuva, joka näyttää esimerkin RNN:n tuottamasta sanasta 'HELLO'.](../../../../../translated_images/fi/rnn-generate.56c54afb52f9781d.png) Kun tuotamme tekstiä (inferenssin aikana), aloitamme jollain **aloitustekstillä**, joka syötetään RNN-soluihin tuottamaan sen välimuistin, ja sitten tästä tilasta alkaa generointi. Tuotamme yhden merkin kerrallaan ja syötämme tilan ja tuotetun merkin seuraavaan RNN-soluun tuottamaan seuraavan, kunnes olemme tuottaneet tarpeeksi merkkejä. diff --git a/translations/fi/lessons/5-NLP/18-Transformers/README.md b/translations/fi/lessons/5-NLP/18-Transformers/README.md index 4d7dcd52..f78d920d 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/fi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN:ien avulla sekvenssi-sekvenssi toteutetaan kahdella toistuvalla verkolla, jo **Huomiomekanismit** tarjoavat tavan painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n kunkin ennusteen kohdalla. Tämä toteutetaan luomalla oikoteitä syötteen RNN:n välitilojen ja tuloksen RNN:n välille. Näin ollen, kun tuotetaan ulostulosymbolia yt, otamme huomioon kaikki syötteen piilotilat hi, eri painokertoimilla αt,i. -![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fi.png) +![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png) > Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Huomiomatriisi {αi,j} edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan tuottamiseen ulostulosekvenssissä. Alla on esimerkki tällaisesta matriisista: -![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fi.png) +![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png) > Kuva [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Tuloksena saadaan positionaalinen upotus, joka upottaa sekä alkuperäisen token Seuraavaksi täytyy tunnistaa kuvioita sekvenssissä. Tätä varten transformerit käyttävät **itsehuomiomekanismia**, joka on käytännössä huomio, joka kohdistetaan samaan sekvenssiin syötteenä ja tuloksena. Itsehuomion soveltaminen mahdollistaa **kontekstin** huomioimisen lauseessa ja sen, mitkä sanat liittyvät toisiinsa. Esimerkiksi se auttaa tunnistamaan, mihin sanat kuten *se* viittaavat, ja ottaa kontekstin huomioon: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.fi.png) +![](../../../../../translated_images/fi/CoreferenceResolution.861924d6d384a7d6.png) > Kuva [Googlen blogista](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Koska jokainen syötteen sijainti kartoitetaan itsenäisesti jokaiseen tuloksen **BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstikorpuksella (Wikipedia + kirjat) käyttämällä valvomatonta koulutusta (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielen ymmärrystä, jota voidaan hyödyntää muilla aineistoilla hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**. -![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fi.png) +![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Kuva [lähde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index b83550ec..94c14e18 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/fi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Huomiomekanismit** tarjoavat tavan painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n jokaisessa tulosennusteessa. Tämä toteutetaan luomalla oikopolkuja syötteen RNN:n välitilojen ja tulos-RNN:n välille. Näin ollen, kun tuotetaan tulossymbolia $y_t$, otamme huomioon kaikki syötteen piilotilat $h_i$, eri painokertoimilla $\\alpha_{t,i}$. \n", "\n", - "![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fi.png)\n", + "![Kuva, joka näyttää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png)\n", "*Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Huomiomatriisi $\\{\\alpha_{i,j}\\}$ edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan muodostumiseen tulossekvenssissä. Alla on esimerkki tällaisesta matriisista:\n", "\n", - "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fi.png)\n", + "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Kuva otettu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Kuva 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstikorpuksella (Wikipedia + kirjat) käyttämällä valvomattua koulutusta (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielen ymmärrystä, jota voidaan hyödyntää muilla aineistoilla hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**. \n", "\n", - "![Kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fi.png)\n", + "![Kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer-arkkitehtuureista on monia variaatioita, kuten BERT, DistilBERT, BigBird, OpenGPT3 ja muita, joita voidaan hienosäätää. [HuggingFace-paketti](https://github.com/huggingface/) tarjoaa kirjaston monien näiden arkkitehtuurien kouluttamiseen PyTorchilla. \n", "\n", diff --git a/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 5eb3ebdd..d89a337d 100644 --- a/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/fi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Huomiomekanismit** tarjoavat keinon painottaa kunkin syötevektorin kontekstuaalista vaikutusta RNN:n jokaisessa tulosennusteessa. Tämä toteutetaan luomalla oikopolkuja syötteen RNN:n välitilojen ja tulos-RNN:n välille. Näin ollen, kun tuotetaan tulossymbolia $y_t$, otamme huomioon kaikki syötteen piilotilat $h_i$, eri painokertoimilla $\\alpha_{t,i}$. \n", "\n", - "![Kuva, joka esittää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fi.png)\n", + "![Kuva, joka esittää enkooderi/dekooderi-mallin additiivisella huomiokerroksella](../../../../../translated_images/fi/encoder-decoder-attention.7a726296894fb567.png)\n", "*Enkooderi-dekooderi-malli additiivisella huomiomekanismilla [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), lainattu [tästä blogikirjoituksesta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Huomiomatriisi $\\{\\alpha_{i,j}\\}$ edustaa sitä, kuinka paljon tietyt syötteen sanat vaikuttavat tietyn sanan muodostumiseen tulosjaksossa. Alla on esimerkki tällaisesta matriisista:\n", "\n", - "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fi.png)\n", + "![Kuva, joka näyttää esimerkkikohdistuksen RNNsearch-50:llä, otettu Bahdanau - arviz.org](../../../../../translated_images/fi/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Kuva otettu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Kuva 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) on erittäin suuri monikerroksinen transformer-verkko, jossa on 12 kerrosta *BERT-base*-mallissa ja 24 kerrosta *BERT-large*-mallissa. Malli esikoulutetaan ensin suurella tekstiaineistolla (WikiPedia + kirjat) käyttämällä valvomatonta oppimista (ennustamalla peitettyjä sanoja lauseessa). Esikoulutuksen aikana malli omaksuu merkittävän määrän kielellistä ymmärrystä, jota voidaan hyödyntää muiden aineistojen kanssa hienosäädön avulla. Tätä prosessia kutsutaan **siirto-oppimiseksi**.\n", "\n", - "![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fi.png)\n", + "![kuva osoitteesta http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer-arkkitehtuureista on monia variaatioita, kuten BERT, DistilBERT, BigBird, OpenGPT3 ja muita, joita voidaan hienosäätää.\n", "\n", diff --git a/translations/fi/lessons/5-NLP/19-NER/README.md b/translations/fi/lessons/5-NLP/19-NER/README.md index d9ed1122..b4ffe3eb 100644 --- a/translations/fi/lessons/5-NLP/19-NER/README.md +++ b/translations/fi/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ lapsella | O Koska meidän täytyy rakentaa yksi-yhteen vastaavuus tokenien ja luokkien välillä, voimme kouluttaa oikeanpuoleisen **moni-moniin** neuroverkkopohjaisen mallin tästä kuvasta: -![Kuva, joka esittää yleisiä toistuvien neuroverkkojen rakenteita.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fi.jpg) +![Kuva, joka esittää yleisiä toistuvien neuroverkkojen rakenteita.](../../../../../translated_images/fi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Kuva [tästä blogikirjoituksesta](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) kirjoittajalta [Andrej Karpathy](http://karpathy.github.io/). NER token-luokittelumallit vastaavat oikeanpuoleista verkkoarkkitehtuuria tässä kuvassa.* diff --git a/translations/fi/lessons/5-NLP/README.md b/translations/fi/lessons/5-NLP/README.md index b82424ab..ec5cd22a 100644 --- a/translations/fi/lessons/5-NLP/README.md +++ b/translations/fi/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Luonnollisen kielen käsittely -![Yhteenveto NLP-tehtävistä doodlena](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.fi.png) +![Yhteenveto NLP-tehtävistä doodlena](../../../../translated_images/fi/ai-nlp.b22dcb8ca4707cea.png) Tässä osiossa keskitymme käyttämään neuroverkkoja **luonnollisen kielen käsittelyyn (NLP)** liittyvien tehtävien ratkaisemiseen. On monia NLP-ongelmia, joita haluamme tietokoneiden pystyvän ratkaisemaan: diff --git a/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md index 458a1de5..e1b100fb 100644 --- a/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/fi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Voit avata yhden malleista, esimerkiksi **Biology → Flocking**. Kun avaat mallin, sinut ohjataan NetLogon pääruutuun. Tässä on esimerkkimalli, joka kuvaa susien ja lampaiden populaatiota rajallisten resurssien (ruohon) avulla. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.fi.png) +![NetLogo Main Screen](../../../../../translated_images/fi/NetLogo-Main.32653711ec1a01b3.png) > Kuvakaappaus Dmitry Soshnikovilta diff --git a/translations/fi/lessons/README.md b/translations/fi/lessons/README.md index 80cd0185..2c7a5c2a 100644 --- a/translations/fi/lessons/README.md +++ b/translations/fi/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Yleiskatsaus -![Yleiskatsaus piirroksena](../../../translated_images/ai-overview.0857791951d19500.fi.png) +![Yleiskatsaus piirroksena](../../../translated_images/fi/ai-overview.0857791951d19500.png) > Piirrosmuistiinpano: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/fi/lessons/X-Extras/X1-MultiModal/README.md b/translations/fi/lessons/X-Extras/X1-MultiModal/README.md index c4292929..b2e8b1ec 100644 --- a/translations/fi/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/fi/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer-mallien menestyksen jälkeen NLP-tehtävissä samoja tai samankaltai CLIP:n pääidea on kyky verrata tekstikehotteita kuvaan ja määrittää, kuinka hyvin kuva vastaa kehotetta. -![CLIP-arkkitehtuuri](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.fi.png) +![CLIP-arkkitehtuuri](../../../../../translated_images/fi/clip-arch.b3dbf20b4e8ed8be.png) > *Kuva [tästä blogikirjoituksesta](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ Kun tämä malli on esikoulutettu, sille voidaan antaa erä kuvia ja tekstikehot Oletetaan, että meidän täytyy luokitella kuvia esimerkiksi kissoihin, koiriin ja ihmisiin. Tässä tapauksessa voimme antaa mallille kuvan ja sarjan tekstikehotteita: "*kuva kissasta*", "*kuva koirasta*", "*kuva ihmisestä*". Tuloksena olevasta kolmen todennäköisyyden vektorista valitsemme vain indeksin, jolla on korkein arvo. -![CLIP kuvien luokitteluun](../../../../../translated_images/clip-class.3af42ef0b2b19369.fi.png) +![CLIP kuvien luokitteluun](../../../../../translated_images/fi/clip-class.3af42ef0b2b19369.png) > *Kuva [tästä blogikirjoituksesta](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ Lisätietoja VQGAN:sta löytyy [Taming Transformers](https://compvis.github.io/t Yksi tärkeä ero VQGAN:n ja perinteisen GAN:n välillä on, että jälkimmäinen voi tuottaa kelvollisen kuvan mistä tahansa syötevektorista, kun taas VQGAN todennäköisesti tuottaa kuvan, joka ei ole koherentti. Siksi kuvan luomisprosessia täytyy ohjata edelleen, ja tämä voidaan tehdä CLIP:llä. -![VQGAN+CLIP-arkkitehtuuri](../../../../../translated_images/vqgan.5027fe05051dfa31.fi.png) +![VQGAN+CLIP-arkkitehtuuri](../../../../../translated_images/fi/vqgan.5027fe05051dfa31.png) Tuottaaksemme kuvan, joka vastaa tekstikehotetta, aloitamme satunnaisella koodausvektorilla, joka syötetään VQGAN:lle kuvan tuottamiseksi. Sitten CLIP:ä käytetään tuottamaan tappiofunktio, joka osoittaa, kuinka hyvin kuva vastaa tekstikehotetta. Tavoitteena on minimoida tämä tappio käyttämällä takaisinkytkentää syötevektorin parametrien säätämiseen. Loistava kirjasto, joka toteuttaa VQGAN+CLIP:n, on [Pixray](http://github.com/pixray/pixray). -![Pixray:n tuottama kuva](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fi.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fi.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fi.png) +![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Kuva, joka on tuotettu kehotteesta *a closeup watercolor portrait of young male teacher of literature with a book* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of young female teacher of computer science with a computer* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of old male teacher of mathematics in front of blackboard* @@ -77,7 +77,7 @@ Toisin kuin CLIP, DALL-E vastaanottaa sekä tekstin että kuvan yhtenä token-vi Suurin ero DALL.E 1:n ja 2:n välillä on, että jälkimmäinen tuottaa realistisempia kuvia ja taidetta. Esimerkkejä DALL-E:n tuottamista kuvista: -![Pixray:n tuottama kuva](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.fi.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.fi.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.fi.png) +![Pixray:n tuottama kuva](../../../../../translated_images/fi/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray:n tuottama kuva](../../../../../translated_images/fi/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Kuva, joka on tuotettu kehotteesta *a closeup watercolor portrait of young male teacher of literature with a book* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of young female teacher of computer science with a computer* | Kuva, joka on tuotettu kehotteesta *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/fr/README.md b/translations/fr/README.md index b8a094fe..d0ddbd29 100644 --- a/translations/fr/README.md +++ b/translations/fr/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](./README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) -> **Préférez cloner localement ?** +> **Préférez-vous cloner localement ?** -> Ce dépôt inclut plus de 50 traductions de langues ce qui augmente considérablement la taille du téléchargement. Pour cloner sans les traductions, utilisez le sparse checkout : +> Ce référentiel inclut plus de 50 traductions linguistiques ce qui augmente significativement la taille du téléchargement. Pour cloner sans les traductions, utilisez le sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Cela vous donne tout ce dont vous avez besoin pour suivre le cours avec un téléchargement beaucoup plus rapide. +> Cela vous donne tout ce dont vous avez besoin pour compléter le cours avec un téléchargement beaucoup plus rapide. -**Si vous souhaitez d'autres langues de traduction, elles sont listées [ici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Si vous souhaitez que d'autres langues soient prises en charge, elles sont listées [ici](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Rejoignez la Communauté [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -56,89 +56,89 @@ Explorez le monde de **l'Intelligence Artificielle** (IA) avec notre programme d **[Carte mentale du cours](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Dans ce programme, vous apprendrez : +Dans ce curriculum, vous apprendrez : -* Différentes approches de l'Intelligence Artificielle, y compris l'approche symbolique "classique" avec la **Représentation des Connaissances** et le raisonnement ([GOFAI](https://fr.wikipedia.org/wiki/Intelligence_artificielle_symbolique)). -* **Les Réseaux de Neurones** et **l’Apprentissage Profond**, qui constituent le cœur de l’IA moderne. Nous illustrerons les concepts derrière ces sujets importants à l’aide de code dans deux des frameworks les plus populaires - [TensorFlow](http://Tensorflow.org) et [PyTorch](http://pytorch.org). -* **Architectures Neuronales** pour travailler avec les images et le texte. Nous couvrirons des modèles récents mais qui peuvent être un peu éloignés de l’état de l’art. +* Différentes approches de l'Intelligence Artificielle, incluant l'approche "classique" symbolique avec la **Représentation des Connaissances** et le raisonnement ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Réseaux de Neurones** et **Apprentissage Profond**, qui sont au cœur de l'IA moderne. Nous illustrerons les concepts derrière ces sujets importants en utilisant du code dans deux des frameworks les plus populaires - [TensorFlow](http://Tensorflow.org) et [PyTorch](http://pytorch.org). +* **Architectures Neuronales** pour travailler avec des images et du texte. Nous couvrirons des modèles récents mais qui peuvent être un peu en retard sur l'état de l'art. * Des approches moins populaires de l’IA, telles que les **Algorithmes Génétiques** et les **Systèmes Multi-Agents**. -Ce que nous ne couvrirons pas dans ce programme : +Ce que nous ne couvrirons pas dans ce curriculum : > [Trouvez toutes les ressources supplémentaires pour ce cours dans notre collection Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Les cas d’usage de **l’IA en entreprise**. Envisagez de suivre le parcours d’apprentissage [Introduction à l’IA pour les utilisateurs métier](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) sur Microsoft Learn, ou l’[École d’Affaires IA](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), développée en coopération avec [INSEAD](https://www.insead.edu/). -* **L’Apprentissage Automatique Classique**, décrit en détail dans notre [Programme pour Débutants en Machine Learning](http://github.com/Microsoft/ML-for-Beginners). -* Les applications pratiques d’IA basées sur **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Pour cela, nous recommandons de commencer avec les modules Microsoft Learn sur [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [traitement du langage naturel](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Générative avec Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** et d’autres. -* Les **Frameworks Cloud ML** spécifiques, tels que [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Envisagez d’utiliser les parcours d’apprentissage [Construire et exploiter des solutions machine learning avec Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) et [Construire et exploiter des solutions machine learning avec Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA Conversationnelle** et **Chat Bots**. Il existe un parcours d’apprentissage séparé [Créer des solutions d’IA conversationnelle](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), et vous pouvez aussi consulter [ce billet de blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) pour plus de détails. -* Les **Mathématiques Approfondies** derrière l’apprentissage profond. Pour cela, nous recommandons [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio et Aaron Courville, également disponible en ligne sur [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Les cas d'utilisation de l'**IA en entreprise**. Pensez à suivre le parcours d’apprentissage [Introduction à l’IA pour les utilisateurs métier](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) sur Microsoft Learn, ou [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), développé en coopération avec [INSEAD](https://www.insead.edu/). +* L'**Apprentissage Machine Classique**, qui est bien décrit dans notre [Curriculum Apprentissage Machine pour Débutants](http://github.com/Microsoft/ML-for-Beginners). +* Applications pratiques de l’IA construites en utilisant **[les Services Cognitifs](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Pour cela, nous recommandons de commencer par les modules Microsoft Learn pour la [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), le [traitement du langage naturel](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Générative avec le service Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** et d’autres. +* **Frameworks Cloud spécifiques pour le ML**, tels que [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Pensez à consulter les parcours d’apprentissage [Construire et exploiter des solutions de machine learning avec Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) et [Construire et exploiter des solutions de Machine Learning avec Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA Conversationnelle** et **Chat Bots**. Il existe un parcours d’apprentissage distinct [Créer des solutions d’IA conversationnelle](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) et vous pouvez aussi consulter [ce billet de blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) pour plus de détails. +* **Mathématiques approfondies** derrière l’apprentissage profond. Pour cela, nous recommandons [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) par Ian Goodfellow, Yoshua Bengio et Aaron Courville, également disponible en ligne à [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Pour une introduction douce aux sujets liés à _l’IA dans le Cloud_, vous pouvez envisager de suivre le parcours d’apprentissage [Démarrer avec l'intelligence artificielle sur Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Pour une introduction douce aux sujets _IA dans le Cloud_, vous pouvez envisager de suivre le parcours d’apprentissage [Commencer avec l’intelligence artificielle sur Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Contenu -| | Lien de la Leçon | PyTorch/Keras/TensorFlow | Laboratoire | +| | Lien de la leçon | PyTorch/Keras/TensorFlow | Laboratoire | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Configuration du cours](./lessons/0-course-setup/setup.md) | [Configurer votre environnement de développement](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Introduction à l’IA**](./lessons/1-Intro/README.md) | | | -| 01 | [Introduction et Histoire de l’IA](./lessons/1-Intro/README.md) | - | - | +| 0 | [Configuration du cours](./lessons/0-course-setup/setup.md) | [Préparez votre environnement de développement](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Introduction à l'IA**](./lessons/1-Intro/README.md) | | | +| 01 | [Introduction et histoire de l'IA](./lessons/1-Intro/README.md) | - | - | | II | **IA Symbolique** | -| 02 | [Représentation des Connaissances et Systèmes Experts](./lessons/2-Symbolic/README.md) | [Systèmes Experts](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graphe Concepts](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Représentation des connaissances et systèmes experts](./lessons/2-Symbolic/README.md) | [Systèmes Experts](./lessons/2-Symbolic/Animals.ipynb) / [Ontologie](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graphe de Concepts](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introduction aux Réseaux de Neurones**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptron Multicouche et Création de notre propre Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 04 | [Perceptron Multi-couches et Création de notre propre Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Introduction aux Frameworks (PyTorch/TensorFlow) et Surapprentissage](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Vision par ordinateur**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explorer la vision par ordinateur sur Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Introduction à la vision par ordinateur. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Réseaux de neurones convolutifs](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architectures CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Réseaux pré-entraînés et apprentissage par transfert](./lessons/4-ComputerVision/08-TransferLearning/README.md) et [Astuces d'entraînement](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| IV | [**Vision par ordinateur**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Introduction à la Vision par Ordinateur. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Réseaux de Neurones Convolutifs](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architectures CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Réseaux pré-entraînés et Apprentissage par Transfert](./lessons/4-ComputerVision/08-TransferLearning/README.md) et [Astuces d'entraînement](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [Autoencodeurs et VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Réseaux antagonistes génératifs et transfert de style artistique](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Détection d'objets](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Segmentation sémantique. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Traitement du langage naturel**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explorer le traitement du langage naturel sur Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Représentation du texte. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | -| 14 | [Embeddings sémantiques. Word2Vec et GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 10 | [Réseaux Adverses Génératifs & Transfert de Style Artistique](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Détection d'Objets](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Segmentation Sémantique. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**Traitement du langage naturel**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Représentation du texte. Sac de mots/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Représentations sémantiques des mots. Word2Vec et GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | | 15 | [Modélisation du langage. Entraînement de vos propres embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Réseaux de neurones récurrents](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Réseaux récurrents génératifs](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 16 | [Réseaux de Neurones Récurrents](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [Réseaux Récurrents Génératifs](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | | 19 | [Reconnaissance d'entités nommées](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Grands modèles de langue, programmation par prompt et tâches en few-shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| 20 | [Grands modèles de langage, programmation par prompt et tâches en few-shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | | VI | **Autres techniques d'IA** || | | 21 | [Algorithmes génétiques](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Apprentissage par renforcement profond](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Systèmes multi-agents](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Éthique de l'IA** | | | | 24 | [Éthique de l'IA et IA responsable](./lessons/7-Ethics/README.md) | [Microsoft Learn : Principes de l'IA responsable](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Extras** | | | -| 25 | [Réseaux multimodaux, CLIP et VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| IX | **Suppléments** | | | +| 25 | [Réseaux multi-modaux, CLIP et VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Chaque leçon contient -* Du matériel à lire avant -* Des notebooks Jupyter exécutables, souvent spécifiques au framework (**PyTorch** ou **TensorFlow**). Le notebook exécutable contient également beaucoup de théorie, donc pour comprendre le sujet il faut parcourir au moins une version du notebook (soit PyTorch soit TensorFlow). -* Des **labs** disponibles pour certains sujets, qui vous donnent l'opportunité de mettre en pratique les connaissances acquises sur un problème spécifique. -* Certaines sections contiennent des liens vers des modules [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) couvrant des sujets connexes. +* Du matériel de pré-lecture +* Des notebooks Jupyter exécutables, souvent spécifiques au framework (**PyTorch** ou **TensorFlow**). Le notebook exécutable contient également beaucoup de théorie, donc pour comprendre le sujet il est nécessaire de parcourir au moins une version du notebook (soit PyTorch soit TensorFlow). +* Des **labs** disponibles pour certains sujets, qui vous donnent l’opportunité d’appliquer le matériel appris à un problème spécifique. +* Certaines sections contiennent des liens vers des modules [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) qui couvrent des sujets connexes. ## Pour commencer ### 🎯 Nouveau en IA ? Commencez ici ! -Si vous débutez complètement en IA et souhaitez des exemples rapides et pratiques, consultez nos [**Exemples adaptés aux débutants**](./examples/README.md) ! Ils incluent : +Si vous êtes complètement novice en IA et souhaitez des exemples rapides et pratiques, consultez nos [**Exemples accessibles aux débutants**](./examples/README.md) ! Ils comprennent : -- 🌟 **Bonjour IA Monde** - Votre premier programme IA (reconnaissance de motifs) -- 🧠 **Réseau de neurones simple** - Construisez un réseau de neurones à partir de zéro -- 🖼️ **Classificateur d'images** - Classifiez des images avec des commentaires détaillés -- 💬 **Sentiment du Texte** - Analyse du texte positif/négatif +- 🌟 **Hello AI World** - Votre premier programme IA (reconnaissance de motifs) +- 🧠 **Réseau neuronal simple** - Construisez un réseau neuronal à partir de zéro +- 🖼️ **Classificateur d'images** - Classifier des images avec des commentaires détaillés +- 💬 **Sentiment du texte** - Analyse du texte positif/négatif Ces exemples sont conçus pour vous aider à comprendre les concepts d'IA avant de plonger dans le programme complet. -### 📚 Configuration du Programme Complet +### 📚 Configuration du programme complet -- Nous avons créé une [leçon de configuration](./lessons/0-course-setup/setup.md) pour vous aider à configurer votre environnement de développement. - Pour les éducateurs, nous avons également créé une [leçon de configuration des programmes](./lessons/0-course-setup/for-teachers.md) pour vous ! -- Comment [Exécuter le code dans VSCode ou Codepace](./lessons/0-course-setup/how-to-run.md) +- Nous avons créé une [leçon d'installation](./lessons/0-course-setup/setup.md) pour vous aider à configurer votre environnement de développement. - Pour les éducateurs, nous avons également créé une [leçon de configuration du programme](./lessons/0-course-setup/for-teachers.md) ! +- Comment [exécuter le code dans VSCode ou un Codespace](./lessons/0-course-setup/how-to-run.md) Suivez ces étapes : @@ -146,33 +146,33 @@ Forkez le dépôt : Cliquez sur le bouton "Fork" en haut à droite de cette page Clonez le dépôt : `git clone https://github.com/microsoft/AI-For-Beginners.git` -N'oubliez pas de mettre une étoile (🌟) sur ce dépôt pour le retrouver plus facilement plus tard. +N’oubliez pas de mettre une étoile (🌟) sur ce dépôt pour le retrouver plus facilement plus tard. ## Rencontrez d'autres apprenants -Rejoignez notre [serveur Discord officiel AI](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) pour rencontrer et réseauter avec d'autres apprenants suivant ce cours et obtenir du support. +Rejoignez notre [serveur Discord officiel de l'IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) pour rencontrer et réseauter avec d'autres apprenants suivant ce cours et obtenir du support. -Si vous avez des retours sur le produit ou des questions pendant la création, visitez notre [Forum des développeurs Azure AI Foundry](https://aka.ms/foundry/forum) +Si vous avez des retours sur le produit ou des questions pendant la construction, visitez notre [Forum développeur Azure AI Foundry](https://aka.ms/foundry/forum). ## Quiz -> **Une note sur les quiz** : Tous les quiz se trouvent dans le dossier Quiz-app dans etc\quiz-app, ou [En ligne Ici](https://ff-quizzes.netlify.app/) Ils sont liés depuis les leçons, l'application quiz peut être exécutée localement ou déployée sur Azure ; suivez les instructions dans le dossier `quiz-app`. Ils sont progressivement localisés. +> **Une note à propos des quiz** : Tous les quiz se trouvent dans le dossier Quiz-app dans etc\quiz-app, ou [en ligne ici](https://ff-quizzes.netlify.app/) Ils sont liés depuis les leçons, l'application quiz peut être exécutée localement ou déployée sur Azure ; suivez les instructions dans le dossier `quiz-app`. Ils sont progressivement localisés. -## Aide Requise +## Besoin d'aide -Avez-vous des suggestions ou avez-vous trouvé des fautes d'orthographe ou des erreurs de code ? Ouvrez un problème ou créez une demande de tirage. +Avez-vous des suggestions ou avez-vous trouvé des fautes d’orthographe ou d’erreurs de code ? Ouvrez une issue ou créez une pull request. -## Remerciements Spéciaux +## Remerciements spéciaux -* **✍️ Auteur Principal :** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **✍️ Auteur principal :** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Éditeur :** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Illustratrice Sketchnote :** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Créatrice de Quiz :** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Contributeurs Principaux :** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✅ Créatrice de quiz :** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Contributeurs principaux :** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Autres Programmes +## Autres programmes -Notre équipe produit d'autres programmes ! Découvrez : +Notre équipe produit d’autres programmes ! Découvrez : ### LangChain @@ -189,7 +189,7 @@ Notre équipe produit d'autres programmes ! Découvrez : --- -### Série IA Générative +### Série IA générative [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) @@ -197,7 +197,7 @@ Notre équipe produit d'autres programmes ! Découvrez : --- -### Apprentissage Fondamental +### Apprentissage fondamental [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -214,13 +214,13 @@ Notre équipe produit d'autres programmes ! Découvrez : [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) -## Obtenir de l'Aide +## Obtenir de l’aide -Si vous êtes bloqué ou avez des questions sur la création d'applications IA. Rejoignez d'autres apprenants et développeurs expérimentés dans les discussions sur MCP. C'est une communauté bienveillante où les questions sont les bienvenues et les connaissances partagées librement. +Si vous êtes bloqué ou si vous avez des questions sur la création d’applications IA. Rejoignez d’autres apprenants et développeurs expérimentés dans des discussions sur MCP. C’est une communauté de soutien où les questions sont les bienvenues et où le savoir est partagé librement. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Si vous avez des retours sur le produit ou des erreurs pendant la création, visitez : +Si vous avez des retours produits ou des erreurs lors de la création, visitez : [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -228,5 +228,5 @@ Si vous avez des retours sur le produit ou des erreurs pendant la création, vis **Avertissement** : -Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d’assurer la précision, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d’origine doit être considéré comme la source faisant foi. Pour les informations critiques, il est recommandé de faire appel à une traduction professionnelle humaine. Nous déclinons toute responsabilité en cas de malentendus ou d’interprétations erronées résultant de l’utilisation de cette traduction. +Ce document a été traduit à l’aide du service de traduction par IA [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforçons d’assurer l’exactitude, veuillez noter que les traductions automatisées peuvent comporter des erreurs ou des imprécisions. Le document original dans sa langue d’origine doit être considéré comme la source fiable. Pour toute information critique, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou d’interprétations erronées résultant de l’utilisation de cette traduction. \ No newline at end of file diff --git a/translations/fr/lessons/0-course-setup/how-to-run.md b/translations/fr/lessons/0-course-setup/how-to-run.md index be6fdf51..62771996 100644 --- a/translations/fr/lessons/0-course-setup/how-to-run.md +++ b/translations/fr/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Comment exécuter le code -Ce programme contient de nombreux exemples exécutables et laboratoires que vous souhaiterez exécuter. Pour ce faire, vous devez pouvoir exécuter du code Python dans des Jupyter Notebooks fournis dans le cadre de ce programme. Vous avez plusieurs options pour exécuter le code : +Ce programme contient beaucoup d’exemples exécutables et de travaux pratiques que vous voudrez exécuter. Pour ce faire, vous avez besoin de pouvoir exécuter du code Python dans les notebooks Jupyter fournis dans le cadre de ce programme. Vous disposez de plusieurs options pour exécuter le code : ## Exécuter localement sur votre ordinateur -Pour exécuter le code localement sur votre ordinateur, vous devez avoir une version de Python installée. Je recommande personnellement d'installer **[miniconda](https://conda.io/en/latest/miniconda.html)** - une installation légère qui prend en charge le gestionnaire de paquets `conda` pour différents **environnements virtuels** Python. +Pour exécuter le code localement sur votre ordinateur, une installation de Python est nécessaire. Une recommandation est d’installer **[miniconda](https://conda.io/en/latest/miniconda.html)** – c’est une installation plutôt légère qui supporte le gestionnaire de paquets `conda` pour différents **environnements virtuels** Python. -Après avoir installé miniconda, vous devez cloner le dépôt et créer un environnement virtuel à utiliser pour ce cours : +Après avoir installé miniconda, clonez le dépôt et créez un environnement virtuel à utiliser pour ce cours : ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,17 +24,17 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Utiliser Visual Studio Code avec l'extension Python +### Utilisation de Visual Studio Code avec l'extension Python -La meilleure façon d'utiliser ce programme est probablement de l'ouvrir dans [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) avec l'[extension Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ce programme est mieux utilisé en l’ouvrant dans [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) avec l’[extension Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note** : Une fois que vous avez cloné et ouvert le répertoire dans VS Code, il vous suggérera automatiquement d'installer les extensions Python. Vous devrez également installer miniconda comme décrit ci-dessus. +> **Note** : Une fois que vous avez cloné et ouvert le répertoire dans VS Code, il vous suggérera automatiquement d’installer les extensions Python. Vous devrez également installer miniconda comme décrit ci-dessus. -> **Note** : Si VS Code vous propose de rouvrir le dépôt dans un conteneur, vous devez refuser pour utiliser l'installation Python locale. +> **Note** : Si VS Code vous suggère de rouvrir le dépôt dans un conteneur, vous devriez refuser cela pour utiliser l’installation Python locale. -### Utiliser Jupyter dans le navigateur +### Utilisation de Jupyter dans le navigateur -Vous pouvez également utiliser l'environnement Jupyter directement depuis le navigateur sur votre propre ordinateur. En fait, Jupyter classique et Jupyter Hub offrent un environnement de développement assez pratique avec autocomplétion, surlignage de code, etc. +Vous pouvez aussi utiliser un environnement Jupyter depuis le navigateur sur votre propre ordinateur. À la fois Jupyter classique et JupyterHub fournissent un environnement de développement pratique avec autocomplétion, coloration syntaxique, etc. Pour démarrer Jupyter localement, allez dans le répertoire du cours et exécutez : @@ -45,32 +45,36 @@ ou ```bash jupyterhub ``` -Vous pouvez ensuite naviguer vers n'importe quel fichier `.ipynb`, l'ouvrir et commencer à travailler. +Vous pouvez alors naviguer vers n’importe lequel des fichiers `.ipynb`, les ouvrir et commencer à travailler. -### Exécuter dans un conteneur +### Exécution dans un conteneur -Une alternative à l'installation de Python serait d'exécuter le code dans un conteneur. Étant donné que notre dépôt contient un dossier spécial `.devcontainer` qui indique comment construire un conteneur pour ce dépôt, VS Code vous proposera de rouvrir le code dans un conteneur. Cela nécessitera l'installation de Docker et sera également plus complexe, donc nous recommandons cette option aux utilisateurs plus expérimentés. +Une alternative à l’installation de Python serait d’exécuter le code dans un conteneur. Puisque notre dépôt fournit un dossier spécial `.devcontainer` qui indique comment construire un conteneur pour ce dépôt, VS Code offre la possibilité de rouvrir le code dans un conteneur. Cela nécessitera l’installation de Docker, et serait aussi plus complexe, c’est pourquoi nous recommandons cela aux utilisateurs plus expérimentés. -## Exécuter dans le cloud +## Exécution dans le cloud -Si vous ne souhaitez pas installer Python localement et que vous avez accès à des ressources cloud, une bonne alternative serait d'exécuter le code dans le cloud. Il existe plusieurs façons de le faire : +Si vous ne souhaitez pas installer Python localement et avez accès à des ressources cloud, une bonne alternative serait d’exécuter le code dans le cloud. Plusieurs options s’offrent à vous : -* Utiliser **[GitHub Codespaces](https://github.com/features/codespaces)**, qui est un environnement virtuel créé pour vous sur GitHub, accessible via l'interface navigateur de VS Code. Si vous avez accès à Codespaces, vous pouvez simplement cliquer sur le bouton **Code** dans le dépôt, démarrer un codespace et commencer à travailler rapidement. -* Utiliser **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) fournit des ressources informatiques gratuites dans le cloud pour permettre aux utilisateurs de tester du code sur GitHub. Il y a un bouton sur la page d'accueil pour ouvrir le dépôt dans Binder - cela vous amènera rapidement au site Binder, qui construira le conteneur sous-jacent et démarrera l'interface web Jupyter pour vous de manière transparente. +* Utilisation de **[GitHub Codespaces](https://github.com/features/codespaces)**, qui est un environnement virtuel créé pour vous sur GitHub, accessible via une interface VS Code dans le navigateur. Si vous avez accès à Codespaces, vous pouvez simplement cliquer sur le bouton **Code** dans le dépôt, démarrer un codespace et vous lancer en un rien de temps. +* Utilisation de **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) offre des ressources informatiques gratuites fournies dans le cloud pour permettre à des personnes comme vous de tester du code sur GitHub. Il y a un bouton sur la page d’accueil pour ouvrir le dépôt dans Binder – cela devrait rapidement vous amener sur le site de Binder, qui construira un conteneur sous-jacent et lancera une interface web Jupyter pour vous de manière transparente. -> **Note** : Pour éviter les abus, Binder bloque l'accès à certaines ressources web. Cela peut empêcher certains codes qui récupèrent des modèles et/ou des ensembles de données depuis Internet de fonctionner. Vous devrez peut-être trouver des solutions alternatives. De plus, les ressources informatiques fournies par Binder sont assez basiques, donc l'entraînement sera lent, en particulier dans les leçons plus complexes. +> **Note** : Pour éviter les abus, Binder a bloqué l’accès à certaines ressources web. Cela peut empêcher certains codes de fonctionner, notamment ceux qui téléchargent des modèles et/ou jeux de données depuis Internet public. Vous devrez peut-être trouver des solutions de contournement. En outre, les ressources de calcul fournies par Binder sont assez basiques, donc l’entraînement sera lent, surtout dans les leçons plus avancées et complexes. -## Exécuter dans le cloud avec GPU +## Exécution dans le cloud avec GPU -Certaines des leçons plus avancées de ce programme bénéficieraient grandement du support GPU, car sinon l'entraînement serait extrêmement lent. Voici quelques options, surtout si vous avez accès au cloud via [Azure pour les étudiants](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou via votre institution : +Certaines leçons avancées de ce programme bénéficieraient grandement du support GPU. L’entraînement de modèles, par exemple, peut être très lent autrement. Vous pouvez suivre quelques options, surtout si vous avez accès au cloud via [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou via votre institution : -* Créez une [Machine Virtuelle Data Science](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) et connectez-vous à celle-ci via Jupyter. Vous pouvez ensuite cloner le dépôt directement sur la machine et commencer à apprendre. Les machines virtuelles de la série NC disposent du support GPU. +* Créez une [Machine virtuelle Data Science](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) et connectez-vous à celle-ci via Jupyter. Vous pourrez alors cloner le dépôt directement sur la machine et commencer à apprendre. Les machines virtuelles série NC offrent un support GPU. -> **Note** : Certaines abonnements, y compris Azure pour les étudiants, ne fournissent pas de support GPU par défaut. Vous devrez peut-être demander des cœurs GPU supplémentaires via une demande de support technique. +> **Note** : Certains abonnements, y compris Azure for Students, ne fournissent pas nativement de support GPU. Vous devrez peut-être demander des cœurs GPU supplémentaires via une demande de support technique. -* Créez un [Espace de travail Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) et utilisez ensuite la fonctionnalité Notebook. [Cette vidéo](https://azure-for-academics.github.io/quickstart/azureml-papers/) montre comment cloner un dépôt dans un notebook Azure ML et commencer à l'utiliser. +* Créez un [Espace de travail Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) puis utilisez la fonctionnalité Notebook. [Cette vidéo](https://azure-for-academics.github.io/quickstart/azureml-papers/) montre comment cloner un dépôt dans un notebook Azure ML et commencer à l’utiliser. -Vous pouvez également utiliser Google Colab, qui offre un support GPU gratuit, et y télécharger des Jupyter Notebooks pour les exécuter un par un. +Vous pouvez aussi utiliser Google Colab, qui offre un support GPU gratuit, et y téléverser vos notebooks Jupyter pour les exécuter un par un. -**Avertissement** : -Ce document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction. \ No newline at end of file +--- + + +**Clause de non-responsabilité** : +Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d’assurer l’exactitude, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d’origine doit être considéré comme la source officielle. Pour les informations critiques, une traduction professionnelle réalisée par un humain est recommandée. Nous ne pouvons être tenus responsables des malentendus ou des erreurs d’interprétation résultant de l’utilisation de cette traduction. + \ No newline at end of file diff --git a/translations/fr/lessons/1-Intro/README.md b/translations/fr/lessons/1-Intro/README.md index 92f8957a..ad8c3c74 100644 --- a/translations/fr/lessons/1-Intro/README.md +++ b/translations/fr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduction à l'IA -![Résumé du contenu de l'introduction à l'IA sous forme de dessin](../../../../translated_images/ai-intro.bf28d1ac4235881c.fr.png) +![Résumé du contenu de l'introduction à l'IA sous forme de dessin](../../../../translated_images/fr/ai-intro.bf28d1ac4235881c.webp) > Sketchnote par [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: À l'origine, les ordinateurs ont été inventés par [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) pour manipuler des nombres en suivant une procédure bien définie - un algorithme. Les ordinateurs modernes, bien qu'extrêmement plus avancés que le modèle initial proposé au XIXe siècle, suivent toujours le même principe de calculs contrôlés. Ainsi, il est possible de programmer un ordinateur pour accomplir une tâche si nous connaissons la séquence exacte d'étapes nécessaires pour atteindre l'objectif. -![Photo d'une personne](../../../../translated_images/dsh_age.d212a30d4e54fb5f.fr.png) +![Photo d'une personne](../../../../translated_images/fr/dsh_age.d212a30d4e54fb5f.webp) > Photo par [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Pour plus d'informations, consultez **[Intelligence Artificielle Générale](htt L'un des problèmes liés au terme **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** est qu'il n'existe pas de définition claire de ce terme. On peut soutenir que l'intelligence est liée à la **pensée abstraite** ou à la **conscience de soi**, mais nous ne pouvons pas la définir correctement. -![Photo d'un chat](../../../../translated_images/photo-cat.8c8e8fb760ffe457.fr.jpg) +![Photo d'un chat](../../../../translated_images/fr/photo-cat.8c8e8fb760ffe457.webp) > [Photo](https://unsplash.com/photos/75715CVEJhI) par [Amber Kipp](https://unsplash.com/@sadmax) sur Unsplash @@ -98,13 +98,13 @@ Alternativement, nous pouvons essayer de modéliser les éléments les plus simp > | Et le ML ? | | > |--------------|-----------| -> | Une partie de l'intelligence artificielle basée sur l'apprentissage par ordinateur pour résoudre un problème à partir de données est appelée **Machine Learning**. Nous n'aborderons pas l'apprentissage automatique classique dans ce cours - nous vous renvoyons à un programme séparé [Machine Learning pour débutants](http://aka.ms/ml-beginners). | ![ML pour débutants](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.fr.png) | +> | Une partie de l'intelligence artificielle basée sur l'apprentissage par ordinateur pour résoudre un problème à partir de données est appelée **Machine Learning**. Nous n'aborderons pas l'apprentissage automatique classique dans ce cours - nous vous renvoyons à un programme séparé [Machine Learning pour débutants](http://aka.ms/ml-beginners). | ![ML pour débutants](../../../../translated_images/fr/ml-for-beginners.9e4fed176fd5817d.webp) | ## Un bref historique de l'IA L'intelligence artificielle a été lancée en tant que domaine au milieu du XXe siècle. Initialement, le raisonnement symbolique était l'approche dominante, et cela a conduit à un certain nombre de succès importants, tels que les systèmes experts – des programmes informatiques capables d'agir comme un expert dans certains domaines problématiques limités. Cependant, il est vite devenu évident que cette approche ne s'adapte pas bien. Extraire les connaissances d'un expert, les représenter dans un ordinateur et maintenir cette base de connaissances précise s'avère être une tâche très complexe et trop coûteuse pour être pratique dans de nombreux cas. Cela a conduit à ce qu'on appelle l'[hiver de l'IA](https://en.wikipedia.org/wiki/AI_winter) dans les années 1970. -Bref historique de l'IA +Bref historique de l'IA > Image par [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ De même, nous pouvons voir comment l'approche pour créer des "programmes parla * Les assistants modernes, tels que Cortana, Siri ou Google Assistant, sont tous des systèmes hybrides qui utilisent des réseaux neuronaux pour convertir la parole en texte et reconnaître notre intention, puis emploient un raisonnement ou des algorithmes explicites pour effectuer les actions requises. * À l'avenir, nous pouvons nous attendre à un modèle entièrement basé sur les réseaux neuronaux pour gérer le dialogue par lui-même. Les récents réseaux neuronaux de la famille GPT et [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) montrent de grands succès dans ce domaine. -l'évolution du test de Turing +l'évolution du test de Turing > Image par Dmitry Soshnikov, [photo](https://unsplash.com/photos/r8LmVbUKgns) par [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Recherche récente en IA diff --git a/translations/fr/lessons/2-Symbolic/Animals.ipynb b/translations/fr/lessons/2-Symbolic/Animals.ipynb index f92f331c..481b8fad 100644 --- a/translations/fr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/fr/lessons/2-Symbolic/Animals.ipynb @@ -6,13 +6,13 @@ "collapsed": true }, "source": [ - "# Mise en œuvre d'un système expert pour les animaux\n", + "# Implémentation d'un système expert animalier\n", "\n", - "Un exemple tiré du [programme d'études AI for Beginners](http://github.com/microsoft/ai-for-beginners).\n", + "Un exemple du [curriculum AI for Beginners](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Dans cet exemple, nous allons mettre en œuvre un système simple basé sur la connaissance pour identifier un animal en fonction de certaines caractéristiques physiques. Le système peut être représenté par l'arbre AND-OR suivant (il s'agit d'une partie de l'arbre complet, nous pouvons facilement ajouter d'autres règles) :\n", + "Dans cet exemple, nous allons implémenter un système simple basé sur la connaissance pour déterminer un animal en fonction de certaines caractéristiques physiques. Le système peut être représenté par l'arbre AND-OR suivant (il s'agit d'une partie de l'arbre complet, nous pouvons facilement ajouter d'autres règles) :\n", "\n", - "![](../../../../lessons/2-Symbolic/images/AND-OR-Tree.png)\n" + "![](../../../../../../translated_images/fr/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { @@ -23,8 +23,8 @@ "\n", "Essayons de définir un langage simple pour la représentation des connaissances basé sur des règles de production. Nous utiliserons des classes Python comme mots-clés pour définir les règles. Il y aurait essentiellement 3 types de classes :\n", "* `Ask` représente une question qui doit être posée à l'utilisateur. Elle contient l'ensemble des réponses possibles.\n", - "* `If` représente une règle, et c'est juste une simplification syntaxique pour stocker le contenu de la règle.\n", - "* `AND`/`OR` sont des classes pour représenter les branches ET/OU de l'arbre. Elles se contentent de stocker la liste des arguments à l'intérieur. Pour simplifier le code, toutes les fonctionnalités sont définies dans la classe parente `Content`.\n" + "* `If` représente une règle, et ce n'est qu'un sucre syntaxique pour stocker le contenu de la règle\n", + "* `AND`/`OR` sont des classes pour représenter les branches ET/OU de l'arbre. Elles stockent simplement la liste des arguments à l'intérieur. Pour simplifier le code, toute la fonctionnalité est définie dans la classe parente `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Dans notre système, la mémoire de travail contiendrait la liste des **faits** sous forme de **paires attribut-valeur**. La base de connaissances peut être définie comme un grand dictionnaire qui associe des actions (nouveaux faits devant être insérés dans la mémoire de travail) à des conditions, exprimées sous forme d'expressions ET-OU. De plus, certains faits peuvent être `Demandés`.\n" + "Dans notre système, la mémoire de travail contiendrait la liste des **faits** sous forme de **paires attribut-valeur**. La base de connaissances peut être définie comme un grand dictionnaire qui associe des actions (nouveaux faits devant être insérés dans la mémoire de travail) à des conditions, exprimées sous forme d'expressions ET-OU. De plus, certains faits peuvent être `Ask`-és.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Pour effectuer l'inférence à rebours, nous allons définir la classe `Knowledgebase`. Elle contiendra :\n", - "* Une `mémoire` de travail - un dictionnaire qui associe des attributs à des valeurs\n", - "* Les `règles` de la base de connaissances dans le format défini ci-dessus\n", + "Pour effectuer l'inférence à rebours, nous définirons la classe `Knowledgebase`. Elle contiendra :\n", + "* Une `memory` de travail - un dictionnaire qui associe des attributs à des valeurs\n", + "* Des `rules` de la base de connaissances au format défini ci-dessus\n", "\n", "Les deux méthodes principales sont :\n", - "* `get` pour obtenir la valeur d'un attribut, en effectuant une inférence si nécessaire. Par exemple, `get('color')` obtiendra la valeur d'un champ de couleur (il posera la question si nécessaire et stockera la valeur pour une utilisation ultérieure dans la mémoire de travail). Si nous demandons `get('color:blue')`, il demandera une couleur, puis retournera une valeur `y`/`n` en fonction de la couleur.\n", - "* `eval` effectue l'inférence proprement dite, c'est-à-dire qu'elle parcourt l'arbre AND/OR, évalue les sous-objectifs, etc.\n" + "* `get` pour obtenir la valeur d'un attribut, en effectuant l'inférence si nécessaire. Par exemple, `get('color')` obtiendra la valeur d'un emplacement de couleur (elle demandera si nécessaire, et stockera la valeur pour une utilisation ultérieure dans la mémoire de travail). Si l'on demande `get('color:blue')`, elle demandera une couleur, puis retournera une valeur `y`/`n` selon la couleur.\n", + "* `eval` effectue l'inférence réelle, c’est-à-dire qu’elle parcourt l’arbre AND/OR, évalue les sous-objectifs, etc.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Maintenant, définissons notre base de connaissances sur les animaux et effectuons la consultation. Notez que cet appel vous posera des questions. Vous pouvez répondre en tapant `y`/`n` pour les questions oui-non, ou en spécifiant un numéro (0..N) pour les questions avec des réponses à choix multiples plus longues.\n" + "Définissons maintenant notre base de connaissances sur les animaux et effectuons la consultation. Notez que cet appel vous posera des questions. Vous pouvez répondre en tapant `y`/`n` pour les questions oui-non, ou en spécifiant un numéro (0..N) pour les questions avec des réponses à choix multiples plus longues.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Utilisation de PyKnow pour l'inférence avant\n", + "## Utilisation d’Experta pour l’inférence avant\n", "\n", - "Dans l'exemple suivant, nous allons essayer de mettre en œuvre l'inférence avant en utilisant l'une des bibliothèques de représentation des connaissances, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** est une bibliothèque permettant de créer des systèmes d'inférence avant en Python, conçue pour être similaire au système classique ancien [CLIPS](http://www.clipsrules.net/index.html).\n", + "Dans l’exemple suivant, nous allons essayer de mettre en œuvre l’inférence avant en utilisant l’une des bibliothèques pour la représentation des connaissances, [Experta](https://github.com/nilp0inter/experta). **Experta** est une bibliothèque pour créer des systèmes d’inférence avant en Python, conçue pour être similaire au système classique ancien [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Nous aurions également pu implémenter nous-mêmes le chaînage avant sans trop de difficultés, mais les implémentations naïves ne sont généralement pas très efficaces. Pour un appariement des règles plus performant, un algorithme spécial appelé [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) est utilisé.\n" + "Nous aurions aussi pu implémenter nous-mêmes un chaînage avant sans trop de problèmes, mais les implémentations naïves ne sont généralement pas très efficaces. Pour un appariement plus efficace des règles, un algorithme spécial [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) est utilisé.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Nous définirons notre système comme une classe qui hérite de `KnowledgeEngine`. Chaque règle est définie par une fonction distincte avec l'annotation `@Rule`, qui spécifie quand la règle doit s'exécuter. À l'intérieur de la règle, nous pouvons ajouter de nouveaux faits en utilisant la fonction `declare`, et l'ajout de ces faits entraînera l'appel de certaines autres règles par le moteur d'inférence avant.\n" + "Nous allons définir notre système comme une classe qui hérite de `KnowledgeEngine`. Chaque règle est définie par une fonction distincte avec l'annotation `@Rule`, qui spécifie quand la règle doit être déclenchée. À l'intérieur de la règle, nous pouvons ajouter de nouveaux faits en utilisant la fonction `declare`, et l'ajout de ces faits entraînera l'appel de certaines règles supplémentaires par le moteur d'inférence avant.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Une fois que nous avons défini une base de connaissances, nous remplissons notre mémoire de travail avec quelques faits initiaux, puis nous appelons la méthode `run()` pour effectuer l'inférence. Vous pouvez voir qu'en conséquence, de nouveaux faits déduits sont ajoutés à la mémoire de travail, y compris le fait final concernant l'animal (si nous avons correctement configuré tous les faits initiaux).\n" + "Une fois que nous avons défini une base de connaissances, nous remplissons notre mémoire de travail avec quelques faits initiaux, puis appelons la méthode `run()` pour effectuer l'inférence. Vous pouvez voir en résultat que de nouveaux faits inférés sont ajoutés à la mémoire de travail, y compris le fait final concernant l'animal (si nous avons correctement configuré tous les faits initiaux).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de recourir à une traduction professionnelle réalisée par un humain. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n" + "---\n\n\n**Avertissement** : \nCe document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforçons d’assurer l’exactitude, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue native doit être considéré comme la source officielle. Pour les informations critiques, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou d’interprétations erronées résultant de l’utilisation de cette traduction.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-31T14:54:39+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:37:57+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "fr" } diff --git a/translations/fr/lessons/2-Symbolic/README.md b/translations/fr/lessons/2-Symbolic/README.md index 033b28f3..ce33de1f 100644 --- a/translations/fr/lessons/2-Symbolic/README.md +++ b/translations/fr/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Représentation des connaissances et systèmes experts +# Représentation des Connaissances et Systèmes Experts -![Résumé du contenu de l'IA symbolique](../../../../translated_images/ai-symbolic.715a30cb610411a6.fr.png) +![Résumé du contenu de l'IA symbolique](../../../../../../translated_images/fr/ai-symbolic.715a30cb610411a6.webp) > Sketchnote par [Tomomi Imura](https://twitter.com/girlie_mac) -La quête de l'intelligence artificielle repose sur une recherche de connaissances, afin de comprendre le monde de manière similaire à celle des humains. Mais comment peut-on y parvenir ? +La quête de l'intelligence artificielle repose sur la recherche de connaissances, afin de comprendre le monde de manière similaire aux humains. Mais comment peut-on s'y prendre ? -## [Quiz avant le cours](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Quiz préalable à la leçon](https://ff-quizzes.netlify.app/en/ai/quiz/3) Aux débuts de l'IA, l'approche descendante pour créer des systèmes intelligents (discutée dans la leçon précédente) était populaire. L'idée était d'extraire les connaissances des personnes dans une forme lisible par machine, puis de les utiliser pour résoudre automatiquement des problèmes. Cette approche reposait sur deux grandes idées : * Représentation des connaissances * Raisonnement -## Représentation des connaissances +## Représentation des Connaissances -Un des concepts importants de l'IA symbolique est **la connaissance**. Il est essentiel de différencier la connaissance de *l'information* ou des *données*. Par exemple, on peut dire que les livres contiennent des connaissances, car on peut les étudier et devenir expert. Cependant, ce que les livres contiennent est en réalité appelé *données*, et en lisant les livres et en intégrant ces données dans notre modèle du monde, nous transformons ces données en connaissances. +Un des concepts importants en IA symbolique est la **connaissance**. Il est important de différencier la connaissance de *l'information* ou des *données*. Par exemple, on peut dire que les livres contiennent des connaissances, car on peut étudier des livres et devenir expert. Cependant, ce que contiennent les livres s'appelle en réalité des *données*, et en lisant les livres et en intégrant ces données à notre modèle du monde, nous convertissons ces données en connaissance. -> ✅ **La connaissance** est ce qui est contenu dans notre esprit et représente notre compréhension du monde. Elle est obtenue par un processus actif d'**apprentissage**, qui intègre les informations reçues dans notre modèle actif du monde. +> ✅ **La connaissance** est quelque chose qui est contenu dans notre tête et représente notre compréhension du monde. Elle est obtenue par un processus actif d’**apprentissage**, qui intègre des morceaux d'informations que nous recevons dans notre modèle actif du monde. -Le plus souvent, nous ne définissons pas strictement la connaissance, mais nous l'alignons avec d'autres concepts connexes en utilisant la [pyramide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Elle contient les concepts suivants : +Le plus souvent, nous ne définissons pas strictement la connaissance, mais nous l'alignons avec d'autres concepts associés en utilisant la [pyramide DIKW](https://fr.wikipedia.org/wiki/Pyramide_DIKW). Elle contient les concepts suivants : -* **Données** : quelque chose représenté sur un support physique, comme du texte écrit ou des mots prononcés. Les données existent indépendamment des êtres humains et peuvent être transmises entre eux. -* **Information** : la manière dont nous interprétons les données dans notre esprit. Par exemple, lorsque nous entendons le mot *ordinateur*, nous avons une certaine compréhension de ce que c'est. -* **Connaissance** : l'information intégrée dans notre modèle du monde. Par exemple, une fois que nous apprenons ce qu'est un ordinateur, nous commençons à avoir des idées sur son fonctionnement, son coût et ses utilisations possibles. Ce réseau de concepts interconnectés forme notre connaissance. -* **Sagesse** : un niveau supplémentaire de compréhension du monde, représentant une *méta-connaissance*, c'est-à-dire une notion sur la manière et le moment d'utiliser la connaissance. +* **Les données** sont quelque chose représenté dans un support physique, comme un texte écrit ou des paroles prononcées. Les données existent indépendamment des êtres humains et peuvent être transmises entre personnes. +* **L'information** est la manière dont nous interprétons les données dans notre tête. Par exemple, lorsque nous entendons le mot *ordinateur*, nous avons une certaine compréhension de ce que c'est. +* **La connaissance** est l'information intégrée dans notre modèle du monde. Par exemple, une fois que nous avons appris ce qu'est un ordinateur, nous commençons à avoir des idées sur son fonctionnement, son coût, et son utilisation possible. Ce réseau de concepts interdépendants forme notre connaissance. +* **La sagesse** est encore un niveau supérieur de notre compréhension du monde, et elle représente la *métaconnaissance*, par exemple une notion de comment et quand la connaissance doit être utilisée. - + -*Image [de Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Par Longlivetheux - Travail personnel, CC BY-SA 4.0* +*Image [de Wikipédia](https://commons.wikimedia.org/w/index.php?curid=37705247), Par Longlivetheux - Travail personnel, CC BY-SA 4.0* -Ainsi, le problème de la **représentation des connaissances** consiste à trouver un moyen efficace de représenter les connaissances dans un ordinateur sous forme de données, afin de les rendre utilisables automatiquement. Cela peut être vu comme un spectre : +Ainsi, le problème de la **représentation des connaissances** est de trouver un moyen efficace de représenter la connaissance à l'intérieur d'un ordinateur sous forme de données, afin de la rendre automatiquement utilisable. Ceci peut être vu comme un spectre : -![Spectre de représentation des connaissances](../../../../translated_images/knowledge-spectrum.b60df631852c0217.fr.png) +![Spectre de la représentation des connaissances](../../../../../../translated_images/fr/knowledge-spectrum.b60df631852c0217.webp) > Image par [Dmitry Soshnikov](http://soshnikov.com) -* À gauche, il y a des types de représentations de connaissances très simples qui peuvent être utilisées efficacement par les ordinateurs. La plus simple est algorithmique, où les connaissances sont représentées par un programme informatique. Cependant, ce n'est pas la meilleure façon de représenter les connaissances, car ce n'est pas flexible. Les connaissances dans notre esprit sont souvent non-algorithmiques. -* À droite, il y a des représentations comme le texte naturel. C'est la plus puissante, mais elle ne peut pas être utilisée pour un raisonnement automatique. +* À gauche, il y a des types très simples de représentations des connaissances qui peuvent être efficacement utilisées par les ordinateurs. La plus simple est algorithmique, lorsque la connaissance est représentée par un programme informatique. Ce n'est cependant pas la meilleure manière de représenter la connaissance, car ce n’est pas flexible. La connaissance dans notre tête est souvent non-algorithmique. +* À droite, il y a des représentations telles que le texte naturel. C’est la plus puissante, mais elle ne peut pas être utilisée pour un raisonnement automatique. -> ✅ Prenez un moment pour réfléchir à la manière dont vous représentez les connaissances dans votre esprit et les convertissez en notes. Y a-t-il un format particulier qui fonctionne bien pour vous et facilite la rétention ? +> ✅ Réfléchissez un instant à la façon dont vous représentez la connaissance dans votre tête et la convertissez en notes. Y a-t-il un format particulier qui fonctionne bien pour vous afin d’aider à la rétention ? -## Classification des représentations de connaissances informatiques +## Classification des Représentations Informatiques des Connaissances -Nous pouvons classer les différentes méthodes de représentation des connaissances informatiques dans les catégories suivantes : +Nous pouvons classer différentes méthodes de représentation des connaissances informatiques dans les catégories suivantes : -* **Représentations en réseau** : basées sur le fait que nous avons un réseau de concepts interconnectés dans notre esprit. Nous pouvons essayer de reproduire ces réseaux sous forme de graphe dans un ordinateur - un **réseau sémantique**. +* **Représentations en réseau** reposent sur le fait que nous avons un réseau de concepts interdépendants dans notre tête. Nous pouvons tenter de reproduire ces mêmes réseaux sous forme de graphe dans un ordinateur - un **réseau sémantique**. -1. **Triplets objet-attribut-valeur** ou **paires attribut-valeur**. Étant donné qu'un graphe peut être représenté dans un ordinateur comme une liste de nœuds et d'arêtes, nous pouvons représenter un réseau sémantique par une liste de triplets contenant des objets, des attributs et des valeurs. Par exemple, nous construisons les triplets suivants sur les langages de programmation : +1. **Triplets objet-attribut-valeur** ou **paires attribut-valeur**. Comme un graphe peut être représenté dans un ordinateur sous forme d'une liste de nœuds et d'arêtes, nous pouvons représenter un réseau sémantique par une liste de triplets, contenant objets, attributs et valeurs. Par exemple, nous construisons les triplets suivants à propos des langages de programmation : Objet | Attribut | Valeur -------|----------|------- +-------|-----------|------ Python | est | Langage non typé -Python | inventé par | Guido van Rossum -Python | syntaxe de bloc | indentation -Langage non typé | n'a pas | définitions de type +Python | inventé-par | Guido van Rossum +Python | syntaxe-bloc | indentation +Langage non typé | ne possède pas | définitions de type -> ✅ Réfléchissez à la manière dont les triplets peuvent être utilisés pour représenter d'autres types de connaissances. +> ✅ Réfléchissez à comment les triplets peuvent être utilisés pour représenter d’autres types de connaissances. -2. **Représentations hiérarchiques** : mettent en avant le fait que nous créons souvent une hiérarchie d'objets dans notre esprit. Par exemple, nous savons qu'un canari est un oiseau, et que tous les oiseaux ont des ailes. Nous avons également une idée de la couleur habituelle d'un canari et de sa vitesse de vol. +2. **Représentations hiérarchiques** mettent en avant le fait que nous créons souvent une hiérarchie d’objets dans notre tête. Par exemple, nous savons qu’un canari est un oiseau, et que tous les oiseaux ont des ailes. Nous avons aussi une idée de la couleur habituelle d’un canari et de sa vitesse de vol. - - **Représentation par cadre** : basée sur la représentation de chaque objet ou classe d'objets sous forme de **cadre** contenant des **slots**. Les slots ont des valeurs par défaut possibles, des restrictions de valeur ou des procédures stockées qui peuvent être appelées pour obtenir la valeur d'un slot. Tous les cadres forment une hiérarchie similaire à une hiérarchie d'objets dans les langages de programmation orientés objet. - - **Scénarios** : un type particulier de cadres qui représentent des situations complexes pouvant évoluer dans le temps. + - La **représentation par cadres** est basée sur la représentation de chaque objet ou classe d’objets comme un **cadre** qui contient des **emplacements**. Les emplacements ont des valeurs possibles par défaut, des restrictions de valeurs, ou des procédures stockées qui peuvent être appelées pour obtenir la valeur d’un emplacement. Tous les cadres forment une hiérarchie similaire à une hiérarchie d’objets dans les langages de programmation orientés objet. + - Les **scénarios** sont un type spécial de cadres qui représentent des situations complexes pouvant se dérouler dans le temps. **Python** -Slot | Valeur | Valeur par défaut | Intervalle | ------|--------|-------------------|------------| +Emplacement | Valeur | Valeur par défaut | Intervalle | +-----|-------|---------------|----------| Nom | Python | | | Est-Un | Langage non typé | | | -Casse des variables | | CamelCase | | +Casse variable | | CamelCase | | Longueur du programme | | | 5-5000 lignes | Syntaxe de bloc | Indentation | | | -3. **Représentations procédurales** : basées sur la représentation des connaissances par une liste d'actions pouvant être exécutées lorsqu'une certaine condition se produit. - - Les règles de production sont des déclarations si-alors qui permettent de tirer des conclusions. Par exemple, un médecin peut avoir une règle disant que **SI** un patient a une forte fièvre **OU** un niveau élevé de protéine C-réactive dans un test sanguin **ALORS** il a une inflammation. Une fois que nous rencontrons une des conditions, nous pouvons conclure à une inflammation, puis l'utiliser dans un raisonnement ultérieur. - - Les algorithmes peuvent être considérés comme une autre forme de représentation procédurale, bien qu'ils ne soient presque jamais utilisés directement dans les systèmes basés sur les connaissances. +3. **Représentations procédurales** sont basées sur la représentation des connaissances par une liste d’actions qui peuvent être exécutées lorsqu’une certaine condition se produit. + - Les règles de production sont des instructions de type si-alors qui nous permettent de tirer des conclusions. Par exemple, un médecin peut avoir une règle disant que **SI** un patient a une forte fièvre **OU** un taux élevé de protéine C-réactive dans le test sanguin **ALORS** il a une inflammation. Dès que nous rencontrons une des conditions, nous pouvons conclure une inflammation, puis l’utiliser dans un raisonnement ultérieur. + - Les algorithmes peuvent être considérés comme une autre forme de représentation procédurale, bien qu’ils soient presque jamais utilisés directement dans les systèmes à base de connaissances. -4. **Logique** : initialement proposée par Aristote comme moyen de représenter les connaissances humaines universelles. - - La logique des prédicats, en tant que théorie mathématique, est trop riche pour être calculable, donc un sous-ensemble est normalement utilisé, comme les clauses de Horn utilisées dans Prolog. - - La logique descriptive est une famille de systèmes logiques utilisés pour représenter et raisonner sur des hiérarchies d'objets et des représentations de connaissances distribuées comme le *web sémantique*. +4. **La logique** a été initialement proposée par Aristote comme moyen de représenter la connaissance humaine universelle. + - La logique des prédicats, en tant que théorie mathématique, est trop riche pour être calculable, donc un sous-ensemble est généralement utilisé, comme les clauses Horn utilisées en Prolog. + - La logique descriptive est une famille de systèmes logiques utilisés pour représenter et raisonner sur des hiérarchies d’objets et des représentations de connaissances distribuées telles que le *web sémantique*. -## Systèmes experts +## Systèmes Experts -Un des premiers succès de l'IA symbolique a été les **systèmes experts** - des systèmes informatiques conçus pour agir comme un expert dans un domaine problématique limité. Ils étaient basés sur une **base de connaissances** extraite d'un ou plusieurs experts humains, et contenaient un **moteur d'inférence** qui effectuait un raisonnement dessus. +Un des premiers succès de l’IA symbolique fut les systèmes dits **experts** – des systèmes informatiques conçus pour agir en tant qu’expert dans un domaine de problème limité. Ils étaient basés sur une **base de connaissances** extraite de un ou plusieurs experts humains, et contenaient un **moteur d’inférence** qui effectuait un raisonnement sur cette base. -![Architecture humaine](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.fr.png) | ![Système basé sur les connaissances](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.fr.png) ------------------------------------------------|------------------------------------------------------ -Structure simplifiée du système neural humain | Architecture d'un système basé sur les connaissances +![Architecture Humaine](../../../../../../translated_images/fr/arch-human.5d4d35f1bba3ab1c.webp) | ![Système à base de connaissances](../../../../../../translated_images/fr/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +Structure simplifiée du système neural humain | Architecture d’un système à base de connaissances -Les systèmes experts sont construits comme le système de raisonnement humain, qui contient une **mémoire à court terme** et une **mémoire à long terme**. De même, dans les systèmes basés sur les connaissances, nous distinguons les composants suivants : +Les systèmes experts sont construits comme le système de raisonnement humain, qui contient une **mémoire à court terme** et une **mémoire à long terme**. De façon similaire, dans les systèmes à base de connaissances nous distinguons les composants suivants : -* **Mémoire du problème** : contient les connaissances sur le problème en cours de résolution, c'est-à-dire la température ou la pression artérielle d'un patient, s'il a une inflammation ou non, etc. Ces connaissances sont également appelées **connaissances statiques**, car elles contiennent un instantané de ce que nous savons actuellement sur le problème - l'état du problème. -* **Base de connaissances** : représente les connaissances à long terme sur un domaine problématique. Elle est extraite manuellement des experts humains et ne change pas d'une consultation à l'autre. Comme elle permet de naviguer d'un état de problème à un autre, elle est également appelée **connaissances dynamiques**. -* **Moteur d'inférence** : orchestre tout le processus de recherche dans l'espace des états du problème, pose des questions à l'utilisateur si nécessaire. Il est également responsable de trouver les bonnes règles à appliquer à chaque état. +* **Mémoire du problème** : contient les connaissances à propos du problème en cours de résolution, par exemple la température ou la pression sanguine d’un patient, s’il a une inflammation ou non, etc. Cette connaissance est aussi appelée **connaissance statique**, car elle contient un instantané de ce que nous savons actuellement sur le problème – l’appelé *état du problème*. +* **Base de connaissances** : représente la connaissance à long terme à propos d’un domaine de problème. Elle est extraite manuellement de spécialistes humains, et ne change pas de consultation en consultation. Parce qu’elle permet de naviguer d’un état du problème à un autre, elle est aussi appelée **connaissance dynamique**. +* **Moteur d'inférence** : orchestre l’ensemble du processus de recherche dans l’espace des états du problème, posant des questions à l’utilisateur quand nécessaire. Il est aussi responsable de trouver les bonnes règles à appliquer à chaque état. -À titre d'exemple, considérons le système expert suivant pour déterminer un animal en fonction de ses caractéristiques physiques : +Par exemple, considérons le système expert suivant permettant de déterminer un animal en fonction de ses caractéristiques physiques : -![Arbre AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.fr.png) +![Arbre ET-OU](../../../../../../translated_images/fr/AND-OR-Tree.5592d2c70187f283.webp) > Image par [Dmitry Soshnikov](http://soshnikov.com) -Ce diagramme est appelé un **arbre AND-OR**, et il représente graphiquement un ensemble de règles de production. Dessiner un arbre est utile au début de l'extraction des connaissances de l'expert. Pour représenter les connaissances dans l'ordinateur, il est plus pratique d'utiliser des règles : +Ce diagramme est appelé un **arbre ET-OU**, et c’est une représentation graphique d’un ensemble de règles de production. Dessiner un arbre est utile au début de l’extraction des connaissances auprès de l’expert. Pour représenter la connaissance dans l’ordinateur il est plus pratique d’utiliser des règles : ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Vous pouvez remarquer que chaque condition sur le côté gauche de la règle et l'action sont essentiellement des triplets objet-attribut-valeur (OAV). La **mémoire de travail** contient l'ensemble des triplets OAV correspondant au problème en cours de résolution. Un **moteur de règles** recherche les règles dont une condition est satisfaite et les applique, ajoutant un autre triplet à la mémoire de travail. +Vous pouvez remarquer que chaque condition du côté gauche de la règle et l'action sont essentiellement des triplets objet-attribut-valeur (OAV). La **mémoire de travail** contient l’ensemble des triplets OAV correspondant au problème en cours de résolution. Un **moteur de règles** recherche les règles dont une condition est satisfaite et les applique, ajoutant un autre triplet à la mémoire de travail. -> ✅ Créez votre propre arbre AND-OR sur un sujet qui vous intéresse ! +> ✅ Écrivez votre propre arbre ET-OU sur un sujet qui vous intéresse ! -### Inférence avant vs. arrière +### Inférence Avant (Forward) vs. Arrière (Backward) -Le processus décrit ci-dessus est appelé **inférence avant**. Il commence avec des données initiales sur le problème disponibles dans la mémoire de travail, puis exécute la boucle de raisonnement suivante : +Le processus décrit ci-dessus est appelé **inférence avant**. Il commence avec certaines données initiales sur le problème disponibles dans la mémoire de travail, puis exécute la boucle de raisonnement suivante : -1. Si l'attribut cible est présent dans la mémoire de travail - arrêter et donner le résultat -2. Rechercher toutes les règles dont la condition est actuellement satisfaite - obtenir un **ensemble de conflit** de règles. -3. Effectuer une **résolution de conflit** - sélectionner une règle qui sera exécutée à cette étape. Il peut y avoir différentes stratégies de résolution de conflit : +1. Si l’attribut cible est présent dans la mémoire de travail – arrêter et donner le résultat +2. Rechercher toutes les règles dont la condition est actuellement satisfaite – obtenir un **ensemble de conflits** de règles. +3. Effectuer une **résolution de conflit** – sélectionner une règle qui sera exécutée à cette étape. Il peut y avoir différentes stratégies de résolution de conflit : - Sélectionner la première règle applicable dans la base de connaissances - Sélectionner une règle aléatoire - - Sélectionner une règle *plus spécifique*, c'est-à-dire celle qui satisfait le plus de conditions dans le côté gauche (LHS) -4. Appliquer la règle sélectionnée et insérer une nouvelle pièce de connaissance dans l'état du problème -5. Recommencer à l'étape 1. + - Sélectionner une règle *plus spécifique*, c’est-à-dire celle qui satisfait le plus de conditions dans le « côté gauche » (LHS) +4. Appliquer la règle sélectionnée et insérer un nouveau morceau de connaissance dans l’état du problème +5. Répéter à partir de l’étape 1. -Cependant, dans certains cas, nous pourrions vouloir commencer avec une connaissance vide sur le problème et poser des questions qui nous aideront à arriver à une conclusion. Par exemple, lors d'un diagnostic médical, nous ne réalisons généralement pas toutes les analyses médicales à l'avance avant de commencer à diagnostiquer le patient. Nous préférons effectuer des analyses lorsque cela est nécessaire pour prendre une décision. +Cependant, dans certains cas on peut vouloir commencer avec une connaissance vide du problème, et poser des questions qui nous aideront à arriver à la conclusion. Par exemple, lors d’un diagnostic médical, nous ne réalisons généralement pas tous les tests médicaux à l’avance avant de commencer à diagnostiquer le patient. Nous préférons faire des analyses quand il faut prendre une décision. -Ce processus peut être modélisé en utilisant **l'inférence arrière**. Il est guidé par le **but** - la valeur de l'attribut que nous cherchons à trouver : +Ce processus peut être modélisé par une **inférence arrière**. Elle est guidée par le **but** – la valeur d’un attribut que nous cherchons à trouver : -1. Sélectionner toutes les règles qui peuvent nous donner la valeur d'un but (c'est-à-dire avec le but sur le côté droit (RHS)) - un ensemble de conflit -1. S'il n'y a pas de règles pour cet attribut, ou s'il existe une règle indiquant que nous devrions demander la valeur à l'utilisateur - la demander, sinon : -1. Utiliser une stratégie de résolution de conflit pour sélectionner une règle que nous utiliserons comme *hypothèse* - nous essaierons de la prouver -1. Répéter récursivement le processus pour tous les attributs dans le LHS de la règle, en essayant de les prouver comme des buts -1. Si à un moment donné le processus échoue - utiliser une autre règle à l'étape 3. +1. Sélectionner toutes les règles pouvant nous donner la valeur d’un but (c’est-à-dire avec le but dans le RHS (« côté droit »)) – un ensemble de conflits +1. S’il n’y a pas de règles pour cet attribut, ou s’il existe une règle disant que nous devons demander la valeur à l’utilisateur – poser la question, sinon : +1. Utiliser la stratégie de résolution des conflits pour sélectionner une règle que nous utiliserons comme *hypothèse* – nous tenterons de la prouver +1. Répéter de manière récurrente le processus pour tous les attributs dans le LHS de la règle, en essayant de les prouver en tant que buts +1. Si à un moment donné le processus échoue – utiliser une autre règle à l’étape 3. > ✅ Dans quelles situations l'inférence avant est-elle plus appropriée ? Et l'inférence arrière ? -### Implémentation des systèmes experts +### Implémentation des Systèmes Experts -Les systèmes experts peuvent être implémentés en utilisant différents outils : +Les systèmes experts peuvent être implémentés avec différents outils : -* Les programmer directement dans un langage de programmation de haut niveau. Ce n'est pas la meilleure idée, car l'avantage principal d'un système basé sur les connaissances est que les connaissances sont séparées de l'inférence, et un expert du domaine problématique devrait potentiellement pouvoir écrire des règles sans comprendre les détails du processus d'inférence. -* Utiliser un **shell de système expert**, c'est-à-dire un système spécialement conçu pour être alimenté par des connaissances en utilisant un langage de représentation des connaissances. +* Programmation directe dans un langage de programmation de haut niveau. Ce n’est pas la meilleure idée, car l’avantage principal d’un système à base de connaissances est que la connaissance est séparée de l’inférence, et potentiellement un expert du domaine devrait pouvoir écrire des règles sans comprendre les détails du processus d’inférence +* Utilisation d’un **shell de systèmes experts**, c’est-à-dire un système spécialement conçu pour être rempli de connaissances à l’aide d’un langage de représentation des connaissances. -## ✍️ Exercice : Inférence animale +## ✍️ Exercice : Inférence Animale -Voir [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) pour un exemple d'implémentation d'un système expert d'inférence avant et arrière. +Consultez [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) pour un exemple d’implémentation d’un système expert d’inférence avant et arrière. -> **Note** : Cet exemple est assez simple et donne seulement une idée de l'apparence d'un système expert. Une fois que vous commencez à créer un tel système, vous ne remarquerez un comportement *intelligent* qu'une fois que vous atteignez un certain nombre de règles, environ 200+. À un moment donné, les règles deviennent trop complexes pour toutes les garder en tête, et à ce moment-là, vous pourriez commencer à vous demander pourquoi un système prend certaines décisions. Cependant, une caractéristique importante des systèmes basés sur les connaissances est que vous pouvez toujours *expliquer* exactement comment chacune des décisions a été prise. +> **Note** : Cet exemple est assez simple, et donne seulement une idée de l’aspect d’un système expert. Une fois que vous commencez à créer un tel système, vous ne remarquerez un comportement *intelligent* qu’une fois que vous aurez atteint un certain nombre de règles, environ 200+. À un moment donné, les règles deviennent trop complexes pour toutes les garder en mémoire, et c’est alors que vous pouvez vous demander pourquoi un système prend certaines décisions. Cependant, le trait important des systèmes à base de connaissances est que vous pouvez toujours *expliquer* exactement comment une décision a été prise. -## Ontologies et le web sémantique +## Ontologies et le Web Sémantique -À la fin du 20e siècle, une initiative a été lancée pour utiliser la représentation des connaissances afin d'annoter les ressources Internet, afin qu'il soit possible de trouver des ressources correspondant à des requêtes très spécifiques. Ce mouvement a été appelé **web sémantique**, et il reposait sur plusieurs concepts : +À la fin du 20ème siècle, une initiative a vu le jour pour utiliser la représentation des connaissances afin d’annoter les ressources Internet, de façon à pouvoir trouver des ressources correspondant à des requêtes très spécifiques. Ce mouvement s’appelait **le Web Sémantique**, et il s’appuyait sur plusieurs concepts : -- Une représentation des connaissances spéciale basée sur **[logiques descriptives](https://en.wikipedia.org/wiki/Description_logic)** (DL). Elle est similaire à la représentation par cadre, car elle construit une hiérarchie d'objets avec des propriétés, mais elle a une sémantique logique formelle et une inférence. Il existe toute une famille de DL qui équilibrent entre expressivité et complexité algorithmique de l'inférence. -- Une représentation des connaissances distribuée, où tous les concepts sont représentés par un identifiant URI global, rendant possible la création de hiérarchies de connaissances qui s'étendent sur Internet. +- Une représentation spéciale des connaissances basée sur les **[logiques descriptives](https://fr.wikipedia.org/wiki/Logique_descriptive)** (DL). Elle est similaire à la représentation de connaissances par cadres, car elle construit une hiérarchie d’objets avec propriétés, mais elle possède une sémantique logique formelle et permet l’inférence. Il existe toute une famille de DL qui équilibrent expressivité et complexité algorithmique de l’inférence. +- Une représentation des connaissances distribuée, où tous les concepts sont représentés par un identifiant URI global, ce qui permet de créer des hiérarchies de connaissances couvrant Internet. - Une famille de langages basés sur XML pour la description des connaissances : RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Un concept central dans le Web sémantique est celui de **l'ontologie**. Il s'agit d'une spécification explicite d'un domaine problématique utilisant une représentation formelle des connaissances. L'ontologie la plus simple peut être une simple hiérarchie d'objets dans un domaine donné, mais les ontologies plus complexes incluront des règles pouvant être utilisées pour l'inférence. +Un concept clé du Web sémantique est celui d'**Ontologie**. Il se réfère à une spécification explicite d'un domaine problématique en utilisant une représentation formelle de la connaissance. L'ontologie la plus simple peut être juste une hiérarchie d'objets dans un domaine problématique, mais les ontologies plus complexes incluront des règles pouvant être utilisées pour l'inférence. -Dans le Web sémantique, toutes les représentations sont basées sur des triplets. Chaque objet et chaque relation sont identifiés de manière unique par un URI. Par exemple, si nous voulons indiquer que ce programme d'IA a été développé par Dmitry Soshnikov le 1er janvier 2022, voici les triplets que nous pouvons utiliser : +Dans le web sémantique, toutes les représentations sont basées sur des triplets. Chaque objet et chaque relation sont identifiés de manière unique par l'URI. Par exemple, si nous voulons énoncer le fait que ce curriculum d'IA a été développé par Dmitry Soshnikov le 1er janvier 2022 - voici les triplets que nous pouvons utiliser : - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Ici, `http://www.example.com/terms/creation-date` et `http://purl.org/dc/elements/1.1/creator` sont des URIs bien connus et universellement acceptés pour exprimer les concepts de *créateur* et de *date de création*. +> ✅ Ici, `http://www.example.com/terms/creation-date` et `http://purl.org/dc/elements/1.1/creator` sont des URI bien connus et universellement acceptés pour exprimer les concepts de *créateur* et de *date de création*. Dans un cas plus complexe, si nous voulons définir une liste de créateurs, nous pouvons utiliser certaines structures de données définies dans RDF. - + -> Diagrammes ci-dessus par [Dmitry Soshnikov](http://soshnikov.com) +> Schémas ci-dessus par [Dmitry Soshnikov](http://soshnikov.com) -Le développement du Web sémantique a été quelque peu ralenti par le succès des moteurs de recherche et des techniques de traitement du langage naturel, qui permettent d'extraire des données structurées à partir de textes. Cependant, dans certains domaines, des efforts significatifs sont encore déployés pour maintenir des ontologies et des bases de connaissances. Quelques projets notables : +Le progrès dans la construction du Web sémantique a été en quelque sorte ralenti par le succès des moteurs de recherche et des techniques de traitement du langage naturel, qui permettent d'extraire des données structurées à partir de textes. Cependant, dans certains domaines, il y a encore des efforts significatifs pour maintenir des ontologies et des bases de connaissances. Quelques projets méritent d’être mentionnés : -* [WikiData](https://wikidata.org/) est une collection de bases de connaissances lisibles par machine associées à Wikipédia. La plupart des données sont extraites des *InfoBoxes* de Wikipédia, des morceaux de contenu structuré dans les pages de Wikipédia. Vous pouvez [interroger](https://query.wikidata.org/) WikiData en SPARQL, un langage de requête spécial pour le Web sémantique. Voici un exemple de requête qui affiche les couleurs d'yeux les plus populaires chez les humains : +* [WikiData](https://wikidata.org/) est une collection de bases de connaissances lisibles par machine associées à Wikipédia. La plupart des données sont extraites des *InfoBoxes* de Wikipédia, des morceaux de contenu structuré à l’intérieur des pages Wikipédia. Vous pouvez [interroger](https://query.wikidata.org/) Wikidata en SPARQL, un langage de requête spécial pour le Web sémantique. Voici une requête d'exemple qui affiche les couleurs d'yeux les plus populaires chez les humains : ```sparql #defaultView:BubbleChart @@ -206,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) est un autre projet similaire à WikiData. +* [DBpedia](https://www.dbpedia.org/) est un autre effort similaire à WikiData. -> ✅ Si vous souhaitez expérimenter la création de vos propres ontologies ou explorer celles existantes, il existe un excellent éditeur visuel d'ontologies appelé [Protégé](https://protege.stanford.edu/). Téléchargez-le ou utilisez-le en ligne. +> ✅ Si vous souhaitez expérimenter la création de vos propres ontologies, ou ouvrir des ontologies existantes, il existe un excellent éditeur d'ontologies visuel appelé [Protégé](https://protege.stanford.edu/). Téléchargez-le ou utilisez-le en ligne. - + *Éditeur Web Protégé ouvert avec l'ontologie de la famille Romanov. Capture d'écran par Dmitry Soshnikov* -## ✍️ Exercice : Une ontologie familiale +## ✍️ Exercice : Une ontologie de famille -Consultez [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) pour un exemple d'utilisation des techniques du Web sémantique afin de raisonner sur les relations familiales. Nous prendrons un arbre généalogique représenté dans le format GEDCOM courant et une ontologie des relations familiales pour construire un graphe de toutes les relations familiales pour un ensemble donné d'individus. + +Voir [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) pour un exemple d'utilisation des techniques du Web sémantique afin de raisonner sur les relations familiales. Nous prendrons un arbre généalogique représenté dans le format GEDCOM courant et une ontologie des relations familiales pour construire un graphe de toutes les relations familiales pour un ensemble donné d'individus. ## Microsoft Concept Graph -Dans la plupart des cas, les ontologies sont soigneusement créées à la main. Cependant, il est également possible de **extraire** des ontologies à partir de données non structurées, par exemple, à partir de textes en langage naturel. +Dans la plupart des cas, les ontologies sont soigneusement créées à la main. Cependant, il est aussi possible de **miner** des ontologies à partir de données non structurées, par exemple, à partir de textes en langage naturel. -Une telle tentative a été réalisée par Microsoft Research, et a abouti au [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Une telle tentative a été faite par Microsoft Research, et a donné lieu au [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Il s'agit d'une vaste collection d'entités regroupées selon une relation d'héritage `is-a`. Cela permet de répondre à des questions comme "Qu'est-ce que Microsoft ?" - la réponse étant quelque chose comme "une entreprise avec une probabilité de 0,87, et une marque avec une probabilité de 0,75". +C’est une grande collection d'entités regroupées en utilisant la relation d'héritage `is-a`. Cela permet de répondre à des questions comme « Qu'est-ce que Microsoft ? » - la réponse étant quelque chose comme « une entreprise avec une probabilité de 0.87, et une marque avec une probabilité de 0.75 ». -Le Graph est disponible soit via une API REST, soit sous forme d'un grand fichier texte téléchargeable qui liste tous les paires d'entités. +Le graphe est disponible soit en tant qu'API REST, soit sous forme d’un grand fichier texte téléchargeable listant toutes les paires d'entités. -## ✍️ Exercice : Un graphe de concepts +## ✍️ Exercice : Un graph des concepts Essayez le notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) pour voir comment nous pouvons utiliser Microsoft Concept Graph pour regrouper des articles de presse en plusieurs catégories. ## Conclusion -De nos jours, l'IA est souvent considérée comme synonyme de *Machine Learning* ou de *Réseaux neuronaux*. Cependant, un être humain fait également preuve de raisonnement explicite, ce qui est quelque chose que les réseaux neuronaux ne gèrent pas actuellement. Dans les projets réels, le raisonnement explicite est encore utilisé pour effectuer des tâches nécessitant des explications ou la capacité de modifier le comportement du système de manière contrôlée. +De nos jours, l’IA est souvent considérée comme synonyme de *Machine Learning* ou de *Réseaux neuronaux*. Cependant, un être humain manifeste aussi un raisonnement explicite, ce qui est quelque chose que les réseaux neuronaux ne gèrent pas actuellement. Dans les projets réels, le raisonnement explicite est encore utilisé pour effectuer des tâches qui nécessitent des explications, ou la capacité de modifier le comportement du système de manière contrôlée. ## 🚀 Défi -Dans le notebook sur l'ontologie familiale associé à cette leçon, il y a une opportunité d'expérimenter avec d'autres relations familiales. Essayez de découvrir de nouvelles connexions entre les personnes dans l'arbre généalogique. +Dans le notebook de l'ontologie de famille associé à cette leçon, il y a une opportunité d’expérimenter avec d’autres relations familiales. Essayez de découvrir de nouvelles connexions entre les personnes dans l’arbre généalogique. -## [Quiz post-lecture](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Quiz post-cours](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Revue & Auto-apprentissage +## Révision & Auto-apprentissage -Faites des recherches sur Internet pour découvrir des domaines où les humains ont essayé de quantifier et de codifier les connaissances. Jetez un œil à la Taxonomie de Bloom, et remontez dans l'histoire pour apprendre comment les humains ont essayé de comprendre leur monde. Explorez le travail de Linné pour créer une taxonomie des organismes, et observez la manière dont Dmitri Mendeleïev a créé un système pour décrire et regrouper les éléments chimiques. Quels autres exemples intéressants pouvez-vous trouver ? +Faites des recherches sur internet pour découvrir des domaines où les humains ont essayé de quantifier et codifier les connaissances. Regardez la Taxonomie de Bloom, et remontez dans l’histoire pour apprendre comment les humains ont essayé de comprendre leur monde. Explorez le travail de Linné pour créer une taxonomie des organismes, et observez la manière dont Dmitri Mendeleev a créé une méthode pour décrire et grouper les éléments chimiques. Quels autres exemples intéressants pouvez-vous trouver ? **Devoir** : [Construire une ontologie](assignment.md) --- + +**Avertissement** : +Ce document a été traduit à l’aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforçons d’assurer l’exactitude, veuillez noter que les traductions automatiques peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d’origine doit être considéré comme la source faisant foi. Pour les informations cruciales, une traduction professionnelle réalisée par un humain est recommandée. Nous déclinons toute responsabilité en cas de malentendus ou de mauvaises interprétations résultant de l’utilisation de cette traduction. + \ No newline at end of file diff --git a/translations/fr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/fr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 0ac1e21d..17716cbb 100644 --- a/translations/fr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/fr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Le surapprentissage est un concept extrêmement important en apprentissage autom Considérons le problème suivant d'approximation de 5 points (représentés par des `x` sur les graphiques ci-dessous) : -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.fr.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.fr.jpg) +![linear](../../../../../translated_images/fr/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/fr/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Modèle linéaire, 2 paramètres** | **Modèle non linéaire, 7 paramètres** Erreur d'entraînement = 5.3 | Erreur d'entraînement = 0 @@ -79,7 +79,7 @@ Il est crucial de trouver un équilibre correct entre la richesse du modèle (no Comme vous pouvez le voir sur le graphique ci-dessus, le surapprentissage peut être détecté par une erreur d'entraînement très faible et une erreur de validation élevée. Normalement, pendant l'entraînement, nous verrons les erreurs d'entraînement et de validation commencer à diminuer, puis à un certain moment, l'erreur de validation pourrait arrêter de diminuer et commencer à augmenter. Ce sera un signe de surapprentissage, et un indicateur que nous devrions probablement arrêter l'entraînement à ce moment-là (ou au moins faire une sauvegarde du modèle). -![surapprentissage](../../../../../translated_images/Overfitting.408ad91cd90b4371.fr.png) +![surapprentissage](../../../../../translated_images/fr/Overfitting.408ad91cd90b4371.webp) ## Comment prévenir le surapprentissage ? diff --git a/translations/fr/lessons/3-NeuralNetworks/README.md b/translations/fr/lessons/3-NeuralNetworks/README.md index ea72b168..3edf05e9 100644 --- a/translations/fr/lessons/3-NeuralNetworks/README.md +++ b/translations/fr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduction aux réseaux neuronaux -![Résumé du contenu d'introduction aux réseaux neuronaux sous forme de dessin](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.fr.png) +![Résumé du contenu d'introduction aux réseaux neuronaux sous forme de dessin](../../../../translated_images/fr/ai-neuralnetworks.1c687ae40bc86e83.webp) Comme nous l'avons vu dans l'introduction, l'une des façons d'atteindre l'intelligence est d'entraîner un **modèle informatique** ou un **cerveau artificiel**. Depuis le milieu du XXe siècle, les chercheurs ont expérimenté différents modèles mathématiques, jusqu'à ce que cette approche s'avère extrêmement fructueuse ces dernières années. Ces modèles mathématiques du cerveau sont appelés **réseaux neuronaux**. @@ -36,13 +36,13 @@ Dans ce programme, nous nous concentrerons uniquement sur les modèles de résea En biologie, nous savons que notre cerveau est constitué de cellules neuronales (neurones), chacune ayant plusieurs "entrées" (dendrites) et une seule "sortie" (axone). Les dendrites et les axones peuvent conduire des signaux électriques, et les connexions entre eux — appelées synapses — peuvent présenter des degrés de conductivité variables, régulés par des neurotransmetteurs. -![Modèle d'un neurone](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.fr.jpg) | ![Modèle d'un neurone](../../../../translated_images/artneuron.1a5daa88d20ebe6f.fr.png) +![Modèle d'un neurone](../../../../translated_images/fr/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modèle d'un neurone](../../../../translated_images/fr/artneuron.1a5daa88d20ebe6f.webp) ----|---- Neurone réel *([Image](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) de Wikipédia)* | Neurone artificiel *(Image par l'auteur)* Ainsi, le modèle mathématique le plus simple d'un neurone contient plusieurs entrées X1, ..., XN et une sortie Y, ainsi qu'une série de poids W1, ..., WN. Une sortie est calculée comme suit : -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) où **f** est une **fonction d'activation** non linéaire. diff --git a/translations/fr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/fr/lessons/4-ComputerVision/06-IntroCV/README.md index 58a1f9b8..6072ebb4 100644 --- a/translations/fr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/fr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Dans notre [OpenCV Notebook](OpenCV.ipynb), nous donnons quelques exemples où l * **Prétraitement d'une photographie d'un livre en braille**. Nous nous concentrons sur la manière dont nous pouvons utiliser le seuil, la détection de caractéristiques, la transformation de perspective et les manipulations NumPy pour séparer les symboles individuels en braille pour une classification ultérieure par un réseau neuronal. -![Image Braille](../../../../../translated_images/braille.341962ff76b1bd70.fr.jpeg) | ![Image Braille prétraitée](../../../../../translated_images/braille-result.46530fea020b03c7.fr.png) | ![Symboles Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.fr.png) +![Image Braille](../../../../../translated_images/fr/braille.341962ff76b1bd70.webp) | ![Image Braille prétraitée](../../../../../translated_images/fr/braille-result.46530fea020b03c7.webp) | ![Symboles Braille](../../../../../translated_images/fr/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Image tirée de [OpenCV.ipynb](OpenCV.ipynb) * **Détection de mouvement dans une vidéo à l'aide de la différence entre les images**. Si la caméra est fixe, les images du flux de la caméra devraient être assez similaires les unes aux autres. Étant donné que les images sont représentées sous forme de tableaux, en soustrayant simplement ces tableaux pour deux images consécutives, nous obtiendrons la différence de pixels, qui devrait être faible pour des images statiques, et devenir plus élevée lorsqu'il y a un mouvement substantiel dans l'image. -![Image des images vidéo et des différences entre les images](../../../../../translated_images/frame-difference.706f805491a0883c.fr.png) +![Image des images vidéo et des différences entre les images](../../../../../translated_images/fr/frame-difference.706f805491a0883c.webp) > Image tirée de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Dans notre [OpenCV Notebook](OpenCV.ipynb), nous donnons quelques exemples où l - **Flux optique dense** calcule le champ vectoriel qui montre pour chaque pixel où il se déplace. - **Flux optique clairsemé** est basé sur la prise de certaines caractéristiques distinctives dans l'image (par exemple, les contours) et la construction de leur trajectoire d'une image à l'autre. -![Image du flux optique](../../../../../translated_images/optical.1f4a94464579a83a.fr.png) +![Image du flux optique](../../../../../translated_images/fr/optical.1f4a94464579a83a.webp) > Image tirée de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/fr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/fr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 80ff1b11..dd24c70c 100644 --- a/translations/fr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/fr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 est un réseau qui a atteint une précision de 92,7 % dans la classification top-5 d'ImageNet en 2014. Il possède la structure de couches suivante : -![Couches ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fr.jpg) +![Couches ImageNet](../../../../../translated_images/fr/vgg-16-arch1.d901a5583b3a51ba.webp) Comme vous pouvez le voir, VGG suit une architecture pyramidale traditionnelle, qui est une séquence de couches de convolution et de pooling. -![Pyramide ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fr.jpg) +![Pyramide ImageNet](../../../../../translated_images/fr/vgg-16-arch.64ff2137f50dd49f.webp) > Image tirée de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/fr/lessons/4-ComputerVision/07-ConvNets/README.md index 80049a2f..8994f3d6 100644 --- a/translations/fr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/fr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Dans la vie réelle, nous souhaitons pouvoir reconnaître des objets sur une ima Pour extraire ces motifs, nous utiliserons la notion de **filtres convolutionnels**. Comme vous le savez, une image est représentée par une matrice 2D ou un tenseur 3D avec une profondeur de couleur. Appliquer un filtre signifie que nous prenons une matrice relativement petite appelée **noyau de filtre**, et pour chaque pixel de l'image originale, nous calculons la moyenne pondérée avec les points voisins. On peut voir cela comme une petite fenêtre glissant sur toute l'image et moyennant tous les pixels selon les poids du noyau de filtre. -![Filtre de bord vertical](../../../../../translated_images/filter-vert.b7148390ca0bc356.fr.png) | ![Filtre de bord horizontal](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.fr.png) +![Filtre de bord vertical](../../../../../translated_images/fr/filter-vert.b7148390ca0bc356.webp) | ![Filtre de bord horizontal](../../../../../translated_images/fr/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Image par Dmitry Soshnikov @@ -38,7 +38,7 @@ Le fonctionnement des CNN repose sur les idées importantes suivantes : * Nous pouvons concevoir le réseau de manière à ce que les filtres soient entraînés automatiquement. * Nous pouvons utiliser la même approche pour détecter des motifs dans des caractéristiques de haut niveau, et pas seulement dans l'image originale. Ainsi, l'extraction de caractéristiques par les CNN fonctionne sur une hiérarchie de caractéristiques, en commençant par des combinaisons de pixels de bas niveau jusqu'à des combinaisons de parties d'image de haut niveau. -![Extraction hiérarchique de caractéristiques](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.fr.png) +![Extraction hiérarchique de caractéristiques](../../../../../translated_images/fr/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Image tirée [d'un article de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basé sur [leurs recherches](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ La plupart des CNN utilisés pour le traitement d'images suivent une architectur À titre d'exemple, examinons l'architecture de VGG-16, un réseau qui a atteint une précision de 92,7 % dans la classification top-5 d'ImageNet en 2014 : -![Couches d'ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.fr.jpg) +![Couches d'ImageNet](../../../../../translated_images/fr/vgg-16-arch1.d901a5583b3a51ba.webp) -![Pyramide d'ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.fr.jpg) +![Pyramide d'ImageNet](../../../../../translated_images/fr/vgg-16-arch.64ff2137f50dd49f.webp) > Image tirée de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/fr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/fr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 7053ce97..bebf65fd 100644 --- a/translations/fr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/fr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vous devez entraîner un réseau neuronal convolutif pour classifier différente Nous utiliserons le [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), qui contient des images de 37 races différentes de chiens et de chats. -![Dataset avec lequel nous travaillerons](../../../../../../translated_images/data.50b2a9d5484bdbf0.fr.png) +![Dataset avec lequel nous travaillerons](../../../../../../translated_images/fr/data.50b2a9d5484bdbf0.webp) Pour télécharger le dataset, utilisez ce fragment de code : diff --git a/translations/fr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/fr/lessons/4-ComputerVision/08-TransferLearning/README.md index 56810308..913c0cae 100644 --- a/translations/fr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/fr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras et PyTorch proposent des fonctions permettant de charger facilement les po Voici des exemples de caractéristiques extraites d'une image de chat par le réseau VGG-16 : -![Caractéristiques extraites par VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.fr.png) +![Caractéristiques extraites par VGG-16](../../../../../translated_images/fr/features.6291f9c7ba3a0b95.webp) ## Ensemble de données "Chats vs. Chiens" @@ -48,19 +48,19 @@ Un réseau neuronal pré-entraîné contient différents motifs dans son *cervea Une approche consiste à partir d'une image aléatoire et à utiliser une technique d'optimisation par **descente de gradient** pour ajuster cette image de manière à ce que le réseau commence à penser qu'il s'agit d'un chat. -![Boucle d'optimisation d'image](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.fr.png) +![Boucle d'optimisation d'image](../../../../../translated_images/fr/ideal-cat-loop.999fbb8ff306e044.webp) Cependant, si nous faisons cela, nous obtiendrons quelque chose qui ressemble beaucoup à un bruit aléatoire. Cela s'explique par le fait qu'*il existe de nombreuses façons de faire croire au réseau que l'image d'entrée est un chat*, y compris certaines qui n'ont aucun sens visuel. Bien que ces images contiennent de nombreux motifs typiques d'un chat, rien ne les contraint à être visuellement distinctives. Pour améliorer le résultat, nous pouvons ajouter un autre terme à la fonction de perte, appelé **perte de variation**. Il s'agit d'une métrique qui montre à quel point les pixels voisins de l'image sont similaires. Minimiser la perte de variation rend l'image plus lisse et élimine le bruit, révélant ainsi des motifs plus visuellement attrayants. Voici un exemple de telles images "idéales", classées comme chat et comme zèbre avec une forte probabilité : -![Chat idéal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.fr.png) | ![Zèbre idéal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.fr.png) +![Chat idéal](../../../../../translated_images/fr/ideal-cat.203dd4597643d6b0.webp) | ![Zèbre idéal](../../../../../translated_images/fr/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Chat idéal* | *Zèbre idéal* Une approche similaire peut être utilisée pour effectuer des **attaques adversariales** sur un réseau neuronal. Supposons que nous souhaitons tromper un réseau neuronal et faire en sorte qu'un chien ressemble à un chat. Si nous prenons une image de chien, reconnue par le réseau comme un chien, nous pouvons la modifier légèrement en utilisant l'optimisation par descente de gradient, jusqu'à ce que le réseau commence à la classer comme un chat : -![Image d'un chien](../../../../../translated_images/original-dog.8f68a67d2fe0911f.fr.png) | ![Image d'un chien classée comme un chat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.fr.png) +![Image d'un chien](../../../../../translated_images/fr/original-dog.8f68a67d2fe0911f.webp) | ![Image d'un chien classée comme un chat](../../../../../translated_images/fr/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Image originale d'un chien* | *Image d'un chien classée comme un chat* diff --git a/translations/fr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/fr/lessons/4-ComputerVision/09-Autoencoders/README.md index 76c79f2c..3f921d4e 100644 --- a/translations/fr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/fr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Cependant, nous pourrions vouloir utiliser des données brutes (non annotées) p Étant donné que nous entraînons un autoencodeur pour capturer autant d'informations que possible de l'image originale afin de la reconstruire avec précision, le réseau cherche à trouver la meilleure **représentation** des images d'entrée pour en saisir le sens. -![Diagramme Autoencodeur](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.fr.jpg) +![Diagramme Autoencodeur](../../../../../translated_images/fr/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Image tirée du [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/fr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/fr/lessons/4-ComputerVision/11-ObjectDetection/README.md index bdaadb5b..11fc7287 100644 --- a/translations/fr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/fr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Les modèles de classification d'images que nous avons abordés jusqu'à présen ## [Quiz avant le cours](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Détection d'objets](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.fr.png) +![Détection d'objets](../../../../../translated_images/fr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Image tirée du [site web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supposons que nous voulions trouver un chat sur une image. Une approche très na 2. Effectuer une classification d'image sur chaque carreau. 3. Les carreaux qui produisent une activation suffisamment élevée peuvent être considérés comme contenant l'objet en question. -![Détection naïve d'objets](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.fr.png) +![Détection naïve d'objets](../../../../../translated_images/fr/naive-detection.e7f1ba220ccd08c6.webp) > *Image tirée du [cahier d'exercices](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Vous pourriez rencontrer les jeux de données suivants pour cette tâche : * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 classes, boîtes englobantes et masques de segmentation -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.fr.jpg) +![COCO](../../../../../translated_images/fr/coco-examples.71bc60380fa6cceb.webp) ## Métriques de détection d'objets @@ -50,7 +50,7 @@ Vous pourriez rencontrer les jeux de données suivants pour cette tâche : Alors que pour la classification d'images, il est facile de mesurer la performance de l'algorithme, pour la détection d'objets, nous devons mesurer à la fois la justesse de la classe et la précision de la localisation de la boîte englobante inférée. Pour cette dernière, nous utilisons ce qu'on appelle **Intersection over Union** (IoU), qui mesure à quel point deux boîtes (ou deux zones arbitraires) se chevauchent. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.fr.png) +![IoU](../../../../../translated_images/fr/iou_equation.9a4751d40fff4e11.webp) > *Figure 2 tirée de [cet excellent article de blog sur IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Il existe deux grandes catégories d'algorithmes de détection d'objets : [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utilise [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) pour générer une structure hiérarchique de régions ROI, qui sont ensuite passées par des extracteurs de caractéristiques CNN et des classificateurs SVM pour déterminer la classe de l'objet, et une régression linéaire pour déterminer les coordonnées de la *boîte englobante*. [Article officiel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.fr.png) +![RCNN](../../../../../translated_images/fr/rcnn1.cae407020dfb1d1f.webp) > *Image tirée de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.fr.png) +![RCNN-1](../../../../../translated_images/fr/rcnn2.2d9530bb83516484.webp) > *Images tirées de [cet article de blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Il existe deux grandes catégories d'algorithmes de détection d'objets : Cette approche est similaire à R-CNN, mais les régions sont définies après l'application des couches de convolution. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.fr.png) +![FRCNN](../../../../../translated_images/fr/f-rcnn.3cda6d9bb4188875.webp) > Image tirée de [l'article officiel](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Cette approche est similaire à R-CNN, mais les régions sont définies après l L'idée principale de cette approche est d'utiliser un réseau neuronal pour prédire les ROI - le *Réseau de propositions de régions*. [Article](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.fr.png) +![FasterRCNN](../../../../../translated_images/fr/faster-rcnn.8d46c099b87ef30a.webp) > Image tirée de [l'article officiel](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Cet algorithme est encore plus rapide que Faster R-CNN. L'idée principale est l 1. Les caractéristiques sont traitées par une **carte de score sensible à la position**. Chaque objet des $C$ classes est divisé en $k\times k$ régions, et nous entraînons le réseau à prédire des parties d'objets. 1. Pour chaque partie des $k\times k$ régions, tous les réseaux votent pour les classes d'objets, et la classe d'objet avec le vote maximum est sélectionnée. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.fr.png) +![r-fcn image](../../../../../translated_images/fr/r-fcn.13eb88158b99a3da.webp) > Image tirée de [l'article officiel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO est un algorithme en temps réel en un seul passage. L'idée principale est * L'image est divisée en $S\times S$ régions. * Pour chaque région, **CNN** prédit $n$ objets possibles, les coordonnées de la *boîte englobante* et la *confiance*=*probabilité* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.fr.png) + ![YOLO](../../../../../translated_images/fr/yolo.a2648ec82ee8bb4e.webp) > Image tirée de [l'article officiel](https://arxiv.org/abs/1506.02640) diff --git a/translations/fr/lessons/4-ComputerVision/README.md b/translations/fr/lessons/4-ComputerVision/README.md index 249969ab..ab7b342c 100644 --- a/translations/fr/lessons/4-ComputerVision/README.md +++ b/translations/fr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vision par ordinateur -![Résumé du contenu sur la vision par ordinateur sous forme de dessin](../../../../translated_images/ai-computervision.6506ebebac3fbf76.fr.png) +![Résumé du contenu sur la vision par ordinateur sous forme de dessin](../../../../translated_images/fr/ai-computervision.6506ebebac3fbf76.webp) Dans cette section, nous allons apprendre : diff --git a/translations/fr/lessons/5-NLP/14-Embeddings/README.md b/translations/fr/lessons/5-NLP/14-Embeddings/README.md index eb88d177..9a2aad22 100644 --- a/translations/fr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/fr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Ainsi, la couche d'embedding prendrait un mot en entrée et produirait un vecteu En utilisant une couche d'embedding comme première couche dans notre réseau de classification, nous pouvons passer d'un modèle bag-of-words à un modèle **embedding bag**, où nous convertissons d'abord chaque mot de notre texte en son embedding correspondant, puis calculons une fonction d'agrégation sur tous ces embeddings, comme `sum`, `average` ou `max`. -![Image montrant un classificateur basé sur des embeddings pour une séquence de cinq mots.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.fr.png) +![Image montrant un classificateur basé sur des embeddings pour une séquence de cinq mots.](../../../../../translated_images/fr/embedding-classifier-example.b77f021a7ee67eee.webp) > Image par l'auteur @@ -40,7 +40,7 @@ Pour cela, nous devons préentraîner notre modèle d'embedding sur une grande c CBoW est plus rapide, tandis que skip-gram est plus lent, mais représente mieux les mots peu fréquents. -![Image montrant les algorithmes CBoW et Skip-Gram pour convertir des mots en vecteurs.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fr.png) +![Image montrant les algorithmes CBoW et Skip-Gram pour convertir des mots en vecteurs.](../../../../../translated_images/fr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Image tirée de [cet article](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/fr/lessons/5-NLP/15-LanguageModeling/README.md index 8a599b27..4f726a37 100644 --- a/translations/fr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/fr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dans nos exemples précédents, nous avons utilisé des embeddings sémantiques * **Continuous Bag-of-Words** (CBoW), où l'on prédit le token central $W_0$ dans une séquence de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, où l'on prédit un ensemble de tokens voisins {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} à partir du token central $W_0$. -![image tirée d'un article sur la conversion des mots en vecteurs](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.fr.png) +![image tirée d'un article sur la conversion des mots en vecteurs](../../../../../translated_images/fr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Image tirée de [cet article](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/fr/lessons/5-NLP/16-RNN/README.md b/translations/fr/lessons/5-NLP/16-RNN/README.md index 9540a7cd..8104ce66 100644 --- a/translations/fr/lessons/5-NLP/16-RNN/README.md +++ b/translations/fr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Dans les sections précédentes, nous avons utilisé des représentations séman Pour capturer le sens d'une séquence de texte, nous devons utiliser une autre architecture de réseau de neurones, appelée **réseau de neurones récurrent**, ou RNN. Dans un RNN, nous faisons passer notre phrase à travers le réseau un symbole à la fois, et le réseau produit un certain **état**, que nous transmettons ensuite au réseau avec le symbole suivant. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.fr.png) +![RNN](../../../../../translated_images/fr/rnn.27f5c29c53d727b5.webp) > Image par l'auteur @@ -61,7 +61,7 @@ Nous avons discuté des réseaux récurrents qui fonctionnent dans une seule dir Un réseau récurrent, qu'il soit unidirectionnel ou bidirectionnel, capture certains motifs au sein d'une séquence et peut les stocker dans un vecteur d'état ou les transmettre en sortie. Comme pour les réseaux convolutionnels, nous pouvons construire une autre couche récurrente au-dessus de la première pour capturer des motifs de niveau supérieur et construire à partir des motifs de bas niveau extraits par la première couche. Cela nous mène à la notion de **RNN multicouche**, qui consiste en deux ou plusieurs réseaux récurrents, où la sortie de la couche précédente est transmise à la couche suivante comme entrée. -![Image montrant un RNN LSTM multicouche](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.fr.jpg) +![Image montrant un RNN LSTM multicouche](../../../../../translated_images/fr/multi-layer-lstm.dd975e29bb2a59fe.webp) *Image tirée de [cet excellent article](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/fr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/fr/lessons/5-NLP/17-GenerativeNetworks/README.md index 7f9ffa65..e3247dc1 100644 --- a/translations/fr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/fr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Dans l'architecture RNN que nous avons abordée dans l'unité précédente, chaq Cela permet différentes architectures neuronales, illustrées dans l'image ci-dessous : -![Image montrant des modèles courants de réseaux neuronaux récurrents.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fr.jpg) +![Image montrant des modèles courants de réseaux neuronaux récurrents.](../../../../../translated_images/fr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Image tirée de l'article de blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) par [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Dans cette unité, nous nous concentrerons sur des modèles génératifs simples Nous entraînerons ce RNN à générer du texte étape par étape. À chaque étape, nous prendrons une séquence de caractères de longueur `nchars` et demanderons au réseau de générer le caractère suivant pour chaque caractère d'entrée : -![Image montrant un exemple de génération du mot 'HELLO' par un RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.fr.png) +![Image montrant un exemple de génération du mot 'HELLO' par un RNN.](../../../../../translated_images/fr/rnn-generate.56c54afb52f9781d.webp) Lors de la génération de texte (pendant l'inférence), nous commençons par une **invite**, qui est passée à travers les cellules RNN pour générer son état intermédiaire, puis la génération commence à partir de cet état. Nous générons un caractère à la fois, et passons l'état et le caractère généré à une autre cellule RNN pour générer le suivant, jusqu'à ce que nous ayons généré suffisamment de caractères. diff --git a/translations/fr/lessons/5-NLP/18-Transformers/README.md b/translations/fr/lessons/5-NLP/18-Transformers/README.md index 9d53bc81..608bdf20 100644 --- a/translations/fr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/fr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Avec les RNNs, la séquence à séquence est mise en œuvre par deux réseaux r Les **mécanismes d'attention** offrent un moyen de pondérer l'impact contextuel de chaque vecteur d'entrée sur chaque prédiction de sortie du RNN. Cela est mis en œuvre en créant des raccourcis entre les états intermédiaires du RNN d'entrée et du RNN de sortie. Ainsi, lors de la génération du symbole de sortie yt, nous prenons en compte tous les états cachés d'entrée hi, avec différents coefficients de poids αt,i. -![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fr.png) +![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/fr/encoder-decoder-attention.7a726296894fb567.webp) > Le modèle encodeur-décodeur avec mécanisme d'attention additive dans [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cité de [ce blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice d'attention {αi,j} représente le degré auquel certains mots d'entrée jouent un rôle dans la génération d'un mot donné dans la séquence de sortie. Voici un exemple de cette matrice : -![Image montrant un alignement trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fr.png) +![Image montrant un alignement trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/fr/bahdanau-fig3.09ba2d37f202a6af.webp) > Figure tirée de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Le résultat obtenu avec l'embedding positionnel intègre à la fois le token or Ensuite, nous devons capturer certains motifs dans notre séquence. Pour ce faire, les transformers utilisent un mécanisme d'**auto-attention**, qui est essentiellement une attention appliquée à la même séquence en tant qu'entrée et sortie. L'application de l'auto-attention nous permet de prendre en compte le **contexte** dans la phrase et de voir quels mots sont interconnectés. Par exemple, cela nous permet de voir quels mots sont référencés par des coréférences, comme *il*, et de prendre également le contexte en compte : -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.fr.png) +![](../../../../../translated_images/fr/CoreferenceResolution.861924d6d384a7d6.webp) > Image tirée du [blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ L'attention encodeur-décodeur est très similaire au mécanisme d'attention uti **BERT** (Bidirectional Encoder Representations from Transformers) est un réseau transformer multi-couches très large avec 12 couches pour *BERT-base*, et 24 pour *BERT-large*. Le modèle est d'abord pré-entraîné sur un large corpus de données textuelles (WikiPedia + livres) en utilisant un entraînement non supervisé (prédiction des mots masqués dans une phrase). Pendant le pré-entraînement, le modèle absorbe des niveaux significatifs de compréhension du langage qui peuvent ensuite être exploités avec d'autres ensembles de données via un ajustement fin. Ce processus est appelé **apprentissage par transfert**. -![image tirée de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fr.png) +![image tirée de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Image [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/fr/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/fr/lessons/5-NLP/18-Transformers/READMEtransformers.md index 9ea6fe51..3afffed2 100644 --- a/translations/fr/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/fr/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Avec les RNN, la séquence à séquence est mise en œuvre par deux réseaux ré Les **Mécanismes d'Attention** fournissent un moyen de pondérer l'impact contextuel de chaque vecteur d'entrée sur chaque prédiction de sortie du RNN. La manière dont cela est mis en œuvre consiste à créer des raccourcis entre les états intermédiaires du RNN d'entrée et le RNN de sortie. De cette manière, lors de la génération du symbole de sortie yt, nous prendrons en compte tous les états cachés d'entrée hi, avec des coefficients de poids différents αt,i. -![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.fr.png) +![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/fr/encoder-decoder-attention.7a726296894fb567.webp) > Le modèle encodeur-décodeur avec mécanisme d'attention additive dans [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cité dans [cet article de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice d'attention {αi,j} représenterait le degré auquel certains mots d'entrée jouent dans la génération d'un mot donné dans la séquence de sortie. Voici un exemple d'une telle matrice : -![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tiré de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.fr.png) +![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tiré de Bahdanau - arviz.org](../../../../../translated_images/fr/bahdanau-fig3.09ba2d37f202a6af.webp) > Figure de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Le résultat que nous obtenons avec l'embedding positionnel intègre à la fois Ensuite, nous devons capturer certains motifs au sein de notre séquence. Pour ce faire, les transformateurs utilisent un mécanisme d'**auto-attention**, qui est essentiellement une attention appliquée à la même séquence en tant qu'entrée et sortie. L'application de l'auto-attention nous permet de prendre en compte le **contexte** au sein de la phrase, et de voir quels mots sont inter-reliés. Par exemple, cela nous permet de voir quels mots sont référencés par des coréférences, telles que *il*, et également de prendre en compte le contexte : -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.fr.png) +![](../../../../../translated_images/fr/CoreferenceResolution.861924d6d384a7d6.webp) > Image du [Blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Puisque chaque position d'entrée est mappée indépendamment à chaque position **BERT** (Représentations Encodeur Bidirectionnelles à partir de Transformateurs) est un très grand réseau de transformateurs multicouches avec 12 couches pour *BERT-base*, et 24 pour *BERT-large*. Le modèle est d'abord pré-entraîné sur un grand corpus de données textuelles (WikiPedia + livres) en utilisant un entraînement non supervisé (prédiction de mots masqués dans une phrase). Pendant le pré-entraînement, le modèle absorbe des niveaux significatifs de compréhension linguistique qui peuvent ensuite être exploités avec d'autres ensembles de données en utilisant un ajustement fin. Ce processus est appelé **apprentissage par transfert**. -![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.fr.png) +![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/fr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Image [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/fr/lessons/5-NLP/19-NER/README.md b/translations/fr/lessons/5-NLP/19-NER/README.md index c1a194a5..8205a377 100644 --- a/translations/fr/lessons/5-NLP/19-NER/README.md +++ b/translations/fr/lessons/5-NLP/19-NER/README.md @@ -60,7 +60,7 @@ nouveau-né | O Puisque nous devons établir une correspondance un-à-un entre les tokens et les classes, nous pouvons entraîner un modèle neuronal **many-to-many** (plusieurs-à-plusieurs) à partir de cette image : -![Image montrant les architectures courantes des réseaux neuronaux récurrents.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.fr.jpg) +![Image montrant les architectures courantes des réseaux neuronaux récurrents.](../../../../../translated_images/fr/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Image tirée de [cet article de blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) par [Andrej Karpathy](http://karpathy.github.io/). Les modèles de classification de tokens NER correspondent à l'architecture de réseau située à l'extrême droite de cette image.* diff --git a/translations/fr/lessons/5-NLP/README.md b/translations/fr/lessons/5-NLP/README.md index 4efa1cd3..7d88a2a6 100644 --- a/translations/fr/lessons/5-NLP/README.md +++ b/translations/fr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Traitement du Langage Naturel -![Résumé des tâches NLP dans un croquis](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.fr.png) +![Résumé des tâches NLP dans un croquis](../../../../translated_images/fr/ai-nlp.b22dcb8ca4707cea.webp) Dans cette section, nous allons nous concentrer sur l'utilisation des réseaux neuronaux pour traiter des tâches liées au **Traitement du Langage Naturel (NLP)**. Il existe de nombreux problèmes NLP que nous souhaitons que les ordinateurs soient capables de résoudre : diff --git a/translations/fr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/fr/lessons/6-Other/23-MultiagentSystems/README.md index 6bc9a085..3b36f698 100644 --- a/translations/fr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/fr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Vous pouvez ouvrir l'un des modèles, par exemple **Biology → Flockin Après avoir ouvert le modèle, vous êtes dirigé vers l'écran principal de NetLogo. Voici un exemple de modèle qui décrit la population de loups et de moutons, compte tenu de ressources limitées (herbe). -![Écran principal de NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.fr.png) +![Écran principal de NetLogo](../../../../../translated_images/fr/NetLogo-Main.32653711ec1a01b3.webp) > Capture d'écran par Dmitry Soshnikov diff --git a/translations/fr/lessons/README.md b/translations/fr/lessons/README.md index e9afc502..72bc1a7b 100644 --- a/translations/fr/lessons/README.md +++ b/translations/fr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vue d'ensemble -![Vue d'ensemble dans un croquis](../../../translated_images/ai-overview.0857791951d19500.fr.png) +![Vue d'ensemble dans un croquis](../../../translated_images/fr/ai-overview.0857791951d19500.webp) > Croquis par [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/fr/lessons/X-Extras/X1-MultiModal/README.md b/translations/fr/lessons/X-Extras/X1-MultiModal/README.md index bd2a0e6b..3baf1218 100644 --- a/translations/fr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/fr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Après le succès des modèles transformateurs pour résoudre des tâches de tra L'idée principale de CLIP est de pouvoir comparer des descriptions textuelles avec une image et déterminer à quel point l'image correspond à la description. -![Architecture CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.fr.png) +![Architecture CLIP](../../../../../translated_images/fr/clip-arch.b3dbf20b4e8ed8be.webp) > *Image tirée de [cet article de blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Une fois ce modèle pré-entraîné, nous pouvons lui fournir un lot d'images et Supposons que nous devons classer des images entre, par exemple, des chats, des chiens et des humains. Dans ce cas, nous pouvons donner au modèle une image et une série de descriptions textuelles : "*une image d'un chat*", "*une image d'un chien*", "*une image d'un humain*". Dans le vecteur résultant de 3 probabilités, il suffit de sélectionner l'indice avec la valeur la plus élevée. -![CLIP pour la Classification d'Images](../../../../../translated_images/clip-class.3af42ef0b2b19369.fr.png) +![CLIP pour la Classification d'Images](../../../../../translated_images/fr/clip-class.3af42ef0b2b19369.webp) > *Image tirée de [cet article de blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Apprenez-en davantage sur VQGAN sur le site [Taming Transformers](https://compvi Une des différences importantes entre VQGAN et les GAN traditionnels est que ces derniers peuvent produire une image correcte à partir de n'importe quel vecteur d'entrée, tandis que VQGAN est susceptible de produire une image incohérente. Ainsi, nous devons guider davantage le processus de création d'image, ce qui peut être fait en utilisant CLIP. -![Architecture VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.fr.png) +![Architecture VQGAN+CLIP](../../../../../translated_images/fr/vqgan.5027fe05051dfa31.webp) Pour générer une image correspondant à une description textuelle, nous commençons par un vecteur d'encodage aléatoire qui est passé à travers VQGAN pour produire une image. Ensuite, CLIP est utilisé pour produire une fonction de perte qui montre à quel point l'image correspond à la description textuelle. L'objectif est alors de minimiser cette perte, en utilisant la rétropropagation pour ajuster les paramètres du vecteur d'entrée. Une excellente bibliothèque qui implémente VQGAN+CLIP est [Pixray](http://github.com/pixray/pixray). -![Image produite par Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.fr.png) | ![Image produite par Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.fr.png) | ![Image produite par Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.fr.png) +![Image produite par Pixray](../../../../../translated_images/fr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Image produite par Pixray](../../../../../translated_images/fr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Image produite par Pixray](../../../../../translated_images/fr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Image générée à partir de la description *un portrait en gros plan à l'aquarelle d'un jeune professeur de littérature avec un livre* | Image générée à partir de la description *un portrait en gros plan à l'huile d'une jeune professeure d'informatique avec un ordinateur* | Image générée à partir de la description *un portrait en gros plan à l'huile d'un vieux professeur de mathématiques devant un tableau noir* @@ -75,7 +75,7 @@ Contrairement à CLIP, DALL-E reçoit à la fois du texte et des images sous for La principale différence entre DALL-E 1 et 2 est que ce dernier génère des images et des œuvres d'art plus réalistes. Exemples de génération d'images avec DALL-E : -![Image produite par DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.fr.png) | ![Image produite par DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.fr.png) | ![Image produite par DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.fr.png) +![Image produite par DALL-E](../../../../../translated_images/fr/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Image produite par DALL-E](../../../../../translated_images/fr/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Image produite par DALL-E](../../../../../translated_images/fr/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Image générée à partir de la description *un portrait en gros plan à l'aquarelle d'un jeune professeur de littérature avec un livre* | Image générée à partir de la description *un portrait en gros plan à l'huile d'une jeune professeure d'informatique avec un ordinateur* | Image générée à partir de la description *un portrait en gros plan à l'huile d'un vieux professeur de mathématiques devant un tableau noir* diff --git a/translations/he/README.md b/translations/he/README.md index aec3fed9..20d8e1cc 100644 --- a/translations/he/README.md +++ b/translations/he/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # בינה מלאכותית למתחילים - תוכנית לימודים -|![רישום סכמטי מאת @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.he.png)| +|![רישום סכמטי מאת @girlie_mac https://twitter.com/girlie_mac](../../translated_images/he/ai-overview.0857791951d19500.png)| |:---:| | בינה מלאכותית למתחילים - _רישום סכמטי מאת [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/he/lessons/1-Intro/README.md b/translations/he/lessons/1-Intro/README.md index 523ef51c..d83cdb9a 100644 --- a/translations/he/lessons/1-Intro/README.md +++ b/translations/he/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # מבוא לבינה מלאכותית -![סיכום תוכן מבוא לבינה מלאכותית בציור](../../../../translated_images/ai-intro.bf28d1ac4235881c.he.png) +![סיכום תוכן מבוא לבינה מלאכותית בציור](../../../../translated_images/he/ai-intro.bf28d1ac4235881c.png) > ציור מאת [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: במקור, מחשבים הומצאו על ידי [צ'ארלס בבג'](https://en.wikipedia.org/wiki/Charles_Babbage) כדי לפעול על מספרים לפי תהליך מוגדר היטב - אלגוריתם. מחשבים מודרניים, למרות שהם מתקדמים בהרבה מהמודל המקורי שהוצע במאה ה-19, עדיין פועלים על פי אותו רעיון של חישובים מבוקרים. לכן, ניתן לתכנת מחשב לבצע משהו אם אנו יודעים את רצף הצעדים המדויק שעלינו לבצע כדי להשיג את המטרה. -![תמונה של אדם](../../../../translated_images/dsh_age.d212a30d4e54fb5f.he.png) +![תמונה של אדם](../../../../translated_images/he/dsh_age.d212a30d4e54fb5f.png) > תמונה מאת [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: אחת הבעיות בהתמודדות עם המונח **[אינטליגנציה](https://en.wikipedia.org/wiki/Intelligence)** היא שאין הגדרה ברורה למונח זה. ניתן לטעון שאינטליגנציה קשורה ל**חשיבה מופשטת**, או ל**מודעות עצמית**, אך איננו יכולים להגדיר אותה כראוי. -![תמונה של חתול](../../../../translated_images/photo-cat.8c8e8fb760ffe457.he.jpg) +![תמונה של חתול](../../../../translated_images/he/photo-cat.8c8e8fb760ffe457.jpg) > [תמונה](https://unsplash.com/photos/75715CVEJhI) מאת [Amber Kipp](https://unsplash.com/@sadmax) מ-Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | מה לגבי ML? | | > |--------------|-----------| -> | חלק מהבינה המלאכותית המבוסס על למידת מחשב לפתור בעיה על סמך נתונים מסוימים נקרא **למידת מכונה**. לא נעסוק בלמידת מכונה קלאסית בקורס זה - אנו מפנים אתכם לתוכנית הלימודים הנפרדת [למידת מכונה למתחילים](http://aka.ms/ml-beginners). | ![ML למתחילים](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.he.png) | +> | חלק מהבינה המלאכותית המבוסס על למידת מחשב לפתור בעיה על סמך נתונים מסוימים נקרא **למידת מכונה**. לא נעסוק בלמידת מכונה קלאסית בקורס זה - אנו מפנים אתכם לתוכנית הלימודים הנפרדת [למידת מכונה למתחילים](http://aka.ms/ml-beginners). | ![ML למתחילים](../../../../translated_images/he/ml-for-beginners.9e4fed176fd5817d.png) | ## היסטוריה קצרה של בינה מלאכותית בינה מלאכותית החלה כתחום באמצע המאה ה-20. בתחילה, גישת ההסקה הסימבולית הייתה הגישה השלטת, והיא הובילה למספר הצלחות חשובות, כמו מערכות מומחה – תוכניות מחשב שיכלו לפעול כמומחה בתחומים מוגבלים. עם זאת, עד מהרה התברר שגישה זו אינה מתאימה להרחבה. חילוץ הידע ממומחה, ייצוגו במחשב, ושמירה על בסיס הידע מדויק מתבררים כמשימה מורכבת מאוד ויקרה מדי במקרים רבים. זה הוביל למה שנקרא [חורף הבינה המלאכותית](https://en.wikipedia.org/wiki/AI_winter) בשנות ה-70. -היסטוריה קצרה של AI +היסטוריה קצרה של AI > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * עוזרים מודרניים, כמו Cortana, Siri או Google Assistant הם כולם מערכות היברידיות שמשתמשות ברשתות נוירונים כדי להמיר דיבור לטקסט ולהבין את כוונתנו, ואז משתמשות בהסקה או באלגוריתמים מפורשים כדי לבצע פעולות נדרשות. * בעתיד, אנו עשויים לצפות למודל מבוסס רשת נוירונים מלא שיטפל בדיאלוג בעצמו. משפחת הרשתות הנוירוניות GPT ו-[Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) מראה הצלחות גדולות בתחום זה. -התפתחות מבחן טיורינג +התפתחות מבחן טיורינג > תמונה מאת דמיטרי סושניקוב, [תמונה](https://unsplash.com/photos/r8LmVbUKgns) מאת [מרינה אברוסימובה](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## מחקר AI עדכני diff --git a/translations/he/lessons/2-Symbolic/Animals.ipynb b/translations/he/lessons/2-Symbolic/Animals.ipynb index 9bc03140..ee2613c2 100644 --- a/translations/he/lessons/2-Symbolic/Animals.ipynb +++ b/translations/he/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "בדוגמה זו, ניישם מערכת פשוטה מבוססת ידע כדי לזהות חיה על סמך כמה מאפיינים פיזיים. ניתן לייצג את המערכת באמצעות עץ AND-OR הבא (זהו חלק מהעץ המלא, ניתן להוסיף בקלות עוד חוקים):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.he.png)\n" + "![](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/he/lessons/2-Symbolic/README.md b/translations/he/lessons/2-Symbolic/README.md index 39d18e15..eb22b6e6 100644 --- a/translations/he/lessons/2-Symbolic/README.md +++ b/translations/he/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ייצוג ידע ומערכות מומחה -![Summary of Symbolic AI content](../../../../translated_images/ai-symbolic.715a30cb610411a6.he.png) +![Summary of Symbolic AI content](../../../../translated_images/he/ai-symbolic.715a30cb610411a6.png) > סקיצה מאת [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: לכן, הבעיה של **ייצוג ידע** היא למצוא דרך יעילה לייצג ידע בתוך מחשב בצורה של נתונים, כדי להפוך אותו לשימושי באופן אוטומטי. ניתן לראות זאת כספקטרום: -![Knowledge representation spectrum](../../../../translated_images/knowledge-spectrum.b60df631852c0217.he.png) +![Knowledge representation spectrum](../../../../translated_images/he/knowledge-spectrum.b60df631852c0217.png) > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | אחד ההישגים הראשונים של AI סמלי היו מערכות מומחה - מערכות מחשב שנועדו לפעול כמומחה בתחום בעיה מוגבל. הן התבססו על **בסיס ידע** שהופק ממומחים אנושיים, וכללו **מנוע הסקה** שביצע הסקת מסקנות על בסיסו. -![Human Architecture](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.he.png) | ![Knowledge-Based System](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.he.png) +![Human Architecture](../../../../translated_images/he/arch-human.5d4d35f1bba3ab1c.png) | ![Knowledge-Based System](../../../../translated_images/he/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ מבנה מפושט של מערכת עצבית אנושית | ארכיטקטורה של מערכת מבוססת ידע @@ -106,7 +106,7 @@ Block Syntax | Indent | | | לדוגמה, נבחן את מערכת המומחה הבאה לקביעת בעל חיים על בסיס מאפייניו הפיזיים: -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.he.png) +![AND-OR Tree](../../../../translated_images/he/AND-OR-Tree.5592d2c70187f283.png) > תמונה מאת [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 9118f5c7..9c34ca09 100644 --- a/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/he/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "במקרה שיש לנו יותר משתי קטגוריות, softmax ינרמל את ההסתברויות בין כולן. הנה תרשים של ארכיטקטורת רשת שמבצעת סיווג ספרות MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.he.png)\n" + "![MNIST Classifier](../../../../../translated_images/he/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* הפסד אימון נמוך - המודל יכול להתאים היטב לנתוני האימון, כי יש לו מספיק כוח ביטוי.\n", "* הפסד האימות יכול להיות גבוה בהרבה מהפסד האימון ואף להתחיל לעלות במהלך האימון - זה קורה כי המודל \"זוכר\" את נקודות האימון ומאבד את \"התמונה הכללית\".\n", "\n", - "![התאמת יתר](../../../../../translated_images/overfit.a0bd57f717c15769.he.png)\n", + "![התאמת יתר](../../../../../translated_images/he/overfit.a0bd57f717c15769.png)\n", "\n", "> בתמונה הזו, `x` מייצג נתוני אימון, `o` - נתוני אימות. משמאל - מודל ליניארי (חד-שכבתי), הוא מתאים את טבע הנתונים בצורה טובה. מימין - מודל עם התאמת יתר, המודל מתאים בצורה מושלמת לנתוני האימון, אבל מפסיק להיות הגיוני עם כל נתון אחר (שגיאת האימות גבוהה מאוד).\n" ] diff --git a/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md index f9763a98..f90d82a2 100644 --- a/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/he/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting הוא מושג חשוב ביותר בלמידת מכונה, וחש שקלו את הבעיה הבאה של התאמת 5 נקודות (מיוצגות על ידי `x` בגרפים למטה): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.he.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.he.jpg) +![linear](../../../../../translated_images/he/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/he/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **מודל ליניארי, 2 פרמטרים** | **מודל לא ליניארי, 7 פרמטרים** שגיאת אימון = 5.3 | שגיאת אימון = 0 @@ -79,7 +79,7 @@ Overfitting הוא מושג חשוב ביותר בלמידת מכונה, וחש כפי שניתן לראות מהגרף למעלה, ניתן לזהות Overfitting על ידי שגיאת אימון נמוכה מאוד ושגיאת ולידציה גבוהה. בדרך כלל במהלך האימון נראה ששגיאות האימון והוולידציה מתחילות לרדת, ואז בשלב מסוים שגיאת הוולידציה עשויה להפסיק לרדת ולהתחיל לעלות. זה יהיה סימן ל-Overfitting, ואינדיקציה לכך שכדאי להפסיק את האימון בנקודה זו (או לפחות לשמור עותק של המודל). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.he.png) +![overfitting](../../../../../translated_images/he/Overfitting.408ad91cd90b4371.png) ## כיצד למנוע Overfitting diff --git a/translations/he/lessons/3-NeuralNetworks/README.md b/translations/he/lessons/3-NeuralNetworks/README.md index 0ffa2456..1ac27032 100644 --- a/translations/he/lessons/3-NeuralNetworks/README.md +++ b/translations/he/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # מבוא לרשתות עצביות -![סיכום תוכן מבוא לרשתות עצביות בציור](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.he.png) +![סיכום תוכן מבוא לרשתות עצביות בציור](../../../../translated_images/he/ai-neuralnetworks.1c687ae40bc86e83.png) כפי שדיברנו במבוא, אחת הדרכים להשיג אינטליגנציה היא לאמן **מודל מחשב** או **מוח מלאכותי**. מאז אמצע המאה ה-20, חוקרים ניסו מודלים מתמטיים שונים, עד שבשנים האחרונות כיוון זה הוכיח את עצמו כהצלחה גדולה. מודלים מתמטיים כאלה של המוח נקראים **רשתות עצביות**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: מהביולוגיה אנו יודעים שהמוח שלנו מורכב מתאי עצב (נוירונים), שלכל אחד מהם יש מספר "קלטים" (דנדריטים) ו"פלט" אחד (אקסון). הן הדנדריטים והן האקסונים יכולים להוליך אותות חשמליים, והחיבורים ביניהם — הידועים כסינפסות — יכולים להציג דרגות שונות של מוליכות, שמוסדרות על ידי נוירוטרנסמיטורים. -![מודל של נוירון](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.he.jpg) | ![מודל של נוירון](../../../../translated_images/artneuron.1a5daa88d20ebe6f.he.png) +![מודל של נוירון](../../../../translated_images/he/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![מודל של נוירון](../../../../translated_images/he/artneuron.1a5daa88d20ebe6f.png) ----|---- נוירון אמיתי *([תמונה](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) מוויקיפדיה)* | נוירון מלאכותי *(תמונה מאת המחבר)* לכן, המודל המתמטי הפשוט ביותר של נוירון מכיל מספר קלטים X1, ..., XN ופלט Y, וסדרה של משקלים W1, ..., WN. הפלט מחושב כך: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) כאשר f היא **פונקציית הפעלה** לא ליניארית. diff --git a/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md index 13336e9e..69f6a491 100644 --- a/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/he/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **עיבוד מקדים של צילום ספר ברייל**. אנו מתמקדים כיצד ניתן להשתמש בסף, זיהוי תכונות, טרנספורמציית פרספקטיבה ומניפולציות NumPy כדי להפריד סמלי ברייל בודדים לסיווג נוסף על ידי רשת נוירונים. -![תמונה של ברייל](../../../../../translated_images/braille.341962ff76b1bd70.he.jpeg) | ![תמונה מעובדת של ברייל](../../../../../translated_images/braille-result.46530fea020b03c7.he.png) | ![סמלי ברייל](../../../../../translated_images/braille-symbols.0159185ab69d5339.he.png) +![תמונה של ברייל](../../../../../translated_images/he/braille.341962ff76b1bd70.jpeg) | ![תמונה מעובדת של ברייל](../../../../../translated_images/he/braille-result.46530fea020b03c7.png) | ![סמלי ברייל](../../../../../translated_images/he/braille-symbols.0159185ab69d5339.png) ----|-----|----- > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) * **זיהוי תנועה בווידאו באמצעות הבדל פריימים**. אם המצלמה קבועה, אז הפריימים מהמצלמה צריכים להיות די דומים זה לזה. מכיוון שפריימים מיוצגים כמערכים, פשוט על ידי חיסור המערכים של שני פריימים עוקבים נקבל את ההבדל בפיקסלים, שאמור להיות נמוך עבור פריימים סטטיים, ולהפוך לגבוה יותר כאשר יש תנועה משמעותית בתמונה. -![תמונה של פריימים בווידאו והבדלי פריימים](../../../../../translated_images/frame-difference.706f805491a0883c.he.png) +![תמונה של פריימים בווידאו והבדלי פריימים](../../../../../translated_images/he/frame-difference.706f805491a0883c.png) > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **זרימה אופטית צפופה** מחשבת את שדה הווקטורים שמראה לכל פיקסל לאן הוא נע. - **זרימה אופטית דלילה** מבוססת על לקיחת תכונות ייחודיות בתמונה (למשל, קצוות), ובניית מסלולן מפריים לפריים. -![תמונה של זרימה אופטית](../../../../../translated_images/optical.1f4a94464579a83a.he.png) +![תמונה של זרימה אופטית](../../../../../translated_images/he/optical.1f4a94464579a83a.png) > תמונה מתוך [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 0f0cd2ea..474c5bad 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 היא רשת שהשיגה דיוק של 92.7% בסיווג ImageNet top-5 בשנת 2014. יש לה את מבנה השכבות הבא: -![שכבות ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.he.jpg) +![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.jpg) כפי שניתן לראות, VGG עוקבת אחר ארכיטקטורת פירמידה מסורתית, שהיא רצף של שכבות קונבולוציה-פולינג. -![פירמידת ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.he.jpg) +![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.jpg) > תמונה מ-[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index efcbe756..61e874ef 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "לכן, ברשת CNN טיפוסית יהיו כמה שכבות קונבולוציה, עם שכבות pooling ביניהן כדי להקטין את הממדים של התמונה. כמו כן, נגדיל את מספר הפילטרים, מכיוון שככל שהדפוסים הופכים למתקדמים יותר - יש יותר שילובים מעניינים שצריך לחפש.\n", "\n", - "![תמונה המציגה כמה שכבות קונבולוציה עם שכבות pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.he.png)\n", + "![תמונה המציגה כמה שכבות קונבולוציה עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "בגלל הקטנת הממדים המרחביים והגדלת ממדי התכונות/הפילטרים, ארכיטקטורה זו נקראת גם **ארכיטקטורת פירמידה**.\n" ] diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 26e42ffe..b78a4a12 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "לכן, ברשת CNN טיפוסית יהיו מספר שכבות קונבולוציוניות, עם שכבות pooling ביניהן כדי להקטין את ממדי התמונה. בנוסף, נגדיל את מספר הפילטרים, מכיוון שככל שהדפוסים הופכים למתקדמים יותר - יש יותר שילובים מעניינים שצריך לחפש.\n", "\n", - "![תמונה המציגה מספר שכבות קונבולוציוניות עם שכבות pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.he.png)\n", + "![תמונה המציגה מספר שכבות קונבולוציוניות עם שכבות pooling.](../../../../../translated_images/he/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "בגלל הקטנת הממדים המרחביים והגדלת ממדי הפיצ'רים/פילטרים, הארכיטקטורה הזו נקראת גם **ארכיטקטורת פירמידה**.\n" ] diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md index cc94a406..932d1073 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: כדי לחלץ תבניות, נשתמש במושג של **פילטרים קונבולוציוניים**. כפי שאתם יודעים, תמונה מיוצגת על ידי מטריצה דו-ממדית, או טנזור תלת-ממדי עם עומק צבע. החלת פילטר פירושה שאנחנו לוקחים מטריצת **ליבת פילטר** קטנה יחסית, ולכל פיקסל בתמונה המקורית מחשבים ממוצע משוקלל עם הנקודות השכנות. ניתן לראות זאת כמו חלון קטן שגולש על פני כל התמונה, וממוצע את כל הפיקסלים לפי המשקלים במטריצת ליבת הפילטר. -![פילטר קצה אנכי](../../../../../translated_images/filter-vert.b7148390ca0bc356.he.png) | ![פילטר קצה אופקי](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.he.png) +![פילטר קצה אנכי](../../../../../translated_images/he/filter-vert.b7148390ca0bc356.png) | ![פילטר קצה אופקי](../../../../../translated_images/he/filter-horiz.59b80ed4feb946ef.png) ----|---- > תמונה מאת דמיטרי סושניקוב @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * ניתן לעצב את הרשת כך שהפילטרים יותאמו באופן אוטומטי * ניתן להשתמש באותה גישה כדי למצוא תבניות בתכונות ברמה גבוהה, ולא רק בתמונה המקורית. כך, חילוץ התכונות ב-CNN עובד על היררכיה של תכונות, החל משילובי פיקסלים ברמה נמוכה ועד לשילובים ברמה גבוהה של חלקי תמונה. -![חילוץ תכונות היררכי](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.he.png) +![חילוץ תכונות היררכי](../../../../../translated_images/he/FeatureExtractionCNN.d9b456cbdae7cb64.png) > תמונה מתוך [מאמר של Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), המבוסס על [המחקר שלהם](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: לדוגמה, בואו נסתכל על הארכיטקטורה של VGG-16, רשת שהשיגה דיוק של 92.7% בסיווג הטופ-5 של ImageNet בשנת 2014: -![שכבות ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.he.jpg) +![שכבות ImageNet](../../../../../translated_images/he/vgg-16-arch1.d901a5583b3a51ba.jpg) -![פירמידת ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.he.jpg) +![פירמידת ImageNet](../../../../../translated_images/he/vgg-16-arch.64ff2137f50dd49f.jpg) > תמונה מתוך [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 03c462cb..e2b8eea2 100644 --- a/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/he/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: נשתמש ב-[מאגר הנתונים של Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/), המכיל תמונות של 37 גזעים שונים של כלבים וחתולים. -![מאגר הנתונים שבו נעסוק](../../../../../../translated_images/data.50b2a9d5484bdbf0.he.png) +![מאגר הנתונים שבו נעסוק](../../../../../../translated_images/he/data.50b2a9d5484bdbf0.png) כדי להוריד את מאגר הנתונים, השתמשו בקטע הקוד הבא: diff --git a/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index cae782a0..85fed709 100644 --- a/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/he/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "כדי לדמיין את החתול האידיאלי, נתחיל עם תמונה של רעש אקראי, וננסה להשתמש בטכניקת אופטימיזציה של ירידת גרדיאנט כדי להתאים את התמונה כך שרשת תזהה חתול.\n", "\n", - "![לולאת אופטימיזציה](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.he.png)\n", + "![לולאת אופטימיזציה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "הנה התמונה ההתחלתית שלנו:\n" ] diff --git a/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md index 7c046f98..084e742a 100644 --- a/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/he/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: להלן דוגמה לתכונות שחולצו מתמונה של חתול על ידי רשת VGG-16: -![תכונות שחולצו על ידי VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.he.png) +![תכונות שחולצו על ידי VGG-16](../../../../../translated_images/he/features.6291f9c7ba3a0b95.png) ## מערך נתונים של חתולים וכלבים @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: גישה אחת שנוכל לקחת היא להתחיל עם תמונה אקראית, ואז לנסות להשתמש בטכניקת **אופטימיזציה בירידת גרדיאנט** כדי להתאים את התמונה כך שהרשת תתחיל לחשוב שמדובר בחתול. -![לולאת אופטימיזציה של תמונה](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.he.png) +![לולאת אופטימיזציה של תמונה](../../../../../translated_images/he/ideal-cat-loop.999fbb8ff306e044.png) עם זאת, אם נעשה זאת, נקבל משהו שדומה מאוד לרעש אקראי. זאת מכיוון ש*יש הרבה דרכים לגרום לרשת לחשוב שהתמונה הקלט היא חתול*, כולל כאלה שאינן הגיוניות מבחינה חזותית. למרות שהתמונות הללו מכילות הרבה דפוסים אופייניים לחתול, אין שום דבר שמגביל אותן להיות מובחנות חזותית. כדי לשפר את התוצאה, נוכל להוסיף מונח נוסף לפונקציית ההפסד, שנקרא **הפסד וריאציה**. זהו מדד שמראה עד כמה פיקסלים סמוכים בתמונה דומים זה לזה. צמצום הפסד וריאציה הופך את התמונה לחלקה יותר ומסלק רעש - ובכך חושף דפוסים מושכים יותר מבחינה חזותית. הנה דוגמה לתמונות "אידיאליות" כאלה, שמסווגות כחתול וכזברה בהסתברות גבוהה: -![חתול אידיאלי](../../../../../translated_images/ideal-cat.203dd4597643d6b0.he.png) | ![זברה אידיאלית](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.he.png) +![חתול אידיאלי](../../../../../translated_images/he/ideal-cat.203dd4597643d6b0.png) | ![זברה אידיאלית](../../../../../translated_images/he/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *חתול אידיאלי* | *זברה אידיאלית* גישה דומה יכולה לשמש לביצוע מה שנקרא **התקפות עוינות** על רשת עצבית. נניח שאנחנו רוצים להטעות רשת עצבית ולגרום לכלב להיראות כמו חתול. אם ניקח תמונה של כלב, שמזוהה על ידי הרשת ככלב, נוכל לשנות אותה מעט באמצעות אופטימיזציה בירידת גרדיאנט, עד שהרשת תתחיל לסווג אותה כחתול: -![תמונה של כלב](../../../../../translated_images/original-dog.8f68a67d2fe0911f.he.png) | ![תמונה של כלב שמסווג כחתול](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.he.png) +![תמונה של כלב](../../../../../translated_images/he/original-dog.8f68a67d2fe0911f.png) | ![תמונה של כלב שמסווג כחתול](../../../../../translated_images/he/adversarial-dog.d9fc7773b0142b89.png) -----|----- *תמונה מקורית של כלב* | *תמונה של כלב שמסווג כחתול* diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 35c602d7..8c49f6c1 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "מכיוון שאנחנו מאמנים את האוטואנקודר ללכוד כמה שיותר מידע מהתמונה המקורית כדי לשחזר אותה בצורה מדויקת, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות שלהן.\n", "\n", - "![תרשים אוטואנקודר](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.he.jpg)\n", + "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> תמונה מתוך [הבלוג של Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index b595e1b7..da8f005d 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "מכיוון שאנחנו מאמנים את האוטואנקודר ללכוד כמה שיותר מידע מהתמונה המקורית לצורך שחזור מדויק, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות.\n", "\n", - "![תרשים אוטואנקודר](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.he.jpg)\n", + "![תרשים אוטואנקודר](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*תמונה מתוך [הבלוג של Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md index 78451853..42c95a88 100644 --- a/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/he/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: מכיוון שאנו מאמנים אוטואנקודר כדי ללכוד כמה שיותר מידע מהתמונה המקורית לצורך שחזור מדויק, הרשת מנסה למצוא את ה**הטמעה** הטובה ביותר של תמונות הקלט כדי ללכוד את המשמעות. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.he.jpg) +![AutoEncoder Diagram](../../../../../translated_images/he/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > תמונה מתוך [בלוג Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md index 78a62cb1..06c5d08b 100644 --- a/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/he/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [שאלון לפני השיעור](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![זיהוי אובייקטים](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.he.png) +![זיהוי אובייקטים](../../../../../translated_images/he/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > תמונה מתוך [אתר YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. להריץ סיווג תמונה על כל אריח. 3. אריחים שמפיקים הפעלה גבוהה מספיק יכולים להיחשב ככאלה שמכילים את האובייקט המדובר. -![זיהוי נאיבי של אובייקטים](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.he.png) +![זיהוי נאיבי של אובייקטים](../../../../../translated_images/he/naive-detection.e7f1ba220ccd08c6.png) > *תמונה מתוך [מחברת התרגילים](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 קטגוריות * [COCO](http://cocodataset.org/#home) - אובייקטים נפוצים בהקשר. 80 קטגוריות, תיבות גבול ומסכות סגמנטציה -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.he.jpg) +![COCO](../../../../../translated_images/he/coco-examples.71bc60380fa6cceb.jpg) ## מדדים לזיהוי אובייקטים @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: בעוד שבסיווג תמונות קל למדוד עד כמה האלגוריתם מצליח, בזיהוי אובייקטים עלינו למדוד גם את נכונות הקטגוריה וגם את דיוק מיקום תיבת הגבול שחוזה האלגוריתם. עבור האחרון, אנו משתמשים במדד שנקרא **חיתוך על איחוד** (IoU), שמודד עד כמה שתי תיבות (או שני אזורים שרירותיים) חופפים. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.he.png) +![IoU](../../../../../translated_images/he/iou_equation.9a4751d40fff4e11.png) > *איור 2 מתוך [פוסט בלוג מצוין על IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) משתמשת ב-[חיפוש סלקטיבי](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) כדי ליצור מבנה היררכי של אזורי ROI, אשר מועברים לאחר מכן דרך מחלצי תכונות של CNN ומסווגי SVM כדי לקבוע את קטגוריית האובייקט, ורגרסיה ליניארית כדי לקבוע את קואורדינטות *תיבת הגבול*. [מאמר רשמי](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.he.png) +![RCNN](../../../../../translated_images/he/rcnn1.cae407020dfb1d1f.png) > *תמונה מתוך van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.he.png) +![RCNN-1](../../../../../translated_images/he/rcnn2.2d9530bb83516484.png) > *תמונות מתוך [הבלוג הזה](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ גישה זו דומה ל-R-CNN, אך האזורים מוגדרים לאחר יישום שכבות הקונבולוציה. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.he.png) +![FRCNN](../../../../../translated_images/he/f-rcnn.3cda6d9bb4188875.png) > תמונה מתוך [המאמר הרשמי](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ הרעיון המרכזי בגישה זו הוא להשתמש ברשת עצבית כדי לנבא ROI - מה שנקרא *רשת הצעת אזורים*. [מאמר](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.he.png) +![FasterRCNN](../../../../../translated_images/he/faster-rcnn.8d46c099b87ef30a.png) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. התכונות מעובדות על ידי **מפת ניקוד רגישה למיקום**. כל אובייקט מתוך $C$ קטגוריות מחולק ל-$k\times k$ אזורים, ואנו מאמנים לחזות חלקים של אובייקטים. 3. עבור כל חלק מתוך $k\times k$ אזורים כל הרשתות מצביעות על קטגוריות אובייקטים, וקטגוריית האובייקט עם ההצבעה המקסימלית נבחרת. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.he.png) +![r-fcn image](../../../../../translated_images/he/r-fcn.13eb88158b99a3da.png) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO הוא אלגוריתם בזמן אמת במעבר אחד. הרעיון ה * התמונה מחולקת ל-$S\times S$ אזורים. * עבור כל אזור, **CNN** מנבא $n$ אובייקטים אפשריים, *קואורדינטות תיבת הגבול* ו-*ביטחון*=*הסתברות* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.he.png) + ![YOLO](../../../../../translated_images/he/yolo.a2648ec82ee8bb4e.png) > תמונה מתוך [המאמר הרשמי](https://arxiv.org/abs/1506.02640) diff --git a/translations/he/lessons/4-ComputerVision/README.md b/translations/he/lessons/4-ComputerVision/README.md index 8ae3dfeb..11c9719d 100644 --- a/translations/he/lessons/4-ComputerVision/README.md +++ b/translations/he/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ראייה ממוחשבת -![סיכום תוכן ראייה ממוחשבת באיור](../../../../translated_images/ai-computervision.6506ebebac3fbf76.he.png) +![סיכום תוכן ראייה ממוחשבת באיור](../../../../translated_images/he/ai-computervision.6506ebebac3fbf76.png) בפרק זה נלמד על: diff --git a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index bee2eb16..03ee4944 100644 --- a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "ייצוג וקטורי **Bag of Words** (BoW) הוא הייצוג הווקטורי המסורתי הנפוץ ביותר. כל מילה מקושרת לאינדקס בווקטור, והאלמנט בווקטור מכיל את מספר ההופעות של מילה מסוימת במסמך נתון.\n", "\n", - "![תמונה שמראה כיצד ייצוג וקטורי בשיטת Bag of Words מיוצג בזיכרון.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.he.png)\n", + "![תמונה שמראה כיצד ייצוג וקטורי בשיטת Bag of Words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: ניתן גם לחשוב על BoW כסכום של כל הווקטורים המקודדים בשיטת one-hot עבור המילים הבודדות בטקסט.\n", "\n", diff --git a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 1c21a0d8..e6dbfd35 100644 --- a/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/he/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "ייצוג וקטורי בשיטת **Bag-of-words** (BoW) הוא הייצוג הווקטורי המסורתי הפשוט ביותר להבנה. כל מילה מקושרת לאינדקס בווקטור, ואלמנט בווקטור מכיל את מספר הפעמים שהמילה מופיעה במסמך נתון.\n", "\n", - "![תמונה שמציגה כיצד ייצוג וקטורי בשיטת Bag-of-words מיוצג בזיכרון.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.he.png) \n", + "![תמונה שמציגה כיצד ייצוג וקטורי בשיטת Bag-of-words מיוצג בזיכרון.](../../../../../translated_images/he/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: ניתן גם לחשוב על BoW כסכום של כל הווקטורים המקודדים בשיטת one-hot עבור המילים הבודדות בטקסט.\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 0b246c57..51d7d7b2 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "על ידי שימוש בשכבת הטמעה כשכבה הראשונה ברשת שלנו, נוכל לעבור ממודל bag-of-words למודל **embedding bag**, שבו קודם כל נמיר כל מילה בטקסט שלנו להטמעה המתאימה שלה, ואז נחשב פונקציית צבירה כלשהי על כל ההטמעות הללו, כמו `sum`, `average` או `max`.\n", "\n", - "![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.he.png)\n", + "![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "רשת העצבים המסווגת שלנו תתחיל עם שכבת הטמעה, לאחר מכן שכבת צבירה, ולבסוף מסווג ליניארי מעליה:\n" ] @@ -176,7 +176,7 @@ "\n", "בארכיטקטורה הקודמת, היינו צריכים לרפד את כל הרצפים לאורך אחיד כדי להתאים אותם למיני-באטץ'. זו לא הדרך היעילה ביותר לייצג רצפים באורך משתנה - גישה אחרת תהיה להשתמש בוקטור **offset**, שמחזיק את ההיסטים של כל הרצפים המאוחסנים בוקטור גדול אחד.\n", "\n", - "![תמונה המציגה ייצוג רצף עם היסטים](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.he.png)\n", + "![תמונה המציגה ייצוג רצף עם היסטים](../../../../../translated_images/he/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: בתמונה למעלה, אנו מציגים רצף של תווים, אך בדוגמה שלנו אנו עובדים עם רצפים של מילים. עם זאת, העיקרון הכללי של ייצוג רצפים באמצעות וקטור היסטים נשאר זהה.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW מהיר יותר, בעוד ש-Skip-Gram איטי יותר, אך מבצע עבודה טובה יותר בייצוג מילים נדירות.\n", "\n", - "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.he.png)\n", + "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "כדי להתנסות בהטמעת Word2Vec שאומנה מראש על מאגר הנתונים של Google News, נוכל להשתמש בספריית **gensim**. להלן נמצא את המילים שהכי דומות ל-'neural'\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index ed30208f..2d396003 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/he/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "על ידי שימוש בשכבת embedding כשכבה הראשונה ברשת שלנו, אנחנו יכולים לעבור ממודל bag-of-words למודל **embedding bag**, שבו תחילה אנו ממירים כל מילה בטקסט שלנו ל-embedding המתאים לה, ואז מחשבים פונקציית צבירה כלשהי על כל ה-embeddings, כמו `sum`, `average` או `max`.\n", "\n", - "![תמונה המציגה מסווג embedding עבור חמישה רצפי מילים.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.he.png)\n", + "![תמונה המציגה מסווג embedding עבור חמישה רצפי מילים.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "רשת הנוירונים המסווגת שלנו מורכבת מהשכבות הבאות:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW מהיר יותר, בעוד ש-Skip-Gram איטי יותר, אך הוא עושה עבודה טובה יותר בייצוג מילים נדירות.\n", "\n", - "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.he.png)\n", + "![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "כדי להתנסות בהטמעת Word2Vec שאומנה מראש על מאגר הנתונים של Google News, ניתן להשתמש בספריית **gensim**. להלן נאתר את המילים הדומות ביותר ל'neural'.\n", "\n", diff --git a/translations/he/lessons/5-NLP/14-Embeddings/README.md b/translations/he/lessons/5-NLP/14-Embeddings/README.md index e2d71053..9555a4fc 100644 --- a/translations/he/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/he/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: על ידי שימוש בשכבת הטמעות כשכבה הראשונה ברשת המסווג שלנו, נוכל לעבור מתיק מילים למודל **תיק הטמעות** (embedding bag), שבו אנו קודם ממירים כל מילה בטקסט שלנו להטמעה המתאימה, ואז מחשבים פונקציית צבירה כלשהי על כל ההטמעות הללו, כמו `sum`, `average` או `max`. -![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.he.png) +![תמונה המציגה מסווג הטמעות עבור חמש מילים ברצף.](../../../../../translated_images/he/embedding-classifier-example.b77f021a7ee67eee.png) > תמונה מאת המחבר @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW מהיר יותר, בעוד ש-skip-gram איטי יותר, אך עושה עבודה טובה יותר בייצוג מילים נדירות. -![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.he.png) +![תמונה המציגה את האלגוריתמים CBoW ו-Skip-Gram להמרת מילים לוקטורים.](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > תמונה מתוך [המאמר הזה](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/he/lessons/5-NLP/15-LanguageModeling/README.md b/translations/he/lessons/5-NLP/15-LanguageModeling/README.md index 6ff966c2..dda4e622 100644 --- a/translations/he/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/he/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW), שבו אנו חוזים את הטוקן האמצעי $W_0$ ברצף טוקנים $W_{-N}$, ..., $W_N$. * **Skip-gram**, שבו אנו חוזים סט של טוקנים סמוכים {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} מתוך הטוקן האמצעי $W_0$. -![תמונה מתוך מאמר על המרת מילים לווקטורים](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.he.png) +![תמונה מתוך מאמר על המרת מילים לווקטורים](../../../../../translated_images/he/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > תמונה מתוך [המאמר הזה](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/he/lessons/5-NLP/16-RNN/README.md b/translations/he/lessons/5-NLP/16-RNN/README.md index 842f3129..4cfbfecf 100644 --- a/translations/he/lessons/5-NLP/16-RNN/README.md +++ b/translations/he/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: כדי לתפוס את המשמעות של רצף טקסט, עלינו להשתמש בארכיטקטורה אחרת של רשת עצבית, הנקראת **רשת עצבית חוזרת**, או RNN. ב-RNN, אנו מעבירים את המשפט דרך הרשת סמל אחד בכל פעם, והרשת מייצרת **מצב** מסוים, אותו אנו מעבירים שוב לרשת עם הסמל הבא. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.he.png) +![RNN](../../../../../translated_images/he/rnn.27f5c29c53d727b5.png) > תמונה מאת המחבר @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: רשת חוזרת, בין אם חד-כיוונית או דו-כיוונית, תופסת דפוסים מסוימים בתוך רצף, ויכולה לאחסן אותם בוקטור מצב או להעבירם לפלט. כמו ברשתות קונבולוציה, אנו יכולים לבנות שכבה חוזרת נוספת מעל הראשונה כדי לתפוס דפוסים ברמה גבוהה יותר ולבנות מדפוסים ברמה נמוכה שנלכדו על ידי השכבה הראשונה. זה מוביל אותנו למושג של **RNN רב-שכבתי** שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט. -![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.he.jpg) +![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg) *תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת פרננדו לופז* diff --git a/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 67b98a57..53178bc7 100644 --- a/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "רשת חוזרת, בין אם היא חד-כיוונית או דו-כיוונית, לוכדת דפוסים מסוימים בתוך רצף, ויכולה לאחסן אותם בווקטור מצב או להעביר אותם לפלט. כמו ברשתות קונבולוציה, ניתן לבנות שכבה חוזרת נוספת מעל הראשונה כדי ללכוד דפוסים ברמה גבוהה יותר, שנבנים מדפוסים ברמה נמוכה שהופקו על ידי השכבה הראשונה. זה מוביל אותנו למושג של **RNN רב-שכבתי**, שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט.\n", "\n", - "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.he.jpg)\n", + "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת Fernando López*\n", "\n", diff --git a/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2446ac10..201cbf1f 100644 --- a/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/he/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "כדי לתפוס את המשמעות של רצף טקסט, נשתמש בארכיטקטורה של רשת עצבית הנקראת **רשת עצבית חוזרת**, או RNN. כאשר משתמשים ב-RNN, אנו מעבירים את המשפט דרך הרשת, טוקן אחד בכל פעם, והרשת מייצרת **מצב** מסוים, אותו אנו מעבירים שוב לרשת יחד עם הטוקן הבא.\n", "\n", - "![תמונה המציגה דוגמה ליצירת רשת עצבית חוזרת.](../../../../../translated_images/rnn.27f5c29c53d727b5.he.png)\n", + "![תמונה המציגה דוגמה ליצירת רשת עצבית חוזרת.](../../../../../translated_images/he/rnn.27f5c29c53d727b5.png)\n", "\n", "בהינתן רצף הטוקנים $X_0,\\dots,X_n$, ה-RNN יוצר רצף של בלוקים של רשת עצבית, ומאמן את הרצף הזה מקצה לקצה באמצעות שיטת ה-backpropagation. כל בלוק ברשת מקבל זוג $(X_i,S_i)$ כקלט, ומייצר $S_{i+1}$ כתוצאה. המצב הסופי $S_n$ או הפלט $Y_n$ מועבר למסווג ליניארי כדי לייצר את התוצאה. כל בלוקי הרשת חולקים את אותם משקלים, ומאומנים מקצה לקצה באמצעות מעבר אחד של backpropagation.\n", "\n", @@ -371,7 +371,7 @@ "\n", "רשתות חוזרות, בין אם חד-כיווניות או דו-כיווניות, לוכדות תבניות בתוך רצף, ושומרות אותן בווקטורי מצב או מחזירות אותן כפלט. כמו ברשתות קונבולוציה, ניתן לבנות שכבה חוזרת נוספת אחרי הראשונה כדי ללכוד תבניות ברמה גבוהה יותר, שנבנות מתבניות ברמה נמוכה יותר שהשכבה הראשונה חילצה. זה מוביל אותנו למושג של **RNN רב-שכבתי**, שמורכב משתי רשתות חוזרות או יותר, כאשר הפלט של השכבה הקודמת מועבר לשכבה הבאה כקלט.\n", "\n", - "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.he.jpg)\n", + "![תמונה המציגה RNN רב-שכבתי מסוג LSTM](../../../../../translated_images/he/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*תמונה מתוך [הפוסט הנהדר הזה](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) מאת Fernando López.*\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 8e34f1b4..042a5f2b 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "הדרך שבה נאמן RNN לייצר טקסט היא כדלקמן. בכל שלב, ניקח רצף של תווים באורך `nchars`, ונבקש מהרשת לייצר את התו הבא עבור כל תו קלט:\n", "\n", - "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.he.png)\n", + "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png)\n", "\n", "בהתאם לתרחיש בפועל, ייתכן שנרצה לכלול גם תווים מיוחדים, כמו *סוף רצף* ``. במקרה שלנו, אנחנו רק רוצים לאמן את הרשת ליצירת טקסט אינסופי, ולכן נקבע את גודל כל רצף להיות שווה ל-`nchars` טוקנים. כתוצאה מכך, כל דוגמת אימון תכלול `nchars` קלטים ו-`nchars` פלטים (שהם רצף הקלט מוזז סמל אחד שמאלה). מיניבאץ' יכלול כמה רצפים כאלה.\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 7353199d..0192d317 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "הדרך שבה נאמן RNN לייצר כותרות חדשות היא כדלקמן. בכל שלב, ניקח כותרת אחת, שתוזן לתוך RNN, ולכל תו קלט נבקש מהרשת לייצר את תו הפלט הבא:\n", "\n", - "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.he.png)\n", + "![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png)\n", "\n", "עבור התו האחרון ברצף שלנו, נבקש מהרשת לייצר את הטוקן ``.\n", "\n", diff --git a/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md index 2aecf195..9dd3fc6c 100644 --- a/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/he/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: זה מאפשר ארכיטקטורות עצביות שונות, כפי שמוצג בתמונה הבאה: -![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.he.jpg) +![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > תמונה מתוך פוסט הבלוג [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) מאת [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: נאמן את ה-RNN הזה ליצירת טקסט שלב אחר שלב. בכל שלב, ניקח רצף של תווים באורך `nchars`, ונבקש מהרשת לייצר את התו הבא עבור כל תו קלט: -![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.he.png) +![תמונה המציגה דוגמה ליצירת המילה 'HELLO' באמצעות RNN.](../../../../../translated_images/he/rnn-generate.56c54afb52f9781d.png) בעת יצירת טקסט (בזמן הסקת מסקנות), נתחיל עם **הנחיה** כלשהי, שתועבר דרך תאי RNN כדי ליצור את מצב הביניים שלה, ואז מהמצב הזה מתחילה היצירה. ניצור תו אחד בכל פעם, ונעביר את המצב ואת התו שנוצר לתא RNN נוסף כדי ליצור את הבא, עד שניצור מספיק תווים. diff --git a/translations/he/lessons/5-NLP/18-Transformers/README.md b/translations/he/lessons/5-NLP/18-Transformers/README.md index 11edfb24..29793e33 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/README.md +++ b/translations/he/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. זה מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN הקלט לבין ה-RNN הפלט. כך, בעת יצירת סמל פלט yt, ניקח בחשבון את כל המצבים המוסתרים של הקלט hi, עם מקדמי משקל שונים αt,i. -![תמונה המציגה מודל encoder/decoder עם שכבת קשב אדיטיבית](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.he.png) +![תמונה המציגה מודל encoder/decoder עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png) > מודל ה-encoder-decoder עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט בבלוג זה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) מטריצת הקשב {αi,j} מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת בפלט. להלן דוגמה למטריצה כזו: -![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.he.png) +![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png) > איור מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: כעת, עלינו לזהות תבניות בתוך הרצף שלנו. לשם כך, טרנספורמרים משתמשים במנגנון **קשב עצמי**, שהוא למעשה קשב המיושם על אותו רצף כקלט וכפלט. יישום קשב עצמי מאפשר לנו לקחת בחשבון **הקשר** בתוך המשפט ולראות אילו מילים קשורות זו לזו. לדוגמה, הוא מאפשר לנו לראות אילו מילים מתייחסות להן באמצעות התייחסויות כמו *it*, וגם לקחת את ההקשר בחשבון: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.he.png) +![](../../../../../translated_images/he/CoreferenceResolution.861924d6d384a7d6.png) > תמונה מתוך [הבלוג של Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) הוא רשת טרנספורמרים גדולה מאוד עם 12 שכבות עבור *BERT-base*, ו-24 עבור *BERT-large*. המודל מאומן תחילה על מאגר טקסט גדול (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (חיזוי מילים מוסתרות במשפט). במהלך האימון הראשוני, המודל סופג רמות משמעותיות של הבנת שפה, שניתן לאחר מכן לנצל עם מערכי נתונים אחרים באמצעות כוונון עדין. תהליך זה נקרא **למידת העברה**. -![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.he.png) +![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > מקור התמונה [כאן](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 4743e14f..2a0c3211 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/he/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. זה מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN של הקלט לבין ה-RNN של הפלט. כך, בעת יצירת סמל פלט $y_t$, ניקח בחשבון את כל המצבים המוסתרים של הקלט $h_i$, עם מקדמי משקל שונים $\\alpha_{t,i}$.\n", "\n", - "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.he.png)\n", + "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png)\n", "*מודל מקודד-מפענח עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט הבלוג הזה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "מטריצת הקשב $\\{\\alpha_{i,j}\\}$ מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת ברצף הפלט. להלן דוגמה למטריצה כזו:\n", "\n", - "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.he.png)\n", + "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*תמונה מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (ייצוגי מקודד דו-כיווניים מטרנספורמרים) הוא רשת טרנספורמרים גדולה מאוד עם 12 שכבות עבור *BERT-base* ו-24 עבור *BERT-large*. המודל מאומן מראש על מאגר טקסטים גדול (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (חיזוי מילים מוסתרות במשפט). במהלך האימון המוקדם, המודל סופג רמה משמעותית של הבנת שפה, שניתן לנצל לאחר מכן עם מערכי נתונים אחרים באמצעות כוונון עדין. תהליך זה נקרא **למידת העברה**.\n", "\n", - "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.he.png)\n", + "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ישנן וריאציות רבות של ארכיטקטורות טרנספורמרים, כולל BERT, DistilBERT, BigBird, OpenGPT3 ועוד, שניתן לכוונן. חבילת [HuggingFace](https://github.com/huggingface/) מספקת מאגר לאימון רבות מהארכיטקטורות הללו עם PyTorch.\n", "\n", diff --git a/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 5dc34765..b94ce148 100644 --- a/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/he/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**מנגנוני קשב** מספקים דרך לשקלל את ההשפעה ההקשרית של כל וקטור קלט על כל תחזית פלט של ה-RNN. הדבר מיושם על ידי יצירת קיצורי דרך בין המצבים הביניים של ה-RNN של הקלט לבין ה-RNN של הפלט. כך, בעת יצירת סמל פלט $y_t$, ניקח בחשבון את כל המצבים המוסתרים של הקלט $h_i$, עם מקדמי משקל שונים $\\alpha_{t,i}$.\n", "\n", - "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.he.png)\n", + "![תמונה המציגה מודל מקודד/מפענח עם שכבת קשב אדיטיבית](../../../../../translated_images/he/encoder-decoder-attention.7a726296894fb567.png)\n", "*מודל מקודד-מפענח עם מנגנון קשב אדיטיבי מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), מצוטט מתוך [פוסט הבלוג הזה](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "מטריצת הקשב $\\{\\alpha_{i,j}\\}$ מייצגת את המידה שבה מילים מסוימות בקלט משפיעות על יצירת מילה מסוימת ברצף הפלט. להלן דוגמה למטריצה כזו:\n", "\n", - "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.he.png)\n", + "![תמונה המציגה יישור דוגמה שנמצא על ידי RNNsearch-50, מתוך Bahdanau - arviz.org](../../../../../translated_images/he/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*תמונה מתוך [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (איור 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) הוא רשת טרנספורמר רב שכבתית גדולה מאוד עם 12 שכבות עבור *BERT-base*, ו-24 עבור *BERT-large*. המודל עובר תחילה אימון מוקדם על מאגר נתונים גדול של טקסט (ויקיפדיה + ספרים) באמצעות אימון לא מפוקח (ניבוי מילים מוסתרות במשפט). במהלך האימון המוקדם, המודל סופג רמה משמעותית של הבנת שפה, שניתן לאחר מכן לנצל עם מערכי נתונים אחרים באמצעות כיוונון עדין. תהליך זה נקרא **למידה מעוברת**.\n", "\n", - "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.he.png)\n", + "![תמונה מתוך http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/he/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ישנם וריאציות רבות של ארכיטקטורות טרנספורמר, כולל BERT, DistilBERT, BigBird, OpenGPT3 ועוד, שניתן לבצע עליהן כיוונון עדין.\n", "\n", diff --git a/translations/he/lessons/5-NLP/19-NER/README.md b/translations/he/lessons/5-NLP/19-NER/README.md index 11449f6f..7dfed10e 100644 --- a/translations/he/lessons/5-NLP/19-NER/README.md +++ b/translations/he/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O מכיוון שעלינו לבנות התאמה של אחד-לאחד בין טוקנים לקטגוריות, נוכל לאמן מודל רשת עצבית **רב-לרב** מהתמונה הבאה: -![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.he.jpg) +![תמונה המציגה דפוסים נפוצים של רשתות עצביות חוזרות.](../../../../../translated_images/he/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *תמונה מתוך [פוסט הבלוג הזה](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) מאת [אנדריי קרפת'י](http://karpathy.github.io/). מודלים של סיווג טוקנים ב-NER תואמים לארכיטקטורת הרשת הימנית ביותר בתמונה זו.* diff --git a/translations/he/lessons/5-NLP/README.md b/translations/he/lessons/5-NLP/README.md index 71c6aaa4..670f4c86 100644 --- a/translations/he/lessons/5-NLP/README.md +++ b/translations/he/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # עיבוד שפה טבעית -![סיכום משימות NLP בציור](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.he.png) +![סיכום משימות NLP בציור](../../../../translated_images/he/ai-nlp.b22dcb8ca4707cea.png) בפרק זה נתמקד בשימוש ברשתות נוירונים לטיפול במשימות הקשורות ל**עיבוד שפה טבעית (NLP)**. ישנם הרבה בעיות NLP שאנו רוצים שמחשבים יוכלו לפתור: diff --git a/translations/he/lessons/6-Other/23-MultiagentSystems/README.md b/translations/he/lessons/6-Other/23-MultiagentSystems/README.md index 1e3081cf..94843e98 100644 --- a/translations/he/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/he/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ לאחר פתיחת המודל, תועברו למסך הראשי של NetLogo. הנה דוגמת מודל שמתאר את אוכלוסיית הזאבים והכבשים, בהתחשב במשאבים מוגבלים (דשא). -![מסך ראשי של NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.he.png) +![מסך ראשי של NetLogo](../../../../../translated_images/he/NetLogo-Main.32653711ec1a01b3.png) > צילום מסך מאת דמיטרי סושניקוב diff --git a/translations/he/lessons/README.md b/translations/he/lessons/README.md index fc7f7f5c..50e2582a 100644 --- a/translations/he/lessons/README.md +++ b/translations/he/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # סקירה כללית -![סקירה כללית באיור](../../../translated_images/ai-overview.0857791951d19500.he.png) +![סקירה כללית באיור](../../../translated_images/he/ai-overview.0857791951d19500.png) > איור מאת [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/he/lessons/X-Extras/X1-MultiModal/README.md b/translations/he/lessons/X-Extras/X1-MultiModal/README.md index 48b30f11..37e1e323 100644 --- a/translations/he/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/he/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: הרעיון המרכזי של CLIP הוא היכולת להשוות בין טקסט לתמונה ולקבוע עד כמה התמונה מתאימה לטקסט. -![ארכיטקטורת CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.he.png) +![ארכיטקטורת CLIP](../../../../../translated_images/he/clip-arch.b3dbf20b4e8ed8be.png) > *תמונה מתוך [הפוסט הזה](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: נניח שעלינו לסווג תמונות בין חתולים, כלבים ובני אדם. במקרה זה, ניתן להזין למודל תמונה וסדרה של טקסטים: "*תמונה של חתול*", "*תמונה של כלב*", "*תמונה של אדם*". בווקטור התוצאות של 3 ההסתברויות, נבחר את האינדקס עם הערך הגבוה ביותר. -![CLIP לסיווג תמונות](../../../../../translated_images/clip-class.3af42ef0b2b19369.he.png) +![CLIP לסיווג תמונות](../../../../../translated_images/he/clip-class.3af42ef0b2b19369.png) > *תמונה מתוך [הפוסט הזה](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CO_OP_TRANSLATOR_METADATA: אחת ההבדלים החשובים בין VQGAN ל-GAN מסורתי היא שהאחרון יכול לייצר תמונה סבירה מכל וקטור קלט, בעוד ש-VQGAN עשוי לייצר תמונה שאינה קוהרנטית. לכן, יש להנחות את תהליך יצירת התמונה, וזה נעשה באמצעות CLIP. -![ארכיטקטורת VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.he.png) +![ארכיטקטורת VQGAN+CLIP](../../../../../translated_images/he/vqgan.5027fe05051dfa31.png) כדי ליצור תמונה שמתאימה לטקסט, מתחילים עם וקטור קידוד אקראי שמועבר דרך VQGAN ליצירת תמונה. לאחר מכן, CLIP משמש ליצירת פונקציית הפסד שמראה עד כמה התמונה מתאימה לטקסט. המטרה היא למזער את ההפסד הזה באמצעות back propagation כדי להתאים את פרמטרי וקטור הקלט. ספרייה מצוינת שמממשת VQGAN+CLIP היא [Pixray](http://github.com/pixray/pixray). -![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.he.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.he.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.he.png) +![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- תמונה שנוצרה מהטקסט *דיוקן בצבעי מים של מורה צעיר לספרות עם ספר* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה צעירה למדעי המחשב עם מחשב* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה מבוגר למתמטיקה מול לוח שחור* @@ -75,7 +75,7 @@ DALL-E הוא גרסה של GPT-3 שאומנה ליצירת תמונות מטק ההבדל המרכזי בין DALL-E 1 ל-DALL-E 2 הוא שגרסה 2 מייצרת תמונות ואמנות ריאליסטיות יותר. דוגמאות ליצירת תמונות עם DALL-E: -![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.he.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.he.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.he.png) +![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![תמונה שנוצרה על ידי Pixray](../../../../../translated_images/he/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- תמונה שנוצרה מהטקסט *דיוקן בצבעי מים של מורה צעיר לספרות עם ספר* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה צעירה למדעי המחשב עם מחשב* | תמונה שנוצרה מהטקסט *דיוקן בשמן של מורה מבוגר למתמטיקה מול לוח שחור* diff --git a/translations/hi/README.md b/translations/hi/README.md index 0913844d..cf542420 100644 --- a/translations/hi/README.md +++ b/translations/hi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # शुरुआती लोगों के लिए आर्टिफिशियल इंटेलिजेंस - एक पाठ्यक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.hi.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hi/ai-overview.0857791951d19500.png)| |:---:| | शुरुआत करने वालों के लिए AI - _स्केचनोट [@girlie_mac](https://twitter.com/girlie_mac) द्वारा_ | diff --git a/translations/hi/lessons/1-Intro/README.md b/translations/hi/lessons/1-Intro/README.md index 515ac365..c843da40 100644 --- a/translations/hi/lessons/1-Intro/README.md +++ b/translations/hi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # एआई का परिचय -![एआई परिचय सामग्री का सारांश एक डूडल में](../../../../translated_images/ai-intro.bf28d1ac4235881c.hi.png) +![एआई परिचय सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-intro.bf28d1ac4235881c.png) > स्केच नोट [टोमोमी इमुरा](https://twitter.com/girlie_mac) द्वारा @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: मूल रूप से, कंप्यूटर को [चार्ल्स बैबेज](https://en.wikipedia.org/wiki/Charles_Babbage) द्वारा संख्याओं पर एक निश्चित प्रक्रिया - एक एल्गोरिदम - के अनुसार काम करने के लिए आविष्कार किया गया था। आधुनिक कंप्यूटर, हालांकि 19वीं सदी में प्रस्तावित मूल मॉडल की तुलना में काफी उन्नत हैं, फिर भी नियंत्रित गणनाओं के उसी विचार का पालन करते हैं। इसलिए, यदि हमें उस लक्ष्य को प्राप्त करने के लिए आवश्यक चरणों का सटीक क्रम पता है, तो कंप्यूटर को कुछ करने के लिए प्रोग्राम करना संभव है। -![एक व्यक्ति की तस्वीर](../../../../translated_images/dsh_age.d212a30d4e54fb5f.hi.png) +![एक व्यक्ति की तस्वीर](../../../../translated_images/hi/dsh_age.d212a30d4e54fb5f.png) > फोटो [विकी सॉश्निकोवा](http://twitter.com/vickievalerie) द्वारा @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** शब्द से निपटने में एक समस्या यह है कि इस शब्द की कोई स्पष्ट परिभाषा नहीं है। कोई तर्क कर सकता है कि बुद्धिमत्ता **सार्वभौमिक सोच** या **आत्म-जागरूकता** से जुड़ी है, लेकिन हम इसे ठीक से परिभाषित नहीं कर सकते। -![एक बिल्ली की तस्वीर](../../../../translated_images/photo-cat.8c8e8fb760ffe457.hi.jpg) +![एक बिल्ली की तस्वीर](../../../../translated_images/hi/photo-cat.8c8e8fb760ffe457.jpg) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [एंबर किप्प](https://unsplash.com/@sadmax) द्वारा Unsplash से @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | एमएल के बारे में क्या? | | > |--------------|-----------| -> | कृत्रिम बुद्धिमत्ता का वह हिस्सा जो किसी समस्या को कुछ डेटा के आधार पर हल करने के लिए कंप्यूटर सीखने पर आधारित है, उसे **मशीन लर्निंग** कहा जाता है। हम इस पाठ्यक्रम में क्लासिकल मशीन लर्निंग पर विचार नहीं करेंगे - हम आपको एक अलग [मशीन लर्निंग फॉर बिगिनर्स](http://aka.ms/ml-beginners) पाठ्यक्रम की ओर संदर्भित करते हैं। | ![एमएल फॉर बिगिनर्स](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.hi.png) | +> | कृत्रिम बुद्धिमत्ता का वह हिस्सा जो किसी समस्या को कुछ डेटा के आधार पर हल करने के लिए कंप्यूटर सीखने पर आधारित है, उसे **मशीन लर्निंग** कहा जाता है। हम इस पाठ्यक्रम में क्लासिकल मशीन लर्निंग पर विचार नहीं करेंगे - हम आपको एक अलग [मशीन लर्निंग फॉर बिगिनर्स](http://aka.ms/ml-beginners) पाठ्यक्रम की ओर संदर्भित करते हैं। | ![एमएल फॉर बिगिनर्स](../../../../translated_images/hi/ml-for-beginners.9e4fed176fd5817d.png) | ## एआई का संक्षिप्त इतिहास कृत्रिम बुद्धिमत्ता को 20वीं सदी के मध्य में एक क्षेत्र के रूप में शुरू किया गया था। प्रारंभ में, प्रतीकात्मक तर्क एक प्रमुख दृष्टिकोण था, और इसने कुछ महत्वपूर्ण सफलताओं को जन्म दिया, जैसे कि विशेषज्ञ प्रणालियाँ – कंप्यूटर प्रोग्राम जो कुछ सीमित समस्या डोमेन में एक विशेषज्ञ के रूप में कार्य करने में सक्षम थे। हालांकि, जल्द ही यह स्पष्ट हो गया कि ऐसा दृष्टिकोण अच्छी तरह से स्केल नहीं करता। किसी विशेषज्ञ से ज्ञान निकालना, उसे कंप्यूटर में प्रस्तुत करना, और उस ज्ञान आधार को सटीक बनाए रखना एक बहुत ही जटिल कार्य और कई मामलों में व्यावहारिक रूप से बहुत महंगा साबित हुआ। इसने 1970 के दशक में तथाकथित [एआई विंटर](https://en.wikipedia.org/wiki/AI_winter) को जन्म दिया। -एआई का संक्षिप्त इतिहास +एआई का संक्षिप्त इतिहास > छवि [दिमित्री सॉश्निकोव](http://soshnikov.com) द्वारा @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * आधुनिक सहायक, जैसे कि कोरटाना, सिरी या गूगल असिस्टेंट सभी हाइब्रिड सिस्टम हैं जो भाषण को टेक्स्ट में बदलने और हमारे इरादे को पहचानने के लिए न्यूरल नेटवर्क का उपयोग करते हैं, और फिर आवश्यक कार्यों को करने के लिए कुछ तर्क या स्पष्ट एल्गोरिदम का उपयोग करते हैं। * भविष्य में, हम उम्मीद कर सकते हैं कि एक पूर्ण न्यूरल-आधारित मॉडल स्वयं संवाद को संभालेगा। हाल ही में GPT और [ट्यूरिंग-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) न्यूरल नेटवर्क परिवार इस क्षेत्र में बड़ी सफलता दिखा रहे हैं। -ट्यूरिंग टेस्ट का विकास +ट्यूरिंग टेस्ट का विकास > चित्र Dmitry Soshnikov द्वारा, [फोटो](https://unsplash.com/photos/r8LmVbUKgns) Marina Abrosimova द्वारा [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## हालिया AI अनुसंधान diff --git a/translations/hi/lessons/2-Symbolic/README.md b/translations/hi/lessons/2-Symbolic/README.md index 21ea7772..4e927e9d 100644 --- a/translations/hi/lessons/2-Symbolic/README.md +++ b/translations/hi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञान प्रतिनिधित्व और विशेषज्ञ प्रणाली -![सिंबोलिक AI सामग्री का सारांश](../../../../translated_images/ai-symbolic.715a30cb610411a6.hi.png) +![सिंबोलिक AI सामग्री का सारांश](../../../../translated_images/hi/ai-symbolic.715a30cb610411a6.png) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा @@ -41,7 +41,7 @@ AI के शुरुआती दिनों में, बुद्धिम इस प्रकार, **ज्ञान प्रतिनिधित्व** की समस्या यह है कि कंप्यूटर के अंदर डेटा के रूप में ज्ञान को प्रभावी ढंग से कैसे प्रस्तुत किया जाए, ताकि इसे स्वचालित रूप से उपयोग किया जा सके। इसे एक स्पेक्ट्रम के रूप में देखा जा सकता है: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/knowledge-spectrum.b60df631852c0217.hi.png) +![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/hi/knowledge-spectrum.b60df631852c0217.png) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा @@ -94,7 +94,7 @@ Untyped-Language | नहीं है | टाइप डिफिनिशन सिंबोलिक AI की शुरुआती सफलताओं में से एक **विशेषज्ञ प्रणाली** थीं - कंप्यूटर प्रणाली जो किसी सीमित समस्या क्षेत्र में विशेषज्ञ के रूप में कार्य करने के लिए डिज़ाइन की गई थीं। ये **ज्ञान आधार** पर आधारित थीं जो एक या अधिक मानव विशेषज्ञों से निकाली गई थीं, और इनमें एक **तर्क इंजन** था जो इसके ऊपर कुछ तर्क करता था। -![मानव संरचना](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.hi.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.hi.png) +![मानव संरचना](../../../../translated_images/hi/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/hi/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------|-------------------------------------------- मानव तंत्रिका प्रणाली की सरलीकृत संरचना | ज्ञान-आधारित प्रणाली की संरचना @@ -106,7 +106,7 @@ Untyped-Language | नहीं है | टाइप डिफिनिशन उदाहरण के लिए, आइए एक जानवर की शारीरिक विशेषताओं के आधार पर उसे निर्धारित करने वाली विशेषज्ञ प्रणाली पर विचार करें: -![AND-OR ट्री](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hi.png) +![AND-OR ट्री](../../../../translated_images/hi/AND-OR-Tree.5592d2c70187f283.png) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा diff --git a/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md index e33ef773..efc4cde8 100644 --- a/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 5 बिंदुओं को (ग्राफ में `x` द्वारा दर्शाए गए) अनुमानित करने की निम्नलिखित समस्या पर विचार करें: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.hi.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.hi.jpg) +![linear](../../../../../translated_images/hi/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/hi/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **लिनियर मॉडल, 2 पैरामीटर्स** | **नॉन-लिनियर मॉडल, 7 पैरामीटर्स** ट्रेनिंग एरर = 5.3 | ट्रेनिंग एरर = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: जैसा कि आप ऊपर दिए गए ग्राफ से देख सकते हैं, ओवरफिटिंग का पता बहुत कम ट्रेनिंग एरर और उच्च वैलिडेशन एरर से लगाया जा सकता है। आमतौर पर ट्रेनिंग के दौरान हम देखेंगे कि ट्रेनिंग और वैलिडेशन एरर दोनों कम होने लगते हैं, और फिर किसी बिंदु पर वैलिडेशन एरर कम होना बंद कर सकता है और बढ़ने लग सकता है। यह ओवरफिटिंग का संकेत होगा, और यह संकेत होगा कि हमें शायद इस बिंदु पर ट्रेनिंग रोक देनी चाहिए (या कम से कम मॉडल का स्नैपशॉट लेना चाहिए)। -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.hi.png) +![overfitting](../../../../../translated_images/hi/Overfitting.408ad91cd90b4371.png) ## ओवरफिटिंग को कैसे रोकें diff --git a/translations/hi/lessons/3-NeuralNetworks/README.md b/translations/hi/lessons/3-NeuralNetworks/README.md index ac674594..c186d6c9 100644 --- a/translations/hi/lessons/3-NeuralNetworks/README.md +++ b/translations/hi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्यूरल नेटवर्क्स का परिचय -![न्यूरल नेटवर्क्स के परिचय की सामग्री का सारांश एक डूडल में](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.hi.png) +![न्यूरल नेटवर्क्स के परिचय की सामग्री का सारांश एक डूडल में](../../../../translated_images/hi/ai-neuralnetworks.1c687ae40bc86e83.png) जैसा कि हमने परिचय में चर्चा की थी, बुद्धिमत्ता प्राप्त करने के तरीकों में से एक है **कंप्यूटर मॉडल** या **कृत्रिम मस्तिष्क** को प्रशिक्षित करना। 20वीं सदी के मध्य से, शोधकर्ताओं ने विभिन्न गणितीय मॉडलों की कोशिश की, और हाल के वर्षों में यह दिशा अत्यधिक सफल साबित हुई। मस्तिष्क के इन गणितीय मॉडलों को **न्यूरल नेटवर्क्स** कहा जाता है। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जीवविज्ञान से, हम जानते हैं कि हमारा मस्तिष्क न्यूरल कोशिकाओं (न्यूरॉन्स) से बना है, जिनमें से प्रत्येक के पास कई "इनपुट" (डेंड्राइट्स) और एक "आउटपुट" (एक्सॉन) होता है। डेंड्राइट्स और एक्सॉन दोनों विद्युत संकेतों का संचालन कर सकते हैं, और उनके बीच के कनेक्शन — जिन्हें सिनैप्स कहा जाता है — विभिन्न स्तरों की चालकता प्रदर्शित कर सकते हैं, जो न्यूरोट्रांसमीटर द्वारा नियंत्रित होती है। -![न्यूरॉन का मॉडल](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.hi.jpg) | ![न्यूरॉन का मॉडल](../../../../translated_images/artneuron.1a5daa88d20ebe6f.hi.png) +![न्यूरॉन का मॉडल](../../../../translated_images/hi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्यूरॉन का मॉडल](../../../../translated_images/hi/artneuron.1a5daa88d20ebe6f.png) ----|---- वास्तविक न्यूरॉन *([छवि](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) विकिपीडिया से)* | कृत्रिम न्यूरॉन *(लेखक द्वारा बनाई गई छवि)* इस प्रकार, न्यूरॉन का सबसे सरल गणितीय मॉडल कई इनपुट्स X1, ..., XN और एक आउटपुट Y, और वज़नों की एक श्रृंखला W1, ..., WN को शामिल करता है। आउटपुट की गणना इस प्रकार की जाती है: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जहां f एक गैर-रेखीय **सक्रियण फ़ंक्शन** (activation function) है। diff --git a/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md index b8cb70e9..defd228c 100644 --- a/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तक की तस्वीर का पूर्व-प्रसंस्करण**। हम इस पर ध्यान केंद्रित करते हैं कि थ्रेशोल्डिंग, फीचर डिटेक्शन, परिप्रेक्ष्य रूपांतरण और NumPy हेरफेर का उपयोग करके ब्रेल प्रतीकों को अलग कैसे किया जा सकता है ताकि उन्हें न्यूरल नेटवर्क द्वारा आगे वर्गीकृत किया जा सके। -![ब्रेल छवि](../../../../../translated_images/braille.341962ff76b1bd70.hi.jpeg) | ![ब्रेल छवि पूर्व-प्रसंस्कृत](../../../../../translated_images/braille-result.46530fea020b03c7.hi.png) | ![ब्रेल प्रतीक](../../../../../translated_images/braille-symbols.0159185ab69d5339.hi.png) +![ब्रेल छवि](../../../../../translated_images/hi/braille.341962ff76b1bd70.jpeg) | ![ब्रेल छवि पूर्व-प्रसंस्कृत](../../../../../translated_images/hi/braille-result.46530fea020b03c7.png) | ![ब्रेल प्रतीक](../../../../../translated_images/hi/braille-symbols.0159185ab69d5339.png) ----|-----|----- > छवि [OpenCV.ipynb](OpenCV.ipynb) से * **फ्रेम अंतर का उपयोग करके वीडियो में गति का पता लगाना**। यदि कैमरा स्थिर है, तो कैमरा फीड से फ्रेम एक-दूसरे के समान होने चाहिए। चूंकि फ्रेम arrays के रूप में दर्शाए जाते हैं, केवल दो लगातार फ्रेम के लिए उन arrays को घटाकर हमें पिक्सल अंतर मिलेगा, जो स्थिर फ्रेम के लिए कम होना चाहिए और छवि में महत्वपूर्ण गति होने पर अधिक हो जाएगा। -![वीडियो फ्रेम और फ्रेम अंतर की छवि](../../../../../translated_images/frame-difference.706f805491a0883c.hi.png) +![वीडियो फ्रेम और फ्रेम अंतर की छवि](../../../../../translated_images/hi/frame-difference.706f805491a0883c.png) > छवि [OpenCV.ipynb](OpenCV.ipynb) से @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **डेंस ऑप्टिकल फ्लो** वह वेक्टर फील्ड गणना करता है जो दिखाता है कि प्रत्येक पिक्सल कहां जा रहा है। - **स्पार्स ऑप्टिकल फ्लो** छवि में कुछ विशिष्ट विशेषताओं (जैसे किनारों) को लेता है और फ्रेम से फ्रेम तक उनकी प्रक्षेपवक्र बनाता है। -![ऑप्टिकल फ्लो की छवि](../../../../../translated_images/optical.1f4a94464579a83a.hi.png) +![ऑप्टिकल फ्लो की छवि](../../../../../translated_images/hi/optical.1f4a94464579a83a.png) > छवि [OpenCV.ipynb](OpenCV.ipynb) से diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8142b2fa..faa226da 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 एक नेटवर्क है जिसने 2014 में ImageNet टॉप-5 क्लासिफिकेशन में 92.7% सटीकता हासिल की। इसका लेयर स्ट्रक्चर निम्नलिखित है: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hi.jpg) +![ImageNet Layers](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.jpg) जैसा कि आप देख सकते हैं, VGG एक पारंपरिक पिरामिड आर्किटेक्चर का अनुसरण करता है, जो कि कॉन्वोल्यूशन-पूलिंग लेयर्स का अनुक्रम है। -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hi.jpg) +![ImageNet Pyramid](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.jpg) > चित्र [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) से लिया गया है diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md index 8c1cae05..785453e1 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: पैटर्न्स निकालने के लिए, हम **कॉन्वोल्यूशनल फिल्टर्स** की अवधारणा का उपयोग करेंगे। जैसा कि आप जानते हैं, एक इमेज को 2D-मैट्रिक्स या रंग गहराई के साथ 3D-टेंसर के रूप में दर्शाया जाता है। फिल्टर लागू करने का मतलब है कि हम एक अपेक्षाकृत छोटा **फिल्टर कर्नल** मैट्रिक्स लेते हैं, और मूल इमेज के प्रत्येक पिक्सल के लिए पड़ोसी बिंदुओं के साथ भारित औसत की गणना करते हैं। इसे हम ऐसे देख सकते हैं जैसे एक छोटी विंडो पूरी इमेज पर स्लाइड कर रही हो और फिल्टर कर्नल मैट्रिक्स में वज़न के अनुसार सभी पिक्सल्स को औसत कर रही हो। -![वर्टिकल एज फिल्टर](../../../../../translated_images/filter-vert.b7148390ca0bc356.hi.png) | ![हॉरिज़ॉन्टल एज फिल्टर](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.hi.png) +![वर्टिकल एज फिल्टर](../../../../../translated_images/hi/filter-vert.b7148390ca0bc356.png) | ![हॉरिज़ॉन्टल एज फिल्टर](../../../../../translated_images/hi/filter-horiz.59b80ed4feb946ef.png) ----|---- > छवि: दिमित्री सोश्निकोव द्वारा @@ -38,7 +38,7 @@ CNNs जिस तरह से काम करते हैं, वह नि * हम नेटवर्क को इस तरह डिज़ाइन कर सकते हैं कि फिल्टर्स स्वचालित रूप से प्रशिक्षित हों * हम केवल मूल इमेज में ही नहीं, बल्कि उच्च-स्तरीय फीचर्स में भी पैटर्न्स ढूंढने के लिए इसी दृष्टिकोण का उपयोग कर सकते हैं। इस प्रकार, CNN फीचर एक्सट्रैक्शन फीचर्स की एक पदानुक्रम पर काम करता है, जो लो-लेवल पिक्सल संयोजनों से शुरू होकर, इमेज के हिस्सों के उच्च-स्तरीय संयोजनों तक जाता है। -![पदानुक्रमिक फीचर एक्सट्रैक्शन](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.hi.png) +![पदानुक्रमिक फीचर एक्सट्रैक्शन](../../../../../translated_images/hi/FeatureExtractionCNN.d9b456cbdae7cb64.png) > छवि: [Hislop-Lynch के पेपर](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) से, उनके [अनुसंधान](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) पर आधारित @@ -55,9 +55,9 @@ CNNs जिस तरह से काम करते हैं, वह नि उदाहरण के लिए, आइए VGG-16 की आर्किटेक्चर पर नज़र डालें, एक नेटवर्क जिसने 2014 में ImageNet के टॉप-5 वर्गीकरण में 92.7% सटीकता प्राप्त की: -![ImageNet लेयर्स](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hi.jpg) +![ImageNet लेयर्स](../../../../../translated_images/hi/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet पिरामिड](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hi.jpg) +![ImageNet पिरामिड](../../../../../translated_images/hi/vgg-16-arch.64ff2137f50dd49f.jpg) > छवि: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) से diff --git a/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index c9c45fd1..b2d90d25 100644 --- a/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: हम [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) का उपयोग करेंगे, जिसमें कुत्तों और बिल्लियों की 37 विभिन्न नस्लों की छवियां शामिल हैं। -![हम जिस डेटासेट पर काम करेंगे](../../../../../../translated_images/data.50b2a9d5484bdbf0.hi.png) +![हम जिस डेटासेट पर काम करेंगे](../../../../../../translated_images/hi/data.50b2a9d5484bdbf0.png) डेटासेट डाउनलोड करने के लिए, इस कोड स्निपेट का उपयोग करें: diff --git a/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md index d93a885f..7e8875bc 100644 --- a/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras और PyTorch दोनों में कुछ सामान्य यहाँ VGG-16 नेटवर्क द्वारा एक बिल्ली की तस्वीर से निकाले गए फीचर्स का एक उदाहरण है: -![VGG-16 द्वारा निकाले गए फीचर्स](../../../../../translated_images/features.6291f9c7ba3a0b95.hi.png) +![VGG-16 द्वारा निकाले गए फीचर्स](../../../../../translated_images/hi/features.6291f9c7ba3a0b95.png) ## कैट्स बनाम डॉग्स डेटासेट @@ -48,19 +48,19 @@ Keras और PyTorch दोनों में कुछ सामान्य एक दृष्टिकोण यह हो सकता है कि हम एक रैंडम इमेज से शुरू करें, और फिर **ग्रेडिएंट डिसेंट ऑप्टिमाइज़ेशन** तकनीक का उपयोग करके उस इमेज को इस तरह से समायोजित करें कि नेटवर्क इसे बिल्ली मानने लगे। -![इमेज ऑप्टिमाइज़ेशन लूप](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hi.png) +![इमेज ऑप्टिमाइज़ेशन लूप](../../../../../translated_images/hi/ideal-cat-loop.999fbb8ff306e044.png) हालांकि, यदि हम ऐसा करते हैं, तो हमें कुछ ऐसा मिलेगा जो रैंडम नॉइज़ के बहुत करीब होगा। ऐसा इसलिए है क्योंकि *नेटवर्क को यह सोचने के लिए कई तरीके हैं कि इनपुट इमेज एक बिल्ली है*, जिनमें से कुछ दृश्य रूप से समझ में नहीं आते। जबकि उन इमेज में बिल्ली के लिए विशिष्ट कई पैटर्न होते हैं, उन्हें दृश्य रूप से विशिष्ट बनाने के लिए कुछ भी बाध्य नहीं करता। परिणाम को बेहतर बनाने के लिए, हम लॉस फंक्शन में एक और टर्म जोड़ सकते हैं, जिसे **वेरिएशन लॉस** कहा जाता है। यह एक मीट्रिक है जो दिखाता है कि इमेज के पड़ोसी पिक्सल कितने समान हैं। वेरिएशन लॉस को कम करने से इमेज स्मूथ हो जाती है और नॉइज़ हट जाता है - जिससे अधिक दृश्य रूप से आकर्षक पैटर्न सामने आते हैं। यहाँ ऐसे "आदर्श" इमेज का एक उदाहरण है, जिन्हें उच्च संभावना के साथ बिल्ली और ज़ेब्रा के रूप में वर्गीकृत किया गया है: -![आदर्श बिल्ली](../../../../../translated_images/ideal-cat.203dd4597643d6b0.hi.png) | ![आदर्श ज़ेब्रा](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.hi.png) +![आदर्श बिल्ली](../../../../../translated_images/hi/ideal-cat.203dd4597643d6b0.png) | ![आदर्श ज़ेब्रा](../../../../../translated_images/hi/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *आदर्श बिल्ली* | *आदर्श ज़ेब्रा* इसी तरह का दृष्टिकोण तथाकथित **एडवर्सेरियल अटैक्स** करने के लिए उपयोग किया जा सकता है। मान लें कि हम एक न्यूरल नेटवर्क को धोखा देना चाहते हैं और कुत्ते को बिल्ली जैसा दिखाना चाहते हैं। यदि हम कुत्ते की इमेज लें, जिसे नेटवर्क द्वारा कुत्ते के रूप में पहचाना जाता है, तो हम इसे थोड़ा सा समायोजित कर सकते हैं, जब तक कि नेटवर्क इसे बिल्ली के रूप में वर्गीकृत करना शुरू न कर दे: -![कुत्ते की तस्वीर](../../../../../translated_images/original-dog.8f68a67d2fe0911f.hi.png) | ![कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.hi.png) +![कुत्ते की तस्वीर](../../../../../translated_images/hi/original-dog.8f68a67d2fe0911f.png) | ![कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया](../../../../../translated_images/hi/adversarial-dog.d9fc7773b0142b89.png) -----|----- *कुत्ते की मूल तस्वीर* | *कुत्ते की तस्वीर जिसे बिल्ली के रूप में वर्गीकृत किया गया* diff --git a/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md index cf61c31e..92f24e4a 100644 --- a/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: चूंकि हम ऑटोएन्कोडर को मूल इमेज से अधिकतम जानकारी को कैप्चर करने के लिए ट्रेन कर रहे हैं ताकि सटीक पुनर्निर्माण हो सके, नेटवर्क इनपुट इमेज का सबसे अच्छा **एम्बेडिंग** खोजने की कोशिश करता है। -![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hi.jpg) +![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/hi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > इमेज [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) से diff --git a/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1a88b26f..bbccfa1a 100644 --- a/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [प्री-लेक्चर क्विज़](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.hi.png) +![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > इमेज [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) से @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइल पर इमेज क्लासिफिकेशन चलाएं। 3. जिन टाइल्स में पर्याप्त उच्च सक्रियता होती है, उन्हें उस वस्तु को शामिल करने वाला माना जा सकता है। -![साधारण ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.hi.png) +![साधारण ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/hi/naive-detection.e7f1ba220ccd08c6.png) > *इमेज [एक्सरसाइज नोटबुक](ObjectDetection-TF.ipynb) से* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 क्लासेस * [COCO](http://cocodataset.org/#home) - कॉमन ऑब्जेक्ट्स इन कॉन्टेक्स्ट। 80 क्लासेस, बॉक्स और सेगमेंटेशन मास्क्स -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.hi.jpg) +![COCO](../../../../../translated_images/hi/coco-examples.71bc60380fa6cceb.jpg) ## ऑब्जेक्ट डिटेक्शन मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: जहां इमेज क्लासिफिकेशन के लिए एल्गोरिदम के प्रदर्शन को मापना आसान है, वहीं ऑब्जेक्ट डिटेक्शन के लिए हमें क्लास की सही पहचान और बॉक्स की लोकेशन की सटीकता दोनों को मापना होता है। लोकेशन की सटीकता के लिए, हम **इंटरसेक्शन ओवर यूनियन** (IoU) का उपयोग करते हैं, जो मापता है कि दो बॉक्स (या दो क्षेत्र) कितने अच्छे से ओवरलैप करते हैं। -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.hi.png) +![IoU](../../../../../translated_images/hi/iou_equation.9a4751d40fff4e11.png) > *फिगर 2 [इस उत्कृष्ट ब्लॉग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) से* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) का उपयोग करता है ताकि ROI क्षेत्रों की एक पदानुक्रमित संरचना उत्पन्न की जा सके, जिन्हें फिर CNN फीचर एक्सट्रैक्टर्स और SVM-क्लासिफायर के माध्यम से पास किया जाता है ताकि ऑब्जेक्ट क्लास निर्धारित किया जा सके, और *बॉक्स* निर्देशांक निर्धारित करने के लिए लीनियर रिग्रेशन का उपयोग किया जाता है। [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.hi.png) +![RCNN](../../../../../translated_images/hi/rcnn1.cae407020dfb1d1f.png) > *इमेज van de Sande et al. ICCV’11 से* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.hi.png) +![RCNN-1](../../../../../translated_images/hi/rcnn2.2d9530bb83516484.png) > *इमेज [इस ब्लॉग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) से* @@ -110,7 +110,7 @@ $$ यह दृष्टिकोण R-CNN के समान है, लेकिन रीजन को परिभाषित किया जाता है जब कॉन्वोल्यूशन लेयर्स लागू हो चुकी होती हैं। -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.hi.png) +![FRCNN](../../../../../translated_images/hi/f-rcnn.3cda6d9bb4188875.png) > इमेज [आधिकारिक पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 से @@ -118,7 +118,7 @@ $$ इस दृष्टिकोण का मुख्य विचार है कि रीजन की भविष्यवाणी करने के लिए न्यूरल नेटवर्क का उपयोग किया जाए - जिसे *रीजन प्रपोजल नेटवर्क* कहा जाता है। [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.hi.png) +![FasterRCNN](../../../../../translated_images/hi/faster-rcnn.8d46c099b87ef30a.png) > इमेज [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497.pdf) से @@ -130,7 +130,7 @@ $$ 2. फीचर्स को **पोजिशन-सेंसिटिव स्कोर मैप** द्वारा प्रोसेस किया जाता है। $C$ क्लासेस के प्रत्येक ऑब्जेक्ट को $k\times k$ क्षेत्रों में विभाजित किया जाता है, और हम ऑब्जेक्ट्स के भागों की भविष्यवाणी करने के लिए प्रशिक्षण देते हैं। 3. $k\times k$ क्षेत्रों के प्रत्येक भाग के लिए सभी नेटवर्क ऑब्जेक्ट क्लासेस के लिए वोट करते हैं, और अधिकतम वोट वाले ऑब्जेक्ट क्लास को चुना जाता है। -![r-fcn इमेज](../../../../../translated_images/r-fcn.13eb88158b99a3da.hi.png) +![r-fcn इमेज](../../../../../translated_images/hi/r-fcn.13eb88158b99a3da.png) > इमेज [आधिकारिक पेपर](https://arxiv.org/abs/1605.06409) से @@ -141,7 +141,7 @@ YOLO एक रियलटाइम वन-पास एल्गोरिद * इमेज को $S\times S$ क्षेत्रों में विभाजित किया जाता है। * प्रत्येक क्षेत्र के लिए, **CNN** $n$ संभावित ऑब्जेक्ट्स, *बॉक्स* निर्देशांक और *कॉन्फिडेंस*=*प्रोबेबिलिटी* * IoU की भविष्यवाणी करता है। - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.hi.png) + ![YOLO](../../../../../translated_images/hi/yolo.a2648ec82ee8bb4e.png) > इमेज [आधिकारिक पेपर](https://arxiv.org/abs/1506.02640) से diff --git a/translations/hi/lessons/5-NLP/14-Embeddings/README.md b/translations/hi/lessons/5-NLP/14-Embeddings/README.md index ebfbd896..a80aca52 100644 --- a/translations/hi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: हमारे क्लासिफायर नेटवर्क में एम्बेडिंग लेयर को पहली लेयर के रूप में उपयोग करके, हम बैग-ऑफ-वर्ड्स से **एम्बेडिंग बैग** मॉडल में स्विच कर सकते हैं, जहां हम पहले अपने टेक्स्ट में प्रत्येक शब्द को संबंधित एम्बेडिंग में बदलते हैं और फिर उन सभी एम्बेडिंग्स पर कुछ समग्र फ़ंक्शन की गणना करते हैं, जैसे `sum`, `average` या `max`। -![पांच अनुक्रम शब्दों के लिए एम्बेडिंग क्लासिफायर दिखाने वाली छवि।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hi.png) +![पांच अनुक्रम शब्दों के लिए एम्बेडिंग क्लासिफायर दिखाने वाली छवि।](../../../../../translated_images/hi/embedding-classifier-example.b77f021a7ee67eee.png) > लेखक द्वारा बनाई गई छवि @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW तेज है, जबकि स्किप-ग्राम धीमा है, लेकिन यह दुर्लभ शब्दों को बेहतर तरीके से रिप्रेजेंट करता है। -![CBoW और स्किप-ग्राम एल्गोरिदम को शब्दों को वेक्टर में बदलने के लिए दिखाने वाली छवि।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hi.png) +![CBoW और स्किप-ग्राम एल्गोरिदम को शब्दों को वेक्टर में बदलने के लिए दिखाने वाली छवि।](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [इस पेपर](https://arxiv.org/pdf/1301.3781.pdf) से ली गई छवि diff --git a/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md index 5a1e4a0b..804f38d9 100644 --- a/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **कंटीन्युअस बैग-ऑफ-वर्ड्स** (CBoW), जिसमें हम टोकन अनुक्रम $W_{-N}$, ..., $W_N$ में मध्य टोकन $W_0$ की भविष्यवाणी करते हैं। * **स्किप-ग्राम**, जिसमें हम मध्य टोकन $W_0$ से पड़ोसी टोकन {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} का सेट भविष्यवाणी करते हैं। -![शब्दों को वेक्टर में बदलने के लिए पेपर से छवि](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hi.png) +![शब्दों को वेक्टर में बदलने के लिए पेपर से छवि](../../../../../translated_images/hi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > छवि [इस पेपर](https://arxiv.org/pdf/1301.3781.pdf) से diff --git a/translations/hi/lessons/5-NLP/16-RNN/README.md b/translations/hi/lessons/5-NLP/16-RNN/README.md index 277ae293..b89afea4 100644 --- a/translations/hi/lessons/5-NLP/16-RNN/README.md +++ b/translations/hi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: टेक्स्ट अनुक्रम के अर्थ को पकड़ने के लिए, हमें एक अन्य न्यूरल नेटवर्क आर्किटेक्चर का उपयोग करना होगा, जिसे **पुनरावर्ती न्यूरल नेटवर्क** या RNN कहा जाता है। RNN में, हम अपने वाक्य को नेटवर्क के माध्यम से एक समय में एक प्रतीक पास करते हैं, और नेटवर्क कुछ **स्थिति** उत्पन्न करता है, जिसे हम अगले प्रतीक के साथ नेटवर्क में फिर से पास करते हैं। -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.hi.png) +![RNN](../../../../../translated_images/hi/rnn.27f5c29c53d727b5.png) > चित्र लेखक द्वारा @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN के समान तरीके से सं एक पुनरावर्ती नेटवर्क, चाहे वह एक-दिशात्मक हो या बाईडायरेक्शनल, अनुक्रम के भीतर कुछ पैटर्न को कैप्चर करता है, और उन्हें स्थिति वेक्टर में संग्रहीत कर सकता है या आउटपुट में पास कर सकता है। जैसे कि कन्वोल्यूशनल नेटवर्क्स के साथ, हम पहले लेयर द्वारा निकाले गए निम्न-स्तरीय पैटर्न से उच्च-स्तरीय पैटर्न को कैप्चर करने के लिए पहले लेयर के ऊपर एक और पुनरावर्ती लेयर बना सकते हैं। यह हमें **मल्टी-लेयर RNN** की धारणा तक ले जाता है, जिसमें दो या अधिक पुनरावर्ती नेटवर्क होते हैं, जहां पिछले लेयर का आउटपुट अगले लेयर में इनपुट के रूप में पास होता है। -![मल्टीलेयर लॉन्ग-शॉर्ट-टर्म-मेमोरी RNN का चित्र](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hi.jpg) +![मल्टीलेयर लॉन्ग-शॉर्ट-टर्म-मेमोरी RNN का चित्र](../../../../../translated_images/hi/multi-layer-lstm.dd975e29bb2a59fe.jpg) *चित्र [इस शानदार पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) से लिया गया है, लेखक: फर्नांडो लोपेज़* diff --git a/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md index 485d5c54..a25dfacf 100644 --- a/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) और उनके gated cell वेरिए यह विभिन्न neural architectures की अनुमति देता है, जो नीचे दी गई तस्वीर में दिखाए गए हैं: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hi.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > चित्र ब्लॉग पोस्ट [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) से लिया गया है, लेखक [Andrej Karpaty](http://karpathy.github.io/)। @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) और उनके gated cell वेरिए हम इस RNN को टेक्स्ट step-by-step उत्पन्न करने के लिए प्रशिक्षित करेंगे। प्रत्येक चरण में, हम `nchars` लंबाई के characters का एक sequence लेंगे, और नेटवर्क से प्रत्येक इनपुट character के लिए अगला आउटपुट character उत्पन्न करने के लिए कहेंगे: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hi.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/hi/rnn-generate.56c54afb52f9781d.png) जब टेक्स्ट उत्पन्न किया जाता है (inference के दौरान), हम कुछ **prompt** के साथ शुरू करते हैं, जिसे RNN cells के माध्यम से पास किया जाता है ताकि इसका intermediate state उत्पन्न हो सके, और फिर इस state से जनरेशन शुरू होती है। हम एक बार में एक character उत्पन्न करते हैं, और state और उत्पन्न character को अगले RNN cell में पास करते हैं ताकि अगला character उत्पन्न हो सके, जब तक कि हम पर्याप्त characters उत्पन्न न कर लें। diff --git a/translations/hi/lessons/5-NLP/18-Transformers/README.md b/translations/hi/lessons/5-NLP/18-Transformers/README.md index cd67af85..9f660533 100644 --- a/translations/hi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस **ध्यान तंत्र** प्रत्येक इनपुट वेक्टर के संदर्भ प्रभाव को प्रत्येक आउटपुट भविष्यवाणी पर वेटिंग प्रदान करने का एक तरीका है। इसे लागू करने का तरीका यह है कि इनपुट RNN और आउटपुट RNN की मध्यवर्ती अवस्थाओं के बीच शॉर्टकट बनाए जाते हैं। इस प्रकार, जब आउटपुट प्रतीक yt उत्पन्न किया जाता है, तो हम सभी इनपुट हिडन स्टेट्स hi को विभिन्न वेट कोएफिशिएंट्स αt,i के साथ ध्यान में रखते हैं। -![एन्कोडर/डिकोडर मॉडल जिसमें एडिटिव ध्यान लेयर है](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hi.png) +![एन्कोडर/डिकोडर मॉडल जिसमें एडिटिव ध्यान लेयर है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) में एडिटिव ध्यान तंत्र के साथ एन्कोडर-डिकोडर मॉडल, [इस ब्लॉग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) से लिया गया। ध्यान मैट्रिक्स {αi,j} यह दर्शाता है कि आउटपुट सीक्वेंस में दिए गए शब्द के निर्माण में कुछ इनपुट शब्दों की भूमिका कितनी है। नीचे एक उदाहरण दिया गया है: -![Bahdanau - arviz.org से लिया गया RNNsearch-50 द्वारा पाया गया एक नमूना संरेखण](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hi.png) +![Bahdanau - arviz.org से लिया गया RNNsearch-50 द्वारा पाया गया एक नमूना संरेखण](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से चित्र (Fig.3) @@ -66,7 +66,7 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस अब हमें अपने सीक्वेंस के भीतर कुछ पैटर्न कैप्चर करने की आवश्यकता है। ऐसा करने के लिए, ट्रांसफॉर्मर्स **सेल्फ-अटेंशन** तंत्र का उपयोग करते हैं, जो मूल रूप से इनपुट और आउटपुट के रूप में एक ही सीक्वेंस पर लागू ध्यान है। सेल्फ-अटेंशन लागू करने से हमें वाक्य के भीतर **संदर्भ** को ध्यान में रखने और यह देखने की अनुमति मिलती है कि कौन से शब्द आपस में संबंधित हैं। उदाहरण के लिए, यह हमें यह देखने की अनुमति देता है कि कौन से शब्द *it* जैसे कोरफेरेंस द्वारा संदर्भित हैं, और संदर्भ को ध्यान में रखता है: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.hi.png) +![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.png) > [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) से छवि @@ -91,7 +91,7 @@ RNNs के साथ, सीक्वेंस-टू-सीक्वेंस **BERT** (Bidirectional Encoder Representations from Transformers) एक बहुत बड़ा मल्टी लेयर ट्रांसफॉर्मर नेटवर्क है जिसमें *BERT-base* के लिए 12 लेयर और *BERT-large* के लिए 24 लेयर हैं। मॉडल को पहले एक बड़े टेक्स्ट डेटा कॉर्पस (WikiPedia + किताबें) पर अनसुपरवाइज्ड ट्रेनिंग (वाक्य में मास्क किए गए शब्दों की भविष्यवाणी) का उपयोग करके प्री-ट्रेन किया जाता है। प्री-ट्रेनिंग के दौरान मॉडल महत्वपूर्ण स्तर की भाषा समझ को अवशोषित करता है, जिसे फिर अन्य डेटासेट्स के साथ फाइन ट्यूनिंग का उपयोग करके लाभ उठाया जा सकता है। इस प्रक्रिया को **ट्रांसफर लर्निंग** कहा जाता है। -![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hi.png) +![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > छवि [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md index c2837f08..2aa3e208 100644 --- a/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/hi/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर **ध्यान तंत्र** प्रत्येक इनपुट वेक्टर के संदर्भात्मक प्रभाव को RNN के प्रत्येक आउटपुट भविष्यवाणी पर वजन देने का एक साधन प्रदान करते हैं। इसे लागू करने का तरीका इनपुट RNN और आउटपुट RNN के मध्यवर्ती राज्यों के बीच शॉर्टकट बनाना है। इस तरह, जब आउटपुट प्रतीक yt उत्पन्न करते हैं, तो हम सभी इनपुट छिपी हुई स्थितियों hi को विभिन्न वजन गुणांक αt,i के साथ ध्यान में रखते हैं। -![एक एन्कोडर/डिकोडर मॉडल को दर्शाने वाली छवि जिसमें एक जोड़ात्मक ध्यान परत है](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hi.png) +![एक एन्कोडर/डिकोडर मॉडल को दर्शाने वाली छवि जिसमें एक जोड़ात्मक ध्यान परत है](../../../../../translated_images/hi/encoder-decoder-attention.7a726296894fb567.png) > एन्कोडर-डिकोडर मॉडल जिसमें जोड़ात्मक ध्यान तंत्र है [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से, [इस ब्लॉग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) से उद्धृत। ध्यान मैट्रिक्स {αi,j} यह दर्शाएगा कि कुछ इनपुट शब्दों का एक दिए गए शब्द के उत्पादन में क्या योगदान है। नीचे एक ऐसे मैट्रिक्स का उदाहरण दिया गया है: -![RNNsearch-50 द्वारा पाए गए एक नमूना संरेखण को दर्शाने वाली छवि, Bahdanau - arviz.org से ली गई](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hi.png) +![RNNsearch-50 द्वारा पाए गए एक नमूना संरेखण को दर्शाने वाली छवि, Bahdanau - arviz.org से ली गई](../../../../../translated_images/hi/bahdanau-fig3.09ba2d37f202a6af.png) > चित्र [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) से (चित्र 3) @@ -57,7 +57,7 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर अगला, हमें अपने अनुक्रम में कुछ पैटर्न कैप्चर करने की आवश्यकता है। ऐसा करने के लिए, ट्रांसफार्मर **आत्म-ध्यान** तंत्र का उपयोग करते हैं, जो मूल रूप से इनपुट और आउटपुट के रूप में उसी अनुक्रम पर लागू ध्यान है। आत्म-ध्यान लागू करने से हमें वाक्य के भीतर **संदर्भ** को ध्यान में रखने की अनुमति मिलती है, और यह देखने की अनुमति मिलती है कि कौन से शब्द आपस में संबंधित हैं। उदाहरण के लिए, यह हमें यह देखने की अनुमति देता है कि कौन से शब्द सहसंबंधों द्वारा संदर्भित हैं, जैसे *यह*, और संदर्भ को भी ध्यान में रखते हैं: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.hi.png) +![](../../../../../translated_images/hi/CoreferenceResolution.861924d6d384a7d6.png) > चित्र [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) से @@ -82,7 +82,7 @@ RNNs के साथ, क्रम-से-क्रम को दो आवर **BERT** (Bidirectional Encoder Representations from Transformers) एक बहुत बड़ा मल्टी लेयर ट्रांसफार्मर नेटवर्क है जिसमें *BERT-base* के लिए 12 परतें और *BERT-large* के लिए 24 परतें हैं। मॉडल को पहले एक बड़े पाठ डेटा (WikiPedia + पुस्तकें) के कॉर्पस पर बिना पर्यवेक्षण प्रशिक्षण का उपयोग करके पूर्व-प्रशिक्षित किया जाता है (एक वाक्य में मास्क किए गए शब्दों की भविष्यवाणी करना)। पूर्व-प्रशिक्षण के दौरान, मॉडल भाषा समझने के महत्वपूर्ण स्तरों को अवशोषित करता है जिसे फिर अन्य डेटासेट के साथ फाइन ट्यूनिंग के माध्यम से लाभ उठाया जा सकता है। इस प्रक्रिया को **हस्तांतरण शिक्षण** कहा जाता है। -![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hi.png) +![चित्र http://jalammar.github.io/illustrated-bert/ से](../../../../../translated_images/hi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > चित्र [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hi/lessons/5-NLP/19-NER/README.md b/translations/hi/lessons/5-NLP/19-NER/README.md index 2e5b999f..1f27cf02 100644 --- a/translations/hi/lessons/5-NLP/19-NER/README.md +++ b/translations/hi/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O चूंकि हमें टोकन और वर्गों के बीच एक-से-एक पत्राचार बनाना है, हम इस चित्र से एक सही **कई-से-कई** तंत्रिका नेटवर्क मॉडल को प्रशिक्षित कर सकते हैं: -![सामान्य पुनरावर्ती तंत्रिका नेटवर्क पैटर्न दिखाने वाली छवि।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hi.jpg) +![सामान्य पुनरावर्ती तंत्रिका नेटवर्क पैटर्न दिखाने वाली छवि।](../../../../../translated_images/hi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *[Andrej Karpathy](http://karpathy.github.io/) द्वारा [इस ब्लॉग पोस्ट](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) से छवि। NER टोकन वर्गीकरण मॉडल इस चित्र में सबसे दाईं ओर नेटवर्क आर्किटेक्चर से मेल खाते हैं।* diff --git a/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md index 2f028373..65776039 100644 --- a/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo की एक शानदार बात यह है कि इस मॉडल खोलने के बाद, आप मुख्य NetLogo स्क्रीन पर ले जाए जाते हैं। यहां एक नमूना मॉडल है जो सीमित संसाधनों (घास) को ध्यान में रखते हुए भेड़ियों और भेड़ों की आबादी का वर्णन करता है। -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.hi.png) +![NetLogo Main Screen](../../../../../translated_images/hi/NetLogo-Main.32653711ec1a01b3.png) > दिमित्री सोश्निकोव द्वारा स्क्रीनशॉट diff --git a/translations/hk/README.md b/translations/hk/README.md index f4c5ff65..1fdae7d7 100644 --- a/translations/hk/README.md +++ b/translations/hk/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 人工智能初學者課程大綱 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.hk.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hk/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _手繪筆記由 [@girlie_mac](https://twitter.com/girlie_mac) 製作_ | diff --git a/translations/hk/lessons/1-Intro/README.md b/translations/hk/lessons/1-Intro/README.md index 6128d710..be1fde8c 100644 --- a/translations/hk/lessons/1-Intro/README.md +++ b/translations/hk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能簡介 -![人工智能簡介內容的手繪圖](../../../../translated_images/ai-intro.bf28d1ac4235881c.hk.png) +![人工智能簡介內容的手繪圖](../../../../translated_images/hk/ai-intro.bf28d1ac4235881c.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦是由 [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) 發明的，用於按照明確定義的程序（即算法）處理數字。現代電腦雖然比19世紀提出的原型先進得多，但仍然遵循受控計算的理念。因此，如果我們知道實現目標所需的精確步驟，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/dsh_age.d212a30d4e54fb5f.hk.png) +![一個人的照片](../../../../translated_images/hk/dsh_age.d212a30d4e54fb5f.png) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 當討論 **[智能](https://en.wikipedia.org/wiki/Intelligence)** 這個術語時，問題之一是我們對這個術語沒有明確的定義。有人認為智能與 **抽象思維** 或 **自我意識** 有關，但我們無法準確定義它。 -![一隻貓的照片](../../../../translated_images/photo-cat.8c8e8fb760ffe457.hk.jpg) +![一隻貓的照片](../../../../translated_images/hk/photo-cat.8c8e8fb760ffe457.jpg) > [照片](https://unsplash.com/photos/75715CVEJhI) 由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那麼機器學習呢？ | | > |--------------|-----------| -> | 基於電腦通過一些數據學習解決問題的人工智能部分被稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議你參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.hk.png) | +> | 基於電腦通過一些數據學習解決問題的人工智能部分被稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議你參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/hk/ml-for-beginners.9e4fed176fd5817d.png) | ## 人工智能的簡史 人工智能作為一個領域始於20世紀中葉。最初，符號推理是一種流行的方法，並且它帶來了一些重要的成功，例如專家系統——能夠在某些有限問題領域中充當專家的電腦程序。然而，很快就發現這種方法並不適用於大規模應用。從專家那裡提取知識、將其表示在電腦中並保持知識庫的準確性，事實證明這是一項非常複雜且在許多情況下成本過高的任務。這導致了20世紀70年代所謂的 [人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能簡史 +人工智能簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助手，例如 Cortana、Siri 或 Google Assistant，都是混合系統，使用神經網絡將語音轉換為文本並識別我們的意圖，然後使用一些推理或明確的算法執行所需的操作。 * 未來，我們可能會期待一個完全基於神經網絡的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 系列神經網絡在這方面表現出色。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智能研究 diff --git a/translations/hk/lessons/2-Symbolic/README.md b/translations/hk/lessons/2-Symbolic/README.md index 48c9a5ed..ecc623b0 100644 --- a/translations/hk/lessons/2-Symbolic/README.md +++ b/translations/hk/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 知識表示與專家系統 -![Symbolic AI內容摘要](../../../../translated_images/ai-symbolic.715a30cb610411a6.hk.png) +![Symbolic AI內容摘要](../../../../translated_images/hk/ai-symbolic.715a30cb610411a6.png) > Sketchnote由 [Tomomi Imura](https://twitter.com/girlie_mac) 創作 @@ -41,7 +41,7 @@ Symbolic AI的一個重要概念是**知識**。需要將知識與*信息*或* 因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： -![知識表示光譜](../../../../translated_images/knowledge-spectrum.b60df631852c0217.hk.png) +![知識表示光譜](../../../../translated_images/hk/knowledge-spectrum.b60df631852c0217.png) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 創作 @@ -94,7 +94,7 @@ Untyped-Language | 沒有 | 類型定義 Symbolic AI的一個早期成功是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個**推理引擎**，在其上進行一些推理。 -![人類架構](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.hk.png) | ![基於知識的系統架構](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.hk.png) +![人類架構](../../../../translated_images/hk/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/hk/arch-kbs.3ec5c150b09fa8da.png) ----------------------------------|----------------------------------------- 人類神經系統的簡化結構 | 基於知識的系統的架構 @@ -106,7 +106,7 @@ Symbolic AI的一個早期成功是所謂的**專家系統**——設計用於 例如，讓我們考慮以下基於動物物理特徵的專家系統： -![AND-OR樹](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hk.png) +![AND-OR樹](../../../../translated_images/hk/AND-OR-Tree.5592d2c70187f283.png) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 創作 diff --git a/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md index ade6fb65..602b8991 100644 --- a/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點（圖中的 `x`）的問題： -![線性模型](../../../../../translated_images/overfit1.f24b71c6f652e59e.hk.jpg) | ![過擬合模型](../../../../../translated_images/overfit2.131f5800ae10ca5e.hk.jpg) +![線性模型](../../../../../translated_images/hk/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/hk/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 如上圖所示，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練和驗證誤差都開始下降，然後在某個時候驗證誤差可能停止下降並開始上升。這就是過擬合的跡象，表明我們可能應該停止訓練（或者至少保存模型的快照）。 -![過擬合](../../../../../translated_images/Overfitting.408ad91cd90b4371.hk.png) +![過擬合](../../../../../translated_images/hk/Overfitting.408ad91cd90b4371.png) ## 如何防止過擬合 diff --git a/translations/hk/lessons/3-NeuralNetworks/README.md b/translations/hk/lessons/3-NeuralNetworks/README.md index b319fe0c..70efe38a 100644 --- a/translations/hk/lessons/3-NeuralNetworks/README.md +++ b/translations/hk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網絡簡介 -![神經網絡簡介內容的手繪圖](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.hk.png) +![神經網絡簡介內容的手繪圖](../../../../translated_images/hk/ai-neuralnetworks.1c687ae40bc86e83.png) 正如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到近年來這一方向證明非常成功。這些模仿大腦的數學模型被稱為**神經網絡**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中我們知道，大腦由神經細胞（神經元）組成，每個神經元有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都可以傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這由神經遞質調節。 -![神經元模型](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.hk.jpg) | ![神經元模型](../../../../translated_images/artneuron.1a5daa88d20ebe6f.hk.png) +![神經元模型](../../../../translated_images/hk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/hk/artneuron.1a5daa88d20ebe6f.png) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含若干輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md index 3f6db96e..3762220d 100644 --- a/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書籍的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便神經網絡進一步分類。 -![盲文圖像](../../../../../translated_images/braille.341962ff76b1bd70.hk.jpeg) | ![盲文圖像預處理](../../../../../translated_images/braille-result.46530fea020b03c7.hk.png) | ![盲文符號](../../../../../translated_images/braille-symbols.0159185ab69d5339.hk.png) +![盲文圖像](../../../../../translated_images/hk/braille.341962ff76b1bd70.jpeg) | ![盲文圖像預處理](../../../../../translated_images/hk/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/hk/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測視頻中的運動**。如果攝像機是固定的，那麼來自攝像機的幀應該彼此非常相似。由於幀表示為數組，只需對兩個連續幀的數組進行減法運算，我們就能得到像素差異，靜態幀的差異應該很小，而當圖像中有顯著運動時，差異會變大。 -![視頻幀和幀差的圖像](../../../../../translated_images/frame-difference.706f805491a0883c.hk.png) +![視頻幀和幀差的圖像](../../../../../translated_images/hk/frame-difference.706f805491a0883c.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -91,7 +91,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算顯示每個像素移動方向的向量場 - **稀疏光流** 基於圖像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流圖像](../../../../../translated_images/optical.1f4a94464579a83a.hk.png) +![光流圖像](../../../../../translated_images/hk/optical.1f4a94464579a83a.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 720172c3..ca9e7bda 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網絡。它的層結構如下： -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hk.jpg) +![ImageNet Layers](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.jpg) 如你所見，VGG 採用了傳統的金字塔架構，即一系列的卷積-池化層。 -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hk.jpg) +![ImageNet Pyramid](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md index b8cfe80a..d2668599 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像是由一個二維矩陣或帶有色彩深度的三維張量表示的。應用濾波器意味著我們取一個相對較小的**濾波核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口在整個圖像上滑動，並根據濾波核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/filter-vert.b7148390ca0bc356.hk.png) | ![水平邊緣濾波器](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.hk.png) +![垂直邊緣濾波器](../../../../../translated_images/hk/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/hk/filter-horiz.59b80ed4feb946ef.png) ----|---- > 圖片由 Dmitry Soshnikov 提供 @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中尋找模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵層次上工作，從低層次的像素組合開始，到更高層次的圖像部分組合。 -![層次特徵提取](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.hk.png) +![層次特徵提取](../../../../../translated_images/hk/FeatureExtractionCNN.d9b456cbdae7cb64.png) > 圖片來自 [Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的前五名分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hk.jpg) +![ImageNet 層](../../../../../translated_images/hk/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet 金字塔](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hk.jpg) +![ImageNet 金字塔](../../../../../translated_images/hk/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來自 [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5f438cc7..47a8b7df 100644 --- a/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含37種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/data.50b2a9d5484bdbf0.hk.png) +![我們將處理的數據集](../../../../../../translated_images/hk/data.50b2a9d5484bdbf0.png) 要下載數據集，請使用以下代碼片段： diff --git a/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md index 99493c89..d48305b8 100644 --- a/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 以下是 VGG-16 網絡從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/features.6291f9c7ba3a0b95.hk.png) +![VGG-16 提取的特徵](../../../../../translated_images/hk/features.6291f9c7ba3a0b95.png) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使得網絡認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hk.png) +![圖像優化循環](../../../../../translated_images/hk/ideal-cat-loop.999fbb8ff306e044.png) 然而，如果我們這樣做，我們會得到一些非常接近隨機噪聲的東西。這是因為*有很多方法可以讓網絡認為輸入圖像是一隻貓*，其中一些方法在視覺上並不合理。雖然這些圖像包含了許多典型於貓的模式，但並沒有任何約束使它們在視覺上具有辨識度。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變化損失**。這是一種衡量圖像中相鄰像素相似程度的指標。最小化變化損失可以使圖像更平滑，並消除噪聲——從而揭示出更具視覺吸引力的模式。以下是一些這樣的「理想」圖像的例子，它們被高概率分類為貓和斑馬： -![理想貓](../../../../../translated_images/ideal-cat.203dd4597643d6b0.hk.png) | ![理想斑馬](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.hk.png) +![理想貓](../../../../../translated_images/hk/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/hk/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用於對神經網絡進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網絡，讓一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網絡識別為狗，然後稍微調整它，使用梯度下降優化，直到網絡開始將其分類為貓： -![狗的圖片](../../../../../translated_images/original-dog.8f68a67d2fe0911f.hk.png) | ![被分類為貓的狗圖片](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.hk.png) +![狗的圖片](../../../../../translated_images/hk/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/hk/adversarial-dog.d9fc7773b0142b89.png) -----|----- *原始狗圖片* | *被分類為貓的狗圖片* diff --git a/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md index 38440e0e..53be7178 100644 --- a/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器以捕捉原始圖像中的盡可能多的信息以進行準確重建，網絡會嘗試找到最佳的**嵌入**方式來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hk.jpg) +![自動編碼器示意圖](../../../../../translated_images/hk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 2af86470..478b5c79 100644 --- a/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.hk.png) +![物件偵測](../../../../../translated_images/hk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 圖片來源：[YOLO v2 官方網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 將分類結果中激活值足夠高的區塊視為包含目標物件的區域。 -![簡單的物件偵測](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.hk.png) +![簡單的物件偵測](../../../../../translated_images/hk/naive-detection.e7f1ba220ccd08c6.png) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件上下文數據集，包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.hk.jpg) +![COCO](../../../../../translated_images/hk/coco-examples.71bc60380fa6cceb.jpg) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 對於影像分類來說，衡量算法表現的方式很簡單，但對於物件偵測，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。後者使用所謂的**交集比聯集** (IoU) 來衡量，這是一種用來測量兩個框（或任意兩個區域）重疊程度的方法。 -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.hk.png) +![IoU](../../../../../translated_images/hk/iou_equation.9a4751d40fff4e11.png) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) 生成 ROI 區域的層次結構，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性回歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.hk.png) +![RCNN](../../../../../translated_images/hk/rcnn1.cae407020dfb1d1f.png) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.hk.png) +![RCNN-1](../../../../../translated_images/hk/rcnn2.2d9530bb83516484.png) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用後定義的。 -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.hk.png) +![FRCNN](../../../../../translated_images/hk/f-rcnn.3cda6d9bb4188875.png) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -117,7 +117,7 @@ $$ 這種方法的主要思想是使用神經網絡來預測 ROI，即所謂的*區域提議網絡* (Region Proposal Network)。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.hk.png) +![FasterRCNN](../../../../../translated_images/hk/faster-rcnn.8d46c099b87ef30a.png) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ $$ 2. 特徵經過**位置敏感得分圖** (Position-Sensitive Score Map) 處理。每個來自 $C$ 類別的物件被劃分為 $k\times k$ 區域，我們訓練網絡來預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網絡對物件類別進行投票，選擇得票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/r-fcn.13eb88158b99a3da.hk.png) +![r-fcn 圖片](../../../../../translated_images/hk/r-fcn.13eb88158b99a3da.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO 是一種實時單次通過算法。主要思想如下： * 將圖片劃分為 $S\times S$ 區域。 * 對於每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.hk.png) + ![YOLO](../../../../../translated_images/hk/yolo.a2648ec82ee8bb4e.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/hk/lessons/4-ComputerVision/README.md b/translations/hk/lessons/4-ComputerVision/README.md index d3033cc1..b7b74236 100644 --- a/translations/hk/lessons/4-ComputerVision/README.md +++ b/translations/hk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要的手繪圖](../../../../translated_images/ai-computervision.6506ebebac3fbf76.hk.png) +![電腦視覺內容摘要的手繪圖](../../../../translated_images/hk/ai-computervision.6506ebebac3fbf76.png) 在這部分，我們將學習以下內容： diff --git a/translations/hk/lessons/5-NLP/14-Embeddings/README.md b/translations/hk/lessons/5-NLP/14-Embeddings/README.md index 0b3eaaa3..5552792f 100644 --- a/translations/hk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![展示五個序列詞嵌入分類器的圖片。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hk.png) +![展示五個序列詞嵌入分類器的圖片。](../../../../../translated_images/hk/embedding-classifier-example.b77f021a7ee67eee.png) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 的速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法圖片。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hk.png) +![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法圖片。](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md index 21a0756d..0787023d 100644 --- a/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型** (CBoW)，通過預測詞元序列 $W_{-N}$, ..., $W_N$ 中的中間詞元 $W_0$。 * **Skip-gram**，通過中間詞元 $W_0$ 預測一組相鄰詞元 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自論文的將詞語轉換為向量的算法示例](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hk.png) +![來自論文的將詞語轉換為向量的算法示例](../../../../../translated_images/hk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hk/lessons/5-NLP/16-RNN/README.md b/translations/hk/lessons/5-NLP/16-RNN/README.md index 6864e043..2a7bf9d5 100644 --- a/translations/hk/lessons/5-NLP/16-RNN/README.md +++ b/translations/hk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地輸入網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次輸入網絡。 -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.hk.png) +![RNN](../../../../../translated_images/hk/rnn.27f5c29c53d727b5.png) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態會從層到層傳 循環網絡，無論是單向還是雙向，都能捕捉序列中的某些模式，並將其存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN**的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為下一層的輸入。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hk.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/hk/multi-layer-lstm.dd975e29bb2a59fe.jpg) *圖片來自 [這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) 作者 Fernando López* diff --git a/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md index 8b3b03b2..c9d0113d 100644 --- a/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hk.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 圖片來自 [Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hk.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/hk/rnn-generate.56c54afb52f9781d.png) 在生成文本（推理過程中）時，我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/hk/lessons/5-NLP/18-Transformers/README.md b/translations/hk/lessons/5-NLP/18-Transformers/README.md index 0a28a8fc..5accdcbe 100644 --- a/translations/hk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意力機制**提供了一種方法，能夠對每個輸入向量對RNN輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態與輸出RNN之間創建捷徑。這樣，在生成輸出符號yt時，我們會考慮所有輸入隱藏狀態hi，並賦予不同的權重係數αt,i。 -![顯示帶有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hk.png) +![顯示帶有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/hk/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau等人, 2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意力機制編碼器-解碼器模型，圖片來源於[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩陣{αi,j}表示某些輸入詞在生成給定輸出序列中的某個詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hk.png) +![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/hk/bahdanau-fig3.09ba2d37f202a6af.png) > 圖片來自[Bahdanau等人, 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3） @@ -66,7 +66,7 @@ Transformer的主要思想之一是避免RNN的序列性質，並創建一個在 接下來，我們需要捕捉序列中的一些模式。為此，Transformer使用了**自注意力**機制，這本質上是將注意力應用於相同的輸入和輸出序列。應用自注意力使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它使我們能夠看到哪些詞由指代詞（如*it*）指代，並考慮上下文： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.hk.png) +![](../../../../../translated_images/hk/CoreferenceResolution.861924d6d384a7d6.png) > 圖片來自[Google博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的主要思想之一是避免RNN的序列性質，並創建一個在 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層Transformer網絡，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據語料庫（維基百科+書籍）上進行無監督預訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程被稱為**遷移學習**。 -![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hk.png) +![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 圖片[來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hk/lessons/5-NLP/19-NER/README.md b/translations/hk/lessons/5-NLP/19-NER/README.md index c23be72a..047be71f 100644 --- a/translations/hk/lessons/5-NLP/19-NER/README.md +++ b/translations/hk/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入標記 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個最右側的 **多對多** 神經網絡模型： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hk.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/hk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *圖片來自 [這篇博客文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) 作者 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於圖片中最右側的網絡架構。* diff --git a/translations/hk/lessons/5-NLP/README.md b/translations/hk/lessons/5-NLP/README.md index 5b316489..64902324 100644 --- a/translations/hk/lessons/5-NLP/README.md +++ b/translations/hk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP 任務的手繪圖概述](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.hk.png) +![NLP 任務的手繪圖概述](../../../../translated_images/hk/ai-nlp.b22dcb8ca4707cea.png) 在本節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)** 相關的任務。我們希望計算機能夠解決許多 NLP 問題： diff --git a/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md index 4991748a..d0555a51 100644 --- a/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優勢是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主屏幕。以下是一個描述狼和羊的種群模型，給定有限資源（草地）。 -![NetLogo主屏幕](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.hk.png) +![NetLogo主屏幕](../../../../../translated_images/hk/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov提供的截圖 diff --git a/translations/hk/lessons/README.md b/translations/hk/lessons/README.md index 221db26b..05c47103 100644 --- a/translations/hk/lessons/README.md +++ b/translations/hk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概覽 -![概覽的手繪圖](../../../translated_images/ai-overview.0857791951d19500.hk.png) +![概覽的手繪圖](../../../translated_images/hk/ai-overview.0857791951d19500.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/hk/lessons/X-Extras/X1-MultiModal/README.md b/translations/hk/lessons/X-Extras/X1-MultiModal/README.md index ca162ed3..933bdd75 100644 --- a/translations/hk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的主要理念是能夠比較文本提示與圖像，並判斷圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.hk.png) +![CLIP 架構](../../../../../translated_images/hk/clip-arch.b3dbf20b4e8ed8be.png) > *圖片來源：[這篇博客文章](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲得。 假設我們需要將圖像分類為例如貓、狗和人。在這種情況下，我們可以向模型提供一張圖像，以及一系列文本提示：“*一張貓的照片*”、“*一張狗的照片*”、“*一張人的照片*”。在結果的三個概率向量中，我們只需選擇值最高的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/clip-class.3af42ef0b2b19369.hk.png) +![CLIP 用於圖像分類](../../../../../translated_images/hk/clip-class.3af42ef0b2b19369.png) > *圖片來源：[這篇博客文章](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別是，後者可以從任何輸入向量生成一張像樣的圖像，而 VQGAN 則可能生成不連貫的圖像。因此，我們需要進一步引導圖像創建過程，這可以通過 CLIP 來完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/vqgan.5027fe05051dfa31.hk.png) +![VQGAN+CLIP 架構](../../../../../translated_images/hk/vqgan.5027fe05051dfa31.png) 為了生成與文本提示相對應的圖像，我們首先使用一些隨機編碼向量，通過 VQGAN 生成一張圖像。然後使用 CLIP 生成損失函數，顯示圖像與文本提示的匹配程度。目標是最小化該損失，通過反向傳播調整輸入向量的參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray)。 -![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.hk.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.hk.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.hk.png) +![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/hk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- 根據提示 *一張年輕男性文學教師拿著書的水彩特寫肖像* 生成的圖片 | 根據提示 *一張年輕女性計算機科學教師拿著電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一張老年男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 @@ -75,7 +75,7 @@ DALL-E 是 GPT-3 的一個版本，訓練用於根據提示生成圖像。它擁 DALL-E 1 和 DALL-E 2 的主要區別在於，後者能生成更逼真的圖像和藝術作品。 以下是使用 DALL-E 生成的圖像示例： -![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.hk.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.hk.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.hk.png) +![由 DALL-E 生成的圖片](../../../../../translated_images/hk/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/hk/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/hk/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- 根據提示 *一張年輕男性文學教師拿著書的水彩特寫肖像* 生成的圖片 | 根據提示 *一張年輕女性計算機科學教師拿著電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一張老年男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/hr/README.md b/translations/hr/README.md index a97a0000..99633172 100644 --- a/translations/hr/README.md +++ b/translations/hr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umjetna inteligencija za početnike - Nastavni plan -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.hr.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hr/ai-overview.0857791951d19500.png)| |:---:| | AI za početnike - _Sketchnote autora [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/hr/lessons/1-Intro/README.md b/translations/hr/lessons/1-Intro/README.md index ca493a83..6464b10e 100644 --- a/translations/hr/lessons/1-Intro/README.md +++ b/translations/hr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod u AI -![Sažetak sadržaja uvoda u AI u obliku crteža](../../../../translated_images/ai-intro.bf28d1ac4235881c.hr.png) +![Sažetak sadržaja uvoda u AI u obliku crteža](../../../../translated_images/hr/ai-intro.bf28d1ac4235881c.png) > Crtež od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Izvorno, računala je izumio [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) kako bi radila s brojevima slijedeći dobro definirani postupak - algoritam. Moderna računala, iako znatno naprednija od originalnog modela predloženog u 19. stoljeću, i dalje slijede istu ideju kontroliranih izračuna. Stoga je moguće programirati računalo da nešto učini ako znamo točan slijed koraka koji trebamo poduzeti kako bismo postigli cilj. -![Fotografija osobe](../../../../translated_images/dsh_age.d212a30d4e54fb5f.hr.png) +![Fotografija osobe](../../../../translated_images/hr/dsh_age.d212a30d4e54fb5f.png) > Fotografija od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -45,7 +45,7 @@ Za više informacija pogledajte **[Umjetna Opća Inteligencija](https://en.wikip Jedan od problema pri bavljenju pojmom **[inteligencija](https://en.wikipedia.org/wiki/Intelligence)** jest taj što ne postoji jasna definicija ovog pojma. Može se tvrditi da je inteligencija povezana s **apstraktnim razmišljanjem** ili **samosviješću**, ali je ne možemo pravilno definirati. -![Fotografija mačke](../../../../translated_images/photo-cat.8c8e8fb760ffe457.hr.jpg) +![Fotografija mačke](../../../../translated_images/hr/photo-cat.8c8e8fb760ffe457.jpg) > [Fotografija](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) s Unsplash-a @@ -97,13 +97,13 @@ Alternativno, možemo pokušati modelirati najjednostavnije elemente unutar naš > | Što je s ML? | | > |--------------|-----------| -> | Dio umjetne inteligencije koji se temelji na učenju računala da riješi problem na temelju nekih podataka naziva se **strojno učenje**. Nećemo razmatrati klasično strojno učenje u ovom tečaju - upućujemo vas na zasebni kurikulum [Strojno učenje za početnike](http://aka.ms/ml-beginners). | ![ML za početnike](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.hr.png) | +> | Dio umjetne inteligencije koji se temelji na učenju računala da riješi problem na temelju nekih podataka naziva se **strojno učenje**. Nećemo razmatrati klasično strojno učenje u ovom tečaju - upućujemo vas na zasebni kurikulum [Strojno učenje za početnike](http://aka.ms/ml-beginners). | ![ML za početnike](../../../../translated_images/hr/ml-for-beginners.9e4fed176fd5817d.png) | ## Kratka povijest AI-a Umjetna inteligencija započela je kao područje sredinom dvadesetog stoljeća. U početku je simboličko zaključivanje bilo prevladavajući pristup, što je dovelo do brojnih važnih uspjeha, poput stručnih sustava – računalnih programa koji su mogli djelovati kao stručnjaci u nekim ograničenim domenama problema. Međutim, ubrzo je postalo jasno da se takav pristup ne skalira dobro. Izdvajanje znanja od stručnjaka, njegovo predstavljanje u računalu i održavanje te baze znanja točnom pokazalo se vrlo složenim zadatkom i preskupim za praktičnu primjenu u mnogim slučajevima. To je dovelo do takozvane [AI zime](https://en.wikipedia.org/wiki/AI_winter) 1970-ih. -Kratka povijest AI-a +Kratka povijest AI-a > Slika od [Dmitry Soshnikov](http://soshnikov.com) @@ -123,7 +123,7 @@ Slično, možemo vidjeti kako se pristup stvaranju "govornih programa" (koji bi * Moderni asistenti, poput Cortane, Siri ili Google Assistanta, svi su hibridni sustavi koji koriste neuronske mreže za pretvaranje govora u tekst i prepoznavanje naše namjere, a zatim koriste neko zaključivanje ili eksplicitne algoritme za obavljanje potrebnih radnji. * U budućnosti možemo očekivati potpuni model temeljen na neuronskim mrežama koji će samostalno upravljati dijalogom. Nedavne GPT i [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) obitelji neuronskih mreža pokazuju veliki uspjeh u tome. -evolucija Turingovog testa +evolucija Turingovog testa > Slika Dmitry Soshnikov, [fotografija](https://unsplash.com/photos/r8LmVbUKgns) od [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedavna istraživanja u području umjetne inteligencije diff --git a/translations/hr/lessons/2-Symbolic/Animals.ipynb b/translations/hr/lessons/2-Symbolic/Animals.ipynb index 1515dc99..239fb768 100644 --- a/translations/hr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "U ovom primjeru implementirat ćemo jednostavan sustav temeljen na znanju za određivanje životinje na temelju nekih fizičkih karakteristika. Sustav se može prikazati sljedećim AND-OR stablom (ovo je dio cijelog stabla, lako možemo dodati još pravila):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hr.png)\n" + "![](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/hr/lessons/2-Symbolic/README.md b/translations/hr/lessons/2-Symbolic/README.md index 20007db2..8130e7a3 100644 --- a/translations/hr/lessons/2-Symbolic/README.md +++ b/translations/hr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Predstavljanje znanja i ekspertni sustavi -![Sažetak sadržaja o simboličkoj umjetnoj inteligenciji](../../../../translated_images/ai-symbolic.715a30cb610411a6.hr.png) +![Sažetak sadržaja o simboličkoj umjetnoj inteligenciji](../../../../translated_images/hr/ai-symbolic.715a30cb610411a6.png) > Sketchnote autorice [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najčešće ne definiramo strogo znanje, već ga povezujemo s drugim srodnim poj Dakle, problem **predstavljanja znanja** je pronaći učinkovit način za predstavljanje znanja unutar računala u obliku podataka kako bi se ono moglo automatski koristiti. To se može promatrati kao spektar: -![Spektar predstavljanja znanja](../../../../translated_images/knowledge-spectrum.b60df631852c0217.hr.png) +![Spektar predstavljanja znanja](../../../../translated_images/hr/knowledge-spectrum.b60df631852c0217.png) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaksa bloka | Uvlačenje | | | Jedan od ranih uspjeha simboličke umjetne inteligencije bili su tzv. **ekspertni sustavi** - računalni sustavi dizajnirani da djeluju kao stručnjaci u nekom ograničenom problematičnom području. Temeljili su se na **bazi znanja** izvučenoj od jednog ili više ljudskih stručnjaka i sadržavali su **mehanizam zaključivanja** koji je provodio zaključivanje na temelju te baze. -![Ljudska arhitektura](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.hr.png) | ![Arhitektura sustava temeljenog na znanju](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.hr.png) +![Ljudska arhitektura](../../../../translated_images/hr/arch-human.5d4d35f1bba3ab1c.png) | ![Arhitektura sustava temeljenog na znanju](../../../../translated_images/hr/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Pojednostavljena struktura ljudskog živčanog sustava | Arhitektura sustava temeljenog na znanju @@ -106,7 +106,7 @@ Ekspertni sustavi izgrađeni su poput ljudskog sustava zaključivanja, koji sadr Kao primjer, razmotrimo sljedeći ekspertni sustav za određivanje životinje na temelju njezinih fizičkih karakteristika: -![AND-OR stablo](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hr.png) +![AND-OR stablo](../../../../translated_images/hr/AND-OR-Tree.5592d2c70187f283.png) > Slika autora [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 8eb792f9..f2ad55ec 100644 --- a/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/hr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Ako imamo više od 2 klase, softmax će normalizirati vjerojatnosti za sve njih. Ovdje je dijagram arhitekture mreže koja klasificira MNIST znamenke:\n", "\n", - "![MNIST Klasifikator](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.hr.png)\n" + "![MNIST Klasifikator](../../../../../translated_images/hr/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1248,7 +1248,7 @@ "* Nizak gubitak na podacima za treniranje - model može dobro aproksimirati podatke za treniranje jer ima dovoljno izražajne moći.\n", "* Gubitak na validaciji može biti znatno veći od gubitka na treniranju i može početi rasti tijekom treniranja - to je zato što model \"pamti\" točke za treniranje i gubi \"širu sliku\".\n", "\n", - "![Prekomjerno prilagođavanje](../../../../../translated_images/overfit.a0bd57f717c15769.hr.png)\n", + "![Prekomjerno prilagođavanje](../../../../../translated_images/hr/overfit.a0bd57f717c15769.png)\n", "\n", "> Na ovoj slici, `x` označava podatke za treniranje, `o` - podatke za validaciju. Lijevo - linearni model (jednoslojni), dobro aproksimira prirodu podataka. Desno - model s prekomjernim prilagođavanjem, savršeno aproksimira podatke za treniranje, ali gubi smisao s bilo kojim drugim podacima (pogreška validacije je vrlo visoka).\n" ] diff --git a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md index c67c6183..d798a639 100644 --- a/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pretreniranje je iznimno važan koncept u strojnom učenju, i vrlo je važno raz Razmotrimo sljedeći problem aproksimacije 5 točaka (prikazanih kao `x` na grafovima dolje): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.hr.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.hr.jpg) +![linear](../../../../../translated_images/hr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/hr/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linearni model, 2 parametra** | **Nelinearni model, 7 parametara** Pogreška na treningu = 5.3 | Pogreška na treningu = 0 @@ -79,7 +79,7 @@ Vrlo je važno pronaći ispravnu ravnotežu između složenosti modela (broja pa Kao što možete vidjeti na grafu iznad, pretreniranje se može otkriti vrlo niskom pogreškom na treningu i visokom pogreškom na validaciji. Obično tijekom treninga vidimo kako pogreške na treningu i validaciji počinju opadati, a zatim u nekom trenutku pogreška na validaciji prestaje opadati i počinje rasti. To će biti znak pretreniranja i pokazatelj da bismo trebali prestati trenirati u tom trenutku (ili barem napraviti snimku modela). -![pretreniranje](../../../../../translated_images/Overfitting.408ad91cd90b4371.hr.png) +![pretreniranje](../../../../../translated_images/hr/Overfitting.408ad91cd90b4371.png) ## Kako spriječiti pretreniranje diff --git a/translations/hr/lessons/3-NeuralNetworks/README.md b/translations/hr/lessons/3-NeuralNetworks/README.md index f2f56b8a..315d8ae0 100644 --- a/translations/hr/lessons/3-NeuralNetworks/README.md +++ b/translations/hr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod u neuronske mreže -![Sažetak sadržaja o uvodu u neuronske mreže u obliku crteža](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.hr.png) +![Sažetak sadržaja o uvodu u neuronske mreže u obliku crteža](../../../../translated_images/hr/ai-neuralnetworks.1c687ae40bc86e83.png) Kao što smo raspravili u uvodu, jedan od načina za postizanje inteligencije je treniranje **računalnog modela** ili **umjetnog mozga**. Od sredine 20. stoljeća, istraživači su isprobavali različite matematičke modele, sve dok se u posljednjim godinama ovaj smjer nije pokazao izuzetno uspješnim. Takvi matematički modeli mozga nazivaju se **neuronske mreže**. @@ -36,13 +36,13 @@ U ovom kurikulumu fokusirat ćemo se isključivo na modele neuronskih mreža. Iz biologije znamo da naš mozak sastoji se od neuralnih stanica (neurona), od kojih svaka ima više "ulaza" (dendrita) i jedan "izlaz" (akson). I dendriti i aksoni mogu provoditi električne signale, a veze između njih — poznate kao sinapse — mogu pokazivati različite stupnjeve provodljivosti, koje reguliraju neurotransmiteri. -![Model neurona](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.hr.jpg) | ![Model neurona](../../../../translated_images/artneuron.1a5daa88d20ebe6f.hr.png) +![Model neurona](../../../../translated_images/hr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neurona](../../../../translated_images/hr/artneuron.1a5daa88d20ebe6f.png) ----|---- Stvarni neuron *([Slika](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) s Wikipedije)* | Umjetni neuron *(Slika autora)* Dakle, najjednostavniji matematički model neurona sadrži nekoliko ulaza X1, ..., XN i jedan izlaz Y, te niz težina W1, ..., WN. Izlaz se računa kao: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) gdje je f neka nelinearna **funkcija aktivacije**. diff --git a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md index 76401dcd..2c2dbdb2 100644 --- a/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ U našem [OpenCV Notebook](OpenCV.ipynb) dajemo neke primjere kada se računalni * **Predobrada fotografije Brailleove knjige**. Fokusiramo se na to kako možemo koristiti pragove, detekciju značajki, perspektivne transformacije i manipulacije NumPy nizovima kako bismo odvojili pojedinačne Brailleove simbole za daljnju klasifikaciju neuronskom mrežom. -![Braille slika](../../../../../translated_images/braille.341962ff76b1bd70.hr.jpeg) | ![Predobrađena Braille slika](../../../../../translated_images/braille-result.46530fea020b03c7.hr.png) | ![Braille simboli](../../../../../translated_images/braille-symbols.0159185ab69d5339.hr.png) +![Braille slika](../../../../../translated_images/hr/braille.341962ff76b1bd70.jpeg) | ![Predobrađena Braille slika](../../../../../translated_images/hr/braille-result.46530fea020b03c7.png) | ![Braille simboli](../../../../../translated_images/hr/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Slika iz [OpenCV.ipynb](OpenCV.ipynb) * **Detekcija kretanja u videu pomoću razlike između okvira**. Ako je kamera fiksna, tada bi okviri iz videozapisa trebali biti prilično slični. Budući da su okviri predstavljeni kao nizovi, jednostavnim oduzimanjem tih nizova za dva uzastopna okvira dobit ćemo razliku piksela, koja bi trebala biti mala za statične okvire, a postati veća kada postoji značajno kretanje na slici. -![Slika video okvira i razlika između okvira](../../../../../translated_images/frame-difference.706f805491a0883c.hr.png) +![Slika video okvira i razlika između okvira](../../../../../translated_images/hr/frame-difference.706f805491a0883c.png) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ U našem [OpenCV Notebook](OpenCV.ipynb) dajemo neke primjere kada se računalni - **Gusti optički tok** izračunava vektorsko polje koje pokazuje za svaki piksel kamo se kreće - **Rijetki optički tok** temelji se na uzimanju nekih prepoznatljivih značajki na slici (npr. rubova) i praćenju njihove putanje od okvira do okvira. -![Slika optičkog toka](../../../../../translated_images/optical.1f4a94464579a83a.hr.png) +![Slika optičkog toka](../../../../../translated_images/hr/optical.1f4a94464579a83a.png) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d0b3347a..11d96f5c 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je mreža koja je postigla 92.7% točnosti u ImageNet top-5 klasifikaciji 2014. godine. Ima sljedeću strukturu slojeva: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hr.jpg) +![ImageNet Layers](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.jpg) Kao što možete vidjeti, VGG slijedi tradicionalnu piramidalnu arhitekturu, koja je niz slojeva konvolucije i pooling-a. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hr.jpg) +![ImageNet Pyramid](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.jpg) > Slika preuzeta s [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 1d54eae7..915241e4 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Dakle, u tipičnom CNN-u postoji nekoliko slojeva konvolucije, sa slojevima za pooling između njih kako bi se smanjile dimenzije slike. Također bismo povećali broj filtera, jer kako uzorci postaju napredniji - postoji više mogućih zanimljivih kombinacija koje trebamo tražiti.\n", "\n", - "![Slika koja prikazuje nekoliko slojeva konvolucije sa slojevima za pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.hr.png)\n", + "![Slika koja prikazuje nekoliko slojeva konvolucije sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Zbog smanjenja prostorne dimenzije i povećanja dimenzije značajki/filtera, ova se arhitektura također naziva **piramidalna arhitektura**.\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index d57bb9d5..7a4c265c 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Dakle, u tipičnom CNN-u postoji nekoliko konvolucijskih slojeva, s pooling slojevima između njih kako bi se smanjile dimenzije slike. Također bismo povećali broj filtera, jer kako uzorci postaju napredniji - postoji više mogućih zanimljivih kombinacija koje trebamo tražiti.\n", "\n", - "![Slika koja prikazuje nekoliko konvolucijskih slojeva sa slojevima za pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.hr.png)\n", + "![Slika koja prikazuje nekoliko konvolucijskih slojeva sa slojevima za pooling.](../../../../../translated_images/hr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Zbog smanjenja prostorne dimenzije i povećanja dimenzije značajki/filtera, ova se arhitektura također naziva **piramidalna arhitektura**.\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md index ea056d2b..dbcee906 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ U stvarnom životu želimo biti u mogućnosti prepoznati objekte na slici bez ob Za izdvajanje uzoraka koristit ćemo pojam **konvolucijskih filtera**. Kao što znate, slika je predstavljena 2D-matricom ili 3D-tenzorom s dubinom boje. Primjena filtera znači da uzimamo relativno malu matricu **jezgre filtera** i za svaki piksel u originalnoj slici izračunavamo ponderirani prosjek s okolnim točkama. To možemo zamisliti kao mali prozor koji klizi preko cijele slike i izračunava prosjek svih piksela prema težinama u matrici jezgre filtera. -![Vertikalni rubni filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.hr.png) | ![Horizontalni rubni filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.hr.png) +![Vertikalni rubni filter](../../../../../translated_images/hr/filter-vert.b7148390ca0bc356.png) | ![Horizontalni rubni filter](../../../../../translated_images/hr/filter-horiz.59b80ed4feb946ef.png) ----|---- > Slika: Dmitry Soshnikov @@ -38,7 +38,7 @@ Način na koji CNN funkcionira temelji se na sljedećim važnim idejama: * Možemo dizajnirati mrežu na način da se filteri automatski treniraju * Možemo koristiti isti pristup za pronalaženje uzoraka u visokorazinskim značajkama, ne samo u originalnoj slici. Tako ekstrakcija značajki u CNN-u funkcionira na hijerarhiji značajki, počevši od niskorazinskih kombinacija piksela do visokorazinskih kombinacija dijelova slike. -![Hijerarhijska ekstrakcija značajki](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.hr.png) +![Hijerarhijska ekstrakcija značajki](../../../../../translated_images/hr/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Slika iz [rada Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), temeljenog na [njihovom istraživanju](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Većina CNN-a koji se koriste za obradu slika slijedi tzv. piramidalnu arhitektu Kao primjer, pogledajmo arhitekturu VGG-16, mreže koja je postigla 92.7% točnosti u ImageNet-ovoj top-5 klasifikaciji 2014. godine: -![ImageNet slojevi](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hr.jpg) +![ImageNet slojevi](../../../../../translated_images/hr/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet piramida](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hr.jpg) +![ImageNet piramida](../../../../../translated_images/hr/vgg-16-arch.64ff2137f50dd49f.jpg) > Slika: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index a6def67f..b7c4816f 100644 --- a/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaš zadatak je trenirati konvolucijsku neuronsku mrežu za klasifikaciju razli Koristit ćemo [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), koji sadrži slike 37 različitih pasmina pasa i mačaka. -![Skup podataka s kojim ćemo raditi](../../../../../../translated_images/data.50b2a9d5484bdbf0.hr.png) +![Skup podataka s kojim ćemo raditi](../../../../../../translated_images/hr/data.50b2a9d5484bdbf0.png) Za preuzimanje skupa podataka, koristite ovaj isječak koda: diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 98b6f386..4a6adcab 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Kako bismo vizualizirali idealnu mačku, započet ćemo s nasumičnom slikom šuma i pokušati koristiti tehniku optimizacije gradijentnog spuštanja kako bismo prilagodili sliku tako da mreža prepozna mačku.\n", "\n", - "![Optimizacijska petlja](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hr.png)\n", + "![Optimizacijska petlja](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Ovo je naša početna slika:\n" ] diff --git a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md index 4ecc64b7..022af723 100644 --- a/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ I Keras i PyTorch sadrže funkcije za jednostavno učitavanje unaprijed treniran Evo primjera značajki koje je VGG-16 mreža izdvojila iz slike mačke: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.hr.png) +![Features extracted by VGG-16](../../../../../translated_images/hr/features.6291f9c7ba3a0b95.png) ## Skup podataka Mačke vs. Psi @@ -48,19 +48,19 @@ Unaprijed trenirana neuronska mreža sadrži različite uzorke unutar svog *mozg Jedan pristup koji možemo koristiti je započeti s nasumičnom slikom, a zatim pokušati koristiti tehniku **optimizacije gradijentnog spuštanja** kako bismo prilagodili tu sliku na način da mreža počne misliti da je to mačka. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hr.png) +![Image Optimization Loop](../../../../../translated_images/hr/ideal-cat-loop.999fbb8ff306e044.png) Međutim, ako to učinimo, dobit ćemo nešto vrlo slično nasumičnom šumu. To je zato što *postoji mnogo načina da mreža pomisli da je ulazna slika mačka*, uključujući neke koji vizualno nemaju smisla. Iako te slike sadrže mnogo uzoraka tipičnih za mačku, ništa ih ne ograničava da budu vizualno prepoznatljive. Kako bismo poboljšali rezultat, možemo dodati još jedan član u funkciju gubitka, koji se naziva **gubitak varijacije**. To je metrika koja pokazuje koliko su slični susjedni pikseli slike. Minimiziranje gubitka varijacije čini sliku glađom i uklanja šum - otkrivajući tako vizualno privlačnije uzorke. Evo primjera takvih "idealnih" slika koje se klasificiraju kao mačka i zebra s visokom vjerojatnošću: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.hr.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.hr.png) +![Ideal Cat](../../../../../translated_images/hr/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/hr/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Idealna mačka* | *Idealna zebra* Sličan pristup može se koristiti za izvođenje takozvanih **adversarijalnih napada** na neuronsku mrežu. Pretpostavimo da želimo zavarati neuronsku mrežu i učiniti da pas izgleda kao mačka. Ako uzmemo sliku psa, koju mreža prepoznaje kao psa, možemo je malo prilagoditi koristeći optimizaciju gradijentnog spuštanja dok mreža ne počne klasificirati sliku kao mačku: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.hr.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.hr.png) +![Picture of a Dog](../../../../../translated_images/hr/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/hr/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Izvorna slika psa* | *Slika psa klasificirana kao mačka* diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 406ca28a..dbe2c66f 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz izvorne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje.\n", "\n", - "![Dijagram Autoenkodera](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hr.jpg)\n", + "![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Slika preuzeta s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 6e0d5745..a9c9eebe 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz originalne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje.\n", "\n", - "![AutoEncoder Dijagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hr.jpg)\n", + "![AutoEncoder Dijagram](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Slika preuzeta s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md index c72d204c..d5548be4 100644 --- a/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Međutim, možda bismo željeli koristiti sirove (neoznačene) podatke za trenir Budući da treniramo autoenkoder kako bi uhvatio što više informacija iz originalne slike za točnu rekonstrukciju, mreža pokušava pronaći najbolju **ugradnju** ulaznih slika kako bi uhvatila njihovo značenje. -![Dijagram Autoenkodera](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hr.jpg) +![Dijagram Autoenkodera](../../../../../translated_images/hr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Slika s [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md index 5be381e8..ef0eeadd 100644 --- a/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modeli za klasifikaciju slika s kojima smo se dosad susretali uzimaju sliku i pr ## [Prethodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekcija objekata](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.hr.png) +![Detekcija objekata](../../../../../translated_images/hr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Slika s [YOLO v2 web stranice](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Pretpostavimo da želimo pronaći mačku na slici. Vrlo naivan pristup detekciji 2. Provoditi klasifikaciju slike na svakoj pločici. 3. Pločice koje rezultiraju dovoljno visokom aktivacijom mogu se smatrati da sadrže traženi objekt. -![Naivna detekcija objekata](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.hr.png) +![Naivna detekcija objekata](../../../../../translated_images/hr/naive-detection.e7f1ba220ccd08c6.png) > *Slika iz [vježbenice](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Možete naići na sljedeće skupove podataka za ovu zadaću: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasa * [COCO](http://cocodataset.org/#home) – Uobičajeni objekti u kontekstu. 80 klasa, okviri i maske za segmentaciju -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.hr.jpg) +![COCO](../../../../../translated_images/hr/coco-examples.71bc60380fa6cceb.jpg) ## Metrike za detekciju objekata @@ -50,7 +50,7 @@ Možete naići na sljedeće skupove podataka za ovu zadaću: Dok je za klasifikaciju slika lako izmjeriti koliko dobro algoritam radi, za detekciju objekata moramo mjeriti i točnost klase, kao i preciznost lokacije predviđenog okvira. Za ovo drugo koristimo metodu **Presjek kroz uniju** (IoU), koja mjeri koliko se dobro dva okvira (ili dva proizvoljna područja) preklapaju. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.hr.png) +![IoU](../../../../../translated_images/hr/iou_equation.9a4751d40fff4e11.png) > *Slika 2 iz [ovog izvrsnog blog posta o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Postoje dvije široke kategorije algoritama za detekciju objekata: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) koristi [Selektivno pretraživanje](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) za generiranje hijerarhijske strukture ROI regija, koje se zatim prosljeđuju kroz CNN ekstraktore značajki i SVM klasifikatore za određivanje klase objekta, te linearnu regresiju za određivanje koordinata *okvira*. [Službeni rad](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.hr.png) +![RCNN](../../../../../translated_images/hr/rcnn1.cae407020dfb1d1f.png) > *Slika iz van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.hr.png) +![RCNN-1](../../../../../translated_images/hr/rcnn2.2d9530bb83516484.png) > *Slike iz [ovog bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Postoje dvije široke kategorije algoritama za detekciju objekata: Ovaj pristup je sličan R-CNN-u, ali regije se definiraju nakon što su primijenjeni slojevi konvolucije. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.hr.png) +![FRCNN](../../../../../translated_images/hr/f-rcnn.3cda6d9bb4188875.png) > Slika iz [službenog rada](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ovaj pristup je sličan R-CNN-u, ali regije se definiraju nakon što su primijen Glavna ideja ovog pristupa je korištenje neuronske mreže za predviđanje ROI – takozvane *Mreže za predlaganje regija*. [Rad](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.hr.png) +![FasterRCNN](../../../../../translated_images/hr/faster-rcnn.8d46c099b87ef30a.png) > Slika iz [službenog rada](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ovaj algoritam je čak brži od Faster R-CNN-a. Glavna ideja je sljedeća: 2. Značajke se obrađuju pomoću **Pozicijski osjetljive mape rezultata**. Svaki objekt iz $C$ klasa dijeli se na $k\times k$ regije, i treniramo mrežu da predviđa dijelove objekata. 3. Za svaki dio iz $k\times k$ regija sve mreže glasaju za klase objekata, a klasa objekta s najviše glasova se odabire. -![r-fcn slika](../../../../../translated_images/r-fcn.13eb88158b99a3da.hr.png) +![r-fcn slika](../../../../../translated_images/hr/r-fcn.13eb88158b99a3da.png) > Slika iz [službenog rada](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritam za detekciju u stvarnom vremenu s jednim prolazom. Glavna idej * Slika se dijeli na $S\times S$ regije. * Za svaku regiju, **CNN** predviđa $n$ mogućih objekata, koordinate *okvira* i *povjerenje*=*vjerojatnost* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.hr.png) + ![YOLO](../../../../../translated_images/hr/yolo.a2648ec82ee8bb4e.png) > Slika iz [službenog rada](https://arxiv.org/abs/1506.02640) diff --git a/translations/hr/lessons/4-ComputerVision/README.md b/translations/hr/lessons/4-ComputerVision/README.md index 0e2dab7a..f322a147 100644 --- a/translations/hr/lessons/4-ComputerVision/README.md +++ b/translations/hr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Računalni vid -![Sažetak sadržaja o računalnom vidu u obliku crteža](../../../../translated_images/ai-computervision.6506ebebac3fbf76.hr.png) +![Sažetak sadržaja o računalnom vidu u obliku crteža](../../../../translated_images/hr/ai-computervision.6506ebebac3fbf76.png) U ovom dijelu ćemo naučiti o: diff --git a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index b62ccf58..59f0af09 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Vreća riječi** (BoW) vektorsko predstavljanje najčešće je korišteno tradicionalno vektorsko predstavljanje. Svaka riječ povezana je s indeksom vektora, a element vektora sadrži broj pojavljivanja riječi u određenom dokumentu.\n", "\n", - "![Slika koja prikazuje kako je vektorsko predstavljanje vreće riječi prikazano u memoriji.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.hr.png) \n", + "![Slika koja prikazuje kako je vektorsko predstavljanje vreće riječi prikazano u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Napomena**: Možete također razmišljati o BoW kao o zbroju svih one-hot kodiranih vektora za pojedinačne riječi u tekstu.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 106d2ef1..6596a023 100644 --- a/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/hr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Vreća riječi** (BoW) je najjednostavnija tradicionalna reprezentacija vektora za razumijevanje. Svaka riječ povezana je s indeksom vektora, a element vektora sadrži broj pojavljivanja svake riječi u danom dokumentu.\n", "\n", - "![Slika koja prikazuje kako je reprezentacija vektora vreće riječi prikazana u memoriji.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.hr.png)\n", + "![Slika koja prikazuje kako je reprezentacija vektora vreće riječi prikazana u memoriji.](../../../../../translated_images/hr/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: BoW možete zamisliti i kao zbroj svih vektora kodiranih metodom \"jedan na jedan\" za pojedinačne riječi u tekstu.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 0550b79d..2e3405d8 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Korištenjem sloja za ugradnju kao prvog sloja u našoj mreži, možemo prijeći s modela vreće riječi na model **vreće ugradnji**, gdje prvo pretvaramo svaku riječ u našem tekstu u odgovarajuću ugradnju, a zatim izračunavamo neku agregatnu funkciju nad svim tim ugradnjama, poput `sum`, `average` ili `max`.\n", "\n", - "![Slika koja prikazuje klasifikator s ugradnjom za pet riječi u nizu.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hr.png)\n", + "![Slika koja prikazuje klasifikator s ugradnjom za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša neuronska mreža za klasifikaciju započet će slojem za ugradnju, zatim slojem za agregaciju, i linearnim klasifikatorom na vrhu:\n" ] @@ -176,7 +176,7 @@ "\n", "U prethodnoj arhitekturi morali smo popuniti sve sekvence na istu duljinu kako bismo ih uklopili u minibatch. Ovo nije najučinkovitiji način za prikazivanje sekvenci promjenjive duljine - drugi pristup bio bi korištenje **offset** vektora, koji bi sadržavao pomake svih sekvenci pohranjenih u jednom velikom vektoru.\n", "\n", - "![Slika koja prikazuje prikaz sekvenci pomoću offseta](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.hr.png)\n", + "![Slika koja prikazuje prikaz sekvenci pomoću offseta](../../../../../translated_images/hr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Napomena**: Na slici iznad prikazana je sekvenca znakova, ali u našem primjeru radimo sa sekvencama riječi. Međutim, osnovni princip prikazivanja sekvenci pomoću offset vektora ostaje isti.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja riječi koje se rjeđe pojavljuju.\n", "\n", - "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hr.png)\n", + "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Za eksperimentiranje s Word2Vec ugradnjom unaprijed obučenom na Google News skupu podataka, možemo koristiti biblioteku **gensim**. Ispod nalazimo riječi najsličnije riječi 'neural':\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2a8d1128..444fa9c4 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/hr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Korištenjem embedding sloja kao prvog sloja u našoj mreži, možemo prijeći s modela vreće riječi (bag-of-words) na model **embedding vreće** (embedding bag), gdje prvo pretvaramo svaku riječ u našem tekstu u odgovarajući embedding, a zatim izračunavamo neku agregacijsku funkciju nad svim tim embeddingima, poput `sum`, `average` ili `max`.\n", "\n", - "![Slika koja prikazuje embedding klasifikator za pet riječi u nizu.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hr.png)\n", + "![Slika koja prikazuje embedding klasifikator za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša neuronska mreža klasifikatora sastoji se od sljedećih slojeva:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja riječi koje se rjeđe pojavljuju.\n", "\n", - "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hr.png)\n", + "![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Kako bismo eksperimentirali s Word2Vec ugradnjom unaprijed istreniranom na Google News skupu podataka, možemo koristiti biblioteku **gensim**. Ispod nalazimo riječi koje su najsličnije riječi 'neural'.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/14-Embeddings/README.md b/translations/hr/lessons/5-NLP/14-Embeddings/README.md index 85a2495a..3a8cc823 100644 --- a/translations/hr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Dakle, sloj za ugrađivanje uzima riječ kao ulaz i proizvodi izlazni vektor odr Koristeći sloj za ugrađivanje kao prvi sloj u našoj mreži klasifikatora, možemo se prebaciti s modela vreće riječi na model **vreće ugrađivanja**, gdje prvo svaku riječ u našem tekstu pretvaramo u odgovarajuće ugrađivanje, a zatim izračunavamo neku agregatnu funkciju preko svih tih ugrađivanja, poput `sum`, `average` ili `max`. -![Slika koja prikazuje klasifikator ugrađivanja za pet riječi u nizu.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hr.png) +![Slika koja prikazuje klasifikator ugrađivanja za pet riječi u nizu.](../../../../../translated_images/hr/embedding-classifier-example.b77f021a7ee67eee.png) > Slika autora @@ -40,7 +40,7 @@ Da bismo to postigli, trebamo unaprijed trenirati naš model za ugrađivanje na CBoW je brži, dok je skip-gram sporiji, ali bolje predstavlja rijetke riječi. -![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hr.png) +![Slika koja prikazuje algoritme CBoW i Skip-Gram za pretvaranje riječi u vektore.](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Slika iz [ovog rada](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md index 8c1ac5b4..4d4d9767 100644 --- a/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ U našim prethodnim primjerima koristili smo unaprijed trenirane semantičke ugr * **Kontinuirana vreća riječi** (CBoW), gdje predviđamo srednji token $W_0$ u nizu tokena $W_{-N}$, ..., $W_N$. * **Skip-gram**, gdje predviđamo skup susjednih tokena {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iz srednjeg tokena $W_0$. -![slika iz rada o pretvaranju riječi u vektore](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hr.png) +![slika iz rada o pretvaranju riječi u vektore](../../../../../translated_images/hr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Slika iz [ovog rada](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hr/lessons/5-NLP/16-RNN/README.md b/translations/hr/lessons/5-NLP/16-RNN/README.md index c1c4846f..5de06255 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/README.md +++ b/translations/hr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ U prethodnim odjeljcima koristili smo bogate semantičke reprezentacije teksta i Kako bismo uhvatili značenje sekvenci teksta, trebamo koristiti drugu arhitekturu neuronske mreže, koja se naziva **rekurentna neuronska mreža** ili RNN. U RNN-u, rečenicu prosljeđujemo kroz mrežu jedan simbol po simbol, a mreža proizvodi određeno **stanje**, koje zatim ponovno prosljeđujemo mreži s idućim simbolom. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.hr.png) +![RNN](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.png) > Slika autora @@ -61,7 +61,7 @@ Razgovarali smo o rekurentnim mrežama koje djeluju u jednom smjeru, od početka Rekurentna mreža, bilo jednosmjerna ili dvosmjerna, hvata određene uzorke unutar sekvence i može ih pohraniti u vektor stanja ili proslijediti u izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi drugi rekurentni sloj na vrhu prvog kako bismo uhvatili uzorke višeg nivoa i izgradili na temelju uzoraka nižeg nivoa koje je izvukao prvi sloj. To nas dovodi do pojma **višeslojnog RNN-a**, koji se sastoji od dva ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz. -![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hr.jpg) +![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Slika iz [ovog izvrsnog posta](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernanda Lópeza* diff --git a/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 35f4bd5c..91218ff3 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentna mreža, bilo jednosmjerna ili dvosmjerna, hvata određene uzorke unutar sekvence i može ih pohraniti u vektor stanja ili proslijediti u izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi još jedan rekurentni sloj na vrhu prvog kako bismo uhvatili uzorke višeg nivoa, izgrađene od uzoraka nižeg nivoa koje je izvukao prvi sloj. To nas dovodi do pojma **višeslojni RNN**, koji se sastoji od dvije ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz.\n", "\n", - "![Slika koja prikazuje višeslojni long-short-term-memory RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hr.jpg)\n", + "![Slika koja prikazuje višeslojni long-short-term-memory RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Slika iz [ovog sjajnog posta](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autora Fernanda Lópeza*\n", "\n", diff --git a/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb index f8c7e54a..bdb3ecc0 100644 --- a/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/hr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Kako bismo uhvatili značenje sekvence teksta, koristit ćemo arhitekturu neuronske mreže zvanu **rekurentna neuronska mreža** ili RNN. Kada koristimo RNN, prosljeđujemo našu rečenicu kroz mrežu jedan token po jedan, a mreža proizvodi određeno **stanje**, koje zatim ponovno prosljeđujemo mreži s idućim tokenom.\n", "\n", - "![Slika koja prikazuje primjer generiranja rekurentne neuronske mreže.](../../../../../translated_images/rnn.27f5c29c53d727b5.hr.png)\n", + "![Slika koja prikazuje primjer generiranja rekurentne neuronske mreže.](../../../../../translated_images/hr/rnn.27f5c29c53d727b5.png)\n", "\n", "S obzirom na ulaznu sekvencu tokena $X_0,\\dots,X_n$, RNN stvara sekvencu blokova neuronske mreže i trenira ovu sekvencu od početka do kraja koristeći unatrag širenje pogreške (backpropagation). Svaki blok mreže uzima par $(X_i,S_i)$ kao ulaz i proizvodi $S_{i+1}$ kao rezultat. Konačno stanje $S_n$ ili izlaz $Y_n$ ide u linearni klasifikator kako bi se proizveo rezultat. Svi blokovi mreže dijele iste težine i treniraju se od početka do kraja koristeći jedan prolaz unatrag širenja pogreške.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentne mreže, bilo jednosmjerne ili dvosmjerne, prepoznaju uzorke unutar sekvence i pohranjuju ih u vektore stanja ili ih vraćaju kao izlaz. Kao i kod konvolucijskih mreža, možemo izgraditi još jedan rekurentni sloj nakon prvog kako bismo prepoznali uzorke višeg nivoa, izgrađene na temelju uzoraka nižeg nivoa koje je izdvojio prvi sloj. To nas dovodi do pojma **višeslojnog RNN-a**, koji se sastoji od dvije ili više rekurentnih mreža, gdje se izlaz prethodnog sloja prosljeđuje sljedećem sloju kao ulaz.\n", "\n", - "![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hr.jpg)\n", + "![Slika koja prikazuje višeslojni LSTM RNN](../../../../../translated_images/hr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Slika preuzeta iz [ovog izvrsnog članka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autora Fernanda Lópeza.*\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 92aa1f37..efd9dc50 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Način na koji ćemo trenirati RNN za generiranje teksta je sljedeći. U svakom koraku uzet ćemo niz znakova duljine `nchars` i tražiti od mreže da generira sljedeći izlazni znak za svaki ulazni znak:\n", "\n", - "![Slika koja prikazuje primjer RNN generiranja riječi 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hr.png)\n", + "![Slika koja prikazuje primjer RNN generiranja riječi 'HELLO'.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Ovisno o stvarnom scenariju, možda ćemo htjeti uključiti i neke posebne znakove, poput *kraj-sekvence* ``. U našem slučaju, želimo trenirati mrežu za beskonačno generiranje teksta, stoga ćemo fiksirati veličinu svakog niza na `nchars` tokena. Posljedično, svaki primjer za treniranje sastojat će se od `nchars` ulaza i `nchars` izlaza (što je ulazni niz pomaknut za jedan simbol ulijevo). Minibatch će se sastojati od nekoliko takvih nizova.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 3c9989ab..4a600cf8 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Način na koji ćemo trenirati RNN za generiranje naslova vijesti je sljedeći. U svakom koraku uzet ćemo jedan naslov, koji će se proslijediti u RNN, i za svaki ulazni znak tražit ćemo od mreže da generira sljedeći izlazni znak:\n", "\n", - "![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hr.png)\n", + "![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Za posljednji znak našeg niza tražit ćemo od mreže da generira `` token.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md index 408bc743..da678c09 100644 --- a/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ U arhitekturi RNN-a koju smo raspravili u prethodnoj jedinici, svaka RNN jedinic To omogućuje različite neuronske arhitekture prikazane na slici ispod: -![Slika koja prikazuje uobičajene uzorke rekurentnih neuronskih mreža.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hr.jpg) +![Slika koja prikazuje uobičajene uzorke rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Slika iz blog posta [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autora [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ U ovoj jedinici fokusirat ćemo se na jednostavne generativne modele koji nam po Trenirat ćemo ovaj RNN da generira tekst korak po korak. Na svakom koraku uzet ćemo niz znakova duljine `nchars` i tražiti od mreže da generira sljedeći izlazni znak za svaki ulazni znak: -![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hr.png) +![Slika koja prikazuje primjer generiranja riječi 'HELLO' pomoću RNN-a.](../../../../../translated_images/hr/rnn-generate.56c54afb52f9781d.png) Tijekom generiranja teksta (tijekom inferencije), počinjemo s nekim **poticajem**, koji se prosljeđuje kroz RNN ćelije kako bi se generiralo njegovo međustanje, a zatim iz tog stanja počinje generiranje. Generiramo jedan znak po jedan, prosljeđujemo stanje i generirani znak sljedećoj RNN ćeliji kako bismo generirali sljedeći znak, sve dok ne generiramo dovoljno znakova. diff --git a/translations/hr/lessons/5-NLP/18-Transformers/README.md b/translations/hr/lessons/5-NLP/18-Transformers/README.md index c256c91e..5077ab96 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Kod RNN-ova, sekvenca-u-sekvencu se implementira pomoću dvije rekurentne mreže **Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, pri generiranju izlaznog simbola yt, uzet ćemo u obzir sva skrivena stanja ulaza hi, s različitim težinskim koeficijentima αt,i. -![Slika koja prikazuje enkoder/dekoder model s aditivnim slojem pažnje](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hr.png) +![Slika koja prikazuje enkoder/dekoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png) > Enkoder-dekoder model s aditivnim mehanizmom pažnje u [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matrica pažnje {αi,j} predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznoj sekvenci. Ispod je primjer takve matrice: -![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hr.png) +![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png) > Slika iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3) @@ -66,7 +66,7 @@ Rezultat koji dobivamo s pozicijskim ugrađivanjem uključuje i originalni token Zatim trebamo uhvatiti neke uzorke unutar naše sekvence. Da bismo to učinili, transformeri koriste mehanizam **samopozornosti**, koji je u osnovi pažnja primijenjena na istu sekvencu kao ulaz i izlaz. Primjena samopozornosti omogućuje nam uzimanje u obzir **konteksta** unutar rečenice i uvid u međusobne odnose između riječi. Na primjer, omogućuje nam da vidimo na koje riječi se odnose zamjenice poput *to*, i također uzimamo kontekst u obzir: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.hr.png) +![](../../../../../translated_images/hr/CoreferenceResolution.861924d6d384a7d6.png) > Slika iz [Googleovog bloga](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Budući da se svaka ulazna pozicija neovisno mapira na svaku izlaznu poziciju, t **BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna transformer mreža s 12 slojeva za *BERT-base*, i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijed treniranja model usvaja značajne razine razumijevanja jezika koje se zatim mogu iskoristiti s drugim skupovima podataka pomoću finog podešavanja. Ovaj proces naziva se **transferno učenje**. -![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hr.png) +![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Slika [izvor](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index b034dd33..40ee2a0a 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/hr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, prilikom generiranja izlaznog simbola $y_t$, uzimamo u obzir sva skrivena stanja ulaza $h_i$, s različitim težinskim koeficijentima $\\alpha_{t,i}$.\n", "\n", - "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hr.png)\n", + "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder model s mehanizmom aditivne pažnje iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrica pažnje $\\{\\alpha_{i,j}\\}$ predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznom nizu. Ispod je primjer takve matrice:\n", "\n", - "![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hr.png)\n", + "![Slika koja prikazuje uzorak poravnanja pronađen od strane RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Slika preuzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna mreža transformera s 12 slojeva za *BERT-base* i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijed treniranja model usvaja značajnu razinu razumijevanja jezika, koja se zatim može iskoristiti s drugim skupovima podataka pomoću finog podešavanja. Ovaj proces naziva se **prijenosno učenje**.\n", "\n", - "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hr.png)\n", + "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Postoji mnogo varijacija arhitektura transformera, uključujući BERT, DistilBERT, BigBird, OpenGPT3 i druge, koje se mogu fino podešavati. Paket [HuggingFace](https://github.com/huggingface/) pruža repozitorij za treniranje mnogih od ovih arhitektura s PyTorchom.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index fba16859..6fb5b85a 100644 --- a/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/hr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pažnje** omogućuju ponderiranje kontekstualnog utjecaja svakog ulaznog vektora na svaku izlaznu predikciju RNN-a. To se implementira stvaranjem prečaca između međustanja ulaznog RNN-a i izlaznog RNN-a. Na taj način, prilikom generiranja izlaznog simbola $y_t$, uzimamo u obzir sva ulazna skrivena stanja $h_i$, s različitim težinskim koeficijentima $\\alpha_{t,i}$.\n", "\n", - "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hr.png)\n", + "![Slika koja prikazuje encoder/decoder model s aditivnim slojem pažnje](../../../../../translated_images/hr/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder model s aditivnim mehanizmom pažnje u [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [ovog blog posta](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrica pažnje $\\{\\alpha_{i,j}\\}$ predstavlja stupanj u kojem određene ulazne riječi sudjeluju u generiranju određene riječi u izlaznom nizu. Ispod je primjer takve matrice:\n", "\n", - "![Slika koja prikazuje uzorak poravnanja pronađenog pomoću RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hr.png)\n", + "![Slika koja prikazuje uzorak poravnanja pronađenog pomoću RNNsearch-50, preuzeto iz Bahdanau - arviz.org](../../../../../translated_images/hr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Slika preuzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je vrlo velika višeslojna transformacijska mreža s 12 slojeva za *BERT-base* i 24 za *BERT-large*. Model se prvo unaprijed trenira na velikom korpusu tekstualnih podataka (WikiPedia + knjige) koristeći nenadzirano učenje (predviđanje maskiranih riječi u rečenici). Tijekom unaprijednog treniranja model usvaja značajnu razinu razumijevanja jezika, što se kasnije može iskoristiti s drugim skupovima podataka putem finog podešavanja. Ovaj proces naziva se **transferno učenje**.\n", "\n", - "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hr.png)\n", + "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Postoji mnogo varijacija transformacijskih arhitektura, uključujući BERT, DistilBERT, BigBird, OpenGPT3 i druge, koje se mogu fino podešavati.\n", "\n", diff --git a/translations/hr/lessons/5-NLP/19-NER/README.md b/translations/hr/lessons/5-NLP/19-NER/README.md index 48cd3498..68de7ab8 100644 --- a/translations/hr/lessons/5-NLP/19-NER/README.md +++ b/translations/hr/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorođenčeta | O Budući da trebamo izgraditi jedno-na-jedno korespondenciju između tokena i klasa, možemo trenirati desno **mnogostruko-na-mnogostruko** neuronski mrežni model iz ove slike: -![Slika koja prikazuje uobičajene obrasce rekurentnih neuronskih mreža.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hr.jpg) +![Slika koja prikazuje uobičajene obrasce rekurentnih neuronskih mreža.](../../../../../translated_images/hr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Slika iz [ovog blog posta](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autora [Andreja Karpathyja](http://karpathy.github.io/). NER modeli za klasifikaciju tokena odgovaraju desno najudaljenijoj arhitekturi mreže na ovoj slici.* diff --git a/translations/hr/lessons/5-NLP/README.md b/translations/hr/lessons/5-NLP/README.md index 73c809cf..c7451f28 100644 --- a/translations/hr/lessons/5-NLP/README.md +++ b/translations/hr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Obrada Prirodnog Jezika -![Sažetak NLP zadataka u crtežu](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.hr.png) +![Sažetak NLP zadataka u crtežu](../../../../translated_images/hr/ai-nlp.b22dcb8ca4707cea.png) U ovom dijelu fokusirat ćemo se na korištenje neuronskih mreža za rješavanje zadataka povezanih s **Obradom Prirodnog Jezika (NLP)**. Postoji mnogo NLP problema koje želimo da računala mogu riješiti: diff --git a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md index 4025dbe2..3e301574 100644 --- a/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Možete otvoriti jedan od modela, na primjer **Biology → Flocking**. Nakon otvaranja modela, dolazite na glavni ekran NetLoga. Evo uzorka modela koji opisuje populaciju vukova i ovaca, s obzirom na ograničene resurse (trava). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.hr.png) +![NetLogo Main Screen](../../../../../translated_images/hr/NetLogo-Main.32653711ec1a01b3.png) > Snimka zaslona Dmitryja Soshnikova diff --git a/translations/hr/lessons/README.md b/translations/hr/lessons/README.md index fc3df6c3..9c50f9ab 100644 --- a/translations/hr/lessons/README.md +++ b/translations/hr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pregled -![Pregled u crtežu](../../../translated_images/ai-overview.0857791951d19500.hr.png) +![Pregled u crtežu](../../../translated_images/hr/ai-overview.0857791951d19500.png) > Crtež bilješki od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md index f00c51ca..e237df41 100644 --- a/translations/hr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Nakon uspjeha transformera u rješavanju zadataka obrade prirodnog jezika (NLP), Glavna ideja CLIP-a je usporediti tekstualne upite sa slikom i odrediti koliko dobro slika odgovara upitu. -![CLIP Arhitektura](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.hr.png) +![CLIP Arhitektura](../../../../../translated_images/hr/clip-arch.b3dbf20b4e8ed8be.png) > *Slika iz [ovog blog posta](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Nakon što je ovaj model prethodno treniran, možemo mu dati batch slika i batch Pretpostavimo da trebamo klasificirati slike, primjerice, između mačaka, pasa i ljudi. U tom slučaju možemo modelu dati sliku i niz tekstualnih upita: "*slika mačke*", "*slika psa*", "*slika čovjeka*". U rezultirajućem vektoru s 3 vjerojatnosti samo trebamo odabrati indeks s najvećom vrijednošću. -![CLIP za klasifikaciju slika](../../../../../translated_images/clip-class.3af42ef0b2b19369.hr.png) +![CLIP za klasifikaciju slika](../../../../../translated_images/hr/clip-class.3af42ef0b2b19369.png) > *Slika iz [ovog blog posta](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Više o VQGAN-u saznajte na web stranici [Taming Transformers](https://compvis.g Jedna od važnih razlika između VQGAN-a i tradicionalnog GAN-a je ta što potonji može proizvesti pristojnu sliku iz bilo kojeg ulaznog vektora, dok VQGAN vjerojatno neće proizvesti koherentnu sliku. Stoga je potrebno dodatno usmjeriti proces stvaranja slike, a to se može učiniti pomoću CLIP-a. -![VQGAN+CLIP Arhitektura](../../../../../translated_images/vqgan.5027fe05051dfa31.hr.png) +![VQGAN+CLIP Arhitektura](../../../../../translated_images/hr/vqgan.5027fe05051dfa31.png) Za generiranje slike koja odgovara tekstualnom upitu, počinjemo s nekim nasumičnim vektorskim kodiranjem koje se prosljeđuje kroz VQGAN kako bi se proizvela slika. Zatim se CLIP koristi za stvaranje funkcije gubitka koja pokazuje koliko dobro slika odgovara tekstualnom upitu. Cilj je minimizirati taj gubitak koristeći backpropagation za prilagodbu parametara ulaznog vektora. Odlična biblioteka koja implementira VQGAN+CLIP je [Pixray](http://github.com/pixray/pixray). -![Slika generirana Pixrayem](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.hr.png) | ![Slika generirana Pixrayem](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.hr.png) | ![Slika generirana Pixrayem](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.hr.png) +![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Slika generirana iz upita *bliski akvarelni portret mladog učitelja književnosti s knjigom* | Slika generirana iz upita *bliski uljani portret mlade učiteljice računalnih znanosti s računalom* | Slika generirana iz upita *bliski uljani portret starijeg učitelja matematike ispred ploče* @@ -75,7 +75,7 @@ Za razliku od CLIP-a, DALL-E prima tekst i sliku kao jedinstveni niz tokena za s Glavna razlika između DALL-E 1 i 2 je ta što DALL-E 2 generira realističnije slike i umjetnička djela. Primjeri generiranja slika s DALL-E: -![Slika generirana Pixrayem](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.hr.png) | ![Slika generirana Pixrayem](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.hr.png) | ![Slika generirana Pixrayem](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.hr.png) +![Slika generirana Pixrayem](../../../../../translated_images/hr/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Slika generirana Pixrayem](../../../../../translated_images/hr/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Slika generirana iz upita *bliski akvarelni portret mladog učitelja književnosti s knjigom* | Slika generirana iz upita *bliski uljani portret mlade učiteljice računalnih znanosti s računalom* | Slika generirana iz upita *bliski uljani portret starijeg učitelja matematike ispred ploče* diff --git a/translations/hu/README.md b/translations/hu/README.md index e6ca1125..72b3826b 100644 --- a/translations/hu/README.md +++ b/translations/hu/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Mesterséges intelligencia kezdőknek – Tananyag -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.hu.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/hu/ai-overview.0857791951d19500.png)| |:---:| | Mesterséges intelligencia kezdőknek – _Vázlat [@girlie_mac](https://twitter.com/girlie_mac) tollából_ | diff --git a/translations/hu/lessons/1-Intro/README.md b/translations/hu/lessons/1-Intro/README.md index 0db05c7f..c7e67306 100644 --- a/translations/hu/lessons/1-Intro/README.md +++ b/translations/hu/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bevezetés a mesterséges intelligenciába -![A mesterséges intelligencia bevezetésének összefoglalása egy rajzban](../../../../translated_images/ai-intro.bf28d1ac4235881c.hu.png) +![A mesterséges intelligencia bevezetésének összefoglalása egy rajzban](../../../../translated_images/hu/ai-intro.bf28d1ac4235881c.png) > Rajz: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Eredetileg a számítógépeket [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) találta fel, hogy számokkal dolgozzanak egy jól meghatározott eljárás – algoritmus – alapján. A modern számítógépek, bár jelentősen fejlettebbek, mint a 19. században javasolt eredeti modell, még mindig ugyanazt az irányított számítási elvet követik. Ezért lehetséges egy számítógépet programozni, hogy valamit elvégezzen, ha pontosan ismerjük a cél eléréséhez szükséges lépések sorrendjét. -![Egy személy fotója](../../../../translated_images/dsh_age.d212a30d4e54fb5f.hu.png) +![Egy személy fotója](../../../../translated_images/hu/dsh_age.d212a30d4e54fb5f.png) > Fotó: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ További információért lásd **[Mesterséges általános intelligencia](https Az egyik probléma az **[intelligencia](https://en.wikipedia.org/wiki/Intelligence)** kifejezéssel kapcsolatban az, hogy nincs egyértelmű definíciója. Egyesek szerint az intelligencia kapcsolódik az **absztrakt gondolkodáshoz**, vagy az **önismerethez**, de nem tudjuk megfelelően meghatározni. -![Macska fotója](../../../../translated_images/photo-cat.8c8e8fb760ffe457.hu.jpg) +![Macska fotója](../../../../translated_images/hu/photo-cat.8c8e8fb760ffe457.jpg) > [Fotó](https://unsplash.com/photos/75715CVEJhI) készítette [Amber Kipp](https://unsplash.com/@sadmax) az Unsplash-en @@ -98,13 +98,13 @@ Alternatívaként megpróbálhatjuk modellezni az agyunk legegyszerűbb elemeit > | Mi a helyzet az ML-lel? | | > |--------------|-----------| -> | A mesterséges intelligencia azon része, amely a számítógép tanulásán alapul, hogy egy problémát megoldjon bizonyos adatok alapján, **gépi tanulásnak** nevezzük. Ebben a kurzusban nem foglalkozunk a klasszikus gépi tanulással – erre külön [Gépi tanulás kezdőknek](http://aka.ms/ml-beginners) tananyagot ajánlunk. | ![ML kezdőknek](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.hu.png) | +> | A mesterséges intelligencia azon része, amely a számítógép tanulásán alapul, hogy egy problémát megoldjon bizonyos adatok alapján, **gépi tanulásnak** nevezzük. Ebben a kurzusban nem foglalkozunk a klasszikus gépi tanulással – erre külön [Gépi tanulás kezdőknek](http://aka.ms/ml-beginners) tananyagot ajánlunk. | ![ML kezdőknek](../../../../translated_images/hu/ml-for-beginners.9e4fed176fd5817d.png) | ## A mesterséges intelligencia rövid története A mesterséges intelligencia mint terület a huszadik század közepén indult. Kezdetben a szimbolikus érvelés volt az uralkodó megközelítés, és számos fontos sikert eredményezett, például szakértői rendszereket – számítógépes programokat, amelyek képesek voltak szakértőként működni bizonyos korlátozott problématerületeken. Azonban hamar világossá vált, hogy ez a megközelítés nem skálázható jól. A tudás kinyerése egy szakértőtől, annak számítógépes ábrázolása és a tudásbázis pontosan tartása rendkívül összetett feladatnak bizonyult, és sok esetben túl drága volt ahhoz, hogy gyakorlati legyen. Ez az úgynevezett [MI télhez](https://en.wikipedia.org/wiki/AI_winter) vezetett az 1970-es években. -A mesterséges intelligencia rövid története +A mesterséges intelligencia rövid története > Kép: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hu/lessons/2-Symbolic/Animals.ipynb b/translations/hu/lessons/2-Symbolic/Animals.ipynb index d0c1fee9..9e40c0cf 100644 --- a/translations/hu/lessons/2-Symbolic/Animals.ipynb +++ b/translations/hu/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Ebben a példában egy egyszerű tudásalapú rendszert valósítunk meg, amely fizikai jellemzők alapján határozza meg az állatot. A rendszert az alábbi AND-OR fa képviseli (ez csak egy része a teljes fának, könnyen hozzáadhatunk további szabályokat):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hu.png)\n" + "![](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/hu/lessons/2-Symbolic/README.md b/translations/hu/lessons/2-Symbolic/README.md index 4b58a5cc..991fe72a 100644 --- a/translations/hu/lessons/2-Symbolic/README.md +++ b/translations/hu/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tudásábrázolás és szakértői rendszerek -![A szimbolikus AI tartalmának összefoglalása](../../../../translated_images/ai-symbolic.715a30cb610411a6.hu.png) +![A szimbolikus AI tartalmának összefoglalása](../../../../translated_images/hu/ai-symbolic.715a30cb610411a6.png) > Sketchnote készítette: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Leggyakrabban nem határozzuk meg szigorúan a tudást, hanem más kapcsolódó Így a **tudásábrázolás** problémája az, hogy hatékony módot találjunk a tudás számítógépen belüli adatként való ábrázolására, hogy automatikusan használható legyen. Ez egy spektrumként értelmezhető: -![Tudásábrázolási spektrum](../../../../translated_images/knowledge-spectrum.b60df631852c0217.hu.png) +![Tudásábrázolási spektrum](../../../../translated_images/hu/knowledge-spectrum.b60df631852c0217.png) > Kép készítette: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokk szintaxis | Indent | | | A szimbolikus AI korai sikerei közé tartoztak az úgynevezett **szakértői rendszerek** - olyan számítógépes rendszerek, amelyeket arra terveztek, hogy egy korlátozott problématerületen szakértőként működjenek. Ezek egy **tudásbázison** alapultak, amelyet egy vagy több emberi szakértőtől nyertek ki, és tartalmaztak egy **következtető motort**, amely ezen tudás alapján végzett következtetéseket. -![Emberi architektúra](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.hu.png) | ![Tudásalapú rendszer](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.hu.png) +![Emberi architektúra](../../../../translated_images/hu/arch-human.5d4d35f1bba3ab1c.png) | ![Tudásalapú rendszer](../../../../translated_images/hu/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Az emberi idegrendszer egyszerűsített szerkezete | Tudásalapú rendszer architektúrája @@ -106,7 +106,7 @@ A szakértői rendszerek felépítése hasonló az emberi következtetési rends Példaként vegyük a következő szakértői rendszert, amely egy állatot határoz meg fizikai jellemzői alapján: -![AND-OR fa](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.hu.png) +![AND-OR fa](../../../../translated_images/hu/AND-OR-Tree.5592d2c70187f283.png) > Kép készítette: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 3c643d88..ddfc9354 100644 --- a/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/hu/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Ha több mint 2 osztályunk van, a softmax normalizálja a valószínűségeket mindegyik között. Itt van egy diagram a hálózati architektúráról, amely MNIST számjegy osztályozást végez:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.hu.png)\n" + "![MNIST Classifier](../../../../../translated_images/hu/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1257,7 +1257,7 @@ "* Alacsony tanulási veszteség – a modell jól tudja közelíteni a tanulási adatokat, mert elegendő kifejezőerővel rendelkezik.\n", "* Az érvényesítési veszteség sokkal magasabb lehet, mint a tanulási veszteség, és az edzés során növekedhet is – ez azért van, mert a modell \"megjegyzi\" a tanulási pontokat, és elveszíti az \"összképet\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.hu.png)\n", + "![Overfitting](../../../../../translated_images/hu/overfit.a0bd57f717c15769.png)\n", "\n", "> Ezen a képen az `x` a tanulási adatokat, az `o` az érvényesítési adatokat jelöli. Bal oldalon – lineáris modell (egyrétegű), amely elég jól közelíti az adatok természetét. Jobb oldalon – túlilleszkedett modell, amely tökéletesen közelíti a tanulási adatokat, de bármilyen más adathalmaz esetén (érvényesítési hiba nagyon magas) már nem működik megfelelően.\n" ] diff --git a/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md index 852ce12c..1759b562 100644 --- a/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/hu/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Az overfitting rendkívül fontos fogalom a gépi tanulásban, és nagyon fontos Vegyük például az alábbi problémát, amelyben 5 pontot próbálunk közelíteni (a grafikonokon `x` jelöli a pontokat): -![lineáris](../../../../../translated_images/overfit1.f24b71c6f652e59e.hu.jpg) | ![overfitting](../../../../../translated_images/overfit2.131f5800ae10ca5e.hu.jpg) +![lineáris](../../../../../translated_images/hu/overfit1.f24b71c6f652e59e.jpg) | ![overfitting](../../../../../translated_images/hu/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineáris modell, 2 paraméter** | **Nemlineáris modell, 7 paraméter** Tanítási hiba = 5.3 | Tanítási hiba = 0 @@ -79,7 +79,7 @@ Nagyon fontos megtalálni a megfelelő egyensúlyt a modell gazdagsága (paramé Ahogy a fenti grafikonon látható, az overfittinget nagyon alacsony tanítási hiba és magas validációs hiba jelezheti. Általában a tanítás során mind a tanítási, mind a validációs hibák csökkenni kezdenek, majd egy ponton a validációs hiba megállhat a csökkenésben, és növekedni kezdhet. Ez az overfitting jele, és annak indikátora, hogy valószínűleg abba kell hagynunk a tanítást (vagy legalábbis készítenünk kell egy pillanatképet a modellről). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.hu.png) +![overfitting](../../../../../translated_images/hu/Overfitting.408ad91cd90b4371.png) ## Hogyan előzhető meg az overfitting? diff --git a/translations/hu/lessons/3-NeuralNetworks/README.md b/translations/hu/lessons/3-NeuralNetworks/README.md index 4f77cf84..ddf264df 100644 --- a/translations/hu/lessons/3-NeuralNetworks/README.md +++ b/translations/hu/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bevezetés a neurális hálózatokba -![Összefoglaló a neurális hálózatok bevezetőjének tartalmáról egy rajzban](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.hu.png) +![Összefoglaló a neurális hálózatok bevezetőjének tartalmáról egy rajzban](../../../../translated_images/hu/ai-neuralnetworks.1c687ae40bc86e83.png) Ahogy a bevezetőben tárgyaltuk, az intelligencia egyik elérési módja egy **számítógépes modell** vagy egy **mesterséges agy** tanítása. A 20. század közepe óta a kutatók különböző matematikai modelleket próbáltak ki, míg az utóbbi években ez az irány rendkívül sikeresnek bizonyult. Az agy ilyen matematikai modelljeit **neurális hálózatoknak** nevezzük. @@ -36,13 +36,13 @@ Ebben a tananyagban kizárólag neurális hálózati modellekre fogunk összpont A biológiából tudjuk, hogy az agyunk neurális sejtekből (neuronokból) áll, amelyek mindegyike több "bemenettel" (dendritek) és egyetlen "kimenettel" (axon) rendelkezik. Mind a dendritek, mind az axonok képesek elektromos jeleket vezetni, és a köztük lévő kapcsolatok — szinapszisok — különböző vezetőképességet mutathatnak, amelyeket neurotranszmitterek szabályoznak. -![Neuron modellje](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.hu.jpg) | ![Neuron modellje](../../../../translated_images/artneuron.1a5daa88d20ebe6f.hu.png) +![Neuron modellje](../../../../translated_images/hu/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neuron modellje](../../../../translated_images/hu/artneuron.1a5daa88d20ebe6f.png) ----|---- Valódi neuron *([Kép](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) a Wikipédiáról)* | Mesterséges neuron *(Kép a szerzőtől)* Így a neuron legegyszerűbb matematikai modellje több bemenetet tartalmaz X1, ..., XN, egy kimenetet Y, valamint egy sor súlyt W1, ..., WN. A kimenet a következőképpen számítható: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ahol f egy nemlineáris **aktivációs függvény**. diff --git a/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md index 68175770..0686aa2a 100644 --- a/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/hu/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Az [OpenCV Notebook](OpenCV.ipynb) példákban bemutatjuk, hogy a számítógép * **Egy Braille könyv fényképének előfeldolgozása**. Arra összpontosítunk, hogyan használhatjuk a küszöbérték alkalmazást, jellemzők detektálását, perspektíva transzformációt és NumPy manipulációkat az egyes Braille szimbólumok elkülönítésére, hogy azokat később neurális hálózat osztályozza. -![Braille kép](../../../../../translated_images/braille.341962ff76b1bd70.hu.jpeg) | ![Braille kép előfeldolgozva](../../../../../translated_images/braille-result.46530fea020b03c7.hu.png) | ![Braille szimbólumok](../../../../../translated_images/braille-symbols.0159185ab69d5339.hu.png) +![Braille kép](../../../../../translated_images/hu/braille.341962ff76b1bd70.jpeg) | ![Braille kép előfeldolgozva](../../../../../translated_images/hu/braille-result.46530fea020b03c7.png) | ![Braille szimbólumok](../../../../../translated_images/hu/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből * **Mozgás detektálása videóban képkocka különbséggel**. Ha a kamera fix, akkor a kamera képkockái elég hasonlóak kell legyenek egymáshoz. Mivel a képkockák tömbként vannak ábrázolva, egyszerűen a két egymást követő képkocka tömbjeinek kivonásával megkapjuk a pixelkülönbséget, amely alacsony lesz statikus képkockák esetén, és magasabb lesz, ha jelentős mozgás van a képen. -![Videó képkockák és képkocka különbségek képe](../../../../../translated_images/frame-difference.706f805491a0883c.hu.png) +![Videó képkockák és képkocka különbségek képe](../../../../../translated_images/hu/frame-difference.706f805491a0883c.png) > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből @@ -89,7 +89,7 @@ Az [OpenCV Notebook](OpenCV.ipynb) példákban bemutatjuk, hogy a számítógép - **Sűrű optikai áramlás** kiszámítja a vektormezőt, amely megmutatja, hogy minden pixel hova mozog - **Ritka optikai áramlás** az alapján működik, hogy néhány jellegzetes jellemzőt vesz a képen (pl. élek), és ezek pályáját építi fel képkockáról képkockára. -![Optikai áramlás képe](../../../../../translated_images/optical.1f4a94464579a83a.hu.png) +![Optikai áramlás képe](../../../../../translated_images/hu/optical.1f4a94464579a83a.png) > Kép az [OpenCV.ipynb](OpenCV.ipynb)-ből diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index a077826d..338108dc 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: A VGG-16 egy hálózat, amely 2014-ben 92,7%-os pontosságot ért el az ImageNet top-5 osztályozásban. Az alábbi rétegstruktúrával rendelkezik: -![ImageNet rétegek](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hu.jpg) +![ImageNet rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.jpg) Ahogy látható, a VGG egy hagyományos piramis architektúrát követ, amely egy konvolúciós és pooling rétegek sorozata. -![ImageNet piramis](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hu.jpg) +![ImageNet piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.jpg) > Kép forrása: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index ea1c0f79..03043789 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Így egy tipikus CNN-ben több konvolúciós réteg található, amelyek között pooling rétegek csökkentik a kép dimenzióit. Emellett növeljük a szűrők számát is, mivel ahogy a minták egyre összetettebbé válnak, több érdekes kombinációt kell keresnünk.\n", "\n", - "![Egy ábra, amely több konvolúciós réteget és pooling réteget mutat be.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.hu.png)\n", + "![Egy ábra, amely több konvolúciós réteget és pooling réteget mutat be.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "A térbeli dimenziók csökkenése és a jellemzők/szűrők dimenzióinak növekedése miatt ezt az architektúrát **piramis architektúrának** is nevezik.\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index c1b9ada1..4d3e1f79 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Így egy tipikus CNN-ben több konvolúciós réteg található, közöttük pooling rétegekkel, amelyek csökkentik a kép dimenzióit. Emellett növeljük a szűrők számát is, mert ahogy a minták bonyolultabbá válnak, több érdekes kombinációt kell keresnünk.\n", "\n", - "![Egy kép, amely több konvolúciós réteget mutat pooling rétegekkel.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.hu.png)\n", + "![Egy kép, amely több konvolúciós réteget mutat pooling rétegekkel.](../../../../../translated_images/hu/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "A térbeli dimenziók csökkenése és a jellemzők/szűrők dimenzióinak növekedése miatt ezt az architektúrát **piramis architektúrának** is nevezik.\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md index 24722281..54b62c33 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ A valóságban azt szeretnénk, hogy képesek legyünk felismerni tárgyakat egy A mintázatok kinyeréséhez a **konvolúciós szűrők** fogalmát használjuk. Mint tudjuk, egy kép egy 2D-mátrixként vagy egy színes mélységgel rendelkező 3D-tenzorként van ábrázolva. Egy szűrő alkalmazása azt jelenti, hogy veszünk egy viszonylag kicsi **szűrőmag** mátrixot, és az eredeti kép minden egyes pixelénél kiszámítjuk a súlyozott átlagot a szomszédos pontokkal. Ezt úgy képzelhetjük el, mint egy kis ablakot, amely végigcsúszik az egész képen, és az összes pixelt az ablakban lévő súlyok szerint átlagolja. -![Függőleges él szűrő](../../../../../translated_images/filter-vert.b7148390ca0bc356.hu.png) | ![Vízszintes él szűrő](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.hu.png) +![Függőleges él szűrő](../../../../../translated_images/hu/filter-vert.b7148390ca0bc356.png) | ![Vízszintes él szűrő](../../../../../translated_images/hu/filter-horiz.59b80ed4feb946ef.png) ----|---- > Kép: Dmitry Soshnikov @@ -38,7 +38,7 @@ A CNN-ek működése a következő fontos ötleteken alapul: * A hálózatot úgy tervezhetjük meg, hogy a szűrők automatikusan tanuljanak. * Ugyanezt a megközelítést használhatjuk magas szintű jellemzők mintázatainak megtalálására is, nem csak az eredeti képen. Így a CNN jellemzők kinyerése egy hierarchikus folyamatban működik, az alacsony szintű pixelkombinációktól kezdve a kép részeinek magasabb szintű kombinációjáig. -![Hierarchikus jellemzők kinyerése](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.hu.png) +![Hierarchikus jellemzők kinyerése](../../../../../translated_images/hu/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Kép a [Hislop-Lynch tanulmányból](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), az [ő kutatásuk alapján](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A legtöbb képfeldolgozásra használt CNN az úgynevezett piramis architektúr Példaként nézzük meg a VGG-16 architektúráját, amely 92,7%-os pontosságot ért el az ImageNet top-5 osztályozásában 2014-ben: -![ImageNet Rétegek](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.hu.jpg) +![ImageNet Rétegek](../../../../../translated_images/hu/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Piramis](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.hu.jpg) +![ImageNet Piramis](../../../../../translated_images/hu/vgg-16-arch.64ff2137f50dd49f.jpg) > Kép a [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) oldalról diff --git a/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9d7c5343..1687a793 100644 --- a/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/hu/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Egy konvolúciós neurális hálózatot kell betanítanod, amely képes különb Az [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) adathalmazt fogjuk használni, amely 37 különböző kutya- és macskafajta képeit tartalmazza. -![Az adathalmaz, amellyel dolgozni fogunk](../../../../../../translated_images/data.50b2a9d5484bdbf0.hu.png) +![Az adathalmaz, amellyel dolgozni fogunk](../../../../../../translated_images/hu/data.50b2a9d5484bdbf0.png) Az adathalmaz letöltéséhez használd az alábbi kódrészletet: diff --git a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 2fe0b513..89cf3509 100644 --- a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Ahhoz, hogy elképzeljük az ideális macskát, egy véletlenszerű zajképpel kezdünk, majd a gradiens-descent optimalizációs technikát alkalmazzuk, hogy a képet úgy módosítsuk, hogy a hálózat felismerjen egy macskát.\n", "\n", - "![Optimalizációs ciklus](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hu.png)\n", + "![Optimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Íme a kiindulási képünk:\n" ] diff --git a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md index 8435c069..3cb68acc 100644 --- a/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/hu/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Mind a Keras, mind a PyTorch tartalmaz funkciókat, amelyekkel könnyen betölth Íme egy példa a VGG-16 hálózat által egy macska képéből kinyert jellemzőkre: -![A VGG-16 által kinyert jellemzők](../../../../../translated_images/features.6291f9c7ba3a0b95.hu.png) +![A VGG-16 által kinyert jellemzők](../../../../../translated_images/hu/features.6291f9c7ba3a0b95.png) ## Macskák és kutyák adathalmaz @@ -48,19 +48,19 @@ Egy előre betanított neurális hálózat különböző mintákat tartalmaz az Egy megközelítés az lehet, hogy egy véletlenszerű képpel kezdünk, majd a **gradiens-deszcendens optimalizációs** technikát alkalmazzuk, hogy úgy módosítsuk a képet, hogy a hálózat elkezdje azt macskának gondolni. -![Képoptimalizációs ciklus](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.hu.png) +![Képoptimalizációs ciklus](../../../../../translated_images/hu/ideal-cat-loop.999fbb8ff306e044.png) Ha azonban ezt tesszük, akkor valami nagyon hasonlót kapunk, mint egy véletlenszerű zaj. Ennek oka, hogy *sokféleképpen lehet a hálózatot rávenni arra, hogy a bemeneti képet macskának gondolja*, beleértve olyanokat is, amelyek vizuálisan nem értelmezhetők. Bár ezek a képek sok, a macskákra jellemző mintát tartalmaznak, semmi sem kényszeríti őket arra, hogy vizuálisan megkülönböztethetők legyenek. Az eredmény javítása érdekében hozzáadhatunk egy másik tagot a veszteségfüggvényhez, amelyet **variációs veszteségnek** nevezünk. Ez egy olyan metrika, amely megmutatja, mennyire hasonlóak a kép szomszédos pixelei. A variációs veszteség minimalizálása simábbá teszi a képet, és megszabadítja a zajtól – így vizuálisan vonzóbb mintákat tár fel. Íme egy példa az ilyen "ideális" képekre, amelyeket nagy valószínűséggel macskának és zebrának osztályoznak: -![Ideális macska](../../../../../translated_images/ideal-cat.203dd4597643d6b0.hu.png) | ![Ideális zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.hu.png) +![Ideális macska](../../../../../translated_images/hu/ideal-cat.203dd4597643d6b0.png) | ![Ideális zebra](../../../../../translated_images/hu/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideális macska* | *Ideális zebra* Hasonló megközelítést lehet alkalmazni úgynevezett **adverzális támadások** végrehajtására egy neurális hálózaton. Tegyük fel, hogy szeretnénk megtéveszteni egy neurális hálózatot, és egy kutyát macskának láttatni. Ha veszünk egy kutya képét, amelyet a hálózat kutyaként ismer fel, akkor azt egy kicsit módosíthatjuk a gradiens-deszcendens optimalizáció segítségével, amíg a hálózat macskaként nem kezdi osztályozni: -![Kép egy kutyáról](../../../../../translated_images/original-dog.8f68a67d2fe0911f.hu.png) | ![Kép egy kutyáról, amelyet macskának osztályoznak](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.hu.png) +![Kép egy kutyáról](../../../../../translated_images/hu/original-dog.8f68a67d2fe0911f.png) | ![Kép egy kutyáról, amelyet macskának osztályoznak](../../../../../translated_images/hu/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Eredeti kép egy kutyáról* | *Kép egy kutyáról, amelyet macskának osztályoznak* diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index babcb0c8..5dc85e1d 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Mivel az autoencoder-t arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadására.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hu.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Kép forrása: [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index f1d89252..33dfda25 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Mivel az autoencoder-t arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadásához.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hu.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Kép a [Keras blogból](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md index 209ea9ad..e21d980e 100644 --- a/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/hu/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Azonban előfordulhat, hogy nyers (címkézetlen) adatokat szeretnénk használn Mivel az autoenkódert arra tanítjuk, hogy minél több információt megőrizzen az eredeti képből a pontos rekonstrukció érdekében, a hálózat megpróbálja megtalálni a legjobb **beágyazást** a bemeneti képek jelentésének megragadásához. -![Autoenkóder diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.hu.jpg) +![Autoenkóder diagram](../../../../../translated_images/hu/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Kép a [Keras blogból](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md index 3cddfcfb..1ca30e95 100644 --- a/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/hu/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Az eddig tárgyalt képosztályozási modellek egy képet vettek bemenetként, ## [Előadás előtti kvíz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektumfelismerés](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.hu.png) +![Objektumfelismerés](../../../../../translated_images/hu/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Kép a [YOLO v2 weboldaláról](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tegyük fel, hogy egy képen szeretnénk megtalálni egy macskát. Egy nagyon eg 2. Képosztályozást futtatunk minden csempén. 3. Azok a csempék, amelyeknél elég magas aktivációt kapunk, tartalmazhatják a keresett objektumot. -![Naiv objektumfelismerés](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.hu.png) +![Naiv objektumfelismerés](../../../../../translated_images/hu/naive-detection.e7f1ba220ccd08c6.png) > *Kép az [Exercise Notebook](ObjectDetection-TF.ipynb)-ból* @@ -42,7 +42,7 @@ Az alábbi adatállományokkal találkozhatsz ezen a területen: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 osztály * [COCO](http://cocodataset.org/#home) - Közönséges tárgyak kontextusban. 80 osztály, körvonalak és szegmentációs maszkok -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.hu.jpg) +![COCO](../../../../../translated_images/hu/coco-examples.71bc60380fa6cceb.jpg) ## Objektumfelismerési metrikák @@ -50,7 +50,7 @@ Az alábbi adatállományokkal találkozhatsz ezen a területen: Míg a képosztályozásnál könnyű mérni az algoritmus teljesítményét, az objektumfelismerésnél nemcsak az osztály helyességét kell mérni, hanem az előre jelzett körvonal helyének pontosságát is. Ehhez az úgynevezett **Metszet az unióhoz viszonyítva** (IoU) metrikát használjuk, amely azt méri, hogy két doboz (vagy két tetszőleges terület) mennyire fedik egymást. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.hu.png) +![IoU](../../../../../translated_images/hu/iou_equation.9a4751d40fff4e11.png) > *2. ábra [ebből a kiváló blogbejegyzésből az IoU-ról](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Az objektumfelismerési algoritmusoknak két fő típusa van: Az [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) a [Szelektív Keresést](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) használja, hogy hierarchikus ROI régiókat generáljon, amelyeket aztán CNN jellemzőkivonókon és SVM-osztályozókon futtatunk, hogy meghatározzuk az objektum osztályát, valamint lineáris regresszióval a *körvonal* koordinátáit. [Hivatalos tanulmány](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.hu.png) +![RCNN](../../../../../translated_images/hu/rcnn1.cae407020dfb1d1f.png) > *Kép van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.hu.png) +![RCNN-1](../../../../../translated_images/hu/rcnn2.2d9530bb83516484.png) > *Képek [ebből a blogból](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Az [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) a [Sze Ez a megközelítés hasonló az R-CNN-hez, de a régiókat a konvolúciós rétegek alkalmazása után határozzuk meg. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.hu.png) +![FRCNN](../../../../../translated_images/hu/f-rcnn.3cda6d9bb4188875.png) > Kép a [Hivatalos tanulmányból](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ez a megközelítés hasonló az R-CNN-hez, de a régiókat a konvolúciós rét Ennek a megközelítésnek az alapötlete, hogy neurális hálózatot használunk az ROI-k előrejelzésére - az úgynevezett *Régiójavasló Hálózat*. [Tanulmány](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.hu.png) +![FasterRCNN](../../../../../translated_images/hu/faster-rcnn.8d46c099b87ef30a.png) > Kép a [hivatalos tanulmányból](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ez az algoritmus még gyorsabb, mint a Gyorsabb R-CNN. Az alapötlet a következ 2. A jellemzőket **Pozíció-Érzékeny Pontszám Térképen** dolgozzuk fel. Minden objektumot $C$ osztályból $k\times k$ régiókra osztunk, és az objektumok részeinek előrejelzésére tanítjuk a hálózatot. 3. Minden részre a $k\times k$ régiókból a hálózatok szavaznak az objektumosztályokra, és a maximális szavazatot kapó osztályt választjuk. -![r-fcn kép](../../../../../translated_images/r-fcn.13eb88158b99a3da.hu.png) +![r-fcn kép](../../../../../translated_images/hu/r-fcn.13eb88158b99a3da.png) > Kép a [hivatalos tanulmányból](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ A YOLO egy valós idejű egyszeri futtatású algoritmus. Az alapötlet a követ * A képet $S\times S$ régiókra osztjuk. * Minden régióra **CNN** előrejelzi $n$ lehetséges objektumot, *körvonal* koordinátákat és *bizalmi szintet*=*valószínűség* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.hu.png) + ![YOLO](../../../../../translated_images/hu/yolo.a2648ec82ee8bb4e.png) > Kép a [hivatalos tanulmányból](https://arxiv.org/abs/1506.02640) diff --git a/translations/hu/lessons/4-ComputerVision/README.md b/translations/hu/lessons/4-ComputerVision/README.md index 34a8077e..0a01e4fb 100644 --- a/translations/hu/lessons/4-ComputerVision/README.md +++ b/translations/hu/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Számítógépes látás -![A számítógépes látás tartalmának összefoglalása egy rajzban](../../../../translated_images/ai-computervision.6506ebebac3fbf76.hu.png) +![A számítógépes látás tartalmának összefoglalása egy rajzban](../../../../translated_images/hu/ai-computervision.6506ebebac3fbf76.png) Ebben a részben megtanuljuk: diff --git a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 2439c87b..319d05be 100644 --- a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "A **Szavak zsákja** (BoW) vektorábrázolás a leggyakrabban használt hagyományos vektorábrázolás. Minden szó egy vektorindexhez van rendelve, a vektor elemei pedig azt mutatják, hogy egy adott dokumentumban hányszor fordul elő az adott szó.\n", "\n", - "![Kép, amely bemutatja, hogyan van ábrázolva a szavak zsákja vektor a memóriában.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.hu.png) \n", + "![Kép, amely bemutatja, hogyan van ábrázolva a szavak zsákja vektor a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Megjegyzés**: A BoW-t úgy is elképzelheted, mint az egyes szavak egy-egy one-hot-kódolt vektorának összegét a szövegben.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 7ad6be1f..d5fdebd1 100644 --- a/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/hu/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "A **szótásképzés** (BoW) vektori ábrázolás a legegyszerűbben érthető hagyományos vektori ábrázolás. Minden szó egy vektor indexhez van kötve, és a vektor elemei azt mutatják, hogy egy adott dokumentumban hányszor fordul elő az adott szó.\n", "\n", - "![Kép, amely bemutatja, hogyan van ábrázolva a szótásképzés vektori reprezentációja a memóriában.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.hu.png) \n", + "![Kép, amely bemutatja, hogyan van ábrázolva a szótásképzés vektori reprezentációja a memóriában.](../../../../../translated_images/hu/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Megjegyzés**: A BoW-t úgy is elképzelhetjük, mint az egyes szavak egy-egy one-hot kódolt vektorának összegét a szövegben.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 171a8955..39ba1655 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ha az embedding réteget használjuk hálózatunk első rétegeként, akkor átállhatunk a bag-of-words modellről az **embedding bag** modellre. Ebben először minden szót a szövegünkben a megfelelő embeddingre konvertálunk, majd valamilyen aggregáló függvényt számítunk ki az összes embedding felett, például `sum`, `average` vagy `max`.\n", "\n", - "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hu.png)\n", + "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Az osztályozó neurális hálózatunk embedding réteggel kezdődik, majd egy aggregáló réteggel, és végül egy lineáris osztályozóval a tetején:\n" ] @@ -176,7 +176,7 @@ "\n", "Az előző architektúrában minden szekvenciát ugyanarra a hosszra kellett kiegészíteni, hogy illeszkedjenek egy minibatch-be. Ez nem a leghatékonyabb módja a változó hosszúságú szekvenciák reprezentálásának – egy másik megközelítés az **offset** vektor használata, amely egy nagy vektorban tárolt összes szekvencia eltolásait tartalmazza.\n", "\n", - "![Kép, amely egy offset szekvencia reprezentációt mutat](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.hu.png)\n", + "![Kép, amely egy offset szekvencia reprezentációt mutat](../../../../../translated_images/hu/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: A fenti képen karakterek szekvenciáját mutatjuk, de példánkban szavak szekvenciáival dolgozunk. Azonban a szekvenciák offset vektorral történő reprezentálásának általános elve ugyanaz marad.\n", "\n", @@ -311,7 +311,7 @@ "\n", "A CBoW gyorsabb, míg a skip-gram lassabb, de jobban reprezentálja a ritkábban előforduló szavakat.\n", "\n", - "![Kép, amely a CBoW és a Skip-Gram algoritmusokat mutatja be a szavak vektorokká alakításához.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hu.png)\n", + "![Kép, amely a CBoW és a Skip-Gram algoritmusokat mutatja be a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Ahhoz, hogy kísérletezzünk a Google News adathalmazon előre betanított word2vec beágyazással, használhatjuk a **gensim** könyvtárat. Az alábbiakban megkeressük a 'neural' szóhoz leginkább hasonló szavakat.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a86d7d09..1219c034 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/hu/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ha az embedding réteget használjuk a hálózatunk első rétegeként, akkor áttérhetünk a bag-of-words modellről egy **embedding bag** modellre. Ebben az esetben először minden szót a szövegünkben a megfelelő embeddingre alakítunk, majd valamilyen aggregáló függvényt számítunk ki az összes embedding felett, például `sum`, `average` vagy `max`.\n", "\n", - "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hu.png)\n", + "![Kép, amely egy embedding osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Az osztályozó neurális hálózatunk a következő rétegekből áll:\n", "\n", @@ -283,7 +283,7 @@ "\n", "A CBoW gyorsabb, míg az ugrógram lassabb, de jobban reprezentálja a ritka szavakat.\n", "\n", - "![Kép, amely bemutatja a CBoW és az ugrógram algoritmusokat a szavak vektorokká alakításához.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hu.png)\n", + "![Kép, amely bemutatja a CBoW és az ugrógram algoritmusokat a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Ahhoz, hogy kísérletezzünk a Google News adathalmazon előtanított Word2Vec beágyazással, használhatjuk a **gensim** könyvtárat. Az alábbiakban megkeressük a 'neural' szóhoz leginkább hasonló szavakat.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/14-Embeddings/README.md b/translations/hu/lessons/5-NLP/14-Embeddings/README.md index 19e992e0..4f9bb829 100644 --- a/translations/hu/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/hu/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ A beágyazási réteg tehát egy szót vesz bemenetként, és egy meghatározott Ha a beágyazási réteget használjuk osztályozó hálózatunk első rétegeként, akkor a szavak zsákja modellről áttérhetünk az **embedding bag** modellre, ahol először minden szót a megfelelő beágyazásra alakítunk, majd valamilyen aggregált függvényt számítunk ki az összes beágyazás felett, például `sum`, `average` vagy `max`. -![Kép, amely egy beágyazási osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.hu.png) +![Kép, amely egy beágyazási osztályozót mutat öt szekvencia szóra.](../../../../../translated_images/hu/embedding-classifier-example.b77f021a7ee67eee.png) > Kép a szerzőtől @@ -40,7 +40,7 @@ Ehhez elő kell tanítanunk a beágyazási modellünket egy nagy szöveggyűjtem A CBoW gyorsabb, míg a skip-gram lassabb, de jobban reprezentálja a ritka szavakat. -![Kép, amely a CBoW és Skip-Gram algoritmusokat mutatja a szavak vektorokká alakításához.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hu.png) +![Kép, amely a CBoW és Skip-Gram algoritmusokat mutatja a szavak vektorokká alakításához.](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Kép ebből a [tanulmányból](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md b/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md index e9301184..e23b1cb3 100644 --- a/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/hu/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Korábbi példáinkban előre betanított szemantikai beágyazásokat használtu * **Folytonos Szózsák** (CBoW), amikor a középső token-t $W_0$ jósoljuk meg egy token sorozatban $W_{-N}$, ..., $W_N$. * **Skip-gram**, ahol a középső token $W_0$ alapján egy szomszédos tokenek halmazát {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} jósoljuk meg. -![kép a szavak vektorokká alakításáról szóló tanulmányból](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.hu.png) +![kép a szavak vektorokká alakításáról szóló tanulmányból](../../../../../translated_images/hu/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Kép [ebből a tanulmányból](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/hu/lessons/5-NLP/16-RNN/README.md b/translations/hu/lessons/5-NLP/16-RNN/README.md index 8fc2cf35..0f23e583 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/README.md +++ b/translations/hu/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Az előző szekciókban gazdag szemantikai reprezentációkat használtunk a sz Ahhoz, hogy a szövegszekvencia jelentését megragadjuk, egy másik neurális hálózati architektúrát kell használnunk, amelyet **rekurrens neurális hálózatnak** (RNN) nevezünk. Az RNN-ben mondatunkat egy szimbólumonként adjuk át a hálózaton, és a hálózat egy **állapotot** hoz létre, amelyet aztán a következő szimbólummal együtt újra átadunk a hálózatnak. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.hu.png) +![RNN](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.png) > Kép a szerzőtől @@ -61,7 +61,7 @@ Olyan rekurrens hálózatokat tárgyaltunk, amelyek egy irányban működnek, a Egy rekurrens hálózat, akár egyirányú, akár kétirányú, bizonyos mintákat ragad meg egy szekvenciában, és ezeket az állapotvektorba menti vagy a kimenetbe továbbítja. Akárcsak a konvolúciós hálózatok esetében, egy másik rekurrens réteget építhetünk az első fölé, hogy magasabb szintű mintákat ragadjunk meg, és az első réteg által kinyert alacsony szintű mintákból építkezzünk. Ez vezet minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő rétegbe. -![Többrétegű hosszú-rövid távú memória RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hu.jpg) +![Többrétegű hosszú-rövid távú memória RNN](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Kép Fernando López [ezen csodálatos bejegyzéséből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 6fe9ad38..ca8e5bcb 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "A rekurrens hálózat, legyen az egyirányú vagy kétirányú, bizonyos mintázatokat rögzít egy szekvencián belül, és ezeket tárolhatja az állapotvektorban vagy továbbíthatja a kimenetbe. Akárcsak a konvolúciós hálózatok esetében, egy másik rekurrens réteget építhetünk az első fölé, hogy magasabb szintű mintázatokat rögzítsünk, amelyeket az első réteg által kinyert alacsony szintű mintázatokból építünk fel. Ez vezet el minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő réteghez.\n", "\n", - "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hu.jpg)\n", + "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Kép Fernando López [ezen csodálatos bejegyzéséből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)*\n", "\n", diff --git a/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb index e1555ced..4695c4b7 100644 --- a/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/hu/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Ahhoz, hogy egy szövegszekvencia jelentését megragadjuk, egy **rekurrens neurális hálózatnak** (angolul recurrent neural network, RNN) nevezett neurális hálózati architektúrát fogunk használni. Az RNN használatakor a mondatot egyesével, tokenenként vezetjük át a hálózaton, amely minden lépésben előállít egy **állapotot**, amit aztán a következő tokennel együtt ismét átadunk a hálózatnak.\n", "\n", - "![Kép, amely egy rekurrens neurális hálózat generálását mutatja.](../../../../../translated_images/rnn.27f5c29c53d727b5.hu.png)\n", + "![Kép, amely egy rekurrens neurális hálózat generálását mutatja.](../../../../../translated_images/hu/rnn.27f5c29c53d727b5.png)\n", "\n", "A tokenekből álló bemeneti szekvencia $X_0,\\dots,X_n$ alapján az RNN egy neurális hálózati blokkokból álló szekvenciát hoz létre, és ezt a szekvenciát végig, visszaterjesztéses tanulással (backpropagation) tanítja. Minden hálózati blokk egy $(X_i,S_i)$ párt kap bemenetként, és eredményként előállítja $S_{i+1}$-et. A végső állapot $S_n$ vagy a kimenet $Y_n$ egy lineáris osztályozóba kerül, amely előállítja az eredményt. Az összes hálózati blokk ugyanazokat a súlyokat osztja meg, és egyetlen visszaterjesztési lépés során tanulják meg azokat.\n", "\n", @@ -369,7 +369,7 @@ "\n", "A rekurrens hálózatok, legyenek egyirányúak vagy kétirányúak, mintákat ragadnak meg egy szekvenciában, és ezeket állapotvektorokba tárolják vagy kimenetként adják vissza. Akárcsak a konvolúciós hálózatok esetében, építhetünk egy másik rekurrens réteget az első után, hogy magasabb szintű mintákat ragadjunk meg, amelyeket az első réteg által kinyert alacsonyabb szintű mintákból építünk fel. Ez vezet el minket a **többrétegű RNN** fogalmához, amely két vagy több rekurrens hálózatból áll, ahol az előző réteg kimenete bemenetként kerül a következő réteghez.\n", "\n", - "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.hu.jpg)\n", + "![Kép egy többrétegű hosszú-rövid távú memória RNN-ről](../../../../../translated_images/hu/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Kép [ebből a nagyszerű bejegyzésből](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López tollából.*\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index a5a7dd04..38a5a945 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Az RNN szöveg generálására való tanításának módja a következő. Minden lépésben veszünk egy `nchars` hosszúságú karakterláncot, és megkérjük a hálózatot, hogy minden bemeneti karakterhez generálja a következő kimeneti karaktert:\n", "\n", - "![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hu.png)\n", + "![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png)\n", "\n", "A konkrét helyzettől függően előfordulhat, hogy speciális karaktereket is be kell vonnunk, például *sorvége* ``. A mi esetünkben azonban csak végtelen szöveg generálására szeretnénk tanítani a hálózatot, ezért minden szekvencia méretét fixen `nchars` tokenre állítjuk. Ennek megfelelően minden tanítási példában `nchars` bemenet és `nchars` kimenet lesz (a bemeneti szekvencia egy szimbólummal balra eltolva). Egy minibatch több ilyen szekvenciából fog állni.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index af878133..5756d20d 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Az RNN betanításának módja a hírcímek generálására a következő. Minden lépésben veszünk egy címet, amelyet betáplálunk egy RNN-be, és minden bemeneti karakterhez megkérjük a hálózatot, hogy generálja a következő kimeneti karaktert:\n", "\n", - "![Kép, amely bemutatja az 'HELLO' szó generálását egy RNN segítségével.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hu.png)\n", + "![Kép, amely bemutatja az 'HELLO' szó generálását egy RNN segítségével.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png)\n", "\n", "A szekvenciánk utolsó karakterénél megkérjük a hálózatot, hogy generálja a `` tokent.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md index 91c7e993..60b3fd37 100644 --- a/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/hu/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Az előző egységben tárgyalt RNN architektúrában minden RNN egység a köve Ez különböző neurális architektúrákat tesz lehetővé, amelyeket az alábbi képen láthatunk: -![Kép, amely a rekurrens neurális hálózatok gyakori mintázatait mutatja.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hu.jpg) +![Kép, amely a rekurrens neurális hálózatok gyakori mintázatait mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Kép Andrej Karpaty [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) című blogbejegyzéséből [Andrej Karpaty](http://karpathy.github.io/) által @@ -32,7 +32,7 @@ Ebben az egységben egyszerű generatív modellekre fogunk összpontosítani, am Ezt az RNN-t arra fogjuk tanítani, hogy lépésről lépésre generáljon szöveget. Minden lépésben egy `nchars` hosszúságú karakter sorozatot veszünk, és megkérjük a hálózatot, hogy generálja a következő kimeneti karaktert minden bemeneti karakterhez: -![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.hu.png) +![Kép, amely az 'HELLO' szó RNN általi generálását mutatja.](../../../../../translated_images/hu/rnn-generate.56c54afb52f9781d.png) Szöveg generálásakor (következtetés során) egy **indítószöveggel** kezdünk, amelyet RNN cellákon keresztül adunk át, hogy előállítsuk annak köztes állapotát, majd ebből az állapotból kezdődik a generálás. Egy karaktert generálunk egyszerre, és az állapotot és a generált karaktert átadjuk egy másik RNN cellának, hogy generálja a következőt, amíg elegendő karaktert nem generálunk. diff --git a/translations/hu/lessons/5-NLP/18-Transformers/README.md b/translations/hu/lessons/5-NLP/18-Transformers/README.md index 28624e1f..1f734222 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/README.md +++ b/translations/hu/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Az RNN-ekkel a sorozat-sorozat feladatot két rekurzív hálózat valósítja me A **figyelem mechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes bemeneti vektorok kontextuális hatását az RNN kimeneti előrejelzéseire. Ez úgy valósul meg, hogy rövidítéseket hozunk létre a bemeneti RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a yt kimeneti szimbólumot generáljuk, figyelembe vesszük az összes bemeneti rejtett állapotot hi, különböző súlyozási együtthatókkal αt,i. -![Kép egy enkóder/dekóder modellről additív figyelemréteggel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hu.png) +![Kép egy enkóder/dekóder modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png) > Az enkóder-dekóder modell additív figyelem mechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A figyelem mátrix {αi,j} azt mutatja, hogy a bemeneti szavak milyen mértékben játszanak szerepet egy adott szó generálásában a kimeneti sorozatban. Az alábbiakban egy ilyen mátrix példáját láthatjuk: -![Kép egy mintázott igazításról, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hu.png) +![Kép egy mintázott igazításról, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png) > Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) @@ -66,7 +66,7 @@ Az eredmény, amelyet a pozíciós beágyazással kapunk, beágyazza mind az ere Ezután meg kell ragadnunk néhány mintát a sorozatunkon belül. Ehhez a transzformerek **önfigyelem** mechanizmust használnak, amely lényegében figyelem, amelyet ugyanarra a sorozatra alkalmazunk bemenetként és kimenetként. Az önfigyelem alkalmazása lehetővé teszi számunkra, hogy figyelembe vegyük a mondaton belüli **kontekztust**, és lássuk, mely szavak kapcsolódnak egymáshoz. Például lehetővé teszi számunkra, hogy lássuk, mely szavakra utalnak visszautalások, mint például *az*, és figyelembe vegyük a kontextust is: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.hu.png) +![](../../../../../translated_images/hu/CoreferenceResolution.861924d6d384a7d6.png) > Kép a [Google Blogból](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Mivel minden bemeneti pozíciót függetlenül térképezünk a kimeneti pozíci A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transzformer hálózat, amelynek 12 rétege van a *BERT-base* esetében, és 24 a *BERT-large* esetében. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanulással (maszkolt szavak előrejelzése egy mondatban). Az előképzés során a modell jelentős nyelvi megértést szerez, amelyet más adathalmazokkal finomhangolással lehet kihasználni. Ezt a folyamatot **transzfer tanulásnak** nevezzük. -![kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hu.png) +![kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Kép [forrása](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 24180cb1..2ed55de2 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/hu/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Figyelemmechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes input vektorok kontextuális hatását az RNN minden egyes kimeneti előrejelzésére. Ez úgy valósul meg, hogy rövidítéseket hozunk létre az input RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a $y_t$ kimeneti szimbólumot generáljuk, figyelembe vesszük az összes input rejtett állapotot $h_i$, különböző súlykoefficiensekkel $\\alpha_{t,i}$. \n", "\n", - "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hu.png)\n", + "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png)\n", "*A kódoló-dekódoló modell additív figyelemmechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A figyelem mátrix $\\{\\alpha_{i,j}\\}$ azt mutatja, hogy az egyes input szavak milyen mértékben játszanak szerepet egy adott szó generálásában a kimeneti szekvenciában. Az alábbiakban egy ilyen mátrix példáját láthatjuk:\n", "\n", - "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hu.png)\n", + "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) alapján*\n", "\n", @@ -35,7 +35,7 @@ "\n", "A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transzformer hálózat, amelynek 12 rétege van a *BERT-base* esetében, és 24 a *BERT-large* esetében. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanítással (maszkolt szavak előrejelzése egy mondatban). Az előtanítás során a modell jelentős nyelvi megértést sajátít el, amelyet aztán más adathalmazokkal lehet finomhangolni. Ezt a folyamatot **transzfer tanulásnak** nevezzük. \n", "\n", - "![Kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hu.png)\n", + "![Kép a http://jalammar.github.io/illustrated-bert/ oldalról](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Számos transzformer architektúra létezik, például BERT, DistilBERT, BigBird, OpenGPT3 és még sok más, amelyek finomhangolhatók. A [HuggingFace csomag](https://github.com/huggingface/) lehetőséget biztosít ezeknek az architektúráknak a tanítására PyTorch segítségével. \n", "\n", diff --git a/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 88e8e75d..e9b7762b 100644 --- a/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/hu/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "A **figyelemmechanizmusok** lehetőséget adnak arra, hogy súlyozzuk az egyes input vektorok kontextuális hatását az RNN kimeneti előrejelzéseire. Ez úgy valósul meg, hogy rövidítéseket hozunk létre az input RNN köztes állapotai és a kimeneti RNN között. Ily módon, amikor a $y_t$ kimeneti szimbólumot generáljuk, figyelembe vesszük az összes input rejtett állapotot $h_i$, különböző súlykoefficiensekkel $\\alpha_{t,i}$. \n", "\n", - "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.hu.png)\n", + "![Kép egy kódoló/dekódoló modellről additív figyelemréteggel](../../../../../translated_images/hu/encoder-decoder-attention.7a726296894fb567.png)\n", "*A kódoló-dekódoló modell additív figyelemmechanizmussal [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) alapján, idézve [ebből a blogbejegyzésből](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "A figyelem mátrix $\\{\\alpha_{i,j}\\}$ azt mutatja, hogy az egyes input szavak milyen mértékben járulnak hozzá egy adott szó generálásához a kimeneti szekvenciában. Az alábbiakban egy ilyen mátrix példáját láthatjuk:\n", "\n", - "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.hu.png)\n", + "![Kép egy mintaillesztésről, amelyet az RNNsearch-50 talált, Bahdanau - arviz.org](../../../../../translated_images/hu/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Ábra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (3. ábra) alapján*\n", "\n", @@ -225,7 +225,7 @@ "\n", "A **BERT** (Bidirectional Encoder Representations from Transformers) egy nagyon nagy, többrétegű transformer hálózat, amely *BERT-base* esetén 12 rétegből, míg *BERT-large* esetén 24 rétegből áll. A modellt először egy nagy szövegkorpuszra (WikiPedia + könyvek) tanítják be felügyelet nélküli tanulással (maszkolt szavak előrejelzése egy mondatban). Az előképzés során a modell jelentős nyelvi megértést sajátít el, amelyet később más adathalmazokkal finomhangolással lehet hasznosítani. Ezt a folyamatot **transzfer tanulásnak** nevezzük.\n", "\n", - "![kép forrása: http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.hu.png)\n", + "![kép forrása: http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/hu/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Számos Transformer architektúra létezik, például BERT, DistilBERT, BigBird, OpenGPT3 és még sok más, amelyek finomhangolhatók.\n", "\n", diff --git a/translations/hu/lessons/5-NLP/19-NER/README.md b/translations/hu/lessons/5-NLP/19-NER/README.md index 85bf4042..e21c304e 100644 --- a/translations/hu/lessons/5-NLP/19-NER/README.md +++ b/translations/hu/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ csecsemőben | O Mivel egy-egy megfeleltetést kell létrehoznunk a tokenek és osztályok között, egy **sok-sokhoz** neurális hálózati modellt tudunk tanítani az alábbi ábráról: -![Kép, amely a gyakori rekurzív neurális hálózati mintákat mutatja.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.hu.jpg) +![Kép, amely a gyakori rekurzív neurális hálózati mintákat mutatja.](../../../../../translated_images/hu/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Kép [ebből a blogbejegyzésből](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) [Andrej Karpathy](http://karpathy.github.io/) tollából. A NER token klasszifikációs modellek megfelelnek az ábra jobb szélső hálózati architektúrájának.* diff --git a/translations/hu/lessons/5-NLP/README.md b/translations/hu/lessons/5-NLP/README.md index 9f714415..faf92568 100644 --- a/translations/hu/lessons/5-NLP/README.md +++ b/translations/hu/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Természetes Nyelvfeldolgozás -![Összefoglaló az NLP feladatokról egy rajzban](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.hu.png) +![Összefoglaló az NLP feladatokról egy rajzban](../../../../translated_images/hu/ai-nlp.b22dcb8ca4707cea.png) Ebben a részben a neurális hálózatok használatára összpontosítunk, hogy megoldjuk a **természetes nyelvfeldolgozással (NLP)** kapcsolatos feladatokat. Számos NLP probléma van, amelyeket szeretnénk, ha a számítógépek meg tudnának oldani: diff --git a/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md b/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md index 4869a5a6..584d2de9 100644 --- a/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/hu/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Megnyithatsz egy modellt, például **Biology → Flocking**. A modell megnyitása után a NetLogo fő képernyőjére kerülsz. Itt egy minta modell látható, amely a farkasok és juhok populációját írja le véges erőforrások (fű) mellett. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.hu.png) +![NetLogo Main Screen](../../../../../translated_images/hu/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov által készített képernyőkép diff --git a/translations/hu/lessons/README.md b/translations/hu/lessons/README.md index a76c7054..7dfdebdc 100644 --- a/translations/hu/lessons/README.md +++ b/translations/hu/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Áttekintés -![Áttekintés egy rajzban](../../../translated_images/ai-overview.0857791951d19500.hu.png) +![Áttekintés egy rajzban](../../../translated_images/hu/ai-overview.0857791951d19500.png) > Vázlatrajz: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/hu/lessons/X-Extras/X1-MultiModal/README.md b/translations/hu/lessons/X-Extras/X1-MultiModal/README.md index f4b3aa5b..cc012a4e 100644 --- a/translations/hu/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/hu/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ A transzformer modellek sikerét követően az NLP feladatok megoldásában, has A CLIP fő ötlete, hogy képes legyen összehasonlítani szöveges utasításokat egy képpel, és meghatározni, mennyire felel meg a kép az utasításnak. -![CLIP Architektúra](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.hu.png) +![CLIP Architektúra](../../../../../translated_images/hu/clip-arch.b3dbf20b4e8ed8be.png) > *Kép [ebből a blogbejegyzésből](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Miután a modellt betanították, megadhatunk neki egy batch képet és egy batc Tegyük fel, hogy képeket kell osztályoznunk például macskák, kutyák és emberek között. Ebben az esetben megadhatjuk a modellnek a képet, és egy sor szöveges utasítást: "*egy macska képe*", "*egy kutya képe*", "*egy ember képe*". A kapott 3 valószínűségi vektorban csak ki kell választanunk a legmagasabb értékű indexet. -![CLIP Képosztályozáshoz](../../../../../translated_images/clip-class.3af42ef0b2b19369.hu.png) +![CLIP Képosztályozáshoz](../../../../../translated_images/hu/clip-class.3af42ef0b2b19369.png) > *Kép [ebből a blogbejegyzésből](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ További információ a VQGAN-ról a [Taming Transformers](https://compvis.githu A VQGAN és a hagyományos GAN egyik fontos különbsége, hogy az utóbbi bármilyen bemeneti vektorból képes elfogadható képet előállítani, míg a VQGAN valószínűleg nem koherens képet hoz létre. Ezért tovább kell irányítanunk a képalkotási folyamatot, amit a CLIP segítségével tehetünk meg. -![VQGAN+CLIP Architektúra](../../../../../translated_images/vqgan.5027fe05051dfa31.hu.png) +![VQGAN+CLIP Architektúra](../../../../../translated_images/hu/vqgan.5027fe05051dfa31.png) Ahhoz, hogy egy szöveges utasításhoz illeszkedő képet generáljunk, egy véletlenszerű kódoló vektorral kezdünk, amelyet a VQGAN-on keresztül egy képpé alakítunk. Ezután a CLIP-et használjuk egy veszteségfüggvény előállítására, amely megmutatja, mennyire felel meg a kép a szöveges utasításnak. A cél ennek a veszteségnek a minimalizálása, a visszaterjesztés segítségével a bemeneti vektor paramétereinek módosításával. Egy nagyszerű könyvtár, amely megvalósítja a VQGAN+CLIP-et, a [Pixray](http://github.com/pixray/pixray). -![Pixray által készített kép](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.hu.png) | ![Pixray által készített kép](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.hu.png) | ![Pixray által készített kép](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.hu.png) +![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray által készített kép](../../../../../translated_images/hu/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Kép generálva az *egy fiatal irodalomtanár akvarell portréja könyvvel* utasítás alapján | Kép generálva az *egy fiatal női informatikatanár olajportréja számítógéppel* utasítás alapján | Kép generálva az *egy idős matematikatanár olajportréja táblával* utasítás alapján @@ -75,7 +75,7 @@ A CLIP-től eltérően a DALL-E egyszerre kapja meg a szöveget és a képet egy A DALL.E 1 és 2 közötti fő különbség, hogy a második verzió valósághűbb képeket és művészeti alkotásokat generál. Példák a DALL-E által generált képekre: -![DALL-E által készített kép](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.hu.png) | ![DALL-E által készített kép](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.hu.png) | ![DALL-E által készített kép](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.hu.png) +![DALL-E által készített kép](../../../../../translated_images/hu/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![DALL-E által készített kép](../../../../../translated_images/hu/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![DALL-E által készített kép](../../../../../translated_images/hu/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Kép generálva az *egy fiatal irodalomtanár akvarell portréja könyvvel* utasítás alapján | Kép generálva az *egy fiatal női informatikatanár olajportréja számítógéppel* utasítás alapján | Kép generálva az *egy idős matematikatanár olajportréja táblával* utasítás alapján diff --git a/translations/id/README.md b/translations/id/README.md index 126fe024..aae77581 100644 --- a/translations/id/README.md +++ b/translations/id/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kecerdasan Buatan untuk Pemula - Kurikulum -|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.id.png)| +|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/id/ai-overview.0857791951d19500.png)| |:---:| | AI Untuk Pemula - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/id/lessons/1-Intro/README.md b/translations/id/lessons/1-Intro/README.md index bbaf3f0a..d9d1707c 100644 --- a/translations/id/lessons/1-Intro/README.md +++ b/translations/id/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengantar AI -![Ringkasan konten Pengantar AI dalam bentuk doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c.id.png) +![Ringkasan konten Pengantar AI dalam bentuk doodle](../../../../translated_images/id/ai-intro.bf28d1ac4235881c.png) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Awalnya, komputer ditemukan oleh [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) untuk mengolah angka dengan mengikuti prosedur yang terdefinisi dengan baik - sebuah algoritma. Komputer modern, meskipun jauh lebih canggih daripada model awal yang diusulkan pada abad ke-19, masih mengikuti ide yang sama tentang perhitungan terkontrol. Oleh karena itu, kita dapat memprogram komputer untuk melakukan sesuatu jika kita mengetahui urutan langkah-langkah yang tepat yang perlu dilakukan untuk mencapai tujuan. -![Foto seseorang](../../../../translated_images/dsh_age.d212a30d4e54fb5f.id.png) +![Foto seseorang](../../../../translated_images/id/dsh_age.d212a30d4e54fb5f.png) > Foto oleh [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Untuk informasi lebih lanjut, lihat **[Artificial General Intelligence](https:// Salah satu masalah ketika membahas istilah **[Kecerdasan](https://en.wikipedia.org/wiki/Intelligence)** adalah tidak adanya definisi yang jelas untuk istilah ini. Seseorang dapat berargumen bahwa kecerdasan terkait dengan **pemikiran abstrak**, atau dengan **kesadaran diri**, tetapi kita tidak dapat mendefinisikannya dengan tepat. -![Foto Kucing](../../../../translated_images/photo-cat.8c8e8fb760ffe457.id.jpg) +![Foto Kucing](../../../../translated_images/id/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) oleh [Amber Kipp](https://unsplash.com/@sadmax) dari Unsplash @@ -98,13 +98,13 @@ Sebaliknya, kita dapat mencoba memodelkan elemen-elemen paling sederhana di dala > | Bagaimana dengan ML? | | > |--------------|-----------| -> | Bagian dari Kecerdasan Buatan yang didasarkan pada komputer yang belajar menyelesaikan masalah berdasarkan beberapa data disebut **Machine Learning**. Kita tidak akan membahas pembelajaran mesin klasik dalam kursus ini - kami merujuk Anda ke kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.id.png) | +> | Bagian dari Kecerdasan Buatan yang didasarkan pada komputer yang belajar menyelesaikan masalah berdasarkan beberapa data disebut **Machine Learning**. Kita tidak akan membahas pembelajaran mesin klasik dalam kursus ini - kami merujuk Anda ke kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/id/ml-for-beginners.9e4fed176fd5817d.png) | ## Sejarah Singkat AI Kecerdasan Buatan dimulai sebagai sebuah bidang pada pertengahan abad ke-20. Awalnya, penalaran simbolik adalah pendekatan yang dominan, dan ini menghasilkan sejumlah keberhasilan penting, seperti sistem pakar – program komputer yang mampu bertindak sebagai ahli dalam beberapa domain masalah terbatas. Namun, segera menjadi jelas bahwa pendekatan semacam itu tidak dapat berkembang dengan baik. Mengekstraksi pengetahuan dari seorang ahli, merepresentasikannya di dalam komputer, dan menjaga basis pengetahuan tersebut tetap akurat ternyata menjadi tugas yang sangat kompleks, dan terlalu mahal untuk praktis dalam banyak kasus. Hal ini menyebabkan apa yang disebut [AI Winter](https://en.wikipedia.org/wiki/AI_winter) pada tahun 1970-an. -Sejarah Singkat AI +Sejarah Singkat AI > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Demikian pula, kita dapat melihat bagaimana pendekatan terhadap pembuatan “pro * Asisten modern, seperti Cortana, Siri, atau Google Assistant semuanya adalah sistem hibrida yang menggunakan jaringan saraf untuk mengubah ucapan menjadi teks dan mengenali niat kita, lalu menggunakan beberapa penalaran atau algoritma eksplisit untuk melakukan tindakan yang diperlukan. * Di masa depan, kita mungkin mengharapkan model berbasis jaringan sepenuhnya untuk menangani dialog secara mandiri. Keluarga jaringan saraf GPT dan [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) baru-baru ini menunjukkan keberhasilan besar dalam hal ini. -evolusi Tes Turing +evolusi Tes Turing > Gambar oleh Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) oleh [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Penelitian AI Terkini diff --git a/translations/id/lessons/2-Symbolic/Animals.ipynb b/translations/id/lessons/2-Symbolic/Animals.ipynb index fd463fd9..7a6e139d 100644 --- a/translations/id/lessons/2-Symbolic/Animals.ipynb +++ b/translations/id/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Dalam contoh ini, kita akan menerapkan sistem berbasis pengetahuan sederhana untuk menentukan jenis hewan berdasarkan beberapa karakteristik fisik. Sistem ini dapat direpresentasikan dengan pohon AND-OR berikut (ini adalah bagian dari keseluruhan pohon, kita dapat dengan mudah menambahkan beberapa aturan lagi):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.id.png)\n" + "![](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/id/lessons/2-Symbolic/README.md b/translations/id/lessons/2-Symbolic/README.md index 7117cc74..3f5cb312 100644 --- a/translations/id/lessons/2-Symbolic/README.md +++ b/translations/id/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representasi Pengetahuan dan Sistem Pakar -![Ringkasan konten AI simbolik](../../../../translated_images/ai-symbolic.715a30cb610411a6.id.png) +![Ringkasan konten AI simbolik](../../../../translated_images/id/ai-symbolic.715a30cb610411a6.png) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Sering kali, kita tidak secara ketat mendefinisikan pengetahuan, tetapi kita men Dengan demikian, masalah **representasi pengetahuan** adalah menemukan cara yang efektif untuk merepresentasikan pengetahuan di dalam komputer dalam bentuk data, agar dapat digunakan secara otomatis. Ini dapat dilihat sebagai spektrum: -![Spektrum representasi pengetahuan](../../../../translated_images/knowledge-spectrum.b60df631852c0217.id.png) +![Spektrum representasi pengetahuan](../../../../translated_images/id/knowledge-spectrum.b60df631852c0217.png) > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaks Blok | Indentasi | | | Salah satu keberhasilan awal AI simbolik adalah **sistem pakar** - sistem komputer yang dirancang untuk bertindak sebagai pakar dalam domain masalah yang terbatas. Sistem ini didasarkan pada **basis pengetahuan** yang diekstraksi dari satu atau lebih pakar manusia, dan mereka memiliki **mesin inferensi** yang melakukan penalaran di atasnya. -![Arsitektur Manusia](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.id.png) | ![Sistem Berbasis Pengetahuan](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.id.png) +![Arsitektur Manusia](../../../../translated_images/id/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem Berbasis Pengetahuan](../../../../translated_images/id/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Struktur sederhana sistem saraf manusia | Arsitektur sistem berbasis pengetahuan @@ -106,7 +106,7 @@ Sistem pakar dibangun seperti sistem penalaran manusia, yang mengandung **memori Sebagai contoh, mari kita pertimbangkan sistem pakar berikut untuk menentukan hewan berdasarkan karakteristik fisiknya: -![Pohon AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.id.png) +![Pohon AND-OR](../../../../translated_images/id/AND-OR-Tree.5592d2c70187f283.png) > Gambar oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 19d01a05..c8fcdeaf 100644 --- a/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/id/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Jika kita memiliki lebih dari 2 kelas, softmax akan menormalkan probabilitas di antara semuanya. Berikut adalah diagram arsitektur jaringan yang melakukan klasifikasi digit MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.id.png)\n" + "![MNIST Classifier](../../../../../translated_images/id/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Loss pelatihan rendah - model dapat mendekati data pelatihan dengan baik karena memiliki kekuatan ekspresif yang cukup.\n", "* Loss validasi bisa jauh lebih tinggi daripada loss pelatihan dan dapat mulai meningkat selama pelatihan - ini terjadi karena model \"mengingat\" titik-titik pelatihan, dan kehilangan \"gambaran keseluruhan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.id.png)\n", + "![Overfitting](../../../../../translated_images/id/overfit.a0bd57f717c15769.png)\n", "\n", "> Pada gambar ini, `x` mewakili data pelatihan, `o` - data validasi. Kiri - model linear (satu layer), mendekati sifat data dengan cukup baik. Kanan - model yang overfitting, model mendekati data pelatihan dengan sangat baik, tetapi kehilangan makna untuk data lainnya (error validasi sangat tinggi).\n" ] diff --git a/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md index c3932014..9d3a9bb3 100644 --- a/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/id/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting adalah konsep yang sangat penting dalam pembelajaran mesin, dan sang Pertimbangkan masalah berikut dalam mendekati 5 titik (diwakili oleh `x` pada grafik di bawah): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.id.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.id.jpg) +![linear](../../../../../translated_images/id/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/id/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Model linear, 2 parameter** | **Model non-linear, 7 parameter** Error pelatihan = 5.3 | Error pelatihan = 0 @@ -79,7 +79,7 @@ Sangat penting untuk menemukan keseimbangan yang tepat antara kompleksitas model Seperti yang dapat Anda lihat dari grafik di atas, overfitting dapat dideteksi dengan error pelatihan yang sangat rendah, dan error validasi yang tinggi. Biasanya selama pelatihan kita akan melihat error pelatihan dan validasi mulai menurun, dan kemudian pada suatu titik error validasi mungkin berhenti menurun dan mulai meningkat. Ini akan menjadi tanda overfitting, dan indikator bahwa kita mungkin harus menghentikan pelatihan pada titik ini (atau setidaknya membuat snapshot model). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.id.png) +![overfitting](../../../../../translated_images/id/Overfitting.408ad91cd90b4371.png) ## Cara mencegah overfitting diff --git a/translations/id/lessons/3-NeuralNetworks/README.md b/translations/id/lessons/3-NeuralNetworks/README.md index 6406b393..ec0bf51f 100644 --- a/translations/id/lessons/3-NeuralNetworks/README.md +++ b/translations/id/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengantar Jaringan Neural -![Ringkasan konten pengantar Jaringan Neural dalam bentuk doodle](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.id.png) +![Ringkasan konten pengantar Jaringan Neural dalam bentuk doodle](../../../../translated_images/id/ai-neuralnetworks.1c687ae40bc86e83.png) Seperti yang telah kita bahas dalam pengantar, salah satu cara untuk mencapai kecerdasan adalah dengan melatih **model komputer** atau **otak buatan**. Sejak pertengahan abad ke-20, para peneliti mencoba berbagai model matematika, hingga beberapa tahun terakhir arah ini terbukti sangat berhasil. Model matematika otak ini disebut **jaringan neural**. @@ -36,13 +36,13 @@ Dalam kurikulum ini, kita hanya akan fokus pada model jaringan neural. Dari biologi, kita tahu bahwa otak kita terdiri dari sel-sel neural (neuron), masing-masing memiliki beberapa "input" (dendrit) dan satu "output" (akson). Baik dendrit maupun akson dapat menghantarkan sinyal listrik, dan koneksi di antara mereka — yang dikenal sebagai sinaps — dapat menunjukkan tingkat konduktivitas yang bervariasi, yang diatur oleh neurotransmiter. -![Model Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.id.jpg) | ![Model Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.id.png) +![Model Neuron](../../../../translated_images/id/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model Neuron](../../../../translated_images/id/artneuron.1a5daa88d20ebe6f.png) ----|---- Neuron Asli *([Gambar](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) dari Wikipedia)* | Neuron Buatan *(Gambar oleh Penulis)* Dengan demikian, model matematika paling sederhana dari neuron memiliki beberapa input X1, ..., XN dan satu output Y, serta serangkaian bobot W1, ..., WN. Output dihitung sebagai: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) di mana f adalah beberapa **fungsi aktivasi** non-linear. diff --git a/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md index 2288b4cf..e8468321 100644 --- a/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/id/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Dalam [OpenCV Notebook](OpenCV.ipynb), kami memberikan beberapa contoh kapan com * **Pra-pemrosesan foto buku Braille**. Kami fokus pada bagaimana kami dapat menggunakan thresholding, deteksi fitur, transformasi perspektif, dan manipulasi NumPy untuk memisahkan simbol Braille individu untuk klasifikasi lebih lanjut oleh jaringan saraf. -![Gambar Braille](../../../../../translated_images/braille.341962ff76b1bd70.id.jpeg) | ![Gambar Braille yang Diproses](../../../../../translated_images/braille-result.46530fea020b03c7.id.png) | ![Simbol Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.id.png) +![Gambar Braille](../../../../../translated_images/id/braille.341962ff76b1bd70.jpeg) | ![Gambar Braille yang Diproses](../../../../../translated_images/id/braille-result.46530fea020b03c7.png) | ![Simbol Braille](../../../../../translated_images/id/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) * **Mendeteksi gerakan dalam video menggunakan perbedaan frame**. Jika kamera tetap, maka frame dari umpan kamera seharusnya cukup mirip satu sama lain. Karena frame direpresentasikan sebagai array, hanya dengan mengurangi array untuk dua frame berturut-turut kita akan mendapatkan perbedaan piksel, yang seharusnya rendah untuk frame statis, dan menjadi lebih tinggi ketika ada gerakan yang signifikan dalam gambar. -![Gambar frame video dan perbedaan frame](../../../../../translated_images/frame-difference.706f805491a0883c.id.png) +![Gambar frame video dan perbedaan frame](../../../../../translated_images/id/frame-difference.706f805491a0883c.png) > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Dalam [OpenCV Notebook](OpenCV.ipynb), kami memberikan beberapa contoh kapan com - **Dense Optical Flow** menghitung medan vektor yang menunjukkan untuk setiap piksel ke mana ia bergerak. - **Sparse Optical Flow** didasarkan pada mengambil beberapa fitur khas dalam gambar (misalnya, tepi), dan membangun trajektorinya dari frame ke frame. -![Gambar Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.id.png) +![Gambar Optical Flow](../../../../../translated_images/id/optical.1f4a94464579a83a.png) > Gambar dari [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 4875c220..0988bea6 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 adalah jaringan yang mencapai akurasi 92,7% dalam klasifikasi top-5 ImageNet pada tahun 2014. Jaringan ini memiliki struktur lapisan sebagai berikut: -![Lapisan ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.id.jpg) +![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.jpg) Seperti yang dapat Anda lihat, VGG mengikuti arsitektur piramida tradisional, yaitu urutan lapisan konvolusi-pooling. -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.id.jpg) +![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.jpg) > Gambar dari [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 3f015840..c1eec964 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Dengan demikian, dalam CNN yang khas, akan ada beberapa lapisan konvolusi, dengan lapisan pooling di antaranya untuk mengurangi dimensi gambar. Kita juga akan meningkatkan jumlah filter, karena seiring pola menjadi lebih kompleks, ada lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.id.png)\n", + "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Karena dimensi spasial yang berkurang dan dimensi fitur/filter yang meningkat, arsitektur ini juga disebut sebagai **arsitektur piramida**.\n" ] diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index e9af3510..b8f38be9 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Dengan demikian, dalam CNN yang khas akan ada beberapa lapisan konvolusi, dengan lapisan pooling di antaranya untuk mengurangi dimensi gambar. Kita juga akan meningkatkan jumlah filter, karena saat pola menjadi lebih kompleks - ada lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.id.png)\n", + "![Gambar yang menunjukkan beberapa lapisan konvolusi dengan lapisan pooling.](../../../../../translated_images/id/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Karena pengurangan dimensi spasial dan peningkatan dimensi fitur/filter, arsitektur ini juga disebut **arsitektur piramida**.\n" ] diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md index 6f3172a2..c265b4a3 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Dalam kehidupan nyata, kita ingin dapat mengenali objek dalam gambar tanpa pedul Untuk mengekstrak pola, kita akan menggunakan konsep **filter konvolusi**. Seperti yang Anda ketahui, gambar direpresentasikan sebagai matriks 2D, atau tensor 3D dengan kedalaman warna. Menerapkan filter berarti kita mengambil matriks **filter kernel** yang relatif kecil, dan untuk setiap piksel dalam gambar asli kita menghitung rata-rata berbobot dengan titik-titik tetangga. Kita dapat melihat ini seperti jendela kecil yang meluncur di seluruh gambar, dan meratakan semua piksel sesuai dengan bobot dalam matriks filter kernel. -![Filter Tepi Vertikal](../../../../../translated_images/filter-vert.b7148390ca0bc356.id.png) | ![Filter Tepi Horizontal](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.id.png) +![Filter Tepi Vertikal](../../../../../translated_images/id/filter-vert.b7148390ca0bc356.png) | ![Filter Tepi Horizontal](../../../../../translated_images/id/filter-horiz.59b80ed4feb946ef.png) ----|---- > Gambar oleh Dmitry Soshnikov @@ -38,7 +38,7 @@ Cara kerja CNN didasarkan pada ide-ide penting berikut: * Kita dapat merancang jaringan sedemikian rupa sehingga filter dilatih secara otomatis * Kita dapat menggunakan pendekatan yang sama untuk menemukan pola dalam fitur tingkat tinggi, bukan hanya dalam gambar asli. Dengan demikian, ekstraksi fitur CNN bekerja pada hierarki fitur, mulai dari kombinasi piksel tingkat rendah hingga kombinasi tingkat tinggi dari bagian gambar. -![Ekstraksi Fitur Hierarkis](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.id.png) +![Ekstraksi Fitur Hierarkis](../../../../../translated_images/id/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Gambar dari [makalah oleh Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), berdasarkan [penelitian mereka](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Sebagian besar CNN yang digunakan untuk pemrosesan gambar mengikuti apa yang dis Sebagai contoh, mari kita lihat arsitektur VGG-16, sebuah jaringan yang mencapai akurasi 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014: -![Lapisan ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.id.jpg) +![Lapisan ImageNet](../../../../../translated_images/id/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.id.jpg) +![Piramida ImageNet](../../../../../translated_images/id/vgg-16-arch.64ff2137f50dd49f.jpg) > Gambar dari [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ac1d32fa..e1265a61 100644 --- a/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/id/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Anda perlu melatih jaringan saraf konvolusi untuk mengklasifikasikan berbagai ra Kita akan menggunakan [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), yang berisi gambar dari 37 ras anjing dan kucing yang berbeda. -![Dataset yang akan kita gunakan](../../../../../../translated_images/data.50b2a9d5484bdbf0.id.png) +![Dataset yang akan kita gunakan](../../../../../../translated_images/id/data.50b2a9d5484bdbf0.png) Untuk mengunduh dataset, gunakan cuplikan kode berikut: diff --git a/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 942e8312..e0683672 100644 --- a/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/id/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Untuk memvisualisasikan kucing yang ideal, kita akan memulai dengan gambar acak berupa noise, dan mencoba menggunakan teknik optimasi gradient descent untuk menyesuaikan gambar agar jaringan dapat mengenali kucing.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.id.png)\n", + "![Optimization Loop](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Berikut adalah gambar awal kita:\n" ] diff --git a/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md index 057e46b2..17a167be 100644 --- a/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/id/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Baik Keras maupun PyTorch memiliki fungsi untuk dengan mudah memuat bobot jaring Berikut adalah contoh fitur yang diekstrak dari gambar kucing oleh jaringan VGG-16: -![Fitur yang diekstrak oleh VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.id.png) +![Fitur yang diekstrak oleh VGG-16](../../../../../translated_images/id/features.6291f9c7ba3a0b95.png) ## Dataset Kucing vs. Anjing @@ -48,19 +48,19 @@ Jaringan neural pra-latih mengandung berbagai pola di dalam "otaknya", termasuk Salah satu pendekatan yang bisa kita ambil adalah memulai dengan gambar acak, lalu mencoba menggunakan teknik **optimisasi penurunan gradien** untuk menyesuaikan gambar tersebut sedemikian rupa sehingga jaringan mulai berpikir bahwa itu adalah kucing. -![Loop Optimisasi Gambar](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.id.png) +![Loop Optimisasi Gambar](../../../../../translated_images/id/ideal-cat-loop.999fbb8ff306e044.png) Namun, jika kita melakukan ini, kita akan mendapatkan sesuatu yang sangat mirip dengan noise acak. Hal ini terjadi karena *ada banyak cara untuk membuat jaringan berpikir bahwa gambar input adalah kucing*, termasuk beberapa yang tidak masuk akal secara visual. Meskipun gambar-gambar tersebut mengandung banyak pola khas untuk kucing, tidak ada yang membatasi mereka untuk menjadi visual yang jelas. Untuk meningkatkan hasil, kita dapat menambahkan istilah lain ke dalam fungsi loss, yang disebut **variation loss**. Ini adalah metrik yang menunjukkan seberapa mirip piksel-piksel yang berdekatan dalam gambar. Meminimalkan variation loss membuat gambar lebih halus, dan menghilangkan noise - sehingga mengungkapkan pola yang lebih menarik secara visual. Berikut adalah contoh gambar "ideal" yang diklasifikasikan sebagai kucing dan zebra dengan probabilitas tinggi: -![Kucing Ideal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.id.png) | ![Zebra Ideal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.id.png) +![Kucing Ideal](../../../../../translated_images/id/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/id/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Kucing Ideal* | *Zebra Ideal* Pendekatan serupa dapat digunakan untuk melakukan apa yang disebut **serangan adversarial** pada jaringan neural. Misalkan kita ingin mengelabui jaringan neural dan membuat gambar anjing terlihat seperti kucing. Jika kita mengambil gambar anjing, yang dikenali oleh jaringan sebagai anjing, kita kemudian dapat sedikit menyesuaikannya menggunakan optimisasi penurunan gradien, hingga jaringan mulai mengklasifikasikannya sebagai kucing: -![Gambar Anjing](../../../../../translated_images/original-dog.8f68a67d2fe0911f.id.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.id.png) +![Gambar Anjing](../../../../../translated_images/id/original-dog.8f68a67d2fe0911f.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/id/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Gambar asli anjing* | *Gambar anjing yang diklasifikasikan sebagai kucing* diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 66abcb22..4c6b29de 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar rekonstruksi akurat, jaringan mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya.\n", "\n", - "![Diagram AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.id.jpg)\n", + "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Gambar dari [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index c3ac3b48..00a33b1e 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar rekonstruksi akurat, jaringan mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya.\n", "\n", - "![Diagram AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.id.jpg)\n", + "![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Gambar dari [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md index 55938003..c011c4d9 100644 --- a/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/id/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Namun, kita mungkin ingin menggunakan data mentah (tidak berlabel) untuk melatih Karena kita melatih autoencoder untuk menangkap sebanyak mungkin informasi dari gambar asli agar dapat merekonstruksi dengan akurat, jaringan ini mencoba menemukan **embedding** terbaik dari gambar input untuk menangkap maknanya. -![Diagram AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.id.jpg) +![Diagram AutoEncoder](../../../../../translated_images/id/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Gambar dari [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md index 7edb8745..fb553c6d 100644 --- a/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/id/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Model klasifikasi gambar yang telah kita bahas sejauh ini mengambil gambar dan m ## [Kuis sebelum pelajaran](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Deteksi Objek](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.id.png) +![Deteksi Objek](../../../../../translated_images/id/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Gambar dari [situs web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Misalkan kita ingin menemukan seekor kucing dalam sebuah gambar, pendekatan yang 2. Melakukan klasifikasi gambar pada setiap ubin. 3. Ubin yang menghasilkan aktivasi yang cukup tinggi dapat dianggap mengandung objek yang dimaksud. -![Deteksi Objek Naif](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.id.png) +![Deteksi Objek Naif](../../../../../translated_images/id/naive-detection.e7f1ba220ccd08c6.png) > *Gambar dari [Notebook Latihan](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Anda mungkin menemukan dataset berikut untuk tugas ini: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 kelas * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 kelas, kotak pembatas, dan masker segmentasi -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.id.jpg) +![COCO](../../../../../translated_images/id/coco-examples.71bc60380fa6cceb.jpg) ## Metrik Deteksi Objek @@ -50,7 +50,7 @@ Anda mungkin menemukan dataset berikut untuk tugas ini: Sementara untuk klasifikasi gambar mudah untuk mengukur seberapa baik algoritma bekerja, untuk deteksi objek kita perlu mengukur baik kebenaran kelas maupun ketepatan lokasi kotak pembatas yang dihasilkan. Untuk yang terakhir, kita menggunakan **Intersection over Union** (IoU), yang mengukur seberapa baik dua kotak (atau dua area arbitrer) saling tumpang tindih. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.id.png) +![IoU](../../../../../translated_images/id/iou_equation.9a4751d40fff4e11.png) > *Gambar 2 dari [blog yang sangat bagus tentang IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Ada dua kelas besar algoritma deteksi objek: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) menggunakan [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) untuk menghasilkan struktur hierarkis dari wilayah ROI, yang kemudian diteruskan melalui ekstraktor fitur CNN dan pengklasifikasi SVM untuk menentukan kelas objek, serta regresi linier untuk menentukan koordinat *bounding box*. [Makalah Resmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.id.png) +![RCNN](../../../../../translated_images/id/rcnn1.cae407020dfb1d1f.png) > *Gambar dari van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.id.png) +![RCNN-1](../../../../../translated_images/id/rcnn2.2d9530bb83516484.png) > *Gambar dari [blog ini](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Ada dua kelas besar algoritma deteksi objek: Pendekatan ini mirip dengan R-CNN, tetapi wilayah didefinisikan setelah lapisan konvolusi diterapkan. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.id.png) +![FRCNN](../../../../../translated_images/id/f-rcnn.3cda6d9bb4188875.png) > Gambar dari [Makalah Resmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Pendekatan ini mirip dengan R-CNN, tetapi wilayah didefinisikan setelah lapisan Ide utama pendekatan ini adalah menggunakan jaringan saraf untuk memprediksi ROI - yang disebut *Region Proposal Network*. [Makalah](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.id.png) +![FasterRCNN](../../../../../translated_images/id/faster-rcnn.8d46c099b87ef30a.png) > Gambar dari [makalah resmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algoritma ini bahkan lebih cepat daripada Faster R-CNN. Ide utamanya adalah seba 2. Fitur diproses oleh **Position-Sensitive Score Map**. Setiap objek dari $C$ kelas dibagi menjadi $k\times k$ wilayah, dan kita melatih untuk memprediksi bagian-bagian objek. 3. Untuk setiap bagian dari wilayah $k\times k$, semua jaringan memberikan suara untuk kelas objek, dan kelas objek dengan suara maksimum dipilih. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.id.png) +![r-fcn image](../../../../../translated_images/id/r-fcn.13eb88158b99a3da.png) > Gambar dari [makalah resmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO adalah algoritma satu kali pengolahan secara real-time. Ide utamanya adalah * Gambar dibagi menjadi $S\times S$ wilayah. * Untuk setiap wilayah, **CNN** memprediksi $n$ objek yang mungkin, koordinat *bounding box*, dan *confidence*=*probabilitas* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.id.png) + ![YOLO](../../../../../translated_images/id/yolo.a2648ec82ee8bb4e.png) > Gambar dari [makalah resmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/id/lessons/4-ComputerVision/README.md b/translations/id/lessons/4-ComputerVision/README.md index f8dc31f5..22dfa4ed 100644 --- a/translations/id/lessons/4-ComputerVision/README.md +++ b/translations/id/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Penglihatan Komputer -![Ringkasan konten Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ai-computervision.6506ebebac3fbf76.id.png) +![Ringkasan konten Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/id/ai-computervision.6506ebebac3fbf76.png) Di bagian ini kita akan mempelajari tentang: diff --git a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 5d664cf1..3777532d 100644 --- a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Representasi vektor **Bag of Words** (BoW) adalah representasi vektor tradisional yang paling umum digunakan. Setiap kata dikaitkan dengan indeks vektor, elemen vektor berisi jumlah kemunculan sebuah kata dalam dokumen tertentu.\n", "\n", - "![Gambar yang menunjukkan bagaimana representasi vektor bag of words disimpan dalam memori.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.id.png) \n", + "![Gambar yang menunjukkan bagaimana representasi vektor bag of words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Anda juga dapat memikirkan BoW sebagai jumlah dari semua vektor one-hot-encoded untuk kata-kata individual dalam teks.\n", "\n", diff --git a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index dcf75b96..ab363522 100644 --- a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Representasi vektor **Bag-of-words** (BoW) adalah representasi vektor tradisional yang paling sederhana untuk dipahami. Setiap kata dikaitkan dengan indeks vektor, dan elemen vektor berisi jumlah kemunculan setiap kata dalam dokumen tertentu.\n", "\n", - "![Gambar yang menunjukkan bagaimana representasi vektor bag-of-words disimpan dalam memori.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.id.png) \n", + "![Gambar yang menunjukkan bagaimana representasi vektor bag-of-words disimpan dalam memori.](../../../../../translated_images/id/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Anda juga dapat memikirkan BoW sebagai jumlah dari semua vektor one-hot-encoded untuk setiap kata dalam teks.\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index e497319c..13737254 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita menjadi embedding yang sesuai, lalu menghitung beberapa fungsi agregat atas semua embedding tersebut, seperti `sum`, `average`, atau `max`.\n", "\n", - "![Gambar yang menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.png)\n", + "![Gambar yang menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Jaringan saraf pengklasifikasi kita akan dimulai dengan lapisan embedding, kemudian lapisan agregasi, dan pengklasifikasi linear di atasnya:\n" ] @@ -176,7 +176,7 @@ "\n", "Dalam arsitektur sebelumnya, kita perlu menambahkan padding pada semua urutan agar memiliki panjang yang sama untuk dimasukkan ke dalam minibatch. Ini bukan cara yang paling efisien untuk merepresentasikan urutan dengan panjang variabel - pendekatan lain adalah menggunakan vektor **offset**, yang akan menyimpan offset dari semua urutan yang disimpan dalam satu vektor besar.\n", "\n", - "![Gambar yang menunjukkan representasi urutan dengan offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.id.png)\n", + "![Gambar yang menunjukkan representasi urutan dengan offset](../../../../../translated_images/id/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Pada gambar di atas, kami menunjukkan urutan karakter, tetapi dalam contoh ini kami bekerja dengan urutan kata. Namun, prinsip umum merepresentasikan urutan dengan vektor offset tetap sama.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi lebih baik dalam merepresentasikan kata-kata yang jarang muncul.\n", "\n", - "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.id.png)\n", + "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Untuk bereksperimen dengan embedding word2vec yang telah dilatih sebelumnya pada dataset Google News, kita dapat menggunakan pustaka **gensim**. Di bawah ini kita menemukan kata-kata yang paling mirip dengan 'neural'\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index e6e779d5..b7f51eda 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/id/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita ke embedding yang sesuai, lalu menghitung fungsi agregat tertentu dari semua embedding tersebut, seperti `sum`, `average`, atau `max`.\n", "\n", - "![Gambar menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.png)\n", + "![Gambar menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Jaringan neural pengklasifikasi kita terdiri dari lapisan-lapisan berikut:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi skip-gram lebih baik dalam merepresentasikan kata-kata yang jarang muncul.\n", "\n", - "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.id.png)\n", + "![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Untuk bereksperimen dengan embedding Word2Vec yang telah dilatih sebelumnya pada dataset Google News, kita dapat menggunakan pustaka **gensim**. Di bawah ini kita menemukan kata-kata yang paling mirip dengan 'neural'.\n", "\n", diff --git a/translations/id/lessons/5-NLP/14-Embeddings/README.md b/translations/id/lessons/5-NLP/14-Embeddings/README.md index 07b296e8..4e550e4c 100644 --- a/translations/id/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/id/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Jadi, lapisan embedding akan mengambil sebuah kata sebagai input, dan menghasilk Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam jaringan classifier kita, kita dapat beralih dari model bag-of-words ke model **embedding bag**, di mana kita pertama-tama mengonversi setiap kata dalam teks kita ke embedding yang sesuai, dan kemudian menghitung beberapa fungsi agregat dari semua embedding tersebut, seperti `sum`, `average`, atau `max`. -![Gambar menunjukkan classifier embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.png) +![Gambar menunjukkan classifier embedding untuk lima kata dalam urutan.](../../../../../translated_images/id/embedding-classifier-example.b77f021a7ee67eee.png) > Gambar oleh penulis @@ -40,7 +40,7 @@ Untuk mencapai itu, kita perlu melatih model embedding kita terlebih dahulu pada CBoW lebih cepat, sedangkan skip-gram lebih lambat, tetapi lebih baik dalam merepresentasikan kata-kata yang jarang muncul. -![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.id.png) +![Gambar menunjukkan algoritma CBoW dan Skip-Gram untuk mengonversi kata-kata menjadi vektor.](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Gambar dari [makalah ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/id/lessons/5-NLP/15-LanguageModeling/README.md b/translations/id/lessons/5-NLP/15-LanguageModeling/README.md index 58c75e4a..f4ba0221 100644 --- a/translations/id/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/id/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dalam contoh sebelumnya, kita menggunakan embedding semantik yang sudah dilatih * **Continuous Bag-of-Words** (CBoW), di mana kita memprediksi token tengah $W_0$ dalam urutan token $W_{-N}$, ..., $W_N$. * **Skip-gram**, di mana kita memprediksi sekumpulan token tetangga {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} dari token tengah $W_0$. -![gambar dari makalah tentang mengonversi kata menjadi vektor](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.id.png) +![gambar dari makalah tentang mengonversi kata menjadi vektor](../../../../../translated_images/id/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Gambar dari [makalah ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/id/lessons/5-NLP/16-RNN/README.md b/translations/id/lessons/5-NLP/16-RNN/README.md index bba487a8..ffd27e3e 100644 --- a/translations/id/lessons/5-NLP/16-RNN/README.md +++ b/translations/id/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Pada bagian sebelumnya, kita telah menggunakan representasi semantik yang kaya d Untuk menangkap makna dari urutan teks, kita perlu menggunakan arsitektur jaringan saraf lain yang disebut **jaringan saraf rekurens**, atau RNN. Dalam RNN, kita melewatkan kalimat kita melalui jaringan satu simbol pada satu waktu, dan jaringan menghasilkan beberapa **state**, yang kemudian kita lewati kembali ke jaringan bersama simbol berikutnya. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.id.png) +![RNN](../../../../../translated_images/id/rnn.27f5c29c53d727b5.png) > Gambar oleh penulis @@ -61,7 +61,7 @@ Kita telah membahas jaringan rekurens yang beroperasi dalam satu arah, dari awal Jaringan rekurens, baik satu arah maupun bidirectional, menangkap pola tertentu dalam urutan, dan dapat menyimpannya ke dalam vektor state atau melewatkannya ke output. Seperti pada jaringan konvolusi, kita dapat membangun lapisan rekurens lain di atas yang pertama untuk menangkap pola tingkat tinggi dan membangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Ini membawa kita pada konsep **RNN multilayer** yang terdiri dari dua atau lebih jaringan rekurens, di mana output dari lapisan sebelumnya dilewatkan ke lapisan berikutnya sebagai input. -![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.jpg) +![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López* diff --git a/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b04bedfa..43a657b4 100644 --- a/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Jaringan rekuren, baik satu arah maupun bidirectional, menangkap pola tertentu dalam sebuah urutan, dan dapat menyimpannya ke dalam vektor state atau meneruskannya ke output. Seperti pada jaringan konvolusional, kita dapat membangun lapisan rekuren lain di atas lapisan pertama untuk menangkap pola tingkat yang lebih tinggi, yang dibangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Ini membawa kita pada konsep **RNN multilayer**, yang terdiri dari dua atau lebih jaringan rekuren, di mana output dari lapisan sebelumnya diteruskan ke lapisan berikutnya sebagai input.\n", "\n", - "![Gambar yang menunjukkan Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.jpg)\n", + "![Gambar yang menunjukkan Multilayer long-short-term-memory- RNN](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López*\n", "\n", diff --git a/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb index 901bad23..3f2082ae 100644 --- a/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/id/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Untuk menangkap makna dari urutan teks, kita akan menggunakan arsitektur jaringan saraf yang disebut **jaringan saraf berulang**, atau RNN. Saat menggunakan RNN, kita melewatkan kalimat kita melalui jaringan satu token pada satu waktu, dan jaringan menghasilkan beberapa **state**, yang kemudian kita lewati kembali ke jaringan bersama token berikutnya.\n", "\n", - "![Gambar menunjukkan contoh pembuatan jaringan saraf berulang.](../../../../../translated_images/rnn.27f5c29c53d727b5.id.png)\n", + "![Gambar menunjukkan contoh pembuatan jaringan saraf berulang.](../../../../../translated_images/id/rnn.27f5c29c53d727b5.png)\n", "\n", "Diberikan urutan input token $X_0,\\dots,X_n$, RNN menciptakan urutan blok jaringan saraf, dan melatih urutan ini secara end-to-end menggunakan backpropagation. Setiap blok jaringan mengambil pasangan $(X_i,S_i)$ sebagai input, dan menghasilkan $S_{i+1}$ sebagai hasil. State akhir $S_n$ atau output $Y_n$ masuk ke dalam pengklasifikasi linier untuk menghasilkan hasil. Semua blok jaringan berbagi bobot yang sama, dan dilatih secara end-to-end menggunakan satu langkah backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Jaringan rekuren, baik unidirectional maupun bidirectional, menangkap pola dalam sebuah urutan, dan menyimpannya ke dalam vektor status atau mengembalikannya sebagai output. Seperti halnya jaringan konvolusi, kita dapat membangun lapisan rekuren lain setelah lapisan pertama untuk menangkap pola tingkat yang lebih tinggi, yang dibangun dari pola tingkat rendah yang diekstraksi oleh lapisan pertama. Hal ini membawa kita pada konsep **RNN multilayer**, yang terdiri dari dua atau lebih jaringan rekuren, di mana output dari lapisan sebelumnya diteruskan ke lapisan berikutnya sebagai input.\n", "\n", - "![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.jpg)\n", + "![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/id/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Gambar dari [postingan luar biasa ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López.*\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b3e04820..9c70464a 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cara kita melatih RNN untuk menghasilkan teks adalah sebagai berikut. Pada setiap langkah, kita akan mengambil urutan karakter dengan panjang `nchars`, dan meminta jaringan untuk menghasilkan karakter keluaran berikutnya untuk setiap karakter masukan:\n", "\n", - "![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.png)\n", + "![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Bergantung pada skenario sebenarnya, kita mungkin juga ingin menyertakan beberapa karakter khusus, seperti *akhir urutan* ``. Dalam kasus kita, kita hanya ingin melatih jaringan untuk menghasilkan teks tanpa henti, sehingga kita akan menetapkan ukuran setiap urutan sama dengan `nchars` token. Akibatnya, setiap contoh pelatihan akan terdiri dari `nchars` masukan dan `nchars` keluaran (yang merupakan urutan masukan yang digeser satu simbol ke kiri). Minibatch akan terdiri dari beberapa urutan seperti itu.\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index e66aec73..0f61f829 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Cara kita melatih RNN untuk menghasilkan judul berita adalah sebagai berikut. Pada setiap langkah, kita akan mengambil satu judul, yang akan dimasukkan ke dalam RNN, dan untuk setiap karakter input, kita akan meminta jaringan untuk menghasilkan karakter output berikutnya:\n", "\n", - "![Gambar yang menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.png)\n", + "![Gambar yang menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Untuk karakter terakhir dari urutan kita, kita akan meminta jaringan untuk menghasilkan token ``.\n", "\n", diff --git a/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md index d0bc5b0c..6c8ab014 100644 --- a/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/id/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Dalam arsitektur RNN yang kita bahas di unit sebelumnya, setiap unit RNN menghas Hal ini memungkinkan berbagai arsitektur neural yang ditunjukkan pada gambar di bawah: -![Gambar menunjukkan pola umum jaringan neural rekuren.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.id.jpg) +![Gambar menunjukkan pola umum jaringan neural rekuren.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Gambar dari blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Di unit ini, kita akan fokus pada model generatif sederhana yang membantu kita m Kita akan melatih RNN ini untuk menghasilkan teks langkah demi langkah. Pada setiap langkah, kita akan mengambil urutan karakter dengan panjang `nchars`, dan meminta jaringan untuk menghasilkan karakter output berikutnya untuk setiap karakter input: -![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.png) +![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/id/rnn-generate.56c54afb52f9781d.png) Saat menghasilkan teks (selama inferensi), kita mulai dengan beberapa **prompt**, yang dilewatkan melalui sel RNN untuk menghasilkan status intermediate-nya, dan kemudian dari status ini proses generasi dimulai. Kita menghasilkan satu karakter pada satu waktu, dan melewatkan status serta karakter yang dihasilkan ke sel RNN lainnya untuk menghasilkan karakter berikutnya, hingga kita menghasilkan cukup karakter. diff --git a/translations/id/lessons/5-NLP/18-Transformers/README.md b/translations/id/lessons/5-NLP/18-Transformers/README.md index c9696d3e..2b67c1c3 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/README.md +++ b/translations/id/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Dengan RNN, sequence-to-sequence diimplementasikan oleh dua jaringan berulang, d **Mekanisme Perhatian** memberikan cara untuk memberi bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan membuat jalur pintas antara keadaan menengah dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output yt, kita akan mempertimbangkan semua keadaan tersembunyi input hi, dengan koefisien bobot yang berbeda αt,i. -![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.png) +![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png) > Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili tingkat di mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Di bawah ini adalah contoh matriks semacam itu: -![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.png) +![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png) > Gambar dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Hasil yang kita dapatkan dengan embedding posisi menggabungkan token asli dan po Selanjutnya, kita perlu menangkap beberapa pola dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian diri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai input dan output. Menerapkan perhatian diri memungkinkan kita mempertimbangkan **konteks** dalam kalimat, dan melihat kata-kata mana yang saling terkait. Misalnya, ini memungkinkan kita melihat kata-kata yang dirujuk oleh coreferensi, seperti *itu*, dan juga mempertimbangkan konteks: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.id.png) +![](../../../../../translated_images/id/CoreferenceResolution.861924d6d384a7d6.png) > Gambar dari [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Karena setiap posisi input dipetakan secara independen ke setiap posisi output, **BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan fine tuning. Proses ini disebut **transfer learning**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Gambar [sumber](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 0a83f6bd..64fc0489 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/id/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan menciptakan jalur pintas antara state antara dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output $y_t$, kita akan mempertimbangkan semua state tersembunyi input $h_i$, dengan koefisien bobot yang berbeda $\\alpha_{t,i}$. \n", "\n", - "![Gambar yang menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.png)\n", + "![Gambar yang menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan merepresentasikan sejauh mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Berikut adalah contoh matriks seperti itu:\n", "\n", - "![Gambar yang menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.png)\n", + "![Gambar yang menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Gambar diambil dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan fine tuning. Proses ini disebut **transfer learning**. \n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Ada banyak variasi arsitektur Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3, dan lainnya yang dapat disesuaikan. Paket [HuggingFace](https://github.com/huggingface/) menyediakan repositori untuk melatih banyak arsitektur ini dengan PyTorch. \n", "\n", diff --git a/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 49067e77..2bad587d 100644 --- a/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/id/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor input terhadap setiap prediksi output dari RNN. Cara ini diimplementasikan dengan menciptakan jalur pintas antara state antara dari RNN input dan RNN output. Dengan cara ini, saat menghasilkan simbol output $y_t$, kita akan mempertimbangkan semua state tersembunyi input $h_i$, dengan koefisien bobot yang berbeda $\\alpha_{t,i}$. \n", "\n", - "![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.png)\n", + "![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/id/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan merepresentasikan sejauh mana kata-kata tertentu dalam input berperan dalam menghasilkan kata tertentu dalam urutan output. Di bawah ini adalah contoh matriks seperti itu:\n", "\n", - "![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.png)\n", + "![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/id/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Gambar diambil dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Gambar 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 lapisan untuk *BERT-large*. Model ini pertama kali dilatih pada korpus teks yang sangat besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata-kata yang disembunyikan dalam sebuah kalimat). Selama pelatihan awal, model ini menyerap pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain melalui proses penyetelan ulang. Proses ini disebut **transfer learning**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/id/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Ada banyak variasi arsitektur Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3, dan lainnya yang dapat disesuaikan lebih lanjut.\n", "\n", diff --git a/translations/id/lessons/5-NLP/19-NER/README.md b/translations/id/lessons/5-NLP/19-NER/README.md index 9d2b6ecc..4f93ce62 100644 --- a/translations/id/lessons/5-NLP/19-NER/README.md +++ b/translations/id/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Karena kita perlu membangun korespondensi satu-ke-satu antara token dan kelas, kita dapat melatih model jaringan neural **many-to-many** yang paling kanan dari gambar ini: -![Gambar menunjukkan pola umum jaringan neural berulang.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.id.jpg) +![Gambar menunjukkan pola umum jaringan neural berulang.](../../../../../translated_images/id/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Gambar dari [blog post ini](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpathy](http://karpathy.github.io/). Model klasifikasi token NER sesuai dengan arsitektur jaringan paling kanan pada gambar ini.* diff --git a/translations/id/lessons/5-NLP/README.md b/translations/id/lessons/5-NLP/README.md index 4ec79243..eafe3415 100644 --- a/translations/id/lessons/5-NLP/README.md +++ b/translations/id/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pemrosesan Bahasa Alami -![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.id.png) +![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/id/ai-nlp.b22dcb8ca4707cea.png) Di bagian ini, kita akan fokus pada penggunaan Jaringan Saraf untuk menangani tugas-tugas yang berkaitan dengan **Pemrosesan Bahasa Alami (Natural Language Processing/NLP)**. Ada banyak masalah NLP yang ingin kita selesaikan dengan bantuan komputer: diff --git a/translations/id/lessons/6-Other/23-MultiagentSystems/README.md b/translations/id/lessons/6-Other/23-MultiagentSystems/README.md index d421702a..b84c075d 100644 --- a/translations/id/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/id/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Anda dapat membuka salah satu model, misalnya **Biology → Flocking**. Setelah membuka model, Anda akan dibawa ke layar utama NetLogo. Berikut adalah contoh model yang menggambarkan populasi serigala dan domba, dengan sumber daya yang terbatas (rumput). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.id.png) +![NetLogo Main Screen](../../../../../translated_images/id/NetLogo-Main.32653711ec1a01b3.png) > Tangkapan layar oleh Dmitry Soshnikov diff --git a/translations/id/lessons/README.md b/translations/id/lessons/README.md index 2f6d1258..260b4821 100644 --- a/translations/id/lessons/README.md +++ b/translations/id/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Gambaran Umum -![Gambaran Umum dalam sebuah sketsa](../../../translated_images/ai-overview.0857791951d19500.id.png) +![Gambaran Umum dalam sebuah sketsa](../../../translated_images/id/ai-overview.0857791951d19500.png) > Sketsa oleh [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/id/lessons/X-Extras/X1-MultiModal/README.md b/translations/id/lessons/X-Extras/X1-MultiModal/README.md index 0fa6b0f9..89172fbe 100644 --- a/translations/id/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/id/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Setelah keberhasilan model transformer dalam menyelesaikan tugas NLP, arsitektur Ide utama dari CLIP adalah untuk dapat membandingkan teks dengan gambar dan menentukan seberapa baik gambar tersebut sesuai dengan teks. -![Arsitektur CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.id.png) +![Arsitektur CLIP](../../../../../translated_images/id/clip-arch.b3dbf20b4e8ed8be.png) > *Gambar dari [blog ini](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Setelah model ini dilatih sebelumnya, kita dapat memberikannya batch gambar dan Misalkan kita perlu mengklasifikasikan gambar antara, misalnya, kucing, anjing, dan manusia. Dalam kasus ini, kita dapat memberikan model sebuah gambar, dan serangkaian teks: "*gambar seekor kucing*", "*gambar seekor anjing*", "*gambar seorang manusia*". Dalam vektor hasil dengan 3 probabilitas, kita hanya perlu memilih indeks dengan nilai tertinggi. -![CLIP untuk Klasifikasi Gambar](../../../../../translated_images/clip-class.3af42ef0b2b19369.id.png) +![CLIP untuk Klasifikasi Gambar](../../../../../translated_images/id/clip-class.3af42ef0b2b19369.png) > *Gambar dari [blog ini](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Pelajari lebih lanjut tentang VQGAN di situs web [Taming Transformers](https://c Salah satu perbedaan penting antara VQGAN dan GAN tradisional adalah bahwa GAN tradisional dapat menghasilkan gambar yang cukup baik dari vektor input apa pun, sementara VQGAN cenderung menghasilkan gambar yang tidak koheren. Oleh karena itu, kita perlu membimbing lebih lanjut proses pembuatan gambar, dan itu dapat dilakukan menggunakan CLIP. -![Arsitektur VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.id.png) +![Arsitektur VQGAN+CLIP](../../../../../translated_images/id/vqgan.5027fe05051dfa31.png) Untuk menghasilkan gambar yang sesuai dengan teks, kita mulai dengan vektor encoding acak yang diteruskan melalui VQGAN untuk menghasilkan gambar. Kemudian CLIP digunakan untuk menghasilkan fungsi loss yang menunjukkan seberapa baik gambar sesuai dengan teks. Tujuannya adalah meminimalkan loss ini, menggunakan backpropagation untuk menyesuaikan parameter vektor input. Pustaka hebat yang mengimplementasikan VQGAN+CLIP adalah [Pixray](http://github.com/pixray/pixray). -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.id.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Gambar yang dihasilkan dari teks *a closeup watercolor portrait of young male teacher of literature with a book* | Gambar yang dihasilkan dari teks *a closeup oil portrait of young female teacher of computer science with a computer* | Gambar yang dihasilkan dari teks *a closeup oil portrait of old male teacher of mathematics in front of blackboard* @@ -75,7 +75,7 @@ Berbeda dengan CLIP, DALL-E menerima teks dan gambar sebagai satu aliran token u Perbedaan utama antara DALL-E 1 dan 2 adalah bahwa DALL-E 2 menghasilkan gambar dan seni yang lebih realistis. Contoh gambar yang dihasilkan dengan DALL-E: -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.id.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/id/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Gambar yang dihasilkan dari teks *a closeup watercolor portrait of young male teacher of literature with a book* | Gambar yang dihasilkan dari teks *a closeup oil portrait of young female teacher of computer science with a computer* | Gambar yang dihasilkan dari teks *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/it/README.md b/translations/it/README.md index c43078b0..d4ccdbde 100644 --- a/translations/it/README.md +++ b/translations/it/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Intelligenza Artificiale per Principianti - Un Curriculum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.it.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/it/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote di [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/it/lessons/1-Intro/README.md b/translations/it/lessons/1-Intro/README.md index 169709b2..ef4d1a13 100644 --- a/translations/it/lessons/1-Intro/README.md +++ b/translations/it/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduzione all'Intelligenza Artificiale -![Riepilogo del contenuto dell'introduzione all'IA in uno schizzo](../../../../translated_images/ai-intro.bf28d1ac4235881c.it.png) +![Riepilogo del contenuto dell'introduzione all'IA in uno schizzo](../../../../translated_images/it/ai-intro.bf28d1ac4235881c.png) > Schizzo di [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originariamente, i computer furono inventati da [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) per operare sui numeri seguendo una procedura ben definita - un algoritmo. I computer moderni, anche se significativamente più avanzati rispetto al modello originale proposto nel XIX secolo, seguono ancora lo stesso principio di calcoli controllati. Pertanto, è possibile programmare un computer per fare qualcosa se conosciamo la sequenza esatta di passaggi necessari per raggiungere l'obiettivo. -![Foto di una persona](../../../../translated_images/dsh_age.d212a30d4e54fb5f.it.png) +![Foto di una persona](../../../../translated_images/it/dsh_age.d212a30d4e54fb5f.png) > Foto di [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Per maggiori informazioni, consulta **[Intelligenza Artificiale Generale](https: Uno dei problemi nel trattare il termine **[Intelligenza](https://en.wikipedia.org/wiki/Intelligence)** è che non esiste una definizione chiara di questo termine. Si potrebbe sostenere che l'intelligenza sia collegata al **pensiero astratto**, o alla **autoconsapevolezza**, ma non possiamo definirla correttamente. -![Foto di un gatto](../../../../translated_images/photo-cat.8c8e8fb760ffe457.it.jpg) +![Foto di un gatto](../../../../translated_images/it/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) di [Amber Kipp](https://unsplash.com/@sadmax) da Unsplash @@ -98,13 +98,13 @@ In alternativa, possiamo cercare di modellare gli elementi più semplici all'int > | E il ML? | | > |--------------|-----------| -> | Parte dell'Intelligenza Artificiale basata sull'apprendimento del computer per risolvere un problema basato su alcuni dati è chiamata **Machine Learning**. Non considereremo il machine learning classico in questo corso - ti rimandiamo al curriculum separato [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML per Principianti](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.it.png) | +> | Parte dell'Intelligenza Artificiale basata sull'apprendimento del computer per risolvere un problema basato su alcuni dati è chiamata **Machine Learning**. Non considereremo il machine learning classico in questo corso - ti rimandiamo al curriculum separato [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML per Principianti](../../../../translated_images/it/ml-for-beginners.9e4fed176fd5817d.png) | ## Breve Storia dell'IA L'Intelligenza Artificiale è iniziata come campo a metà del ventesimo secolo. Inizialmente, il ragionamento simbolico era l'approccio prevalente e portò a una serie di successi importanti, come i sistemi esperti – programmi informatici in grado di agire come esperti in alcuni domini di problemi limitati. Tuttavia, presto divenne chiaro che tale approccio non si scala bene. Estrarre la conoscenza da un esperto, rappresentarla in un computer e mantenere accurata quella base di conoscenza si rivelò un compito molto complesso e troppo costoso per essere pratico in molti casi. Questo portò al cosiddetto [AI Winter](https://en.wikipedia.org/wiki/AI_winter) negli anni '70. -Breve Storia dell'IA +Breve Storia dell'IA > Immagine di [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Allo stesso modo, possiamo vedere come l'approccio verso la creazione di "progra * Gli assistenti moderni, come Cortana, Siri o Google Assistant, sono tutti sistemi ibridi che utilizzano reti neurali per convertire il discorso in testo e riconoscere il nostro intento, e poi impiegano qualche ragionamento o algoritmi espliciti per eseguire le azioni richieste. * In futuro, possiamo aspettarci un modello completamente basato su reti neurali per gestire il dialogo autonomamente. Le recenti famiglie di reti neurali GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostrano grandi successi in questo. -l'evoluzione del Test di Turing +l'evoluzione del Test di Turing > Immagine di Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) di [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Ricerca recente sull'IA diff --git a/translations/it/lessons/2-Symbolic/Animals.ipynb b/translations/it/lessons/2-Symbolic/Animals.ipynb index 8f76c4d3..d28ce9d3 100644 --- a/translations/it/lessons/2-Symbolic/Animals.ipynb +++ b/translations/it/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "In questo esempio, implementeremo un semplice sistema basato sulla conoscenza per determinare un animale in base ad alcune caratteristiche fisiche. Il sistema può essere rappresentato dal seguente albero AND-OR (questa è solo una parte dell'intero albero, possiamo facilmente aggiungere altre regole):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.it.png)\n" + "![](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/it/lessons/2-Symbolic/README.md b/translations/it/lessons/2-Symbolic/README.md index a2d49b3e..7b8b34e2 100644 --- a/translations/it/lessons/2-Symbolic/README.md +++ b/translations/it/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Rappresentazione della Conoscenza e Sistemi Esperti -![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/ai-symbolic.715a30cb610411a6.it.png) +![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/it/ai-symbolic.715a30cb610411a6.png) > Sketchnote di [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Spesso non definiamo rigorosamente la conoscenza, ma la allineiamo ad altri conc Pertanto, il problema della **rappresentazione della conoscenza** è trovare un modo efficace per rappresentare la conoscenza all'interno di un computer sotto forma di dati, per renderla automaticamente utilizzabile. Questo può essere visto come uno spettro: -![Spettro della rappresentazione della conoscenza](../../../../translated_images/knowledge-spectrum.b60df631852c0217.it.png) +![Spettro della rappresentazione della conoscenza](../../../../translated_images/it/knowledge-spectrum.b60df631852c0217.png) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintassi Blocco | Indentazione | | | Uno dei primi successi dell'IA simbolica furono i cosiddetti **sistemi esperti** - sistemi informatici progettati per agire come esperti in un dominio di problemi limitato. Si basavano su una **base di conoscenza** estratta da uno o più esperti umani e contenevano un **motore di inferenza** che eseguiva un ragionamento su di essa. -![Architettura umana](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.it.png) | ![Sistema basato sulla conoscenza](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.it.png) +![Architettura umana](../../../../translated_images/it/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema basato sulla conoscenza](../../../../translated_images/it/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Struttura semplificata del sistema neurale umano | Architettura di un sistema basato sulla conoscenza @@ -106,7 +106,7 @@ I sistemi esperti sono costruiti come il sistema di ragionamento umano, che cont Come esempio, consideriamo il seguente sistema esperto per determinare un animale basandosi sulle sue caratteristiche fisiche: -![Albero AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.it.png) +![Albero AND-OR](../../../../translated_images/it/AND-OR-Tree.5592d2c70187f283.png) > Immagine di [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 16cd7449..e7937dad 100644 --- a/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Nel caso in cui abbiamo più di 2 classi, softmax normalizzerà le probabilità tra tutte. Ecco un diagramma dell'architettura della rete che esegue la classificazione delle cifre MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.it.png)\n" + "![MNIST Classifier](../../../../../translated_images/it/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1256,7 +1256,7 @@ "* Bassa perdita sui dati di addestramento - il modello può approssimare bene i dati di addestramento, perché ha abbastanza potenza espressiva.\n", "* La perdita sui dati di validazione può essere molto più alta rispetto a quella sui dati di addestramento e può iniziare ad aumentare durante l'addestramento - questo accade perché il modello \"memorizza\" i punti di addestramento, perdendo la \"visione d'insieme\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.it.png)\n", + "![Overfitting](../../../../../translated_images/it/overfit.a0bd57f717c15769.png)\n", "\n", "> In questa immagine, `x` rappresenta i dati di addestramento, `o` i dati di validazione. A sinistra - modello lineare (a uno strato), approssima abbastanza bene la natura dei dati. A destra - modello sovradattato, il modello approssima perfettamente i dati di addestramento, ma perde di significato con qualsiasi altro dato (l'errore di validazione è molto alto).\n" ] diff --git a/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md index a8a03c46..76d8a0fe 100644 --- a/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/it/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ L'overfitting è un concetto estremamente importante nel machine learning, ed è Considera il seguente problema di approssimazione di 5 punti (rappresentati da `x` nei grafici sottostanti): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.it.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.it.jpg) +![linear](../../../../../translated_images/it/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/it/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Modello lineare, 2 parametri** | **Modello non lineare, 7 parametri** Errore di training = 5.3 | Errore di training = 0 @@ -79,7 +79,7 @@ Errore di validazione = 5.1 | Errore di validazione = 20 Come puoi vedere dal grafico sopra, l'overfitting può essere rilevato da un errore di training molto basso e un errore di validazione molto alto. Normalmente durante l'allenamento vedremo sia l'errore di training che quello di validazione iniziare a diminuire, e poi a un certo punto l'errore di validazione potrebbe smettere di diminuire e iniziare a salire. Questo sarà un segnale di overfitting e un indicatore che probabilmente dovremmo interrompere l'allenamento a questo punto (o almeno fare uno snapshot del modello). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.it.png) +![overfitting](../../../../../translated_images/it/Overfitting.408ad91cd90b4371.png) ## Come prevenire l'overfitting diff --git a/translations/it/lessons/3-NeuralNetworks/README.md b/translations/it/lessons/3-NeuralNetworks/README.md index 58be16ef..6a99d120 100644 --- a/translations/it/lessons/3-NeuralNetworks/README.md +++ b/translations/it/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduzione alle Reti Neurali -![Riassunto del contenuto di Introduzione alle Reti Neurali in uno schizzo](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.it.png) +![Riassunto del contenuto di Introduzione alle Reti Neurali in uno schizzo](../../../../translated_images/it/ai-neuralnetworks.1c687ae40bc86e83.png) Come discusso nell'introduzione, uno dei modi per raggiungere l'intelligenza è addestrare un **modello informatico** o un **cervello artificiale**. Dalla metà del XX secolo, i ricercatori hanno sperimentato diversi modelli matematici, finché negli ultimi anni questa direzione si è dimostrata estremamente promettente. Questi modelli matematici del cervello sono chiamati **reti neurali**. @@ -36,13 +36,13 @@ In questo curriculum, ci concentreremo solo sui modelli di reti neurali. Dalla biologia, sappiamo che il nostro cervello è composto da cellule neurali (neuroni), ciascuna delle quali ha molteplici "input" (dendriti) e un singolo "output" (assone). Sia i dendriti che gli assoni possono condurre segnali elettrici, e le connessioni tra di loro — note come sinapsi — possono mostrare diversi gradi di conduttività, regolati dai neurotrasmettitori. -![Modello di un Neurone](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.it.jpg) | ![Modello di un Neurone](../../../../translated_images/artneuron.1a5daa88d20ebe6f.it.png) +![Modello di un Neurone](../../../../translated_images/it/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modello di un Neurone](../../../../translated_images/it/artneuron.1a5daa88d20ebe6f.png) ----|---- Neurone Reale *([Immagine](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipedia)* | Neurone Artificiale *(Immagine dell'autore)* Pertanto, il modello matematico più semplice di un neurone contiene diversi input X1, ..., XN e un output Y, e una serie di pesi W1, ..., WN. L'output viene calcolato come: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) dove f è una **funzione di attivazione** non lineare. diff --git a/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md index a67f9d59..6149db42 100644 --- a/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/it/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Nel nostro [OpenCV Notebook](OpenCV.ipynb), forniamo alcuni esempi di quando la * **Pre-elaborazione di una fotografia di un libro in Braille**. Ci concentriamo su come possiamo utilizzare thresholding, rilevamento delle caratteristiche, trasformazione prospettica e manipolazioni NumPy per separare i singoli simboli Braille per una successiva classificazione tramite una rete neurale. -![Immagine Braille](../../../../../translated_images/braille.341962ff76b1bd70.it.jpeg) | ![Immagine Braille Pre-elaborata](../../../../../translated_images/braille-result.46530fea020b03c7.it.png) | ![Simboli Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.it.png) +![Immagine Braille](../../../../../translated_images/it/braille.341962ff76b1bd70.jpeg) | ![Immagine Braille Pre-elaborata](../../../../../translated_images/it/braille-result.46530fea020b03c7.png) | ![Simboli Braille](../../../../../translated_images/it/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Immagine da [OpenCV.ipynb](OpenCV.ipynb) * **Rilevamento del movimento in video utilizzando la differenza tra fotogrammi**. Se la fotocamera è fissa, i fotogrammi del feed della fotocamera dovrebbero essere abbastanza simili tra loro. Poiché i fotogrammi sono rappresentati come array, semplicemente sottraendo questi array per due fotogrammi consecutivi otterremo la differenza dei pixel, che dovrebbe essere bassa per fotogrammi statici e diventare più alta quando c'è un movimento sostanziale nell'immagine. -![Immagine dei fotogrammi video e differenze tra fotogrammi](../../../../../translated_images/frame-difference.706f805491a0883c.it.png) +![Immagine dei fotogrammi video e differenze tra fotogrammi](../../../../../translated_images/it/frame-difference.706f805491a0883c.png) > Immagine da [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Nel nostro [OpenCV Notebook](OpenCV.ipynb), forniamo alcuni esempi di quando la - **Dense Optical Flow** calcola il campo vettoriale che mostra per ogni pixel dove si sta muovendo. - **Sparse Optical Flow** si basa sull'individuazione di alcune caratteristiche distintive nell'immagine (ad esempio, bordi) e sulla costruzione della loro traiettoria da fotogramma a fotogramma. -![Immagine di Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.it.png) +![Immagine di Optical Flow](../../../../../translated_images/it/optical.1f4a94464579a83a.png) > Immagine da [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 763d598e..daf0bd43 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 è una rete che ha raggiunto il 92,7% di accuratezza nella classificazione top-5 di ImageNet nel 2014. Ha la seguente struttura di livelli: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.it.jpg) +![ImageNet Layers](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.jpg) Come puoi vedere, VGG segue una tradizionale architettura a piramide, che consiste in una sequenza di livelli di convoluzione e pooling. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.it.jpg) +![ImageNet Pyramid](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.jpg) > Immagine da [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 16fc6e83..446b2a4e 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Pertanto, in una CNN tipica ci sarebbero diversi livelli di convoluzione, con livelli di pooling tra di essi per ridurre le dimensioni dell'immagine. Aumenteremmo anche il numero di filtri, perché man mano che gli schemi diventano più complessi, ci sono più combinazioni interessanti che dobbiamo cercare.\n", "\n", - "![Un'immagine che mostra diversi livelli di convoluzione con livelli di pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.it.png)\n", + "![Un'immagine che mostra diversi livelli di convoluzione con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "A causa della diminuzione delle dimensioni spaziali e dell'aumento delle dimensioni delle caratteristiche/filtri, questa architettura è anche chiamata **architettura a piramide**.\n" ] diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index a74217f9..c9b275f3 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Pertanto, in una CNN tipica ci sarebbero diversi strati convoluzionali, con livelli di pooling tra di essi per ridurre le dimensioni dell'immagine. Inoltre, aumenteremmo il numero di filtri, perché man mano che gli schemi diventano più avanzati, ci sono più possibili combinazioni interessanti che dobbiamo cercare.\n", "\n", - "![Un'immagine che mostra diversi strati convoluzionali con livelli di pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.it.png)\n", + "![Un'immagine che mostra diversi strati convoluzionali con livelli di pooling.](../../../../../translated_images/it/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "A causa della diminuzione delle dimensioni spaziali e dell'aumento delle dimensioni delle caratteristiche/filtri, questa architettura è anche chiamata **architettura a piramide**.\n" ] diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md index 0eb0b78f..e15d7791 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Nella vita reale, vogliamo essere in grado di riconoscere oggetti in un'immagine Per estrarre schemi, utilizzeremo il concetto di **filtri convoluzionali**. Come sapete, un'immagine è rappresentata da una matrice 2D o da un tensore 3D con profondità di colore. Applicare un filtro significa prendere una matrice relativamente piccola chiamata **kernel del filtro**, e per ogni pixel dell'immagine originale calcolare la media ponderata con i punti vicini. Possiamo immaginare questo processo come una piccola finestra che scorre su tutta l'immagine, mediando tutti i pixel secondo i pesi nella matrice del kernel del filtro. -![Filtro per bordi verticali](../../../../../translated_images/filter-vert.b7148390ca0bc356.it.png) | ![Filtro per bordi orizzontali](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.it.png) +![Filtro per bordi verticali](../../../../../translated_images/it/filter-vert.b7148390ca0bc356.png) | ![Filtro per bordi orizzontali](../../../../../translated_images/it/filter-horiz.59b80ed4feb946ef.png) ----|---- > Immagine di Dmitry Soshnikov @@ -38,7 +38,7 @@ Il funzionamento delle CNN si basa sulle seguenti idee fondamentali: * Possiamo progettare la rete in modo che i filtri vengano addestrati automaticamente * Possiamo utilizzare lo stesso approccio per trovare schemi in caratteristiche di alto livello, non solo nell'immagine originale. Pertanto, l'estrazione delle caratteristiche nelle CNN funziona su una gerarchia di caratteristiche, partendo da combinazioni di pixel di basso livello fino ad arrivare a combinazioni di alto livello di parti dell'immagine. -![Estrazione gerarchica delle caratteristiche](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.it.png) +![Estrazione gerarchica delle caratteristiche](../../../../../translated_images/it/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Immagine tratta da [un articolo di Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basato sulla [loro ricerca](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ La maggior parte delle CNN utilizzate per l'elaborazione delle immagini segue un Ad esempio, osserviamo l'architettura di VGG-16, una rete che ha raggiunto il 92,7% di accuratezza nella classificazione top-5 di ImageNet nel 2014: -![Strati di ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.it.jpg) +![Strati di ImageNet](../../../../../translated_images/it/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramide di ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.it.jpg) +![Piramide di ImageNet](../../../../../translated_images/it/vgg-16-arch.64ff2137f50dd49f.jpg) > Immagine tratta da [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 090efe1f..bfa744f5 100644 --- a/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/it/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Devi addestrare una rete neurale convoluzionale per classificare le diverse razz Utilizzeremo il [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), che contiene immagini di 37 diverse razze di cani e gatti. -![Dataset con cui lavoreremo](../../../../../../translated_images/data.50b2a9d5484bdbf0.it.png) +![Dataset con cui lavoreremo](../../../../../../translated_images/it/data.50b2a9d5484bdbf0.png) Per scaricare il dataset, utilizza questo frammento di codice: diff --git a/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index a9594d06..27116a57 100644 --- a/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/it/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Per visualizzare il gatto ideale, inizieremo con un'immagine di rumore casuale e cercheremo di utilizzare la tecnica di ottimizzazione della discesa del gradiente per modificare l'immagine in modo che una rete riconosca un gatto.\n", "\n", - "![Ciclo di Ottimizzazione](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.it.png)\n", + "![Ciclo di Ottimizzazione](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Ecco la nostra immagine di partenza:\n" ] diff --git a/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md index dc2e1e81..8b8e1959 100644 --- a/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/it/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Sia Keras che PyTorch contengono funzioni per caricare facilmente i pesi di reti Ecco alcune caratteristiche estratte da un'immagine di un gatto dalla rete VGG-16: -![Caratteristiche estratte da VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.it.png) +![Caratteristiche estratte da VGG-16](../../../../../translated_images/it/features.6291f9c7ba3a0b95.png) ## Dataset Gatti vs. Cani @@ -48,19 +48,19 @@ Una rete neurale pre-addestrata contiene diversi schemi all'interno del suo *cer Un approccio che possiamo adottare è partire da un'immagine casuale e poi cercare di utilizzare la tecnica di **ottimizzazione con discesa del gradiente** per modificare quell'immagine in modo tale che la rete inizi a pensare che sia un gatto. -![Ciclo di Ottimizzazione dell'Immagine](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.it.png) +![Ciclo di Ottimizzazione dell'Immagine](../../../../../translated_images/it/ideal-cat-loop.999fbb8ff306e044.png) Tuttavia, se facciamo questo, otterremo qualcosa di molto simile a un rumore casuale. Questo perché *ci sono molti modi per far pensare alla rete che l'immagine di input sia un gatto*, inclusi alcuni che non hanno senso visivamente. Sebbene queste immagini contengano molti schemi tipici di un gatto, non c'è nulla che le vincoli a essere visivamente distintive. Per migliorare il risultato, possiamo aggiungere un altro termine alla funzione di perdita, chiamato **variation loss**. È una metrica che mostra quanto sono simili i pixel vicini dell'immagine. Minimizzare la variation loss rende l'immagine più liscia e elimina il rumore, rivelando così schemi più visivamente piacevoli. Ecco un esempio di queste immagini "ideali", classificate come gatto e zebra con alta probabilità: -![Gatto Ideale](../../../../../translated_images/ideal-cat.203dd4597643d6b0.it.png) | ![Zebra Ideale](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.it.png) +![Gatto Ideale](../../../../../translated_images/it/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideale](../../../../../translated_images/it/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Gatto Ideale* | *Zebra Ideale* Un approccio simile può essere utilizzato per eseguire i cosiddetti **attacchi avversari** su una rete neurale. Supponiamo di voler ingannare una rete neurale e far sembrare un cane un gatto. Se prendiamo l'immagine di un cane, che è riconosciuta dalla rete come un cane, possiamo modificarla leggermente utilizzando l'ottimizzazione con discesa del gradiente, fino a quando la rete inizia a classificarla come un gatto: -![Immagine di un Cane](../../../../../translated_images/original-dog.8f68a67d2fe0911f.it.png) | ![Immagine di un cane classificata come gatto](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.it.png) +![Immagine di un Cane](../../../../../translated_images/it/original-dog.8f68a67d2fe0911f.png) | ![Immagine di un cane classificata come gatto](../../../../../translated_images/it/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Immagine originale di un cane* | *Immagine di un cane classificata come gatto* diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index aa76c121..243111c8 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Poiché stiamo addestrando l'autoencoder per catturare quante più informazioni possibili dall'immagine originale al fine di ottenere una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per coglierne il significato.\n", "\n", - "![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Immagine dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 00f8c0fb..bc657cb6 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Poiché stiamo addestrando l'autoencoder per catturare quante più informazioni possibili dall'immagine originale al fine di ottenere una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per rappresentarne il significato.\n", "\n", - "![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Immagine tratta dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md index 18f3ff76..49d94d0a 100644 --- a/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/it/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tuttavia, potremmo voler utilizzare dati grezzi (non etichettati) per allenare g Poiché stiamo allenando un autoencoder per catturare quante più informazioni possibili dall'immagine originale per una ricostruzione accurata, la rete cerca di trovare il miglior **embedding** delle immagini di input per catturarne il significato. -![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.jpg) +![Diagramma AutoEncoder](../../../../../translated_images/it/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Immagine dal [blog di Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md index cb1fc9ec..0653028e 100644 --- a/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/it/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ I modelli di classificazione delle immagini che abbiamo trattato finora prendeva ## [Quiz pre-lezione](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Rilevamento degli Oggetti](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.it.png) +![Rilevamento degli Oggetti](../../../../../translated_images/it/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Immagine dal [sito web di YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supponendo di voler trovare un gatto in un'immagine, un approccio molto semplice 2. Eseguire la classificazione delle immagini su ciascun riquadro. 3. I riquadri che producono un'attivazione sufficientemente alta possono essere considerati contenere l'oggetto in questione. -![Rilevamento Naïf](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.it.png) +![Rilevamento Naïf](../../../../../translated_images/it/naive-detection.e7f1ba220ccd08c6.png) > *Immagine dal [Notebook di Esercizi](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Potresti imbatterti nei seguenti dataset per questo compito: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classi * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 classi, riquadri delimitatori e maschere di segmentazione -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.it.jpg) +![COCO](../../../../../translated_images/it/coco-examples.71bc60380fa6cceb.jpg) ## Metriche per il Rilevamento degli Oggetti @@ -50,7 +50,7 @@ Potresti imbatterti nei seguenti dataset per questo compito: Mentre per la classificazione delle immagini è facile misurare quanto bene l'algoritmo performa, per il rilevamento degli oggetti dobbiamo misurare sia la correttezza della classe, sia la precisione della posizione del riquadro delimitatore inferito. Per quest'ultimo, utilizziamo la cosiddetta **Intersezione su Unione** (IoU), che misura quanto bene due riquadri (o due aree arbitrarie) si sovrappongono. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.it.png) +![IoU](../../../../../translated_images/it/iou_equation.9a4751d40fff4e11.png) > *Figura 2 da [questo eccellente post sul blog su IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Esistono due grandi classi di algoritmi di rilevamento degli oggetti: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utilizza [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) per generare una struttura gerarchica di regioni ROI, che vengono poi passate attraverso estrattori di caratteristiche CNN e classificatori SVM per determinare la classe dell'oggetto, e regressione lineare per determinare le coordinate del *riquadro delimitatore*. [Paper ufficiale](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.it.png) +![RCNN](../../../../../translated_images/it/rcnn1.cae407020dfb1d1f.png) > *Immagine da van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.it.png) +![RCNN-1](../../../../../translated_images/it/rcnn2.2d9530bb83516484.png) > *Immagini da [questo blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Esistono due grandi classi di algoritmi di rilevamento degli oggetti: Questo approccio è simile a R-CNN, ma le regioni vengono definite dopo che i livelli di convoluzione sono stati applicati. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.it.png) +![FRCNN](../../../../../translated_images/it/f-rcnn.3cda6d9bb4188875.png) > Immagine dal [Paper ufficiale](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Questo approccio è simile a R-CNN, ma le regioni vengono definite dopo che i li L'idea principale di questo approccio è utilizzare una rete neurale per prevedere le ROI - la cosiddetta *Rete di Proposta di Regione*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.it.png) +![FasterRCNN](../../../../../translated_images/it/faster-rcnn.8d46c099b87ef30a.png) > Immagine dal [Paper ufficiale](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Questo algoritmo è ancora più veloce di Faster R-CNN. L'idea principale è la 1. Le caratteristiche vengono elaborate da **Position-Sensitive Score Map**. Ogni oggetto delle classi $C$ è suddiviso in regioni $k\times k$, e si addestra per prevedere parti degli oggetti. 1. Per ogni parte delle regioni $k\times k$ tutte le reti votano per le classi degli oggetti, e la classe dell'oggetto con il massimo voto viene selezionata. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.it.png) +![r-fcn image](../../../../../translated_images/it/r-fcn.13eb88158b99a3da.png) > Immagine dal [Paper ufficiale](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO è un algoritmo in tempo reale a passaggio unico. L'idea principale è la s * L'immagine è suddivisa in regioni $S\times S$ * Per ogni regione, **CNN** prevede $n$ oggetti possibili, le coordinate del *riquadro delimitatore* e la *confidence*=*probabilità* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.it.png) + ![YOLO](../../../../../translated_images/it/yolo.a2648ec82ee8bb4e.png) > Immagine dal [Paper ufficiale](https://arxiv.org/abs/1506.02640) diff --git a/translations/it/lessons/4-ComputerVision/README.md b/translations/it/lessons/4-ComputerVision/README.md index b95e14a1..5b816f65 100644 --- a/translations/it/lessons/4-ComputerVision/README.md +++ b/translations/it/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visione Artificiale -![Riassunto dei contenuti sulla Visione Artificiale in uno schizzo](../../../../translated_images/ai-computervision.6506ebebac3fbf76.it.png) +![Riassunto dei contenuti sulla Visione Artificiale in uno schizzo](../../../../translated_images/it/ai-computervision.6506ebebac3fbf76.png) In questa sezione impareremo: diff --git a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index ddb36143..a926625f 100644 --- a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "La rappresentazione vettoriale **Bag of Words** (BoW) è la rappresentazione vettoriale tradizionale più comunemente utilizzata. Ogni parola è collegata a un indice del vettore, e l'elemento del vettore contiene il numero di occorrenze di una parola in un determinato documento.\n", "\n", - "![Immagine che mostra come una rappresentazione vettoriale Bag of Words sia rappresentata in memoria.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.it.png) \n", + "![Immagine che mostra come una rappresentazione vettoriale Bag of Words sia rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Nota**: Puoi anche pensare al BoW come alla somma di tutti i vettori one-hot-encoded per le singole parole nel testo.\n", "\n", diff --git a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c06734f7..ccdeb12f 100644 --- a/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/it/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "La rappresentazione vettoriale **Bag-of-words** (BoW) è la più semplice da comprendere tra le rappresentazioni vettoriali tradizionali. Ogni parola è collegata a un indice vettoriale, e un elemento del vettore contiene il numero di occorrenze di ciascuna parola in un determinato documento.\n", "\n", - "![Immagine che mostra come una rappresentazione vettoriale bag-of-words è rappresentata in memoria.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.it.png) \n", + "![Immagine che mostra come una rappresentazione vettoriale bag-of-words è rappresentata in memoria.](../../../../../translated_images/it/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Nota**: Puoi anche pensare al BoW come alla somma di tutti i vettori one-hot-encoded per le singole parole nel testo.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 5e824257..c3c8f50a 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Utilizzando il livello di embedding come primo livello nella nostra rete, possiamo passare dal modello bag-of-words al modello **embedding bag**, dove prima convertiamo ogni parola nel nostro testo nel corrispondente embedding e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`.\n", "\n", - "![Immagine che mostra un classificatore embedding per cinque parole in sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.png)\n", + "![Immagine che mostra un classificatore embedding per cinque parole in sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "La nostra rete neurale classificatrice inizierà con un livello di embedding, seguito da un livello di aggregazione e un classificatore lineare sopra di esso:\n" ] @@ -176,7 +176,7 @@ "\n", "Nell'architettura precedente, era necessario riempire tutte le sequenze fino alla stessa lunghezza per adattarle a un minibatch. Questo non è il modo più efficiente per rappresentare sequenze a lunghezza variabile - un altro approccio sarebbe utilizzare un vettore di **offset**, che contiene gli offset di tutte le sequenze memorizzate in un unico grande vettore.\n", "\n", - "![Immagine che mostra una rappresentazione di sequenza con offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.it.png)\n", + "![Immagine che mostra una rappresentazione di sequenza con offset](../../../../../translated_images/it/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Nota**: Nell'immagine sopra, mostriamo una sequenza di caratteri, ma nel nostro esempio stiamo lavorando con sequenze di parole. Tuttavia, il principio generale di rappresentare le sequenze con un vettore di offset rimane lo stesso.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW è più veloce, mentre skip-gram è più lento, ma rappresenta meglio le parole meno frequenti.\n", "\n", - "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.it.png)\n", + "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Per sperimentare con embedding word2vec pre-addestrati sul dataset Google News, possiamo utilizzare la libreria **gensim**. Di seguito troviamo le parole più simili a 'neural'.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 4ef895fb..aa1f1b2c 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/it/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Utilizzando un livello di embedding come primo livello nella nostra rete, possiamo passare da un modello bag-of-words a un modello **embedding bag**, dove prima convertiamo ogni parola del nostro testo nel corrispondente embedding e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`.\n", "\n", - "![Immagine che mostra un classificatore con embedding per cinque parole di una sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.png)\n", + "![Immagine che mostra un classificatore con embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "La nostra rete neurale classificatrice è composta dai seguenti livelli:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW è più veloce, mentre skip-gram, pur essendo più lento, rappresenta meglio le parole meno frequenti.\n", "\n", - "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire le parole in vettori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.it.png)\n", + "![Immagine che mostra entrambi gli algoritmi CBoW e Skip-Gram per convertire le parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Per sperimentare con l'embedding Word2Vec pre-addestrato sul dataset di Google News, possiamo utilizzare la libreria **gensim**. Di seguito troviamo le parole più simili a 'neural'.\n", "\n", diff --git a/translations/it/lessons/5-NLP/14-Embeddings/README.md b/translations/it/lessons/5-NLP/14-Embeddings/README.md index 2efc97a1..c7554783 100644 --- a/translations/it/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/it/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Quindi, il livello di embedding prenderebbe una parola come input e produrrebbe Utilizzando un livello di embedding come primo livello nella nostra rete di classificazione, possiamo passare da un modello bag-of-words a un modello **embedding bag**, dove prima convertiamo ogni parola nel nostro testo nel corrispondente embedding, e poi calcoliamo una funzione aggregata su tutti questi embedding, come `sum`, `average` o `max`. -![Immagine che mostra un classificatore embedding per cinque parole di una sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.png) +![Immagine che mostra un classificatore embedding per cinque parole di una sequenza.](../../../../../translated_images/it/embedding-classifier-example.b77f021a7ee67eee.png) > Immagine dell'autore @@ -40,7 +40,7 @@ Per fare ciò, dobbiamo pre-addestrare il nostro modello di embedding su una gra CBoW è più veloce, mentre skip-gram è più lento, ma rappresenta meglio le parole meno frequenti. -![Immagine che mostra gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.it.png) +![Immagine che mostra gli algoritmi CBoW e Skip-Gram per convertire parole in vettori.](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Immagine tratta da [questo articolo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/it/lessons/5-NLP/15-LanguageModeling/README.md b/translations/it/lessons/5-NLP/15-LanguageModeling/README.md index 8682a762..1e9333d1 100644 --- a/translations/it/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/it/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nei nostri esempi precedenti, abbiamo utilizzato embedding semantici pre-addestr * **Continuous Bag-of-Words** (CBoW), in cui si predice il token centrale $W_0$ in una sequenza di token $W_{-N}$, ..., $W_N$. * **Skip-gram**, in cui si predice un insieme di token vicini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partire dal token centrale $W_0$. -![immagine tratta da un articolo sulla conversione di parole in vettori](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.it.png) +![immagine tratta da un articolo sulla conversione di parole in vettori](../../../../../translated_images/it/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Immagine tratta da [questo articolo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/it/lessons/5-NLP/16-RNN/README.md b/translations/it/lessons/5-NLP/16-RNN/README.md index 9379d36e..7fb39a60 100644 --- a/translations/it/lessons/5-NLP/16-RNN/README.md +++ b/translations/it/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nelle sezioni precedenti, abbiamo utilizzato rappresentazioni semantiche ricche Per catturare il significato di una sequenza di testo, dobbiamo utilizzare un'altra architettura di rete neurale, chiamata **rete neurale ricorrente**, o RNN. Nelle RNN, passiamo la nostra frase attraverso la rete un simbolo alla volta, e la rete produce uno **stato**, che poi passiamo nuovamente alla rete insieme al simbolo successivo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.it.png) +![RNN](../../../../../translated_images/it/rnn.27f5c29c53d727b5.png) > Immagine dell'autore @@ -61,7 +61,7 @@ Abbiamo discusso reti ricorrenti che operano in una direzione, dall'inizio di un Una rete ricorrente, sia unidirezionale che bidirezionale, cattura certi schemi all'interno di una sequenza e può memorizzarli in un vettore di stato o passarli all'output. Come con le reti convoluzionali, possiamo costruire un altro strato ricorrente sopra il primo per catturare schemi di livello superiore e costruire a partire dagli schemi di basso livello estratti dal primo strato. Questo ci porta al concetto di **RNN multistrato**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input. -![Immagine che mostra una RNN LSTM multistrato](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.jpg) +![Immagine che mostra una RNN LSTM multistrato](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Immagine tratta da [questo meraviglioso post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López* diff --git a/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index f84315c2..0ea4901b 100644 --- a/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Una rete ricorrente, sia unidirezionale che bidirezionale, cattura determinati schemi all'interno di una sequenza e può memorizzarli nel vettore di stato o passarli all'output. Come con le reti convoluzionali, possiamo costruire un altro livello ricorrente sopra il primo per catturare schemi di livello superiore, costruiti a partire dagli schemi di basso livello estratti dal primo livello. Questo ci porta al concetto di **RNN multilivello**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input.\n", "\n", - "![Immagine che mostra una rete RNN LSTM multilivello](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.jpg)\n", + "![Immagine che mostra una rete RNN LSTM multilivello](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Immagine tratta da [questo fantastico articolo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López*\n", "\n", diff --git a/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb index 7ee62f17..526b2028 100644 --- a/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/it/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Per catturare il significato di una sequenza di testo, utilizzeremo un'architettura di rete neurale chiamata **rete neurale ricorrente**, o RNN. Quando utilizziamo una RNN, passiamo la nostra frase attraverso la rete un token alla volta, e la rete produce uno **stato**, che poi passiamo nuovamente alla rete insieme al token successivo.\n", "\n", - "![Immagine che mostra un esempio di generazione con rete neurale ricorrente.](../../../../../translated_images/rnn.27f5c29c53d727b5.it.png)\n", + "![Immagine che mostra un esempio di generazione con rete neurale ricorrente.](../../../../../translated_images/it/rnn.27f5c29c53d727b5.png)\n", "\n", "Data la sequenza di input di token $X_0,\\dots,X_n$, la RNN crea una sequenza di blocchi di rete neurale e allena questa sequenza end-to-end utilizzando la retropropagazione. Ogni blocco di rete prende una coppia $(X_i,S_i)$ come input e produce $S_{i+1}$ come risultato. Lo stato finale $S_n$ o l'output $Y_n$ viene inviato a un classificatore lineare per produrre il risultato. Tutti i blocchi di rete condividono gli stessi pesi e vengono allenati end-to-end con un unico passaggio di retropropagazione.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Le reti ricorrenti, unidirezionali o bidirezionali, catturano schemi all'interno di una sequenza e li memorizzano in vettori di stato o li restituiscono come output. Come per le reti convoluzionali, possiamo costruire un altro livello ricorrente dopo il primo per catturare schemi di livello superiore, costruiti a partire dagli schemi di livello inferiore estratti dal primo livello. Questo ci porta al concetto di **RNN multilivello**, che consiste in due o più reti ricorrenti, dove l'output del livello precedente viene passato al livello successivo come input.\n", "\n", - "![Immagine che mostra una RNN multilivello con memoria a lungo termine](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.jpg)\n", + "![Immagine che mostra una RNN multilivello con memoria a lungo termine](../../../../../translated_images/it/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Immagine tratta da [questo fantastico articolo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) di Fernando López.*\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 21f5f65f..9a4ee6e3 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Il modo in cui addestreremo l'RNN per generare testo è il seguente. A ogni passo, prenderemo una sequenza di caratteri di lunghezza `nchars` e chiederemo alla rete di generare il carattere successivo per ogni carattere di input:\n", "\n", - "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png)\n", "\n", "A seconda dello scenario specifico, potremmo anche voler includere alcuni caratteri speciali, come *fine-sequenza* ``. Nel nostro caso, vogliamo semplicemente addestrare la rete per una generazione di testo continua, quindi fisseremo la dimensione di ogni sequenza uguale a `nchars` token. Di conseguenza, ogni esempio di addestramento sarà composto da `nchars` input e `nchars` output (che sono la sequenza di input spostata di un simbolo a sinistra). Il minibatch sarà composto da diverse di queste sequenze.\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 11fa2ee2..8ca2175c 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Il modo in cui addestreremo l'RNN per generare titoli di notizie è il seguente. A ogni passo, prenderemo un titolo, che verrà fornito a un RNN, e per ogni carattere di input chiederemo alla rete di generare il carattere di output successivo:\n", "\n", - "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Per l'ultimo carattere della nostra sequenza, chiederemo alla rete di generare il token ``.\n", "\n", diff --git a/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md index e50dc60f..b7384d01 100644 --- a/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/it/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Nell'architettura RNN discussa nell'unità precedente, ogni unità RNN produceva Questo consente diverse architetture neurali, come mostrato nell'immagine seguente: -![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.it.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Immagine tratta dal post del blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) di [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In questa unità, ci concentreremo su modelli generativi semplici che ci aiutano Addestreremo questa RNN per generare testo passo dopo passo. A ogni passo, prenderemo una sequenza di caratteri di lunghezza `nchars` e chiederemo alla rete di generare il carattere successivo per ciascun carattere di input: -![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.png) +![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/it/rnn-generate.56c54afb52f9781d.png) Durante la generazione del testo (in fase di inferenza), iniziamo con un **prompt**, che viene passato attraverso le celle RNN per generare il suo stato intermedio, e da questo stato inizia la generazione. Generiamo un carattere alla volta, passando lo stato e il carattere generato a un'altra cella RNN per generare il successivo, fino a quando non abbiamo generato un numero sufficiente di caratteri. diff --git a/translations/it/lessons/5-NLP/18-Transformers/README.md b/translations/it/lessons/5-NLP/18-Transformers/README.md index e231bd77..4691c93e 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/README.md +++ b/translations/it/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Con gli RNN, il sequence-to-sequence viene implementato da due reti ricorrenti, I **Meccanismi di Attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Questo viene implementato creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output yt, si prendono in considerazione tutti gli stati nascosti di input hi, con diversi coefficienti di peso αt,i. -![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.png) +![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png) > Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice di attenzione {αi,j} rappresenta il grado in cui alcune parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice: -![Immagine che mostra un allineamento di esempio trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.png) +![Immagine che mostra un allineamento di esempio trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png) > Figura da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Il risultato che otteniamo con l'embedding posizionale incorpora sia il token or Successivamente, dobbiamo catturare alcuni schemi all'interno della nostra sequenza. Per fare ciò, i transformers utilizzano un meccanismo di **auto-attenzione**, che è essenzialmente attenzione applicata alla stessa sequenza come input e output. Applicare l'auto-attenzione ci consente di tenere conto del **contesto** all'interno della frase e vedere quali parole sono interconnesse. Ad esempio, ci consente di vedere quali parole sono riferite da coreferenze, come *it*, e di considerare il contesto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.it.png) +![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.png) > Immagine dal [Blog di Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Poiché ogni posizione di input viene mappata indipendentemente a ogni posizione **BERT** (Bidirectional Encoder Representations from Transformers) è una rete transformer multi-strato molto grande con 12 strati per *BERT-base* e 24 per *BERT-large*. Il modello viene prima pre-addestrato su un ampio corpus di dati testuali (Wikipedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante il pre-addestramento, il modello acquisisce livelli significativi di comprensione del linguaggio che possono poi essere sfruttati con altri dataset utilizzando il fine tuning. Questo processo è chiamato **transfer learning**. -![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.png) +![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Immagine [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md index 3cff3f6f..7d792b97 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/it/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Avec les RNN, la séquence-à-séquence est mise en œuvre par deux réseaux ré **Les Mécanismes d'Attention** fournissent un moyen de pondérer l'impact contextuel de chaque vecteur d'entrée sur chaque prédiction de sortie du RNN. La façon dont cela est mis en œuvre consiste à créer des raccourcis entre les états intermédiaires du RNN d'entrée et du RNN de sortie. De cette manière, lors de la génération du symbole de sortie yt, nous prendrons en compte tous les états cachés d'entrée hi, avec différents coefficients de poids αt,i. -![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.png) +![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png) > Le modèle encodeur-décodeur avec un mécanisme d'attention additive dans [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cité depuis [ce billet de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) La matrice d'attention {αi,j} représenterait le degré d'importance de certains mots d'entrée dans la génération d'un mot donné dans la séquence de sortie. Ci-dessous se trouve un exemple de telle matrice : -![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.png) +![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png) > Figure de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Le résultat que nous obtenons avec l'embedding positionnel incorpore à la fois Ensuite, nous devons capturer certains motifs au sein de notre séquence. Pour ce faire, les transformateurs utilisent un mécanisme d'**auto-attention**, qui est essentiellement une attention appliquée à la même séquence en tant qu'entrée et sortie. L'application de l'auto-attention nous permet de prendre en compte le **contexte** au sein de la phrase et de voir quels mots sont inter-reliés. Par exemple, cela nous permet de voir quels mots sont référés par des co-références, telles que *il*, et également de prendre en compte le contexte : -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.it.png) +![](../../../../../translated_images/it/CoreferenceResolution.861924d6d384a7d6.png) > Image provenant du [Blog de Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Puisque chaque position d'entrée est mappée indépendamment à chaque position **BERT** (Représentations d'Encodeur Bidirectionnelles à partir de Transformateurs) est un très grand réseau de transformateurs à plusieurs couches avec 12 couches pour *BERT-base*, et 24 pour *BERT-large*. Le modèle est d'abord pré-entraîné sur un grand corpus de données textuelles (WikiPedia + livres) en utilisant un entraînement non supervisé (prédiction de mots masqués dans une phrase). Au cours de la pré-formation, le modèle absorbe des niveaux significatifs de compréhension linguistique qui peuvent ensuite être exploités avec d'autres ensembles de données via un ajustement fin. Ce processus est appelé **apprentissage par transfert**. -![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.png) +![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Image [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 3c81fdfd..12eac6ce 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/it/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**I meccanismi di attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Il modo in cui viene implementato è creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output $y_t$, si prenderanno in considerazione tutti gli stati nascosti di input $h_i$, con diversi coefficienti di peso $\\alpha_{t,i}$.\n", "\n", - "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png)\n", "*Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo post sul blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "La matrice di attenzione $\\{\\alpha_{i,j}\\}$ rappresenta il grado in cui certe parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice:\n", "\n", - "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura tratta da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) è una rete Transformer multi-strato molto grande con 12 strati per *BERT-base* e 24 per *BERT-large*. Il modello viene prima pre-addestrato su un ampio corpus di dati testuali (Wikipedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante il pre-addestramento, il modello acquisisce un livello significativo di comprensione del linguaggio che può poi essere sfruttato con altri dataset tramite il fine-tuning. Questo processo è chiamato **transfer learning**.\n", "\n", - "![Immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.png)\n", + "![Immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Esistono molte varianti delle architetture Transformer, tra cui BERT, DistilBERT, BigBird, OpenGPT3 e altre, che possono essere ottimizzate. Il pacchetto [HuggingFace](https://github.com/huggingface/) fornisce un repository per l'addestramento di molte di queste architetture con PyTorch.\n", "\n", diff --git a/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 6c60d451..f8e99207 100644 --- a/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/it/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "I **meccanismi di attenzione** forniscono un mezzo per pesare l'impatto contestuale di ciascun vettore di input su ciascuna previsione di output dell'RNN. Questo viene implementato creando scorciatoie tra gli stati intermedi dell'RNN di input e l'RNN di output. In questo modo, quando si genera il simbolo di output $y_t$, si tiene conto di tutti gli stati nascosti di input $h_i$, con diversi coefficienti di peso $\\alpha_{t,i}$. \n", "\n", - "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/it/encoder-decoder-attention.7a726296894fb567.png)\n", "*Il modello encoder-decoder con meccanismo di attenzione additiva in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citato da [questo post sul blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "La matrice di attenzione $\\{\\alpha_{i,j}\\}$ rappresenta il grado in cui certe parole di input influenzano la generazione di una determinata parola nella sequenza di output. Di seguito è riportato un esempio di tale matrice:\n", "\n", - "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/it/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura tratta da [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) è una rete transformer multilivello molto grande con 12 livelli per *BERT-base* e 24 per *BERT-large*. Il modello viene inizialmente pre-addestrato su un ampio corpus di dati testuali (WikiPedia + libri) utilizzando un addestramento non supervisionato (predizione di parole mascherate in una frase). Durante la fase di pre-addestramento, il modello acquisisce un livello significativo di comprensione del linguaggio che può essere poi sfruttato con altri dataset attraverso il fine tuning. Questo processo è chiamato **apprendimento trasferibile**.\n", "\n", - "![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.png)\n", + "![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/it/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Esistono molte varianti delle architetture Transformer, tra cui BERT, DistilBERT, BigBird, OpenGPT3 e altre, che possono essere ottimizzate.\n", "\n", diff --git a/translations/it/lessons/5-NLP/19-NER/README.md b/translations/it/lessons/5-NLP/19-NER/README.md index cb408130..2b894b45 100644 --- a/translations/it/lessons/5-NLP/19-NER/README.md +++ b/translations/it/lessons/5-NLP/19-NER/README.md @@ -59,7 +59,7 @@ neonato | O Poiché dobbiamo costruire una corrispondenza uno-a-uno tra token e classi, possiamo allenare un modello neurale **molti-a-molti** come mostrato in questa immagine: -![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.it.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/it/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Immagine tratta da [questo post sul blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) di [Andrej Karpathy](http://karpathy.github.io/). I modelli di classificazione dei token NER corrispondono all'architettura di rete più a destra in questa immagine.* diff --git a/translations/it/lessons/5-NLP/README.md b/translations/it/lessons/5-NLP/README.md index d7b40cbd..0b5eb4b6 100644 --- a/translations/it/lessons/5-NLP/README.md +++ b/translations/it/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Elaborazione del Linguaggio Naturale -![Riepilogo dei compiti NLP in uno schizzo](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.it.png) +![Riepilogo dei compiti NLP in uno schizzo](../../../../translated_images/it/ai-nlp.b22dcb8ca4707cea.png) In questa sezione ci concentreremo sull'utilizzo delle reti neurali per gestire compiti legati all'**Elaborazione del Linguaggio Naturale (NLP)**. Ci sono molti problemi di NLP che vogliamo che i computer siano in grado di risolvere: diff --git a/translations/it/lessons/6-Other/23-MultiagentSystems/README.md b/translations/it/lessons/6-Other/23-MultiagentSystems/README.md index 6c299bc7..c30f5155 100644 --- a/translations/it/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/it/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Puoi aprire uno dei modelli, ad esempio **Biology → Flocking**. Dopo aver aperto il modello, verrai portato alla schermata principale di NetLogo. Ecco un esempio di modello che descrive la popolazione di lupi e pecore, date risorse finite (erba). -![Schermata Principale di NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.it.png) +![Schermata Principale di NetLogo](../../../../../translated_images/it/NetLogo-Main.32653711ec1a01b3.png) > Screenshot di Dmitry Soshnikov diff --git a/translations/it/lessons/README.md b/translations/it/lessons/README.md index f08c41d4..86ce5ea7 100644 --- a/translations/it/lessons/README.md +++ b/translations/it/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panoramica -![Panoramica in uno schizzo](../../../translated_images/ai-overview.0857791951d19500.it.png) +![Panoramica in uno schizzo](../../../translated_images/it/ai-overview.0857791951d19500.png) > Schizzo di [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/it/lessons/X-Extras/X1-MultiModal/README.md b/translations/it/lessons/X-Extras/X1-MultiModal/README.md index 19928cd7..1de3f143 100644 --- a/translations/it/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/it/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Dopo il successo dei modelli transformer per risolvere compiti di NLP, le stesse L'idea principale di CLIP è quella di confrontare i prompt testuali con un'immagine e determinare quanto bene l'immagine corrisponda al prompt. -![Architettura CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.it.png) +![Architettura CLIP](../../../../../translated_images/it/clip-arch.b3dbf20b4e8ed8be.png) > *Immagine tratta da [questo post sul blog](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Una volta che questo modello è stato pre-addestrato, possiamo fornire un batch Supponiamo di dover classificare immagini tra, ad esempio, gatti, cani e esseri umani. In questo caso, possiamo fornire al modello un'immagine e una serie di prompt testuali: "*una foto di un gatto*", "*una foto di un cane*", "*una foto di un essere umano*". Nel vettore risultante di 3 probabilità, dobbiamo semplicemente selezionare l'indice con il valore più alto. -![CLIP per la Classificazione delle Immagini](../../../../../translated_images/clip-class.3af42ef0b2b19369.it.png) +![CLIP per la Classificazione delle Immagini](../../../../../translated_images/it/clip-class.3af42ef0b2b19369.png) > *Immagine tratta da [questo post sul blog](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Scopri di più su VQGAN sul sito web [Taming Transformers](https://compvis.githu Una delle differenze importanti tra VQGAN e i GAN tradizionali è che questi ultimi possono produrre un'immagine decente da qualsiasi vettore di input, mentre VQGAN è più propenso a produrre un'immagine incoerente. Pertanto, è necessario guidare ulteriormente il processo di creazione dell'immagine, e questo può essere fatto utilizzando CLIP. -![Architettura VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.it.png) +![Architettura VQGAN+CLIP](../../../../../translated_images/it/vqgan.5027fe05051dfa31.png) Per generare un'immagine corrispondente a un prompt testuale, iniziamo con un vettore di codifica casuale che viene passato attraverso VQGAN per produrre un'immagine. Successivamente, CLIP viene utilizzato per produrre una funzione di perdita che mostra quanto bene l'immagine corrisponda al prompt testuale. L'obiettivo è quindi minimizzare questa perdita, utilizzando la retropropagazione per regolare i parametri del vettore di input. Una grande libreria che implementa VQGAN+CLIP è [Pixray](http://github.com/pixray/pixray). -![Immagine prodotta da Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.it.png) +![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Immagine generata dal prompt *un ritratto ravvicinato ad acquerello di un giovane insegnante di letteratura con un libro* | Immagine generata dal prompt *un ritratto ravvicinato a olio di una giovane insegnante di informatica con un computer* | Immagine generata dal prompt *un ritratto ravvicinato a olio di un anziano insegnante di matematica davanti a una lavagna* @@ -75,7 +75,7 @@ A differenza di CLIP, DALL-E riceve sia testo che immagine come un unico flusso La principale differenza tra DALL-E 1 e 2 è che quest'ultimo genera immagini e arte più realistiche. Esempi di generazione di immagini con DALL-E: -![Immagine prodotta da Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.it.png) +![Immagine prodotta da Pixray](../../../../../translated_images/it/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/it/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Immagine generata dal prompt *un ritratto ravvicinato ad acquerello di un giovane insegnante di letteratura con un libro* | Immagine generata dal prompt *un ritratto ravvicinato a olio di una giovane insegnante di informatica con un computer* | Immagine generata dal prompt *un ritratto ravvicinato a olio di un anziano insegnante di matematica davanti a una lavagna* diff --git a/translations/ja/README.md b/translations/ja/README.md index b5c319b0..161a1998 100644 --- a/translations/ja/README.md +++ b/translations/ja/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 初心者向け人工知能 - カリキュラム -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ja.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ja/ai-overview.0857791951d19500.png)| |:---:| | 初心者向けAI - _スケッチノート [@girlie_mac](https://twitter.com/girlie_mac)による_ | diff --git a/translations/ja/lessons/1-Intro/README.md b/translations/ja/lessons/1-Intro/README.md index df1c591f..9b18b2ed 100644 --- a/translations/ja/lessons/1-Intro/README.md +++ b/translations/ja/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AIの紹介 -![AIの紹介内容をまとめたスケッチノート](../../../../translated_images/ai-intro.bf28d1ac4235881c.ja.png) +![AIの紹介内容をまとめたスケッチノート](../../../../translated_images/ja/ai-intro.bf28d1ac4235881c.png) > スケッチノート: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: もともとコンピュータは、[チャールズ・バベッジ](https://en.wikipedia.org/wiki/Charles_Babbage)によって、明確に定義された手順（アルゴリズム）に従って数値を操作するために発明されました。現代のコンピュータは、19世紀に提案された元のモデルよりもはるかに高度ですが、依然として制御された計算という同じ考えに基づいています。そのため、目標を達成するために必要な手順を正確に知っていれば、コンピュータに何かをプログラムすることが可能です。 -![人物の写真](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ja.png) +![人物の写真](../../../../translated_images/ja/dsh_age.d212a30d4e54fb5f.png) > 写真: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[知能](https://en.wikipedia.org/wiki/Intelligence)** という用語を扱う際の問題の1つは、この用語に明確な定義がないことです。知能は**抽象的思考**や**自己認識**に関連していると主張することができますが、それを適切に定義することはできません。 -![猫の写真](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ja.jpg) +![猫の写真](../../../../translated_images/ja/photo-cat.8c8e8fb760ffe457.jpg) > [写真](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) (Unsplashより) @@ -98,13 +98,13 @@ AGIについて話すとき、私たちは本当に知能を持つシステム > | 機械学習については？ | | > |--------------|-----------| -> | データに基づいて問題を解決する方法をコンピュータが学ぶ人工知能の一部は、**機械学習**と呼ばれます。このコースでは古典的な機械学習は扱いません。別のカリキュラム [Machine Learning for Beginners](http://aka.ms/ml-beginners) を参照してください。 | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ja.png) | +> | データに基づいて問題を解決する方法をコンピュータが学ぶ人工知能の一部は、**機械学習**と呼ばれます。このコースでは古典的な機械学習は扱いません。別のカリキュラム [Machine Learning for Beginners](http://aka.ms/ml-beginners) を参照してください。 | ![ML for Beginners](../../../../translated_images/ja/ml-for-beginners.9e4fed176fd5817d.png) | ## AIの簡単な歴史 人工知能は20世紀中頃に分野として始まりました。当初は記号的推論が主流のアプローチであり、専門家システムのような重要な成功を収めました。専門家システムは、限られた問題領域で専門家として行動できるコンピュータプログラムです。しかし、このアプローチがスケールしにくいことがすぐに明らかになりました。専門家から知識を抽出し、それをコンピュータに表現し、その知識ベースを正確に保つことは非常に複雑で、多くの場合実用的ではないほど高価な作業であることが判明しました。この結果、1970年代にいわゆる[AIの冬](https://en.wikipedia.org/wiki/AI_winter)が訪れました。 -AIの簡単な歴史 +AIの簡単な歴史 > 画像: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ja/lessons/2-Symbolic/Animals.ipynb b/translations/ja/lessons/2-Symbolic/Animals.ipynb index 3cb41293..31498d48 100644 --- a/translations/ja/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ja/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "このサンプルでは、いくつかの物理的特徴に基づいて動物を特定するための簡単な知識ベースシステムを実装します。このシステムは以下の AND-OR ツリーで表すことができます（これはツリーの一部であり、簡単にさらにルールを追加することができます）:\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ja.png)\n" + "![](../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ja/lessons/2-Symbolic/README.md b/translations/ja/lessons/2-Symbolic/README.md index 7b3c6c85..9799524d 100644 --- a/translations/ja/lessons/2-Symbolic/README.md +++ b/translations/ja/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 知識表現とエキスパートシステム -![Symbolic AIの内容の概要](../../../../translated_images/ai-symbolic.715a30cb610411a6.ja.png) +![Symbolic AIの内容の概要](../../../../translated_images/ja/ai-symbolic.715a30cb610411a6.png) > スケッチノート: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Symbolic AIにおける重要な概念の1つが**知識**です。知識を*情 したがって、**知識表現**の問題は、コンピュータ内で知識をデータとして効果的に表現し、自動的に利用可能にする方法を見つけることです。これは以下のようなスペクトラムとして見ることができます： -![知識表現のスペクトラム](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ja.png) +![知識表現のスペクトラム](../../../../translated_images/ja/knowledge-spectrum.b60df631852c0217.png) > 画像: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Is-A | Untyped-Language | | | Symbolic AIの初期の成功例の1つが、**エキスパートシステム**と呼ばれるものでした。これは、限定された問題領域で専門家として機能するように設計されたコンピュータシステムです。これらは、1人以上の人間の専門家から抽出された**知識ベース**に基づいており、その上で推論を行う**推論エンジン**を含んでいました。 -![人間の構造](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ja.png) | ![知識ベースシステムの構造](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ja.png) +![人間の構造](../../../../translated_images/ja/arch-human.5d4d35f1bba3ab1c.png) | ![知識ベースシステムの構造](../../../../translated_images/ja/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ 人間の神経系の簡略化された構造 | 知識ベースシステムの構造 @@ -106,7 +106,7 @@ Symbolic AIの初期の成功例の1つが、**エキスパートシステム** 例として、動物の物理的特徴に基づいて動物を特定するエキスパートシステムを考えてみましょう： -![AND-ORツリー](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ja.png) +![AND-ORツリー](../../../../translated_images/ja/AND-OR-Tree.5592d2c70187f283.png) > 画像: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index d9201098..4cddb4c9 100644 --- a/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "クラスが2つ以上ある場合、softmaxはすべてのクラスにわたって確率を正規化します。以下は、MNISTの数字分類を行うネットワークアーキテクチャの図です：\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ja.png)\n" + "![MNIST Classifier](../../../../../translated_images/ja/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* トレーニング損失が低い - モデルは十分な表現力を持っているため、トレーニングデータを正確に近似できます。\n", "* 検証損失がトレーニング損失よりもはるかに高くなり、トレーニング中に増加し始めることがあります。これは、モデルがトレーニングデータを「記憶」してしまい、「全体像」を見失うためです。\n", "\n", - "![過学習](../../../../../translated_images/overfit.a0bd57f717c15769.ja.png)\n", + "![過学習](../../../../../translated_images/ja/overfit.a0bd57f717c15769.png)\n", "\n", "> この図では、`x`はトレーニングデータ、`o`は検証データを表しています。左側は線形モデル（一層）で、データの本質をうまく近似しています。右側は過学習したモデルで、トレーニングデータを完全に近似していますが、他のデータに対しては意味をなさなくなっています（検証誤差が非常に高い）。\n" ] diff --git a/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md index 1c45dbfd..d51628e2 100644 --- a/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ja/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 以下の5つの点（グラフ上の`x`で表される）を近似する問題を考えてみましょう： -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ja.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ja.jpg) +![linear](../../../../../translated_images/ja/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ja/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **線形モデル、2つのパラメータ** | **非線形モデル、7つのパラメータ** 学習誤差 = 5.3 | 学習誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 上記のグラフからわかるように、過学習は非常に低い学習誤差と高い検証誤差によって検出できます。通常、学習中は学習誤差と検証誤差の両方が減少し始めますが、ある時点で検証誤差が減少を止めて上昇し始めることがあります。これが過学習の兆候であり、この時点で学習を停止するべき（または少なくともモデルのスナップショットを作成するべき）という指標になります。 -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ja.png) +![overfitting](../../../../../translated_images/ja/Overfitting.408ad91cd90b4371.png) ## 過学習を防ぐ方法 diff --git a/translations/ja/lessons/3-NeuralNetworks/README.md b/translations/ja/lessons/3-NeuralNetworks/README.md index 68ba7f5e..c1636df2 100644 --- a/translations/ja/lessons/3-NeuralNetworks/README.md +++ b/translations/ja/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ニューラルネットワーク入門 -![ニューラルネットワーク入門の内容をまとめたイラスト](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ja.png) +![ニューラルネットワーク入門の内容をまとめたイラスト](../../../../translated_images/ja/ai-neuralnetworks.1c687ae40bc86e83.png) 序章で述べたように、知能を実現する方法の一つは、**コンピュータモデル**や**人工の脳**を訓練することです。20世紀中頃から研究者たちはさまざまな数学的モデルを試みてきましたが、近年になってこの方向性が非常に成功を収めることが証明されました。このような脳の数学的モデルは**ニューラルネットワーク**と呼ばれます。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 生物学から、私たちの脳はニューロン（神経細胞）で構成されており、それぞれが複数の「入力」（樹状突起）と1つの「出力」（軸索）を持っていることがわかっています。樹状突起と軸索は電気信号を伝達することができ、これらの間の接続—シナプスとして知られる—は、神経伝達物質によって調節されるさまざまな伝導度を示すことができます。 -![ニューロンのモデル](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ja.jpg) | ![ニューロンのモデル](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ja.png) +![ニューロンのモデル](../../../../translated_images/ja/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ニューロンのモデル](../../../../translated_images/ja/artneuron.1a5daa88d20ebe6f.png) ----|---- 実際のニューロン *([画像](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipediaより)* | 人工ニューロン *(著者による画像)* したがって、ニューロンの最も単純な数学的モデルは、いくつかの入力X1, ..., XNと出力Y、および一連の重みW1, ..., WNを含みます。出力は次のように計算されます： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ここで、fは非線形の**活性化関数**です。 diff --git a/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md index fbcb30b4..0a8e12aa 100644 --- a/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ja/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCVを使用してビデオをフレームごとに読み込むことも可 * **点字本の写真の前処理**。閾値処理、特徴検出、透視変換、NumPy操作を使用して、個々の点字記号を分離し、ニューラルネットワークによる分類に備える方法に焦点を当てています。 -![点字画像](../../../../../translated_images/braille.341962ff76b1bd70.ja.jpeg) | ![前処理された点字画像](../../../../../translated_images/braille-result.46530fea020b03c7.ja.png) | ![点字記号](../../../../../translated_images/braille-symbols.0159185ab69d5339.ja.png) +![点字画像](../../../../../translated_images/ja/braille.341962ff76b1bd70.jpeg) | ![前処理された点字画像](../../../../../translated_images/ja/braille-result.46530fea020b03c7.png) | ![点字記号](../../../../../translated_images/ja/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 * **フレーム差分を使用したビデオ内の動きの検出**。カメラが固定されている場合、カメラフィードのフレームは互いに非常に似ているはずです。フレームが配列として表現されているため、2つの連続するフレームの配列を引き算するだけでピクセル差分が得られます。静的なフレームでは差分は低く、画像内に大きな動きがあると差分が高くなります。 -![ビデオフレームとフレーム差分の画像](../../../../../translated_images/frame-difference.706f805491a0883c.ja.png) +![ビデオフレームとフレーム差分の画像](../../../../../translated_images/ja/frame-difference.706f805491a0883c.png) > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 @@ -89,7 +89,7 @@ OpenCVを使用してビデオをフレームごとに読み込むことも可 - **密なオプティカルフロー**は、各ピクセルがどこに移動しているかを示すベクトルフィールドを計算します。 - **疎なオプティカルフロー**は、画像内の特徴的な部分（例：エッジ）を取り、それらのフレーム間の軌跡を構築します。 -![オプティカルフローの画像](../../../../../translated_images/optical.1f4a94464579a83a.ja.png) +![オプティカルフローの画像](../../../../../translated_images/ja/optical.1f4a94464579a83a.png) > 画像は[OpenCV.ipynb](OpenCV.ipynb)から引用 diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 4b6c7287..9d07ebbe 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16は、2014年にImageNetのトップ5分類で92.7%の精度を達成したネットワークです。そのレイヤー構造は以下の通りです： -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ja.jpg) +![ImageNet Layers](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.jpg) ご覧の通り、VGGは従来のピラミッド型アーキテクチャを採用しており、畳み込み層とプーリング層が順番に並んでいます。 -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ja.jpg) +![ImageNet Pyramid](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.jpg) > 画像出典: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 03725aaa..66a52d13 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "このようにして、典型的なCNNではいくつかの畳み込み層があり、その間にプーリング層を挟むことで画像の次元を減少させます。また、フィルターの数を増やします。これは、パターンがより高度になるにつれて、探すべき興味深い組み合わせが増えるためです。\n", "\n", - "![プーリング層を含む複数の畳み込み層を示す画像。](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ja.png)\n", + "![プーリング層を含む複数の畳み込み層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "空間次元が減少し、特徴やフィルターの次元が増加するため、このアーキテクチャは**ピラミッドアーキテクチャ**とも呼ばれます。\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 277a8c89..b6232934 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "このようにして、典型的なCNNではいくつかの畳み込み層があり、その間にプーリング層を挟むことで画像の次元を縮小します。また、フィルターの数を増やします。パターンがより高度になるにつれて、注目すべき興味深い組み合わせが増えるためです。\n", "\n", - "![複数の畳み込み層とプーリング層を示す画像。](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ja.png)\n", + "![複数の畳み込み層とプーリング層を示す画像。](../../../../../translated_images/ja/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "空間次元が縮小し、特徴/フィルター次元が増加するため、このアーキテクチャは**ピラミッドアーキテクチャ**とも呼ばれます。\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md index 2b44b7b1..b18f6f6e 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: パターンを抽出するために、**畳み込みフィルター**という概念を使用します。ご存じの通り、画像は2D行列、または色深度を持つ3Dテンソルとして表されます。フィルターを適用するとは、比較的小さな**フィルターカーネル**行列を取り、元の画像の各ピクセルに対して隣接する点との加重平均を計算することを意味します。これは、小さな窓が画像全体をスライドし、フィルターカーネル行列の重みに従ってすべてのピクセルを平均化するようなものと考えることができます。 -![垂直エッジフィルター](../../../../../translated_images/filter-vert.b7148390ca0bc356.ja.png) | ![水平エッジフィルター](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ja.png) +![垂直エッジフィルター](../../../../../translated_images/ja/filter-vert.b7148390ca0bc356.png) | ![水平エッジフィルター](../../../../../translated_images/ja/filter-horiz.59b80ed4feb946ef.png) ----|---- > Dmitry Soshnikovによる画像 @@ -38,7 +38,7 @@ CNNが機能する仕組みは、以下の重要なアイデアに基づいて * フィルターが自動的に学習されるようにネットワークを設計できる * 元の画像だけでなく、高レベルの特徴におけるパターンを見つけるためにも同じアプローチを使用できる。そのため、CNNの特徴抽出は低レベルのピクセルの組み合わせから始まり、画像の部分の高レベルの組み合わせに至るまで、特徴の階層で機能する。 -![階層的特徴抽出](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ja.png) +![階層的特徴抽出](../../../../../translated_images/ja/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Hislop-Lynchによる論文からの画像 [研究に基づく](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNNが機能する仕組みは、以下の重要なアイデアに基づいて 例として、2014年にImageNetのトップ5分類で92.7%の精度を達成したVGG-16のアーキテクチャを見てみましょう： -![ImageNet層](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ja.jpg) +![ImageNet層](../../../../../translated_images/ja/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNetピラミッド](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ja.jpg) +![ImageNetピラミッド](../../../../../translated_images/ja/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493)からの画像 diff --git a/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1214dba7..75629605 100644 --- a/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ja/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) を使用します。このデータセットには、37種類の犬と猫の品種の画像が含まれています。 -![扱うデータセット](../../../../../../translated_images/data.50b2a9d5484bdbf0.ja.png) +![扱うデータセット](../../../../../../translated_images/ja/data.50b2a9d5484bdbf0.png) データセットをダウンロードするには、以下のコードスニペットを使用してください： diff --git a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index c071a709..50a27bfc 100644 --- a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "理想的な猫を視覚化するために、ランダムなノイズ画像から始めます。そして、勾配降下法の最適化技術を使って画像を調整し、ネットワークが猫を認識できるようにします。\n", "\n", - "![最適化ループ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ja.png)\n", + "![最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "こちらが初期の画像です:\n" ] diff --git a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md index 8fd87840..6c64d032 100644 --- a/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ja/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ KerasとPyTorchには、一般的なアーキテクチャの事前学習済み 以下は、VGG-16ネットワークによって猫の画像から抽出された特徴の例です： -![VGG-16による特徴抽出](../../../../../translated_images/features.6291f9c7ba3a0b95.ja.png) +![VGG-16による特徴抽出](../../../../../translated_images/ja/features.6291f9c7ba3a0b95.png) ## 猫 vs 犬データセット @@ -48,19 +48,19 @@ KerasとPyTorchには、一般的なアーキテクチャの事前学習済み 一つのアプローチとして、ランダムな画像から始めて、**勾配降下法**を使用してその画像を調整し、ネットワークがそれを猫だと認識するようにする方法があります。 -![画像最適化ループ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ja.png) +![画像最適化ループ](../../../../../translated_images/ja/ideal-cat-loop.999fbb8ff306e044.png) しかし、この方法ではランダムノイズに非常に近いものが得られます。これは、*ネットワークが入力画像を猫だと認識する方法が多数存在する*ためであり、その中には視覚的に意味をなさないものも含まれます。これらの画像には猫に典型的な多くのパターンが含まれていますが、視覚的に特徴的であるように制約するものはありません。 結果を改善するために、**変動損失**と呼ばれる項を損失関数に追加することができます。これは、画像の隣接するピクセルがどれだけ似ているかを示す指標です。変動損失を最小化することで画像が滑らかになり、ノイズが除去され、より視覚的に魅力的なパターンが現れます。以下は、猫とシマウマとして高い確率で分類される「理想的な」画像の例です： -![理想的な猫](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ja.png) | ![理想的なシマウマ](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ja.png) +![理想的な猫](../../../../../translated_images/ja/ideal-cat.203dd4597643d6b0.png) | ![理想的なシマウマ](../../../../../translated_images/ja/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *理想的な猫* | *理想的なシマウマ* 同様のアプローチを使用して、いわゆる**敵対的攻撃**をニューラルネットワークに対して行うことができます。例えば、犬を猫のように見せてネットワークを欺きたい場合、ネットワークが犬として認識する犬の画像を取り、それを少し調整してネットワークがそれを猫として分類するようにすることができます： -![犬の画像](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ja.png) | ![猫として分類される犬の画像](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ja.png) +![犬の画像](../../../../../translated_images/ja/original-dog.8f68a67d2fe0911f.png) | ![猫として分類される犬の画像](../../../../../translated_images/ja/adversarial-dog.d9fc7773b0142b89.png) -----|----- *元の犬の画像* | *猫として分類される犬の画像* diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index a2882f5b..3758cd08 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "オートエンコーダをトレーニングする際には、元の画像から可能な限り多くの情報を正確に再構築するためにネットワークが最適な**埋め込み**を見つけようとします。これにより、入力画像の意味を捉えることができます。\n", "\n", - "![オートエンコーダの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> 画像出典: [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9363906a..ffed6a18 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "オートエンコーダをトレーニングする際には、元の画像から可能な限り多くの情報を正確に再構築するために捉える必要があるため、ネットワークは入力画像の意味を捉える最適な**埋め込み**を見つけようとします。\n", "\n", - "![オートエンコーダの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*画像出典: [Kerasブログ](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md index bb269e4a..547c234f 100644 --- a/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ja/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNをトレーニングする際の問題の一つは、多くのラベル付 オートエンコーダーをトレーニングして、元の画像から可能な限り多くの情報をキャプチャし、正確に再構築するため、ネットワークは入力画像の意味を捉える最適な**埋め込み**を見つけようとします。 -![オートエンコーダーの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.jpg) +![オートエンコーダーの図](../../../../../translated_images/ja/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 画像は[Kerasブログ](https://blog.keras.io/building-autoencoders-in-keras.html)より diff --git a/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md index cd5e58b9..235fc3a2 100644 --- a/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ja/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [事前クイズ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![オブジェクト検出](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ja.png) +![オブジェクト検出](../../../../../translated_images/ja/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 画像出典: [YOLO v2 ウェブサイト](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 各タイルに対して画像分類を実行する 3. 十分に高い活性化を示したタイルを、対象の物体を含むとみなす -![単純なオブジェクト検出](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ja.png) +![単純なオブジェクト検出](../../../../../translated_images/ja/naive-detection.e7f1ba220ccd08c6.png) > *画像出典: [演習ノートブック](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20クラス * [COCO](http://cocodataset.org/#home) - コンテキスト内の一般的な物体。80クラス、境界ボックスとセグメンテーションマスクを含む -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ja.jpg) +![COCO](../../../../../translated_images/ja/coco-examples.71bc60380fa6cceb.jpg) ## オブジェクト検出の評価指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 画像分類ではアルゴリズムの性能を測定するのは簡単ですが、オブジェクト検出ではクラスの正確性だけでなく、推定された境界ボックスの位置の精度も測定する必要があります。そのために使用されるのが**Intersection over Union** (IoU)です。これは、2つのボックス（または任意の領域）がどれだけ重なっているかを測定します。 -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ja.png) +![IoU](../../../../../translated_images/ja/iou_equation.9a4751d40fff4e11.png) > *図2 出典: [IoUに関する優れたブログ記事](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)は、[Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) を使用してROI領域の階層構造を生成します。それらはCNN特徴抽出器とSVM分類器を通じて物体クラスを決定し、線形回帰を使用して*境界ボックス*の座標を決定します。[公式論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ja.png) +![RCNN](../../../../../translated_images/ja/rcnn1.cae407020dfb1d1f.png) > *画像出典: van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ja.png) +![RCNN-1](../../../../../translated_images/ja/rcnn2.2d9530bb83516484.png) > *画像出典: [このブログ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC このアプローチはR-CNNに似ていますが、領域は畳み込み層が適用された後に定義されます。 -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ja.png) +![FRCNN](../../../../../translated_images/ja/f-rcnn.3cda6d9bb4188875.png) > 画像出典: [公式論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC このアプローチの主なアイデアは、ROIを予測するためにニューラルネットワークを使用することです。これを*領域提案ネットワーク* (Region Proposal Network) と呼びます。[論文](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ja.png) +![FasterRCNN](../../../../../translated_images/ja/faster-rcnn.8d46c099b87ef30a.png) > 画像出典: [公式論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoUが一定値以上の検出のみを考慮します。例えば、PASCAL VOC 1. 特徴は**位置感知スコアマップ**で処理されます。$C$クラスの各物体は$k\times k$領域に分割され、物体の部分を予測するように学習します。 1. $k\times k$領域の各部分について、すべてのネットワークが物体クラスに投票し、最大票を得た物体クラスが選択されます。 -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ja.png) +![r-fcn image](../../../../../translated_images/ja/r-fcn.13eb88158b99a3da.png) > 画像出典: [公式論文](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLOはリアルタイムのワンパスアルゴリズムです。主なアイ * 画像を$S\times S$領域に分割 * 各領域について、**CNN**が$n$個の可能な物体、*境界ボックス*の座標、*信頼度*=*確率* * IoUを予測 - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ja.png) + ![YOLO](../../../../../translated_images/ja/yolo.a2648ec82ee8bb4e.png) > 画像出典: [公式論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/ja/lessons/4-ComputerVision/README.md b/translations/ja/lessons/4-ComputerVision/README.md index 6250f4e3..840a305e 100644 --- a/translations/ja/lessons/4-ComputerVision/README.md +++ b/translations/ja/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # コンピュータビジョン -![コンピュータビジョンの内容をまとめたイラスト](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ja.png) +![コンピュータビジョンの内容をまとめたイラスト](../../../../translated_images/ja/ai-computervision.6506ebebac3fbf76.png) このセクションでは以下について学びます: diff --git a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 5e0f4610..9ed03646 100644 --- a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) ベクトル表現は、最も一般的に使用される伝統的なベクトル表現です。各単語はベクトルのインデックスにリンクされ、ベクトル要素には特定の文書内での単語の出現回数が含まれます。\n", "\n", - "![Bag of Words ベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ja.png) \n", + "![Bag of Words ベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW は、テキスト内の個々の単語に対するすべてのワンホットエンコードされたベクトルの合計として考えることもできます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index b6d3d399..25bcdee8 100644 --- a/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ja/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW)ベクトル表現は、最も理解しやすい伝統的なベクトル表現です。各単語がベクトルのインデックスにリンクされ、ベクトルの要素には、特定の文書内で各単語が出現した回数が含まれます。\n", "\n", - "![Bag-of-wordsベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ja.png) \n", + "![Bag-of-wordsベクトル表現がメモリ内でどのように表現されるかを示す画像。](../../../../../translated_images/ja/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoWは、テキスト内の個々の単語に対するすべてのone-hotエンコードされたベクトルの合計として考えることもできます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index b8ce58de..7381388f 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ネットワークの最初の層として埋め込み層を使用することで、バッグオブワードモデルから**埋め込みバッグ**モデルに切り替えることができます。このモデルでは、まずテキスト内の各単語を対応する埋め込みに変換し、それらの埋め込み全体に対して`sum`、`average`、`max`などの集約関数を計算します。\n", "\n", - "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ja.png)\n", + "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "私たちの分類器ニューラルネットワークは、埋め込み層、集約層、そしてその上に線形分類器を持つ構造になります。\n" ] @@ -176,7 +176,7 @@ "\n", "以前のアーキテクチャでは、すべてのシーケンスを同じ長さにパディングしてミニバッチに収める必要がありました。しかし、これは可変長シーケンスを表現する最も効率的な方法ではありません。別のアプローチとして、**オフセット**ベクトルを使用する方法があります。このベクトルは、1つの大きなベクトルに格納されたすべてのシーケンスのオフセットを保持します。\n", "\n", - "![オフセットシーケンス表現を示す画像](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ja.png)\n", + "![オフセットシーケンス表現を示す画像](../../../../../translated_images/ja/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: 上の図では文字のシーケンスを示していますが、この例では単語のシーケンスを扱っています。ただし、オフセットベクトルでシーケンスを表現するという基本的な原則は同じです。\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoWは高速ですが、スキップグラムは遅いものの、頻度の低い単語をより良く表現することができます。\n", "\n", - "![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.png)\n", + "![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google Newsデータセットで事前学習されたWord2Vec埋め込みを試すには、**gensim** ライブラリを使用することができます。以下は「neural」に最も類似した単語を見つける例です。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 7c34a3fa..c920ceb2 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ja/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ネットワークの最初の層として埋め込み層を使用することで、バッグオブワード（bag-of-words）モデルから **埋め込みバッグ（embedding bag）** モデルに切り替えることができます。このモデルでは、まずテキスト内の各単語を対応する埋め込みに変換し、その後、`sum`、`average`、`max` などの集約関数をこれらの埋め込み全体に対して計算します。\n", "\n", - "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ja.png)\n", + "![5つのシーケンス単語に対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "私たちの分類器ニューラルネットワークは以下の層で構成されています：\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoWは高速ですが、スキップグラムは処理が遅いものの、頻度の低い単語をより良く表現することができます。\n", "\n", - "![単語をベクトルに変換するCBoWとスキップグラムのアルゴリズムを示す画像。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.png)\n", + "![単語をベクトルに変換するCBoWとスキップグラムのアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Googleニュースのデータセットで事前学習されたWord2Vec埋め込みを試すには、**gensim**ライブラリを使用することができます。以下に、'neural'に最も類似した単語を見つける例を示します。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/14-Embeddings/README.md b/translations/ja/lessons/5-NLP/14-Embeddings/README.md index 01ab6f68..bf8cf7ea 100644 --- a/translations/ja/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ja/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoWやTF/IDFに基づく分類器を訓練する際、高次元の単語袋ベ 分類器ネットワークの最初の層として埋め込み層を使用することで、単語袋モデルから**埋め込み袋**モデルに切り替えることができます。このモデルでは、テキスト内の各単語を対応する埋め込みに変換し、それらの埋め込み全体に対して`sum`、`average`、`max`などの集約関数を計算します。 -![5つの単語シーケンスに対する埋め込み分類器を示す画像。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ja.png) +![5つの単語シーケンスに対する埋め込み分類器を示す画像。](../../../../../translated_images/ja/embedding-classifier-example.b77f021a7ee67eee.png) > 著者による画像 @@ -40,7 +40,7 @@ BoWやTF/IDFに基づく分類器を訓練する際、高次元の単語袋ベ CBoWは高速ですが、スキップグラムは遅いものの、頻度の低い単語をより良く表現します。 -![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.png) +![単語をベクトルに変換するためのCBoWとスキップグラムアルゴリズムを示す画像。](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [この論文](https://arxiv.org/pdf/1301.3781.pdf)からの画像 diff --git a/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md index 287433dc..06fdf547 100644 --- a/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ja/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2VecやGloVeのようなセマンティック埋め込みは、実際には* * **Continuous Bag-of-Words** (CBoW)：トークン列$W_{-N}$, ..., $W_N$の中間トークン$W_0$を予測する。 * **Skip-gram**：中間トークン$W_0$から、隣接するトークンの集合{$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}を予測する。 -![単語をベクトルに変換するアルゴリズムに関する論文の画像](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.png) +![単語をベクトルに変換するアルゴリズムに関する論文の画像](../../../../../translated_images/ja/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 画像出典：[この論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ja/lessons/5-NLP/16-RNN/README.md b/translations/ja/lessons/5-NLP/16-RNN/README.md index b31e96d7..f687713e 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/README.md +++ b/translations/ja/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: テキストシーケンスの意味を捉えるためには、**リカレントニューラルネットワーク**（RNN）と呼ばれる別のニューラルネットワークアーキテクチャを使用する必要があります。RNNでは、文をネットワークに1つずつシンボルを通し、ネットワークは**状態**を生成します。この状態を次のシンボルとともに再びネットワークに渡します。 -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ja.png) +![RNN](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.png) > 著者による画像 @@ -61,7 +61,7 @@ LSTMネットワークはRNNと似た構成ですが、層から層へ渡され リカレントネットワークは、1方向または双方向のいずれであっても、シーケンス内の特定のパターンを捉え、それを状態ベクトルに保存するか、出力に渡すことができます。畳み込みネットワークと同様に、最初の層によって抽出された低レベルのパターンから構築し、高レベルのパターンを捉えるために、最初の層の上に別のリカレント層を構築することができます。これにより、**多層RNN**の概念に至ります。これは2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。 -![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.jpg) +![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Fernando Lópezによる[この素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像* diff --git a/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 89a94997..af0dbc92 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "リカレントネットワーク（単方向でも双方向でも）は、シーケンス内の特定のパターンを捉え、それを状態ベクトルに保存したり、出力に渡したりすることができます。畳み込みネットワークと同様に、最初の層によって抽出された低レベルのパターンを基に、より高次のパターンを捉えるために、もう1つのリカレント層をその上に構築することができます。これにより、**多層RNN**という概念が生まれます。これは2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。\n", "\n", - "![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando Lópezによる[素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像*\n", "\n", diff --git a/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb index db4308f7..0c47cf10 100644 --- a/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ja/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "テキストシーケンスの意味を捉えるために、**再帰型ニューラルネットワーク**（Recurrent Neural Network、RNN）と呼ばれるニューラルネットワークのアーキテクチャを使用します。RNNを使用する際には、文をネットワークに1トークンずつ通し、ネットワークが生成する**状態**を次のトークンとともに再びネットワークに渡します。\n", "\n", - "![再帰型ニューラルネットワーク生成の例を示す画像](../../../../../translated_images/rnn.27f5c29c53d727b5.ja.png)\n", + "![再帰型ニューラルネットワーク生成の例を示す画像](../../../../../translated_images/ja/rnn.27f5c29c53d727b5.png)\n", "\n", "トークンの入力シーケンス $X_0,\\dots,X_n$ が与えられると、RNNはニューラルネットワークブロックのシーケンスを作成し、このシーケンスをバックプロパゲーションを使用してエンドツーエンドで学習します。各ネットワークブロックは、入力としてペア $(X_i,S_i)$ を受け取り、結果として $S_{i+1}$ を生成します。最終状態 $S_n$ または出力 $Y_n$ は線形分類器に渡され、結果を生成します。すべてのネットワークブロックは同じ重みを共有し、1回のバックプロパゲーションパスでエンドツーエンドで学習されます。\n", "\n", @@ -369,7 +369,7 @@ "\n", "リカレントネットワーク（単方向でも双方向でも）は、シーケンス内のパターンを捉え、それを状態ベクトルに保存したり、出力として返したりします。畳み込みネットワークと同様に、最初の層で抽出された低レベルのパターンから構築された高レベルのパターンを捉えるために、最初の層の後に別のリカレント層を追加することができます。これにより、**多層RNN**という概念が生まれます。これは、2つ以上のリカレントネットワークで構成され、前の層の出力が次の層の入力として渡されます。\n", "\n", - "![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/ja/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando Lópezによる[素晴らしい投稿](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)からの画像。*\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 55e51cef..77be5afc 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNNを使ってテキストを生成する方法は以下の通りです。各ステップで、`nchars`の長さの文字列を入力として取り、ネットワークに対して各入力文字に対する次の出力文字を生成するように求めます。\n", "\n", - "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ja.png)\n", + "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png)\n", "\n", "実際のシナリオによっては、*end-of-sequence* `` のような特別な文字を含めることもあります。しかし、今回の場合は無限にテキストを生成するネットワークをトレーニングしたいので、各シーケンスのサイズを`nchars`トークンに固定します。その結果、各トレーニング例は`nchars`の入力と`nchars`の出力（入力シーケンスを1文字左にシフトしたもの）で構成されます。ミニバッチはこのようなシーケンスをいくつかまとめたものになります。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index e892008e..83c33af0 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "ニュースタイトルを生成するためにRNNをトレーニングする方法は以下の通りです。各ステップで1つのタイトルを取り出し、それをRNNに入力します。そして、各入力文字に対してネットワークに次の出力文字を生成させます。\n", "\n", - "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ja.png)\n", + "![単語 'HELLO' を生成するRNNの例を示す画像。](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png)\n", "\n", "シーケンスの最後の文字に対しては、ネットワークに `` トークンを生成させます。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md index 21e0a428..a335e453 100644 --- a/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ja/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: これにより、以下の図に示されるようなさまざまなニューラルアーキテクチャが可能になります： -![一般的なリカレントニューラルネットワークのパターンを示す画像](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ja.jpg) +![一般的なリカレントニューラルネットワークのパターンを示す画像](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 画像は [Andrej Karpaty](http://karpathy.github.io/) のブログ記事 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) より引用 @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: このRNNを訓練してステップごとにテキストを生成します。各ステップで、`nchars`の長さの文字列を取り、ネットワークに各入力文字に対して次の出力文字を生成させます： -![単語 'HELLO' を生成するRNNの例を示す画像](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ja.png) +![単語 'HELLO' を生成するRNNの例を示す画像](../../../../../translated_images/ja/rnn-generate.56c54afb52f9781d.png) テキスト生成（推論中）では、まず**プロンプト**を使用し、それをRNNセルに通して中間状態を生成します。その後、この状態から生成が始まります。1文字ずつ生成し、状態と生成された文字を次のRNNセルに渡して次の文字を生成します。このプロセスを繰り返して十分な文字数を生成します。 diff --git a/translations/ja/lessons/5-NLP/18-Transformers/README.md b/translations/ja/lessons/5-NLP/18-Transformers/README.md index 53181c64..a6a308e2 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ja/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット **注意機構**は、RNNの各出力予測に対する各入力ベクトルの文脈的な影響を重み付けする手段を提供します。これを実現する方法は、入力RNNの中間状態と出力RNNの間にショートカットを作成することです。この方法では、出力記号ytを生成する際に、異なる重み係数αt,iを用いてすべての入力隠れ状態hiを考慮します。 -![エンコーダ/デコーダモデルと加法型注意層を示す画像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ja.png) +![エンコーダ/デコーダモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意機構を持つエンコーダ-デコーダモデル。引用元：[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意行列{αi,j}は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度関与しているかを表します。以下はそのような行列の例です： -![BahdanauによるRNNsearch-50のサンプルアラインメントを示す画像](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ja.png) +![BahdanauによるRNNsearch-50のサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)からの図（Fig.3） @@ -66,7 +66,7 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット 次に、シーケンス内のパターンを捉える必要があります。これを行うために、トランスフォーマーは**自己注意**機構を使用します。これは入力と出力が同じシーケンスに対して適用される注意です。自己注意を適用することで、文内の**文脈**を考慮し、どの単語が相互に関連しているかを確認できます。例えば、*it*のような共参照が指す単語を確認したり、文脈を考慮することができます： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ja.png) +![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.png) > [Googleのブログ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)からの画像 @@ -91,7 +91,7 @@ RNNを使用したシーケンス間変換は、2つのリカレントネット **BERT**（Bidirectional Encoder Representations from Transformers）は非常に大規模な多層トランスフォーマーネットワークで、*BERT-base*では12層、*BERT-large*では24層を持ちます。このモデルはまず、WikiPediaや書籍などの大規模なテキストデータコーパスで教師なし学習（文中のマスクされた単語を予測する）を使用して事前学習されます。事前学習中にモデルは言語理解の重要なレベルを吸収し、その後他のデータセットで微調整することで活用できます。このプロセスは**転移学習**と呼ばれます。 -![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 画像の[出典](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md index f0b37ae8..6d501824 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ja/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの **注意機構**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。これは、入力RNNの中間状態と出力RNNの間にショートカットを作成することによって実装されます。この方法では、出力シンボルytを生成する際に、異なる重み係数αt,iを持つすべての入力隠れ状態hiを考慮します。 -![加法注意層を持つエンコーダ/デコーダモデルの画像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ja.png) +![加法注意層を持つエンコーダ/デコーダモデルの画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法注意機構を持つエンコーダ-デコーダモデル、[このブログ投稿](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用 注意行列 {αi,j} は、特定の入力単語が出力シーケンス内の特定の単語の生成にどの程度寄与しているかを表します。以下はそのような行列の例です： -![RNNsearch-50によって見つかったサンプルアラインメントの画像、Bahdanau - arviz.orgから](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ja.png) +![RNNsearch-50によって見つかったサンプルアラインメントの画像、Bahdanau - arviz.orgから](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)からの図（Fig.3） @@ -57,7 +57,7 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの 次に、シーケンス内のパターンをキャプチャする必要があります。これを行うために、トランスフォーマーは**自己注意**機構を使用します。これは基本的に、同じシーケンスに対して入力と出力に適用される注意です。自己注意を適用することで、文内の**コンテキスト**を考慮し、どの単語が相互関連しているかを確認できます。例えば、*it*のようなコリファレンスによって参照される単語を確認し、コンテキストも考慮に入れることができます： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ja.png) +![](../../../../../translated_images/ja/CoreferenceResolution.861924d6d384a7d6.png) > [Googleブログ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)からの画像 @@ -82,7 +82,7 @@ RNNを使用したシーケンスからシーケンスの実装は、二つの **BERT**（Bidirectional Encoder Representations from Transformers）は、*BERT-base*用の12層、*BERT-large*用の24層を持つ非常に大きなマルチレイヤートランスフォーマーネットワークです。このモデルは、無監督トレーニング（文中のマスクされた単語を予測）を使用して、大規模なテキストデータコーパス（WikiPedia + 書籍）で事前トレーニングされます。事前トレーニング中に、モデルは言語理解の重要なレベルを吸収し、その後ファインチューニングを使用して他のデータセットと活用できます。このプロセスは**転移学習**と呼ばれます。 -![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 画像 [出典](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 083bfede..b2e0b661 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ja/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**注意メカニズム**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。これを実現する方法は、入力RNNの中間状態と出力RNNの間にショートカットを作成することです。この方法では、出力記号$y_t$を生成する際に、異なる重み係数$\\alpha_{t,i}$を用いてすべての入力隠れ状態$h_i$を考慮します。\n", "\n", - "![エンコーダーデコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ja.png)\n", + "![エンコーダーデコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意メカニズムを持つエンコーダーデコーダーモデル。画像は[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用。]*\n", "\n", "注意行列$\\{\\alpha_{i,j}\\}$は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度影響を与えるかを表します。以下はそのような行列の例です：\n", "\n", - "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ja.png)\n", + "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)から引用された図]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers）は、非常に大規模な多層トランスフォーマーネットワークであり、*BERT-base*では12層、*BERT-large*では24層を持ちます。このモデルは、まず大規模なテキストデータ（Wikipedia + 書籍）を使用して教師なし学習（文中のマスクされた単語を予測する）で事前学習されます。事前学習中にモデルは言語理解の重要なレベルを吸収し、その後、他のデータセットで微調整することで活用できます。このプロセスは**転移学習**と呼ばれます。\n", "\n", - "![http://jalammar.github.io/illustrated-bert/から引用された画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.png)\n", + "![http://jalammar.github.io/illustrated-bert/から引用された画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT、DistilBERT、BigBird、OpenGPT3など、微調整可能なトランスフォーマーアーキテクチャには多くのバリエーションがあります。[HuggingFaceパッケージ](https://github.com/huggingface/)は、PyTorchを使用してこれらのアーキテクチャの多くをトレーニングするためのリポジトリを提供しています。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 9c815ace..41047714 100644 --- a/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ja/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**注意メカニズム**は、RNNの各出力予測に対する各入力ベクトルの文脈的影響を重み付けする手段を提供します。このメカニズムは、入力RNNの中間状態と出力RNNの間にショートカットを作成することで実装されます。この方法では、出力記号$y_t$を生成する際に、異なる重み係数$\\alpha_{t,i}$を用いてすべての入力隠れ状態$h_i$を考慮します。\n", "\n", - "![エンコーダー/デコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ja.png)\n", + "![エンコーダー/デコーダーモデルと加法型注意層を示す画像](../../../../../translated_images/ja/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)の加法型注意メカニズムを備えたエンコーダーデコーダーモデル。画像は[このブログ記事](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)から引用。]*\n", "\n", "注意行列$\\{\\alpha_{i,j}\\}$は、出力シーケンス内の特定の単語の生成において、入力単語がどの程度影響を与えるかを表します。以下はそのような行列の例です：\n", "\n", - "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ja.png)\n", + "![Bahdanau - arviz.orgから引用されたRNNsearch-50によるサンプルアラインメントを示す画像](../../../../../translated_images/ja/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（図3）から引用された図]*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers）は、非常に大規模な多層トランスフォーマーネットワークで、*BERT-base* では12層、*BERT-large* では24層の構造を持っています。このモデルは、まず大規模なテキストデータ（Wikipedia + 書籍）を使用して、教師なし学習（文中のマスクされた単語を予測する）で事前学習されます。事前学習の過程で、モデルは言語理解の高度な知識を吸収し、その後、他のデータセットで微調整を行うことで活用できます。このプロセスは**転移学習**と呼ばれます。\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.png)\n", + "![http://jalammar.github.io/illustrated-bert/ からの画像](../../../../../translated_images/ja/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT、DistilBERT、BigBird、OpenGPT3 など、微調整可能なトランスフォーマーアーキテクチャには多くのバリエーションがあります。\n", "\n", diff --git a/translations/ja/lessons/5-NLP/19-NER/README.md b/translations/ja/lessons/5-NLP/19-NER/README.md index e4dc37ac..db547f17 100644 --- a/translations/ja/lessons/5-NLP/19-NER/README.md +++ b/translations/ja/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ Token | Tag トークンとクラスの1対1の対応を構築する必要があるため、この図から右端の**多対多**ニューラルネットワークモデルをトレーニングできます： -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ja.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ja/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *[Andrej Karpathy](http://karpathy.github.io/)による[このブログ記事](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)からの画像。NERトークン分類モデルは、この画像の右端のネットワークアーキテクチャに対応します。* diff --git a/translations/ja/lessons/5-NLP/README.md b/translations/ja/lessons/5-NLP/README.md index 9d74d65e..a2f54bb9 100644 --- a/translations/ja/lessons/5-NLP/README.md +++ b/translations/ja/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然言語処理 -![NLPタスクの概要を示すイラスト](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ja.png) +![NLPタスクの概要を示すイラスト](../../../../translated_images/ja/ai-nlp.b22dcb8ca4707cea.png) このセクションでは、**自然言語処理 (NLP)** に関連するタスクを扱うためにニューラルネットワークを使用する方法に焦点を当てます。コンピュータに解決してほしい多くのNLP問題があります。 diff --git a/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md index 2ee54bc4..1cd31628 100644 --- a/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ja/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogoの素晴らしい点は、試すことができる動作するモデル モデルを開くと、NetLogoのメイン画面に移動します。ここでは、有限の資源（草）を考慮した狼と羊の個体数を記述するサンプルモデルを見てみましょう。 -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ja.png) +![NetLogo Main Screen](../../../../../translated_images/ja/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikovによるスクリーンショット diff --git a/translations/ja/lessons/README.md b/translations/ja/lessons/README.md index 6d8d4a89..d8f36b34 100644 --- a/translations/ja/lessons/README.md +++ b/translations/ja/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概要 -![概要のイラスト](../../../translated_images/ai-overview.0857791951d19500.ja.png) +![概要のイラスト](../../../translated_images/ja/ai-overview.0857791951d19500.png) > スケッチノート作成者：[Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ja/lessons/X-Extras/X1-MultiModal/README.md b/translations/ja/lessons/X-Extras/X1-MultiModal/README.md index c66a4a74..aa2e608a 100644 --- a/translations/ja/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ja/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIPの主なアイデアは、テキストプロンプトと画像を比較し、画像がプロンプトにどれだけ対応しているかを判断できるようにすることです。 -![CLIP アーキテクチャ](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ja.png) +![CLIP アーキテクチャ](../../../../../translated_images/ja/clip-arch.b3dbf20b4e8ed8be.png) > *画像は[このブログ記事](https://openai.com/blog/clip/)から引用* @@ -29,7 +29,7 @@ CLIPモデル/ライブラリは[OpenAI GitHub](https://github.com/openai/CLIP) 例えば、画像を猫、犬、人間に分類する必要があるとします。この場合、モデルに画像と一連のテキストプロンプト（例: "*a picture of a cat*"、"*a picture of a dog*"、"*a picture of a human*"）を与えます。結果として得られる3つの確率のベクトルの中で、最も高い値を持つインデックスを選択すればよいのです。 -![CLIPによる画像分類](../../../../../translated_images/clip-class.3af42ef0b2b19369.ja.png) +![CLIPによる画像分類](../../../../../translated_images/ja/clip-class.3af42ef0b2b19369.png) > *画像は[このブログ記事](https://openai.com/blog/clip/)から引用* @@ -53,13 +53,13 @@ VQGANの詳細については、[Taming Transformers](https://compvis.github.io/ VQGANと従来のGANの重要な違いの一つは、後者が任意の入力ベクトルから適切な画像を生成できるのに対し、VQGANは一貫性のない画像を生成する可能性が高いことです。そのため、画像生成プロセスをさらに誘導する必要があり、それがCLIPを使用して行われます。 -![VQGAN+CLIP アーキテクチャ](../../../../../translated_images/vqgan.5027fe05051dfa31.ja.png) +![VQGAN+CLIP アーキテクチャ](../../../../../translated_images/ja/vqgan.5027fe05051dfa31.png) テキストプロンプトに対応する画像を生成するには、まずランダムなエンコーディングベクトルを用意し、それをVQGANに通して画像を生成します。その後、CLIPを使用して、画像がテキストプロンプトにどれだけ対応しているかを示す損失関数を生成します。その損失を最小化することを目指し、逆伝播を使用して入力ベクトルのパラメータを調整します。 VQGAN+CLIPを実装した優れたライブラリとして[Pixray](http://github.com/pixray/pixray)があります。 -![Pixrayによる生成画像](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ja.png) | ![Pixrayによる生成画像](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ja.png) | ![Pixrayによる生成画像](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ja.png) +![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- プロンプト *a closeup watercolor portrait of young male teacher of literature with a book* から生成された画像 | プロンプト *a closeup oil portrait of young female teacher of computer science with a computer* から生成された画像 | プロンプト *a closeup oil portrait of old male teacher of mathematics in front of blackboard* から生成された画像 @@ -75,7 +75,7 @@ CLIPとは異なり、DALL-Eはテキストと画像の両方を、画像とテ DALL-E 1と2の主な違いは、よりリアルな画像やアートを生成できる点です。 DALL-Eによる画像生成の例： -![Pixrayによる生成画像](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ja.png) | ![Pixrayによる生成画像](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ja.png) | ![Pixrayによる生成画像](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ja.png) +![Pixrayによる生成画像](../../../../../translated_images/ja/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixrayによる生成画像](../../../../../translated_images/ja/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- プロンプト *a closeup watercolor portrait of young male teacher of literature with a book* から生成された画像 | プロンプト *a closeup oil portrait of young female teacher of computer science with a computer* から生成された画像 | プロンプト *a closeup oil portrait of old male teacher of mathematics in front of blackboard* から生成された画像 diff --git a/translations/kn/README.md b/translations/kn/README.md index 50f2b9d4..36a0d815 100644 --- a/translations/kn/README.md +++ b/translations/kn/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ಹೊಸವರಿಗಾಗಿ искусственный ಬುದ್ಧಿಮತ್ತೆ - ಒಂದು ಪಠ್ಯಕ್ರಮ -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.kn.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/kn/ai-overview.0857791951d19500.png)| |:---:| | ಹೊಸವರಿಗಾಗಿ искусственный ಬುದ್ಧಿಮತ್ತೆ - _ಸ್ಕೆಎಚ್‌ನೋಟ್ [@girlie_mac](https://twitter.com/girlie_mac) ಅವರಿಂದ_ | diff --git a/translations/kn/lessons/1-Intro/README.md b/translations/kn/lessons/1-Intro/README.md index d00d8035..017d6287 100644 --- a/translations/kn/lessons/1-Intro/README.md +++ b/translations/kn/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ಪರಿಚಯ -![AI ವಿಷಯದ ಪರಿಚಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/ai-intro.bf28d1ac4235881c.kn.png) +![AI ವಿಷಯದ ಪರಿಚಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-intro.bf28d1ac4235881c.png) > ಸ್ಕೆಚ್‌ನೋಟ್: [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ಆರಂಭದಲ್ಲಿ, ಕಂಪ್ಯೂಟರ್‌ಗಳನ್ನು [ಚಾರ್ಲ್ಸ್ ಬ್ಯಾಬೇಜ್](https://en.wikipedia.org/wiki/Charles_Babbage) ಸಂಖ್ಯೆಗಳ ಮೇಲೆ ನಿರ್ದಿಷ್ಟ ಕ್ರಮವನ್ನು ಅನುಸರಿಸಿ ಕಾರ್ಯನಿರ್ವಹಿಸಲು ಕಂಡುಹಿಡಿದರು - ಅಲ್ಗೋರಿದಮ್. 19ನೇ ಶತಮಾನದಲ್ಲಿ ಪ್ರಸ್ತಾಪಿಸಲಾದ ಮೂಲ ಮಾದರಿಗಿಂತ ಬಹಳ ಮುಂದುವರೆದಿದ್ದರೂ, ಆಧುನಿಕ ಕಂಪ್ಯೂಟರ್‌ಗಳು ಇನ್ನೂ ನಿಯಂತ್ರಿತ ಗಣನೆಗಳ ಆಲೋಚನೆಯನ್ನು ಅನುಸರಿಸುತ್ತವೆ. ಆದ್ದರಿಂದ, ಗುರಿಯನ್ನು ಸಾಧಿಸಲು ಬೇಕಾದ ಕ್ರಮಗಳನ್ನು ನಾವು ತಿಳಿದಿದ್ದರೆ, ಕಂಪ್ಯೂಟರ್‌ಗೆ ಆ ಕಾರ್ಯವನ್ನು ಪ್ರೋಗ್ರಾಮ್ ಮಾಡಬಹುದು. -![ವ್ಯಕ್ತಿಯ ಫೋಟೋ](../../../../translated_images/dsh_age.d212a30d4e54fb5f.kn.png) +![ವ್ಯಕ್ತಿಯ ಫೋಟೋ](../../../../translated_images/kn/dsh_age.d212a30d4e54fb5f.png) > ಫೋಟೋ: [ವಿಕ್ಕಿ ಸೋಶ್ನಿಕೋವಾ](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ಬುದ್ಧಿಮತ್ತೆ](https://en.wikipedia.org/wiki/Intelligence)** ಎಂಬ ಪದದ ಸ್ಪಷ್ಟ ವ್ಯಾಖ್ಯಾನ ಇಲ್ಲದಿರುವುದು ಒಂದು ಸಮಸ್ಯೆ. ಬುದ್ಧಿಮತ್ತೆ abstract thinking ಅಥವಾ self-awareness ಗೆ ಸಂಬಂಧಿಸಿದೆ ಎಂದು ಹೇಳಬಹುದು, ಆದರೆ ಸರಿಯಾಗಿ ವ್ಯಾಖ್ಯಾನಿಸಲು ಸಾಧ್ಯವಿಲ್ಲ. -![ಬೆಕ್ಕಿನ ಫೋಟೋ](../../../../translated_images/photo-cat.8c8e8fb760ffe457.kn.jpg) +![ಬೆಕ್ಕಿನ ಫೋಟೋ](../../../../translated_images/kn/photo-cat.8c8e8fb760ffe457.jpg) > [ಫೋಟೋ](https://unsplash.com/photos/75715CVEJhI) - [ಅಂಬರ್ ಕಿಪ್](https://unsplash.com/@sadmax) ಅವರಿಂದ Unsplash @@ -98,13 +98,13 @@ AGI ಬಗ್ಗೆ ಮಾತನಾಡುವಾಗ, ನಾವು ನಿಜವಾ > | ML ಬಗ್ಗೆ ಏನು? | | > |--------------|-----------| -> | ಕೆಲವು ಡೇಟಾ ಆಧಾರಿತ ಸಮಸ್ಯೆ ಪರಿಹಾರಕ್ಕಾಗಿ ಕಂಪ್ಯೂಟರ್ ಕಲಿಕೆಯನ್ನು ಆಧರಿಸಿದ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆಯ ಭಾಗವನ್ನು **ಮಷೀನ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಈ ಕೋರ್ಸ್‌ನಲ್ಲಿ ನಾವು ಸಾಂಪ್ರದಾಯಿಕ ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಒಳಗೊಂಡಿಲ್ಲ - ನೀವು ಪ್ರತ್ಯೇಕ [ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](http://aka.ms/ml-beginners) ಪಠ್ಯಕ್ರಮವನ್ನು ನೋಡಿ.| ![ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.kn.png) | +> | ಕೆಲವು ಡೇಟಾ ಆಧಾರಿತ ಸಮಸ್ಯೆ ಪರಿಹಾರಕ್ಕಾಗಿ ಕಂಪ್ಯೂಟರ್ ಕಲಿಕೆಯನ್ನು ಆಧರಿಸಿದ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆಯ ಭಾಗವನ್ನು **ಮಷೀನ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಈ ಕೋರ್ಸ್‌ನಲ್ಲಿ ನಾವು ಸಾಂಪ್ರದಾಯಿಕ ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಒಳಗೊಂಡಿಲ್ಲ - ನೀವು ಪ್ರತ್ಯೇಕ [ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](http://aka.ms/ml-beginners) ಪಠ್ಯಕ್ರಮವನ್ನು ನೋಡಿ.| ![ಮಷೀನ್ ಲರ್ನಿಂಗ್ ಫಾರ್ ಬಿಗಿನರ್ಸ್](../../../../translated_images/kn/ml-for-beginners.9e4fed176fd5817d.png) | ## AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಪರಿಚಯ ಕೃತಕ ಬುದ್ಧಿಮತ್ತೆ 20ನೇ ಶತಮಾನ ಮಧ್ಯದಲ್ಲಿ ಕ್ಷೇತ್ರವಾಗಿ ಪ್ರಾರಂಭವಾಯಿತು. ಆರಂಭದಲ್ಲಿ, ಪ್ರತೀಕಾತ್ಮಕ ತರ್ಕವು ಪ್ರಮುಖ ದೃಷ್ಠಿಕೋನವಾಗಿತ್ತು ಮತ್ತು ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳಂತಹ ಯಶಸ್ಸುಗಳನ್ನು ತಂದಿತು. ಆದರೆ ಈ ವಿಧಾನವು ವ್ಯಾಪಕವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುವುದಿಲ್ಲ ಎಂದು ಸ್ಪಷ್ಟವಾಯಿತು. ತಜ್ಞರಿಂದ ಜ್ಞಾನ ತೆಗೆದು, ಅದನ್ನು ಕಂಪ್ಯೂಟರ್‌ನಲ್ಲಿ ಪ್ರತಿನಿಧಿಸಿ, ಜ್ಞಾನವನ್ನು ನಿಖರವಾಗಿರಿಸುವುದು ಬಹಳ ಸಂಕೀರ್ಣ ಮತ್ತು ದುಬಾರಿ ಕೆಲಸ. ಇದರಿಂದ 1970ರ ದಶಕದಲ್ಲಿ [AI ವಿಂಟರ್](https://en.wikipedia.org/wiki/AI_winter) ಉಂಟಾಯಿತು. -AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಚಿತ್ರ +AI ಇತಿಹಾಸದ ಸಂಕ್ಷಿಪ್ತ ಚಿತ್ರ > ಚಿತ್ರ: [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) diff --git a/translations/kn/lessons/2-Symbolic/Animals.ipynb b/translations/kn/lessons/2-Symbolic/Animals.ipynb index 72f99b30..4ec72ca4 100644 --- a/translations/kn/lessons/2-Symbolic/Animals.ipynb +++ b/translations/kn/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ಈ ಉದಾಹರಣೆಯಲ್ಲಿ, ಕೆಲವು ದೈಹಿಕ ಲಕ್ಷಣಗಳ ಆಧಾರದ ಮೇಲೆ ಪ್ರಾಣಿಯನ್ನು ನಿರ್ಧರಿಸಲು ಸರಳ ಜ್ಞಾನಾಧಾರಿತ ವ್ಯವಸ್ಥೆಯನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವೆವು. ಈ ವ್ಯವಸ್ಥೆಯನ್ನು ಕೆಳಗಿನ AND-OR ಮರದಿಂದ ಪ್ರತಿನಿಧಿಸಬಹುದು (ಇದು ಸಂಪೂರ್ಣ ಮರದ ಒಂದು ಭಾಗ, ನಾವು ಸುಲಭವಾಗಿ ಇನ್ನಷ್ಟು ನಿಯಮಗಳನ್ನು ಸೇರಿಸಬಹುದು):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.kn.png)\n" + "![](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/kn/lessons/2-Symbolic/README.md b/translations/kn/lessons/2-Symbolic/README.md index 5fd06379..69db57ec 100644 --- a/translations/kn/lessons/2-Symbolic/README.md +++ b/translations/kn/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಮತ್ತು ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳು -![ಸಾಂಕೆತಿಕ AI ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/ai-symbolic.715a30cb610411a6.kn.png) +![ಸಾಂಕೆತಿಕ AI ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-symbolic.715a30cb610411a6.png) > ಸ್ಕೆಚ್‌ನೋಟ್ [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) ಅವರಿಂದ @@ -35,13 +35,13 @@ AI ಆರಂಭಿಕ ದಿನಗಳಲ್ಲಿ, ಬುದ್ಧಿವಂತ * **ಜ್ಞಾನ** ಎಂದರೆ ಮಾಹಿತಿಯನ್ನು ನಮ್ಮ ಜಗತ್ತಿನ ಮಾದರಿಯಲ್ಲಿ ಸಂಯೋಜಿಸುವುದು. ಉದಾಹರಣೆಗೆ, ಕಂಪ್ಯೂಟರ್ ಎಂದರೇನು ಎಂದು ಕಲಿತ ಮೇಲೆ, ಅದು ಹೇಗೆ ಕೆಲಸ ಮಾಡುತ್ತದೆ, ಅದರ ಬೆಲೆ ಎಷ್ಟು, ಮತ್ತು ಅದನ್ನು ಏಕೆ ಬಳಸಬಹುದು ಎಂಬುದರ ಬಗ್ಗೆ ನಮಗೆ ಕೆಲವು ಕಲ್ಪನೆಗಳು ಬರುತ್ತವೆ. ಈ ಪರಸ್ಪರ ಸಂಬಂಧಿತ ಕಲ್ಪನೆಗಳ ಜಾಲವೇ ನಮ್ಮ ಜ್ಞಾನ. * **ಜ್ಞಾನತೆ** ಎಂದರೆ ಜಗತ್ತಿನ ನಮ್ಮ ಇನ್ನೊಂದು ಮಟ್ಟದ ಅರ್ಥ, ಮತ್ತು ಇದು *ಮೆಟಾ-ಜ್ಞಾನ* ಅನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ, ಉದಾ. ಜ್ಞಾನವನ್ನು ಯಾವಾಗ ಮತ್ತು ಹೇಗೆ ಬಳಸಬೇಕು ಎಂಬ ಕಲ್ಪನೆ. - + *ಚಿತ್ರ [ವಿಕಿಪೀಡಿಯದಿಂದ](https://commons.wikimedia.org/w/index.php?curid=37705247), ಲಾಂಗ್ಲಿವಥಿಯುಕ್ಸ್ ಅವರ ಸ್ವಂತ ಕೆಲಸ, CC BY-SA 4.0* ಹೀಗಾಗಿ, **ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನದ** ಸಮಸ್ಯೆ ಎಂದರೆ ಜ್ಞಾನವನ್ನು ಡೇಟಾ ರೂಪದಲ್ಲಿ ಕಂಪ್ಯೂಟರ್ ಒಳಗೆ ಪರಿಣಾಮಕಾರಿಯಾಗಿ ಪ್ರತಿನಿಧಿಸುವ ವಿಧಾನವನ್ನು ಕಂಡುಹಿಡಿಯುವುದು, ಅದನ್ನು ಸ್ವಯಂಚಾಲಿತವಾಗಿ ಬಳಸಲು ಸಾಧ್ಯವಾಗುವಂತೆ ಮಾಡುವುದು. ಇದನ್ನು ಒಂದು ಸ್ಪೆಕ್ಟ್ರಮ್ ಆಗಿ ನೋಡಬಹುದು: -![ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಸ್ಪೆಕ್ಟ್ರಮ್](../../../../translated_images/knowledge-spectrum.b60df631852c0217.kn.png) +![ಜ್ಞಾನ ಪ್ರತಿನಿಧಾನ ಸ್ಪೆಕ್ಟ್ರಮ್](../../../../translated_images/kn/knowledge-spectrum.b60df631852c0217.png) > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರಿಂದ @@ -94,7 +94,7 @@ Python | ಬ್ಲಾಕ್-ಸಿಂಟ್ಯಾಕ್ಸ್ | ಇನ್‌ಡ ಸಾಂಕೆತಿಕ AIಯ ಮೊದಲ ಯಶಸ್ಸುಗಳಲ್ಲಿ ಒಂದಾಗಿದ್ದವು **ತಜ್ಞ ವ್ಯವಸ್ಥೆಗಳು** - ಕೆಲವು ನಿರ್ದಿಷ್ಟ ಸಮಸ್ಯಾ ಕ್ಷೇತ್ರದಲ್ಲಿ ತಜ್ಞರಂತೆ ಕಾರ್ಯನಿರ್ವಹಿಸಲು ವಿನ್ಯಾಸಗೊಳಿಸಿದ ಕಂಪ್ಯೂಟರ್ ವ್ಯವಸ್ಥೆಗಳು. ಅವು ಮಾನವ ತಜ್ಞರಿಂದ ತೆಗೆದುಕೊಂಡ **ಜ್ಞಾನ ಭಂಡಾರ** ಮತ್ತು ಅದರಲ್ಲಿ ತರ್ಕ ನಡೆಸುವ **ನಿರ್ಣಯ ಯಂತ್ರ** ಹೊಂದಿದ್ದವು. -![ಮಾನವ ವಾಸ್ತುಶಿಲ್ಪ](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.kn.png) | ![ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆ](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.kn.png) +![ಮಾನವ ವಾಸ್ತುಶಿಲ್ಪ](../../../../translated_images/kn/arch-human.5d4d35f1bba3ab1c.png) | ![ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆ](../../../../translated_images/kn/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ ಮಾನವ ನ್ಯೂರಲ್ ವ್ಯವಸ್ಥೆಯ ಸರಳೀಕೃತ ರಚನೆ | ಜ್ಞಾನ ಆಧಾರಿತ ವ್ಯವಸ್ಥೆಯ ವಾಸ್ತುಶಿಲ್ಪ @@ -106,7 +106,7 @@ Python | ಬ್ಲಾಕ್-ಸಿಂಟ್ಯಾಕ್ಸ್ | ಇನ್‌ಡ ಉದಾಹರಣೆಗೆ, ಪ್ರಾಣಿಯನ್ನು ಅದರ ಭೌತಿಕ ಲಕ್ಷಣಗಳ ಆಧಾರದ ಮೇಲೆ ಗುರುತಿಸುವ ತಜ್ಞ ವ್ಯವಸ್ಥೆಯನ್ನು ಪರಿಗಣಿಸೋಣ: -![AND-OR ಮರ](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.kn.png) +![AND-OR ಮರ](../../../../translated_images/kn/AND-OR-Tree.5592d2c70187f283.png) > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರಿಂದ @@ -166,7 +166,7 @@ THEN the animal is a carnivore ಸೆಮ್ಯಾಂಟಿಕ್ ವೆಬ್‌ನಲ್ಲಿ ಎಲ್ಲಾ ಪ್ರತಿನಿಧಾನಗಳು ತ್ರಿಪುಟಗಳ ಮೇಲೆ ಆಧಾರಿತವಾಗಿವೆ. ಪ್ರತಿ ವಸ್ತು ಮತ್ತು ಪ್ರತಿ ಸಂಬಂಧವನ್ನು ಯುಆರ್‌ಐ ಮೂಲಕ ವಿಶಿಷ್ಟವಾಗಿ ಗುರುತಿಸಲಾಗುತ್ತದೆ. ಉದಾಹರಣೆಗೆ, ಈ AI ಪಠ್ಯಕ್ರಮವನ್ನು ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಜನವರಿ 1, 2022 ರಂದು ಅಭಿವೃದ್ಧಿಪಡಿಸಿದ್ದೆಂದು ಹೇಳಬೇಕಾದರೆ, ನಾವು ಬಳಸಬಹುದಾದ ತ್ರಿಪುಟಗಳು ಇವು: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -177,7 +177,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre ಹೆಚ್ಚು ಸಂಕೀರ್ಣ ಪ್ರಕರಣದಲ್ಲಿ, ರಚಯಿತೃಗಳ ಪಟ್ಟಿಯನ್ನು ನಿರ್ಧರಿಸಲು RDF ನಲ್ಲಿ ನಿರ್ದಿಷ್ಟಪಡಿಸಿದ ಡೇಟಾ ರಚನೆಗಳನ್ನು ಬಳಸಬಹುದು. - + > ಮೇಲಿನ ಚಿತ್ರಗಳು [ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ಅವರವರು ರಚಿಸಿದ್ದಾರೆ @@ -201,7 +201,7 @@ GROUP BY ?eyeColorLabel > ✅ ನಿಮ್ಮದೇ ಒಂಟಾಲಜಿಗಳನ್ನು ನಿರ್ಮಿಸಲು ಅಥವಾ ಇತ್ತೀಚಿನ ಒಂಟಾಲಜಿಗಳನ್ನು ತೆರೆಯಲು ಆಸಕ್ತಿ ಇದ್ದರೆ, [Protégé](https://protege.stanford.edu/) ಎಂಬ ಅದ್ಭುತ ದೃಶ್ಯ ಒಂಟಾಲಜಿ ಸಂಪಾದಕವನ್ನು ಬಳಸಬಹುದು. ಅದನ್ನು ಡೌನ್‌ಲೋಡ್ ಮಾಡಿ ಅಥವಾ ಆನ್‌ಲೈನ್‌ನಲ್ಲಿ ಬಳಸಿ. - + *Web Protégé ಸಂಪಾದಕ ರೋಮ್ಯಾನೋವ್ ಕುಟುಂಬ ಒಂಟಾಲಜಿಯೊಂದಿಗೆ ತೆರೆಯಲಾಗಿದೆ. ಚಿತ್ರ ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರವರು ತೆಗೆದಿದ್ದಾರೆ* diff --git a/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md index d4c5f9cd..26764c54 100644 --- a/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > ಚಿತ್ರಗಳು [ವಿಕಿಪೀಡಿಯದಿಂದ](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ಇಲ್ಲಿ f ಒಂದು ಸ್ಟೆಪ್ ಸಕ್ರಿಯತೆ ಕಾರ್ಯವಾಗಿದೆ - + ## ಪರ್ಸೆಪ್ಟ್ರಾನ್ ತರಬೇತಿ diff --git a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index ed850b8f..fddec4b1 100644 --- a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "ನಾವು ದ್ವಿಮೌಲ್ಯ ವರ್ಗೀಕರಣ ಸಮಸ್ಯೆಗೆ ಡೇಟಾಸೆಟ್ ಅನ್ನು ರಚಿಸಿದ್ದೇವೆ. ಆದಾಗ್ಯೂ, ನಾವು ಅದನ್ನು ಆರಂಭದಿಂದಲೇ ಬಹು-ವರ್ಗ ವರ್ಗೀಕರಣವೆಂದು ಪರಿಗಣಿಸೋಣ, ಇದರಿಂದ ನಾವು ನಮ್ಮ ಕೋಡ್ ಅನ್ನು ಸುಲಭವಾಗಿ ಬಹು-ವರ್ಗ ವರ್ಗೀಕರಣಕ್ಕೆ ಬದಲಾಯಿಸಬಹುದು. ಈ ಸಂದರ್ಭದಲ್ಲಿ, ನಮ್ಮ ಒನ್-ಲೆಯರ್ ಪರ್ಸೆಪ್ಟ್ರಾನ್ ಕೆಳಗಿನ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಹೊಂದಿರುತ್ತದೆ:\n", "\n", - "\n", + "\n", "\n", "ನೆಟ್‌ವರ್ಕ್‌ನ ಎರಡು ಔಟ್‌ಪುಟ್‌ಗಳು ಎರಡು ವರ್ಗಗಳಿಗೆ ಹೊಂದಿವೆ, ಮತ್ತು ಎರಡು ಔಟ್‌ಪುಟ್‌ಗಳಲ್ಲಿನ ಅತ್ಯಧಿಕ ಮೌಲ್ಯ ಹೊಂದಿರುವ ವರ್ಗವೇ ಸರಿಯಾದ ಪರಿಹಾರವಾಗುತ್ತದೆ.\n", "\n", @@ -489,7 +489,7 @@ "\n", "ನಾವು 2 ಕ್ಕಿಂತ ಹೆಚ್ಚು ವರ್ಗಗಳಿದ್ದರೆ, ಸಾಫ್ಟ್‌ಮ್ಯಾಕ್ಸ್ ಎಲ್ಲಾ ವರ್ಗಗಳಲ್ಲಿಯೂ ಪ್ರಾಬಬಿಲಿಟಿಗಳನ್ನು ಸಾಮಾನ್ಯೀಕರಿಸುತ್ತದೆ. ಇಲ್ಲಿ MNIST ಅಂಕಿ ವರ್ಗೀಕರಣ ಮಾಡುವ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪದ ಚಿತ್ರಣ ಇದೆ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.kn.png)\n" + "![MNIST Classifier](../../../../../translated_images/kn/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್\n", "\n", - "\n", + "\n", "\n", "ಈ ಕ್ಷಣದವರೆಗೆ, ನಾವು ನೆಟ್‌ವರ್ಕ್‌ನ ವಿಭಿನ್ನ ಲೇಯರ್‌ಗಳಿಗೆ ವಿಭಿನ್ನ ಕ್ಲಾಸ್‌ಗಳನ್ನು ವ್ಯಾಖ್ಯಾನಿಸಿದ್ದೇವೆ. ಆ ಲೇಯರ್‌ಗಳ ಸಂಯೋಜನೆಯನ್ನು **ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್** ಎಂದು ಪ್ರತಿನಿಧಿಸಬಹುದು. ಈಗ ನಾವು ಕೆಳಗಿನ ರೀತಿಯಲ್ಲಿ ನೀಡಲಾದ ತರಬೇತಿ ಡೇಟಾಸೆಟ್ (ಅಥವಾ ಅದರ ಭಾಗ) ಗೆ ಲಾಸ್ ಅನ್ನು ಲೆಕ್ಕಹಾಕಬಹುದು:\n" ] @@ -687,7 +687,7 @@ "source": [ "## ಹಿಂದುಮುಖ ಪ್ರಸರಣ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ಇದು ನೋಡ್ $z$ ಗೆ $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$ ಬದಲಾವಣೆಗಳಿಗೆ ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ\n", "* ಈ ದೋಷವನ್ನು ಕಡಿಮೆ ಮಾಡಲು, ನಾವು ಪ್ಯಾರಾಮೀಟರ್‌ಗಳನ್ನು ಅನುಗುಣವಾಗಿ ಸರಿಹೊಂದಿಸಬೇಕಾಗುತ್ತದೆ: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (ಮತ್ತು $b$ ಗಾಗಿ ಕೂಡ ಇದೇ)\n", "\n", - "\n", + "\n", "\n", "ಈ ಪ್ರಕ್ರಿಯೆ ನೆಟ್‌ವರ್ಕ್‌ನ ಔಟ್‌ಪುಟ್‌ನಿಂದ ಅದರ ಪ್ಯಾರಾಮೀಟರ್‌ಗಳ ಕಡೆಗೆ ಲಾಸ್ ದೋಷವನ್ನು ಹಂಚಿಕೊಳ್ಳಲು ಪ್ರಾರಂಭವಾಗುತ್ತದೆ. ಆದ್ದರಿಂದ ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಬ್ಯಾಕ್ ಪ್ರೋಪಾಗೇಶನ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", @@ -1265,7 +1265,7 @@ "* ತರಬೇತಿ ನಷ್ಟ ಕಡಿಮೆ - ಮಾದರಿ ತರಬೇತಿ ಡೇಟಾವನ್ನು ಚೆನ್ನಾಗಿ ಅಂದಾಜು ಮಾಡಬಹುದು, ಏಕೆಂದರೆ ಅದಕ್ಕೆ ಸಾಕಷ್ಟು ವ್ಯಕ್ತಪಡಿಸುವ ಶಕ್ತಿ ಇದೆ.\n", "* ಮಾನ್ಯತೆ ನಷ್ಟ ತರಬೇತಿ ನಷ್ಟಕ್ಕಿಂತ ಬಹಳ ಹೆಚ್ಚು ಇರಬಹುದು ಮತ್ತು ತರಬೇತಿ ಸಮಯದಲ್ಲಿ ಹೆಚ್ಚಾಗಬಹುದು - ಇದಕ್ಕೆ ಕಾರಣ ಮಾದರಿ ತರಬೇತಿ ಬಿಂದುಗಳನ್ನು \"ಸ್ಮರಿಸುತ್ತದೆ\", ಮತ್ತು \"ಒಟ್ಟು ಚಿತ್ರ\" ಕಳೆದುಕೊಳ್ಳುತ್ತದೆ.\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.kn.png)\n", + "![Overfitting](../../../../../translated_images/kn/overfit.a0bd57f717c15769.png)\n", "\n", "> ಈ ಚಿತ್ರದಲ್ಲಿ, `x` ತರಬೇತಿ ಡೇಟಾವನ್ನು ಸೂಚಿಸುತ್ತದೆ, `o` - ಮಾನ್ಯತೆ ಡೇಟಾವನ್ನು. ಎಡಭಾಗ - ರೇಖೀಯ ಮಾದರಿ (ಒಂದು-ಪರತೆಯ), ಇದು ಡೇಟಾದ ಸ್ವಭಾವವನ್ನು ಚೆನ್ನಾಗಿ ಅಂದಾಜು ಮಾಡುತ್ತದೆ. ಬಲಭಾಗ - ಅತಿವೈಯಕ್ತಿಕ ಮಾದರಿ, ಮಾದರಿ ತರಬೇತಿ ಡೇಟಾವನ್ನು ಪರಿಪೂರ್ಣವಾಗಿ ಅಂದಾಜು ಮಾಡುತ್ತದೆ, ಆದರೆ ಯಾವುದೇ ಇತರ ಡೇಟಾದೊಂದಿಗೆ ಅರ್ಥವಿಲ್ಲದಂತೆ ನಿಲ್ಲುತ್ತದೆ (ಮಾನ್ಯತೆ ದೋಷ ಬಹಳ ಹೆಚ್ಚು).\n" ] diff --git a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 46ee4dd1..6a1570b8 100644 --- a/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: ಎಲ್ಲಾ ಅಭಿವ್ಯಕ್ತಿಗಳ ಎಡಭಾಗವು ಒಂದೇ ಆಗಿರುವುದರಿಂದ, ನಾವು ಲಾಸ್ ಫಂಕ್ಷನ್‌ನಿಂದ ಪ್ರಾರಂಭಿಸಿ ಗಣನಾತ್ಮಕ ಗ್ರಾಫ್ ಮೂಲಕ "ಹಿಂದಕ್ಕೆ" ಹೋಗಿ ಡೆರಿವೇಟಿವ್‌ಗಳನ್ನು ಪರಿಣಾಮಕಾರಿಯಾಗಿ ಲೆಕ್ಕಿಸಬಹುದು. ಆದ್ದರಿಂದ ಬಹು-ಪರತೆಯ ಪರ್ಸೆಪ್ಟ್ರಾನ್ ತರಬೇತಿಯ ವಿಧಾನವನ್ನು **ಬ್ಯಾಕ್‌ಪ್ರೊಪಾಗೇಶನ್** ಅಥವಾ 'ಬ್ಯಾಕ್‌ಪ್ರೊಪ್' ಎಂದು ಕರೆಯುತ್ತಾರೆ. -compute graph +compute graph > TODO: ಚಿತ್ರ ಉಲ್ಲೇಖ diff --git a/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md index 66750da9..f66d1c12 100644 --- a/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: ಕೆಳಗಿನ 5 ಬಿಂದುಗಳನ್ನು (ಗ್ರಾಫ್‌ಗಳಲ್ಲಿ `x` ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಲಾಗಿದೆ) ಅಂದಾಜಿಸುವ ಸಮಸ್ಯೆಯನ್ನು ಪರಿಗಣಿಸಿ: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.kn.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.kn.jpg) +![linear](../../../../../translated_images/kn/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/kn/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **ರೇಖೀಯ ಮಾದರಿ, 2 ಪ್ಯಾರಾಮೀಟರ್‌ಗಳು** | **ಅರೇಖೀಯ ಮಾದರಿ, 7 ಪ್ಯಾರಾಮೀಟರ್‌ಗಳು** ತರಬೇತಿ ದೋಷ = 5.3 | ತರಬೇತಿ ದೋಷ = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: ಮೇಲಿನ ಗ್ರಾಫ್‌ನಿಂದ ನೀವು ನೋಡಬಹುದು, ಓವರ್‌ಫಿಟಿಂಗ್ ಅನ್ನು ತುಂಬಾ ಕಡಿಮೆ ತರಬೇತಿ ದೋಷ ಮತ್ತು ಹೆಚ್ಚು ಮಾನ್ಯತೆ ದೋಷದಿಂದ ಪತ್ತೆಮಾಡಬಹುದು. ಸಾಮಾನ್ಯವಾಗಿ ತರಬೇತಿ ಸಮಯದಲ್ಲಿ ತರಬೇತಿ ಮತ್ತು ಮಾನ್ಯತೆ ದೋಷಗಳು ಎರಡೂ ಕಡಿಮೆಯಾಗುತ್ತವೆ, ನಂತರ ಕೆಲವೊಂದು ಸಮಯದಲ್ಲಿ ಮಾನ್ಯತೆ ದೋಷ ಕಡಿಮೆಯಾಗುವುದನ್ನು ನಿಲ್ಲಿಸಿ ಏರಿಕೆಯಾಗಬಹುದು. ಇದು ಓವರ್‌ಫಿಟಿಂಗ್ ಸೂಚನೆ ಆಗಿದ್ದು, ಈ ಸಮಯದಲ್ಲಿ ತರಬೇತಿಯನ್ನು ನಿಲ್ಲಿಸುವುದು (ಅಥವಾ ಕನಿಷ್ಠ ಮಾದರಿಯ ಸ্নಾಪ್‌ಶಾಟ್ ತೆಗೆದುಕೊಳ್ಳುವುದು) ಸೂಕ್ತ. -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.kn.png) +![overfitting](../../../../../translated_images/kn/Overfitting.408ad91cd90b4371.png) ## ಓವರ್‌ಫಿಟಿಂಗ್ ಅನ್ನು ತಡೆಯುವುದು ಹೇಗೆ diff --git a/translations/kn/lessons/3-NeuralNetworks/README.md b/translations/kn/lessons/3-NeuralNetworks/README.md index 732bbb4f..2530fb45 100644 --- a/translations/kn/lessons/3-NeuralNetworks/README.md +++ b/translations/kn/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಗೆ ಪರಿಚಯ -![ಡೂಡಲ್‌ನಲ್ಲಿ ಇಂಟ್ರೋ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.kn.png) +![ಡೂಡಲ್‌ನಲ್ಲಿ ಇಂಟ್ರೋ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಿಷಯದ ಸಾರಾಂಶ](../../../../translated_images/kn/ai-neuralnetworks.1c687ae40bc86e83.png) ನಾವು ಪರಿಚಯದಲ್ಲಿ ಚರ್ಚಿಸಿದಂತೆ, ಬುದ್ಧಿಮತ್ತೆಯನ್ನು ಸಾಧಿಸುವ ಒಂದು ಮಾರ್ಗವೆಂದರೆ **ಕಂಪ್ಯೂಟರ್ ಮಾದರಿ** ಅಥವಾ **ಕೃತಕ ಮೆದುಳು** ಅನ್ನು ತರಬೇತುಗೊಳಿಸುವುದು. 20ನೇ ಶತಮಾನ ಮಧ್ಯಭಾಗದಿಂದ, ಸಂಶೋಧಕರು ವಿವಿಧ ಗಣಿತ ಮಾದರಿಗಳನ್ನು ಪ್ರಯತ್ನಿಸಿದರು, ಇತ್ತೀಚಿನ ವರ್ಷಗಳಲ್ಲಿ ಈ ದಿಕ್ಕು ಬಹುಮಟ್ಟಿಗೆ ಯಶಸ್ವಿಯಾಗಿದೆ. ಮೆದುಳಿನ ಇಂತಹ ಗಣಿತ ಮಾದರಿಗಳನ್ನು **ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳು** ಎಂದು ಕರೆಯುತ್ತಾರೆ. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ಜೈವಶಾಸ್ತ್ರದಿಂದ, ನಮ್ಮ ಮೆದುಳು ನ್ಯೂರಲ್ ಸೆಲ್‌ಗಳು (ನ್ಯೂರಾನ್‌ಗಳು) ಹೊಂದಿದೆ ಎಂದು ತಿಳಿದಿದೆ, ಪ್ರತಿಯೊಂದು ನ್ಯೂರಾನ್‌ಗೂ ಹಲವಾರು "ಇನ್‌ಪುಟ್‌ಗಳು" (ಡೆಂಡ್ರೈಟ್‌ಗಳು) ಮತ್ತು ಒಂದು "ಔಟ್‌ಪುಟ್" (ಆಕ್ಸಾನ್) ಇರುತ್ತದೆ. ಡೆಂಡ್ರೈಟ್‌ಗಳು ಮತ್ತು ಆಕ್ಸಾನ್‌ಗಳು ವಿದ್ಯುತ್ ಸಂಕೆತಗಳನ್ನು ಸಾಗಿಸಬಹುದು, ಮತ್ತು ಅವುಗಳ ನಡುವಿನ ಸಂಪರ್ಕಗಳು — ಸೈನಾಪ್ಸ್ ಎಂದು ಕರೆಯಲ್ಪಡುವವು — ವಿವಿಧ ಮಟ್ಟದ ಚಾಲಕತೆಯನ್ನು ತೋರಬಹುದು, ಅವು ನ್ಯೂರೋ ಟ್ರಾನ್ಸ್‌ಮಿಟರ್‌ಗಳ ಮೂಲಕ ನಿಯಂತ್ರಿಸಲ್ಪಡುತ್ತವೆ. -![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.kn.jpg) | ![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/artneuron.1a5daa88d20ebe6f.kn.png) +![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ನ್ಯೂರಾನ್ ಮಾದರಿ](../../../../translated_images/kn/artneuron.1a5daa88d20ebe6f.png) ----|---- ನಿಜವಾದ ನ್ಯೂರಾನ್ *([ಚಿತ್ರ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) ವಿಕಿಪೀಡಿಯದಿಂದ)* | ಕೃತಕ ನ್ಯೂರಾನ್ *(ಲೇಖಕರ ಚಿತ್ರ)* ಹೀಗಾಗಿ, ನ್ಯೂರಾನ್‌ನ ಸರಳ ಗಣಿತ ಮಾದರಿಯಲ್ಲಿ ಹಲವಾರು ಇನ್‌ಪುಟ್‌ಗಳು X1, ..., XN ಮತ್ತು ಒಂದು ಔಟ್‌ಪುಟ್ Y, ಮತ್ತು ಸರಣಿಯಾದ ತೂಕಗಳು W1, ..., WN ಇರುತ್ತವೆ. ಔಟ್‌ಪುಟ್ ಅನ್ನು ಹೀಗೆ ಲೆಕ್ಕಹಾಕಲಾಗುತ್ತದೆ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ಇಲ್ಲಿ f ಎಂಬುದು ಕೆಲವು ರೇಖೀಯವಲ್ಲದ **ಸಕ್ರಿಯ ಕಾರ್ಯ**. diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md index a144c405..22286261 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV ಬಳಸಿ ವಿಡಿಯೋವನ್ನು ಫ್ರೇಮ್-ಬೈ- * **ಬ್ರೈಲ್ ಪುಸ್ತಕದ ಫೋಟೋವನ್ನು ಪೂರ್ವ-ಸಂಸ್ಕರಿಸುವುದು**. ನಾವು ಥ್ರೆಶೋಲ್ಡಿಂಗ್, ವೈಶಿಷ್ಟ್ಯ ಪತ್ತೆ, ಪರ್ಸ್ಪೆಕ್ಟಿವ್ ಪರಿವರ್ತನೆ ಮತ್ತು NumPy ಮ್ಯಾನಿಪ್ಯುಲೇಶನ್‌ಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಪ್ರತ್ಯೇಕ ಬ್ರೈಲ್ ಚಿಹ್ನೆಗಳನ್ನು neural network ಮೂಲಕ ವರ್ಗೀಕರಿಸಲು ಹೇಗೆ ಪ್ರಕ್ರಿಯೆ ಮಾಡಬಹುದು ಎಂಬುದರ ಮೇಲೆ ಗಮನಹರಿಸುತ್ತೇವೆ. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.kn.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.kn.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.kn.png) +![Braille Image](../../../../../translated_images/kn/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/kn/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/kn/braille-symbols.0159185ab69d5339.png) ----|-----|----- > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ * **ವೀಡಿಯೋದಲ್ಲಿ ಚಲನವಲನ ಪತ್ತೆಹಚ್ಚುವುದು ಫ್ರೇಮ್ ವ್ಯತ್ಯಾಸ ಬಳಸಿ**. ಕ್ಯಾಮೆರಾ ಸ್ಥಿರವಾಗಿದ್ದರೆ, ಕ್ಯಾಮೆರಾ ಫೀಡ್‌ನ ಫ್ರೇಮ್‌ಗಳು ಪರಸ್ಪರ ಬಹಳ ಸಮಾನವಾಗಿರುತ್ತವೆ. ಫ್ರೇಮ್‌ಗಳು ಅರೆಗಳಾಗಿ ಪ್ರತಿನಿಧಿಸಲ್ಪಟ್ಟಿರುವುದರಿಂದ, ಎರಡು ಕ್ರಮಬದ್ಧ ಫ್ರೇಮ್‌ಗಳ ಅರೆಗಳನ್ನು ವಜಾ ಮಾಡಿದರೆ ಪಿಕ್ಸೆಲ್ ವ್ಯತ್ಯಾಸ ಸಿಗುತ್ತದೆ, ಇದು ಸ್ಥಿರ ಫ್ರೇಮ್‌ಗಳಿಗೆ ಕಡಿಮೆ ಮತ್ತು ಚಲನೆಯಾಗಿರುವ ಚಿತ್ರಗಳಿಗೆ ಹೆಚ್ಚು ಆಗುತ್ತದೆ. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.kn.png) +![Image of video frames and frame differences](../../../../../translated_images/kn/frame-difference.706f805491a0883c.png) > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ @@ -89,7 +89,7 @@ OpenCV ಬಳಸಿ ವಿಡಿಯೋವನ್ನು ಫ್ರೇಮ್-ಬೈ- - **Dense Optical Flow** ಪ್ರತಿ ಪಿಕ್ಸೆಲ್ ಯಾವ ಕಡೆಗೆ ಚಲಿಸುತ್ತಿದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ವೆಕ್ಟರ್ ಕ್ಷೇತ್ರವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತದೆ - **Sparse Optical Flow** ಚಿತ್ರದಲ್ಲಿ ಕೆಲವು ವಿಶಿಷ್ಟ ಲಕ್ಷಣಗಳನ್ನು (ಉದಾ: ಅಂಚುಗಳು) ತೆಗೆದು, ಅವುಗಳ ಪಥವನ್ನು ಫ್ರೇಮ್‌ಗಳಿಂದ ಫ್ರೇಮ್‌ಗೆ ನಿರ್ಮಿಸುತ್ತದೆ. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.kn.png) +![Image of Optical Flow](../../../../../translated_images/kn/optical.1f4a94464579a83a.png) > ಚಿತ್ರ [OpenCV.ipynb](OpenCV.ipynb) ನಿಂದ @@ -115,7 +115,7 @@ AI ಶೋದಿಂದ [ಈ ವೀಡಿಯೋವನ್ನು](https://docs.micro ಈ ಪ್ರಯೋಗಶಾಲೆಯಲ್ಲಿ, ನೀವು ಸರಳ ಸಂಕೆತಗಳೊಂದಿಗೆ ಒಂದು ವೀಡಿಯೋ ತೆಗೆದುಕೊಳ್ಳುತ್ತೀರಿ, ಮತ್ತು ನಿಮ್ಮ ಗುರಿ ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲನೆಗಳನ್ನು ಹೊರತೆಗೆಯುವುದು. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index d4109c36..8f9d02c7 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ಸ್ಥಿರ ಹಿನ್ನೆಲೆಯ ಮೇಲೆ ಒಬ್ಬ ವ್ಯಕ್ತಿಯ ಕೈಬುಟ್ಟಿ ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ/ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ ಚಲಿಸುವ [ಈ ವೀಡಿಯೋ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ಯನ್ನು ಪರಿಗಣಿಸಿ.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**ನಿಮ್ಮ ಗುರಿ** ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ, ವೀಡಿಯೋದಲ್ಲಿ ಯಾವ ಭಾಗಗಳು ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲಿಸುತ್ತಿವೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು.\n", "\n", diff --git a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 2ee370f8..6684b504 100644 --- a/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/kn/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [ಈ ವೀಡಿಯೋ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ಯನ್ನು ಪರಿಗಣಿಸಿ, ಇದರಲ್ಲಿ ಒಬ್ಬ ವ್ಯಕ್ತಿಯ ಹಸ್ತದ ತೊಡೆಯು ಸ್ಥಿರ ಹಿನ್ನೆಲೆಯ ಮೇಲೆ ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ/ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ ಚಲಿಸುತ್ತದೆ. -ಹಸ್ತದ ಚಲನೆ ಫ್ರೇಮ್ +ಹಸ್ತದ ಚಲನೆ ಫ್ರೇಮ್ **ನಿಮ್ಮ ಗುರಿ** ಆಪ್ಟಿಕಲ್ ಫ್ಲೋ ಬಳಸಿ ವೀಡಿಯೋದಲ್ಲಿ ಯಾವ ಭಾಗಗಳಲ್ಲಿ ಮೇಲಕ್ಕೆ/ಕೆಳಗೆ/ಎಡಕ್ಕೆ/ಬಲಕ್ಕೆ ಚಲನೆಗಳಿವೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು. diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 205d3638..264a8379 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದಲ್ಲಿ 92.7% ನಿಖರತೆಯನ್ನು ಸಾಧಿಸಿದ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದೆ. ಇದರ ಲೇಯರ್ ರಚನೆ ಹೀಗಿದೆ: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.kn.jpg) +![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.jpg) ನೀವು ನೋಡಬಹುದು, VGG ಪರಂಪರাগত ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಅನುಸರಿಸುತ್ತದೆ, ಇದು ಸಂಯೋಜನೆ-ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳ ಸರಣಿಯಾಗಿದೆ. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.kn.jpg) +![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.jpg) > ಚಿತ್ರ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ನಿಂದ @@ -25,7 +25,7 @@ VGG-16 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದ ResNet 2015 ರಲ್ಲಿ Microsoft Research ಪ್ರಸ್ತಾಪಿಸಿದ ಮಾದರಿಗಳ ಕುಟುಂಬವಾಗಿದೆ. ResNet ನ ಮುಖ್ಯ ಆಲೋಚನೆ **residual blocks** ಬಳಕೆ: - + > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1512.03385.pdf) ನಿಂದ @@ -37,7 +37,7 @@ ResNet 2015 ರಲ್ಲಿ Microsoft Research ಪ್ರಸ್ತಾಪಿಸಿ Google Inception ವಾಸ್ತುಶಿಲ್ಪ ಈ ಆಲೋಚನೆಯನ್ನು ಇನ್ನೊಂದು ಹಂತಕ್ಕೆ ತೆಗೆದುಕೊಂಡು ಹೋಗುತ್ತದೆ ಮತ್ತು ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಲೇಯರ್ ಅನ್ನು ಹಲವು ವಿಭಿನ್ನ ಮಾರ್ಗಗಳ ಸಂಯೋಜನೆಯಾಗಿ ನಿರ್ಮಿಸುತ್ತದೆ: - + > ಚಿತ್ರ [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index f46c85f8..0a71ddb0 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "ಹೀಗಾಗಿ, ಸಾಮಾನ್ಯ CNNನಲ್ಲಿ ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಇರುತ್ತವೆ, ಅವುಗಳ ನಡುವೆ ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳು ಚಿತ್ರದ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡಲು ಇರುತ್ತವೆ. ನಾವು ಫಿಲ್ಟರ್‌ಗಳ ಸಂಖ್ಯೆಯನ್ನು ಕೂಡ ಹೆಚ್ಚಿಸುತ್ತೇವೆ, ಏಕೆಂದರೆ ಮಾದರಿಗಳು ಹೆಚ್ಚು ಉನ್ನತ ಮಟ್ಟದಾಗುತ್ತವೆ - ನಾವು ಹುಡುಕಬೇಕಾದ ಹೆಚ್ಚಿನ ಆಸಕ್ತಿದಾಯಕ ಸಂಯೋಜನೆಗಳಿವೆ.\n", "\n", - "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.kn.png)\n", + "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "ಸ್ಥಳೀಯ ಆಯಾಮಗಳು ಕಡಿಮೆಯಾಗುತ್ತಾ ಮತ್ತು ವೈಶಿಷ್ಟ್ಯ/ಫಿಲ್ಟರ್ ಆಯಾಮಗಳು ಹೆಚ್ಚಾಗುತ್ತಾ ಇರುವುದರಿಂದ, ಈ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪ** ಎಂದು ಕೂಡ ಕರೆಯುತ್ತಾರೆ.\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index abef2be1..84b9bcab 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -340,7 +340,7 @@ "\n", "ಹೀಗಾಗಿ, ಸಾಮಾನ್ಯ CNNನಲ್ಲಿ ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಇರುತ್ತವೆ, ಅವುಗಳ ನಡುವೆ ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳು ಚಿತ್ರದ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡಲು ಇರುತ್ತವೆ. ನಾವು ಫಿಲ್ಟರ್‌ಗಳ ಸಂಖ್ಯೆಯನ್ನು ಹೆಚ್ಚಿಸುತ್ತೇವೆ, ಏಕೆಂದರೆ ಮಾದರಿಗಳು ಹೆಚ್ಚು ಸುಧಾರಿತವಾಗುತ್ತವೆ - ನಾವು ಹುಡುಕಬೇಕಾದ ಹೆಚ್ಚಿನ ಆಸಕ್ತಿದಾಯಕ ಸಂಯೋಜನೆಗಳಿವೆ.\n", "\n", - "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.kn.png)\n", + "![ಹಲವಾರು ಕನ್ವಲ್ಯೂಷನಲ್ ಲೇಯರ್‌ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್ ಲೇಯರ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "ಸ್ಥಳೀಯ ಆಯಾಮಗಳನ್ನು ಕಡಿಮೆ ಮಾಡುವುದು ಮತ್ತು ವೈಶಿಷ್ಟ್ಯ/ಫಿಲ್ಟರ್ ಆಯಾಮಗಳನ್ನು ಹೆಚ್ಚಿಸುವುದರಿಂದ, ಈ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **ಪಿರಮಿಡ್ ವಾಸ್ತುಶಿಲ್ಪ** ಎಂದು ಕರೆಯಲಾಗುತ್ತದೆ.\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md index fea9c6d8..f1697343 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: ನಮೂನೆಗಳನ್ನು ಹೊರತೆಗೆಯಲು, ನಾವು **ಕನ್ವಲ್ಯೂಷನಲ್ ಫಿಲ್ಟರ್‌ಗಳು** ಎಂಬ ಕಲ್ಪನೆಯನ್ನು ಬಳಸುತ್ತೇವೆ. ನೀವು ತಿಳಿದಿರುವಂತೆ, ಚಿತ್ರವನ್ನು 2D-ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಅಥವಾ ಬಣ್ಣ ಆಳದ 3D-ಟೆನ್ಸರ್ ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ. ಫಿಲ್ಟರ್ ಅನ್ವಯಿಸುವುದು ಎಂದರೆ, ನಾವು ಸಾಪೇಕ್ಷವಾಗಿ ಸಣ್ಣ **ಫಿಲ್ಟರ್ ಕರ್ಣಲ್** ಮ್ಯಾಟ್ರಿಕ್ಸ್ ತೆಗೆದುಕೊಳ್ಳುತ್ತೇವೆ, ಮತ್ತು ಮೂಲ ಚಿತ್ರದಲ್ಲಿನ ಪ್ರತಿ ಪಿಕ್ಸೆಲ್‌ಗೆ ಸಮೀಪದ ಬಿಂದುಗಳೊಂದಿಗೆ ತೂಕಿತ ಸರಾಸರಿಯನ್ನು ಲೆಕ್ಕಿಸುತ್ತೇವೆ. ಇದನ್ನು ನಾವು ಒಂದು ಸಣ್ಣ ಕಿಟಕಿ ಚಿತ್ರದ ಮೇಲೆ ಸ್ಲೈಡ್ ಆಗುತ್ತಾ, ಫಿಲ್ಟರ್ ಕರ್ಣಲ್ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ನ ತೂಕಗಳ ಪ್ರಕಾರ ಎಲ್ಲಾ ಪಿಕ್ಸೆಲ್‌ಗಳನ್ನು ಸರಾಸರಿಗೊಳಿಸುವಂತೆ ನೋಡಬಹುದು. -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.kn.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.kn.png) +![Vertical Edge Filter](../../../../../translated_images/kn/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/kn/filter-horiz.59b80ed4feb946ef.png) ----|---- > ಚಿತ್ರ: Dmitry Soshnikov ಉದಾಹರಣೆಗೆ, ನಾವು 3x3 ಲಂಬ ಮತ್ತು ಆಡುವ ಎಡ್ಜ್ ಫಿಲ್ಟರ್‌ಗಳನ್ನು MNIST ಅಂಕಿಗಳ ಮೇಲೆ ಅನ್ವಯಿಸಿದರೆ, ಮೂಲ ಚಿತ್ರದಲ್ಲಿ ಲಂಬ ಮತ್ತು ಆಡುವ ಎಡ್ಜ್‌ಗಳಿರುವ ಸ್ಥಳಗಳಲ್ಲಿ ಹೈಲೈಟ್ಸ್ (ಹೆಚ್ಚಿನ ಮೌಲ್ಯಗಳು) ಸಿಗುತ್ತವೆ. ಆದ್ದರಿಂದ ಆ ಎರಡು ಫಿಲ್ಟರ್‌ಗಳನ್ನು ಎಡ್ಜ್‌ಗಳನ್ನು "ಹುಡುಕಲು" ಬಳಸಬಹುದು. ಹಾಗೆಯೇ, ನಾವು ಬೇರೆ ಕಡಿಮೆ ಮಟ್ಟದ ನಮೂನೆಗಳನ್ನು ಹುಡುಕಲು ವಿಭಿನ್ನ ಫಿಲ್ಟರ್‌ಗಳನ್ನು ವಿನ್ಯಾಸಗೊಳಿಸಬಹುದು: - + > [Leung-Malik ಫಿಲ್ಟರ್ ಬ್ಯಾಂಕ್](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) ಚಿತ್ರ @@ -38,7 +38,7 @@ CNNಗಳು ಕಾರ್ಯನಿರ್ವಹಿಸುವ ವಿಧಾನವು * ಫಿಲ್ಟರ್‌ಗಳನ್ನು ಸ್ವಯಂಚಾಲಿತವಾಗಿ ತರಬೇತುಗೊಳಿಸುವಂತೆ ನೆಟ್‌ವರ್ಕ್ ವಿನ್ಯಾಸಗೊಳಿಸಬಹುದು * ನಾವು ಮೂಲ ಚಿತ್ರದಲ್ಲಿನ ಮಾತ್ರವಲ್ಲ, ಹೆಚ್ಚಿನ ಮಟ್ಟದ ವೈಶಿಷ್ಟ್ಯಗಳಲ್ಲಿ ನಮೂನೆಗಳನ್ನು ಹುಡುಕಲು ಇದೇ ವಿಧಾನವನ್ನು ಬಳಸಬಹುದು. ಆದ್ದರಿಂದ CNN ವೈಶಿಷ್ಟ್ಯ ಹೊರತೆಗೆಯುವಿಕೆ ಕಡಿಮೆ ಮಟ್ಟದ ಪಿಕ್ಸೆಲ್ ಸಂಯೋಜನೆಗಳಿಂದ ಪ್ರಾರಂಭಿಸಿ, ಚಿತ್ರ ಭಾಗಗಳ ಹೆಚ್ಚಿನ ಮಟ್ಟದ ಸಂಯೋಜನೆಗಳವರೆಗೆ ವೈಶಿಷ್ಟ್ಯಗಳ ಹಿರarchy ಮೇಲೆ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ. -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.kn.png) +![Hierarchical Feature Extraction](../../../../../translated_images/kn/FeatureExtractionCNN.d9b456cbdae7cb64.png) > [Hislop-Lynch ಅವರ ಪೇಪರ್](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ನಿಂದ ಚಿತ್ರ, ಅವರ [ಶೋಧನೆ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ಆಧಾರಿತ @@ -55,9 +55,9 @@ CNNಗಳು ಕಾರ್ಯನಿರ್ವಹಿಸುವ ವಿಧಾನವು ಉದಾಹರಣೆಗೆ, 2014 ರಲ್ಲಿ ImageNet ಟಾಪ್-5 ವರ್ಗೀಕರಣದಲ್ಲಿ 92.7% ನಿಖರತೆಯನ್ನು ಸಾಧಿಸಿದ VGG-16 ನೆಟ್‌ವರ್ಕ್‌ನ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ನೋಡೋಣ: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.kn.jpg) +![ImageNet Layers](../../../../../translated_images/kn/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.kn.jpg) +![ImageNet Pyramid](../../../../../translated_images/kn/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ನಿಂದ ಚಿತ್ರ diff --git a/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 008f0b22..5054e953 100644 --- a/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/kn/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ಅನ್ನು ಬಳಸಲಿದ್ದೇವೆ, ಇದರಲ್ಲಿ 37 ವಿಭಿನ್ನ ನಾಯಿ ಮತ್ತು ಬೆಕ್ಕು ಜಾತಿಗಳ ಚಿತ್ರಗಳಿವೆ. -![ನಾವು ಕೈಗಾರಿಕೆ ಮಾಡಲಿರುವ ಡೇಟಾಸೆಟ್](../../../../../../translated_images/data.50b2a9d5484bdbf0.kn.png) +![ನಾವು ಕೈಗಾರಿಕೆ ಮಾಡಲಿರುವ ಡೇಟಾಸೆಟ್](../../../../../../translated_images/kn/data.50b2a9d5484bdbf0.png) ಡೇಟಾಸೆಟ್ ಡೌನ್‌ಲೋಡ್ ಮಾಡಲು, ಈ ಕೋಡ್ ಸ್ನಿಪೆಟ್ ಬಳಸಿ: diff --git a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index cf6637b2..b0702fb7 100644 --- a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ಆದರ್ಶ ಬೆಕ್ಕನ್ನು ದೃಶ್ಯೀಕರಿಸಲು, ನಾವು ಯಾದೃಚ್ಛಿಕ ಶಬ್ದ ಚಿತ್ರದಿಂದ ಪ್ರಾರಂಭಿಸಿ, ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್ ತಂತ್ರವನ್ನು ಬಳಸಿಕೊಂಡು ಚಿತ್ರವನ್ನು ಸರಿಹೊಂದಿಸಿ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಬೆಕ್ಕನ್ನು ಗುರುತಿಸಲು ಪ್ರಯತ್ನಿಸುವೆವು.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.kn.png)\n", + "![Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ಇದು ನಮ್ಮ ಪ್ರಾರಂಭಿಕ ಚಿತ್ರ:\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md index 1f42cd56..73d1383c 100644 --- a/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/kn/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CNNಗಳನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಹಳ ಸಮ ಇಲ್ಲಿ VGG-16 ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಬೆಕ್ಕಿನ ಚಿತ್ರದಿಂದ ಹೊರತೆಗೆಯಲಾದ ಕೆಲವು ಲಕ್ಷಣಗಳ ಉದಾಹರಣೆ ಇದೆ: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.kn.png) +![Features extracted by VGG-16](../../../../../translated_images/kn/features.6291f9c7ba3a0b95.png) ## ಬೆಕ್ಕು ಮತ್ತು ನಾಯಿ ಡೇಟಾಸೆಟ್ @@ -48,19 +48,19 @@ CNNಗಳನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಹಳ ಸಮ ನಾವು ತೆಗೆದುಕೊಳ್ಳಬಹುದಾದ ಒಂದು ವಿಧಾನವೆಂದರೆ, ಯಾದೃಚ್ಛಿಕ ಚಿತ್ರದಿಂದ ಪ್ರಾರಂಭಿಸಿ, ನಂತರ ಆ ಚಿತ್ರವನ್ನು **ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್** ತಂತ್ರವನ್ನು ಬಳಸಿ ಸರಿಹೊಂದಿಸುವುದು, ಹೀಗೆ ನೆಟ್‌ವರ್ಕ್ ಅದನ್ನು ಬೆಕ್ಕಾಗಿ ಭಾವಿಸಲು ಪ್ರಾರಂಭಿಸುತ್ತದೆ. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.kn.png) +![Image Optimization Loop](../../../../../translated_images/kn/ideal-cat-loop.999fbb8ff306e044.png) ಆದರೆ, ನಾವು ಇದನ್ನು ಮಾಡಿದರೆ, ಯಾದೃಚ್ಛಿಕ ಶಬ್ದದಂತೆ ಕಾಣುವ ಏನನ್ನಾದರೂ ಪಡೆಯುತ್ತೇವೆ. ಇದಕ್ಕೆ ಕಾರಣವೆಂದರೆ *ನೆಟ್‌ವರ್ಕ್ ಇನ್ಪುಟ್ ಚಿತ್ರವನ್ನು ಬೆಕ್ಕಾಗಿ ಭಾವಿಸಲು ಅನೇಕ ಮಾರ್ಗಗಳಿವೆ*, ಅವುಗಳಲ್ಲಿ ಕೆಲವು ದೃಶ್ಯವಾಗಿ ಅರ್ಥವಿಲ್ಲದವುಗಳೂ ಇರುತ್ತವೆ. ಆ ಚಿತ್ರಗಳಲ್ಲಿ ಬೆಕ್ಕಿಗೆ ಸಾಮಾನ್ಯವಾದ ಹಲವಾರು ಮಾದರಿಗಳು ಇದ್ದರೂ, ಅವು ದೃಶ್ಯವಾಗಿ ವಿಭಿನ್ನವಾಗಿರಬೇಕೆಂದು ಯಾವುದೇ ನಿಯಂತ್ರಣವಿಲ್ಲ. ಫಲಿತಾಂಶವನ್ನು ಸುಧಾರಿಸಲು, ನಾವು ನಷ್ಟ ಕಾರ್ಯದಲ್ಲಿ ಮತ್ತೊಂದು ಪದವನ್ನು ಸೇರಿಸಬಹುದು, ಅದನ್ನು **ವೈವಿಧ್ಯ ನಷ್ಟ** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಇದು ಚಿತ್ರದಲ್ಲಿನ ಹತ್ತಿರದ ಪಿಕ್ಸೆಲ್‌ಗಳು ಎಷ್ಟು ಸಮಾನವಾಗಿವೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಮಾನದಂಡ. ವೈವಿಧ್ಯ ನಷ್ಟವನ್ನು ಕಡಿಮೆ ಮಾಡುವುದು ಚಿತ್ರವನ್ನು ಮೃದುಗೊಳಿಸುತ್ತದೆ ಮತ್ತು ಶಬ್ದವನ್ನು ದೂರ ಮಾಡುತ್ತದೆ - ಹೀಗಾಗಿ ದೃಶ್ಯವಾಗಿ ಆಕರ್ಷಕ ಮಾದರಿಗಳನ್ನು ಬಹಿರಂಗಪಡಿಸುತ್ತದೆ. ಇಲ್ಲಿ "ಆದರ್ಶ" ಚಿತ್ರಗಳ ಉದಾಹರಣೆ ಇದೆ, ಅವು ಬೆಕ್ಕಾಗಿ ಮತ್ತು ಜೆಬ್ರಾ ಆಗಿ ಹೆಚ್ಚಿನ ಸಾಧ್ಯತೆಯಿಂದ ವರ್ಗೀಕರಿಸಲ್ಪಟ್ಟಿವೆ: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.kn.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.kn.png) +![Ideal Cat](../../../../../translated_images/kn/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/kn/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *ಆದರ್ಶ ಬೆಕ್ಕು* | *ಆದರ್ಶ ಜೆಬ್ರಾ* ಇದೇ ವಿಧಾನವನ್ನು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಮೇಲೆ **ವಿರೋಧಾತ್ಮಕ ದಾಳಿಗಳು** ನಡೆಸಲು ಬಳಸಬಹುದು. ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ಮೋಸಗೊಳಿಸಿ ನಾಯಿಯನ್ನು ಬೆಕ್ಕಾಗಿ ತೋರಿಸಲು ಬಯಸಿದರೆ, ನೆಟ್‌ವರ್ಕ್ ನಾಯಿಯಾಗಿ ಗುರುತಿಸಿದ ನಾಯಿಯ ಚಿತ್ರವನ್ನು ತೆಗೆದುಕೊಂಡು, ಗ್ರೇಡಿಯಂಟ್ ಡಿಸೆಂಟ್ ಆಪ್ಟಿಮೈಜೆಷನ್ ಬಳಸಿ ಸ್ವಲ್ಪ ತಿದ್ದುಪಡಿ ಮಾಡಬಹುದು, ಹೀಗೆ ನೆಟ್‌ವರ್ಕ್ ಅದನ್ನು ಬೆಕ್ಕಾಗಿ ವರ್ಗೀಕರಿಸಲು ಪ್ರಾರಂಭಿಸುತ್ತದೆ: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.kn.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.kn.png) +![Picture of a Dog](../../../../../translated_images/kn/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/kn/adversarial-dog.d9fc7773b0142b89.png) -----|----- *ನಾಯಿಯ ಮೂಲ ಚಿತ್ರ* | *ಬೆಕ್ಕಾಗಿ ವರ್ಗೀಕರಿಸಲ್ಪಟ್ಟ ನಾಯಿಯ ಚಿತ್ರ* diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 1473080b..34b43f53 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ನಾವು ಆಟೋಎನ್‌ಕೋಡರ್ ಅನ್ನು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ತರಬೇತಿಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಅತ್ಯುತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.kn.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ\n", "\n", @@ -941,7 +941,7 @@ " * ನಾವು $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ ವಿತರಣೆಯಿಂದ `sample(z_val in code)` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಮಾದರಿಮಾಡುತ್ತೇವೆ\n", " * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಡಿಕೋಡ್ ಮಾಡಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ\n", "\n", - " \n", + " \n", "\n", " > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ನಿಂದ, ಇಸಾಕ್ ಡೈಕೆಮನ್ ಅವರಿಂದ\n" ] @@ -1264,7 +1264,7 @@ "\n", "ಈ ವಿಧಾನದಲ್ಲಿ ನಮಗೆ **ಮೂರು ನಷ್ಟ ಕಾರ್ಯಗಳು** ಇವೆ: GAN ಗಳಿಂದ ಜನರೇಟರ್ ನಷ್ಟ, ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್ ನಷ್ಟ ಮತ್ತು VAE ನಿಂದ ಪುನರ್ ನಿರ್ಮಾಣ ನಷ್ಟ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) ನಿಂದ ಫೆಲಿಪೆ ಡುಕೋ ಅವರಿಂದ\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 6fefc1dc..864cfaa4 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "ನಾವು ಆಟೋಎನ್‌ಕೋಡರ್ ಅನ್ನು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ತರಬೇತುಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಅತ್ಯುತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.kn.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ*\n", "\n", @@ -888,7 +888,7 @@ " * ನಾವು $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ ವಿತರಣೆಯಿಂದ `sample` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಮಾದರಿಮಾಡುತ್ತೇವೆ\n", " * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಡಿಕೋಡ್ ಮಾಡಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md index 769612c3..e2757a15 100644 --- a/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/kn/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNಗಳನ್ನು ತರಬೇತುಗೊಳಿಸುವಾಗ, ಒಂದ ನಾವು ಮೂಲ ಚಿತ್ರದಿಂದ ಸಾಧ್ಯವಾದಷ್ಟು ಮಾಹಿತಿ ಹಿಡಿಯಲು ಆಟೋಎನ್‌ಕೋಡರ್ ತರಬೇತುಗೊಳಿಸುತ್ತಿದ್ದೇವೆ, ಆದ್ದರಿಂದ ನೆಟ್‌ವರ್ಕ್ ಇನ್‌ಪುಟ್ ಚಿತ್ರಗಳ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು ಉತ್ತಮ **ಎಂಬೆಡ್ಡಿಂಗ್** ಅನ್ನು ಹುಡುಕಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.kn.jpg) +![AutoEncoder Diagram](../../../../../translated_images/kn/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ಚಿತ್ರ [Keras ಬ್ಲಾಗ್](https://blog.keras.io/building-autoencoders-in-keras.html) ನಿಂದ @@ -46,7 +46,7 @@ VAE ಒಂದು ಆಟೋಎನ್‌ಕೋಡರ್ ಆಗಿದ್ದು, ಲ * ನಾವು ವಿತರಣೆಯಿಂದ `sample` ಎಂಬ ವೆಕ್ಟರ್ ಅನ್ನು ಆಯ್ಕೆ ಮಾಡುತ್ತೇವೆ N(zmean,exp(zlog_sigma)) * ಡಿಕೋಡರ್ `sample` ಅನ್ನು ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಆಗಿ ಬಳಸಿಕೊಂಡು ಮೂಲ ಚಿತ್ರವನ್ನು ಮರುನಿರ್ಮಿಸಲು ಪ್ರಯತ್ನಿಸುತ್ತದೆ - + > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ನಿಂದ, ಇಸಾಕ್ ಡೈಕೆಮನ್ ರಚನೆ @@ -57,13 +57,13 @@ VAE ಒಂದು ಆಟೋಎನ್‌ಕೋಡರ್ ಆಗಿದ್ದು, ಲ VAEಗಳ ಪ್ರಮುಖ ಲಾಭವೆಂದರೆ, ನಾವು ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಯಾವ ವಿತರಣೆಯಿಂದ ಆಯ್ಕೆ ಮಾಡಬೇಕೆಂದು ತಿಳಿದಿರುವುದರಿಂದ, ಹೊಸ ಚಿತ್ರಗಳನ್ನು ಸುಲಭವಾಗಿ ರಚಿಸಬಹುದು. ಉದಾಹರಣೆಗೆ, 2D ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ನೊಂದಿಗೆ MNIST ಮೇಲೆ VAE ತರಬೇತಿಗೊಳಿಸಿದರೆ, ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ನ ಅಂಶಗಳನ್ನು ಬದಲಾಯಿಸಿ ವಿಭಿನ್ನ ಅಂಕಿಗಳನ್ನು ಪಡೆಯಬಹುದು: -vaemnist +vaemnist > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ರಚನೆ ಲ್ಯಾಟೆಂಟ್ ಪರಿಮಾಣ ಸ್ಥಳದ ವಿಭಿನ್ನ ಭಾಗಗಳಿಂದ ಲ್ಯಾಟೆಂಟ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಪಡೆಯಲು ಪ್ರಾರಂಭಿಸಿದಂತೆ ಚಿತ್ರಗಳು ಪರಸ್ಪರ ಮಿಶ್ರಣವಾಗುತ್ತಿರುವುದನ್ನು ಗಮನಿಸಿ. ನಾವು ಈ ಸ್ಥಳವನ್ನು 2Dಯಲ್ಲಿ ದೃಶ್ಯೀಕರಿಸಬಹುದು: -vaemnist cluster +vaemnist cluster > ಚಿತ್ರ [ಡ್ಮಿತ್ರಿ ಸೋಶ್ನಿಕೋವ್](http://soshnikov.com) ರಚನೆ diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 70849474..b429009c 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **ಜನರೇಟರ್** ಒಂದು ಯಾದೃಚ್ಛಿಕ ವೆಕ್ಟರ್ ಅನ್ನು ತೆಗೆದು, ಅದರಿಂದ ಒಂದು ಚಿತ್ರವನ್ನು ರಚಿಸಬೇಕು\n", "* **ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್** ಒಂದು ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, ಮೂಲ ಚಿತ್ರ (ತರಬೇತಿ ಡೇಟಾಸೆಟ್‌ನಿಂದ) ಮತ್ತು ಜನರೇಟರ್ ರಚಿಸಿದ ಚಿತ್ರವನ್ನು ವಿಭಿನ್ನಗೊಳಿಸಬೇಕು.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಟ್ಯುಟೋರಿಯಲ್](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) ನಿಂದ\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 0d6406c5..12976264 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **ಜನರೇಟರ್** ಒಂದು ಯಾದೃಚ್ಛಿಕ ವೆಕ್ಟರ್ ಅನ್ನು ತೆಗೆದು, ಅದರಿಂದ ಚಿತ್ರವನ್ನು ರಚಿಸಬೇಕು\n", "* **ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್** ಒಂದು ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, ಮೂಲ ಚಿತ್ರ (ತರಬೇತಿ ಡೇಟಾಸೆಟ್‌ನಿಂದ) ಮತ್ತು ಜನರೇಟರ್ ರಚಿಸಿದ ಚಿತ್ರವನ್ನು ವಿಭಿನ್ನಗೊಳಿಸಬೇಕು.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/kn/lessons/4-ComputerVision/10-GANs/README.md b/translations/kn/lessons/4-ComputerVision/10-GANs/README.md index 19c00c34..0fea65ea 100644 --- a/translations/kn/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/kn/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GANನ ಮುಖ್ಯ ಕಲ್ಪನೆ ಎರಡು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳನ್ನು ಪರಸ್ಪರ ಎದುರಾಗಿ ತರಬೇತಿಗೊಳಿಸುವುದು: - + > ಚಿತ್ರ: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN ಡಿಸ್ಕ್ರಿಮಿನೇಟರ್‌ನಲ್ಲಿ ಹಲವಾ > ✅ ಕನ್ವಲ್ಯೂಷನ್ ಪದರವು ಚಿತ್ರವನ್ನು ತಲುಪುವ ರೇಖೀಯ ಫಿಲ್ಟರ್ ಆಗಿರುವುದರಿಂದ, ಡಿಕನ್ವಲ್ಯೂಷನ್ ಮೂಲತಃ ಕನ್ವಲ್ಯೂಷನ್‌ಗೆ ಸಮಾನವಾಗಿದ್ದು, ಅದೇ ಪದರ ಲಾಜಿಕ್ ಬಳಸಿ ಅನುಷ್ಠಾನಗೊಳ್ಳಬಹುದು. - + > ಚಿತ್ರ: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md index daba354d..5d169ba9 100644 --- a/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/kn/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ಪೂರ್ವ-ಪಾಠ ಪ್ರಶ್ನೋತ್ತರ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.kn.png) +![ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ಚಿತ್ರ [YOLO v2 ವೆಬ್ ಸೈಟ್](https://pjreddie.com/darknet/yolov2/) ನಿಂದ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ಪ್ರತಿ ಟೈಲ್ನಲ್ಲಿ ಚಿತ್ರ ವರ್ಗೀಕರಣವನ್ನು ನಡೆಸಿ. 3. ಸಾಕಷ್ಟು ಉತ್ಸಾಹದೊಂದಿಗೆ ಫಲಿತಾಂಶ ನೀಡುವ ಟೈಲ್ಗಳನ್ನು ಆ ವಸ್ತು ಹೊಂದಿದೆ ಎಂದು ಪರಿಗಣಿಸಬಹುದು. -![ಸರಳ ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.kn.png) +![ಸರಳ ವಸ್ತು ಪತ್ತೆ](../../../../../translated_images/kn/naive-detection.e7f1ba220ccd08c6.png) > *ಚಿತ್ರ [ಅಭ್ಯಾಸ ನೋಟ್ಬುಕ್](ObjectDetection-TF.ipynb) ನಿಂದ* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ವರ್ಗಗಳು * [COCO](http://cocodataset.org/#home) - ಸಾಮಾನ್ಯ ವಸ್ತುಗಳು ಸನ್ನಿವೇಶದಲ್ಲಿ. 80 ವರ್ಗಗಳು, ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್‌ಗಳು ಮತ್ತು ವಿಭಾಗ ಚಿಹ್ನೆಗಳು -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.kn.jpg) +![COCO](../../../../../translated_images/kn/coco-examples.71bc60380fa6cceb.jpg) ## ವಸ್ತು ಪತ್ತೆ ಮೌಲ್ಯಮಾಪನ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ಚಿತ್ರ ವರ್ಗೀಕರಣದಲ್ಲಿ ಆಲ್ಗಾರಿದಮ್ ಎಷ್ಟು ಚೆನ್ನಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ ಎಂದು ಅಳೆಯುವುದು ಸುಲಭ, ಆದರೆ ವಸ್ತು ಪತ್ತೆಯಲ್ಲಿ ವರ್ಗದ ಸರಿಯಾದತೆ ಮತ್ತು ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್ ನಿಖರತೆಯನ್ನು ಎರಡನ್ನೂ ಅಳೆಯಬೇಕಾಗುತ್ತದೆ. ನಂತರದದಕ್ಕಾಗಿ ನಾವು **Intersection over Union** (IoU) ಅನ್ನು ಬಳಸುತ್ತೇವೆ, ಇದು ಎರಡು ಬಾಕ್ಸ್‌ಗಳು (ಅಥವಾ ಎರಡು ಯಾವುದೇ ಪ್ರದೇಶಗಳು) ಎಷ್ಟು ಒಟ್ಟಿಗೆ ಬರುತ್ತವೆ ಎಂದು ಅಳೆಯುತ್ತದೆ. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.kn.png) +![IoU](../../../../../translated_images/kn/iou_equation.9a4751d40fff4e11.png) > *ಚಿತ್ರ 2 [ಈ ಅದ್ಭುತ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) ನಿಂದ* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ಬಳಸಿ ROI ಪ್ರದೇಶಗಳ ಹೈರಾರ್ಕಿಕ ರಚನೆಯನ್ನು ರಚಿಸುತ್ತದೆ, ಅವುಗಳನ್ನು ನಂತರ CNN ವೈಶಿಷ್ಟ್ಯ ಸಂಗ್ರಾಹಕ ಮತ್ತು SVM ವರ್ಗೀಕರಣಕಾರರ ಮೂಲಕ ವಸ್ತು ವರ್ಗವನ್ನು ನಿರ್ಧರಿಸಲು ಮತ್ತು ರೇಖೀಯ ರಿಗ್ರೆಷನ್ ಮೂಲಕ *ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್* ಸಂಯೋಜನೆಗಳನ್ನು ಊಹಿಸಲು ಬಳಸಲಾಗುತ್ತದೆ. [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.kn.png) +![RCNN](../../../../../translated_images/kn/rcnn1.cae407020dfb1d1f.png) > *ಚಿತ್ರ van de Sande et al. ICCV’11 ನಿಂದ* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.kn.png) +![RCNN-1](../../../../../translated_images/kn/rcnn2.2d9530bb83516484.png) > *ಚಿತ್ರಗಳು [ಈ ಬ್ಲಾಗ್](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) ನಿಂದ* @@ -109,7 +109,7 @@ $$ ಈ ವಿಧಾನ R-CNN ಗೆ ಸಮಾನ, ಆದರೆ ಪ್ರದೇಶಗಳನ್ನು ಕನ್ವಲ್ಯೂಷನ್ ಲೇಯರ್‌ಗಳ ನಂತರ ನಿರ್ಧರಿಸಲಾಗುತ್ತದೆ. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.kn.png) +![FRCNN](../../../../../translated_images/kn/f-rcnn.3cda6d9bb4188875.png) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 ನಿಂದ @@ -117,7 +117,7 @@ $$ ಈ ವಿಧಾನದಲ್ಲಿ ಮುಖ್ಯ ಯೋಚನೆ ROI ಗಳನ್ನು ಊಹಿಸಲು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬಳಸುವುದು - ಇದನ್ನು *ಪ್ರದೇಶ ಪ್ರಸ್ತಾವನೆ ಜಾಲ* ಎಂದು ಕರೆಯುತ್ತಾರೆ. [ಪೇಪರ್](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.kn.png) +![FasterRCNN](../../../../../translated_images/kn/faster-rcnn.8d46c099b87ef30a.png) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/pdf/1506.01497.pdf) ನಿಂದ @@ -129,7 +129,7 @@ $$ 2. ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು **ಸ್ಥಾನ-ಸಂವೇದನಾತ್ಮಕ ಸ್ಕೋರ್ ನಕ್ಷೆ** ಮೂಲಕ ಪ್ರಕ್ರಿಯೆಗೊಳಿಸುವುದು. $C$ ವರ್ಗಗಳ ಪ್ರತಿಯೊಂದು ವಸ್ತುವನ್ನು $k\times k$ ಪ್ರದೇಶಗಳಾಗಿ ವಿಭಜಿಸಿ, ವಸ್ತುಗಳ ಭಾಗಗಳನ್ನು ಊಹಿಸಲು ತರಬೇತಿ ನೀಡಲಾಗುತ್ತದೆ. 3. $k\times k$ ಪ್ರದೇಶಗಳ ಪ್ರತಿಯೊಂದು ಭಾಗಕ್ಕೆ ಎಲ್ಲಾ ಜಾಲಗಳು ವಸ್ತು ವರ್ಗಗಳಿಗೆ ಮತದಾನ ಮಾಡುತ್ತವೆ, ಗರಿಷ್ಠ ಮತ ಪಡೆದ ವಸ್ತು ವರ್ಗ ಆಯ್ಕೆಮಾಡಲಾಗುತ್ತದೆ. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.kn.png) +![r-fcn image](../../../../../translated_images/kn/r-fcn.13eb88158b99a3da.png) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/abs/1605.06409) ನಿಂದ @@ -140,7 +140,7 @@ YOLO ಒಂದು ರಿಯಲ್-ಟೈಮ್ ಒಂದು-ಪಾಸ್ ಆಲ * ಚಿತ್ರವನ್ನು $S\times S$ ಪ್ರದೇಶಗಳಾಗಿ ವಿಭಜಿಸುವುದು * ಪ್ರತಿ ಪ್ರದೇಶಕ್ಕೆ, **CNN** $n$ ಸಾಧ್ಯವಿರುವ ವಸ್ತುಗಳನ್ನು, *ಸುತ್ತುವರೆದ ಬಾಕ್ಸ್* ಸಂಯೋಜನೆಗಳನ್ನು ಮತ್ತು *ನಂಬಿಕೆ* = *ಸಂಭಾವ್ಯತೆ* * IoU ಅನ್ನು ಊಹಿಸುವುದು. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.kn.png) + ![YOLO](../../../../../translated_images/kn/yolo.a2648ec82ee8bb4e.png) > ಚಿತ್ರ [ಅಧಿಕೃತ ಪೇಪರ್](https://arxiv.org/abs/1506.02640) ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md index 01b9ed79..4e1e25df 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ಘಟಕ ವಿಭಾಗೀಕರಣದಲ್ಲಿ, ಈ ಕುರಿಗಳು ವಿಭಿನ್ನ ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಸಾಮಾನ್ಯ ವಿಭಾಗೀಕರಣದಲ್ಲಿ ಎಲ್ಲಾ ಕುರಿಗಳು ಒಂದೇ ವರ್ಗದಿಂದ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತವೆ. - + > ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **ಎನ್‌ಕೋಡರ್** ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ಲಕ್ಷಣಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ * **ಡಿಕೋಡರ್** ಆ ಲಕ್ಷಣಗಳನ್ನು **ಮಾಸ್ಕ್ ಚಿತ್ರ** ಆಗಿ ಪರಿವರ್ತಿಸುತ್ತದೆ, ಅದೇ ಗಾತ್ರ ಮತ್ತು ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಹೊಂದಿಕೊಂಡ ಚಾನೆಲ್‌ಗಳೊಂದಿಗೆ. - + > ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ಈ ತಂತ್ರಜ್ಞಾನ ವೈದ್ಯಕೀಯ ಚಿತ್ರಣಕ್ಕೆ ವಿಶೇಷವಾಗಿ ಸೂಕ್ತವಾಗಿದೆ, ಆದರೆ ನೀವು ಇನ್ನೇನು ನೈಜ ಜಗತ್ತಿನ ಅನ್ವಯಗಳನ್ನು ಊಹಿಸಬಹುದು? -navi +navi > ಚಿತ್ರ PH2 ಡೇಟಾಬೇಸ್‌ನಿಂದ diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index 84a7a497..a6e7286a 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ಉದಾಹರಣೆಗೆ, ಘಟಕ ವಿಭಾಗೀಕರಣದಲ್ಲಿ 10 ಕುರಿಗಳು ವಿಭಿನ್ನ ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಅರ್ಥಪೂರ್ಣ ವಿಭಾಗೀಕರಣದಲ್ಲಿ ಎಲ್ಲಾ ಕುರಿಗಳು ಒಂದೇ ವರ್ಗದಿಂದ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತವೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ\n", "\n", @@ -26,7 +26,7 @@ "* **ಎನ್‌ಕೋಡರ್** ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ\n", "* **ಡಿಕೋಡರ್** ಆ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು **ಮಾಸ್ಕ್ ಚಿತ್ರ** ಆಗಿ ಪರಿವರ್ತಿಸುತ್ತದೆ, ಅದೇ ಗಾತ್ರ ಮತ್ತು ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಹೊಂದಿಕೊಂಡ ಚಾನೆಲ್‌ಗಳೊಂದಿಗೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -252,7 +252,7 @@ "\n", "ಎಲ್ಲಾದರೂ ಸರಳವಾದ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು **SegNet** ಎಂದು ಕರೆಯುತ್ತಾರೆ. ಇದು ಎನ್‌ಕೋಡರ್‌ನಲ್ಲಿ ಸಂಪ್ರದಾಯಬದ್ಧ CNN ಅನ್ನು ಸಂವೇದನೆಗಳು ಮತ್ತು ಪೂಲಿಂಗ್‌ಗಳೊಂದಿಗೆ ಬಳಸುತ್ತದೆ, ಮತ್ತು ಡಿಕೋಡರ್‌ನಲ್ಲಿ ಸಂವೇದನೆಗಳು ಮತ್ತು ಅಪ್ಸ್ಯಾಂಪಿಂಗ್‌ಗಳನ್ನು ಒಳಗೊಂಡ ಡಿಕೋನ್ವಲ್ಯೂಷನ್ CNN ಅನ್ನು ಬಳಸುತ್ತದೆ. ಬಹು-ಮಟ್ಟದ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ಯಶಸ್ವಿಯಾಗಿ ತರಬೇತುಗೊಳಿಸಲು ಇದು ಬ್ಯಾಚ್ ನಾರ್ಮಲೈಜೆಶನ್ ಮೇಲೆ ಅವಲಂಬಿತವಾಗಿದೆ.\n", "\n", - "\n", + "\n", "\n", "> ಈ ಚಿತ್ರ ಈ ಪೇಪರ್‌ನಿಂದ: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ನಾವು ಇಲ್ಲಿ ಬಹಳ ಸರಳ CNN ವಿನ್ಯಾಸವನ್ನು ಬಳಸುತ್ತೇವೆ, ಆದರೆ U-Net ಹೆಚ್ಚು ಸಂಕೀರ್ಣ ಎನ್‌ಕೋಡರ್ ಅನ್ನು ವೈಶಿಷ್ಟ್ಯಗಳ ಹೊರತೆಗೆಯಲು ಬಳಸಬಹುದು, ಉದಾಹರಣೆಗೆ ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ ಮೂಲ: Ronneberger, Olaf, Philipp Fischer, ಮತ್ತು Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index 8af7f6c0..ec55f9c4 100644 --- a/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/kn/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ಉದಾಹರಣೆಗೆ, ಇನ್ಸ್ಟಾನ್ಸ್ ಸೆಗ್ಮೆಂಟೇಶನ್‌ನಲ್ಲಿ ಹತ್ತು ಕಾರುಗಳು **ವಿಭಿನ್ನ** ವಸ್ತುಗಳಾಗಿವೆ, ಆದರೆ ಸಿಮೆಂಟಿಕ್ ಸೆಗ್ಮೆಂಟೇಶನ್‌ನಲ್ಲಿ **ಎಲ್ಲಾ** ಕಾರುಗಳು ಒಂದೇ ವರ್ಗ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) ನಿಂದ\n", "\n", "ಸುಮಾರು ಎಲ್ಲಾ ವಾಸ್ತುಶಿಲ್ಪಗಳು ಒಂದೇ ರಚನೆಯನ್ನು ಹೊಂದಿವೆ. ಮೊದಲ ಭಾಗವು **ಎನ್‌ಕೋಡರ್** ಆಗಿದ್ದು, ಇನ್‌ಪುಟ್ ಚಿತ್ರದಿಂದ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ, ಎರಡನೇ ಭಾಗವು **ಡಿಕೋಡರ್** ಆಗಿದ್ದು, ಈ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ಅದೇ ಎತ್ತರ ಮತ್ತು ಅಗಲದ ಚಿತ್ರವಾಗಿ ಮತ್ತು ಕೆಲವು ಚಾನೆಲ್‌ಗಳ ಸಂಖ್ಯೆಯೊಂದಿಗೆ (ವರ್ಗಗಳ ಸಂಖ್ಯೆಗೆ ಸಮಾನವಾಗಬಹುದು) ಪರಿವರ್ತಿಸುತ್ತದೆ.\n", "\n", - "\n", + "\n", "\n", "> ಚಿತ್ರ [ಈ ಪ್ರಕಟಣೆಯಿಂದ](https://arxiv.org/pdf/2001.05566.pdf)\n" ] @@ -210,7 +210,7 @@ "\n", "ಸರಳ ಎನ್‌ಕೋಡರ್ - ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪ, ಎನ್‌ಕೋಡರ್‌ನಲ್ಲಿ ಕಾಂವೊಲ್ಯೂಶನ್ಗಳು ಮತ್ತು ಪೂಲಿಂಗ್‌ಗಳು, ಡಿಕೋಡರ್‌ನಲ್ಲಿ ಕಾಂವೊಲ್ಯೂಶನ್ಗಳು ಮತ್ತು ಅಪ್ಸ್ಯಾಂಪ್ಲಿಂಗ್‌ಗಳೊಂದಿಗೆ.\n", "\n", - "\n", + "\n", "\n", "* ಬದ್ರಿನಾರಾಯಣನ್, ವಿ., ಕೆಂಡಾಲ್, ಎ., & ಸಿಪೊಲ್ಲಾ, ಆರ್. (2015). [SegNet: ಚಿತ್ರ ವಿಭಾಗಕ್ಕಾಗಿ ಆಳವಾದ ಕಾಂವೊಲ್ಯೂಶನಲ್ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ವಾಸ್ತುಶಿಲ್ಪ](https://arxiv.org/pdf/1511.00561.pdf)\n" ] @@ -601,7 +601,7 @@ "\n", "U-Net ಸಾಮಾನ್ಯವಾಗಿ ವೈಶಿಷ್ಟ್ಯಗಳನ್ನು ಹೊರತೆಗೆಯಲು ಡೀಫಾಲ್ಟ್ ಎನ್‌ಕೋಡರ್ ಹೊಂದಿರುತ್ತದೆ, ಉದಾಹರಣೆಗೆ resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, ಮತ್ತು Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/kn/lessons/4-ComputerVision/README.md b/translations/kn/lessons/4-ComputerVision/README.md index 8106c5ca..dc0a579a 100644 --- a/translations/kn/lessons/4-ComputerVision/README.md +++ b/translations/kn/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ -![ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ ವಿಷಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/ai-computervision.6506ebebac3fbf76.kn.png) +![ಕಂಪ್ಯೂಟರ್ ವೀಕ್ಷಣೆ ವಿಷಯದ ಸಾರಾಂಶ ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-computervision.6506ebebac3fbf76.png) ಈ ವಿಭಾಗದಲ್ಲಿ ನಾವು ಕಲಿಯಲಿದ್ದೇವೆ: diff --git a/translations/kn/lessons/5-NLP/13-TextRep/README.md b/translations/kn/lessons/5-NLP/13-TextRep/README.md index 986ef86f..54e08905 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/README.md +++ b/translations/kn/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳೊಂದಿಗೆ ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ (NLP) ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸಲು ಬಯಸಿದರೆ, ಪಠ್ಯವನ್ನು ಟೆನ್ಸರ್‌ಗಳಾಗಿ ಪ್ರತಿನಿಧಿಸುವ ವಿಧಾನ ಬೇಕಾಗುತ್ತದೆ. ಕಂಪ್ಯೂಟರ್‌ಗಳು ಈಗಾಗಲೇ ASCII ಅಥವಾ UTF-8 ಎಂಬ ಎನ್ಕೋಡಿಂಗ್‌ಗಳನ್ನು ಬಳಸಿ ನಿಮ್ಮ ಪರದೆ上的 ಫಾಂಟ್‌ಗಳಿಗೆ ನಕ್ಷೆ ಮಾಡಲಾದ ಸಂಖ್ಯೆಗಳಾಗಿ ಪಠ್ಯ ಅಕ್ಷರಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತವೆ. -ಅಕ್ಷರವನ್ನು ASCII ಮತ್ತು ಬೈನರಿ ಪ್ರತಿನಿಧಿಸುವ ಡಯಾಗ್ರಾಂ ತೋರಿಸುವ ಚಿತ್ರ +ಅಕ್ಷರವನ್ನು ASCII ಮತ್ತು ಬೈನರಿ ಪ್ರತಿನಿಧಿಸುವ ಡಯಾಗ್ರಾಂ ತೋರಿಸುವ ಚಿತ್ರ > [ಚಿತ್ರ ಮೂಲ](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: ಪಠ್ಯ ವರ್ಗೀಕರಣದಂತಹ ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸುವಾಗ, ನಾವು ಪಠ್ಯವನ್ನು ಒಂದು ನಿಶ್ಚಿತ ಗಾತ್ರದ ವೆಕ್ಟರ್ ಮೂಲಕ ಪ್ರತಿನಿಧಿಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು ಅಂತಿಮ ಡೆನ್ಸ್ ವರ್ಗೀಕರಣಕ್ಕೆ ಇನ್ಪುಟ್ ಆಗಿ ಬಳಸುತ್ತೇವೆ. ಇದಕ್ಕೆ ಸರಳ ವಿಧಾನಗಳಲ್ಲಿ ಒಂದಾಗಿದೆ ಎಲ್ಲಾ ಪದಗಳ ಪ್ರತಿನಿಧನೆಗಳನ್ನು ಸೇರಿಸುವುದು. ಪ್ರತಿಯೊಂದು ಪದದ ಒನ್-ಹಾಟ್ ಎನ್ಕೋಡಿಂಗ್‌ಗಳನ್ನು ಸೇರಿಸಿದರೆ, ನಾವು ಪದಗಳ ಆವರ್ತನೆಗಳ ವೆಕ್ಟರ್ ಅನ್ನು ಪಡೆಯುತ್ತೇವೆ, ಇದು ಪಠ್ಯದಲ್ಲಿ ಪ್ರತಿ ಪದ ಎಷ್ಟು ಬಾರಿ ಬರುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುತ್ತದೆ. ಈ ರೀತಿಯ ಪಠ್ಯ ಪ್ರತಿನಿಧನೆಯನ್ನು **ಬ್ಯಾಗ್ ಆಫ್ ವರ್ಡ್ಸ್** (BoW) ಎಂದು ಕರೆಯುತ್ತಾರೆ. - + > ಚಿತ್ರ ಲೇಖಕರಿಂದ diff --git a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 3fc281e8..01085cd7 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**ಪದಗಳ ಬ್ಯಾಗ್** (BoW) ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವು ಅತ್ಯಂತ ಸಾಮಾನ್ಯವಾಗಿ ಬಳಸುವ ಪರಂಪರাগত ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವಾಗಿದೆ. ಪ್ರತಿ ಪದವು ಒಂದು ವೆಕ್ಟರ್ ಸೂಚ್ಯಂಕಕ್ಕೆ ಜೋಡಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ವೆಕ್ಟರ್ ಅಂಶವು ನೀಡಲಾದ ದಾಖಲೆಗಳಲ್ಲಿ ಆ ಪದದ ಸಂಭವನೆಯ ಸಂಖ್ಯೆಯನ್ನು ಹೊಂದಿರುತ್ತದೆ.\n", "\n", - "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.kn.png) \n", + "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW ಅನ್ನು ಪಠ್ಯದ ಪ್ರತ್ಯೇಕ ಪದಗಳ ಒನ್-ಹಾಟ್-ಎನ್‌ಕೋಡ್ ಮಾಡಿದ ವೆಕ್ಟರ್‌ಗಳ ಮೊತ್ತವೆಂದು ಕೂಡ ಪರಿಗಣಿಸಬಹುದು.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 3c76e5fd..0b6d2f2c 100644 --- a/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/kn/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ಪದಗಳ ಬ್ಯಾಗ್** (BoW) ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವು ಅತಿ ಸರಳವಾಗಿ ಅರ್ಥಮಾಡಿಕೊಳ್ಳಬಹುದಾದ ಪರಂಪರাগত ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವಾಗಿದೆ. ಪ್ರತಿ ಪದವು ಒಂದು ವೆಕ್ಟರ್ ಸೂಚ್ಯಂಕಕ್ಕೆ ಜೋಡಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಒಂದು ವೆಕ್ಟರ್ ಅಂಶವು ನೀಡಲಾದ ದಾಖಲೆಗಳಲ್ಲಿ ಪ್ರತಿ ಪದದ ಸಂಭವನಗಳ ಸಂಖ್ಯೆಯನ್ನು ಹೊಂದಿರುತ್ತದೆ.\n", "\n", - "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.kn.png) \n", + "![ಪದಗಳ ಬ್ಯಾಗ್ ವೆಕ್ಟರ್ ಪ್ರತಿನಿಧಾನವನ್ನು ಮೆಮೊರಿಯಲ್ಲಿ ಹೇಗೆ ಪ್ರತಿನಿಧಿಸಲಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW ಅನ್ನು ಪಠ್ಯದ ಪ್ರತ್ಯೇಕ ಪದಗಳ ಒನ್-ಹಾಟ್-ಎನ್‌ಕೋಡ್ ಮಾಡಿದ ವೆಕ್ಟರ್‌ಗಳ ಮೊತ್ತವೆಂದು ಕೂಡ ಪರಿಗಣಿಸಬಹುದು.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 4db798ae..79c026d0 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ನಮ್ಮ ನೆಟ್‌ವರ್ಕ್‌ನಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಬಳಸಿ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ನಾವು ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದ ಪ್ರತಿಯೊಂದು ಪದವನ್ನು ಅದರ ಸಂಬಂಧಿತ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸಿ, ನಂತರ ಆ ಎಲ್ಲ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹಣಾ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ.\n", "\n", - "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.kn.png)\n", + "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ನಮ್ಮ ವರ್ಗೀಕರಣ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಎಂಬೆಡ್ಡಿಂಗ್ ಲೇಯರ್‌ನಿಂದ ಪ್ರಾರಂಭವಾಗಿ, ನಂತರ ಸಂಗ್ರಹಣಾ ಲೇಯರ್ ಮತ್ತು ಅದರ ಮೇಲೆ ಲೀನಿಯರ್ ವರ್ಗೀಕರಣ ಲೇಯರ್ ಇರುತ್ತದೆ:\n" ] @@ -176,7 +176,7 @@ "\n", "ಹಿಂದಿನ ವಾಸ್ತುಶಿಲ್ಪದಲ್ಲಿ, ನಾವು ಎಲ್ಲಾ ಕ್ರಮಗಳನ್ನು ಒಂದೇ ಉದ್ದಕ್ಕೆ ಪ್ಯಾಡ್ ಮಾಡಬೇಕಾಗಿತ್ತು ताकि ಅವುಗಳನ್ನು ಒಂದು ಮಿನಿಬ್ಯಾಚ್‌ಗೆ ಹೊಂದಿಸಬಹುದು. ಇದು ಬದಲಾಗುವ ಉದ್ದದ ಕ್ರಮಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವ ಅತ್ಯಂತ ಪರಿಣಾಮಕಾರಿ ವಿಧಾನವಲ್ಲ - ಇನ್ನೊಂದು ವಿಧಾನವೆಂದರೆ **offset** ವೆಕ್ಟರ್ ಅನ್ನು ಬಳಸುವುದು, ಇದು ಒಂದು ದೊಡ್ಡ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಸಂಗ್ರಹಿಸಲಾದ ಎಲ್ಲಾ ಕ್ರಮಗಳ ಆಫ್‌ಸೆಟ್‌ಗಳನ್ನು ಹಿಡಿದಿರುತ್ತದೆ.\n", "\n", - "![offset ಕ್ರಮದ ಪ್ರತಿನಿಧಾನವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.kn.png)\n", + "![offset ಕ್ರಮದ ಪ್ರತಿನಿಧಾನವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: ಮೇಲಿನ ಚಿತ್ರದಲ್ಲಿ, ನಾವು ಅಕ್ಷರಗಳ ಕ್ರಮವನ್ನು ತೋರಿಸಿದ್ದೇವೆ, ಆದರೆ ನಮ್ಮ ಉದಾಹರಣೆಯಲ್ಲಿ ನಾವು ಪದಗಳ ಕ್ರಮಗಳೊಂದಿಗೆ ಕೆಲಸ ಮಾಡುತ್ತಿದ್ದೇವೆ. ಆದಾಗ್ಯೂ, ಆಫ್‌ಸೆಟ್ ವೆಕ್ಟರ್‌ನೊಂದಿಗೆ ಕ್ರಮಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವ ಸಾಮಾನ್ಯ ತತ್ವ ಅದೇ ಆಗಿರುತ್ತದೆ.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಆದರೆ ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದು, ಅಪರೂಪದ ಪದಗಳನ್ನು ಉತ್ತಮವಾಗಿ ಪ್ರತಿನಿಧಿಸುತ್ತದೆ.\n", "\n", - "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.kn.png)\n", + "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ಡೇಟಾಸೆಟ್‌ನಲ್ಲಿ ಪೂರ್ವ-ಪ್ರಶಿಕ್ಷಿತ word2vec ಎम्बೆಡ್ಡಿಂಗ್‌ನೊಂದಿಗೆ ಪ್ರಯೋಗ ಮಾಡಲು, ನಾವು **gensim** ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸಬಹುದು. ಕೆಳಗೆ 'neural' ಗೆ ಅತ್ಯಂತ ಸಮಾನವಾದ ಪದಗಳನ್ನು ಕಂಡುಹಿಡಿಯಲಾಗಿದೆ\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index f401e407..812bea82 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/kn/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ನಮ್ಮ ನೆಟ್‌ವರ್ಕ್‌ನಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಅನ್ನು ಬಳಸುವುದರಿಂದ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಯ ಕಡೆಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ನಾವು ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದ ಪ್ರತಿಯೊಂದು ಪದವನ್ನು ಅದರ ಸಂಬಂಧಿಸಿದ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸುತ್ತೇವೆ, ನಂತರ ಆ ಎಲ್ಲಾ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹಣಾ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ.\n", "\n", - "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.kn.png)\n", + "![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ನಮ್ಮ ವರ್ಗೀಕರಣ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಕೆಳಗಿನ ಲೇಯರ್‌ಗಳಿಂದ ಕೂಡಿದೆ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದರೂ ಅಪರೂಪದ ಪದಗಳನ್ನು ಪ್ರತಿನಿಧಿಸುವಲ್ಲಿ ಉತ್ತಮ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ.\n", "\n", - "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸಲು CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.kn.png)\n", + "![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸಲು CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ಡೇಟಾಸೆಟ್‌ನಲ್ಲಿ ಪೂರ್ವಪ್ರಶಿಕ್ಷಿತ Word2Vec ಎम्बೆಡ್ಡಿಂಗ್‌ನೊಂದಿಗೆ ಪ್ರಯೋಗ ಮಾಡಲು, ನಾವು **gensim** ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸಬಹುದು. ಕೆಳಗೆ 'neural' ಗೆ ಅತ್ಯಂತ ಸಮಾನವಾದ ಪದಗಳನ್ನು ಕಂಡುಹಿಡಿಯಲಾಗಿದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/14-Embeddings/README.md b/translations/kn/lessons/5-NLP/14-Embeddings/README.md index 57173a11..57f5ca5e 100644 --- a/translations/kn/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/kn/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW ಅಥವಾ TF/IDF ಆಧಾರಿತ ವರ್ಗೀಕರಣಗಳನ್ ನಮ್ಮ ವರ್ಗೀಕರಣ ಜಾಲದಲ್ಲಿ ಮೊದಲ ಲೇಯರ್ ಆಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ಲೇಯರ್ ಬಳಸಿ, ನಾವು ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್‌ನಿಂದ **ಎಂಬೆಡ್ಡಿಂಗ್ ಬ್ಯಾಗ್** ಮಾದರಿಗೆ ಬದಲಾಗಬಹುದು, ಅಲ್ಲಿ ಮೊದಲಿಗೆ ನಮ್ಮ ಪಠ್ಯದಲ್ಲಿನ ಪ್ರತಿ ಪದವನ್ನು ಸಂಬಂಧಿತ ಎम्बೆಡ್ಡಿಂಗ್‌ಗೆ ಪರಿವರ್ತಿಸಿ, ನಂತರ ಆ ಎಲ್ಲ ಎम्बೆಡ್ಡಿಂಗ್‌ಗಳ ಮೇಲೆ `sum`, `average` ಅಥವಾ `max` ಮುಂತಾದ ಸಂಗ್ರಹ ಕಾರ್ಯವನ್ನು ಲೆಕ್ಕಹಾಕುತ್ತೇವೆ. -![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.kn.png) +![ಐದು ಕ್ರಮ ಪದಗಳಿಗಾಗಿ ಎम्बೆಡ್ಡಿಂಗ್ ವರ್ಗೀಕರಣವನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/embedding-classifier-example.b77f021a7ee67eee.png) > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -40,7 +40,7 @@ BoW ಅಥವಾ TF/IDF ಆಧಾರಿತ ವರ್ಗೀಕರಣಗಳನ್ CBoW ವೇಗವಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತದೆ, ಆದರೆ ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ನಿಧಾನವಾಗಿದ್ದು, ಅಪರೂಪದ ಪದಗಳನ್ನು ಉತ್ತಮವಾಗಿ ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. -![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.kn.png) +![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ CBoW ಮತ್ತು ಸ್ಕಿಪ್-ಗ್ರಾಮ್ ಆಲ್ಗಾರಿಥಮ್‌ಗಳನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1301.3781.pdf) ನಿಂದ diff --git a/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md b/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md index bbeebee3..e08b5812 100644 --- a/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/kn/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **ಕಂಟಿನ್ಯೂಯಸ್ ಬ್ಯಾಗ್-ಆಫ್-ವರ್ಡ್ಸ್** (CBoW), ಇಲ್ಲಿ ನಾವು ಟೋಕನ್ ಸರಣಿಯಲ್ಲಿ ಮಧ್ಯದ ಟೋಕನ್ $W_0$ ಅನ್ನು ಊಹಿಸುತ್ತೇವೆ $W_{-N}$, ..., $W_N$. * **ಸ್ಕಿಪ್-ಗ್ರಾಮ್**, ಇಲ್ಲಿ ನಾವು ಮಧ್ಯದ ಟೋಕನ್ $W_0$ ರಿಂದ ಸುತ್ತಲೂ ಇರುವ ಟೋಕನ್‌ಗಳ ಸಮೂಹ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ಅನ್ನು ಊಹಿಸುತ್ತೇವೆ. -![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ ಕುರಿತು ಪೇಪರ್‌ನ ಚಿತ್ರ](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.kn.png) +![ಪದಗಳನ್ನು ವೆಕ್ಟರ್‌ಗಳಿಗೆ ಪರಿವರ್ತಿಸುವ ಕುರಿತು ಪೇಪರ್‌ನ ಚಿತ್ರ](../../../../../translated_images/kn/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ಚಿತ್ರ [ಈ ಪೇಪರ್](https://arxiv.org/pdf/1301.3781.pdf) ನಿಂದ diff --git a/translations/kn/lessons/5-NLP/16-RNN/README.md b/translations/kn/lessons/5-NLP/16-RNN/README.md index f770f2b2..856f4ccc 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/README.md +++ b/translations/kn/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ಪಠ್ಯದ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು ಮತ್ತೊಂದು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುತ್ತಾರೆ. RNN ನಲ್ಲಿ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಸಂಕೇತವನ್ನು ಒಂದೇ ಸಮಯದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಹಾದುಹೋಗಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ**ಗಳನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ಮುಂದಿನ ಸಂಕೇತದೊಂದಿಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ನೀಡುತ್ತೇವೆ. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.kn.png) +![RNN](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.png) > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ಸರಳ RNN ಸೆಲ್ ಒಳಗೆ ಎರಡು ತೂಕ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ಗಳಿವೆ: ಒಂದು ಇನ್‌ಪುಟ್ ಸಂಕೇತವನ್ನು ಪರಿವರ್ತಿಸುತ್ತದೆ (ನಾವು ಅದನ್ನು W ಎಂದು ಕರೆಯೋಣ), ಮತ್ತೊಂದು ಇನ್‌ಪುಟ್ ಸ್ಥಿತಿಯನ್ನು ಪರಿವರ್ತಿಸುತ್ತದೆ (H). ಈ ಸಂದರ್ಭದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಔಟ್‌ಪುಟ್ ಅನ್ನು σ(W×Xi+H×Si-1+b) ಎಂದು ಲೆಕ್ಕಹಾಕಲಾಗುತ್ತದೆ, ಇಲ್ಲಿ σ ಸಕ್ರಿಯಕರಣ ಕಾರ್ಯ ಮತ್ತು b ಹೆಚ್ಚುವರಿ ಬಯಾಸ್. -RNN Cell Anatomy +RNN Cell Anatomy > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -61,7 +61,7 @@ LSTM ನೆಟ್‌ವರ್ಕ್ RNN ಗೆ ಹೋಲುವ ರೀತಿಯ ಒಂದು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್, ಏತಾದರೂ ಒಂದು ದಿಕ್ಕಿನ ಅಥವಾ ದ್ವಿಮುಖಿ, ಸರಣಿಯೊಳಗಿನ ಕೆಲವು ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್ ಅಥವಾ ಔಟ್‌ಪುಟ್‌ಗೆ ಸಂಗ್ರಹಿಸುತ್ತದೆ. ಕಾಂವಲ್ಯೂಷನಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳಂತೆ, ನಾವು ಮೊದಲ ಲೇಯರ್‌ನ ಮೇಲೆ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಲೇಯರ್ ನಿರ್ಮಿಸಬಹುದು, ಇದರಿಂದ ಹೆಚ್ಚಿನ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳಬಹುದು ಮತ್ತು ಮೊದಲ ಲೇಯರ್ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿಸಬಹುದು. ಇದರಿಂದ **ಬಹು-ಲೇಯರ್ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಂದ ಕೂಡಿದೆ, ಇಲ್ಲಿ ಹಿಂದಿನ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಲೇಯರ್‌ಗೆ ಇನ್‌ಪುಟ್ ಆಗಿ ನೀಡಲಾಗುತ್ತದೆ. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.kn.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg) *ಚಿತ್ರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರಿಂದ* diff --git a/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index a0506b59..28f3240e 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "ಪಠ್ಯದ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು ಮತ್ತೊಂದು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸಬೇಕಾಗುತ್ತದೆ, ಇದನ್ನು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುತ್ತಾರೆ. RNN ನಲ್ಲಿ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಸಂಕೇತವನ್ನು ಪ್ರತಿ ಬಾರಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಕಳುಹಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ** ಅನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ನಂತರ ಮುಂದಿನ ಸಂಕೇತದೊಂದಿಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಕಳುಹಿಸುತ್ತೇವೆ.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ನಮೂದಿಸಿದ ಟೋಕನ್ ಸರಣಿಯಾದ $X_0,\\dots,X_n$ ನೀಡಿದಾಗ, RNN ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳ ಸರಣಿಯನ್ನು ರಚಿಸುತ್ತದೆ ಮತ್ತು ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಬಳಸಿ ಈ ಸರಣಿಯನ್ನು ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳಿಸುತ್ತದೆ. ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್ ಒಂದು ಜೋಡಿ $(X_i,S_i)$ ಅನ್ನು ಇನ್‌ಪುಟ್ ಆಗಿ ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು $S_{i+1}$ ಅನ್ನು ಫಲಿತಾಂಶವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತದೆ. ಅಂತಿಮ ಸ್ಥಿತಿ $S_n$ ಅಥವಾ ಔಟ್‌ಪುಟ್ $X_n$ ರೇಖೀಯ ವರ್ಗೀಕರಣಕ್ಕೆ ಹೋಗಿ ಫಲಿತಾಂಶವನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ. ಎಲ್ಲಾ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳು ಒಂದೇ ತೂಕಗಳನ್ನು ಹಂಚಿಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಒಂದು ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಪಾಸ್ ಬಳಸಿ ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳ್ಳುತ್ತವೆ.\n", "\n", @@ -428,7 +428,7 @@ "\n", "ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್, ಒಂದು ದಿಕ್ಕಿನ ಅಥವಾ ದ್ವಿಮುಖಿ, ಕ್ರಮದೊಳಗಿನ ನಿರ್ದಿಷ್ಟ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಸಂಗ್ರಹಿಸಬಹುದು ಅಥವಾ ಔಟ್‌ಪುಟ್‌ಗೆ ಪಾಸ್ ಮಾಡಬಹುದು. ಸಂಯೋಜಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಂತೆ, ನಾವು ಮೊದಲನೆಯದಿನ ಮೇಲೆ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಪದರವನ್ನು ನಿರ್ಮಿಸಬಹುದು, ಮೊದಲನೆಯ ಪದರದಿಂದ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿತ ಉನ್ನತ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಲು. ಇದರಿಂದ ನಮಗೆ **ಬಹುಮಟ್ಟದ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ನೆಟ್‌ವರ್ಕ್‌ಗಳಿಂದ ಕೂಡಿದ್ದು, ಹಿಂದಿನ ಪದರದ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಪದರಕ್ಕೆ ಇನ್‌ಪುಟ್ ಆಗಿ ಪಾಸ್ ಆಗುತ್ತದೆ.\n", "\n", - "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.kn.jpg)\n", + "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಚಿತ್ರ*\n", "\n", diff --git a/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb index ce89cd60..ffc60ba3 100644 --- a/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/kn/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ಪಠ್ಯ ಕ್ರಮದ ಅರ್ಥವನ್ನು ಹಿಡಿಯಲು, ನಾವು **ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್** ಅಥವಾ RNN ಎಂದು ಕರೆಯುವ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪವನ್ನು ಬಳಸುತ್ತೇವೆ. RNN ಬಳಸುವಾಗ, ನಾವು ನಮ್ಮ ವಾಕ್ಯವನ್ನು ಒಂದು ಟೋಕನ್‌ವನ್ನೊಂದು ಸಮಯದಲ್ಲಿ ನೆಟ್‌ವರ್ಕ್ ಮೂಲಕ ಹಾದುಹೋಗಿಸುತ್ತೇವೆ, ಮತ್ತು ನೆಟ್‌ವರ್ಕ್ ಕೆಲವು **ಸ್ಥಿತಿ** ಅನ್ನು ಉತ್ಪಾದಿಸುತ್ತದೆ, ಅದನ್ನು ನಂತರ ಮುಂದಿನ ಟೋಕನ್ ಜೊತೆಗೆ ಮತ್ತೆ ನೆಟ್‌ವರ್ಕ್‌ಗೆ ನೀಡುತ್ತೇವೆ.\n", "\n", - "![ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ರಚನೆಯ ಉದಾಹರಣೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/rnn.27f5c29c53d727b5.kn.png)\n", + "![ಪುನರಾವರ್ತಿತ ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ರಚನೆಯ ಉದಾಹರಣೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn.27f5c29c53d727b5.png)\n", "\n", "ನಮೂದಿಸಿದ ಟೋಕನ್ ಕ್ರಮ $X_0,\\dots,X_n$ ನೀಡಿದಾಗ, RNN ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳ ಸರಣಿಯನ್ನು ರಚಿಸುತ್ತದೆ ಮತ್ತು ಬ್ಯಾಕ್ಪ್ರೊಪಗೇಶನ್ ಬಳಸಿ ಈ ಸರಣಿಯನ್ನು ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳಿಸುತ್ತದೆ. ಪ್ರತಿ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್ ಒಂದು ಜೋಡಿ $(X_i,S_i)$ ಅನ್ನು ಇನ್ಪುಟ್ ಆಗಿ ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ ಮತ್ತು $S_{i+1}$ ಅನ್ನು ಫಲಿತಾಂಶವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತದೆ. ಅಂತಿಮ ಸ್ಥಿತಿ $S_n$ ಅಥವಾ ಔಟ್‌ಪುಟ್ $Y_n$ ಅನ್ನು ಲೀನಿಯರ್ ವರ್ಗೀಕರಣಕಾರಿಗೆ ನೀಡಲಾಗುತ್ತದೆ ಫಲಿತಾಂಶವನ್ನು ಉತ್ಪಾದಿಸಲು. ಎಲ್ಲಾ ನೆಟ್‌ವರ್ಕ್ ಬ್ಲಾಕ್‌ಗಳು ಒಂದೇ ತೂಕಗಳನ್ನು ಹಂಚಿಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಒಂದು ಬ್ಯಾಕ್ಪ್ರೊಪಗೇಶನ್ ಪಾಸ್ ಬಳಸಿ ಅಂತ್ಯದಿಂದ ಅಂತ್ಯಕ್ಕೆ ತರಬೇತುಗೊಳ್ಳುತ್ತವೆ.\n", "\n", @@ -371,7 +371,7 @@ "\n", "ಪುನರಾವರ್ತಿತ ಜಾಲಗಳು, ಏಕದಿಕ್ಕಿ ಅಥವಾ ದ್ವಿಮುಖವಾಗಿರಲಿ, ಕ್ರಮದೊಳಗಿನ ಮಾದರಿಗಳನ್ನು ಹಿಡಿದುಕೊಳ್ಳುತ್ತವೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸ್ಥಿತಿ ವೆಕ್ಟರ್‌ಗಳಲ್ಲಿ ಸಂಗ್ರಹಿಸುತ್ತವೆ ಅಥವಾ ಔಟ್‌ಪುಟ್ ಆಗಿ ನೀಡುತ್ತವೆ. ಸಂಯೋಜಕ ಜಾಲಗಳಂತೆ, ನಾವು ಮೊದಲ ಲೇಯರ್‌ನ ನಂತರ ಮತ್ತೊಂದು ಪುನರಾವರ್ತಿತ ಲೇಯರ್ ಅನ್ನು ನಿರ್ಮಿಸಿ, ಮೊದಲ ಲೇಯರ್ ತೆಗೆದುಕೊಂಡ ಕಡಿಮೆ ಮಟ್ಟದ ಮಾದರಿಗಳಿಂದ ನಿರ್ಮಿತ ಉನ್ನತ ಮಟ್ಟದ ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಬಹುದು. ಇದರಿಂದ ನಮಗೆ **ಬಹುಮಟ್ಟದ RNN** ಎಂಬ ಕಲ್ಪನೆ ಬರುತ್ತದೆ, ಇದು ಎರಡು ಅಥವಾ ಹೆಚ್ಚು ಪುನರಾವರ್ತಿತ ಜಾಲಗಳನ್ನು ಒಳಗೊಂಡಿದ್ದು, ಹಿಂದಿನ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಮುಂದಿನ ಲೇಯರ್‌ಗೆ ಇನ್ಪುಟ್ ಆಗಿ ನೀಡಲಾಗುತ್ತದೆ.\n", "\n", - "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.kn.jpg)\n", + "![ಬಹುಮಟ್ಟದ ದೀರ್ಘಕಾಲಿಕ-ಸ್ಮೃತಿ- RNN ಅನ್ನು ತೋರಿಸುವ ಚಿತ್ರ](../../../../../translated_images/kn/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ಚಿತ್ರ [ಈ ಅದ್ಭುತ ಪೋಸ್ಟ್](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ನಿಂದ ಫೆರ್ನಾಂಡೋ ಲೋಪೆಜ್ ಅವರಿಂದ.*\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b96bb687..64e782f0 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ನಾವು RNN ಅನ್ನು ಪಠ್ಯವನ್ನು ರಚಿಸಲು ತರಬೇತಿಗೊಳಿಸುವ ವಿಧಾನ ಹೀಗಿದೆ. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು `nchars` ಉದ್ದದ ಅಕ್ಷರಗಳ ಸರಣಿಯನ್ನು ತೆಗೆದು, ಪ್ರತಿಯೊಂದು ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ನೆಟ್‌ವರ್ಕ್ ರಚಿಸಲು ಕೇಳುತ್ತೇವೆ:\n", "\n", - "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.kn.png)\n", + "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ವಾಸ್ತವಿಕ ಪರಿಸ್ಥಿತಿಯ ಮೇಲೆ ಅವಲಂಬಿಸಿ, ನಾವು ಕೆಲವು ವಿಶೇಷ ಅಕ್ಷರಗಳನ್ನು ಸೇರಿಸಲು ಬಯಸಬಹುದು, ಉದಾಹರಣೆಗೆ *end-of-sequence* ``. ನಮ್ಮ ಪ್ರಕರಣದಲ್ಲಿ, ನಾವು ನಿರಂತರ ಪಠ್ಯ ರಚನೆಗಾಗಿ ನೆಟ್‌ವರ್ಕ್ ಅನ್ನು ತರಬೇತಿಗೊಳಿಸಲು ಬಯಸುತ್ತೇವೆ, ಆದ್ದರಿಂದ ಪ್ರತಿ ಸರಣಿಯ ಗಾತ್ರವನ್ನು `nchars` ಟೋಕನ್‌ಗಳಿಗೆ ಸಮಾನವಾಗಿರಿಸುವೆವು. ಪರಿಣಾಮವಾಗಿ, ಪ್ರತಿ ತರಬೇತಿ ಉದಾಹರಣೆ `nchars` ಇನ್‌ಪುಟ್‌ಗಳು ಮತ್ತು `nchars` ಔಟ್‌ಪುಟ್‌ಗಳಿಂದ (ಇನ್‌ಪುಟ್ ಸರಣಿಯನ್ನು ಎಡಕ್ಕೆ ಒಂದು ಚಿಹ್ನೆ ಸರಿಸಿದವು) ಕೂಡಿರುತ್ತದೆ. ಮಿನಿಬ್ಯಾಚ್ ಹಲವಾರು ಇಂತಹ ಸರಣಿಗಳಿಂದ ಕೂಡಿರುತ್ತದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 5c9cec63..1fbb96f3 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "ನಾವು ಸುದ್ದಿಯ ಶೀರ್ಷಿಕೆಗಳನ್ನು ರಚಿಸಲು RNN ಅನ್ನು ತರಬೇತಿಗೊಳಿಸುವ ವಿಧಾನ ಹೀಗಿದೆ. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು ಒಂದು ಶೀರ್ಷಿಕೆಯನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತೇವೆ, ಅದನ್ನು RNN ಗೆ ನೀಡಲಾಗುತ್ತದೆ, ಮತ್ತು ಪ್ರತಿ ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ನೆಟ್‌ವರ್ಕ್ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು ಕೇಳಲಾಗುತ್ತದೆ:\n", "\n", - "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.kn.png)\n", + "!['HELLO' ಎಂಬ ಪದದ ಉದಾಹರಣೆಯಾಗಿ RNN ರಚನೆಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ನಮ್ಮ ಕ್ರಮದ ಕೊನೆಯ ಅಕ್ಷರಕ್ಕೆ, ನಾವು ನೆಟ್‌ವರ್ಕ್ `` ಟೋಕನ್ ರಚಿಸಲು ಕೇಳುತ್ತೇವೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md index 80b566a3..ae4c03fd 100644 --- a/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/kn/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ಇದು ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ತೋರಿಸಿದಂತೆ ವಿಭಿನ್ನ ನ್ಯೂರಲ್ ವಾಸ್ತುಶಿಲ್ಪಗಳಿಗೆ ಅವಕಾಶ ನೀಡುತ್ತದೆ: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.kn.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > ಚಿತ್ರವು [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ಬ್ಲಾಗ್ ಪೋಸ್ಟ್‌ನಿಂದ [Andrej Karpaty](http://karpathy.github.io/) ಅವರಿಂದ @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು ಈ RNN ಅನ್ನು ಹಂತ ಹಂತವಾಗಿ ಪಠ್ಯ ರಚಿಸಲು ತರಬೇತುಗೊಳಿಸುವೆವು. ಪ್ರತಿ ಹಂತದಲ್ಲಿ, ನಾವು `nchars` ಉದ್ದದ ಅಕ್ಷರ ಸರಣಿಯನ್ನು ತೆಗೆದು, ಪ್ರತಿ ಇನ್‌ಪುಟ್ ಅಕ್ಷರಕ್ಕೆ ಮುಂದಿನ ಔಟ್‌ಪುಟ್ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು ನೆಟ್‌ವರ್ಕ್‌ಗೆ ಕೇಳುತ್ತೇವೆ: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.kn.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/kn/rnn-generate.56c54afb52f9781d.png) ಪಠ್ಯ ರಚಿಸುವಾಗ (ಇನ್ಫರೆನ್ಸ್ ಸಮಯದಲ್ಲಿ), ನಾವು ಕೆಲವು **ಪ್ರಾಂಪ್ಟ್**ನಿಂದ ಪ್ರಾರಂಭಿಸುತ್ತೇವೆ, ಅದನ್ನು RNN ಸೆಲ್‌ಗಳ ಮೂಲಕ ಹೋದರೆ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಯನ್ನು ರಚಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಆ ಸ್ಥಿತಿಯಿಂದ ರಚನೆ ಪ್ರಾರಂಭವಾಗುತ್ತದೆ. ನಾವು ಒಂದೊಂದು ಅಕ್ಷರವನ್ನು ರಚಿಸಿ, ಆ ಸ್ಥಿತಿಯನ್ನು ಮತ್ತು ರಚಿಸಿದ ಅಕ್ಷರವನ್ನು ಮತ್ತೊಂದು RNN ಸೆಲ್‌ಗೆ ನೀಡುತ್ತೇವೆ ಮುಂದಿನ ಅಕ್ಷರವನ್ನು ರಚಿಸಲು, ಇದನ್ನು ಬೇಕಾದಷ್ಟು ಅಕ್ಷರಗಳು ರಚಿಸುವವರೆಗೆ ಮುಂದುವರಿಸುತ್ತೇವೆ. - + > ಚಿತ್ರ ಲೇಖಕರಿಂದ diff --git a/translations/kn/lessons/5-NLP/18-Transformers/README.md b/translations/kn/lessons/5-NLP/18-Transformers/README.md index a38ef4be..bb2ef635 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/README.md +++ b/translations/kn/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ **ಗಮನ ಯಂತ್ರಗಳು** RNN ನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿಗೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNN ಮತ್ತು ಔಟ್‌ಪುಟ್ RNN ನಡುವಿನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ yt ಅನ್ನು ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು hiಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳು αt,iಗಳೊಂದಿಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ. -![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.kn.png) +![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರದೊಂದಿಗೆ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ {αi,j} ಒಂದು ನಿರ್ದಿಷ್ಟ ಔಟ್‌ಪುಟ್ ಪದವನ್ನು ರಚಿಸುವಲ್ಲಿ ಕೆಲವು ಇನ್‌ಪುಟ್ ಪದಗಳು ಎಷ್ಟು ಪ್ರಭಾವ ಬೀರುತ್ತವೆ ಎಂಬುದನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಉದಾಹರಣೆ ನೀಡಲಾಗಿದೆ: -![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.kn.png) +![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ @@ -56,7 +56,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ * ಟೋಕನ್ ಎಂಬೆಡ್ಡಿಂಗ್‌ಗೆ ಹೋಲುವ ತರಬೇತಿಗೊಳ್ಳಬಹುದಾದ ಎಂಬೆಡ್ಡಿಂಗ್. ಇದನ್ನು ಇಲ್ಲಿ ನಾವು ಪರಿಗಣಿಸುತ್ತೇವೆ. ಟೋಕನ್‌ಗಳು ಮತ್ತು ಅವುಗಳ ಸ್ಥಾನಗಳ ಮೇಲೆ ಎಂಬೆಡ್ಡಿಂಗ್ ಪದರಗಳನ್ನು ಅನ್ವಯಿಸಿ, ಸಮಾನ ಆಯಾಮಗಳ ಎಂಬೆಡ್ಡಿಂಗ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಪಡೆಯುತ್ತೇವೆ ಮತ್ತು ಅವುಗಳನ್ನು ಸೇರಿಸುತ್ತೇವೆ. * ಮೂಲ ಕಾಗದದಲ್ಲಿ ಪ್ರಸ್ತಾಪಿಸಿದ ಸ್ಥಿರ ಸ್ಥಾನ ಎನ್‌ಕೋಡಿಂಗ್ ಕಾರ್ಯ. - + > ಲೇಖಕರಿಂದ ಚಿತ್ರ @@ -66,7 +66,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ ಮುಂದೆ, ನಾವು ಕ್ರಮದೊಳಗಿನ ಕೆಲವು ಮಾದರಿಗಳನ್ನು ಹಿಡಿಯಬೇಕಾಗುತ್ತದೆ. ಇದಕ್ಕಾಗಿ, ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್‌ಗಳು **ಸ್ವ-ಗಮನ** ಯಂತ್ರವನ್ನು ಬಳಸುತ್ತವೆ, ಇದು ಇನ್‌ಪುಟ್ ಮತ್ತು ಔಟ್‌ಪುಟ್ ಕ್ರಮ ಒಂದೇ ಆಗಿರುವ ಗಮನವಾಗಿದೆ. ಸ್ವ-ಗಮನವನ್ನು ಅನ್ವಯಿಸುವುದರಿಂದ ವಾಕ್ಯದ **ಸಂದರ್ಭ**ವನ್ನು ಪರಿಗಣಿಸಲು ಸಾಧ್ಯವಾಗುತ್ತದೆ ಮತ್ತು ಯಾವ ಪದಗಳು ಪರಸ್ಪರ ಸಂಬಂಧ ಹೊಂದಿವೆ ಎಂದು ನೋಡಬಹುದು. ಉದಾಹರಣೆಗೆ, ಇದು *it* ಮುಂತಾದ ಕೋರೆಫರೆನ್ಸ್‌ಗಳ ಮೂಲಕ ಯಾವ ಪದಗಳನ್ನು ಸೂಚಿಸಲಾಗುತ್ತಿದೆ ಎಂಬುದನ್ನು ತಿಳಿಯಲು ಸಹಾಯ ಮಾಡುತ್ತದೆ ಮತ್ತು ಸಾಂದರ್ಭಿಕ ಮಾಹಿತಿಯನ್ನು ಪರಿಗಣಿಸುತ್ತದೆ: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.kn.png) +![](../../../../../translated_images/kn/CoreferenceResolution.861924d6d384a7d6.png) > [Google ಬ್ಲಾಗ್](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) ನಿಂದ ಚಿತ್ರ @@ -91,7 +91,7 @@ RNN ಗಳೊಂದಿಗೆ, ಕ್ರಮ-ದಿಂದ-ಕ್ರಮ ಕಾರ **BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಜಾಲವಾಗಿದೆ, *BERT-base* ಗೆ 12 ಪದರಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಪದರಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಸಂಗ್ರಹ (ವಿಕಿಪೀಡಿಯ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಸ್ವಯಂನಿರೀಕ್ಷಿತ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಿದ ಪದಗಳನ್ನು ಊಹಿಸುವುದು) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿಯ ಸಮಯದಲ್ಲಿ, ಮಾದರಿ ಭಾಷಾ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವಪೂರ್ಣ ಮಟ್ಟವನ್ನು ಅಳವಡಿಸಿಕೊಂಡು, ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಸೂಕ್ಷ್ಮ-ತರಬೇತಿ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ. -![ಚಿತ್ರ http://jalammar.github.io/illustrated-bert/ ನಿಂದ](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.kn.png) +![ಚಿತ್ರ http://jalammar.github.io/illustrated-bert/ ನಿಂದ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > ಚಿತ್ರ [ಮೂಲ](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 3ef924fa..d266373c 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/kn/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ಗಮನ ಯಂತ್ರಗಳು** RNNನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿ ಮೇಲೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNN ಮತ್ತು ಔಟ್‌ಪುಟ್ RNNನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ $y_t$ ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು $h_i$ಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳು $\\alpha_{t,i}$ ಜೊತೆಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ.\n", "\n", - "![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.kn.png)\n", + "![ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ತೋರಿಸುವ ಚಿತ್ರ, ಜೊತೆಗೆ ಸೇರಿಸಿದ ಗಮನ ಪದರ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿನ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರದೊಂದಿಗೆ ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ*\n", "\n", "ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ $\\{\\alpha_{i,j}\\}$ ನಿರ್ದಿಷ್ಟ ಇನ್‌ಪುಟ್ ಪದಗಳು ಔಟ್‌ಪುಟ್ ಸರಣಿಯ ನಿರ್ದಿಷ್ಟ ಪದ ರಚನೆಯಲ್ಲಿ ಎಷ್ಟು ಪಾತ್ರ ವಹಿಸುತ್ತವೆ ಎಂಬ ಮಟ್ಟವನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರವು ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್‌ನ ಉದಾಹರಣೆಯಾಗಿದೆ:\n", "\n", - "![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.kn.png)\n", + "![RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆ ಚಿತ್ರ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ ತೆಗೆದಿದೆ*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹಳ ದೊಡ್ಡ ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, *BERT-base* ಗೆ 12 ಮಟ್ಟಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಮಟ್ಟಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಸಂಗ್ರಹ (ವಿಕಿಪೀಡಿಯಾ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಸ್ವಯಂನಿರೀಕ್ಷಿತ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಲಾದ ಪದಗಳನ್ನು ಭವಿಷ್ಯವಾಣಿ ಮಾಡುವ ಮೂಲಕ) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿಯ ಸಮಯದಲ್ಲಿ ಮಾದರಿ ಭಾಷಾ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವಪೂರ್ಣ ಮಟ್ಟವನ್ನು ಶೋಷಿಸುತ್ತದೆ, ಇದನ್ನು ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಸೂಕ್ಷ್ಮ-ಸಂಯೋಜನೆಯ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ನಿಂದ ಚಿತ್ರ](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.kn.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ನಿಂದ ಚಿತ್ರ](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 ಮತ್ತು ಇನ್ನಷ್ಟು ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಅನೇಕ ರೂಪಾಂತರಗಳಿವೆ, ಅವುಗಳನ್ನು ಸೂಕ್ಷ್ಮ-ಸಂಯೋಜನೆ ಮಾಡಬಹುದು. [HuggingFace ಪ್ಯಾಕೇಜ್](https://github.com/huggingface/) PyTorch ಬಳಸಿ ಈ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಬಹುತೇಕ ತರಬೇತಿಗಾಗಿ ಸಂಗ್ರಹಾಲಯವನ್ನು ಒದಗಿಸುತ್ತದೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index deca4a4d..1cdaa8d0 100644 --- a/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/kn/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ಗಮನ ಯಂತ್ರಗಳು** RNNನ ಪ್ರತಿ ಔಟ್‌ಪುಟ್ ಭವಿಷ್ಯವಾಣಿ ಮೇಲೆ ಪ್ರತಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನ ಸಾಂದರ್ಭಿಕ ಪ್ರಭಾವವನ್ನು ತೂಕ ನೀಡುವ ವಿಧಾನವನ್ನು ಒದಗಿಸುತ್ತವೆ. ಇದನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ವಿಧಾನವೆಂದರೆ ಇನ್‌ಪುಟ್ RNNನ ಮಧ್ಯಂತರ ಸ್ಥಿತಿಗಳ ಮತ್ತು ಔಟ್‌ಪುಟ್ RNNನ ನಡುವೆ ಶಾರ್ಟ್‌ಕಟ್‌ಗಳನ್ನು ಸೃಷ್ಟಿಸುವುದು. ಈ ರೀತಿಯಲ್ಲಿ, ಔಟ್‌ಪುಟ್ ಚಿಹ್ನೆ $y_t$ ರಚಿಸುವಾಗ, ನಾವು ಎಲ್ಲಾ ಇನ್‌ಪುಟ್ ಗುಪ್ತ ಸ್ಥಿತಿಗಳು $h_i$ಗಳನ್ನು ವಿಭಿನ್ನ ತೂಕ ಗುಣಾಂಕಗಳ $\\alpha_{t,i}$ೊಂದಿಗೆ ಪರಿಗಣಿಸುತ್ತೇವೆ.\n", "\n", - "![ಚಿತ್ರವು ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ಸೇರಿಸಿದ ಗಮನ ಪದರದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.kn.png)\n", + "![ಚಿತ್ರವು ಎನ್‌ಕೋಡರ್/ಡಿಕೋಡರ್ ಮಾದರಿಯನ್ನು ಸೇರಿಸಿದ ಗಮನ ಪದರದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ](../../../../../translated_images/kn/encoder-decoder-attention.7a726296894fb567.png)\n", "*ಎನ್‌ಕೋಡರ್-ಡಿಕೋಡರ್ ಮಾದರಿಯೊಂದಿಗೆ ಸೇರಿಸಿದ ಗಮನ ಯಂತ್ರವನ್ನು [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ನಲ್ಲಿ ವಿವರಿಸಲಾಗಿದೆ, [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ನಿಂದ ಉಲ್ಲೇಖಿಸಲಾಗಿದೆ*\n", "\n", "ಗಮನ ಮ್ಯಾಟ್ರಿಕ್ಸ್ $\\{\\alpha_{i,j}\\}$ ನಿರ್ದಿಷ್ಟ ಇನ್‌ಪುಟ್ ಪದಗಳು ಔಟ್‌ಪುಟ್ ಸರಣಿಯ ನಿರ್ದಿಷ್ಟ ಪದ ರಚನೆಯಲ್ಲಿ ಎಷ್ಟು ಪಾತ್ರ ವಹಿಸುತ್ತವೆ ಎಂಬುದನ್ನು ಪ್ರತಿನಿಧಿಸುತ್ತದೆ. ಕೆಳಗಿನ ಚಿತ್ರದಲ್ಲಿ ಇಂತಹ ಮ್ಯಾಟ್ರಿಕ್ಸ್ ಉದಾಹರಣೆ ನೀಡಲಾಗಿದೆ:\n", "\n", - "![ಚಿತ್ರವು RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆಯನ್ನು ತೋರಿಸುತ್ತದೆ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.kn.png)\n", + "![ಚಿತ್ರವು RNNsearch-50 ಮೂಲಕ ಕಂಡುಬಂದ ಮಾದರಿ ಹೊಂದಾಣಿಕೆಯನ್ನು ತೋರಿಸುತ್ತದೆ, Bahdanau - arviz.org ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ](../../../../../translated_images/kn/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*ಚಿತ್ರ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ಚಿತ್ರ 3) ನಿಂದ ತೆಗೆದುಕೊಂಡಿದೆ*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ಈ ಲೇಯರ್ ಎರಡು `Embedding` ಲೇಯರ್‌ಗಳಿಂದ ಕೂಡಿದೆ: ಟೋಕನ್‌ಗಳನ್ನು ಎम्बೆಡ್ ಮಾಡಲು (ನಾವು ಹಿಂದಿನಂತೆ ಚರ್ಚಿಸಿದ ರೀತಿಯಲ್ಲಿ) ಮತ್ತು ಟೋಕನ್ ಸ್ಥಾನಗಳನ್ನು ಎम्बೆಡ್ ಮಾಡಲು. ಟೋಕನ್ ಸ್ಥಾನಗಳನ್ನು 0 ರಿಂದ `maxlen` ವರೆಗೆ ನೈಸರ್ಗಿಕ ಸಂಖ್ಯೆಗಳ ಸರಣಿಯಾಗಿ `tf.range` ಬಳಸಿ ರಚಿಸಲಾಗುತ್ತದೆ, ನಂತರ ಅದನ್ನು ಎम्बೆಡಿಂಗ್ ಲೇಯರ್ ಮೂಲಕ ಹಾದುಹೋಗುತ್ತದೆ. ಎರಡು ಫಲಿತಾಂಶ ಎम्बೆಡಿಂಗ್ ವೆಕ್ಟರ್‌ಗಳನ್ನು ಸೇರಿಸಲಾಗುತ್ತದೆ, ಇದರಿಂದ `maxlen`$\\times$`embed_dim` ಆಕಾರದ ಸ್ಥಾನಾನುಕ್ರಮಿತ ಎम्बೆಡಿಂಗ್ ಪ್ರತಿನಿಧಾನ ಸೃಷ್ಟಿಯಾಗುತ್ತದೆ.\n", "\n", - "\n", + "\n", "\n", "ಈಗ, ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಬ್ಲಾಕ್ ಅನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸೋಣ. ಇದು ಹಿಂದಿನದಾಗಿ ವ್ಯಾಖ್ಯಾನಿಸಿದ ಎम्बೆಡಿಂಗ್ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ಅನ್ನು ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ:\n" ] @@ -134,7 +134,7 @@ "\n", "ಈ ಲೇಯರ್‌ನ ಔಟ್‌ಪುಟ್ ನಂತರ `Dense` ನೆಟ್‌ವರ್ಕ್ (ನಮ್ಮ ಪ್ರಕರಣದಲ್ಲಿ - ಎರಡು ಲೇಯರ್ ಪರ್ಸೆಪ್ಟ್ರಾನ್) ಮೂಲಕ ಸಾಗಿಸಲಾಗುತ್ತದೆ, ಮತ್ತು ಫಲಿತಾಂಶವನ್ನು ಅಂತಿಮ ಔಟ್‌ಪುಟ್‌ಗೆ ಸೇರಿಸಲಾಗುತ್ತದೆ (ಅದು ಮತ್ತೆ ಸಾಮಾನ್ಯೀಕರಣಕ್ಕೆ ಒಳಗಾಗುತ್ತದೆ).\n", "\n", - "\n", + "\n", "\n", "ಈಗ, ನಾವು ಸಂಪೂರ್ಣ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ಮಾದರಿಯನ್ನು ವ್ಯಾಖ್ಯಾನಿಸಲು ಸಿದ್ಧರಾಗಿದ್ದೇವೆ:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ಒಂದು ಬಹಳ ದೊಡ್ಡ ಬಹುಮಟ್ಟದ ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ನೆಟ್‌ವರ್ಕ್ ಆಗಿದ್ದು, *BERT-base* ಗೆ 12 ಲೇಯರ್‌ಗಳು ಮತ್ತು *BERT-large* ಗೆ 24 ಲೇಯರ್‌ಗಳಿವೆ. ಈ ಮಾದರಿಯನ್ನು ಮೊದಲು ದೊಡ್ಡ ಪಠ್ಯ ಡೇಟಾ (ವಿಕಿಪೀಡಿಯಾ + ಪುಸ್ತಕಗಳು) ಮೇಲೆ ಅನ್‌ಸೂಪರ್ವೈಸ್‌ಡ್ ತರಬೇತಿಯಲ್ಲಿ (ವಾಕ್ಯದಲ್ಲಿ ಮಸ್ಕ್ ಮಾಡಲಾದ ಪದಗಳನ್ನು ಊಹಿಸುವ ಮೂಲಕ) ಪೂರ್ವ-ತರಬೇತಿ ಮಾಡಲಾಗುತ್ತದೆ. ಪೂರ್ವ-ತರಬೇತಿ ಸಮಯದಲ್ಲಿ, ಮಾದರಿ ಭಾಷೆಯ ಅರ್ಥಮಾಡಿಕೊಳ್ಳುವ ಮಹತ್ವದ ಮಟ್ಟವನ್ನು ಅಳವಡಿಸಿಕೊಂಡು, ನಂತರ ಇತರ ಡೇಟಾಸೆಟ್‌ಗಳೊಂದಿಗೆ ಫೈನ್ ಟ್ಯೂನಿಂಗ್ ಮೂಲಕ ಉಪಯೋಗಿಸಬಹುದು. ಈ ಪ್ರಕ್ರಿಯೆಯನ್ನು **ಟ್ರಾನ್ಸ್‌ಫರ್ ಲರ್ನಿಂಗ್** ಎಂದು ಕರೆಯುತ್ತಾರೆ.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.kn.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/kn/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ಟ್ರಾನ್ಸ್‌ಫಾರ್ಮರ್ ವಾಸ್ತುಶಿಲ್ಪಗಳ ಹಲವು ಬದಲಾವಣೆಗಳಿವೆ, ಅವುಗಳಲ್ಲಿ BERT, DistilBERT, BigBird, OpenGPT3 ಮತ್ತು ಇನ್ನಷ್ಟು ಫೈನ್ ಟ್ಯೂನಿಂಗ್ ಮಾಡಬಹುದಾದವುಗಳಾಗಿವೆ.\n", "\n", diff --git a/translations/kn/lessons/5-NLP/19-NER/README.md b/translations/kn/lessons/5-NLP/19-NER/README.md index eb880989..e60a7602 100644 --- a/translations/kn/lessons/5-NLP/19-NER/README.md +++ b/translations/kn/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ನೀವು ಅಮೆಜಾನ್ ಅಲೆಕ್ಸಾ ಅಥವಾ ಗೂಗಲ್ ಅಸಿಸ್ಟೆಂಟ್‌ನಂತೆ ನೈಸರ್ಗಿಕ ಭಾಷೆ ಚಾಟ್ ಬಾಟ್ ಅಭಿವೃದ್ಧಿಪಡಿಸಲು ಬಯಸಿದರೆ, ಬುದ್ಧಿವಂತ ಚಾಟ್ ಬಾಟ್‌ಗಳು ಬಳಕೆದಾರನು ಏನು ಬಯಸುತ್ತಾನೆ ಎಂಬುದನ್ನು *ಅರ್ಥಮಾಡಿಕೊಳ್ಳಲು* ಇನ್‌ಪುಟ್ ವಾಕ್ಯದಲ್ಲಿ ಪಠ್ಯ ವರ್ಗೀಕರಣವನ್ನು ಮಾಡುತ್ತವೆ. ಈ ವರ್ಗೀಕರಣದ ಫಲಿತಾಂಶವನ್ನು **ಉದ್ದೇಶ** ಎಂದು ಕರೆಯುತ್ತಾರೆ, ಇದು ಚಾಟ್ ಬಾಟ್ ಏನು ಮಾಡಬೇಕು ಎಂದು ನಿರ್ಧರಿಸುತ್ತದೆ. -Bot NER +Bot NER > ಚಿತ್ರ ಲೇಖಕರಿಂದ @@ -58,7 +58,7 @@ infant | O ಟೋಕನ್‌ಗಳು ಮತ್ತು ವರ್ಗಗಳ ನಡುವೆ ಒಂದರೊಂದರ ಹೊಂದಾಣಿಕೆಯನ್ನು ನಿರ್ಮಿಸಬೇಕಾಗಿರುವುದರಿಂದ, ಈ ಚಿತ್ರದಿಂದ ನಾವು ಬಲಭಾಗದ **ಬಹು-ದಿಂದ-ಬಹು** ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್ ಮಾದರಿಯನ್ನು ತರಬೇತುಗೊಳಿಸಬಹುದು: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.kn.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/kn/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ನಿಂದ [ಅಂದ್ರೇಜ್ ಕಾರ್ಪಥಿ](http://karpathy.github.io/) ಅವರಿಂದ. NER ಟೋಕನ್ ವರ್ಗೀಕರಣ ಮಾದರಿಗಳು ಈ ಚಿತ್ರದಲ್ಲಿ ಬಲಭಾಗದ ನೆಟ್‌ವರ್ಕ್ ವಾಸ್ತುಶಿಲ್ಪಕ್ಕೆ ಹೊಂದಿಕೆಯಾಗುತ್ತವೆ.* diff --git a/translations/kn/lessons/5-NLP/README.md b/translations/kn/lessons/5-NLP/README.md index ccc2b77c..aa0ab1d5 100644 --- a/translations/kn/lessons/5-NLP/README.md +++ b/translations/kn/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ -![NLP ಕಾರ್ಯಗಳ ಸಾರಾಂಶವನ್ನು ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.kn.png) +![NLP ಕಾರ್ಯಗಳ ಸಾರಾಂಶವನ್ನು ಡೂಡಲ್‌ನಲ್ಲಿ](../../../../translated_images/kn/ai-nlp.b22dcb8ca4707cea.png) ಈ ವಿಭಾಗದಲ್ಲಿ, ನಾವು ನ್ಯೂರಲ್ ನೆಟ್‌ವರ್ಕ್‌ಗಳನ್ನು ಬಳಸಿಕೊಂಡು **ನೈಸರ್ಗಿಕ ಭಾಷಾ ಪ್ರಕ್ರಿಯೆ (NLP)** ಸಂಬಂಧಿತ ಕಾರ್ಯಗಳನ್ನು ನಿರ್ವಹಿಸುವುದರ ಮೇಲೆ ಗಮನಹರಿಸುವೆವು. ಕಂಪ್ಯೂಟರ್‌ಗಳು ಪರಿಹರಿಸಬೇಕಾದ ಅನೇಕ NLP ಸಮಸ್ಯೆಗಳಿವೆ: diff --git a/translations/kn/lessons/6-Other/22-DeepRL/README.md b/translations/kn/lessons/6-Other/22-DeepRL/README.md index bc84d44d..0ca4fe1d 100644 --- a/translations/kn/lessons/6-Other/22-DeepRL/README.md +++ b/translations/kn/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL ಗೆ ಅತ್ಯುತ್ತಮ ಸಾಧನವೆಂದರೆ [OpenAI Gym ಬ್ಯಾಲೆನ್ಸಿಂಗ್‌ನ ಸರಳೀಕೃತ ಆವೃತ್ತಿಯನ್ನು **ಕಾರ್ಟ್‌ಪೋಲ್** ಸಮಸ್ಯೆ ಎಂದು ಕರೆಯಲಾಗುತ್ತದೆ. ಕಾರ್ಟ್‌ಪೋಲ್ ಜಗತ್ತಿನಲ್ಲಿ, ನಾವು ಎಡಕ್ಕೆ ಅಥವಾ ಬಲಕ್ಕೆ ಚಲಿಸುವ ಹೋರಿಜಾಂಟಲ್ ಸ್ಲೈಡರ್ ಹೊಂದಿದ್ದೇವೆ, ಮತ್ತು ಗುರಿ ಸ್ಲೈಡರ್ ಮೇಲ್ಭಾಗದಲ್ಲಿ ಲಂಬ ಪೋಲ್ ಅನ್ನು ಬ್ಯಾಲೆನ್ಸ್ ಮಾಡುವುದು. -a cartpole +a cartpole ಈ ಪರಿಸರವನ್ನು ರಚಿಸಲು ಮತ್ತು ಬಳಸಲು, ನಮಗೆ ಕೆಲವು ಪೈಥಾನ್ ಕೋಡ್ ಸಾಲುಗಳು ಬೇಕಾಗುತ್ತವೆ: diff --git a/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md b/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md index cccb9c58..c0d3a3a8 100644 --- a/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/kn/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ನಿಮ್ಮ ಗುರಿ RL ಏಜೆಂಟ್ ಅನ್ನು OpenAI ಪರಿಸರದಲ್ಲಿ [ಪರ್ವತ ಕಾರ್](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ಅನ್ನು ನಿಯಂತ್ರಿಸಲು ತರಬೇತಿ ನೀಡುವುದು. -Mountain Car +Mountain Car ## ಪರಿಸರ diff --git a/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md b/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md index 8c4eb48b..874297b4 100644 --- a/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/kn/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ ask turtles [ ನೆಟ್‌ಲೋಗೋದಲ್ಲಿ ಒಂದು ಅದ್ಭುತ ವಿಷಯವೆಂದರೆ, ನೀವು ಪ್ರಯತ್ನಿಸಬಹುದಾದ ಕಾರ್ಯನಿರ್ವಹಿಸುವ ಮಾದರಿಗಳ ಗ್ರಂಥಾಲಯವಿದೆ. **File → Models Library** ಗೆ ಹೋಗಿ, ಮತ್ತು ನೀವು ಅನೇಕ ವರ್ಗಗಳ ಮಾದರಿಗಳನ್ನು ಆಯ್ಕೆಮಾಡಬಹುದು. -NetLogo Models Library +NetLogo Models Library > ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರ ಮಾದರಿ ಗ್ರಂಥಾಲಯದ ಸ್ಕ್ರೀನ್‌ಶಾಟ್ @@ -70,7 +70,7 @@ ask turtles [ ಮಾದರಿಯನ್ನು ತೆರೆಯುವ ನಂತರ, ನೀವು ಮುಖ್ಯ ನೆಟ್‌ಲೋಗೋ ಪರದೆಗೆ ಹೋಗುತ್ತೀರಿ. ಇಲ್ಲಿ ನಾಯಿ ಮತ್ತು ಕುರಿಗಳ ಜನಸಂಖ್ಯೆಯನ್ನು ವಿವರಿಸುವ ಮಾದರಿ ಇದೆ, ನಿರ್ದಿಷ್ಟ ಸಂಪನ್ಮೂಲಗಳ (ಹುಲ್ಲು)ೊಂದಿಗೆ. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.kn.png) +![NetLogo Main Screen](../../../../../translated_images/kn/NetLogo-Main.32653711ec1a01b3.png) > ಡಿಮಿಟ್ರಿ ಸೋಶ್ನಿಕೋವ್ ಅವರ ಸ್ಕ್ರೀನ್‌ಶಾಟ್ diff --git a/translations/kn/lessons/README.md b/translations/kn/lessons/README.md index 88c233a0..c7251622 100644 --- a/translations/kn/lessons/README.md +++ b/translations/kn/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಅವಲೋಕನ -![ಡೂಡಲ್‌ನಲ್ಲಿ ಅವಲೋಕನ](../../../translated_images/ai-overview.0857791951d19500.kn.png) +![ಡೂಡಲ್‌ನಲ್ಲಿ ಅವಲೋಕನ](../../../translated_images/kn/ai-overview.0857791951d19500.png) > ಸ್ಕೆಚ್‌ನೋಟ್ [ಟೊಮೊಮಿ ಇಮುರು](https://twitter.com/girlie_mac) ಅವರಿಂದ diff --git a/translations/kn/lessons/X-Extras/X1-MultiModal/README.md b/translations/kn/lessons/X-Extras/X1-MultiModal/README.md index 5321e83c..c632a4a5 100644 --- a/translations/kn/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/kn/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ಕಾರ್ಯಗಳನ್ನು ಪರಿಹರಿಸಲು ಟ್ರಾ CLIP ನ ಮುಖ್ಯ ಆಲೋಚನೆ ಎಂದರೆ ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗಳನ್ನು ಚಿತ್ರದೊಂದಿಗೆ ಹೋಲಿಸಿ, ಚಿತ್ರವು ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಎಷ್ಟು ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ ಎಂದು ನಿರ್ಧರಿಸುವುದು. -![CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.kn.png) +![CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/clip-arch.b3dbf20b4e8ed8be.png) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://openai.com/blog/clip/) ನಿಂದ* @@ -29,7 +29,7 @@ CLIP ಮಾದರಿ/ಲೈಬ್ರರಿ [OpenAI GitHub](https://github.com/op ನಾವು ಚಿತ್ರಗಳನ್ನು, ಉದಾಹರಣೆಗೆ, ಬೆಕ್ಕುಗಳು, ನಾಯಿ ಮತ್ತು ಮಾನವರ ನಡುವೆ ವರ್ಗೀಕರಿಸಬೇಕಾದರೆ, ಈ ಸಂದರ್ಭದಲ್ಲಿ ನಾವು ಮಾದರಿಗೆ ಒಂದು ಚಿತ್ರ ಮತ್ತು ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗಳ ಸರಣಿಯನ್ನು ನೀಡಬಹುದು: "*ಬೆಕ್ಕಿನ ಚಿತ್ರ*", "*ನಾಯಿಯ ಚಿತ್ರ*", "*ಮಾನವರ ಚಿತ್ರ*". 3 ಪ್ರಾಬಬಿಲಿಟಿಗಳ ಫಲಿತಾಂಶ ವೆಕ್ಟರ್‌ನಲ್ಲಿ ಅತ್ಯಧಿಕ ಮೌಲ್ಯದ ಸೂಚ್ಯಂಕವನ್ನು ಆಯ್ಕೆ ಮಾಡಬೇಕಾಗುತ್ತದೆ. -![ಚಿತ್ರ ವರ್ಗೀಕರಣಕ್ಕೆ CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.kn.png) +![ಚಿತ್ರ ವರ್ಗೀಕರಣಕ್ಕೆ CLIP](../../../../../translated_images/kn/clip-class.3af42ef0b2b19369.png) > *ಚಿತ್ರ [ಈ ಬ್ಲಾಗ್ ಪೋಸ್ಟ್](https://openai.com/blog/clip/) ನಿಂದ* @@ -53,13 +53,13 @@ VQGAN ಬಗ್ಗೆ ಹೆಚ್ಚಿನ ಮಾಹಿತಿಗಾಗಿ [Tami VQGAN ಮತ್ತು ಸಾಂಪ್ರದಾಯಿಕ GAN ನಡುವಿನ ಪ್ರಮುಖ ವ್ಯತ್ಯಾಸವೆಂದರೆ, GAN ಯಾವುದೇ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್‌ನಿಂದ ಸಮರ್ಪಕ ಚಿತ್ರವನ್ನು ಉತ್ಪಾದಿಸಬಹುದು, ಆದರೆ VQGAN ಅಸಂಬದ್ಧ ಚಿತ್ರವನ್ನು ಉತ್ಪಾದಿಸುವ ಸಾಧ್ಯತೆ ಇದೆ. ಆದ್ದರಿಂದ, ಚಿತ್ರ ರಚನೆ ಪ್ರಕ್ರಿಯೆಯನ್ನು ಇನ್ನಷ್ಟು ಮಾರ್ಗದರ್ಶನ ಮಾಡಬೇಕಾಗುತ್ತದೆ, ಮತ್ತು ಅದನ್ನು CLIP ಬಳಸಿ ಮಾಡಬಹುದು. -![VQGAN+CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/vqgan.5027fe05051dfa31.kn.png) +![VQGAN+CLIP ವಾಸ್ತುಶಿಲ್ಪ](../../../../../translated_images/kn/vqgan.5027fe05051dfa31.png) ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಹೊಂದುವ ಚಿತ್ರವನ್ನು ರಚಿಸಲು, ನಾವು ಕೆಲವು ಯಾದೃಚ್ಛಿಕ ಎನ್‌ಕೋಡಿಂಗ್ ವೆಕ್ಟರ್‌ನಿಂದ ಪ್ರಾರಂಭಿಸಿ ಅದನ್ನು VQGAN ಮೂಲಕ ಚಿತ್ರವಾಗಿ ಉತ್ಪಾದಿಸುತ್ತೇವೆ. ನಂತರ CLIP ಅನ್ನು ಬಳಸಿಕೊಂಡು ಚಿತ್ರವು ಪಠ್ಯ ಪ್ರಾಂಪ್ಟ್‌ಗೆ ಎಷ್ಟು ಹೊಂದಿಕೆಯಾಗುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಲಾಸ್ ಫಂಕ್ಷನ್ ರಚಿಸಲಾಗುತ್ತದೆ. ಗುರಿ ಈ ಲಾಸ್ ಅನ್ನು ಕನಿಷ್ಠಗೊಳಿಸುವುದು, ಬ್ಯಾಕ್ ಪ್ರೋಪಗೇಶನ್ ಬಳಸಿ ಇನ್‌ಪುಟ್ ವೆಕ್ಟರ್ ಪರಿಮಾಣಗಳನ್ನು ಸರಿಹೊಂದಿಸುವುದು. VQGAN+CLIP ಅನ್ನು ಅನುಷ್ಠಾನಗೊಳಿಸುವ ಅತ್ಯುತ್ತಮ ಲೈಬ್ರರಿ [Pixray](http://github.com/pixray/pixray) -![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.kn.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.kn.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.kn.png) +![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- ಪ್ರಾಂಪ್ಟ್ *ಪುಸ್ತಕದೊಂದಿಗೆ ಯುವ ಸಾಹಿತ್ಯ ಶಿಕ್ಷಕರ ನೀರಾವರಿ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಕಂಪ್ಯೂಟರ್ ವಿಜ್ಞಾನ ಯುವ ಶಿಕ್ಷಕಿ ಕಂಪ್ಯೂಟರ್ ಜೊತೆಗೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಹಳೆಯ ಗಣಿತ ಶಿಕ್ಷಕ ಬ್ಲ್ಯಾಕ್ಬೋರ್ಡ್ ಮುಂದೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ @@ -75,7 +75,7 @@ CLIP ಗಿಂತ ಭಿನ್ನವಾಗಿ, DALL-E ಪಠ್ಯ ಮತ್ತ DALL.E 1 ಮತ್ತು 2 ನಡುವಿನ ಮುಖ್ಯ ವ್ಯತ್ಯಾಸವೆಂದರೆ, DALL.E 2 ಹೆಚ್ಚು ವಾಸ್ತವಿಕ ಚಿತ್ರಗಳು ಮತ್ತು ಕಲೆಯನ್ನು ರಚಿಸುತ್ತದೆ. DALL-E ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರಗಳ ಉದಾಹರಣೆಗಳು: -![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.kn.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.kn.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.kn.png) +![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray ಮೂಲಕ ರಚಿಸಲಾದ ಚಿತ್ರ](../../../../../translated_images/kn/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- ಪ್ರಾಂಪ್ಟ್ *ಪುಸ್ತಕದೊಂದಿಗೆ ಯುವ ಸಾಹಿತ್ಯ ಶಿಕ್ಷಕರ ನೀರಾವರಿ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಕಂಪ್ಯೂಟರ್ ವಿಜ್ಞಾನ ಯುವ ಶಿಕ್ಷಕಿ ಕಂಪ್ಯೂಟರ್ ಜೊತೆಗೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ | ಪ್ರಾಂಪ್ಟ್ *ಹಳೆಯ ಗಣಿತ ಶಿಕ್ಷಕ ಬ್ಲ್ಯಾಕ್ಬೋರ್ಡ್ ಮುಂದೆ ತೈಲ ಚಿತ್ರ* ನಿಂದ ರಚಿಸಲಾದ ಚಿತ್ರ diff --git a/translations/ko/README.md b/translations/ko/README.md index 42c6b4d1..13ff1ef0 100644 --- a/translations/ko/README.md +++ b/translations/ko/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 초보자를 위한 인공 지능 - 커리큘럼 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ko.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ko/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _스케치노트 by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ko/lessons/1-Intro/README.md b/translations/ko/lessons/1-Intro/README.md index ba86a96f..fe5900b4 100644 --- a/translations/ko/lessons/1-Intro/README.md +++ b/translations/ko/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI 소개 -![AI 소개 내용 요약을 담은 스케치](../../../../translated_images/ai-intro.bf28d1ac4235881c.ko.png) +![AI 소개 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-intro.bf28d1ac4235881c.png) > 스케치노트: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 원래 컴퓨터는 [찰스 배비지](https://en.wikipedia.org/wiki/Charles_Babbage)에 의해 명확히 정의된 절차, 즉 알고리즘을 따라 숫자를 처리하기 위해 발명되었습니다. 현대의 컴퓨터는 19세기에 제안된 원래 모델보다 훨씬 더 발전했지만, 여전히 제어된 계산이라는 동일한 아이디어를 따릅니다. 따라서 목표를 달성하기 위해 필요한 정확한 단계의 순서를 알고 있다면 컴퓨터를 프로그래밍하여 작업을 수행할 수 있습니다. -![한 사람의 사진](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ko.png) +![한 사람의 사진](../../../../translated_images/ko/dsh_age.d212a30d4e54fb5f.png) > 사진 제공: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[지능](https://en.wikipedia.org/wiki/Intelligence)**이라는 용어를 다룰 때의 문제 중 하나는 이 용어에 대한 명확한 정의가 없다는 점입니다. 지능이 **추상적 사고** 또는 **자기 인식**과 연결되어 있다고 주장할 수 있지만, 이를 제대로 정의할 수는 없습니다. -![고양이 사진](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ko.jpg) +![고양이 사진](../../../../translated_images/ko/photo-cat.8c8e8fb760ffe457.jpg) > [사진](https://unsplash.com/photos/75715CVEJhI) 제공: [Amber Kipp](https://unsplash.com/@sadmax) (Unsplash) @@ -98,13 +98,13 @@ AGI에 대해 이야기할 때, 우리가 진정으로 지능적인 시스템을 > | 기계 학습은? | | > |--------------|-----------| -> | 일부 데이터를 기반으로 문제를 해결하도록 컴퓨터가 학습하는 인공지능의 한 부분을 **기계 학습**이라고 합니다. 이 강의에서는 고전적인 기계 학습을 다루지 않습니다. 별도의 [기계 학습 초보자용](http://aka.ms/ml-beginners) 커리큘럼을 참조하세요. | ![기계 학습 초보자용](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ko.png) | +> | 일부 데이터를 기반으로 문제를 해결하도록 컴퓨터가 학습하는 인공지능의 한 부분을 **기계 학습**이라고 합니다. 이 강의에서는 고전적인 기계 학습을 다루지 않습니다. 별도의 [기계 학습 초보자용](http://aka.ms/ml-beginners) 커리큘럼을 참조하세요. | ![기계 학습 초보자용](../../../../translated_images/ko/ml-for-beginners.9e4fed176fd5817d.png) | ## AI의 간략한 역사 인공지능은 20세기 중반에 하나의 분야로 시작되었습니다. 초기에는 기호적 추론이 우세한 접근법이었으며, 제한된 문제 영역에서 전문가처럼 행동할 수 있는 컴퓨터 프로그램인 전문가 시스템과 같은 중요한 성공을 거두었습니다. 그러나 곧 이러한 접근법이 잘 확장되지 않는다는 것이 명확해졌습니다. 전문가로부터 지식을 추출하고, 이를 컴퓨터에 표현하며, 지식 기반을 정확하게 유지하는 것은 매우 복잡하고 많은 경우 실용적이지 않을 정도로 비용이 많이 드는 작업임이 밝혀졌습니다. 이는 1970년대의 소위 [AI 겨울](https://en.wikipedia.org/wiki/AI_winter)로 이어졌습니다. -AI의 간략한 역사 +AI의 간략한 역사 > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ AGI에 대해 이야기할 때, 우리가 진정으로 지능적인 시스템을 * Cortana, Siri, Google Assistant와 같은 현대의 비서는 모두 음성을 텍스트로 변환하고 우리의 의도를 인식하기 위해 신경망을 사용하는 하이브리드 시스템이며, 이후 필요한 작업을 수행하기 위해 일부 추론이나 명시적 알고리즘을 사용합니다. * 미래에는 대화 자체를 처리할 수 있는 완전한 신경망 기반 모델을 기대할 수 있습니다. 최근의 GPT 및 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 계열의 신경망은 이 분야에서 큰 성공을 보여주고 있습니다. -튜링 테스트의 진화 +튜링 테스트의 진화 > 이미지 제공: Dmitry Soshnikov, [사진](https://unsplash.com/photos/r8LmVbUKgns) 제공: [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## 최근 AI 연구 diff --git a/translations/ko/lessons/2-Symbolic/README.md b/translations/ko/lessons/2-Symbolic/README.md index 14887180..9cad7b31 100644 --- a/translations/ko/lessons/2-Symbolic/README.md +++ b/translations/ko/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 지식 표현과 전문가 시스템 -![Symbolic AI 내용 요약](../../../../translated_images/ai-symbolic.715a30cb610411a6.ko.png) +![Symbolic AI 내용 요약](../../../../translated_images/ko/ai-symbolic.715a30cb610411a6.png) > 스케치노트 제공: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Symbolic AI에서 중요한 개념 중 하나는 **지식**입니다. 지식을 따라서 **지식 표현**의 문제는 컴퓨터 내부에서 데이터를 통해 지식을 효과적으로 표현하여 자동으로 사용할 수 있도록 하는 방법을 찾는 것입니다. 이는 다음과 같은 스펙트럼으로 볼 수 있습니다: -![지식 표현 스펙트럼](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ko.png) +![지식 표현 스펙트럼](../../../../translated_images/ko/knowledge-spectrum.b60df631852c0217.png) > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Is-A | Untyped-Language | | | Symbolic AI의 초기 성공 사례 중 하나는 **전문가 시스템**이라 불리는 컴퓨터 시스템이었습니다. 이는 제한된 문제 영역에서 전문가처럼 행동하도록 설계되었습니다. 이러한 시스템은 인간 전문가로부터 추출된 **지식 기반**과 이를 기반으로 추론을 수행하는 **추론 엔진**을 포함하고 있었습니다. -![인간 구조](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ko.png) | ![지식 기반 시스템](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ko.png) +![인간 구조](../../../../translated_images/ko/arch-human.5d4d35f1bba3ab1c.png) | ![지식 기반 시스템](../../../../translated_images/ko/arch-kbs.3ec5c150b09fa8da.png) ----------------------------------|-------------------------------------- 인간 신경 시스템의 단순화된 구조 | 지식 기반 시스템의 구조 @@ -106,7 +106,7 @@ Symbolic AI의 초기 성공 사례 중 하나는 **전문가 시스템**이라 예를 들어, 동물의 신체적 특성을 기반으로 동물을 결정하는 다음 전문가 시스템을 고려해 보겠습니다: -![AND-OR 트리](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ko.png) +![AND-OR 트리](../../../../translated_images/ko/AND-OR-Tree.5592d2c70187f283.png) > 이미지 제공: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md index 96f2f71f..3f189960 100644 --- a/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ko/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 다음 그래프에서 5개의 점(`x`로 표시된 점)을 근사하는 문제를 고려해봅시다: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ko.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ko.jpg) +![linear](../../../../../translated_images/ko/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ko/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **선형 모델, 2개의 매개변수** | **비선형 모델, 7개의 매개변수** 학습 오류 = 5.3 | 학습 오류 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 위 그래프에서 볼 수 있듯이, 과적합은 매우 낮은 학습 오류와 높은 검증 오류로 감지할 수 있습니다. 일반적으로 학습 중에는 학습 오류와 검증 오류가 모두 감소하다가, 어느 시점에서 검증 오류가 감소를 멈추고 증가하기 시작할 수 있습니다. 이는 과적합의 신호이며, 이 시점에서 학습을 멈추거나 모델의 스냅샷을 저장해야 한다는 표시입니다. -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ko.png) +![overfitting](../../../../../translated_images/ko/Overfitting.408ad91cd90b4371.png) ## 과적합을 방지하는 방법 diff --git a/translations/ko/lessons/3-NeuralNetworks/README.md b/translations/ko/lessons/3-NeuralNetworks/README.md index 4cd2e78d..a8de557e 100644 --- a/translations/ko/lessons/3-NeuralNetworks/README.md +++ b/translations/ko/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 신경망 소개 -![신경망 소개 내용 요약을 담은 그림](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ko.png) +![신경망 소개 내용 요약을 담은 그림](../../../../translated_images/ko/ai-neuralnetworks.1c687ae40bc86e83.png) 소개에서 논의했듯이, 지능을 구현하는 방법 중 하나는 **컴퓨터 모델** 또는 **인공 두뇌**를 훈련시키는 것입니다. 20세기 중반부터 연구자들은 다양한 수학적 모델을 시도했으며, 최근 몇 년간 이 방향이 큰 성공을 거두었습니다. 이러한 두뇌의 수학적 모델을 **신경망**이라고 합니다. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 생물학적으로 우리의 뇌는 신경 세포(뉴런)로 구성되어 있으며, 각 뉴런은 여러 개의 "입력"(수상돌기)과 하나의 "출력"(축삭)을 가지고 있습니다. 수상돌기와 축삭은 모두 전기 신호를 전달할 수 있으며, 이들 간의 연결(시냅스)은 신경전달물질에 의해 전도도가 조절될 수 있습니다. -![뉴런 모델](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ko.jpg) | ![뉴런 모델](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ko.png) +![뉴런 모델](../../../../translated_images/ko/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![뉴런 모델](../../../../translated_images/ko/artneuron.1a5daa88d20ebe6f.png) ----|---- 실제 뉴런 *([이미지](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 출처: 위키피디아)* | 인공 뉴런 *(이미지 제공: 저자)* 따라서 뉴런의 가장 간단한 수학적 모델은 여러 입력 X1, ..., XN과 출력 Y, 그리고 일련의 가중치 W1, ..., WN을 포함합니다. 출력은 다음과 같이 계산됩니다: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 여기서 f는 비선형 **활성화 함수**입니다. diff --git a/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md index 4f063cb1..2d839ef3 100644 --- a/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ko/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV를 사용하여 비디오를 프레임별로 로드할 수도 있습니 * **점자 책 사진 전처리**. 임계값 처리, 특징 탐지, 투시 변환 및 NumPy 조작을 사용하여 개별 점자 기호를 분리하고, 이를 신경망으로 추가 분류하는 방법에 초점을 맞춥니다. -![점자 이미지](../../../../../translated_images/braille.341962ff76b1bd70.ko.jpeg) | ![전처리된 점자 이미지](../../../../../translated_images/braille-result.46530fea020b03c7.ko.png) | ![점자 기호](../../../../../translated_images/braille-symbols.0159185ab69d5339.ko.png) +![점자 이미지](../../../../../translated_images/ko/braille.341962ff76b1bd70.jpeg) | ![전처리된 점자 이미지](../../../../../translated_images/ko/braille-result.46530fea020b03c7.png) | ![점자 기호](../../../../../translated_images/ko/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) * **프레임 차이를 사용한 비디오에서의 움직임 탐지**. 카메라가 고정되어 있다면, 카메라 피드의 프레임은 서로 매우 유사해야 합니다. 프레임이 배열로 표현되므로, 두 연속 프레임의 배열을 빼면 픽셀 차이를 얻을 수 있습니다. 정적인 프레임에서는 차이가 작고, 이미지에 상당한 움직임이 있을 때 차이가 커집니다. -![비디오 프레임 및 프레임 차이 이미지](../../../../../translated_images/frame-difference.706f805491a0883c.ko.png) +![비디오 프레임 및 프레임 차이 이미지](../../../../../translated_images/ko/frame-difference.706f805491a0883c.png) > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ OpenCV를 사용하여 비디오를 프레임별로 로드할 수도 있습니 - **밀집 광학 흐름**: 각 픽셀이 어디로 이동하는지 보여주는 벡터 필드를 계산합니다. - **희소 광학 흐름**: 이미지에서 일부 특징적인 요소(예: 가장자리)를 선택하고, 프레임 간의 궤적을 생성합니다. -![광학 흐름 이미지](../../../../../translated_images/optical.1f4a94464579a83a.ko.png) +![광학 흐름 이미지](../../../../../translated_images/ko/optical.1f4a94464579a83a.png) > 이미지 출처: [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index e4f66216..85a97d1f 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16은 2014년 ImageNet의 top-5 분류에서 92.7%의 정확도를 달성한 네트워크입니다. 이 네트워크는 다음과 같은 계층 구조를 가지고 있습니다: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ko.jpg) +![ImageNet Layers](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.jpg) 보시다시피, VGG는 전통적인 피라미드 아키텍처를 따르며, 이는 컨볼루션-풀링 계층의 연속입니다. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ko.jpg) +![ImageNet Pyramid](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.jpg) > 이미지 출처: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md index f117e379..ff1d81be 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 패턴을 추출하기 위해 **컨볼루션 필터**라는 개념을 사용할 것입니다. 이미지는 2D-매트릭스 또는 색상 깊이를 가진 3D-텐서로 표현됩니다. 필터를 적용한다는 것은 비교적 작은 **필터 커널** 매트릭스를 가져와 원본 이미지의 각 픽셀에 대해 이웃한 점들과 가중 평균을 계산하는 것을 의미합니다. 이를 작은 창이 전체 이미지를 슬라이딩하며 필터 커널 매트릭스의 가중치에 따라 모든 픽셀을 평균화하는 것으로 볼 수 있습니다. -![수직 엣지 필터](../../../../../translated_images/filter-vert.b7148390ca0bc356.ko.png) | ![수평 엣지 필터](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ko.png) +![수직 엣지 필터](../../../../../translated_images/ko/filter-vert.b7148390ca0bc356.png) | ![수평 엣지 필터](../../../../../translated_images/ko/filter-horiz.59b80ed4feb946ef.png) ----|---- > 이미지 제공: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN이 작동하는 방식은 다음과 같은 중요한 아이디어를 기반 * 필터가 자동으로 학습되도록 네트워크를 설계할 수 있다. * 원본 이미지뿐만 아니라 고수준 특징에서도 패턴을 찾는 데 동일한 접근 방식을 사용할 수 있다. 따라서 CNN 특징 추출은 저수준 픽셀 조합에서 시작하여 이미지 부분의 고수준 조합까지 특징의 계층 구조에서 작동한다. -![계층적 특징 추출](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ko.png) +![계층적 특징 추출](../../../../../translated_images/ko/FeatureExtractionCNN.d9b456cbdae7cb64.png) > 이미지 출처: [Hislop-Lynch의 논문](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), [그들의 연구](https://dl.acm.org/doi/abs/10.1145/1553374.1553453)를 기반으로 함 @@ -55,9 +55,9 @@ CNN이 작동하는 방식은 다음과 같은 중요한 아이디어를 기반 예를 들어, 2014년 ImageNet의 상위 5개 분류에서 92.7%의 정확도를 달성한 VGG-16 네트워크의 아키텍처를 살펴봅시다: -![ImageNet 계층](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ko.jpg) +![ImageNet 계층](../../../../../translated_images/ko/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet 피라미드](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ko.jpg) +![ImageNet 피라미드](../../../../../translated_images/ko/vgg-16-arch.64ff2137f50dd49f.jpg) > 이미지 출처: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md index d78be7b3..1c4e1925 100644 --- a/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ko/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 우리는 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)을 사용할 것입니다. 이 데이터셋은 37가지 다른 품종의 개와 고양이 이미지를 포함하고 있습니다. -![우리가 다룰 데이터셋](../../../../../../translated_images/data.50b2a9d5484bdbf0.ko.png) +![우리가 다룰 데이터셋](../../../../../../translated_images/ko/data.50b2a9d5484bdbf0.png) 데이터셋을 다운로드하려면 아래 코드 스니펫을 사용하세요: diff --git a/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md index fac27bfd..d7e1626b 100644 --- a/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ko/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras와 PyTorch는 대부분 ImageNet 이미지로 훈련된 일반적인 아 다음은 VGG-16 네트워크가 고양이 사진에서 추출한 특징의 예입니다: -![VGG-16이 추출한 특징](../../../../../translated_images/features.6291f9c7ba3a0b95.ko.png) +![VGG-16이 추출한 특징](../../../../../translated_images/ko/features.6291f9c7ba3a0b95.png) ## 고양이 vs. 개 데이터셋 @@ -48,19 +48,19 @@ Keras와 PyTorch는 대부분 ImageNet 이미지로 훈련된 일반적인 아 한 가지 접근법은 랜덤 이미지를 시작점으로 사용하여 **경사 하강 최적화** 기법을 통해 이미지를 조정하는 것입니다. 이를 통해 네트워크가 해당 이미지를 고양이라고 생각하도록 만들 수 있습니다. -![이미지 최적화 루프](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ko.png) +![이미지 최적화 루프](../../../../../translated_images/ko/ideal-cat-loop.999fbb8ff306e044.png) 하지만 이렇게 하면 랜덤 노이즈와 매우 유사한 결과를 얻을 수 있습니다. 이는 *네트워크가 입력 이미지를 고양이라고 생각하도록 만드는 방법이 많기 때문*이며, 그중 일부는 시각적으로 의미가 없을 수 있습니다. 이러한 이미지는 고양이에 일반적인 많은 패턴을 포함하고 있지만, 시각적으로 뚜렷하게 보이도록 제한하는 요소가 없습니다. 결과를 개선하기 위해 손실 함수에 **변동 손실**이라는 항목을 추가할 수 있습니다. 이는 이미지의 인접 픽셀이 얼마나 유사한지를 보여주는 메트릭입니다. 변동 손실을 최소화하면 이미지가 더 부드러워지고 노이즈가 제거되어 시각적으로 더 매력적인 패턴이 드러납니다. 아래는 고양이와 얼룩말로 높은 확률로 분류된 "이상적인" 이미지의 예입니다: -![이상적인 고양이](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ko.png) | ![이상적인 얼룩말](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ko.png) +![이상적인 고양이](../../../../../translated_images/ko/ideal-cat.203dd4597643d6b0.png) | ![이상적인 얼룩말](../../../../../translated_images/ko/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *이상적인 고양이* | *이상적인 얼룩말* 유사한 접근법은 **적대적 공격**을 수행하는 데도 사용할 수 있습니다. 예를 들어, 신경망을 속여 개를 고양이처럼 보이게 만들고 싶다고 가정해봅시다. 네트워크가 개로 인식하는 개 이미지를 가져와 경사 하강 최적화를 사용해 약간 조정하면 네트워크가 이를 고양이로 분류하기 시작할 때까지 조정할 수 있습니다: -![개 사진](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ko.png) | ![고양이로 분류된 개 사진](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ko.png) +![개 사진](../../../../../translated_images/ko/original-dog.8f68a67d2fe0911f.png) | ![고양이로 분류된 개 사진](../../../../../translated_images/ko/adversarial-dog.d9fc7773b0142b89.png) -----|----- *원래 개 사진* | *고양이로 분류된 개 사진* diff --git a/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md index 54dad918..5f619503 100644 --- a/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ko/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN을 훈련할 때, 문제 중 하나는 많은 라벨링된 데이터가 필 오토인코더를 훈련하여 원본 이미지의 정보를 최대한 많이 캡처하여 정확히 재구성하려고 할 때, 네트워크는 입력 이미지를 가장 잘 표현할 수 있는 **임베딩**을 찾으려고 합니다. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ko.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ko/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 이미지 출처: [Keras 블로그](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md index 11277880..6e4f54bb 100644 --- a/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ko/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [강의 전 퀴즈](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![객체 탐지](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ko.png) +![객체 탐지](../../../../../translated_images/ko/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 이미지 출처: [YOLO v2 웹사이트](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 각 타일에 대해 이미지 분류를 실행합니다. 3. 충분히 높은 활성화를 보이는 타일은 해당 객체를 포함하고 있다고 간주합니다. -![단순 객체 탐지](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ko.png) +![단순 객체 탐지](../../../../../translated_images/ko/naive-detection.e7f1ba220ccd08c6.png) > *이미지 출처: [실습 노트북](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20개의 클래스 * [COCO](http://cocodataset.org/#home) - 컨텍스트 내 일반 객체. 80개의 클래스, 경계 상자 및 세분화 마스크 제공 -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ko.jpg) +![COCO](../../../../../translated_images/ko/coco-examples.71bc60380fa6cceb.jpg) ## 객체 탐지 평가 지표 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 이미지 분류에서는 알고리즘의 성능을 측정하기 쉽지만, 객체 탐지에서는 클래스의 정확성과 예측된 경계 상자 위치의 정밀도를 모두 측정해야 합니다. 후자를 위해 **교집합 비율**(IoU)을 사용합니다. 이는 두 상자(또는 임의의 두 영역)가 얼마나 잘 겹치는지를 측정합니다. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ko.png) +![IoU](../../../../../translated_images/ko/iou_equation.9a4751d40fff4e11.png) > *출처: [이 훌륭한 IoU 블로그 글](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)은 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)를 사용하여 ROI 영역의 계층 구조를 생성합니다. 그런 다음 이를 CNN 특징 추출기와 SVM 분류기를 통해 객체 클래스를 결정하고, 선형 회귀를 통해 *경계 상자* 좌표를 결정합니다. [공식 논문](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ko.png) +![RCNN](../../../../../translated_images/ko/rcnn1.cae407020dfb1d1f.png) > *이미지 출처: van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ko.png) +![RCNN-1](../../../../../translated_images/ko/rcnn2.2d9530bb83516484.png) > *이미지 출처: [이 블로그](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 이 접근법은 R-CNN과 유사하지만, 영역이 합성곱 계층이 적용된 후에 정의됩니다. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ko.png) +![FRCNN](../../../../../translated_images/ko/f-rcnn.3cda6d9bb4188875.png) > 이미지 출처: [공식 논문](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 이 접근법의 주요 아이디어는 ROI를 예측하기 위해 신경망을 사용하는 것입니다. 이를 *영역 제안 네트워크*라고 합니다. [논문](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ko.png) +![FasterRCNN](../../../../../translated_images/ko/faster-rcnn.8d46c099b87ef30a.png) > 이미지 출처: [공식 논문](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoU가 특정 값 이상인 탐지만 고려합니다. 예를 들어, PASCAL VOC 2. 특징은 **위치 민감 점수 맵**으로 처리됩니다. $C$ 클래스의 각 객체는 $k\times k$ 영역으로 나뉘며, 객체의 부분을 예측하도록 학습합니다. 3. $k\times k$ 영역의 각 부분에 대해 모든 네트워크가 객체 클래스를 투표하며, 최대 투표를 받은 객체 클래스가 선택됩니다. -![r-fcn 이미지](../../../../../translated_images/r-fcn.13eb88158b99a3da.ko.png) +![r-fcn 이미지](../../../../../translated_images/ko/r-fcn.13eb88158b99a3da.png) > 이미지 출처: [공식 논문](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO는 실시간 한 번 처리 알고리즘입니다. 주요 아이디어는 * 이미지를 $S\times S$ 영역으로 나눕니다. * 각 영역에 대해 **CNN**이 $n$개의 가능한 객체, *경계 상자* 좌표 및 *신뢰도*=*확률* * IoU를 예측합니다. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ko.png) + ![YOLO](../../../../../translated_images/ko/yolo.a2648ec82ee8bb4e.png) > 이미지 출처: [공식 논문](https://arxiv.org/abs/1506.02640) diff --git a/translations/ko/lessons/4-ComputerVision/README.md b/translations/ko/lessons/4-ComputerVision/README.md index 3901140e..4d40dc98 100644 --- a/translations/ko/lessons/4-ComputerVision/README.md +++ b/translations/ko/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 컴퓨터 비전 -![컴퓨터 비전 내용 요약을 담은 스케치](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ko.png) +![컴퓨터 비전 내용 요약을 담은 스케치](../../../../translated_images/ko/ai-computervision.6506ebebac3fbf76.png) 이 섹션에서는 다음 내용을 학습합니다: diff --git a/translations/ko/lessons/5-NLP/14-Embeddings/README.md b/translations/ko/lessons/5-NLP/14-Embeddings/README.md index 45d7cec4..3c34d21c 100644 --- a/translations/ko/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ko/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW 또는 TF/IDF 기반의 분류기를 훈련할 때, 우리는 `vocab_size` 분류기 네트워크의 첫 번째 레이어로 임베딩 레이어를 사용하면 bag-of-words 모델에서 **embedding bag** 모델로 전환할 수 있습니다. 여기서 텍스트의 각 단어를 해당 임베딩으로 변환한 후, 이러한 모든 임베딩에 대해 `sum`, `average`, `max`와 같은 집계 함수를 계산합니다. -![다섯 개의 시퀀스 단어에 대한 임베딩 분류기를 보여주는 이미지.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ko.png) +![다섯 개의 시퀀스 단어에 대한 임베딩 분류기를 보여주는 이미지.](../../../../../translated_images/ko/embedding-classifier-example.b77f021a7ee67eee.png) > 작성자 제공 이미지 @@ -40,7 +40,7 @@ BoW 또는 TF/IDF 기반의 분류기를 훈련할 때, 우리는 `vocab_size` CBoW는 더 빠르지만, skip-gram은 더 느리며 드문 단어를 더 잘 표현합니다. -![단어를 벡터로 변환하는 CBoW와 Skip-Gram 알고리즘을 보여주는 이미지.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ko.png) +![단어를 벡터로 변환하는 CBoW와 Skip-Gram 알고리즘을 보여주는 이미지.](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [이 논문](https://arxiv.org/pdf/1301.3781.pdf)에서 제공된 이미지 diff --git a/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md index b7e17950..08fee905 100644 --- a/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ko/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec와 GloVe 같은 의미 임베딩은 사실 **언어 모델링**의 첫 * **연속적인 단어 묶음** (CBoW): 토큰 시퀀스 $W_{-N}$, ..., $W_N$에서 가운데 토큰 $W_0$을 예측하는 방식 * **스킵그램**: 가운데 토큰 $W_0$에서 주변 토큰 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}을 예측하는 방식 -![단어를 벡터로 변환하는 알고리즘에 대한 논문 이미지](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ko.png) +![단어를 벡터로 변환하는 알고리즘에 대한 논문 이미지](../../../../../translated_images/ko/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 이미지 출처: [이 논문](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ko/lessons/5-NLP/16-RNN/README.md b/translations/ko/lessons/5-NLP/16-RNN/README.md index 95523660..f1ea8e6f 100644 --- a/translations/ko/lessons/5-NLP/16-RNN/README.md +++ b/translations/ko/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 텍스트 시퀀스의 의미를 포착하려면 **순환 신경망**(Recurrent Neural Network, RNN)이라는 다른 신경망 아키텍처를 사용해야 합니다. RNN에서는 문장을 네트워크에 한 번에 하나의 기호씩 전달하고, 네트워크는 **상태**를 생성하며, 이를 다음 기호와 함께 네트워크에 다시 전달합니다. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ko.png) +![RNN](../../../../../translated_images/ko/rnn.27f5c29c53d727b5.png) > 저자 제공 이미지 @@ -61,7 +61,7 @@ LSTM 네트워크는 RNN과 유사하게 구성되지만, 레이어 간에 전 순환 네트워크는 단일 방향이든 양방향이든 시퀀스 내 특정 패턴을 캡처하고 이를 상태 벡터에 저장하거나 출력으로 전달할 수 있습니다. 합성곱 네트워크와 마찬가지로 첫 번째 레이어에서 추출한 저수준 패턴을 기반으로 더 높은 수준의 패턴을 캡처하기 위해 첫 번째 레이어 위에 또 다른 순환 레이어를 구축할 수 있습니다. 이는 **다층 RNN**의 개념으로 이어지며, 두 개 이상의 순환 네트워크로 구성되며 이전 레이어의 출력이 다음 레이어의 입력으로 전달됩니다. -![다층 장단기 메모리 RNN을 보여주는 이미지](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ko.jpg) +![다층 장단기 메모리 RNN을 보여주는 이미지](../../../../../translated_images/ko/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Fernando López의 [이 훌륭한 글](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)에서 가져온 그림* diff --git a/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md index b7e37e00..8e081f4f 100644 --- a/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ko/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 이를 통해 아래 그림에 표시된 다양한 신경망 아키텍처를 구현할 수 있습니다: -![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ko.jpg) +![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 이미지 출처: [Andrej Karpaty](http://karpathy.github.io/)의 블로그 게시물 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 이 RNN을 훈련시켜 단계별로 텍스트를 생성할 것입니다. 각 단계에서 `nchars` 길이의 문자 시퀀스를 받아 각 입력 문자에 대해 다음 출력 문자를 생성하도록 네트워크에 요청합니다: -![단어 'HELLO'를 생성하는 RNN 예제를 보여주는 이미지.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ko.png) +![단어 'HELLO'를 생성하는 RNN 예제를 보여주는 이미지.](../../../../../translated_images/ko/rnn-generate.56c54afb52f9781d.png) 텍스트를 생성할 때(추론 중), **프롬프트**를 시작점으로 사용하여 RNN 셀을 통해 중간 상태를 생성한 후 이 상태에서 생성을 시작합니다. 한 번에 한 문자씩 생성하며, 상태와 생성된 문자를 다음 RNN 셀에 전달하여 다음 문자를 생성합니다. 이 과정을 필요한 문자 수만큼 반복합니다. diff --git a/translations/ko/lessons/5-NLP/18-Transformers/README.md b/translations/ko/lessons/5-NLP/18-Transformers/README.md index 7165db62..19ab881a 100644 --- a/translations/ko/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ko/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 **어텐션 메커니즘**은 RNN의 각 출력 예측에 대해 각 입력 벡터의 맥락적 영향을 가중치로 부여하는 방법을 제공합니다. 이는 입력 RNN의 중간 상태와 출력 RNN 사이에 지름길을 만드는 방식으로 구현됩니다. 이렇게 하면 출력 심볼 yt를 생성할 때, 서로 다른 가중치 계수 αt,i를 사용하여 모든 입력 은닉 상태 hi를 고려하게 됩니다. -![어텐션 레이어가 포함된 인코더/디코더 모델](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ko.png) +![어텐션 레이어가 포함된 인코더/디코더 모델](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)의 어텐션 메커니즘이 포함된 인코더-디코더 모델. [이 블로그 글](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)에서 인용. 어텐션 행렬 {αi,j}는 출력 시퀀스의 특정 단어를 생성하는 데 특정 입력 단어가 얼마나 중요한지를 나타냅니다. 아래는 이러한 행렬의 예입니다: -![Bahdanau - arviz.org에서 가져온 RNNsearch-50의 샘플 정렬](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ko.png) +![Bahdanau - arviz.org에서 가져온 RNNsearch-50의 샘플 정렬](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서 발췌 (그림 3) @@ -66,7 +66,7 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 다음으로, 시퀀스 내에서 패턴을 캡처해야 합니다. 이를 위해 트랜스포머는 **셀프 어텐션** 메커니즘을 사용합니다. 이는 입력과 출력이 동일한 시퀀스에 어텐션을 적용하는 것입니다. 셀프 어텐션을 적용하면 문장 내 **맥락**을 고려하고, 어떤 단어들이 서로 관련이 있는지 확인할 수 있습니다. 예를 들어, *it*과 같은 대명사가 참조하는 단어를 확인하고, 맥락을 반영할 수 있습니다: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ko.png) +![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.png) > [Google 블로그](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)에서 발췌. @@ -91,7 +91,7 @@ RNN을 사용하면 시퀀스-투-시퀀스 작업은 두 개의 순환 신경 **BERT**(Bidirectional Encoder Representations from Transformers)는 매우 큰 다층 트랜스포머 네트워크로, *BERT-base*는 12개 층, *BERT-large*는 24개 층으로 구성됩니다. 이 모델은 대규모 텍스트 데이터(Wikipedia + 책) 코퍼스에서 비지도 학습(문장에서 마스킹된 단어 예측)을 통해 사전 학습됩니다. 사전 학습 동안 모델은 상당한 수준의 언어 이해를 흡수하며, 이후 다른 데이터셋에서 미세 조정을 통해 이를 활용할 수 있습니다. 이 과정을 **전이 학습**이라고 합니다. -![http://jalammar.github.io/illustrated-bert/에서 가져온 그림](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ko.png) +![http://jalammar.github.io/illustrated-bert/에서 가져온 그림](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 이미지 [출처](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md index fc7b42e2..2d989f20 100644 --- a/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ko/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 **주의 메커니즘**은 RNN의 각 출력 예측에 대한 각 입력 벡터의 맥락적 영향을 가중치로 조정하는 수단을 제공합니다. 이를 구현하는 방법은 입력 RNN의 중간 상태와 출력 RNN 사이에 단축 경로를 생성하는 것입니다. 이렇게 하면 출력 기호 yt를 생성할 때 모든 입력 숨겨진 상태 hi를 서로 다른 가중치 계수 αt,i와 함께 고려합니다. -![인코더/디코더 모델과 추가적인 주의 레이어를 보여주는 이미지](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ko.png) +![인코더/디코더 모델과 추가적인 주의 레이어를 보여주는 이미지](../../../../../translated_images/ko/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서 인용된 추가적인 주의 메커니즘을 가진 인코더-디코더 모델, [이 블로그 게시물](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)에서 인용됨 주의 행렬 {αi,j}는 특정 입력 단어가 출력 시퀀스의 주어진 단어 생성에 기여하는 정도를 나타냅니다. 아래는 이러한 행렬의 예입니다: -![RNNsearch-50에 의해 발견된 샘플 정렬을 보여주는 이미지, Bahdanau - arviz.org에서 가져옴](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ko.png) +![RNNsearch-50에 의해 발견된 샘플 정렬을 보여주는 이미지, Bahdanau - arviz.org에서 가져옴](../../../../../translated_images/ko/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)에서의 그림 (Fig.3) @@ -57,7 +57,7 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 다음으로, 우리는 시퀀스 내에서 몇 가지 패턴을 포착해야 합니다. 이를 위해 트랜스포머는 **자기 주의** 메커니즘을 사용하며, 이는 기본적으로 입력과 출력으로 동일한 시퀀스에 적용되는 주의입니다. 자기 주의를 적용하면 문장 내의 **맥락**을 고려하고 어떤 단어가 서로 관련되어 있는지를 확인할 수 있습니다. 예를 들어, 이는 *it*와 같은 대명사가 지칭하는 단어를 확인하고 맥락을 고려할 수 있게 해줍니다: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ko.png) +![](../../../../../translated_images/ko/CoreferenceResolution.861924d6d384a7d6.png) > [Google 블로그](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html)에서의 이미지 @@ -82,7 +82,7 @@ RNN을 사용하면 시퀀스-투-시퀀스는 두 개의 순환 네트워크로 **BERT** (Bidirectional Encoder Representations from Transformers)는 *BERT-base*의 경우 12층, *BERT-large*의 경우 24층으로 구성된 매우 큰 다층 트랜스포머 네트워크입니다. 이 모델은 먼저 대규모 텍스트 데이터(위키피디아 + 책)에서 비지도 학습(문장에서 마스킹된 단어 예측)을 사용하여 사전 훈련됩니다. 사전 훈련 동안 모델은 상당한 수준의 언어 이해를 흡수하며, 이는 이후 다른 데이터 세트와 함께 미세 조정하여 활용될 수 있습니다. 이 과정을 **전이 학습**이라고 합니다. -![http://jalammar.github.io/illustrated-bert/에서 가져온 이미지](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ko.png) +![http://jalammar.github.io/illustrated-bert/에서 가져온 이미지](../../../../../translated_images/ko/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 이미지 [출처](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ko/lessons/5-NLP/19-NER/README.md b/translations/ko/lessons/5-NLP/19-NER/README.md index 20ffdf4d..c5f1d67d 100644 --- a/translations/ko/lessons/5-NLP/19-NER/README.md +++ b/translations/ko/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O 토큰과 클래스 간의 일대일 대응을 구축해야 하므로, 아래 그림에서 보이는 것처럼 **다대다** 신경망 모델을 훈련할 수 있습니다: -![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ko.jpg) +![일반적인 순환 신경망 패턴을 보여주는 이미지.](../../../../../translated_images/ko/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *이미지 출처: [Andrej Karpathy](http://karpathy.github.io/)의 [블로그 글](http://karpathy.github.io/2015/05/21/rnn-effectiveness/). NER 토큰 분류 모델은 이 그림의 가장 오른쪽 네트워크 아키텍처에 해당합니다.* diff --git a/translations/ko/lessons/5-NLP/README.md b/translations/ko/lessons/5-NLP/README.md index 20805e40..676ec4af 100644 --- a/translations/ko/lessons/5-NLP/README.md +++ b/translations/ko/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 자연어 처리 -![NLP 작업 요약 스케치](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ko.png) +![NLP 작업 요약 스케치](../../../../translated_images/ko/ai-nlp.b22dcb8ca4707cea.png) 이 섹션에서는 **자연어 처리(NLP)**와 관련된 작업을 처리하기 위해 신경망을 사용하는 방법에 대해 집중적으로 다룹니다. 컴퓨터가 해결할 수 있기를 바라는 많은 NLP 문제들이 있습니다: diff --git a/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md index 6efb063a..d1f3d12b 100644 --- a/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ko/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo의 훌륭한 점은 작동하는 모델 라이브러리를 포함하고 모델을 열면 NetLogo의 주요 화면으로 이동합니다. 여기에는 유한한 자원(풀)을 고려한 늑대와 양의 개체군을 설명하는 샘플 모델이 있습니다. -![NetLogo 주요 화면](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ko.png) +![NetLogo 주요 화면](../../../../../translated_images/ko/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov의 스크린샷 diff --git a/translations/ko/lessons/README.md b/translations/ko/lessons/README.md index dc8e427a..1532861b 100644 --- a/translations/ko/lessons/README.md +++ b/translations/ko/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 개요 -![낙서로 그린 개요](../../../translated_images/ai-overview.0857791951d19500.ko.png) +![낙서로 그린 개요](../../../translated_images/ko/ai-overview.0857791951d19500.png) > 스케치노트: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ko/lessons/X-Extras/X1-MultiModal/README.md b/translations/ko/lessons/X-Extras/X1-MultiModal/README.md index 720e8e7f..867ae3fb 100644 --- a/translations/ko/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ko/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer 모델이 NLP 작업을 해결하는 데 성공한 이후, 동일하 CLIP의 주요 아이디어는 텍스트 프롬프트와 이미지를 비교하여 이미지가 프롬프트와 얼마나 잘 일치하는지 판단하는 것입니다. -![CLIP 아키텍처](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ko.png) +![CLIP 아키텍처](../../../../../translated_images/ko/clip-arch.b3dbf20b4e8ed8be.png) > *[이 블로그 글](https://openai.com/blog/clip/)에서 가져온 이미지* @@ -31,7 +31,7 @@ CLIP 모델/라이브러리는 [OpenAI GitHub](https://github.com/openai/CLIP) 예를 들어, 고양이, 개, 인간을 분류해야 한다고 가정해 봅시다. 이 경우 모델에 이미지를 제공하고, "*고양이 사진*", "*개 사진*", "*인간 사진*"과 같은 일련의 텍스트 프롬프트를 제공합니다. 결과 벡터에서 가장 높은 값을 가진 인덱스를 선택하면 됩니다. -![CLIP을 이용한 이미지 분류](../../../../../translated_images/clip-class.3af42ef0b2b19369.ko.png) +![CLIP을 이용한 이미지 분류](../../../../../translated_images/ko/clip-class.3af42ef0b2b19369.png) > *[이 블로그 글](https://openai.com/blog/clip/)에서 가져온 이미지* @@ -55,13 +55,13 @@ VQGAN에 대해 더 알아보려면 [Taming Transformers](https://compvis.github VQGAN과 전통적인 GAN의 중요한 차이점 중 하나는 후자가 어떤 입력 벡터로도 괜찮은 이미지를 생성할 수 있는 반면, VQGAN은 일관성이 없는 이미지를 생성할 가능성이 높다는 점입니다. 따라서 이미지 생성 과정을 추가로 안내해야 하며, 이를 CLIP을 사용하여 수행할 수 있습니다. -![VQGAN+CLIP 아키텍처](../../../../../translated_images/vqgan.5027fe05051dfa31.ko.png) +![VQGAN+CLIP 아키텍처](../../../../../translated_images/ko/vqgan.5027fe05051dfa31.png) 텍스트 프롬프트에 해당하는 이미지를 생성하려면, 먼저 VQGAN을 통해 이미지를 생성하는 임의의 인코딩 벡터로 시작합니다. 그런 다음 CLIP을 사용하여 이미지가 텍스트 프롬프트와 얼마나 잘 일치하는지 보여주는 손실 함수를 생성합니다. 이후 목표는 이 손실을 최소화하는 것이며, 역전파를 사용하여 입력 벡터 매개변수를 조정합니다. VQGAN+CLIP을 구현한 훌륭한 라이브러리는 [Pixray](http://github.com/pixray/pixray)입니다. -![Pixray로 생성된 이미지](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ko.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ko.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ko.png) +![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- 프롬프트 *문학을 가르치는 젊은 남성 교사의 근접 수채화 초상화, 책을 들고 있음*으로 생성된 이미지 | 프롬프트 *컴퓨터 과학을 가르치는 젊은 여성 교사의 근접 유화 초상화, 컴퓨터와 함께 있음*으로 생성된 이미지 | 프롬프트 *수학을 가르치는 나이 든 남성 교사의 근접 유화 초상화, 칠판 앞에 있음*으로 생성된 이미지 @@ -77,7 +77,7 @@ CLIP과 달리, DALL-E는 텍스트와 이미지를 단일 토큰 스트림으 DALL-E 1과 2의 주요 차이점은 DALL-E 2가 더 현실적인 이미지와 예술 작품을 생성한다는 점입니다. DALL-E를 사용한 이미지 생성 예: -![Pixray로 생성된 이미지](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ko.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ko.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ko.png) +![Pixray로 생성된 이미지](../../../../../translated_images/ko/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray로 생성된 이미지](../../../../../translated_images/ko/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- 프롬프트 *문학을 가르치는 젊은 남성 교사의 근접 수채화 초상화, 책을 들고 있음*으로 생성된 이미지 | 프롬프트 *컴퓨터 과학을 가르치는 젊은 여성 교사의 근접 유화 초상화, 컴퓨터와 함께 있음*으로 생성된 이미지 | 프롬프트 *수학을 가르치는 나이 든 남성 교사의 근접 유화 초상화, 칠판 앞에 있음*으로 생성된 이미지 diff --git a/translations/lt/README.md b/translations/lt/README.md index e65bca77..b3f48860 100644 --- a/translations/lt/README.md +++ b/translations/lt/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Dirbtinis intelektas pradedantiesiems - mokymo programa -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.lt.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/lt/ai-overview.0857791951d19500.png)| |:---:| | Dirbtinis intelektas pradedantiesiems - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/lt/lessons/1-Intro/README.md b/translations/lt/lessons/1-Intro/README.md index f2a52f90..fbde8d4b 100644 --- a/translations/lt/lessons/1-Intro/README.md +++ b/translations/lt/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Įvadas į dirbtinį intelektą -![Santrauka apie dirbtinio intelekto įvadą piešinyje](../../../../translated_images/ai-intro.bf28d1ac4235881c.lt.png) +![Santrauka apie dirbtinio intelekto įvadą piešinyje](../../../../translated_images/lt/ai-intro.bf28d1ac4235881c.png) > Piešinys sukurtas [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Iš pradžių kompiuterius išrado [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage), kad jie galėtų atlikti skaičiavimus pagal aiškiai apibrėžtą procedūrą – algoritmą. Šiuolaikiniai kompiuteriai, nors ir gerokai pažangesni nei XIX amžiuje pasiūlytas modelis, vis dar remiasi ta pačia kontroliuojamų skaičiavimų idėja. Todėl galima užprogramuoti kompiuterį atlikti tam tikrą užduotį, jei žinome tikslų veiksmų seką, reikalingą tikslui pasiekti. -![Asmens nuotrauka](../../../../translated_images/dsh_age.d212a30d4e54fb5f.lt.png) +![Asmens nuotrauka](../../../../translated_images/lt/dsh_age.d212a30d4e54fb5f.png) > Nuotrauka [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Daugiau informacijos rasite **[Dirbtinis bendrasis intelektas](https://en.wikipe Viena iš problemų, susijusių su terminu **[intelektas](https://en.wikipedia.org/wiki/Intelligence)**, yra ta, kad nėra aiškaus šio termino apibrėžimo. Galima teigti, kad intelektas susijęs su **abstrakčiu mąstymu** arba **savimone**, tačiau mes negalime jo tinkamai apibrėžti. -![Katės nuotrauka](../../../../translated_images/photo-cat.8c8e8fb760ffe457.lt.jpg) +![Katės nuotrauka](../../../../translated_images/lt/photo-cat.8c8e8fb760ffe457.jpg) > [Nuotrauka](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) iš Unsplash @@ -98,13 +98,13 @@ Kita vertus, galime pabandyti modeliuoti paprasčiausius mūsų smegenų element > | O kaip su ML? | | > |--------------|-----------| -> | Dirbtinio intelekto dalis, pagrįsta kompiuterio mokymusi spręsti problemą remiantis tam tikrais duomenimis, vadinama **mašininiu mokymusi**. Šiame kurse neaptarsime klasikinio mašininio mokymosi – siūlome atskirą [Mašininio mokymosi pradedantiesiems](http://aka.ms/ml-beginners) mokymo programą. | ![ML pradedantiesiems](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.lt.png) | +> | Dirbtinio intelekto dalis, pagrįsta kompiuterio mokymusi spręsti problemą remiantis tam tikrais duomenimis, vadinama **mašininiu mokymusi**. Šiame kurse neaptarsime klasikinio mašininio mokymosi – siūlome atskirą [Mašininio mokymosi pradedantiesiems](http://aka.ms/ml-beginners) mokymo programą. | ![ML pradedantiesiems](../../../../translated_images/lt/ml-for-beginners.9e4fed176fd5817d.png) | ## Trumpa DI istorija Dirbtinis intelektas kaip sritis pradėtas XX amžiaus viduryje. Iš pradžių simbolinis samprotavimas buvo vyraujantis požiūris, ir jis lėmė nemažai svarbių pasiekimų, tokių kaip ekspertų sistemos – kompiuterinės programos, galinčios veikti kaip ekspertas tam tikrose ribotose problemų srityse. Tačiau netrukus tapo aišku, kad toks požiūris nėra gerai pritaikomas. Žinių išgavimas iš eksperto, jų pateikimas kompiuteryje ir žinių bazės tikslumo palaikymas pasirodė esąs labai sudėtingas ir per brangus daugeliu atvejų. Tai lėmė vadinamąją [DI žiemą](https://en.wikipedia.org/wiki/AI_winter) 1970-aisiais. -Trumpa DI istorija +Trumpa DI istorija > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/lt/lessons/2-Symbolic/README.md b/translations/lt/lessons/2-Symbolic/README.md index ffa97bc0..864ddeee 100644 --- a/translations/lt/lessons/2-Symbolic/README.md +++ b/translations/lt/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Žinių Atvaizdavimas ir Ekspertinės Sistemos -![Santrauka apie simbolinį AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.lt.png) +![Santrauka apie simbolinį AI](../../../../translated_images/lt/ai-symbolic.715a30cb610411a6.png) > Sketchnote sukūrė [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Dažniausiai mes griežtai neapibrėžiame žinių, bet jas susiejame su kitais Taigi, **žinių atvaizdavimo** problema yra rasti efektyvų būdą atvaizduoti žinias kompiuteryje duomenų forma, kad jos būtų automatiškai naudojamos. Tai galima laikyti spektru: -![Žinių atvaizdavimo spektras](../../../../translated_images/knowledge-spectrum.b60df631852c0217.lt.png) +![Žinių atvaizdavimo spektras](../../../../translated_images/lt/knowledge-spectrum.b60df631852c0217.png) > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokų sintaksė | Įtraukimas | | | Vienas iš ankstyvųjų simbolinio AI pasiekimų buvo vadinamosios **ekspertinės sistemos** - kompiuterinės sistemos, sukurtos veikti kaip ekspertas tam tikroje ribotoje problemų srityje. Jos buvo pagrįstos **žinių baze**, išgauta iš vieno ar daugiau žmonių ekspertų, ir turėjo **išvadų variklį**, kuris atliko tam tikrą samprotavimą remdamasis šia baze. -![Žmogaus architektūra](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.lt.png) | ![Žinių pagrindu veikianti sistema](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.lt.png) +![Žmogaus architektūra](../../../../translated_images/lt/arch-human.5d4d35f1bba3ab1c.png) | ![Žinių pagrindu veikianti sistema](../../../../translated_images/lt/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Supaprastinta žmogaus nervų sistemos struktūra | Žinių pagrindu veikiančios sistemos architektūra @@ -106,7 +106,7 @@ Ekspertinės sistemos yra sukurtos panašiai kaip žmogaus samprotavimo sistema, Kaip pavyzdį, apsvarstykime šią ekspertinę sistemą, skirtą gyvūnui nustatyti pagal jo fizines charakteristikas: -![AND-OR medis](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.lt.png) +![AND-OR medis](../../../../translated_images/lt/AND-OR-Tree.5592d2c70187f283.png) > Vaizdas sukurtas [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md index 31f6dc7a..5d0aa6fe 100644 --- a/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/lt/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Per didelis pritaikymas yra itin svarbi sąvoka mašininio mokymosi srityje, ir Apsvarstykite šią problemą, kurioje reikia aproksimuoti 5 taškus (grafikuose pažymėtus `x`): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.lt.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.lt.jpg) +![linear](../../../../../translated_images/lt/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/lt/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linijinis modelis, 2 parametrai** | **Nelinijinis modelis, 7 parametrai** Mokymo klaida = 5.3 | Mokymo klaida = 0 @@ -79,7 +79,7 @@ Labai svarbu rasti tinkamą pusiausvyrą tarp modelio sudėtingumo (parametrų s Kaip matote iš aukščiau pateikto grafiko, per didelį pritaikymą galima aptikti pagal labai mažą mokymo klaidą ir didelę validacijos klaidą. Paprastai mokymo metu matysime, kaip tiek mokymo, tiek validacijos klaidos pradeda mažėti, o tada tam tikru momentu validacijos klaida gali nustoti mažėti ir pradėti didėti. Tai bus per didelio pritaikymo ženklas ir indikatorius, kad turėtume sustabdyti mokymą (arba bent jau išsaugoti modelio būseną). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.lt.png) +![overfitting](../../../../../translated_images/lt/Overfitting.408ad91cd90b4371.png) ## Kaip išvengti per didelio pritaikymo diff --git a/translations/lt/lessons/3-NeuralNetworks/README.md b/translations/lt/lessons/3-NeuralNetworks/README.md index 2b3f1bd3..9bfaf990 100644 --- a/translations/lt/lessons/3-NeuralNetworks/README.md +++ b/translations/lt/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Įvadas į neuroninius tinklus -![Santrauka apie neuroninių tinklų įvadą piešinyje](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.lt.png) +![Santrauka apie neuroninių tinklų įvadą piešinyje](../../../../translated_images/lt/ai-neuralnetworks.1c687ae40bc86e83.png) Kaip aptarėme įvade, vienas iš būdų pasiekti intelektą yra treniruoti **kompiuterinį modelį** arba **dirbtinį smegenų modelį**. Nuo XX amžiaus vidurio mokslininkai bandė įvairius matematinius modelius, kol pastaraisiais metais šis metodas pasirodė itin sėkmingas. Tokie smegenų matematiniai modeliai vadinami **neuroniniais tinklais**. @@ -36,13 +36,13 @@ Mes apsvarstysime dvi dažniausiai pasitaikančias mašininio mokymosi problemas Iš biologijos žinome, kad mūsų smegenys susideda iš neuroninių ląstelių (neuronų), kiekviena iš jų turi kelis "įėjimus" (dendritus) ir vieną "išėjimą" (aksoną). Tiek dendritai, tiek aksonai gali perduoti elektrinius signalus, o jungtys tarp jų — vadinamos sinapsėmis — gali turėti skirtingą laidumą, kurį reguliuoja neurotransmiteriai. -![Neurono modelis](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.lt.jpg) | ![Neurono modelis](../../../../translated_images/artneuron.1a5daa88d20ebe6f.lt.png) +![Neurono modelis](../../../../translated_images/lt/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Neurono modelis](../../../../translated_images/lt/artneuron.1a5daa88d20ebe6f.png) ----|---- Tikras neuronas *([Vaizdas](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) iš Vikipedijos)* | Dirbtinis neuronas *(Vaizdas autoriaus)* Taigi, paprasčiausias matematinis neurono modelis turi kelis įėjimus X1, ..., XN ir vieną išėjimą Y, bei svorių seriją W1, ..., WN. Išėjimas apskaičiuojamas taip: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kur f yra tam tikra nelinijinė **aktyvavimo funkcija**. diff --git a/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md index 549a975f..011ee593 100644 --- a/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/lt/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Mūsų [OpenCV Notebook](OpenCV.ipynb) pateikiame keletą pavyzdžių, kada komp * **Brailio knygos nuotraukos išankstinis apdorojimas**. Mes sutelkiame dėmesį į tai, kaip galima naudoti slenksčio nustatymą, funkcijų aptikimą, perspektyvos transformaciją ir NumPy manipuliacijas, kad atskirtume atskirus Brailio simbolius tolimesnei klasifikacijai neuroniniu tinklu. -![Brailio vaizdas](../../../../../translated_images/braille.341962ff76b1bd70.lt.jpeg) | ![Brailio vaizdas apdorotas](../../../../../translated_images/braille-result.46530fea020b03c7.lt.png) | ![Brailio simboliai](../../../../../translated_images/braille-symbols.0159185ab69d5339.lt.png) +![Brailio vaizdas](../../../../../translated_images/lt/braille.341962ff76b1bd70.jpeg) | ![Brailio vaizdas apdorotas](../../../../../translated_images/lt/braille-result.46530fea020b03c7.png) | ![Brailio simboliai](../../../../../translated_images/lt/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) * **Judėjimo aptikimas vaizdo įraše naudojant kadrų skirtumą**. Jei kamera yra fiksuota, tuomet kadrai iš kameros turėtų būti gana panašūs vienas į kitą. Kadangi kadrai atvaizduojami kaip masyvai, tiesiog atimant šiuos masyvus dviejų iš eilės einančių kadrų atveju gausime pikselių skirtumą, kuris turėtų būti mažas statiniams kadrams ir didėti, kai vaizde yra reikšmingas judėjimas. -![Vaizdo kadrų ir kadrų skirtumų vaizdas](../../../../../translated_images/frame-difference.706f805491a0883c.lt.png) +![Vaizdo kadrų ir kadrų skirtumų vaizdas](../../../../../translated_images/lt/frame-difference.706f805491a0883c.png) > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Mūsų [OpenCV Notebook](OpenCV.ipynb) pateikiame keletą pavyzdžių, kada komp - **Tankus optinis srautas** apskaičiuoja vektorių lauką, kuris rodo, kur kiekvienas pikselis juda. - **Retas optinis srautas** remiasi tam tikrų išskirtinių vaizdo bruožų (pvz., kraštų) paėmimu ir jų trajektorijos kūrimu nuo kadro iki kadro. -![Optinio srauto vaizdas](../../../../../translated_images/optical.1f4a94464579a83a.lt.png) +![Optinio srauto vaizdas](../../../../../translated_images/lt/optical.1f4a94464579a83a.png) > Vaizdas iš [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index b583fa63..bbeee05a 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 yra tinklas, kuris 2014 m. pasiekė 92,7% tikslumą ImageNet top-5 klasifikacijoje. Jo sluoksnių struktūra yra tokia: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.lt.jpg) +![ImageNet Layers](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.jpg) Kaip matote, VGG seka tradicinę piramidės architektūrą, kuri yra konvoliucinių ir kaupimo sluoksnių seka. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.lt.jpg) +![ImageNet Pyramid](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.jpg) > Vaizdas iš [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md index 6dbb9eee..e2b25f00 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Realiame gyvenime norime atpažinti objektus nuotraukoje, nepaisant jų tikslios Norėdami išgauti raštus, naudosime **konvoliucinių filtrų** sąvoką. Kaip žinote, vaizdas yra pateikiamas kaip 2D-matrica arba 3D-tensoras su spalvų gylio dimensija. Filtrą taikyti reiškia, kad imame palyginti mažą **filtrų branduolio** matricą ir kiekvienam pikseliui originaliame vaizde apskaičiuojame svertinį vidurkį su kaimyniniais taškais. Galime tai įsivaizduoti kaip mažą langą, kuris slysta per visą vaizdą ir vidutiniškai apskaičiuoja pikselius pagal filtrų branduolio matricos svorius. -![Vertikalus kraštų filtras](../../../../../translated_images/filter-vert.b7148390ca0bc356.lt.png) | ![Horizontalus kraštų filtras](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.lt.png) +![Vertikalus kraštų filtras](../../../../../translated_images/lt/filter-vert.b7148390ca0bc356.png) | ![Horizontalus kraštų filtras](../../../../../translated_images/lt/filter-horiz.59b80ed4feb946ef.png) ----|---- > Vaizdas: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN veikimas grindžiamas šiomis svarbiomis idėjomis: * Galime sukurti tinklą taip, kad filtrai būtų mokomi automatiškai * Galime naudoti tą patį metodą aukšto lygio savybių raštų paieškai, ne tik originaliame vaizde. Taigi CNN savybių išgavimas veikia hierarchijos principu – pradedant nuo žemo lygio pikselių kombinacijų iki aukšto lygio vaizdo dalių kombinacijų. -![Hierarchinis savybių išgavimas](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.lt.png) +![Hierarchinis savybių išgavimas](../../../../../translated_images/lt/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Vaizdas iš [Hislop-Lynch straipsnio](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), remiantis [jų tyrimu](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Dauguma CNN, naudojamų vaizdų apdorojimui, seka vadinamąją piramidės archit Pavyzdžiui, pažvelkime į VGG-16 architektūrą – tinklą, kuris 2014 m. pasiekė 92,7% tikslumą ImageNet top-5 klasifikacijoje: -![ImageNet sluoksniai](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.lt.jpg) +![ImageNet sluoksniai](../../../../../translated_images/lt/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet piramidė](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.lt.jpg) +![ImageNet piramidė](../../../../../translated_images/lt/vgg-16-arch.64ff2137f50dd49f.jpg) > Vaizdas iš [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 3593b2b3..91af04de 100644 --- a/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/lt/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Jums reikia išmokyti konvoliucinį neuroninį tinklą klasifikuoti skirtingas k Naudosime [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), kuriame yra 37 skirtingų šunų ir kačių veislių nuotraukos. -![Duomenų rinkinys, su kuriuo dirbsime](../../../../../../translated_images/data.50b2a9d5484bdbf0.lt.png) +![Duomenų rinkinys, su kuriuo dirbsime](../../../../../../translated_images/lt/data.50b2a9d5484bdbf0.png) Norėdami atsisiųsti duomenų rinkinį, naudokite šį kodo fragmentą: diff --git a/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md index f0950cc8..63b1e94d 100644 --- a/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/lt/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tiek Keras, tiek PyTorch turi funkcijas, leidžiančias lengvai įkelti iš anks Štai pavyzdinės savybės, išgautos iš katės nuotraukos naudojant VGG-16 tinklą: -![Savybės, išgautos naudojant VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.lt.png) +![Savybės, išgautos naudojant VGG-16](../../../../../translated_images/lt/features.6291f9c7ba3a0b95.png) ## Kačių ir šunų duomenų rinkinys @@ -48,19 +48,19 @@ Iš anksto apmokytas neuroninis tinklas savo „smegenyse“ turi įvairius šab Vienas iš būdų, kurį galime naudoti, yra pradėti nuo atsitiktinio vaizdo ir tada bandyti naudoti **gradientinio nusileidimo optimizavimo** techniką, kad pakoreguotume tą vaizdą taip, jog tinklas pradėtų manyti, kad tai yra katė. -![Vaizdo optimizavimo ciklas](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.lt.png) +![Vaizdo optimizavimo ciklas](../../../../../translated_images/lt/ideal-cat-loop.999fbb8ff306e044.png) Tačiau, jei tai padarysime, gausime kažką labai panašaus į atsitiktinį triukšmą. Taip yra todėl, kad *yra daug būdų, kaip tinklas gali manyti, kad įvesties vaizdas yra katė*, įskaitant kai kuriuos, kurie vizualiai neturi prasmės. Nors tie vaizdai turi daug šablonų, būdingų katei, nėra nieko, kas juos apribotų vizualiai išskirtiniais. Norėdami pagerinti rezultatą, galime pridėti dar vieną terminą į nuostolių funkciją, vadinamą **variacijos nuostoliu**. Tai metrika, rodanti, kaip panašūs yra kaimyniniai vaizdo pikseliai. Mažinant variacijos nuostolį vaizdas tampa lygesnis ir atsikratoma triukšmo – taip atskleidžiami vizualiai patrauklesni šablonai. Štai pavyzdys tokių „idealių“ vaizdų, kurie su didele tikimybe klasifikuojami kaip katė ir kaip zebra: -![Ideali katė](../../../../../translated_images/ideal-cat.203dd4597643d6b0.lt.png) | ![Ideali zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.lt.png) +![Ideali katė](../../../../../translated_images/lt/ideal-cat.203dd4597643d6b0.png) | ![Ideali zebra](../../../../../translated_images/lt/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideali katė* | *Ideali zebra* Panašus metodas gali būti naudojamas atliekant vadinamuosius **priešiškus išpuolius** prieš neuroninį tinklą. Tarkime, norime apgauti neuroninį tinklą ir priversti šunį atrodyti kaip katę. Jei paimsime šuns vaizdą, kurį tinklas atpažįsta kaip šunį, galime šiek tiek jį pakoreguoti naudodami gradientinio nusileidimo optimizavimą, kol tinklas pradės jį klasifikuoti kaip katę: -![Šuns nuotrauka](../../../../../translated_images/original-dog.8f68a67d2fe0911f.lt.png) | ![Šuns nuotrauka, klasifikuojama kaip katė](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.lt.png) +![Šuns nuotrauka](../../../../../translated_images/lt/original-dog.8f68a67d2fe0911f.png) | ![Šuns nuotrauka, klasifikuojama kaip katė](../../../../../translated_images/lt/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originali šuns nuotrauka* | *Šuns nuotrauka, klasifikuojama kaip katė* diff --git a/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md index ce595462..a66c7809 100644 --- a/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/lt/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tačiau galime norėti naudoti neapdorotus (nepažymėtus) duomenis CNN funkcij Kadangi mokome autoenkoderį užfiksuoti kuo daugiau informacijos iš originalaus vaizdo, kad būtų galima tiksliai jį atkurti, tinklas stengiasi rasti geriausią **įterpimą** įvesties vaizdams, kad užfiksuotų jų prasmę. -![Autoenkoderio schema](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.lt.jpg) +![Autoenkoderio schema](../../../../../translated_images/lt/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Vaizdas iš [Keras tinklaraščio](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md index 437a7488..f3dd1be3 100644 --- a/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/lt/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Vaizdų klasifikavimo modeliai, su kuriais dirbome iki šiol, paimdavo vaizdą i ## [Prieš paskaitą: testas](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektų atpažinimas](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.lt.png) +![Objektų atpažinimas](../../../../../translated_images/lt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Vaizdas iš [YOLO v2 svetainės](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tarkime, norime rasti katę paveikslėlyje. Labai naivus požiūris į objektų 2. Atlikti vaizdų klasifikavimą kiekvienoje plytelėje. 3. Tos plytelės, kurios duoda pakankamai aukštą aktyvaciją, gali būti laikomos turinčiomis ieškomą objektą. -![Naivus objektų atpažinimas](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.lt.png) +![Naivus objektų atpažinimas](../../../../../translated_images/lt/naive-detection.e7f1ba220ccd08c6.png) > *Vaizdas iš [užduočių sąsiuvinio](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Galite susidurti su šiais duomenų rinkiniais: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasių * [COCO](http://cocodataset.org/#home) – Įprasti objektai kontekste. 80 klasių, ribų dėžutės ir segmentavimo kaukės -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.lt.jpg) +![COCO](../../../../../translated_images/lt/coco-examples.71bc60380fa6cceb.jpg) ## Objektų atpažinimo metrikos @@ -50,7 +50,7 @@ Galite susidurti su šiais duomenų rinkiniais: Vaizdų klasifikavimui lengva išmatuoti, kaip gerai veikia algoritmas, tačiau objektų atpažinimui reikia įvertinti tiek klasės teisingumą, tiek numatytos ribų dėžutės vietos tikslumą. Pastarajam naudojama vadinamoji **Sankirta per sąjungą** (IoU), kuri matuoja, kaip gerai sutampa dvi dėžutės (arba dvi savavališkos sritys). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.lt.png) +![IoU](../../../../../translated_images/lt/iou_equation.9a4751d40fff4e11.png) > *2 paveikslas iš [puikaus tinklaraščio apie IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) naudoja [Selektyvų paiešką](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf), kad sukurtų hierarchinę ROI regionų struktūrą, kuri vėliau perduodama per CNN funkcijų ištraukėjus ir SVM klasifikatorius, kad būtų nustatyta objekto klasė, o linijinė regresija naudojama *ribų dėžutės* koordinatėms nustatyti. [Oficialus straipsnis](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.lt.png) +![RCNN](../../../../../translated_images/lt/rcnn1.cae407020dfb1d1f.png) > *Vaizdas iš van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.lt.png) +![RCNN-1](../../../../../translated_images/lt/rcnn2.2d9530bb83516484.png) > *Vaizdai iš [šio tinklaraščio](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: Šis metodas panašus į R-CNN, tačiau regionai apibrėžiami po konvoliucinių sluoksnių taikymo. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.lt.png) +![FRCNN](../../../../../translated_images/lt/f-rcnn.3cda6d9bb4188875.png) > Vaizdas iš [oficialaus straipsnio](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Yra dvi pagrindinės objektų atpažinimo algoritmų klasės: Pagrindinė šio metodo idėja – naudoti neuroninį tinklą ROI prognozavimui – vadinamąjį *Regionų pasiūlymo tinklą*. [Straipsnis](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.lt.png) +![FasterRCNN](../../../../../translated_images/lt/faster-rcnn.8d46c099b87ef30a.png) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Pagrindinė šio metodo idėja – naudoti neuroninį tinklą ROI prognozavimui 2. Funkcijos apdorojamos **Pozicijos jautriu rezultatų žemėlapiu**. Kiekvienas objektas iš $C$ klasių padalijamas į $k\times k$ regionus, ir mes treniruojame tinklą prognozuoti objektų dalis. 3. Kiekvienai daliai iš $k\times k$ regionų visi tinklai balsuoja už objektų klases, ir klasė su didžiausiu balsų skaičiumi yra pasirinkta. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.lt.png) +![r-fcn image](../../../../../translated_images/lt/r-fcn.13eb88158b99a3da.png) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO yra realaus laiko vieno perėjimo algoritmas. Pagrindinė idėja yra tokia: * Vaizdas padalijamas į $S\times S$ regionus. * Kiekvienam regionui **CNN** prognozuoja $n$ galimų objektų, *ribų dėžutės* koordinates ir *pasitikėjimą*=*tikimybę* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.lt.png) + ![YOLO](../../../../../translated_images/lt/yolo.a2648ec82ee8bb4e.png) > Vaizdas iš [oficialaus straipsnio](https://arxiv.org/abs/1506.02640) diff --git a/translations/lt/lessons/4-ComputerVision/README.md b/translations/lt/lessons/4-ComputerVision/README.md index 51be2200..3102619c 100644 --- a/translations/lt/lessons/4-ComputerVision/README.md +++ b/translations/lt/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kompiuterinis matymas -![Kompiuterinio matymo turinio santrauka piešinyje](../../../../translated_images/ai-computervision.6506ebebac3fbf76.lt.png) +![Kompiuterinio matymo turinio santrauka piešinyje](../../../../translated_images/lt/ai-computervision.6506ebebac3fbf76.png) Šiame skyriuje sužinosime apie: diff --git a/translations/lt/lessons/5-NLP/14-Embeddings/README.md b/translations/lt/lessons/5-NLP/14-Embeddings/README.md index b9160d8b..e58f0182 100644 --- a/translations/lt/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/lt/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Taigi, įterpimo sluoksnis priims žodį kaip įvestį ir pateiks išvesties vek Naudodami įterpimo sluoksnį kaip pirmąjį sluoksnį mūsų klasifikatoriaus tinkle, galime pereiti nuo žodžių maišo prie **įterpinių maišo** modelio, kuriame pirmiausia kiekvieną žodį mūsų tekste konvertuojame į atitinkamą įterpinį, o tada apskaičiuojame tam tikrą agregavimo funkciją visiems tiems įterpiniams, pvz., `sum`, `average` arba `max`. -![Vaizdas, rodantis įterpinio klasifikatorių penkiems sekos žodžiams.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.lt.png) +![Vaizdas, rodantis įterpinio klasifikatorių penkiems sekos žodžiams.](../../../../../translated_images/lt/embedding-classifier-example.b77f021a7ee67eee.png) > Vaizdas sukurtas autoriaus @@ -40,7 +40,7 @@ Norėdami tai pasiekti, turime iš anksto apmokyti savo įterpimo modelį didel CBoW yra greitesnis, o praleidimo gramų modelis yra lėtesnis, tačiau geriau reprezentuoja retus žodžius. -![Vaizdas, rodantis CBoW ir praleidimo gramų algoritmus žodžių konvertavimui į vektorius.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.lt.png) +![Vaizdas, rodantis CBoW ir praleidimo gramų algoritmus žodžių konvertavimui į vektorius.](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Vaizdas iš [šio straipsnio](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md b/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md index 0852fc38..8e5bad56 100644 --- a/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/lt/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Ankstesniuose pavyzdžiuose naudojome iš anksto apmokytus semantinius įterpini * **Nuolatinis žodžių maišas** (CBoW), kai prognozuojame vidurinį žodį $W_0$ žodžių sekoje $W_{-N}$, ..., $W_N$. * **Skip-gram**, kai prognozuojame kaimyninių žodžių rinkinį {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iš vidurinio žodžio $W_0$. -![vaizdas iš straipsnio apie žodžių konvertavimą į vektorius](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.lt.png) +![vaizdas iš straipsnio apie žodžių konvertavimą į vektorius](../../../../../translated_images/lt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Vaizdas iš [šio straipsnio](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/lt/lessons/5-NLP/16-RNN/README.md b/translations/lt/lessons/5-NLP/16-RNN/README.md index e8f084eb..461ae351 100644 --- a/translations/lt/lessons/5-NLP/16-RNN/README.md +++ b/translations/lt/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Ankstesnėse dalyse naudojome turtingas semantines teksto reprezentacijas ir pap Norint užfiksuoti teksto sekos prasmę, reikia naudoti kitą neuroninio tinklo architektūrą, vadinamą **rekurentiniais neuroniniais tinklais** (RNN). RNN tinkluose sakinį perduodame per tinklą po vieną simbolį, o tinklas generuoja tam tikrą **būseną**, kurią vėliau perduodame tinklui kartu su kitu simboliu. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.lt.png) +![RNN](../../../../../translated_images/lt/rnn.27f5c29c53d727b5.png) > Vaizdas sukurtas autoriaus @@ -61,7 +61,7 @@ Aptarėme rekurentinius tinklus, kurie veikia viena kryptimi, nuo sekos pradžio Rekurentinis tinklas, nesvarbu, ar vienkryptis, ar dvikryptis, užfiksuoja tam tikrus sekos modelius ir gali juos saugoti būsenos vektoriuje arba perduoti į išvestį. Kaip ir konvoliuciniuose tinkluose, galime sukurti kitą rekurentinį sluoksnį virš pirmojo, kad užfiksuotume aukštesnio lygio modelius ir sukurtume iš žemo lygio modelių, kuriuos ištraukė pirmasis sluoksnis. Tai veda mus prie **daugiasluoksnio RNN** sąvokos, kurią sudaro du ar daugiau rekurentinių tinklų, kur ankstesnio sluoksnio išvestis perduodama kitam sluoksniui kaip įvestis. -![Vaizdas, rodantis daugiasluoksnį ilgalaikės trumpalaikės atminties RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.lt.jpg) +![Vaizdas, rodantis daugiasluoksnį ilgalaikės trumpalaikės atminties RNN](../../../../../translated_images/lt/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Paveikslas iš [šio puikaus įrašo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López* diff --git a/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md index 231adc36..1b480ed7 100644 --- a/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/lt/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ RNN architektūroje, kurią aptarėme ankstesniame skyriuje, kiekvienas RNN vien Tai leidžia sukurti skirtingas neuronines architektūras, kurios parodytos žemiau esančiame paveikslėlyje: -![Paveikslėlis, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.lt.jpg) +![Paveikslėlis, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Paveikslėlis iš tinklaraščio įrašo [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autoriaus [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Tai leidžia sukurti skirtingas neuronines architektūras, kurios parodytos žem Mes išmokysime šį RNN generuoti tekstą žingsnis po žingsnio. Kiekviename žingsnyje imsime simbolių seką, kurios ilgis yra `nchars`, ir paprašysime tinklo generuoti kitą išvesties simbolį kiekvienam įvesties simboliui: -![Paveikslėlis, rodantis RNN generavimo pavyzdį su žodžiu 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.lt.png) +![Paveikslėlis, rodantis RNN generavimo pavyzdį su žodžiu 'HELLO'.](../../../../../translated_images/lt/rnn-generate.56c54afb52f9781d.png) Generuojant tekstą (inference metu), pradedame nuo tam tikro **pradžios taško**, kuris perduodamas per RNN ląsteles, kad būtų generuojama tarpinė būsena, o tada iš šios būsenos prasideda generavimas. Generuojame po vieną simbolį, perduodame būseną ir sugeneruotą simbolį kitai RNN ląstelei, kad sugeneruotume kitą, kol sugeneruojame pakankamai simbolių. diff --git a/translations/lt/lessons/5-NLP/18-Transformers/README.md b/translations/lt/lessons/5-NLP/18-Transformers/README.md index 4f78347a..a86fde6d 100644 --- a/translations/lt/lessons/5-NLP/18-Transformers/README.md +++ b/translations/lt/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Naudojant RNN, sekos į seką užduotis įgyvendinama naudojant du rekursinius t **Dėmesio mechanizmai** suteikia galimybę įvertinti kiekvieno įvesties vektoriaus kontekstinį poveikį kiekvienai RNN išvesties prognozei. Tai įgyvendinama sukuriant trumpesnius ryšius tarp įvesties RNN tarpinių būsenų ir išvesties RNN. Tokiu būdu, generuojant išvesties simbolį yt, atsižvelgiama į visas įvesties paslėptas būsenas hi, su skirtingais svorio koeficientais αt,i. -![Vaizdas, rodantis koduotojo/dekoduotojo modelį su papildomu dėmesio sluoksniu](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.lt.png) +![Vaizdas, rodantis koduotojo/dekoduotojo modelį su papildomu dėmesio sluoksniu](../../../../../translated_images/lt/encoder-decoder-attention.7a726296894fb567.png) > Koduotojo-dekoduotojo modelis su papildomu dėmesio mechanizmu [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cituota iš [šio tinklaraščio įrašo](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Dėmesio matrica {αi,j} atspindėtų, kokiu mastu tam tikri įvesties žodžiai dalyvauja generuojant tam tikrą žodį išvesties sekoje. Žemiau pateiktas tokios matricos pavyzdys: -![Vaizdas, rodantis pavyzdinį suderinimą, rastą RNNsearch-50, paimta iš Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.lt.png) +![Vaizdas, rodantis pavyzdinį suderinimą, rastą RNNsearch-50, paimta iš Bahdanau - arviz.org](../../../../../translated_images/lt/bahdanau-fig3.09ba2d37f202a6af.png) > Paveikslas iš [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Rezultatas, kurį gauname su poziciniu įterpimu, įterpia tiek originalų žeto Toliau mums reikia užfiksuoti tam tikrus modelius mūsų sekoje. Tam transformatoriai naudoja **savidėmesio** mechanizmą, kuris iš esmės yra dėmesys, taikomas tai pačiai sekai kaip įvestis ir išvestis. Taikant savidėmesį, galime atsižvelgti į **kontekstą** sakinyje ir pamatyti, kurie žodžiai yra tarpusavyje susiję. Pavyzdžiui, tai leidžia pamatyti, į ką nurodo koreferencijos, tokios kaip *tai*, ir taip pat atsižvelgti į kontekstą: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.lt.png) +![](../../../../../translated_images/lt/CoreferenceResolution.861924d6d384a7d6.png) > Vaizdas iš [Google tinklaraščio](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kadangi kiekviena įvesties pozicija yra nepriklausomai susieta su kiekviena iš **BERT** (Bidirectional Encoder Representations from Transformers) yra labai didelis daugiasluoksnis transformatorių tinklas su 12 sluoksnių *BERT-base* ir 24 sluoksniais *BERT-large*. Modelis pirmiausia iš anksto apmokomas naudojant didelį tekstų korpusą (WikiPedia + knygos) taikant nesupervizuotą mokymą (prognozuojant užmaskuotus žodžius sakinyje). Per išankstinį mokymą modelis įgyja reikšmingą kalbos supratimą, kurį vėliau galima panaudoti su kitais duomenų rinkiniais taikant smulkų derinimą. Šis procesas vadinamas **perkėlimo mokymu**. -![paveikslas iš http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.lt.png) +![paveikslas iš http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/lt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Vaizdo [šaltinis](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/lt/lessons/5-NLP/19-NER/README.md b/translations/lt/lessons/5-NLP/19-NER/README.md index b357da79..9ce80d96 100644 --- a/translations/lt/lessons/5-NLP/19-NER/README.md +++ b/translations/lt/lessons/5-NLP/19-NER/README.md @@ -55,7 +55,7 @@ naujagimiui | O Kadangi reikia sukurti vienas prie vieno atitikimą tarp žodžių ir klasių, galime treniruoti tinkamą **daugelio prie daugelio** neuroninio tinklo modelį pagal šį paveikslą: -![Vaizdas, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.lt.jpg) +![Vaizdas, rodantis įprastus pasikartojančių neuroninių tinklų modelius.](../../../../../translated_images/lt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Vaizdas iš [šio tinklaraščio įrašo](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autoriaus [Andrejaus Karpathy](http://karpathy.github.io/). NER žodžių klasifikacijos modeliai atitinka dešiniausią tinklo architektūrą šiame paveikslėlyje.* diff --git a/translations/lt/lessons/5-NLP/README.md b/translations/lt/lessons/5-NLP/README.md index 1241bfaa..5dd2dea4 100644 --- a/translations/lt/lessons/5-NLP/README.md +++ b/translations/lt/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natūralios kalbos apdorojimas -![NLP užduočių santrauka piešinyje](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.lt.png) +![NLP užduočių santrauka piešinyje](../../../../translated_images/lt/ai-nlp.b22dcb8ca4707cea.png) Šiame skyriuje mes sutelksime dėmesį į neuroninių tinklų naudojimą užduotims, susijusioms su **natūralios kalbos apdorojimu (NLP)**. Yra daugybė NLP problemų, kurias norime, kad kompiuteriai galėtų išspręsti: diff --git a/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md b/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md index 615ab0a0..4a8cee77 100644 --- a/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/lt/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Galite atidaryti vieną iš modelių, pavyzdžiui, **Biology → Flocki Atidarius modelį, būsite nukreipti į pagrindinį NetLogo ekraną. Čia pateikiamas pavyzdinis modelis, aprašantis vilkų ir avių populiaciją, turint ribotus išteklius (žolę). -![NetLogo Pagrindinis ekranas](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.lt.png) +![NetLogo Pagrindinis ekranas](../../../../../translated_images/lt/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov ekrano kopija diff --git a/translations/lt/lessons/README.md b/translations/lt/lessons/README.md index 84386aea..7fe9501b 100644 --- a/translations/lt/lessons/README.md +++ b/translations/lt/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Apžvalga -![Apžvalga piešinyje](../../../translated_images/ai-overview.0857791951d19500.lt.png) +![Apžvalga piešinyje](../../../translated_images/lt/ai-overview.0857791951d19500.png) > Piešinys sukurtas [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/lt/lessons/X-Extras/X1-MultiModal/README.md b/translations/lt/lessons/X-Extras/X1-MultiModal/README.md index 56cef483..046ae040 100644 --- a/translations/lt/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/lt/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po transformatorių modelių sėkmės sprendžiant NLP užduotis, tos pačios ar Pagrindinė CLIP idėja yra gebėjimas palyginti tekstinius užklausimus su vaizdu ir nustatyti, kaip gerai vaizdas atitinka užklausimą. -![CLIP Architektūra](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.lt.png) +![CLIP Architektūra](../../../../../translated_images/lt/clip-arch.b3dbf20b4e8ed8be.png) > *Paveikslėlis iš [šio tinklaraščio įrašo](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kai modelis yra iš anksto apmokytas, galime jam pateikti vaizdų paketą ir tek Tarkime, mums reikia klasifikuoti vaizdus, pavyzdžiui, į kategorijas: katės, šunys ir žmonės. Tokiu atveju galime modeliui pateikti vaizdą ir seriją tekstinių užklausų: "*katės paveikslas*", "*šuns paveikslas*", "*žmogaus paveikslas*". Gautame 3 tikimybių vektoriuje tiesiog reikia pasirinkti indeksą su didžiausia reikšme. -![CLIP vaizdų klasifikavimui](../../../../../translated_images/clip-class.3af42ef0b2b19369.lt.png) +![CLIP vaizdų klasifikavimui](../../../../../translated_images/lt/clip-class.3af42ef0b2b19369.png) > *Paveikslėlis iš [šio tinklaraščio įrašo](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Daugiau apie VQGAN sužinokite [Taming Transformers](https://compvis.github.io/t Vienas svarbus skirtumas tarp VQGAN ir tradicinio GAN yra tas, kad pastarasis gali sukurti padorų vaizdą iš bet kokio įvesties vektoriaus, o VQGAN greičiausiai sukurs vaizdą, kuris nebus nuoseklus. Todėl reikia papildomai vadovauti vaizdo kūrimo procesui, ir tai galima padaryti naudojant CLIP. -![VQGAN+CLIP Architektūra](../../../../../translated_images/vqgan.5027fe05051dfa31.lt.png) +![VQGAN+CLIP Architektūra](../../../../../translated_images/lt/vqgan.5027fe05051dfa31.png) Norint sugeneruoti vaizdą, atitinkantį tekstinę užklausą, pradedame nuo atsitiktinio kodavimo vektoriaus, kuris perduodamas per VQGAN, kad būtų sukurtas vaizdas. Tada CLIP naudojamas nuostolių funkcijai sukurti, kuri parodo, kaip gerai vaizdas atitinka tekstinę užklausą. Tikslas yra sumažinti šiuos nuostolius, naudojant atgalinį sklidimą, kad būtų koreguojami įvesties vektoriaus parametrai. Puiki biblioteka, įgyvendinanti VQGAN+CLIP, yra [Pixray](http://github.com/pixray/pixray). -![Vaizdas sukurtas Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.lt.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.lt.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.lt.png) +![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Vaizdas sukurtas pagal užklausą *artimas akvarelės portretas jauno literatūros mokytojo su knyga* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas jaunos kompiuterių mokslų mokytojos su kompiuteriu* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas seno matematikos mokytojo priešais lentą* @@ -75,7 +75,7 @@ Skirtingai nei CLIP, DALL-E gauna tiek tekstą, tiek vaizdą kaip vieną žeton Pagrindinis skirtumas tarp DALL-E 1 ir 2 yra tas, kad pastarasis generuoja realistiškesnius vaizdus ir meną. DALL-E vaizdų generavimo pavyzdžiai: -![Vaizdas sukurtas Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.lt.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.lt.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.lt.png) +![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Vaizdas sukurtas Pixray](../../../../../translated_images/lt/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Vaizdas sukurtas pagal užklausą *artimas akvarelės portretas jauno literatūros mokytojo su knyga* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas jaunos kompiuterių mokslų mokytojos su kompiuteriu* | Vaizdas sukurtas pagal užklausą *artimas aliejinis portretas seno matematikos mokytojo priešais lentą* diff --git a/translations/ml/README.md b/translations/ml/README.md index 6387d264..73c5c322 100644 --- a/translations/ml/README.md +++ b/translations/ml/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ആരംഭക്കാർക്കായി കൃത്രിമ ബുദ്ധിമുട്ട് - ഒരു പ്രോഗ്രാം -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ml.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ml/ai-overview.0857791951d19500.png)| |:---:| | ആരംഭക്കാർക്കായുള്ള AI - _സ്കെച്ച് നോട്ടുകൾ [@girlie_mac](https://twitter.com/girlie_mac) തുടങ്ങിയവശേഷങ്ങൾ_ | diff --git a/translations/ml/lessons/1-Intro/README.md b/translations/ml/lessons/1-Intro/README.md index 095369ee..bfff6c86 100644 --- a/translations/ml/lessons/1-Intro/README.md +++ b/translations/ml/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # എഐയുടെ പരിചയം -![എഐയുടെ പരിചയത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ai-intro.bf28d1ac4235881c.ml.png) +![എഐയുടെ പരിചയത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-intro.bf28d1ac4235881c.png) > സ്കെച്ച്നോട്ട്: [ടോമോമി ഇമുര](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ആദ്യമായി, കമ്പ്യൂട്ടറുകൾ [ചാൾസ് ബാബേജ്](https://en.wikipedia.org/wiki/Charles_Babbage) നിർമിച്ചത് സംഖ്യകളിൽ നിശ്ചിത ക്രമത്തിൽ പ്രവർത്തിക്കുന്നതിനായി - ഒരു ആൽഗോരിതം. 19-ാം നൂറ്റാണ്ടിൽ നിർദ്ദേശിച്ച ആദ്യ മാതൃകയേക്കാൾ ആധുനിക കമ്പ്യൂട്ടറുകൾ വളരെ മുന്നേറ്റം നേടിയിട്ടുണ്ടെങ്കിലും, അവ ഇപ്പോഴും നിയന്ത്രിത കണക്കുകൂട്ടലുകൾ പിന്തുടരുന്നു. അതിനാൽ, ലക്ഷ്യം നേടാൻ വേണ്ട കൃത്യമായ നടപടികൾ അറിയുകയാണെങ്കിൽ, കമ്പ്യൂട്ടറിനെ പ്രോഗ്രാം ചെയ്യാൻ കഴിയും. -![ഒരു വ്യക്തിയുടെ ഫോട്ടോ](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ml.png) +![ഒരു വ്യക്തിയുടെ ഫോട്ടോ](../../../../translated_images/ml/dsh_age.d212a30d4e54fb5f.png) > ഫോട്ടോ: [വിക്കി സോഷ്നിക്കോവ](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ബുദ്ധിമുട്ട്](https://en.wikipedia.org/wiki/Intelligence)** എന്ന പദം ഉപയോഗിക്കുമ്പോൾ ഒരു പ്രശ്നം ഉണ്ട്, അതായത് ഈ പദത്തിന് വ്യക്തമായ നിർവചനമില്ല. ബുദ്ധിമുട്ട് **അബ്സ്ട്രാക്ട് ചിന്തന**യുമായി ബന്ധപ്പെട്ടു എന്ന് പറയാം, അല്ലെങ്കിൽ **സ്വയം ബോധം**യുമായി ബന്ധപ്പെട്ടു എന്ന് പറയാം, പക്ഷേ നാം അതിനെ ശരിയായി നിർവചിക്കാൻ കഴിയുന്നില്ല. -![ഒരു പൂച്ചയുടെ ഫോട്ടോ](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ml.jpg) +![ഒരു പൂച്ചയുടെ ഫോട്ടോ](../../../../translated_images/ml/photo-cat.8c8e8fb760ffe457.jpg) > [ഫോട്ടോ](https://unsplash.com/photos/75715CVEJhI) - [ആംബർ കിപ്പ്](https://unsplash.com/@sadmax) Unsplash-ൽ നിന്നു @@ -98,13 +98,13 @@ AGIയെക്കുറിച്ച് സംസാരിക്കുമ്പ > | ML എന്ത്? | | > |--------------|-----------| -> | കൃത്രിമ ബുദ്ധിമുട്ടിന്റെ ഭാഗമായും, ഡാറ്റയുടെ അടിസ്ഥാനത്തിൽ പ്രശ്നം പരിഹരിക്കാൻ കമ്പ്യൂട്ടർ പഠിക്കുന്നതിനെ **മെഷീൻ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. ഈ കോഴ്സിൽ ക്ലാസിക്കൽ മെഷീൻ ലേണിംഗ് പരിഗണിക്കില്ല - നിങ്ങൾക്ക് വേറെ [Machine Learning for Beginners](http://aka.ms/ml-beginners) പാഠ്യപദ്ധതി കാണാം. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ml.png) | +> | കൃത്രിമ ബുദ്ധിമുട്ടിന്റെ ഭാഗമായും, ഡാറ്റയുടെ അടിസ്ഥാനത്തിൽ പ്രശ്നം പരിഹരിക്കാൻ കമ്പ്യൂട്ടർ പഠിക്കുന്നതിനെ **മെഷീൻ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. ഈ കോഴ്സിൽ ക്ലാസിക്കൽ മെഷീൻ ലേണിംഗ് പരിഗണിക്കില്ല - നിങ്ങൾക്ക് വേറെ [Machine Learning for Beginners](http://aka.ms/ml-beginners) പാഠ്യപദ്ധതി കാണാം. | ![ML for Beginners](../../../../translated_images/ml/ml-for-beginners.9e4fed176fd5817d.png) | ## എഐയുടെ ഒരു സംക്ഷിപ്ത ചരിത്രം കൃത്രിമ ബുദ്ധിമുട്ട് 20-ാം നൂറ്റാണ്ടിന്റെ മധ്യത്തിൽ ഒരു ശാഖയായി ആരംഭിച്ചു. ആദ്യം, സിംബോളിക് റീസണിംഗ് പ്രചാരത്തിലായിരുന്നു, ഇത് ചില പ്രധാന വിജയങ്ങൾക്കു വഴിതെളിച്ചു, ഉദാഹരണത്തിന് വിദഗ്ധ സിസ്റ്റങ്ങൾ – ചില പരിമിത പ്രശ്ന മേഖലകളിൽ വിദഗ്ധൻപോലെ പ്രവർത്തിക്കുന്ന കമ്പ്യൂട്ടർ പ്രോഗ്രാമുകൾ. എന്നാൽ, ഈ സമീപനം വലിയ തോതിൽ വ്യാപിപ്പിക്കാൻ കഴിയില്ലെന്ന് ഉടൻ മനസ്സിലായി. വിദഗ്ധനിൽ നിന്നുള്ള ജ്ഞാനം എടുക്കുകയും, അത് കമ്പ്യൂട്ടറിലേക്കു പ്രതിനിധാനം ചെയ്യുകയും, ആ ജ്ഞാനശേഖരം കൃത്യമായി നിലനിർത്തുകയും ചെയ്യുന്നത് വളരെ സങ്കീർണ്ണവും ചെലവേറിയതുമായ ജോലി ആയിരുന്നു. ഇതാണ് 1970-കളിലെ [AI വിന്റർ](https://en.wikipedia.org/wiki/AI_winter) ന്റെ കാരണമായത്. -എഐയുടെ സംക്ഷിപ്ത ചരിത്രം +എഐയുടെ സംക്ഷിപ്ത ചരിത്രം > ചിത്രം: [ഡ്മിത്രി സോഷ്നിക്കോവ്](http://soshnikov.com) diff --git a/translations/ml/lessons/2-Symbolic/Animals.ipynb b/translations/ml/lessons/2-Symbolic/Animals.ipynb index 7fb47fc1..f92bd22a 100644 --- a/translations/ml/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ml/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ഈ സാമ്പിളിൽ, ചില ശാരീരിക ലക്ഷണങ്ങളുടെ അടിസ്ഥാനത്തിൽ ഒരു മൃഗം നിർണയിക്കുന്ന ഒരു ലളിതമായ അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം നടപ്പിലാക്കും. സിസ്റ്റം താഴെ കാണുന്ന AND-OR വൃക്ഷം ഉപയോഗിച്ച് പ്രതിനിധീകരിക്കാം (ഇത് മുഴുവൻ വൃക്ഷത്തിന്റെ ഒരു ഭാഗമാണ്, നാം എളുപ്പത്തിൽ കൂടുതൽ നിയമങ്ങൾ ചേർക്കാം):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ml.png)\n" + "![](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ml/lessons/2-Symbolic/README.md b/translations/ml/lessons/2-Symbolic/README.md index d47bf677..8fd24d59 100644 --- a/translations/ml/lessons/2-Symbolic/README.md +++ b/translations/ml/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # അറിവ് പ്രതിനിധാനം ചെയ്യലും വിദഗ്ധ സിസ്റ്റങ്ങളും -![സിംബോളിക് AI ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം](../../../../translated_images/ai-symbolic.715a30cb610411a6.ml.png) +![സിംബോളിക് AI ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം](../../../../translated_images/ml/ai-symbolic.715a30cb610411a6.png) > സ്കെച്ച്നോട്ട്: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -35,13 +35,13 @@ AIയുടെ ആദ്യകാലങ്ങളിൽ, ബുദ്ധിമു * **അറിവ്**: വിവരങ്ങൾ നമ്മുടെ ലോക മോഡലിൽ ചേർക്കപ്പെടുന്നത്. ഉദാഹരണത്തിന്, കമ്പ്യൂട്ടർ എന്താണെന്ന് പഠിച്ചാൽ, അത് എങ്ങനെ പ്രവർത്തിക്കുന്നു, വില എത്രയാണ്, എന്തിന് ഉപയോഗിക്കാം തുടങ്ങിയ ആശയങ്ങൾ ഉണ്ടാകുന്നു. ഈ ബന്ധമുള്ള ആശയങ്ങളുടെ ശൃംഖലയാണ് അറിവ്. * **ബുദ്ധി**: ലോകത്തെ മനസ്സിലാക്കലിന്റെ ഒരു ഉയർന്ന തലമാണ്, ഇത് *മെറ്റാ-അറിവ്* പ്രതിനിധാനം ചെയ്യുന്നു, ഉദാ: അറിവ് എപ്പോൾ എങ്ങനെ ഉപയോഗിക്കണം എന്ന ധാരണ. - + *ചിത്രം [വിക്കിപീഡിയയിൽ നിന്ന്](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - സ്വന്തം കൃതി, CC BY-SA 4.0* അതിനാൽ, **അറിവ് പ്രതിനിധാനം** എന്ന പ്രശ്നം കമ്പ്യൂട്ടറിനുള്ളിൽ അറിവ് ഡാറ്റ രൂപത്തിൽ പ്രതിനിധാനം ചെയ്ത് അത് സ്വയം ഉപയോഗിക്കാൻ കഴിയുന്ന വിധം കണ്ടെത്തലാണ്. ഇത് ഒരു സ്പെക്ട്രം ആയി കാണാം: -![അറിവ് പ്രതിനിധാന സ്പെക്ട്രം](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ml.png) +![അറിവ് പ്രതിനിധാന സ്പെക്ട്രം](../../../../translated_images/ml/knowledge-spectrum.b60df631852c0217.png) > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | സിംബോളിക് AIയുടെ ആദ്യ വിജയങ്ങളിൽ ഒന്നായിരുന്നു **വിദഗ്ധ സിസ്റ്റങ്ങൾ** - ചില പരിമിത പ്രശ്ന മേഖലകളിൽ വിദഗ്ധനായി പ്രവർത്തിക്കാൻ രൂപകൽപ്പന ചെയ്ത കമ്പ്യൂട്ടർ സിസ്റ്റങ്ങൾ. ഇവ മനുഷ്യ വിദഗ്ധരിൽ നിന്നുള്ള **അറിവ് ബേസ്** ഉപയോഗിച്ച് നിർമ്മിക്കപ്പെട്ടിരുന്നു, അതിന്മേൽ പ്രവർത്തിക്കുന്ന **ഇൻഫറൻസ് എഞ്ചിൻ** ഉണ്ടായിരുന്നു. -![മനുഷ്യൻ്റെ ഘടന](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ml.png) | ![അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ml.png) +![മനുഷ്യൻ്റെ ഘടന](../../../../translated_images/ml/arch-human.5d4d35f1bba3ab1c.png) | ![അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റം](../../../../translated_images/ml/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ മനുഷ്യ നാഡീകുഴപ്പത്തിന്റെ ലളിതമായ ഘടന | അറിവ് അടിസ്ഥാനമാക്കിയ സിസ്റ്റത്തിന്റെ ഘടന @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ഉദാഹരണമായി, ഒരു ജീവിയെ അതിന്റെ ശാരീരിക സവിശേഷതകളുടെ അടിസ്ഥാനത്തിൽ തിരിച്ചറിയുന്ന വിദഗ്ധ സിസ്റ്റം പരിഗണിക്കാം: -![AND-OR ട്രീ](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ml.png) +![AND-OR ട്രീ](../../../../translated_images/ml/AND-OR-Tree.5592d2c70187f283.png) > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -168,7 +168,7 @@ THEN the animal is a carnivore സെമാന്റിക് വെബിൽ എല്ലാ പ്രതിനിധാനങ്ങളും ട്രിപ്പിളുകളിലാണ് അടിസ്ഥാനമാക്കുന്നത്. ഓരോ വസ്തുവും ഓരോ ബന്ധവും യുണീക്ക് ആയി URI ഉപയോഗിച്ച് തിരിച്ചറിയപ്പെടുന്നു. ഉദാഹരണത്തിന്, ഈ AI പാഠ്യപദ്ധതി 2022 ജനുവരി 1-ന് Dmitry Soshnikov വികസിപ്പിച്ചുവെന്ന് പറയാൻ താഴെ കാണുന്ന ട്രിപ്പിളുകൾ ഉപയോഗിക്കാം: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -179,7 +179,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre കൂടുതൽ സങ്കീർണ്ണമായ സാഹചര്യത്തിൽ, സൃഷ്ടാക്കളുടെ പട്ടിക നിർവചിക്കാൻ RDF ൽ നിർവചിച്ചിട്ടുള്ള ചില ഡാറ്റാ ഘടനകൾ ഉപയോഗിക്കാം. - + > മുകളിൽ കാണുന്ന ചിത്രങ്ങൾ [Dmitry Soshnikov](http://soshnikov.com) യുടെതാണ് @@ -203,7 +203,7 @@ GROUP BY ?eyeColorLabel > ✅ നിങ്ങളുടെ സ്വന്തം ഓന്റോളജികൾ നിർമ്മിക്കാൻ, അല്ലെങ്കിൽ നിലവിലുള്ളവ തുറക്കാൻ ആഗ്രഹിക്കുന്നുവെങ്കിൽ, [Protégé](https://protege.stanford.edu/) എന്ന മികച്ച ദൃശ്യ ഓന്റോളജി എഡിറ്റർ ഉണ്ട്. ഡൗൺലോഡ് ചെയ്യുക അല്ലെങ്കിൽ ഓൺലൈനിൽ ഉപയോഗിക്കുക. - + *Web Protégé എഡിറ്റർ റോമാനോവ് കുടുംബ ഓന്റോളജിയുമായി തുറന്നിരിക്കുന്ന ചിത്രം. Dmitry Soshnikov യുടെ സ്ക്രീൻഷോട്ട്* diff --git a/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md index f9be662b..91358a81 100644 --- a/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > ചിത്രങ്ങൾ [വിക്കിപീഡിയയിൽ നിന്ന്](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ഇവിടെ f ഒരു സ്റ്റെപ്പ് ആക്ടിവേഷൻ ഫംഗ്ഷനാണ് - + ## പേഴ്സെപ്ട്രോൺ പരിശീലനം diff --git a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 2e091042..a16e113d 100644 --- a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "ബൈനറി ക്ലാസിഫിക്കേഷൻ പ്രശ്നത്തിനായി ഒരു ഡാറ്റാസെറ്റ് ഞങ്ങൾ സൃഷ്ടിച്ചിട്ടുണ്ട്. എങ്കിലും, തുടക്കത്തിൽ തന്നെ ഇത് മൾട്ടി-ക്ലാസ് ക്ലാസിഫിക്കേഷനായി പരിഗണിക്കാം, അതിനാൽ പിന്നീട് നമുക്ക് എളുപ്പത്തിൽ കോഡ് മൾട്ടി-ക്ലാസ് ക്ലാസിഫിക്കേഷനായി മാറ്റാൻ കഴിയും. ഈ സാഹചര്യത്തിൽ, നമ്മുടെ ഒറ്റ-ലെയർ പെർസെപ്ട്രോൺ താഴെ കാണുന്ന ആർക്കിടെക്ചർ ഉണ്ടാകും:\n", "\n", - "\n", + "\n", "\n", "നെറ്റ്‌വർക്കിന്റെ രണ്ട് ഔട്ട്പുട്ടുകൾ രണ്ട് ക്ലാസുകളെ പ്രതിനിധീകരിക്കുന്നു, രണ്ട് ഔട്ട്പുട്ടുകളിൽ ഏറ്റവും ഉയർന്ന മൂല്യമുള്ള ക്ലാസ് ശരിയായ പരിഹാരമായി കണക്കാക്കപ്പെടുന്നു.\n", "\n", @@ -489,7 +489,7 @@ "\n", "ക്ലാസുകൾ 2-ലധികമുണ്ടെങ്കിൽ, softmax അവയിൽ എല്ലാം സാധ്യതകൾ സാധാരണവത്കരിക്കും. MNIST അക്കങ്ങൾ തിരിച്ചറിയുന്ന ഒരു നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചറിന്റെ ചിത്രരൂപം ഇതാ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.ml.png)\n" + "![MNIST Classifier](../../../../../translated_images/ml/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## കംപ്യൂട്ടേഷണൽ ഗ്രാഫ്\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾവരെ, നാം നെറ്റ്‌വർക്കിലെ വ്യത്യസ്ത ലെയറുകൾക്കായി വ്യത്യസ്ത ക്ലാസുകൾ നിർവചിച്ചിട്ടുണ്ട്. ആ ലെയറുകളുടെ സംയോജനം **കംപ്യൂട്ടേഷണൽ ഗ്രാഫ്** ആയി പ്രതിനിധീകരിക്കാം. ഇപ്പോൾ നാം നൽകിയ ട്രെയിനിംഗ് ഡാറ്റാസെറ്റിനോ അതിന്റെ ഭാഗത്തിനോ ലോസ് കണക്കാക്കാൻ താഴെ പറയുന്ന രീതിയിൽ കഴിയും:\n" ] @@ -687,7 +687,7 @@ "source": [ "## ബാക്ക്വർഡ് പ്രൊപ്പഗേഷൻ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ഇത് നോഡ് $z$-യിലെ മാറ്റങ്ങളുമായി ബന്ധപ്പെട്ടിരിക്കുന്നു: $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", "* ഈ പിശക് കുറയ്ക്കാൻ, പാരാമീറ്ററുകൾ അനുസരിച്ച് ക്രമീകരിക്കണം: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (അതുപോലെ $b$-ക്കും)\n", "\n", - "\n", + "\n", "\n", "ഈ പ്രക്രിയ നെറ്റ്‌വർക്കിന്റെ ഔട്ട്പുട്ടിൽ നിന്നുള്ള നഷ്ട പിശക് പാരാമീറ്ററുകളിലേക്ക് തിരിച്ച് വിതരണം ചെയ്യുന്നതായി ആരംഭിക്കുന്നു. അതുകൊണ്ടുതന്നെ ഈ പ്രക്രിയയെ **ബാക്ക് പ്രൊപ്പഗേഷൻ** എന്ന് വിളിക്കുന്നു.\n", "\n", @@ -1265,7 +1265,7 @@ "* പരിശീലന നഷ്ടം കുറവാണ് - മോഡലിന് പരിശീലന ഡാറ്റയെ നന്നായി അനുകരിക്കാൻ മതിയായ പ്രകടനശക്തി ഉണ്ട്.\n", "* വാലിഡേഷൻ നഷ്ടം പരിശീലന നഷ്ടത്തേക്കാൾ വളരെ കൂടുതലായിരിക്കാം, കൂടാതെ പരിശീലനത്തിനിടെ ഇത് വർദ്ധിക്കാനും തുടങ്ങാം - കാരണം മോഡൽ പരിശീലന പോയിന്റുകൾ \"ഓർമ്മിച്ച്\" വെക്കുന്നു, അതിനാൽ \"മൊത്തത്തിലുള്ള ചിത്രം\" നഷ്ടപ്പെടുന്നു.\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.ml.png)\n", + "![Overfitting](../../../../../translated_images/ml/overfit.a0bd57f717c15769.png)\n", "\n", "> ഈ ചിത്രത്തിൽ, `x` പരിശീലന ഡാറ്റയെ സൂചിപ്പിക്കുന്നു, `o` - വാലിഡേഷൻ ഡാറ്റ. ഇടത് ഭാഗം - ലീനിയർ മോഡൽ (ഒറ്റ ലെയർ), ഇത് ഡാറ്റയുടെ സ്വഭാവം നന്നായി അനുകരിക്കുന്നു. വലത് ഭാഗം - ഓവർഫിറ്റഡ് മോഡൽ, മോഡൽ പരിശീലന ഡാറ്റയെ പൂർണ്ണമായും അനുകരിക്കുന്നു, പക്ഷേ മറ്റ് ഡാറ്റയുമായി (വാലിഡേഷൻ പിശക് വളരെ ഉയർന്നതാണ്) യോജിപ്പിക്കാനാകുന്നില്ല.\n" ] diff --git a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md index a615c8ae..da298e89 100644 --- a/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: ഈ എല്ലാ വ്യഞ്ജനങ്ങളുടെയും ഇടത്തരം ഭാഗം ഒരുപോലെയാണ്, അതിനാൽ ലോസ് ഫംഗ്ഷനിൽ നിന്ന് "പിന്നിലേക്ക്" കണക്കുകൂട്ടി ഡെരിവേറ്റീവുകൾ എളുപ്പത്തിൽ കണ്ടെത്താം. അതുകൊണ്ട് മൾട്ടി-ലെയർഡ് പേഴ്സെപ്ട്രോൺ പരിശീലന രീതി **ബാക്ക്‌പ്രൊപ്പഗേഷൻ** അല്ലെങ്കിൽ 'ബാക്ക്‌പ്രോപ്പ്' എന്ന് വിളിക്കുന്നു. -compute graph +compute graph > TODO: ചിത്രം സൈറ്റേഷൻ diff --git a/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md index a2c61fcc..704e619b 100644 --- a/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 5 പോയിന്റുകൾ (ഗ്രാഫുകളിൽ `x` ആയി പ്രതിനിധീകരിച്ചിരിക്കുന്നു) ഏകീകരിക്കുന്ന പ്രശ്നം പരിഗണിക്കൂ: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ml.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ml.jpg) +![linear](../../../../../translated_images/ml/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ml/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **രേഖീയ മോഡൽ, 2 പാരാമീറ്ററുകൾ** | **അരേഖീയ മോഡൽ, 7 പാരാമീറ്ററുകൾ** പരിശീലന പിശക് = 5.3 | പരിശീലന പിശക് = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: മുകളിൽ കാണിച്ച ഗ്രാഫിൽ നിന്ന്, ഓവർഫിറ്റിംഗ് വളരെ കുറഞ്ഞ പരിശീലന പിശക്, കൂടിയ വാലിഡേഷൻ പിശക് എന്നിവയാൽ കണ്ടെത്താം. സാധാരണയായി പരിശീലന സമയത്ത് പരിശീലനവും വാലിഡേഷനും പിശകുകൾ കുറയാൻ തുടങ്ങും, പിന്നീട് വാലിഡേഷൻ പിശക് കുറയുന്നത് നിർത്തി ഉയരാൻ തുടങ്ങും. ഇത് ഓവർഫിറ്റിംഗിന്റെ സൂചനയാണ്, ഈ ഘട്ടത്തിൽ പരിശീലനം നിർത്തേണ്ടതായിരിക്കും (അല്ലെങ്കിൽ മോഡലിന്റെ സ്നാപ്ഷോട്ട് എടുക്കാം). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ml.png) +![overfitting](../../../../../translated_images/ml/Overfitting.408ad91cd90b4371.png) ## ഓവർഫിറ്റിംഗ് തടയാനുള്ള മാർഗങ്ങൾ diff --git a/translations/ml/lessons/3-NeuralNetworks/README.md b/translations/ml/lessons/3-NeuralNetworks/README.md index c7086a8e..ebc10b03 100644 --- a/translations/ml/lessons/3-NeuralNetworks/README.md +++ b/translations/ml/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ന്യൂറൽ നെറ്റ്വർക്കുകളിലേക്ക് പരിചയം -![Intro Neural Networks ഉള്ളടക്കത്തിന്റെ ഒരു ഡൂഡിൽ സംഗ്രഹം](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ml.png) +![Intro Neural Networks ഉള്ളടക്കത്തിന്റെ ഒരു ഡൂഡിൽ സംഗ്രഹം](../../../../translated_images/ml/ai-neuralnetworks.1c687ae40bc86e83.png) പരിചയത്തിൽ ചർച്ച ചെയ്തതുപോലെ, ബുദ്ധിമുട്ട് നേടാനുള്ള ഒരു മാർഗം **കമ്പ്യൂട്ടർ മോഡൽ** അല്ലെങ്കിൽ **കൃത്രിമ മസ്തിഷ്കം** പരിശീലിപ്പിക്കുകയാണ്. 20-ആം നൂറ്റാണ്ടിന്റെ മധ്യത്തിൽ നിന്ന് ഗവേഷകർ വിവിധ ഗണിത മോഡലുകൾ പരീക്ഷിച്ചു, അടുത്തിടെ ഈ ദിശ വളരെ വിജയകരമായി തെളിഞ്ഞു. മസ്തിഷ്കത്തിന്റെ ഇത്തരം ഗണിത മോഡലുകൾ **ന്യൂറൽ നെറ്റ്വർക്കുകൾ** എന്ന് വിളിക്കുന്നു. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ജീവശാസ്ത്രത്തിൽനിന്ന്, നമ്മുടെ മസ്തിഷ്കം ന്യൂറൽ സെല്ലുകൾ (ന്യൂറോണുകൾ) കൊണ്ട് നിർമ്മിതമാണ്, ഓരോന്നിനും നിരവധി "ഇൻപുട്ടുകൾ" (ഡെൻഡ്രൈറ്റുകൾ) ഉണ്ട്, ഒറ്റ "ഔട്ട്പുട്ട്" (ആക്സൺ) ഉണ്ട്. ഡെൻഡ്രൈറ്റുകളും ആക്സണുകളും വൈദ്യുത സിഗ്നലുകൾ കൈമാറാൻ കഴിയും, അവ തമ്മിലുള്ള ബന്ധങ്ങൾ — സിനാപ്സുകൾ എന്ന് അറിയപ്പെടുന്നു — വൈദ്യുത ചാലകതയുടെ വ്യത്യസ്ത നിലകൾ കാണിക്കുന്നു, ഇത് ന്യൂറോട്രാൻസ്മിറ്ററുകൾ നിയന്ത്രിക്കുന്നു. -![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ml.jpg) | ![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ml.png) +![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ന്യൂറോണിന്റെ മോഡൽ](../../../../translated_images/ml/artneuron.1a5daa88d20ebe6f.png) ----|---- യഥാർത്ഥ ന്യൂറോൺ *([ചിത്രം](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) വിക്കിപീഡിയയിൽ നിന്നുള്ളത്)* | കൃത്രിമ ന്യൂറോൺ *(ചിത്രം രചയിതാവ്)* അതിനാൽ, ഒരു ന്യൂറോണിന്റെ ഏറ്റവും ലളിതമായ ഗണിത മോഡൽ X1, ..., XN എന്ന നിരവധി ഇൻപുട്ടുകളും Y എന്ന ഔട്ട്പുട്ടും, W1, ..., WN എന്ന ഭാരങ്ങൾ ഉൾക്കൊള്ളുന്നു. ഔട്ട്പുട്ട് കണക്കാക്കുന്നത്: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ഇവിടെ f ഒരു നോൺ-ലിനിയർ **ആക്ടിവേഷൻ ഫംഗ്ഷൻ** ആണ്. diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md index 3faafc6d..c8c5d3c8 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ഉപയോഗിച്ച് വീഡിയോ ഫ്രെയിമ * **ബ്രെയിൽ പുസ്തകത്തിന്റെ ഫോട്ടോ പ്രീ-പ്രോസസ്സിംഗ്**. thresholding, ഫീച്ചർ കണ്ടെത്തൽ, perspective transformation, NumPy മാനിപ്പുലേഷനുകൾ ഉപയോഗിച്ച് വ്യക്തിഗത ബ്രെയിൽ ചിഹ്നങ്ങൾ വേർതിരിച്ച് പിന്നീട് ന്യൂറൽ നെറ്റ്വർക്കിൽ വർഗ്ഗീകരിക്കാൻ തയ്യാറാക്കൽ. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.ml.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.ml.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.ml.png) +![Braille Image](../../../../../translated_images/ml/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/ml/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/ml/braille-symbols.0159185ab69d5339.png) ----|-----|----- > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് * **ഫ്രെയിം വ്യത്യാസം ഉപയോഗിച്ച് വീഡിയോയിൽ ചലനം കണ്ടെത്തൽ**. ക്യാമറ സ്ഥിരമാണെങ്കിൽ, ക്യാമറ ഫീഡിലെ ഫ്രെയിമുകൾ തമ്മിൽ വളരെ സമാനമായിരിക്കും. ഫ്രെയിമുകൾ അറേകളായി പ്രതിനിധീകരിക്കപ്പെടുന്നതിനാൽ, രണ്ട് തുടർച്ചയായ ഫ്രെയിമുകളുടെ അറേകൾ തമ്മിൽ വ്യത്യാസം എടുത്താൽ പിക്‌സൽ വ്യത്യാസം കിട്ടും, ഇത് സ്ഥിരമായ ഫ്രെയിമുകൾക്കായി കുറവായിരിക്കും, ചിത്രത്തിൽ വലിയ ചലനം ഉണ്ടാകുമ്പോൾ ഉയരും. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.ml.png) +![Image of video frames and frame differences](../../../../../translated_images/ml/frame-difference.706f805491a0883c.png) > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് @@ -91,7 +91,7 @@ OpenCV ഉപയോഗിച്ച് വീഡിയോ ഫ്രെയിമ - **Dense Optical Flow** ഓരോ പിക്‌സലും എവിടെ പോകുന്നു എന്ന് കാണിക്കുന്ന വെക്ടർ ഫീൽഡ് കണക്കാക്കുന്നു - **Sparse Optical Flow** ചിത്രത്തിലെ ചില വ്യത്യസ്തമായ ഫീച്ചറുകൾ (ഉദാ: അരികുകൾ) എടുത്ത്, അവയുടെ ട്രാജക്ടറി ഫ്രെയിമിൽ നിന്ന് ഫ്രെയിമിലേക്ക് നിർമ്മിക്കുന്നു. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.ml.png) +![Image of Optical Flow](../../../../../translated_images/ml/optical.1f4a94464579a83a.png) > ചിത്രം [OpenCV.ipynb](OpenCV.ipynb) നിന്നാണ് @@ -117,7 +117,7 @@ AI ഷോയിൽ നിന്നുള്ള [ഈ വീഡിയോ](https:// ഈ ലാബിൽ, ലളിതമായ ജെസ്റ്ററുകളുള്ള ഒരു വീഡിയോ എടുത്ത്, optical flow ഉപയോഗിച്ച് മുകളിൽ/താഴെ/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ കണ്ടെത്തുക. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index 92c777b1..4b4ea33d 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ഒരു വ്യക്തിയുടെ കൈവിരൽ സ്ഥിരമായ പശ്ചാത്തലത്തിൽ ഇടത്തേക്ക്/വലത്തേക്ക്/മുകളിലേക്ക്/താഴേക്ക് ചലിക്കുന്ന [ഈ വീഡിയോ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) പരിഗണിക്കുക.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**നിങ്ങളുടെ ലക്ഷ്യം** ഓപ്റ്റിക്കൽ ഫ്ലോ ഉപയോഗിച്ച് വീഡിയോയിലെ ഏത് ഭാഗങ്ങളിൽ മുകളിലേക്ക്/താഴേക്ക്/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ ഉണ്ടെന്ന് കണ്ടെത്തുക എന്നതാണ്.\n", "\n", diff --git a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 257ff502..66c22c27 100644 --- a/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/ml/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ഒരു വ്യക്തിയുടെ കൈവിരൽ സ്ഥിരമായ പശ്ചാത്തലത്തിൽ ഇടത്തേക്ക്/വലത്തേക്ക്/മുകളിലേക്ക്/താഴേക്ക് ചലിക്കുന്ന [ഈ വീഡിയോ](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) പരിഗണിക്കുക. -Palm Movement Frame +Palm Movement Frame **നിങ്ങളുടെ ലക്ഷ്യം** ഓപ്റ്റിക്കൽ ഫ്ലോ ഉപയോഗിച്ച് വീഡിയോയിലെ ഏത് ഭാഗങ്ങളിൽ മുകളിലേക്ക്/താഴേക്ക്/ഇടത്തേക്ക്/വലത്തേക്ക് ചലനങ്ങൾ ഉണ്ടെന്ന് കണ്ടെത്താൻ കഴിയുക എന്നതാണ്. diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index fcdd5619..772d83c9 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്കേഷനിൽ 92.7% കൃത്യത നേടിയ ഒരു നെറ്റ്‌വർക്കാണ്. ഇതിന് താഴെ പറയുന്ന ലെയർ ഘടനയുണ്ട്: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ml.jpg) +![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.jpg) നിങ്ങൾക്ക് കാണാമല്ലോ, VGG പരമ്പരാഗത പിരമിഡ് ആർക്കിടെക്ചർ പിന്തുടരുന്നു, convolution-pooling ലെയറുകളുടെ ഒരു ശ്രേണിയാണ് ഇത്. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ml.jpg) +![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.jpg) > ചിത്രം [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) നിന്നാണ് @@ -25,7 +25,7 @@ VGG-16 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്ക ResNet മൈക്രോസോഫ്റ്റ് റിസർച്ച് 2015-ൽ നിർദ്ദേശിച്ച മോഡലുകളുടെ ഒരു കുടുംബമാണ്. ResNet-ന്റെ പ്രധാന ആശയം **residual blocks** ഉപയോഗിക്കുകയാണ്: - + > ചിത്രം [ഈ പേപ്പർ](https://arxiv.org/pdf/1512.03385.pdf) നിന്നാണ് @@ -37,7 +37,7 @@ ResNet മൈക്രോസോഫ്റ്റ് റിസർച്ച് 2015- Google Inception ആർക്കിടെക്ചർ ഈ ആശയം ഒരു പടി മുന്നോട്ട് കൊണ്ടുപോകുന്നു, ഓരോ നെറ്റ്‌വർക്ക് ലെയറും പല വ്യത്യസ്ത പാതകളുടെ സംയോജനം ആയി നിർമ്മിക്കുന്നു: - + > ചിത്രം [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) നിന്നാണ് diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index ac459443..8bd637cd 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "അതിനാൽ, സാധാരണ CNN-ൽ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ ഉണ്ടാകും, അവയുടെ ഇടയിൽ ചിത്രത്തിന്റെ വലുപ്പം കുറയ്ക്കാൻ പൂലിംഗ് ലെയറുകൾ ഉപയോഗിക്കും. പാറ്റേണുകൾ കൂടുതൽ സങ്കീർണ്ണമാകുമ്പോൾ, കൂടുതൽ ഫിൽട്ടറുകളുടെ എണ്ണം വർദ്ധിപ്പിക്കും, കാരണം അന്വേഷിക്കേണ്ട രസകരമായ സംയോജനങ്ങൾ കൂടുതലാകും.\n", "\n", - "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ml.png)\n", + "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "സ്ഥലം വലുപ്പം കുറയുകയും ഫീച്ചർ/ഫിൽട്ടർ വലുപ്പം വർദ്ധിക്കുകയും ചെയ്യുന്നതിനാൽ, ഈ ആർക്കിടെക്ചർ **പിരമിഡ് ആർക്കിടെക്ചർ** എന്നും വിളിക്കുന്നു.\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 21c20492..cc871370 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "പരമ്പരാഗത കമ്പ്യൂട്ടർ ദൃശ്യശാസ്ത്രത്തിൽ, ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ സൃഷ്ടിക്കാൻ പല ഫിൽട്ടറുകളും ഉപയോഗിച്ചിരുന്നു, പിന്നീട് അവ മെഷീൻ ലേണിംഗ് ആൽഗോരിതം ഉപയോഗിച്ച് ക്ലാസിഫയർ നിർമ്മിക്കാൻ ഉപയോഗിച്ചിരുന്നു. ആ ഫിൽട്ടറുകൾ ചില മൃഗങ്ങളുടെ ദൃശ്യ സംവിധാനത്തിൽ ലഭ്യമായ ന്യുറൽ ഘടനകളോട് സാമ്യമുള്ളതാണ്.\n", "\n", - "\n", + "\n", "\n", "എങ്കിലും, ഡീപ്പ് ലേണിങ്ങിൽ, ക്ലാസിഫിക്കേഷൻ പ്രശ്നം പരിഹരിക്കാൻ ഏറ്റവും മികച്ച കോൺവല്യൂഷണൽ ഫിൽട്ടറുകൾ **കൽപ്പിക്കാൻ പഠിക്കുന്ന** നെറ്റ്‌വർക്കുകൾ നിർമ്മിക്കുന്നു. അതിനായി, **കോൺവല്യൂഷണൽ ലെയറുകൾ** പരിചയപ്പെടുത്തുന്നു.\n" ] @@ -358,7 +358,7 @@ "\n", "അതിനാൽ, സാധാരണ CNN-ൽ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ ഉണ്ടാകും, അവയുടെ ഇടയിൽ പൂലിംഗ് ലെയറുകൾ ചിത്രത്തിന്റെ വലുപ്പം കുറയ്ക്കാൻ ഉപയോഗിക്കും. പാറ്റേണുകൾ കൂടുതൽ സങ്കീർണ്ണമായതിനാൽ, കൂടുതൽ ഫിൽട്ടറുകളുടെ എണ്ണം വർദ്ധിപ്പിക്കും, കാരണം കൂടുതൽ സങ്കീർണ്ണമായ സംയോജനങ്ങൾ കണ്ടെത്തേണ്ടതുണ്ട്.\n", "\n", - "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ml.png)\n", + "![പൂളിംഗ് ലെയറുകളോടുകൂടിയ നിരവധി കോൺവല്യൂഷണൽ ലെയറുകൾ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "സ്ഥലം വലുപ്പം കുറയുകയും ഫീച്ചർ/ഫിൽട്ടർ വലുപ്പം വർദ്ധിക്കുകയും ചെയ്യുന്നതിനാൽ, ഈ ആർക്കിടെക്ചർ **പിരമിഡ് ആർക്കിടെക്ചർ** എന്നും വിളിക്കുന്നു.\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md index 738184db..718b2caf 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: പാറ്റേണുകൾ എടുക്കാൻ, നാം **കോൺവല്യൂഷണൽ ഫിൽട്ടറുകൾ** എന്ന ആശയം ഉപയോഗിക്കും. നിങ്ങൾക്ക് അറിയാമല്ലോ, ഒരു ചിത്രം 2D-മാട്രിക്സ് അല്ലെങ്കിൽ നിറത്തിന്റെ ആഴമുള്ള 3D-ടെൻസർ ആയി പ്രതിനിധീകരിക്കപ്പെടുന്നു. ഒരു ഫിൽട്ടർ പ്രയോഗിക്കുന്നത് അർത്ഥമാക്കുന്നത്, നാം ചെറിയ **ഫിൽട്ടർ കർണൽ** മാട്രിക്സ് എടുത്ത്, യഥാർത്ഥ ചിത്രത്തിലെ ഓരോ പിക്‌സലിനും സമീപമുള്ള പോയിന്റുകളുമായി ഭാരിത ശരാശരി കണക്കാക്കുകയാണ്. ഇത് ഒരു ചെറിയ വിൻഡോ മുഴുവൻ ചിത്രത്തിലൂടെ സ്ലൈഡ് ചെയ്യുന്നതുപോലെ കാണാം, ഫിൽട്ടർ കർണൽ മാട്രിക്സിലെ ഭാരങ്ങൾ അനുസരിച്ച് എല്ലാ പിക്‌സലുകളും ശരാശരി ചെയ്യുന്നു. -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.ml.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ml.png) +![Vertical Edge Filter](../../../../../translated_images/ml/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/ml/filter-horiz.59b80ed4feb946ef.png) ----|---- > ചിത്രം: Dmitry Soshnikov ഉദാഹരണത്തിന്, 3x3 വെർട്ടിക്കൽ എഡ്ജ്, ഹോരിസോണ്ടൽ എഡ്ജ് ഫിൽട്ടറുകൾ MNIST അക്കങ്ങളിൽ പ്രയോഗിച്ചാൽ, യഥാർത്ഥ ചിത്രത്തിലെ വെർട്ടിക്കൽ, ഹോരിസോണ്ടൽ എഡ്ജുകൾ ഉള്ള സ്ഥലങ്ങളിൽ ഹൈലൈറ്റുകൾ (ഉയർന്ന മൂല്യങ്ങൾ) ലഭിക്കും. അതിനാൽ ആ രണ്ട് ഫിൽട്ടറുകൾ എഡ്ജുകൾ "തിരയാൻ" ഉപയോഗിക്കാം. അതുപോലെ, നാം മറ്റ് താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകൾ കണ്ടെത്താൻ വ്യത്യസ്ത ഫിൽട്ടറുകൾ രൂപകൽപ്പന ചെയ്യാം: - + > [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) ചിത്രം @@ -38,7 +38,7 @@ CNN-കൾ പ്രവർത്തിക്കുന്നത് താഴെ * ഫിൽട്ടറുകൾ സ്വയം പരിശീലിക്കപ്പെടുന്ന വിധത്തിൽ നെറ്റ്വർക്ക് രൂപകൽപ്പന ചെയ്യാം * നാം ഈ സമീപനം ഉപയോഗിച്ച് ഉയർന്ന തലത്തിലുള്ള ഫീച്ചറുകളിലും പാറ്റേണുകൾ കണ്ടെത്താം, യഥാർത്ഥ ചിത്രത്തിൽ മാത്രമല്ല. അതായത് CNN ഫീച്ചർ എക്സ്ട്രാക്ഷൻ പിക്‌സൽ സംയോജനങ്ങളിൽ നിന്നാരംഭിച്ച് ചിത്രഭാഗങ്ങളുടെ ഉയർന്ന തലത്തിലുള്ള സംയോജനങ്ങളിലേക്കുള്ള ഫീച്ചറുകളുടെ ഹയർആർക്കിയിൽ പ്രവർത്തിക്കുന്നു. -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ml.png) +![Hierarchical Feature Extraction](../../../../../translated_images/ml/FeatureExtractionCNN.d9b456cbdae7cb64.png) > [Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) എന്ന പേപ്പറിൽ നിന്നുള്ള ചിത്രം, അവരുടെ [ഗവേഷണത്തെ അടിസ്ഥാനമാക്കി](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN-കൾ പ്രവർത്തിക്കുന്നത് താഴെ ഉദാഹരണമായി, 2014-ൽ ImageNet ടോപ്പ്-5 ക്ലാസിഫിക്കേഷനിൽ 92.7% കൃത്യത നേടിയ VGG-16 നെറ്റ്വർക്ക് ആർക്കിടെക്ചർ നോക്കാം: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ml.jpg) +![ImageNet Layers](../../../../../translated_images/ml/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ml.jpg) +![ImageNet Pyramid](../../../../../translated_images/ml/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) നിന്നുള്ള ചിത്രം diff --git a/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 02d33303..2acd7888 100644 --- a/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ml/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: നാം ഉപയോഗിക്കുന്നത് [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ആണ്, ഇതിൽ 37 വ്യത്യസ്ത നായയും പൂച്ചയും ജാതികളുടെ ചിത്രങ്ങൾ ഉൾപ്പെടുന്നു. -![നാം കൈകാര്യം ചെയ്യാൻ പോകുന്ന ഡാറ്റാസെറ്റ്](../../../../../../translated_images/data.50b2a9d5484bdbf0.ml.png) +![നാം കൈകാര്യം ചെയ്യാൻ പോകുന്ന ഡാറ്റാസെറ്റ്](../../../../../../translated_images/ml/data.50b2a9d5484bdbf0.png) ഡാറ്റാസെറ്റ് ഡൗൺലോഡ് ചെയ്യാൻ, ഈ കോഡ് സ്നിപ്പെറ്റ് ഉപയോഗിക്കുക: diff --git a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 0f1034cd..028016e2 100644 --- a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ആദർശമായ പൂച്ചയെ ദൃശ്യവൽക്കരിക്കാൻ, നാം ഒരു യാദൃച്ഛിക ശബ്ദചിത്രം ഉപയോഗിച്ച് തുടങ്ങും, പിന്നീട് ഗ്രേഡിയന്റ് ഡിസെന്റ് മെച്ചപ്പെടുത്തൽ സാങ്കേതിക വിദ്യ ഉപയോഗിച്ച് ചിത്രം ക്രമീകരിച്ച് ഒരു നെറ്റ്‌വർക്ക് പൂച്ചയെ തിരിച്ചറിയാൻ ശ്രമിക്കും.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ml.png)\n", + "![Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ഇതാണ് നമ്മുടെ ആരംഭചിത്രം:\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md index f2484517..d5045e8b 100644 --- a/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ml/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras-നും PyTorch-നും ImageNet ചിത്രങ്ങളിൽ പ ഇവിടെ VGG-16 നെറ്റ്‌വർക്ക് ഒരു പൂച്ചയുടെ ചിത്രത്തിൽ നിന്നെടുത്ത ഫീച്ചറുകളുടെ ഉദാഹരണം കാണാം: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ml.png) +![Features extracted by VGG-16](../../../../../translated_images/ml/features.6291f9c7ba3a0b95.png) ## പൂച്ചകളും നായകളും ഡാറ്റാസെറ്റ് @@ -48,19 +48,19 @@ Keras-നും PyTorch-നും ImageNet ചിത്രങ്ങളിൽ പ ഒരു സമീപനം, ഒരു യാദൃച്ഛിക ചിത്രം കൊണ്ട് ആരംഭിച്ച്, **ഗ്രേഡിയന്റ് ഡിസെന്റ് ഓപ്റ്റിമൈസേഷൻ** സാങ്കേതിക വിദ്യ ഉപയോഗിച്ച് ആ ചിത്രം അങ്ങനെ ക്രമീകരിക്കാൻ ശ്രമിക്കുക എന്നതാണ്, നെറ്റ്‌വർക്ക് അത് പൂച്ചയാണെന്ന് കരുതാൻ തുടങ്ങും വിധം. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ml.png) +![Image Optimization Loop](../../../../../translated_images/ml/ideal-cat-loop.999fbb8ff306e044.png) എങ്കിലും, ഇത് ചെയ്താൽ, നമുക്ക് യാദൃച്ഛിക ശബ്ദം പോലുള്ള ഒന്നാണ് ലഭിക്കുന്നത്. കാരണം *നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രം പൂച്ചയാണെന്ന് കരുതാൻ നിരവധി മാർഗ്ഗങ്ങൾ ഉണ്ട്*, ചിലത് ദൃശ്യമായി അർത്ഥവത്തല്ലാത്തവയും. ആ ചിത്രങ്ങളിൽ പൂച്ചയ്ക്ക് സാധാരണമായ പല പാറ്റേണുകളും ഉണ്ടെങ്കിലും, അവ ദൃശ്യമായി വ്യത്യസ്തമാകാൻ യാതൊരു നിയന്ത്രണവും ഇല്ല. ഫലം മെച്ചപ്പെടുത്താൻ, നാം നഷ്ട ഫംഗ്ഷനിൽ മറ്റൊരു പദം ചേർക്കാം, അത് **വേരിയേഷൻ ലോസ്** എന്ന് വിളിക്കുന്നു. ഇത് ചിത്രത്തിലെ സമീപമുള്ള പിക്‌സലുകൾ എത്രത്തോളം സമാനമാണെന്ന് കാണിക്കുന്ന ഒരു മെട്രിക് ആണ്. വേരിയേഷൻ ലോസ് കുറയ്ക്കുന്നത് ചിത്രം മൃദുവാക്കുകയും ശബ്ദം നീക്കം ചെയ്യുകയും ചെയ്യുന്നു - അതിലൂടെ കൂടുതൽ ദൃശ്യപരമായി ആകർഷകമായ പാറ്റേണുകൾ വെളിപ്പെടുത്തുന്നു. താഴെ "ആദർശ" ചിത്രങ്ങളുടെ ഉദാഹരണം കാണാം, പൂച്ചയും സീബ്രയും ഉയർന്ന സാധ്യതയോടെ വർഗ്ഗീകരിക്കപ്പെട്ടവ: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ml.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ml.png) +![Ideal Cat](../../../../../translated_images/ml/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/ml/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *ആദർശ പൂച്ച* | *ആദർശ സീബ്ര* ഇത്തരത്തിലുള്ള സമീപനം ന്യൂറൽ നെറ്റ്‌വർക്കിൽ **എതിരാളി ആക്രമണങ്ങൾ** നടത്താനും ഉപയോഗിക്കാം. ഒരു നായയെ പൂച്ചയെന്നു തോന്നിക്കാൻ നമുക്ക് ആഗ്രഹമുണ്ടെന്ന് കരുതുക. ഒരു നായയുടെ ചിത്രം, നെറ്റ്‌വർക്ക് നായയെന്നു തിരിച്ചറിയുന്ന ചിത്രം, നാം ഗ്രേഡിയന്റ് ഡിസെന്റ് ഓപ്റ്റിമൈസേഷൻ ഉപയോഗിച്ച് ചെറിയ മാറ്റങ്ങൾ വരുത്തി, നെറ്റ്‌വർക്ക് അത് പൂച്ചയെന്നു തിരിച്ചറിയാൻ തുടങ്ങും വരെ ക്രമീകരിക്കാം: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ml.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ml.png) +![Picture of a Dog](../../../../../translated_images/ml/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/ml/adversarial-dog.d9fc7773b0142b89.png) -----|----- *നായയുടെ യഥാർത്ഥ ചിത്രം* | *പൂച്ചയെന്നു തിരിച്ചറിയപ്പെട്ട നായയുടെ ചിത്രം* diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 0704df86..7845f29f 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ഓട്ടോഎൻകോഡർ യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പരമാവധി പിടിച്ചുപറ്റി കൃത്യമായ പുനഃസൃഷ്ടിക്ക് പരിശീലിപ്പിക്കപ്പെടുന്നതിനാൽ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ ഏറ്റവും മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ml.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", @@ -941,7 +941,7 @@ " * നാം $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ എന്ന വിതരണത്തിൽ നിന്ന് `sample(z_val in code)` എന്ന വെക്ടർ സാമ്പിൾ ചെയ്യുന്നു\n", " * ഡീകോഡർ `sample` എന്ന ഇൻപുട്ട് വെക്ടർ ഉപയോഗിച്ച് ഒറിജിനൽ ചിത്രം പുനരുദ്ധരിക്കാൻ ശ്രമിക്കുന്നു\n", "\n", - " \n", + " \n", "\n", " > ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ഇസാക്ക് ഡൈക്മാൻ എഴുതിയത്\n" ] @@ -1264,7 +1264,7 @@ "\n", "ഈ സമീപനത്തിൽ നമുക്ക് **മൂന്ന് നഷ്ട ഫംഗ്ഷനുകൾ** ഉണ്ട്: GAN-കളിൽ നിന്നുള്ള generator നഷ്ടം, discriminator നഷ്ടം, കൂടാതെ VAE-യിൽ നിന്നുള്ള പുനർനിർമ്മാണ നഷ്ടം.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം Felipe Ducau എഴുതിയ [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) ൽ നിന്നാണ്\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 43a0aa0f..f875df69 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "ഓട്ടോഎൻകോഡർ യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പരമാവധി പിടിച്ചുപറ്റി കൃത്യമായ പുനഃസൃഷ്ടിക്ക് പരിശീലിപ്പിക്കപ്പെടുന്നതിനാൽ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ml.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", @@ -888,7 +888,7 @@ " * നാം $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ എന്ന വിതരണത്തിൽ നിന്ന് ഒരു വെക്ടർ `sample` സാമ്പിൾ ചെയ്യുന്നു\n", " * ഡീകോഡർ `sample` എന്ന ഇൻപുട്ട് വെക്ടർ ഉപയോഗിച്ച് യഥാർത്ഥ ചിത്രം പുനഃസംരചിക്കാൻ ശ്രമിക്കുന്നു\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md index 66a3724c..89902894 100644 --- a/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ml/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN-കൾ പരിശീലിപ്പിക്കുമ്പോൾ, ഒര യഥാർത്ഥ ചിത്രത്തിൽ നിന്നുള്ള വിവരങ്ങൾ പൂർണ്ണമായി പിടിച്ചുപറ്റി കൃത്യമായി പുനഃസൃഷ്ടിക്കാൻ ഓട്ടോഎൻകോഡർ പരിശീലിപ്പിക്കുമ്പോൾ, നെറ്റ്‌വർക്ക് ഇൻപുട്ട് ചിത്രങ്ങളുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ മികച്ച **എംബെഡ്ഡിംഗ്** കണ്ടെത്താൻ ശ്രമിക്കുന്നു. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ml.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ml/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ചിത്രം [Keras ബ്ലോഗിൽ നിന്നുള്ളത്](https://blog.keras.io/building-autoencoders-in-keras.html) @@ -46,7 +46,7 @@ VAE ഒരു ഓട്ടോഎൻകോഡറാണ്, അത് ലാറ് * N(zmean, exp(zlog_sigma)) എന്ന വിതരണത്തിൽ നിന്ന് ഒരു `sample` വെക്ടർ സാമ്പിൾ ചെയ്യുന്നു * ഡീകോഡർ `sample` ഉപയോഗിച്ച് യഥാർത്ഥ ചിത്രം പുനഃസൃഷ്ടിക്കാൻ ശ്രമിക്കുന്നു - + > ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നുള്ളത്](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) - Isaak Dykeman @@ -57,13 +57,13 @@ VAE ഒരു ഓട്ടോഎൻകോഡറാണ്, അത് ലാറ് VAE-കളുടെ പ്രധാന ഗുണം, നാം ലാറ്റന്റ് വെക്ടറുകൾ സാമ്പിൾ ചെയ്യേണ്ട വിതരണത്തെ അറിയുന്നതിനാൽ, പുതിയ ചിത്രങ്ങൾ സൃഷ്ടിക്കുന്നത് എളുപ്പമാണ്. ഉദാഹരണത്തിന്, 2D ലാറ്റന്റ് വെക്ടർ ഉപയോഗിച്ച് MNIST-ൽ VAE പരിശീലിപ്പിച്ചാൽ, ലാറ്റന്റ് വെക്ടറിന്റെ ഘടകങ്ങൾ മാറ്റി വ്യത്യസ്ത അക്കങ്ങൾ ലഭിക്കാം: -vaemnist +vaemnist > ചിത്രം [Dmitry Soshnikov](http://soshnikov.com) എന്നവന്റെ ലാറ്റന്റ് പാരാമീറ്റർ സ്പേസിന്റെ വ്യത്യസ്ത ഭാഗങ്ങളിൽ നിന്നുള്ള ലാറ്റന്റ് വെക്ടറുകൾ ഉപയോഗിച്ച് ചിത്രങ്ങൾ എങ്ങനെ പരസ്പരം മിശ്രിതമാകുന്നു എന്ന് ശ്രദ്ധിക്കുക. നാം ഈ സ്പേസ് 2D-ൽ ദൃശ്യവൽക്കരിക്കാനും കഴിയും: -vaemnist cluster +vaemnist cluster > ചിത്രം [Dmitry Soshnikov](http://soshnikov.com) എന്നവന്റെ diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 03ba87ab..c1e4a89d 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **ജനറേറ്റർ** ഒരു റാൻഡം വെക്ടർ സ്വീകരിച്ച് അതിൽ നിന്ന് ഒരു ചിത്രം സൃഷ്ടിക്കണം\n", "* **ഡിസ്ക്രിമിനേറ്റർ** ഒരു നെറ്റ്‌വർക്ക് ആണ്, അത് യഥാർത്ഥ ചിത്രം (പരിശീലന ഡാറ്റാസെറ്റിൽ നിന്നുള്ളത്)യും ജനറേറ്റർ സൃഷ്ടിച്ച ചിത്രവും വേർതിരിക്കണം.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> ചിത്രം [ഈ ട്യൂട്ടോറിയൽ](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) നിന്നാണ്\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index e6e6dce0..2024b47b 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **ജനറേറ്റർ** ഒരു യാദൃച്ഛിക വെക്ടർ സ്വീകരിച്ച് അതിൽ നിന്ന് ഒരു ചിത്രം സൃഷ്ടിക്കണം\n", "* **ഡിസ്ക്രിമിനേറ്റർ** ഒരു നെറ്റ്വർക്ക് ആണ്, അത് യഥാർത്ഥ ചിത്രം (പരിശീലന ഡാറ്റാസെറ്റിൽ നിന്നുള്ളത്)യും ജനറേറ്റർ സൃഷ്ടിച്ച ചിത്രവും വേർതിരിക്കണം.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/ml/lessons/4-ComputerVision/10-GANs/README.md b/translations/ml/lessons/4-ComputerVision/10-GANs/README.md index 609d71f9..b1d00a06 100644 --- a/translations/ml/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/ml/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN-ന്റെ പ്രധാന ആശയം രണ്ട് ന്യൂറൽ നെറ്റ്വർക്കുകൾ തമ്മിൽ പരസ്പരം മത്സരിച്ച് പരിശീലിക്കപ്പെടുക എന്നതാണ്: - + > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN ഡിസ്ക്രിമിനേറ്റർ താഴെപ്പറയ > ✅ കോൺവല്യൂഷൻ ലെയർ ചിത്രം താണ്ടുന്ന ലീനിയർ ഫിൽട്ടറായതിനാൽ, ഡീകോൺവല്യൂഷൻ അടിസ്ഥാനപരമായി കോൺവല്യൂഷനോട് സമാനമാണ്, അതേ ലെയർ ലജിക് ഉപയോഗിച്ച് നടപ്പിലാക്കാം. - + > ചിത്രം: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md index c939c9f2..401a322b 100644 --- a/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ml/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [പ്രീ-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ml.png) +![Object Detection](../../../../../translated_images/ml/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ചിത്രം [YOLO v2 വെബ്‌സൈറ്റ്](https://pjreddie.com/darknet/yolov2/) നിന്നാണ് @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ഓരോ ടൈലിലും ഇമേജ് ക്ലാസിഫിക്കേഷൻ നടത്തുക 3. ഉയർന്ന ആക്ടിവേഷൻ ലഭിക്കുന്ന ടൈലുകൾ ആ വസ്തു അടങ്ങിയതായി കരുതാം -![Naive Object Detection](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ml.png) +![Naive Object Detection](../../../../../translated_images/ml/naive-detection.e7f1ba220ccd08c6.png) > *ചിത്രം [Exercise Notebook](ObjectDetection-TF.ipynb) നിന്നാണ്* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ക്ലാസുകൾ * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 ക്ലാസുകൾ, ബൗണ്ടിംഗ് ബോക്സുകളും സെഗ്മെന്റേഷൻ മാസ്കുകളും -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ml.jpg) +![COCO](../../../../../translated_images/ml/coco-examples.71bc60380fa6cceb.jpg) ## ഒബ്ജക്റ്റ് ഡിറ്റക്ഷൻ മെട്രിക്‌സ് @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ഇമേജ് ക്ലാസിഫിക്കേഷനിൽ ആൽഗോരിതത്തിന്റെ പ്രകടനം എളുപ്പത്തിൽ അളക്കാമെങ്കിലും, ഒബ്ജക്റ്റ് ഡിറ്റക്ഷനിൽ ക്ലാസിന്റെ ശരിതത്വവും ബൗണ്ടിംഗ് ബോക്സിന്റെ കൃത്യതയും അളക്കേണ്ടതുണ്ട്. ഇതിന് **Intersection over Union** (IoU) ഉപയോഗിക്കുന്നു, ഇത് രണ്ട് ബോക്സുകൾ (അഥവാ രണ്ട് ഏരിയകൾ) എത്രമാത്രം ഒതുക്കപ്പെടുന്നുവെന്ന് അളക്കുന്നു. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ml.png) +![IoU](../../../../../translated_images/ml/iou_equation.9a4751d40fff4e11.png) > *ചിത്രം [ഈ മികച്ച ബ്ലോഗ് പോസ്റ്റ്](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) നിന്നാണ്* @@ -97,11 +97,11 @@ IoU ഒരു നിശ്ചിത മൂല്യത്തിന് മുക [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ഉപയോഗിച്ച് ROI പ്രദേശങ്ങളുടെ ഹയർആർക്കിക്കൽ ഘടന സൃഷ്ടിക്കുന്നു, പിന്നീട് CNN ഫീച്ചർ എക്സ്ട്രാക്ടറുകളും SVM ക്ലാസിഫയറുകളും ഉപയോഗിച്ച് വസ്തു ക്ലാസ് നിർണയിക്കുന്നു, ലീനിയർ റെഗ്രഷൻ ഉപയോഗിച്ച് *ബൗണ്ടിംഗ് ബോക്സ്* കോഓർഡിനേറ്റുകൾ കണ്ടെത്തുന്നു. [അധികൃത പേപ്പർ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ml.png) +![RCNN](../../../../../translated_images/ml/rcnn1.cae407020dfb1d1f.png) > *ചിത്രം van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ml.png) +![RCNN-1](../../../../../translated_images/ml/rcnn2.2d9530bb83516484.png) > *ചിത്രങ്ങൾ [ഈ ബ്ലോഗ്](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) നിന്നാണ്* @@ -109,7 +109,7 @@ IoU ഒരു നിശ്ചിത മൂല്യത്തിന് മുക R-CNN പോലെയാണ്, പക്ഷേ പ്രദേശങ്ങൾ കോൺവല്യൂഷൻ ലെയറുകൾ പ്രയോഗിച്ചതിന് ശേഷം നിർവചിക്കുന്നു. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ml.png) +![FRCNN](../../../../../translated_images/ml/f-rcnn.3cda6d9bb4188875.png) > ചിത്രം [അധികൃത പേപ്പർ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ R-CNN പോലെയാണ്, പക്ഷേ പ്രദേശങ്ങൾ ഈ സമീപനത്തിന്റെ പ്രധാന ആശയം ROIകൾ പ്രവചിക്കാൻ ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉപയോഗിക്കുക എന്നതാണ് - ഇതാണ് *Region Proposal Network*. [പേപ്പർ](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ml.png) +![FasterRCNN](../../../../../translated_images/ml/faster-rcnn.8d46c099b87ef30a.png) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ Faster R-CNN-നേക്കാൾ വേഗത്തിൽ പ്രവർത 2. ഫീച്ചറുകൾ **Position-Sensitive Score Map** ഉപയോഗിച്ച് പ്രോസസ്സ് ചെയ്യുന്നു. $C$ ക്ലാസുകളിലുള്ള ഓരോ വസ്തുവും $k\times k$ പ്രദേശങ്ങളായി വിഭജിച്ച്, വസ്തുവിന്റെ ഭാഗങ്ങൾ പ്രവചിക്കാൻ പരിശീലനം നൽകുന്നു. 3. $k\times k$ പ്രദേശങ്ങളിലെ ഓരോ ഭാഗത്തിനും എല്ലാ നെറ്റ്‌വർക്കുകളും വസ്തു ക്ലാസുകൾക്ക് വോട്ട് ചെയ്യുന്നു, പരമാവധി വോട്ട് ലഭിച്ച ക്ലാസ് തിരഞ്ഞെടുക്കുന്നു. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ml.png) +![r-fcn image](../../../../../translated_images/ml/r-fcn.13eb88158b99a3da.png) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO ഒരു റിയൽടൈം ഒന്ന്-പാസ്സ് ആൽ * ചിത്രം $S\times S$ പ്രദേശങ്ങളായി വിഭജിക്കുന്നു * ഓരോ പ്രദേശത്തിനും **CNN** $n$ സാധ്യതയുള്ള വസ്തുക്കൾ, *ബൗണ്ടിംഗ് ബോക്സ്* കോഓർഡിനേറ്റുകൾ, *confidence*=*probability* * IoU പ്രവചിക്കുന്നു. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ml.png) + ![YOLO](../../../../../translated_images/ml/yolo.a2648ec82ee8bb4e.png) > ചിത്രം [അധികൃത പേപ്പർ](https://arxiv.org/abs/1506.02640) diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md index c57096da..8b33bd5d 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ, ഈ ആടുകൾ വ്യത്യസ്ത വസ്തുക്കളാണ്, എന്നാൽ സെമാന്റിക് സെഗ്മെന്റേഷനിൽ എല്ലാ ആടുകളും ഒരേ ക്ലാസ്സായി പ്രതിനിധീകരിക്കുന്നു. - + > ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ് @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **എൻകോഡർ** ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു * **ഡികോഡർ** ആ ഫീച്ചറുകൾ **മാസ്‌ക് ചിത്രം** ആക്കുന്നു, അതിന്റെ വലിപ്പവും ചാനലുകളുടെ എണ്ണം ക്ലാസുകളുടെ എണ്ണം അനുസരിച്ചുള്ളതാണ്. - + > ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ് @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ഈ സാങ്കേതികവിദ്യ ഈ തരത്തിലുള്ള മെഡിക്കൽ ഇമേജിംഗിന് പ്രത്യേകിച്ച് അനുയോജ്യമാണ്, എന്നാൽ മറ്റേതെങ്കിലും യാഥാർത്ഥ്യപ്രയോഗങ്ങൾ നിങ്ങൾക്ക് കാണാമോ? -navi +navi > ചിത്രം PH2 ഡാറ്റാബേസിൽ നിന്നാണ് diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index dcd13b0d..13f2705e 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ഉദാഹരണത്തിന്, ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ 10 ആട് വ്യത്യസ്ത ഒബ്ജക്റ്റുകളാണ്, സെമാന്റിക് സെഗ്മെന്റേഷനിൽ എല്ലാ ആടുകളും ഒരേ ക്ലാസ്സായി പ്രതിനിധീകരിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ്\n", "\n", @@ -26,7 +26,7 @@ "* **എൻകോഡർ** ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു\n", "* **ഡികോഡർ** ആ ഫീച്ചറുകൾ **മാസ്‌ക് ഇമേജായി** മാറ്റുന്നു, അതിന്റെ വലിപ്പവും ചാനലുകളുടെ എണ്ണം ക്ലാസുകളുടെ എണ്ണത്തോടനുസരിച്ചുള്ളതാണ്.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ്\n" ] @@ -252,7 +252,7 @@ "\n", "ഏറ്റവും ലളിതമായ എൻകോഡർ-ഡീകോഡർ ആർക്കിടെക്ചർ **സെഗ്‌നെറ്റ്** എന്നാണ് വിളിക്കുന്നത്. എൻകോഡറിൽ കോൺവല്യൂഷനുകളും പൂലിംഗുകളും ഉപയോഗിക്കുന്ന സ്റ്റാൻഡേർഡ് CNN ആണ് ഇത് ഉപയോഗിക്കുന്നത്, ഡീകോഡറിൽ കോൺവല്യൂഷനുകളും അപ്സാമ്പ്ലിംഗുകളും ഉൾക്കൊള്ളുന്ന ഡീകോൺവല്യൂഷൻ CNN ആണ് ഉപയോഗിക്കുന്നത്. ബാച്ച് നോർമലൈസേഷൻ ഉപയോഗിച്ച് മൾട്ടി-ലെയർ നെറ്റ്‌വർക്ക് വിജയകരമായി പരിശീലിപ്പിക്കാനും ഇത് ആശ്രയിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ഈ ചിത്രം ഈ പേപ്പറിൽ നിന്നാണ്: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ഇവിടെ നാം വളരെ ലളിതമായ CNN ആർക്കിടെക്ചർ ഉപയോഗിക്കും, പക്ഷേ U-Net കൂടുതൽ സങ്കീർണ്ണമായ എൻകോഡർ ഫീച്ചർ എക്സ്ട്രാക്ഷനായി ഉപയോഗിക്കാം, ഉദാഹരണത്തിന് ResNet-50.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം പേപ്പറിൽ നിന്നാണ്: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index 99a354fd..beacb927 100644 --- a/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/ml/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ഉദാഹരണത്തിന്, ഇൻസ്റ്റൻസ് സെഗ്മെന്റേഷനിൽ പത്ത് കാറുകൾ **വ്യത്യസ്ത** വസ്തുക്കളാണ്, സെമാന്റിക് സെഗ്മെന്റേഷനിൽ **എല്ലാ** കാറുകളും ഒരേ ക്ലാസായി കണക്കാക്കപ്പെടുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) നിന്നാണ്\n", "\n", "ഏതാണ്ട് എല്ലാ ആർക്കിടെക്ചറുകളും ഒരേ ഘടനയുള്ളതാണ്. ആദ്യഭാഗം **എൻകോഡർ** ആണ്, ഇത് ഇൻപുട്ട് ചിത്രത്തിൽ നിന്ന് ഫീച്ചറുകൾ എടുക്കുന്നു, രണ്ടാം ഭാഗം **ഡികോഡർ** ആണ്, ഇത് ഈ ഫീച്ചറുകൾ ചിത്രത്തിന്റെ സമാന ഉയരം, വീതി, ചിലപ്പോൾ ക്ലാസുകളുടെ എണ്ണം തുല്യമായ ചാനലുകളുള്ള ചിത്രമായി മാറ്റുന്നു.\n", "\n", - "\n", + "\n", "\n", "> ചിത്രം [ഈ പ്രസിദ്ധീകരണം](https://arxiv.org/pdf/2001.05566.pdf) നിന്നാണ്\n" ] @@ -210,7 +210,7 @@ "\n", "എൻകോഡറിൽ കോൺവല്യൂഷനുകളും പൂലിംഗുകളും, ഡികോഡറിൽ കോൺവല്യൂഷനുകളും അപ്സാമ്പ്ലിംഗുകളും ഉള്ള ലളിതമായ എൻകോഡർ-ഡികോഡർ ആർക്കിടെക്ചർ.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net സാധാരണയായി ഫീച്ചർ എക്സ്ട്രാക്ഷനിനായി ഒരു ഡിഫോൾട്ട് എൻകോഡർ ഉപയോഗിക്കുന്നു, ഉദാഹരണത്തിന് resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ml/lessons/4-ComputerVision/README.md b/translations/ml/lessons/4-ComputerVision/README.md index b444a6ff..831c3cba 100644 --- a/translations/ml/lessons/4-ComputerVision/README.md +++ b/translations/ml/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # കമ്പ്യൂട്ടർ വിഷൻ -![കമ്പ്യൂട്ടർ വിഷൻ ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ml.png) +![കമ്പ്യൂട്ടർ വിഷൻ ഉള്ളടക്കത്തിന്റെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-computervision.6506ebebac3fbf76.png) ഈ വിഭാഗത്തിൽ നാം പഠിക്കാനിരിക്കുന്നവ: diff --git a/translations/ml/lessons/5-NLP/13-TextRep/README.md b/translations/ml/lessons/5-NLP/13-TextRep/README.md index b639e249..db892b66 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/README.md +++ b/translations/ml/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: നാചുറൽ ലാംഗ്വേജ് പ്രോസസ്സിംഗ് (NLP) ടാസ്കുകൾ ന്യൂറൽ നെറ്റ്‌വർക്കുകളിലൂടെ പരിഹരിക്കാൻ, ടെക്സ്റ്റ് ടെൻസറുകളായി പ്രതിനിധാനം ചെയ്യാനുള്ള മാർഗ്ഗം വേണം. കമ്പ്യൂട്ടറുകൾ ഇതിനകം തന്നെ ASCII അല്ലെങ്കിൽ UTF-8 പോലുള്ള എൻകോഡിങ്ങുകൾ ഉപയോഗിച്ച് സ്ക്രീനിലെ ഫോണ്ടുകളുമായി മാപ്പ് ചെയ്യുന്ന സംഖ്യകളായി ടെക്സ്റ്റ് അക്ഷരങ്ങളെ പ്രതിനിധാനം ചെയ്യുന്നു. -ഒരു അക്ഷരത്തെ ASCII, ബൈനറി പ്രതിനിധാനങ്ങളായി മാപ്പ് ചെയ്യുന്ന ഡയഗ്രാം കാണിക്കുന്ന ചിത്രം +ഒരു അക്ഷരത്തെ ASCII, ബൈനറി പ്രതിനിധാനങ്ങളായി മാപ്പ് ചെയ്യുന്ന ഡയഗ്രാം കാണിക്കുന്ന ചിത്രം > [ചിത്രം സ്രോതസ്സ്](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: ടെക്സ്റ്റ് ക്ലാസിഫിക്കേഷൻ പോലുള്ള ടാസ്കുകൾ പരിഹരിക്കുമ്പോൾ, ടെക്സ്റ്റ് ഒരു സ്ഥിരമായ വലിപ്പമുള്ള വെക്ടറായി പ്രതിനിധാനം ചെയ്യാൻ കഴിയണം, ഇത് ഫൈനൽ ഡെൻസ് ക്ലാസിഫയറിലേക്ക് ഇൻപുട്ടായി ഉപയോഗിക്കും. ഏറ്റവും ലളിതമായ മാർഗ്ഗങ്ങളിൽ ഒന്ന് എല്ലാ വ്യക്തിഗത വാക്കുകളുടെ പ്രതിനിധാനങ്ങൾ ചേർക്കലാണ്. ഓരോ വാക്കിന്റെയും ഒന്ന്-ഹോട്ട് എൻകോഡിങ്ങുകൾ ചേർത്താൽ, ഓരോ വാക്കും ടെക്സ്റ്റിൽ എത്ര തവണ വന്നുവെന്ന് കാണിക്കുന്ന ഫ്രീക്വൻസി വെക്ടർ ലഭിക്കും. ഈ ടെക്സ്റ്റ് പ്രതിനിധാനം **ബാഗ് ഓഫ് വേർഡ്സ്** (BoW) എന്ന് വിളിക്കുന്നു. - + > ചിത്രകാരൻ: ലേഖകൻ diff --git a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 214c694a..4e98717f 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) വെക്ടർ പ്രതിനിധാനം ഏറ്റവും സാധാരണമായി ഉപയോഗിക്കുന്ന പരമ്പരാഗത വെക്ടർ പ്രതിനിധാനമാണ്. ഓരോ വാക്കും ഒരു വെക്ടർ ഇൻഡക്സുമായി ബന്ധിപ്പിച്ചിരിക്കുന്നു, വെക്ടർ ഘടകം ഒരു നൽകിയ ഡോക്യുമെന്റിൽ ആ വാക്കിന്റെ ആവർത്തനങ്ങളുടെ എണ്ണം ഉൾക്കൊള്ളുന്നു.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ml.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-നെ ടെക്സ്റ്റിലെ ഓരോ വാക്കിനും ഉള്ള ഒന്ന്-ഹോട്ട്-എൻകോഡഡ് വെക്ടറുകളുടെ മൊത്തം കൂട്ടമായി കാണാം.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index a49ab9b1..c2bd527f 100644 --- a/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ml/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) വെക്ടർ പ്രതിനിധാനം ഏറ്റവും എളുപ്പത്തിൽ മനസ്സിലാക്കാവുന്ന പരമ്പരാഗത വെക്ടർ പ്രതിനിധാനമാണ്. ഓരോ വാക്കിനും ഒരു വെക്ടർ ഇൻഡക്സ് ബന്ധിപ്പിച്ചിരിക്കുന്നു, ഒരു വെക്ടർ ഘടകം ഒരു നൽകിയ ഡോക്യുമെന്റിൽ ഓരോ വാക്കിന്റെയും ആവർത്തനങ്ങളുടെ എണ്ണം ഉൾക്കൊള്ളുന്നു.\n", "\n", - "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ml.png) \n", + "![Image showing how a bag of words vector representation is represented in memory.](../../../../../translated_images/ml/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-നെ ടെക്സ്റ്റിലെ ഓരോ വാക്കിനും ഉള്ള ഒന്ന്-ഹോട്ട്-എൻകോഡഡ് വെക്ടറുകളുടെ മൊത്തം കൂട്ടമായി കാണാം.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index f53f9ccb..4ed062b6 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "നമ്മുടെ നെറ്റ്‌വർക്കിലെ ആദ്യ ലെയറായി എമ്പെഡ്ഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡ്സ് മോഡലിൽ നിന്ന് **എമ്പെഡ്ഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം നമ്മുടെ ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എമ്പെഡ്ഡിങ്ങിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എമ്പെഡ്ഡിങ്ങുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഏതെങ്കിലും സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും.\n", "\n", - "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ml.png)\n", + "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "നമ്മുടെ ക്ലാസിഫയർ ന്യൂറൽ നെറ്റ്‌വർക്ക് എമ്പെഡ്ഡിംഗ് ലെയറോടെ ആരംഭിച്ച്, തുടർന്ന് അഗ്രിഗേഷൻ ലെയർ, അതിന്റെ മുകളിൽ ലീനിയർ ക്ലാസിഫയർ എന്നിവയാകും:\n" ] @@ -176,7 +176,7 @@ "\n", "മുൻവർഷത്തെ ആർക്കിടെക്ചറിൽ, മിനിബാച്ചിൽ ഫിറ്റ് ചെയ്യാൻ എല്ലാ സീക്വൻസുകളും ഒരേ നീളത്തിലേക്ക് പാഡ് ചെയ്യേണ്ടിവന്നു. വ്യത്യസ്ത നീളമുള്ള സീക്വൻസുകൾ പ്രതിനിധാനം ചെയ്യാനുള്ള ഏറ്റവും കാര്യക്ഷമമായ മാർഗം ഇത് അല്ല - മറ്റൊരു സമീപനം **ഓഫ്സെറ്റ്** വെക്ടർ ഉപയോഗിക്കുകയാണ്, ഇത് ഒരു വലിയ വെക്ടറിൽ സൂക്ഷിച്ചിരിക്കുന്ന എല്ലാ സീക്വൻസുകളുടെ ഓഫ്സെറ്റുകൾ സൂക്ഷിക്കും.\n", "\n", - "![ഓഫ്സെറ്റ് സീക്വൻസ് പ്രതിനിധാനം കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ml.png)\n", + "![ഓഫ്സെറ്റ് സീക്വൻസ് പ്രതിനിധാനം കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: മുകളിൽ കാണുന്ന ചിത്രത്തിൽ, ഒരു അക്ഷരങ്ങളുടെ സീക്വൻസ് കാണിക്കുന്നു, എന്നാൽ നമ്മുടെ ഉദാഹരണത്തിൽ നാം വാക്കുകളുടെ സീക്വൻസുകളുമായി പ്രവർത്തിക്കുന്നു. എന്നിരുന്നാലും, ഓഫ്സെറ്റ് വെക്ടർ ഉപയോഗിച്ച് സീക്വൻസുകൾ പ്രതിനിധാനം ചെയ്യാനുള്ള പൊതുവായ സിദ്ധാന്തം അതേപോലെ തുടരുന്നു.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW വേഗത്തിൽ പ്രവർത്തിക്കുന്നു, സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, പക്ഷേ അപൂർവമായ വാക്കുകൾ പ്രതിനിധാനം ചെയ്യുന്നതിൽ മികച്ചതാണ്.\n", "\n", - "![വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ml.png)\n", + "![വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ഡാറ്റാസെറ്റിൽ പ്രീ-ട്രെയിൻ ചെയ്ത word2vec എംബെഡ്ഡിംഗ് പരീക്ഷിക്കാൻ, നാം **gensim** ലൈബ്രറി ഉപയോഗിക്കാം. താഴെ 'neural' എന്ന വാക്കിനോട് ഏറ്റവും സമാനമായ വാക്കുകൾ കാണിക്കുന്നു\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 54bd479e..546ad34f 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ml/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "നമ്മുടെ നെറ്റ്‌വർക്കിലെ ആദ്യ ലെയറായി ഒരു എമ്പെഡ്ഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡ്സ് മോഡലിൽ നിന്ന് **എമ്പെഡ്ഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം നമ്മുടെ ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എമ്പെഡ്ഡിങ്ങിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എമ്പെഡ്ഡിങ്ങുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഒരു സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും.\n", "\n", - "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള ഒരു എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ml.png)\n", + "![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള ഒരു എമ്പെഡ്ഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "നമ്മുടെ ക്ലാസിഫയർ ന്യൂറൽ നെറ്റ്‌വർക്ക് താഴെപ്പറയുന്ന ലെയറുകൾ ഉൾക്കൊള്ളുന്നു:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW വേഗത്തിലാണ്, സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, എന്നാൽ അപൂർവമായ വാക്കുകൾ പ്രതിനിധാനം ചെയ്യുന്നതിൽ മികച്ചതാണ്.\n", "\n", - "![വാക്കുകൾ വെക്ടറുകളായി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ml.png)\n", + "![വാക്കുകൾ വെക്ടറുകളായി മാറ്റാൻ CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ഡാറ്റാസെറ്റിൽ പ്രീട്രെയിൻ ചെയ്ത Word2Vec എംബെഡിംഗ് പരീക്ഷിക്കാൻ, **gensim** ലൈബ്രറി ഉപയോഗിക്കാം. താഴെ 'neural' എന്ന വാക്കിനോട് ഏറ്റവും സമാനമായ വാക്കുകൾ കാണിക്കുന്നു.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/14-Embeddings/README.md b/translations/ml/lessons/5-NLP/14-Embeddings/README.md index 51b8f9f6..fbdb2273 100644 --- a/translations/ml/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ml/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW അല്ലെങ്കിൽ TF/IDF അടിസ്ഥാനമാക് ക്ലാസിഫയർ നെറ്റ്വർക്കിലെ ആദ്യ ലെയറായി എംബെഡിംഗ് ലെയർ ഉപയോഗിച്ച്, നാം ബാഗ്-ഓഫ്-വേർഡിൽ നിന്ന് **എംബെഡിംഗ് ബാഗ്** മോഡലിലേക്ക് മാറാം, ഇവിടെ ആദ്യം ടെക്സ്റ്റിലെ ഓരോ വാക്കും അനുയോജ്യമായ എംബെഡിംഗിലേക്ക് മാറ്റുകയും, പിന്നീട് ആ എംബെഡിംഗുകളുടെ മേൽ `sum`, `average` അല്ലെങ്കിൽ `max` പോലുള്ള ഒരു സംഗ്രഹ ഫംഗ്ഷൻ കണക്കാക്കുകയും ചെയ്യും. -![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എംബെഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ml.png) +![അഞ്ച് സീക്വൻസ് വാക്കുകൾക്കുള്ള എംബെഡിംഗ് ക്ലാസിഫയർ കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/embedding-classifier-example.b77f021a7ee67eee.png) > ചിത്രം: രചയിതാവ് @@ -40,7 +40,7 @@ BoW അല്ലെങ്കിൽ TF/IDF അടിസ്ഥാനമാക് CBoW വേഗത്തിൽ പ്രവർത്തിക്കുന്നു, എന്നാൽ സ്കിപ്പ്-ഗ്രാം മന്ദഗതിയിലാണ്, പക്ഷേ അപൂർവമായ വാക്കുകൾ പ്രതിനിധീകരിക്കുന്നതിൽ മികച്ചതാണ്. -![CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റുന്നത് കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ml.png) +![CBoWയും സ്കിപ്പ്-ഗ്രാം ആൽഗോരിതങ്ങളും വാക്കുകളെ വെക്ടറുകളാക്കി മാറ്റുന്നത് കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ചിത്രം ഈ [പേപ്പറിൽ](https://arxiv.org/pdf/1301.3781.pdf) നിന്നാണ് diff --git a/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md index 0ce36b6f..44bf7943 100644 --- a/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ml/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **കണ്ടിന്യൂവസ് ബാഗ്-ഓഫ്-വേർഡ്സ്** (CBoW), ടോക്കൺ ശ്രേണിയിൽ മധ്യത്തിലുള്ള ടോക്കൺ $W_0$ പ്രവചിക്കുന്നത്, $W_{-N}$, ..., $W_N$. * **സ്കിപ്പ്-ഗ്രാം**, മധ്യത്തിലുള്ള ടോക്കൺ $W_0$ ഉപയോഗിച്ച് സമീപവരുന്ന ടോക്കണുകളുടെ സെറ്റ് {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} പ്രവചിക്കുന്നത്. -![വാക്കുകൾ വെക്ടറുകളാക്കി മാറ്റുന്നതിന് പത്രത്തിൽ നിന്നുള്ള ചിത്രം](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ml.png) +![വാക്കുകൾ വെക്ടറുകളാക്കി മാറ്റുന്നതിന് പത്രത്തിൽ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ചിത്രം [ഈ പത്രത്തിൽ നിന്നുള്ളത്](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ml/lessons/5-NLP/16-RNN/README.md b/translations/ml/lessons/5-NLP/16-RNN/README.md index 83fbc5e4..1c886655 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/README.md +++ b/translations/ml/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ടെക്സ്റ്റ് സീക്വൻസിന്റെ അർത്ഥം പിടികൂടാൻ, നമുക്ക് മറ്റൊരു ന്യൂറൽ നെറ്റ്വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കേണ്ടതുണ്ട്, അതാണ് **റികറന്റ് ന്യൂറൽ നെറ്റ്വർക്ക്** അല്ലെങ്കിൽ RNN. RNN-ൽ, നാം വാക്യം ഒരു സിംബോളായി ഒരു സമയം നെറ്റ്വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത സിംബോളിനൊപ്പം വീണ്ടും നെറ്റ്വർക്കിലേക്ക് കടത്തുന്നു. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ml.png) +![RNN](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.png) > ചിത്രകാരൻ: ലേഖകൻ @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ഒരു ലളിതമായ RNN സെലിൽ രണ്ട് വെയ്റ്റ് മാട്രിസുകൾ ഉണ്ട്: ഒന്ന് ഇൻപുട്ട് സിംബോളിനെ മാറ്റുന്നു (W എന്ന് വിളിക്കാം), മറ്റൊന്ന് ഇൻപുട്ട് സ്റ്റേറ്റ് മാറ്റുന്നു (H). ഈ സാഹചര്യത്തിൽ, നെറ്റ്വർക്ക് ഔട്ട്പുട്ട് σ(W×Xi+H×Si-1+b) ആയി കണക്കാക്കുന്നു, ഇവിടെ σ ആക്ടിവേഷൻ ഫംഗ്ഷനും b അധിക ബയാസും ആണ്. -RNN Cell Anatomy +RNN Cell Anatomy > ചിത്രകാരൻ: ലേഖകൻ @@ -61,7 +61,7 @@ LSTM നെറ്റ്വർക്ക് RNN-നെപ്പോലെ ക്ര ഒരു റികറന്റ് നെറ്റ്വർക്ക്, ഒരോ ദിശയിലായാലും, സീക്വൻസിൽ ചില പാറ്റേണുകൾ പിടികൂടുകയും അവ സ്റ്റേറ്റ് വെക്ടറിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുകയും ചെയ്യുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, നാം ആദ്യ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി ഉപയോഗിച്ച് ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടികൂടാൻ മറ്റൊരു റികറന്റ് ലെയർ നിർമ്മിക്കാം. ഇതാണ് **മൾട്ടി-ലെയർ RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ കൂടുതൽ റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി കടത്തുന്നു. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ml.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg) *ചിത്രം Fernando López-ന്റെ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നാണ്* diff --git a/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 9f2176a3..b0087cbe 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "ടെക്സ്റ്റ് സീക്വൻസിന്റെ അർത്ഥം പിടിച്ചുപറ്റാൻ, നാം മറ്റൊരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കേണ്ടതുണ്ട്, അതാണ് **പുനരാവർത്തന ന്യൂറൽ നെറ്റ്‌വർക്ക്** അല്ലെങ്കിൽ RNN. RNN-ൽ, നാം നമ്മുടെ വാക്യം ഒരു സിംബോളായി ഓരോ തവണയും നെറ്റ്‌വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്‌വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത സിംബോളിനൊപ്പം വീണ്ടും നെറ്റ്‌വർക്കിലേക്ക് നൽകുന്നു.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ഇൻപുട്ട് ടോക്കൺ സീക്വൻസ് $X_0,\\dots,X_n$ നൽകിയാൽ, RNN ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളുടെ സീക്വൻസ് സൃഷ്ടിക്കുന്നു, ഈ സീക്വൻസ് എന്റു-ടു-എൻഡ് ബാക്ക് പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് പരിശീലിപ്പിക്കുന്നു. ഓരോ നെറ്റ്‌വർക്ക് ബ്ലോക്കും ഒരു ജോഡി $(X_i,S_i)$ ഇൻപുട്ടായി സ്വീകരിച്ച്, $S_{i+1}$ ഫലമായി ഉൽപ്പാദിപ്പിക്കുന്നു. അന്തിമ സ്റ്റേറ്റ് $S_n$ അല്ലെങ്കിൽ ഔട്ട്പുട്ട് $X_n$ ഒരു ലീനിയർ ക്ലാസിഫയറിലേക്ക് പോകുന്നു ഫലം ഉൽപ്പാദിപ്പിക്കാൻ. എല്ലാ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളും ഒരേ വെയ്റ്റുകൾ പങ്കുവെക്കുന്നു, ഒറ്റ ബാക്ക് പ്രൊപ്പഗേഷൻ പാസിലൂടെ എന്റു-ടു-എൻഡ് പരിശീലനം നടത്തുന്നു.\n", "\n", @@ -428,7 +428,7 @@ "\n", "റികറന്റ് നെറ്റ്വർക്ക്, ഒരുദിശയിലോ ദ്വിദിശയിലോ, ഒരു സീക്വൻസിനുള്ളിൽ ചില പാറ്റേണുകൾ പിടിച്ചുപറ്റുകയും അവ സ്റ്റേറ്റ് വെക്ടറിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുകയും ചെയ്യുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, നാം ആദ്യ ലെയറിന്റെ മുകളിൽ മറ്റൊരു റികറന്റ് ലെയർ നിർമ്മിച്ച് ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിച്ചുപറ്റാം, ആദ്യ ലെയർ കണ്ടെത്തിയ താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകളിൽ നിന്നാണ് ഇത് നിർമ്മിക്കുന്നത്. ഇതാണ് **ബഹുസ്തര RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ അതിലധികം റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി പാസ്സ് ചെയ്യപ്പെടുന്നു.\n", "\n", - "![Multilayer long-short-term-memory- RNN കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ml.jpg)\n", + "![Multilayer long-short-term-memory- RNN കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ഫെർണാണ്ടോ ലോപ്പസ് എഴുതിയ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നുള്ള ചിത്രം*\n", "\n", diff --git a/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb index f5af2408..7795daac 100644 --- a/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ml/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ഒരു വാചക ശ്രേണിയുടെ അർത്ഥം പിടിച്ചുപറ്റാൻ, നാം **പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക്** എന്നറിയപ്പെടുന്ന ഒരു ന്യൂറൽ നെറ്റ്‌വർക്ക് ആർക്കിടെക്ചർ ഉപയോഗിക്കും, അതായത് RNN. RNN ഉപയോഗിക്കുമ്പോൾ, നാം വാചകം ഓരോ ടോക്കണും ഒരിക്കൽ씩 നെറ്റ്‌വർക്കിലൂടെ കടത്തുന്നു, നെറ്റ്‌വർക്ക് ചില **സ്റ്റേറ്റ്** ഉൽപ്പാദിപ്പിക്കുന്നു, അത് പിന്നീട് അടുത്ത ടോക്കണുമായി വീണ്ടും നെറ്റ്‌വർക്കിലേക്ക് നൽകുന്നു.\n", "\n", - "![പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉദാഹരണ സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/rnn.27f5c29c53d727b5.ml.png)\n", + "![പുനരാവർത്തിത ന്യൂറൽ നെറ്റ്‌വർക്ക് ഉദാഹരണ സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn.27f5c29c53d727b5.png)\n", "\n", "ഇൻപുട്ട് ടോക്കൺ ശ്രേണി $X_0,\\dots,X_n$ നൽകിയാൽ, RNN ന്യൂറൽ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളുടെ ഒരു ശ്രേണി സൃഷ്ടിക്കുന്നു, ഈ ശ്രേണി ബാക്ക്‌പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് എന്റു-ടു-എൻഡ് പരിശീലിപ്പിക്കുന്നു. ഓരോ നെറ്റ്‌വർക്ക് ബ്ലോക്കും $(X_i,S_i)$ എന്ന ജോഡി ഇൻപുട്ടായി സ്വീകരിച്ച് $S_{i+1}$ എന്ന ഔട്ട്പുട്ട് നൽകുന്നു. അവസാന സ്റ്റേറ്റ് $S_n$ അല്ലെങ്കിൽ ഔട്ട്പുട്ട് $Y_n$ ഒരു ലീനിയർ ക്ലാസിഫയറിലേക്ക് പോകുന്നു ഫലം ഉൽപ്പാദിപ്പിക്കാൻ. എല്ലാ നെറ്റ്‌വർക്ക് ബ്ലോക്കുകളും ഒരേ ഭാരങ്ങൾ പങ്കുവെക്കുന്നു, ഒറ്റ ബാക്ക്‌പ്രൊപ്പഗേഷൻ പാസിലൂടെ എന്റു-ടു-എൻഡ് പരിശീലിപ്പിക്കുന്നു.\n", "\n", @@ -371,7 +371,7 @@ "\n", "യൂണിഡിരക്ഷണോ ദ്വിദിശ RNN-കളോ സീക്വൻസിനുള്ളിൽ പാറ്റേണുകൾ പിടിച്ചുപറ്റി അവയെ സ്റ്റേറ്റ് വെക്ടറുകളിലോ ഔട്ട്പുട്ടിലോ സൂക്ഷിക്കുന്നു. കോൺവല്യൂഷണൽ നെറ്റ്വർക്കുകളെപ്പോലെ, ആദ്യ ലെയറിന്റെ താഴ്ന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിച്ചുപറ്റി ഉയർന്ന തലത്തിലുള്ള പാറ്റേണുകൾ പിടിക്കാൻ മറ്റൊരു റികറന്റ് ലെയർ ചേർക്കാം. ഇതാണ് **ബഹുസ്തര RNN** എന്ന ആശയം, ഇത് രണ്ട് അല്ലെങ്കിൽ അതിലധികം റികറന്റ് നെറ്റ്വർക്കുകൾ ഉൾക്കൊള്ളുന്നു, മുൻ ലെയറിന്റെ ഔട്ട്പുട്ട് അടുത്ത ലെയറിന്റെ ഇൻപുട്ടായി നൽകുന്നു.\n", "\n", - "![ബഹുസ്തര ലോങ്-ഷോർട്ട്-ടേം-മെമ്മറി RNN-നെ കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ml.jpg)\n", + "![ബഹുസ്തര ലോങ്-ഷോർട്ട്-ടേം-മെമ്മറി RNN-നെ കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ഫെർണാണ്ടോ ലോപ്പസ് എഴുതിയ [ഈ മനോഹരമായ പോസ്റ്റ്](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) നിന്നുള്ള ചിത്രം.*\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 47c8e7dd..08227327 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ഉപയോഗിച്ച് ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യാൻ നാം സ്വീകരിക്കുന്ന മാർഗം ഇപ്രകാരമാണ്. ഓരോ ഘട്ടത്തിലും, നീളം `nchars` ഉള്ള ഒരു അക്ഷരക്രമം എടുത്ത്, ഓരോ ഇൻപുട്ട് അക്ഷരത്തിനും അടുത്ത ഔട്ട്പുട്ട് അക്ഷരം ജനറേറ്റ് ചെയ്യാൻ നെറ്റ്‌വർക്ക് ആവശ്യപ്പെടും:\n", "\n", - "!['HELLO' എന്ന വാക്ക് RNN ഉപയോഗിച്ച് ജനറേറ്റ് ചെയ്യുന്നതിന്റെ ഉദാഹരണം കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ml.png)\n", + "!['HELLO' എന്ന വാക്ക് RNN ഉപയോഗിച്ച് ജനറേറ്റ് ചെയ്യുന്നതിന്റെ ഉദാഹരണം കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png)\n", "\n", "യഥാർത്ഥ സാഹചര്യത്തെ ആശ്രയിച്ച്, *end-of-sequence* `` പോലുള്ള ചില പ്രത്യേക അക്ഷരങ്ങൾ ഉൾപ്പെടുത്തേണ്ടതുണ്ടാകാം. നമ്മുടെ കേസിൽ, നാം അനന്തമായ ടെക്സ്റ്റ് ജനറേഷനായി നെറ്റ്‌വർക്ക് പരിശീലിപ്പിക്കാനാണ് ഉദ്ദേശിക്കുന്നത്, അതിനാൽ ഓരോ സീക്വൻസിന്റെയും വലിപ്പം `nchars` ടോക്കണുകളായി നിശ്ചയിക്കും. അതിനാൽ, ഓരോ പരിശീലന ഉദാഹരണവും `nchars` ഇൻപുട്ടുകളും `nchars` ഔട്ട്പുട്ടുകളും (ഇൻപുട്ട് സീക്വൻസ് ഒരു സിംബോളിന് ഇടത്തേക്ക് ഷിഫ്റ്റ് ചെയ്തതും) ഉൾക്കൊള്ളും. മിനിബാച്ച് ഇത്തരത്തിലുള്ള നിരവധി സീക്വൻസുകൾ അടങ്ങിയിരിക്കും.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 3822c313..84e6dfc9 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "നാം വാർത്താ തലക്കെട്ടുകൾ സൃഷ്ടിക്കാൻ RNN എങ്ങനെ പരിശീലിപ്പിക്കുമെന്ന് പറയുന്നത് ഇങ്ങനെ ആണ്. ഓരോ ഘട്ടത്തിലും, ഒരു തലക്കെട്ട് എടുത്ത് അത് RNN-ലേക്ക് നൽകും, ഓരോ ഇൻപുട്ട് അക്ഷരത്തിനും നെറ്റ്‌വർക്കിന് അടുത്ത ഔട്ട്പുട്ട് അക്ഷരം സൃഷ്ടിക്കാൻ ആവശ്യപ്പെടും:\n", "\n", - "!['HELLO' എന്ന വാക്കിന്റെ ഉദാഹരണ RNN സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ml.png)\n", + "!['HELLO' എന്ന വാക്കിന്റെ ഉദാഹരണ RNN സൃഷ്ടി കാണിക്കുന്ന ചിത്രം.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png)\n", "\n", "നമ്മുടെ സീക്വൻസിലെ അവസാന അക്ഷരത്തിന്, നെറ്റ്‌വർക്കിന് `` ടോക്കൺ സൃഷ്ടിക്കാൻ ആവശ്യപ്പെടും.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md index 1f38dd0a..fb611854 100644 --- a/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ml/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ഇത് താഴെ കാണുന്ന ചിത്രത്തിൽ കാണുന്ന വ്യത്യസ്ത ന്യൂറൽ ആർക്കിടെക്ചറുകൾക്ക് വഴിയൊരുക്കുന്നു: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ml.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > [Andrej Karpaty](http://karpathy.github.io/) എഴുതിയ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) എന്ന ബ്ലോഗ് പോസ്റ്റിൽ നിന്നുള്ള ചിത്രം @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: നാം ഈ RNN ഓരോ ഘട്ടത്തിലും ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യാൻ പരിശീലിപ്പിക്കും. ഓരോ ഘട്ടത്തിലും, നീളം `nchars` ഉള്ള കറക്റ്ററുകളുടെ ഒരു സീക്വൻസ് എടുത്ത്, ഓരോ ഇൻപുട്ട് കറക്റ്ററിനും അടുത്ത ഔട്ട്പുട്ട് കറക്റ്റർ ജനറേറ്റ് ചെയ്യാൻ നെറ്റ്വർക്കിനെ ചോദിക്കും: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ml.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/ml/rnn-generate.56c54afb52f9781d.png) ടെക്സ്റ്റ് ജനറേറ്റ് ചെയ്യുമ്പോൾ (ഇൻഫറൻസ് സമയത്ത്), നാം ഒരു **പ്രോംപ്റ്റ്** ഉപയോഗിച്ച് തുടങ്ങുന്നു, അത് RNN സെല്ലുകൾ വഴി കടന്നുപോകുന്നു, ഇടക്കാല സ്റ്റേറ്റ് ഉത്പാദിപ്പിക്കുന്നു, തുടർന്ന് ആ സ്റ്റേറ്റിൽ നിന്നാണ് ജനറേഷൻ ആരംഭിക്കുന്നത്. ഓരോ കറക്റ്ററും ഒറ്റത്തവണയായി ജനറേറ്റ് ചെയ്ത്, സ്റ്റേറ്റ് കൂടാതെ ജനറേറ്റ് ചെയ്ത കറക്റ്റർ മറ്റൊരു RNN സെലിലേക്ക് നൽകുന്നു, അടുത്തത് ജനറേറ്റ് ചെയ്യാൻ, ആവശ്യമായ കറക്റ്ററുകൾ വരെ തുടരും. - + > എഴുത്തുകാരന്റെ ചിത്രം diff --git a/translations/ml/lessons/5-NLP/18-Transformers/README.md b/translations/ml/lessons/5-NLP/18-Transformers/README.md index 7a9d5d12..c6ab76db 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ml/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് **ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കത്തോടെ നൽകാനുള്ള മാർഗമാണ്. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം yt സൃഷ്ടിക്കുമ്പോൾ, എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും hi വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ αt,i ഉപയോഗിച്ച് പരിഗണിക്കും. -![എൻകോഡർ/ഡീകോഡർ മോഡലും അഡിറ്റീവ് ശ്രദ്ധാ ലെയറും കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ml.png) +![എൻകോഡർ/ഡീകോഡർ മോഡലും അഡിറ്റീവ് ശ്രദ്ധാ ലെയറും കാണിക്കുന്ന ചിത്രം](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ അഡിറ്റീവ് ശ്രദ്ധാ യന്ത്രം ഉൾപ്പെടുത്തിയ എൻകോഡർ-ഡീകോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നുള്ള ഉദ്ധരണം ശ്രദ്ധാ മാട്രിക്സ് {αi,j} ഒരു ഔട്ട്പുട്ട് വാക്കിന്റെ സൃഷ്ടിയിൽ ചില ഇൻപുട്ട് വാക്കുകൾ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു: -![RNNsearch-50 ഉപയോഗിച്ച് കണ്ടെത്തിയ ഒരു സാംപിൾ അലൈന്മെന്റ്, Bahdanau - arviz.org നിന്നുള്ള ചിത്രം](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ml.png) +![RNNsearch-50 ഉപയോഗിച്ച് കണ്ടെത്തിയ ഒരു സാംപിൾ അലൈന്മെന്റ്, Bahdanau - arviz.org നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) @@ -56,7 +56,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് * ടോക്കൺ എംബെഡിംഗിനോട് സമാനമായ ട്രെയിനബിൾ എംബെഡിംഗ്. ഇതാണ് ഇവിടെ പരിഗണിക്കുന്നത്. ടോക്കണുകൾക്കും അവരുടെ സ്ഥാനങ്ങൾക്കും എംബെഡിംഗ് ലെയറുകൾ പ്രയോഗിച്ച് ഒരേ ഡൈമെൻഷനിലുള്ള എംബെഡിംഗ് വെക്ടറുകൾ ലഭിച്ച് അവ ചേർക്കുന്നു. * ഒറിജിനൽ പേപ്പറിൽ നിർദ്ദേശിച്ച സ്ഥിരം പൊസിഷൻ എൻകോഡിംഗ് ഫംഗ്ഷൻ. - + > എഴുത്തുകാരന്റെ ചിത്രം @@ -66,7 +66,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് അടുത്തത്, സീക്വൻസിനുള്ളിൽ ചില പാറ്റേണുകൾ പിടിക്കേണ്ടതാണ്. ഇതിന് ട്രാൻസ്ഫോർമറുകൾ **സെൽഫ്-അറ്റൻഷൻ** മെക്കാനിസം ഉപയോഗിക്കുന്നു, അതായത് ഇൻപുട്ടും ഔട്ട്പുട്ടും ഒരേ സീക്വൻസായാണ് ശ്രദ്ധ പ്രയോഗിക്കുന്നത്. സെൽഫ്-അറ്റൻഷൻ ഉപയോഗിച്ച് വാചകത്തിലെ **സന്ദർഭം** പരിഗണിക്കാനും വാക്കുകൾ തമ്മിലുള്ള ബന്ധം കാണാനും കഴിയും. ഉദാഹരണത്തിന്, *it* പോലുള്ള കോറഫറൻസുകൾ ഏത് വാക്കുകളെ സൂചിപ്പിക്കുന്നു എന്ന് കാണാനും, കൂടാതെ സന്ദർഭം പരിഗണിക്കാനും ഇത് സഹായിക്കുന്നു: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ml.png) +![](../../../../../translated_images/ml/CoreferenceResolution.861924d6d384a7d6.png) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) നിന്നുള്ള ചിത്രം @@ -91,7 +91,7 @@ RNN-കളിൽ, sequence-to-sequence രണ്ട് റിക്കറന് **BERT** (Bidirectional Encoder Representations from Transformers) വളരെ വലിയ 12 ലെയർ *BERT-base* ഉം 24 ലെയർ *BERT-large* ഉം ഉള്ള മൾട്ടി-ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്വർക്ക് ആണ്. മോഡൽ ആദ്യം വലിയ ടെക്സ്റ്റ് കോർപ്പസിൽ (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) അൺസൂപ്പർവൈസ്ഡ് ട്രെയിനിംഗിലൂടെ (വാചകത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിൽ മോഡൽ ഭാഷാ ബോധം ഗഹനമായി ഉൾക്കൊള്ളുന്നു, പിന്നീട് ഫൈൻ ട്യൂണിങ്ങ് വഴി മറ്റ് ഡാറ്റാസെറ്റുകളുമായി ഉപയോഗിക്കാം. ഈ പ്രക്രിയ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു. -![http://jalammar.github.io/illustrated-bert/ നിന്നുള്ള ചിത്രം](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ml.png) +![http://jalammar.github.io/illustrated-bert/ നിന്നുള്ള ചിത്രം](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > ചിത്രം [മൂലം](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 399a7e62..19f68310 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ml/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കമിടാനുള്ള മാർഗം നൽകുന്നു. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനിലവസ്ഥകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനിലവസ്ഥകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം $y_t$ സൃഷ്ടിക്കുമ്പോൾ, വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ $\\alpha_{t,i}$ ഉപയോഗിച്ച് എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും $h_i$ പരിഗണിക്കും.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ml.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ ആഡിറ്റീവ് ശ്രദ്ധാ യന്ത്രം ഉൾപ്പെടുത്തിയ എൻകോഡർ-ഡികോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നെടുത്തത്]*\n", "\n", "ശ്രദ്ധാ മാട്രിക്സ് $\\{\\alpha_{i,j}\\}$ ഒരു പ്രത്യേക ഔട്ട്പുട്ട് വാക്കിന്റെ സൃഷ്ടിയിൽ ചില ഇൻപുട്ട് വാക്കുകൾ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ml.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) നിന്നെടുത്ത ചിത്രം]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) വളരെ വലിയ, 12 ലെയറുള്ള *BERT-base*നും 24 ലെയറുള്ള *BERT-large*നും ഉള്ള മൾട്ടി ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്‌വർക്കാണ്. മോഡൽ ആദ്യം വലിയ ടെക്സ്റ്റ് കോർപ്പസ് (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) ഉപയോഗിച്ച് സ്വയംപരിശീലനത്തിലൂടെ (വാക്യത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിൽ മോഡൽ ഭാഷാ ബോധം ഗഹനമായി ഉൾക്കൊള്ളുന്നു, പിന്നീട് ഇത് മറ്റ് ഡാറ്റാസെറ്റുകൾ ഉപയോഗിച്ച് ഫൈൻ ട്യൂണിങ്ങിലൂടെ പ്രയോജനപ്പെടുത്താം. ഈ പ്രക്രിയ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ml.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 തുടങ്ങിയ നിരവധി ട്രാൻസ്ഫോർമർ ആർക്കിടെക്ചറുകളുടെ വ്യത്യാസങ്ങൾ ഉണ്ട്, ഇവ ഫൈൻ ട്യൂൺ ചെയ്യാവുന്നതാണ്. [HuggingFace പാക്കേജ്](https://github.com/huggingface/) PyTorch ഉപയോഗിച്ച് ഈ ആർക്കിടെക്ചറികളുടെ പരിശീലനത്തിനുള്ള റിപോസിറ്ററി നൽകുന്നു.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 2056bbb7..c4034abc 100644 --- a/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ml/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ശ്രദ്ധാ യന്ത്രങ്ങൾ** RNN-ന്റെ ഓരോ ഔട്ട്പുട്ട് പ്രവചനത്തിലും ഓരോ ഇൻപുട്ട് വെക്ടറിന്റെ സാന്ദർഭിക സ്വാധീനം തൂക്കമിടാനുള്ള മാർഗം നൽകുന്നു. ഇത് നടപ്പിലാക്കുന്നത് ഇൻപുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും ഔട്ട്പുട്ട് RNN-ന്റെ ഇടനില സ്റ്റേറ്റുകളും തമ്മിൽ ഷോർട്ട്കട്ടുകൾ സൃഷ്ടിച്ച് ആണ്. ഈ രീതിയിൽ, ഔട്ട്പുട്ട് ചിഹ്നം $y_t$ സൃഷ്ടിക്കുമ്പോൾ, വ്യത്യസ്ത തൂക്കം കോഫിഷ്യന്റുകളായ $\\alpha_{t,i}$ ഉപയോഗിച്ച് എല്ലാ ഇൻപുട്ട് ഹിഡൻ സ്റ്റേറ്റുകളും $h_i$ പരിഗണിക്കും.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ml.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ml/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ലെ കൂട്ടിച്ചേർത്ത ശ്രദ്ധാ യന്ത്രം ഉള്ള എൻകോഡർ-ഡികോഡർ മോഡൽ, [ഈ ബ്ലോഗ് പോസ്റ്റ്](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) നിന്നെടുത്തത്]*\n", "\n", "ശ്രദ്ധാ മാട്രിക്സ് $\\{\\alpha_{i,j}\\}$ ഒരു പ്രത്യേക ഇൻപുട്ട് വാക്ക് ഔട്ട്പുട്ട് ശൃംഖലയിൽ ഒരു വാക്ക് സൃഷ്ടിക്കുന്നതിൽ എത്രമാത്രം പങ്കുവഹിക്കുന്നുവെന്ന് പ്രതിനിധീകരിക്കും. താഴെ ഒരു ഉദാഹരണ മാട്രിക്സ് കാണിക്കുന്നു:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ml.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ml/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (ചിത്രം 3) നിന്നെടുത്ത ചിത്രം]*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ഈ ലെയർ രണ്ട് `Embedding` ലെയറുകളടങ്ങിയതാണ്: ടോക്കണുകൾ (മുൻപ് ചർച്ച ചെയ്ത രീതിയിൽ) എമ്പെഡ് ചെയ്യുന്നതിനും ടോക്കൺ സ്ഥാനങ്ങൾ എമ്പെഡ് ചെയ്യുന്നതിനും. ടോക്കൺ സ്ഥാനങ്ങൾ 0 മുതൽ `maxlen` വരെ സ്വാഭാവിക സംഖ്യകളുടെ ഒരു ശ്രേണിയായി `tf.range` ഉപയോഗിച്ച് സൃഷ്ടിക്കപ്പെടുന്നു, പിന്നീട് എമ്പെഡിംഗ് ലെയറിലൂടെ കടന്നുപോകുന്നു. രണ്ട് ഫലമായ എമ്പെഡിംഗ് വെക്ടറുകളും ചേർത്ത്, `maxlen`$\\times$`embed_dim` ആകൃതിയിലുള്ള സ്ഥാനാനുസൃതമായി എമ്പെഡുചെയ്ത ഇൻപുട്ട് പ്രതിനിധാനം സൃഷ്ടിക്കുന്നു.\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾ, ട്രാൻസ്ഫോർമർ ബ്ലോക്ക് നടപ്പിലാക്കാം. ഇത് മുൻപ് നിർവചിച്ച എമ്പെഡിംഗ് ലെയറിന്റെ ഔട്ട്പുട്ട് സ്വീകരിക്കും:\n" ] @@ -134,7 +134,7 @@ "\n", "ഈ ലെയറിന്റെ ഔട്ട്പുട്ട് പിന്നീട് `Dense` നെറ്റ്‌വർക്കിലൂടെ (നമ്മുടെ കേസിൽ - രണ്ട് ലെയർ പെർസെപ്ട്രോൺ) കടന്നുപോകുകയും, ഫലം അന്തിമ ഔട്ട്പുട്ടിൽ ചേർക്കുകയും ചെയ്യുന്നു (അത് വീണ്ടും സാധാരണവത്കരണത്തിലൂടെ കടന്നുപോകുന്നു).\n", "\n", - "\n", + "\n", "\n", "ഇപ്പോൾ, നാം പൂർണ്ണമായ transformer മോഡൽ നിർവചിക്കാൻ തയ്യാറാണ്:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ഒരു വളരെ വലിയ, 12 ലെയറുള്ള *BERT-base* മൾട്ടി ലെയർ ട്രാൻസ്ഫോർമർ നെറ്റ്‌വർക്കും, 24 ലെയറുള്ള *BERT-large* മോഡലും ആണ്. മോഡൽ ആദ്യം വലിയ വാചക ഡാറ്റാ കോർപ്പസിൽ (വിക്കിപീഡിയ + പുസ്തകങ്ങൾ) അനിയന്ത്രിത പരിശീലനത്തിലൂടെ (വാചകത്തിലെ മറച്ചുവച്ച വാക്കുകൾ പ്രവചിച്ച്) പ്രീ-ട്രെയിൻ ചെയ്യപ്പെടുന്നു. പ്രീ-ട്രെയിനിംഗിനിടെ മോഡൽ ഭാഷാ മനസ്സിലാക്കലിന്റെ ഒരു വലിയ തോതിൽ അറിവ് സമ്പാദിക്കുന്നു, പിന്നീട് ഇത് മറ്റ് ഡാറ്റാസെറ്റുകളുമായി ഫൈൻ ട്യൂണിങ്ങ് ഉപയോഗിച്ച് പ്രയോജനപ്പെടുത്താം. ഈ പ്രക്രിയയെ **ട്രാൻസ്ഫർ ലേണിംഗ്** എന്ന് വിളിക്കുന്നു.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ml.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ml/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 തുടങ്ങിയ നിരവധി ട്രാൻസ്ഫോർമർ ആർക്കിടെക്ചറുകളുടെ വ്യത്യാസങ്ങൾ ഉണ്ട്, അവ ഫൈൻ ട്യൂൺ ചെയ്യാവുന്നതാണ്.\n", "\n", diff --git a/translations/ml/lessons/5-NLP/19-NER/README.md b/translations/ml/lessons/5-NLP/19-NER/README.md index c6f0e812..940e3b85 100644 --- a/translations/ml/lessons/5-NLP/19-NER/README.md +++ b/translations/ml/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങൾക്ക് ആമസോൺ അലക്സാ അല്ലെങ്കിൽ ഗൂഗിൾ അസിസ്റ്റന്റ് പോലുള്ള ഒരു നാച്ചുറൽ ലാംഗ്വേജ് ചാറ്റ് ബോട്ട് വികസിപ്പിക്കണമെന്ന് കരുതുക. ബുദ്ധിമുട്ടുള്ള ചാറ്റ് ബോട്ടുകൾ പ്രവർത്തിക്കുന്നത് ഉപയോക്താവ് എന്ത് ആഗ്രഹിക്കുന്നു എന്ന് *അർത്ഥമാക്കുന്നതിലൂടെ* ആണ്, ഇൻപുട്ട് വാക്യത്തിൽ ടെക്സ്റ്റ് ക്ലാസിഫിക്കേഷൻ നടത്തിയാണ് ഇത് സാധ്യമാകുന്നത്. ഈ ക്ലാസിഫിക്കേഷന്റെ ഫലം **ഇന്റന്റ്** എന്നറിയപ്പെടുന്നു, ഇത് ചാറ്റ് ബോട്ട് എന്ത് ചെയ്യണമെന്ന് നിർണ്ണയിക്കുന്നു. -Bot NER +Bot NER > ചിത്രകാരൻ: ലേഖകൻ @@ -58,7 +58,7 @@ infant | O ടോക്കണുകളും ക്ലാസുകളും തമ്മിൽ ഒന്ന് ഒന്ന് പൊരുത്തപ്പെടുത്തേണ്ടതിനാൽ, ഈ ചിത്രത്തിൽ നിന്ന് ഒരു വലതുവശത്തെ **many-to-many** ന്യൂറൽ നെറ്റ്വർക്ക് മോഡൽ പരിശീലിപ്പിക്കാം: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ml.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/ml/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റ്](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ൽ നിന്നുള്ളതാണ്, ലേഖകൻ [Andrej Karpathy](http://karpathy.github.io/). NER ടോക്കൺ ക്ലാസിഫിക്കേഷൻ മോഡലുകൾ ഈ ചിത്രത്തിലെ വലതുവശത്തെ നെറ്റ്വർക്ക് ആർക്കിടെക്ചറിനോട് പൊരുത്തപ്പെടുന്നു.* diff --git a/translations/ml/lessons/5-NLP/README.md b/translations/ml/lessons/5-NLP/README.md index c405b318..9575be17 100644 --- a/translations/ml/lessons/5-NLP/README.md +++ b/translations/ml/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ് -![NLP ടാസ്കുകളുടെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ml.png) +![NLP ടാസ്കുകളുടെ സംഗ്രഹം ഒരു ഡ്രോയിങ്ങിൽ](../../../../translated_images/ml/ai-nlp.b22dcb8ca4707cea.png) ഈ ഭാഗത്തിൽ, നാം **സ്വാഭാവിക ഭാഷാ പ്രോസസ്സിംഗ് (NLP)** സംബന്ധിച്ച ടാസ്കുകൾ കൈകാര്യം ചെയ്യാൻ ന്യൂറൽ നെറ്റ്‌വർക്കുകൾ ഉപയോഗിക്കുന്നതിൽ ശ്രദ്ധ കേന്ദ്രീകരിക്കും. കമ്പ്യൂട്ടറുകൾക്ക് പരിഹരിക്കാൻ കഴിയേണ്ട നിരവധി NLP പ്രശ്നങ്ങൾ ഉണ്ട്: diff --git a/translations/ml/lessons/6-Other/22-DeepRL/README.md b/translations/ml/lessons/6-Other/22-DeepRL/README.md index b24f1e8c..6ed3eff6 100644 --- a/translations/ml/lessons/6-Other/22-DeepRL/README.md +++ b/translations/ml/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL-ക്കായി മികച്ച ഉപകരണം [OpenAI Gym](https:/ ബാലൻസിംഗ് ഒരു ലളിതമായ പതിപ്പ് **കാർട്ട്‌പോൾ** പ്രശ്നമായി അറിയപ്പെടുന്നു. കാർട്ട്‌പോൾ ലോകത്ത്, ഒരു ഹോരിസോണ്ടൽ സ്ലൈഡർ ഇടത്തോ വലത്തോ നീങ്ങാൻ കഴിയും, സ്ലൈഡറിന്റെ മുകളിൽ ഒരു വെർട്ടിക്കൽ പോൾ ബാലൻസ് ചെയ്യുകയാണ് ലക്ഷ്യം. -a cartpole +a cartpole ഈ പരിസ്ഥിതി സൃഷ്ടിക്കുകയും ഉപയോഗിക്കുകയും ചെയ്യാൻ, നമുക്ക് പൈതൺ കോഡിന്റെ കുറച്ച് വരികൾ ആവശ്യമാണ്: diff --git a/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md b/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md index 28b7b257..44658f55 100644 --- a/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/ml/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: OpenAI പരിസ്ഥിതിയിൽ [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) നിയന്ത്രിക്കാൻ RL ഏജന്റിനെ പരിശീലിപ്പിക്കുക. -Mountain Car +Mountain Car ## പരിസ്ഥിതി diff --git a/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md index 0f4e7fa4..d5410496 100644 --- a/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ml/lessons/6-Other/23-MultiagentSystems/README.md @@ -61,7 +61,7 @@ ask turtles [ നെറ്റ്‌ലോഗോയുടെ മികച്ച പ്രത്യേകത ഇതിൽ പ്രവർത്തനക്ഷമമായ മോഡലുകളുടെ ലൈബ്രറി ഉള്ളതാണ്. **File → Models Library** എന്ന വഴി പോകുക, അവിടെ നിരവധി മോഡൽ വിഭാഗങ്ങൾ തിരഞ്ഞെടുക്കാം. -NetLogo Models Library +NetLogo Models Library > മോഡൽ ലൈബ്രറിയുടെ സ്ക്രീൻഷോട്ട് - Dmitry Soshnikov @@ -71,7 +71,7 @@ ask turtles [ മോഡൽ തുറന്ന ശേഷം, നിങ്ങൾ പ്രധാന നെറ്റ്‌ലോഗോ സ്ക്രീനിലേക്ക് എത്തും. ഇവിടെ finite resources (പുല്ല്) ഉള്ള ഒരു വുൾഫ്-ഷീപ്പ് ജനസംഖ്യയെ വിവരിക്കുന്ന ഒരു മാതൃകയാണ്. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ml.png) +![NetLogo Main Screen](../../../../../translated_images/ml/NetLogo-Main.32653711ec1a01b3.png) > സ്ക്രീൻഷോട്ട് - Dmitry Soshnikov diff --git a/translations/ml/lessons/README.md b/translations/ml/lessons/README.md index 5307ad93..807d430f 100644 --- a/translations/ml/lessons/README.md +++ b/translations/ml/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # അവലോകനം -![ഒരു ഡ്രോയിങ്ങിൽ അവലോകനം](../../../translated_images/ai-overview.0857791951d19500.ml.png) +![ഒരു ഡ്രോയിങ്ങിൽ അവലോകനം](../../../translated_images/ml/ai-overview.0857791951d19500.png) > സ്കെച്ച്നോട്ട് [ടോമോമി ഇമുര](https://twitter.com/girlie_mac) എഴുതിയത് diff --git a/translations/ml/lessons/X-Extras/X1-MultiModal/README.md b/translations/ml/lessons/X-Extras/X1-MultiModal/README.md index 57abaabc..9672fbc7 100644 --- a/translations/ml/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ml/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ടാസ്കുകൾ പരിഹരിക്കുന്നതിന് CLIP-ന്റെ പ്രധാന ആശയം ഒരു ടെക്സ്റ്റ് പ്രോംപ്റ്റും ഒരു ചിത്രവും താരതമ്യം ചെയ്ത് ചിത്രം പ്രോംപ്റ്റിനോട് എത്രത്തോളം പൊരുത്തപ്പെടുന്നു എന്ന് നിർണ്ണയിക്കാനാകുക എന്നതാണ്. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ml.png) +![CLIP Architecture](../../../../../translated_images/ml/clip-arch.b3dbf20b4e8ed8be.png) > *ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നാണ്](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP മോഡൽ/ലൈബ്രറി [OpenAI GitHub](https://github.com/opena ഉദാഹരണത്തിന്, പൂച്ചകൾ, നായകൾ, മനുഷ്യർ എന്നിങ്ങനെ ചിത്രങ്ങൾ വർഗ്ഗീകരിക്കേണ്ടതുണ്ടെങ്കിൽ, മോഡലിന് ഒരു ചിത്രം നൽകുകയും, "*ഒരു പൂച്ചയുടെ ചിത്രം*", "*ഒരു നായയുടെ ചിത്രം*", "*ഒരു മനുഷ്യന്റെ ചിത്രം*" എന്നിങ്ങനെ ടെക്സ്റ്റ് പ്രോംപ്റ്റുകൾ നൽകുകയും ചെയ്യാം. 3 പ്രോബബിലിറ്റികളുള്ള വെക്ടറിൽ ഏറ്റവും ഉയർന്ന മൂല്യമുള്ള ഇൻഡക്സ് തിരഞ്ഞെടുക്കുക മതി. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.ml.png) +![CLIP for Image Classification](../../../../../translated_images/ml/clip-class.3af42ef0b2b19369.png) > *ഈ ചിത്രം [ഈ ബ്ലോഗ് പോസ്റ്റിൽ നിന്നാണ്](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN-നെ കുറിച്ച് കൂടുതൽ അറിയാൻ [Tam VQGAN-നും പരമ്പരാഗത GAN-നും ഇടയിലെ പ്രധാന വ്യത്യാസം, GAN ഏതെങ്കിലും ഇൻപുട്ട് വെക്ടറിൽ നിന്ന് നല്ല ചിത്രം സൃഷ്ടിക്കാമെങ്കിലും, VQGAN സൃഷ്ടിക്കുന്ന ചിത്രം സുസംയോജിതമല്ലായിരിക്കാം. അതിനാൽ, ചിത്രം സൃഷ്ടിക്കൽ പ്രക്രിയ കൂടുതൽ മാർഗ്ഗനിർദ്ദേശം ആവശ്യമാണ്, അത് CLIP ഉപയോഗിച്ച് സാധ്യമാക്കാം. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.ml.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/ml/vqgan.5027fe05051dfa31.png) ഒരു ടെക്സ്റ്റ് പ്രോംപ്റ്റിനോട് പൊരുത്തമുള്ള ചിത്രം സൃഷ്ടിക്കാൻ, ആദ്യം ഒരു യാദൃച്ഛിക എൻകോഡിംഗ് വെക്ടർ എടുത്ത് VQGAN വഴി ചിത്രം സൃഷ്ടിക്കുന്നു. തുടർന്ന് CLIP ഉപയോഗിച്ച് ചിത്രം പ്രോംപ്റ്റിനോട് എത്രത്തോളം പൊരുത്തപ്പെടുന്നു എന്ന് കാണിക്കുന്ന ലോസ് ഫംഗ്ഷൻ നിർമ്മിക്കുന്നു. ഈ ലോസ് കുറയ്ക്കാൻ ബാക്ക് പ്രൊപ്പഗേഷൻ ഉപയോഗിച്ച് ഇൻപുട്ട് വെക്ടർ പാരാമീറ്ററുകൾ ക്രമീകരിക്കുന്നു. VQGAN+CLIP നടപ്പിലാക്കുന്ന മികച്ച ലൈബ്രറിയാണ് [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ml.png) | ![Picture produced by pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ml.png) | ![Picture produced by Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ml.png) +![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/ml/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- *ഒരു പുസ്തകമുള്ള സാഹിത്യ അധ്യാപകനായ യുവ പുരുഷന്റെ ക്ലോസ്അപ്പ് വാട്ടർകോളർ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ഒരു കമ്പ്യൂട്ടർ ഉള്ള യുവ വനിതാ കമ്പ്യൂട്ടർ സയൻസ് അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ബ്ലാക്ക്ബോർഡിന് മുന്നിലുള്ള പ്രായമായ പുരുഷ ഗണിത അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം @@ -75,7 +75,7 @@ CLIP-നോട് വ്യത്യസ്തമായി, DALL-E ടെക് DALL.E 1-നും 2-നും ഇടയിലെ പ്രധാന വ്യത്യാസം, 2-ാം പതിപ്പ് കൂടുതൽ യാഥാർത്ഥ്യസമൃദ്ധമായ ചിത്രങ്ങളും കലാസൃഷ്ടികളും സൃഷ്ടിക്കുന്നു എന്നതാണ്. DALL-E ഉപയോഗിച്ച് സൃഷ്ടിച്ച ചിത്രങ്ങളുടെ ഉദാഹരണങ്ങൾ: -![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ml.png) | ![Picture produced by pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ml.png) | ![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ml.png) +![Picture produced by Pixray](../../../../../translated_images/ml/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Picture produced by pixray](../../../../../translated_images/ml/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Picture produced by Pixray](../../../../../translated_images/ml/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- *ഒരു പുസ്തകമുള്ള സാഹിത്യ അധ്യാപകനായ യുവ പുരുഷന്റെ ക്ലോസ്അപ്പ് വാട്ടർകോളർ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ഒരു കമ്പ്യൂട്ടർ ഉള്ള യുവ വനിതാ കമ്പ്യൂട്ടർ സയൻസ് അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം | *ബ്ലാക്ക്ബോർഡിന് മുന്നിലുള്ള പ്രായമായ പുരുഷ ഗണിത അധ്യാപകന്റെ ക്ലോസ്അപ്പ് ഓയിൽ പോർട്രെയിറ്റ്* എന്ന പ്രോംപ്റ്റിൽ നിന്നുള്ള ചിത്രം diff --git a/translations/mo/README.md b/translations/mo/README.md index e7448b06..254279d2 100644 --- a/translations/mo/README.md +++ b/translations/mo/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 初學者人工智能課程 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.mo.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/mo/ai-overview.0857791951d19500.png)| |:---:| | 初學者人工智能 - _速寫筆記由 [@girlie_mac](https://twitter.com/girlie_mac) 提供_ | diff --git a/translations/mo/lessons/1-Intro/README.md b/translations/mo/lessons/1-Intro/README.md index a5415517..e34d8bbe 100644 --- a/translations/mo/lessons/1-Intro/README.md +++ b/translations/mo/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能簡介 -![人工智能簡介內容的手繪圖](../../../../translated_images/ai-intro.bf28d1ac4235881c.mo.png) +![人工智能簡介內容的手繪圖](../../../../translated_images/mo/ai-intro.bf28d1ac4235881c.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦是由 [查爾斯·巴貝奇](https://en.wikipedia.org/wiki/Charles_Babbage) 發明的，用於按照明確定義的程序（算法）處理數字。現代電腦雖然比19世紀提出的原始模型先進得多，但仍然遵循受控計算的理念。因此，如果我們知道實現目標所需的精確步驟序列，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/dsh_age.d212a30d4e54fb5f.mo.png) +![一個人的照片](../../../../translated_images/mo/dsh_age.d212a30d4e54fb5f.png) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 處理 **[智能](https://en.wikipedia.org/wiki/Intelligence)** 這個術語時的一個問題是，對於這個術語並沒有明確的定義。有人認為智能與 **抽象思維** 或 **自我意識** 有關，但我們無法準確定義它。 -![一隻貓的照片](../../../../translated_images/photo-cat.8c8e8fb760ffe457.mo.jpg) +![一隻貓的照片](../../../../translated_images/mo/photo-cat.8c8e8fb760ffe457.jpg) > [照片](https://unsplash.com/photos/75715CVEJhI) 由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那麼機器學習呢？ | | > |--------------|-----------| -> | 基於計算機學習如何根據某些數據解決問題的人工智能部分稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.mo.png) | +> | 基於計算機學習如何根據某些數據解決問題的人工智能部分稱為 **機器學習**。我們不會在本課程中考慮經典的機器學習——我們建議參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/mo/ml-for-beginners.9e4fed176fd5817d.png) | ## 人工智能的簡史 人工智能作為一個領域始於20世紀中葉。最初，符號推理是一種流行的方法，並且它帶來了一些重要的成功，例如專家系統——能夠在某些有限問題領域中充當專家的計算機程序。然而，很快就發現這種方法並不適用於大規模應用。從專家那裡提取知識、將其表示在計算機中並保持知識庫的準確性，事實證明這是一項非常複雜且在許多情況下成本過高的任務。這導致了20世紀70年代所謂的 [人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能簡史 +人工智能簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助手（如 Cortana、Siri 或 Google Assistant）都是混合系統，使用神經網絡將語音轉換為文本並識別我們的意圖，然後使用一些推理或明確的算法執行所需的操作。 * 未來，我們可能會期待一個完全基於神經網絡的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 神經網絡家族在這方面表現出色。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智能研究 diff --git a/translations/mo/lessons/2-Symbolic/Animals.ipynb b/translations/mo/lessons/2-Symbolic/Animals.ipynb index 2409b727..f87df3dc 100644 --- a/translations/mo/lessons/2-Symbolic/Animals.ipynb +++ b/translations/mo/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "在這個範例中，我們將實作一個簡單的知識型系統，根據一些外觀特徵來判斷動物。此系統可以用以下的 AND-OR 樹來表示（這只是整個樹的一部分，我們可以輕鬆地添加更多規則）：\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.mo.png)\n" + "![](../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/mo/lessons/2-Symbolic/README.md b/translations/mo/lessons/2-Symbolic/README.md index f5af6bd5..2ef830a7 100644 --- a/translations/mo/lessons/2-Symbolic/README.md +++ b/translations/mo/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 知識表示與專家系統 -![象徵式 AI 內容摘要](../../../../translated_images/ai-symbolic.715a30cb610411a6.mo.png) +![象徵式 AI 內容摘要](../../../../translated_images/mo/ai-symbolic.715a30cb610411a6.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： -![知識表示光譜](../../../../translated_images/knowledge-spectrum.b60df631852c0217.mo.png) +![知識表示光譜](../../../../translated_images/mo/knowledge-spectrum.b60df631852c0217.png) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -94,7 +94,7 @@ Python | 區塊語法 | 縮排 象徵式 AI 的早期成功之一是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個在其上執行推理的**推理引擎**。 -![人類架構](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.mo.png) | ![基於知識的系統架構](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.mo.png) +![人類架構](../../../../translated_images/mo/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/mo/arch-kbs.3ec5c150b09fa8da.png) ----------------------------------|---------------------------------------- 人類神經系統的簡化結構 | 基於知識的系統的架構 @@ -106,7 +106,7 @@ Python | 區塊語法 | 縮排 例如，讓我們考慮以下基於動物物理特徵的專家系統： -![AND-OR 樹](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.mo.png) +![AND-OR 樹](../../../../translated_images/mo/AND-OR-Tree.5592d2c70187f283.png) > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 diff --git a/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 08fa107e..1f4c7bee 100644 --- a/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/mo/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "如果我們有超過兩個類別，softmax會在所有類別之間對概率進行正規化。以下是用於 MNIST 數字分類的網絡架構圖：\n", "\n", - "![MNIST 分類器](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.mo.png)\n" + "![MNIST 分類器](../../../../../translated_images/mo/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* 訓練損失低——模型能很好地接近訓練數據，因為它具有足夠的表達能力。\n", "* 驗證損失可能比訓練損失高得多，並且在訓練過程中可能開始增加——這是因為模型“記住”了訓練數據點，卻失去了對“整體情況”的把握。\n", "\n", - "![過擬合](../../../../../translated_images/overfit.a0bd57f717c15769.mo.png)\n", + "![過擬合](../../../../../translated_images/mo/overfit.a0bd57f717c15769.png)\n", "\n", "> 在這張圖中，`x` 代表訓練數據，`o` 代表驗證數據。左邊是線性模型（單層），它很好地接近了數據的本質。右邊是過擬合模型，該模型完美地接近了訓練數據，但對其他數據（驗證數據）的表現卻變得毫無意義（驗證誤差非常高）。\n" ] diff --git a/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md index 28d21219..98a4200e 100644 --- a/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/mo/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點（圖中的 `x`）的問題： -![線性模型](../../../../../translated_images/overfit1.f24b71c6f652e59e.mo.jpg) | ![過擬合模型](../../../../../translated_images/overfit2.131f5800ae10ca5e.mo.jpg) +![線性模型](../../../../../translated_images/mo/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/mo/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 如上圖所示，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練誤差和驗證誤差都開始下降，然後在某個時候驗證誤差可能停止下降並開始上升。這將是過擬合的跡象，也是我們應該停止訓練的指示（或者至少保存模型的快照）。 -![過擬合](../../../../../translated_images/Overfitting.408ad91cd90b4371.mo.png) +![過擬合](../../../../../translated_images/mo/Overfitting.408ad91cd90b4371.png) ## 如何防止過擬合 diff --git a/translations/mo/lessons/3-NeuralNetworks/README.md b/translations/mo/lessons/3-NeuralNetworks/README.md index 07270577..dc2ca5f6 100644 --- a/translations/mo/lessons/3-NeuralNetworks/README.md +++ b/translations/mo/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網絡簡介 -![神經網絡簡介內容摘要的手繪圖](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.mo.png) +![神經網絡簡介內容摘要的手繪圖](../../../../translated_images/mo/ai-neuralnetworks.1c687ae40bc86e83.png) 如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到最近這一方向取得了巨大成功。這些模仿大腦的數學模型被稱為**神經網絡**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中，我們知道大腦由神經細胞（神經元）組成，每個神經元都有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都可以傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這些導電性由神經遞質調節。 -![神經元模型](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.mo.jpg) | ![神經元模型](../../../../translated_images/artneuron.1a5daa88d20ebe6f.mo.png) +![神經元模型](../../../../translated_images/mo/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/mo/artneuron.1a5daa88d20ebe6f.png) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含幾個輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md index 8451697b..b0126ccc 100644 --- a/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/mo/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便進一步由神經網路進行分類。 -![盲文影像](../../../../../translated_images/braille.341962ff76b1bd70.mo.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/braille-result.46530fea020b03c7.mo.png) | ![盲文符號](../../../../../translated_images/braille-symbols.0159185ab69d5339.mo.png) +![盲文影像](../../../../../translated_images/mo/braille.341962ff76b1bd70.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/mo/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/mo/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測影片中的運動**。如果相機固定，則來自相機的幀應該彼此非常相似。由於幀表示為陣列，只需對兩個連續幀的陣列進行相減，我們就能得到像素差異，靜態幀的差異應該很低，而當影像中有顯著運動時，差異會變高。 -![影片幀和幀差的影像](../../../../../translated_images/frame-difference.706f805491a0883c.mo.png) +![影片幀和幀差的影像](../../../../../translated_images/mo/frame-difference.706f805491a0883c.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -88,7 +88,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算顯示每個像素移動方向的向量場 - **稀疏光流** 基於提取影像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流影像](../../../../../translated_images/optical.1f4a94464579a83a.mo.png) +![光流影像](../../../../../translated_images/mo/optical.1f4a94464579a83a.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d7ebf40f..6f4124bb 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網路。它的層結構如下： -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.mo.jpg) +![ImageNet Layers](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.jpg) 如圖所示，VGG 採用傳統的金字塔架構，即一系列的卷積-池化層。 -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.mo.jpg) +![ImageNet Pyramid](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 490a9958..a90e6f39 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "因此，在典型的 CNN 中，會有多個卷積層，並在它們之間加入池化層以減少影像的維度。我們還會增加濾波器的數量，因為隨著模式變得更複雜，我們需要尋找的可能組合也會更多。\n", "\n", - "![一張展示多個卷積層和池化層的圖片。](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.mo.png)\n", + "![一張展示多個卷積層和池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "由於空間維度減少以及特徵/濾波器維度增加，這種架構也被稱為 **金字塔架構**。\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 40d28c0a..ef541c45 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "因此，在典型的 CNN 中，會有多個卷積層，並在它們之間插入池化層以減少圖片的尺寸。同時，我們還會增加濾波器的數量，因為隨著模式變得更加複雜，我們需要尋找的可能組合也會更多。\n", "\n", - "![一張展示多個卷積層與池化層的圖片。](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.mo.png)\n", + "![一張展示多個卷積層與池化層的圖片。](../../../../../translated_images/mo/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "由於空間尺寸逐漸減小，而特徵/濾波器的維度逐漸增加，這種架構也被稱為 **金字塔架構**。\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md index bf286d21..78a552bb 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像可以用二維矩陣或帶有色彩深度的三維張量來表示。應用濾波器意味著我們取一個相對較小的**濾波核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口在整個圖像上滑動，並根據濾波核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/filter-vert.b7148390ca0bc356.mo.png) | ![水平邊緣濾波器](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.mo.png) +![垂直邊緣濾波器](../../../../../translated_images/mo/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/mo/filter-horiz.59b80ed4feb946ef.png) ----|---- > 圖片來源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中找到模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵層次上工作，從低層次的像素組合開始，到更高層次的圖片部分組合。 -![層次特徵提取](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.mo.png) +![層次特徵提取](../../../../../translated_images/mo/FeatureExtractionCNN.d9b456cbdae7cb64.png) > 圖片來源：[Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的 top-5 分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.mo.jpg) +![ImageNet 層](../../../../../translated_images/mo/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet 金字塔](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.mo.jpg) +![ImageNet 金字塔](../../../../../translated_images/mo/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 27f86839..b0533466 100644 --- a/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/mo/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含 37 種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/data.50b2a9d5484bdbf0.mo.png) +![我們將處理的數據集](../../../../../../translated_images/mo/data.50b2a9d5484bdbf0.png) 要下載數據集，請使用以下程式碼片段： diff --git a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 17f00a7f..4d879215 100644 --- a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "為了呈現理想的貓，我們將從一張隨機噪點圖片開始，並嘗試使用梯度下降優化技術來調整圖片，使網絡能夠識別出貓。\n", "\n", - "![優化循環](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.mo.png)\n", + "![優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "以下是我們的起始圖片：\n" ] diff --git a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md index 6487b42c..64f4d00a 100644 --- a/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/mo/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含函數，可以輕鬆加載一些常見架構的預 以下是 VGG-16 網絡從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/features.6291f9c7ba3a0b95.mo.png) +![VGG-16 提取的特徵](../../../../../translated_images/mo/features.6291f9c7ba3a0b95.png) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含函數，可以輕鬆加載一些常見架構的預 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使網絡開始認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.mo.png) +![圖像優化循環](../../../../../translated_images/mo/ideal-cat-loop.999fbb8ff306e044.png) 然而，如果我們這樣做，結果會非常接近隨機噪聲。這是因為*有很多方法可以讓網絡認為輸入圖像是一隻貓*，包括一些在視覺上沒有意義的方式。雖然這些圖像包含了許多典型的貓的模式，但並沒有任何約束使它們在視覺上更具辨識性。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變異損失**。它是一種度量，顯示圖像中相鄰像素的相似程度。最小化變異損失可以使圖像更平滑，並消除噪聲——從而揭示更具視覺吸引力的模式。以下是一些“理想”圖像的示例，它們被高概率分類為貓和斑馬： -![理想貓](../../../../../translated_images/ideal-cat.203dd4597643d6b0.mo.png) | ![理想斑馬](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.mo.png) +![理想貓](../../../../../translated_images/mo/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/mo/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用於對神經網絡進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網絡，使一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網絡識別為狗，然後稍微調整它，使用梯度下降優化，直到網絡開始將其分類為貓： -![狗的圖片](../../../../../translated_images/original-dog.8f68a67d2fe0911f.mo.png) | ![被分類為貓的狗圖片](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.mo.png) +![狗的圖片](../../../../../translated_images/mo/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/mo/adversarial-dog.d9fc7773b0142b89.png) -----|----- *狗的原始圖片* | *被分類為貓的狗圖片* diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 52aa08d1..b3f6bb2b 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "由於我們訓練自編碼器的目的是儘可能捕捉原始圖像中的信息以進行準確的重建，網絡會嘗試找到輸入圖像的最佳**嵌入（embedding）**來捕捉其含義。\n", "\n", - "![自編碼器示意圖](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mo.jpg)\n", + "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 2920fce7..1badcdb9 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "由於我們訓練自編碼器的目的是捕捉原始圖像中的盡可能多的信息以進行準確的重建，網絡會嘗試找到輸入圖像的最佳**嵌入**來捕捉其含義。\n", "\n", - "![自編碼器示意圖](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mo.jpg)\n", + "![自編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*圖片來源：[Keras 部落格](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md index 4b3c59a4..8b7f1ad2 100644 --- a/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/mo/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器的目的是捕捉原始圖像中的盡可能多的信息以進行準確的重建，網絡會嘗試找到最佳的**嵌入**方式來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mo.jpg) +![自動編碼器示意圖](../../../../../translated_images/mo/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md index 4ea6a22c..9ac3af8e 100644 --- a/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/mo/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.mo.png) +![物件偵測](../../../../../translated_images/mo/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 圖片來源：[YOLO v2 網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 將分類結果中激活值足夠高的區塊視為包含目標物件的區域。 -![簡單的物件偵測](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.mo.png) +![簡單的物件偵測](../../../../../translated_images/mo/naive-detection.e7f1ba220ccd08c6.png) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件的上下文。包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.mo.jpg) +![COCO](../../../../../translated_images/mo/coco-examples.71bc60380fa6cceb.jpg) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 對於影像分類來說，衡量演算法的表現相對簡單；但對於物件偵測，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。後者使用所謂的**交集比聯集**（IoU）來衡量，這是一種用來評估兩個框（或任意兩個區域）重疊程度的方法。 -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.mo.png) +![IoU](../../../../../translated_images/mo/iou_equation.9a4751d40fff4e11.png) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用 [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) 生成層次結構的 ROI 區域，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性迴歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.mo.png) +![RCNN](../../../../../translated_images/mo/rcnn1.cae407020dfb1d1f.png) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.mo.png) +![RCNN-1](../../../../../translated_images/mo/rcnn2.2d9530bb83516484.png) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用之後定義的。 -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.mo.png) +![FRCNN](../../../../../translated_images/mo/f-rcnn.3cda6d9bb4188875.png) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 這種方法的主要思想是使用神經網路來預測 ROI，即所謂的*區域提議網路*。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.mo.png) +![FasterRCNN](../../../../../translated_images/mo/faster-rcnn.8d46c099b87ef30a.png) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特徵經過**位置敏感分數圖**處理。每個來自 $C$ 類別的物件被劃分為 $k\times k$ 區域，並訓練網路預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網路對物件類別進行投票，選擇得票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/r-fcn.13eb88158b99a3da.mo.png) +![r-fcn 圖片](../../../../../translated_images/mo/r-fcn.13eb88158b99a3da.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO 是一種實時的單次通過演算法。主要思想如下： * 將圖片劃分為 $S\times S$ 區域。 * 對於每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.mo.png) + ![YOLO](../../../../../translated_images/mo/yolo.a2648ec82ee8bb4e.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/mo/lessons/4-ComputerVision/README.md b/translations/mo/lessons/4-ComputerVision/README.md index 3a3cd7d4..243c3a13 100644 --- a/translations/mo/lessons/4-ComputerVision/README.md +++ b/translations/mo/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要的手繪圖](../../../../translated_images/ai-computervision.6506ebebac3fbf76.mo.png) +![電腦視覺內容摘要的手繪圖](../../../../translated_images/mo/ai-computervision.6506ebebac3fbf76.png) 在本章節中，我們將學習以下內容： diff --git a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 1600d7e2..e30ff6e3 100644 --- a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**詞袋** (BoW) 向量表示法是最常用的傳統向量表示法。每個詞語都與一個向量索引相關聯，向量元素包含某個詞語在特定文檔中出現的次數。\n", "\n", - "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.mo.png) \n", + "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: 你也可以將 BoW 理解為文本中每個詞語的單熱編碼向量的總和。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index ffd00a44..322f66df 100644 --- a/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/mo/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**詞袋**（Bag-of-words, BoW）向量表示法是最簡單易懂的傳統向量表示法。每個詞語都對應到向量中的一個索引，而向量中的元素則表示該詞語在特定文檔中出現的次數。\n", "\n", - "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.mo.png) \n", + "![顯示詞袋向量表示法在記憶體中如何表示的圖片。](../../../../../translated_images/mo/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **注意**：你也可以將 BoW 理解為文本中每個詞語的單熱編碼（one-hot-encoded）向量的總和。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index f46e3367..3ef91809 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "通過將嵌入層作為我們網絡的第一層，我們可以從詞袋模型（bag-of-words）切換到 **嵌入袋模型**（embedding bag model）。在這種模型中，我們首先將文本中的每個單詞轉換為對應的嵌入向量，然後對所有這些嵌入向量執行某種聚合函數，例如 `sum`、`average` 或 `max`。\n", "\n", - "![展示一個針對五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mo.png)\n", + "![展示一個針對五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "我們的分類器神經網絡將以嵌入層開始，接著是聚合層，最後在其上添加一個線性分類器：\n" ] @@ -176,7 +176,7 @@ "\n", "在之前的架構中，我們需要將所有序列填充（pad）到相同的長度，才能將它們放入一個小批次中。這並不是表示變長序列最有效率的方法——另一種方法是使用 **offset** 向量，該向量會保存所有序列在一個大型向量中的偏移量。\n", "\n", - "![顯示偏移序列表示法的圖片](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.mo.png)\n", + "![顯示偏移序列表示法的圖片](../../../../../translated_images/mo/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **注意**：在上圖中，我們展示的是一個字符序列，但在我們的例子中，我們處理的是單詞序列。然而，使用偏移向量表示序列的基本原則是相同的。\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW 的訓練速度較快，而 Skip-Gram 雖然較慢，但在表示不常見單詞方面表現更好。\n", "\n", - "![顯示 CBoW 和 Skip-Gram 算法如何將單詞轉換為向量的圖片。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mo.png)\n", + "![顯示 CBoW 和 Skip-Gram 算法如何將單詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "為了試驗在 Google News 數據集上預訓練的 Word2Vec 嵌入，我們可以使用 **gensim** 庫。以下是找到與 'neural' 最相似的單詞的示例：\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index aec0b96e..0ecb1e0d 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/mo/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "通過將嵌入層作為我們網絡的第一層，我們可以從詞袋模型（bag-of-words）切換到 **嵌入袋模型**（embedding bag），在這裡我們首先將文本中的每個單詞轉換為對應的嵌入，然後對所有這些嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。\n", "\n", - "![顯示五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mo.png)\n", + "![顯示五個序列單詞的嵌入分類器的圖片。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "我們的分類器神經網絡由以下幾層組成：\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW 的速度較快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面表現更好。\n", "\n", - "![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mo.png)\n", + "![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "為了試驗基於 Google News 數據集預訓練的 Word2Vec 嵌入，我們可以使用 **gensim** 庫。以下是我們找到與「neural」最相似的詞。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/14-Embeddings/README.md b/translations/mo/lessons/5-NLP/14-Embeddings/README.md index d3e6514a..eecae6c8 100644 --- a/translations/mo/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/mo/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![嵌入分類器處理五個序列詞的示例圖。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mo.png) +![嵌入分類器處理五個序列詞的示例圖。](../../../../../translated_images/mo/embedding-classifier-example.b77f021a7ee67eee.png) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 的速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法示例圖。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mo.png) +![展示 CBoW 和 Skip-Gram 將詞轉換為向量的算法示例圖。](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md b/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md index d687b595..abab2d12 100644 --- a/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/mo/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型**（CBoW）：在標記序列 $W_{-N}$, ..., $W_N$ 中預測中間的標記 $W_0$。 * **Skip-gram**：從中間標記 $W_0$ 預測一組相鄰的標記 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自將單詞轉換為向量的論文的圖片](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mo.png) +![來自將單詞轉換為向量的論文的圖片](../../../../../translated_images/mo/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/mo/lessons/5-NLP/16-RNN/README.md b/translations/mo/lessons/5-NLP/16-RNN/README.md index 7b4ecfbc..6bed1bde 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/README.md +++ b/translations/mo/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地傳遞給網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次傳遞給網絡。 -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.mo.png) +![RNN](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.png) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態從層到層傳遞 循環網絡，無論是單向還是雙向，都能捕捉序列中的某些模式，並將它們存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN** 的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為輸入傳遞到下一層。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mo.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg) *圖片來自 Fernando López 的[這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index f8c92108..691955ff 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "無論是單向還是雙向的循環網路，都能捕捉序列中的某些模式，並將其存儲到狀態向量中或傳遞到輸出中。與卷積網路類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，這些模式是由第一層提取的低層次模式構成的。這引出了 **多層 RNN** 的概念，它由兩層或更多的循環網路組成，前一層的輸出作為下一層的輸入。\n", "\n", - "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mo.jpg)\n", + "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*圖片來源：[這篇精彩的文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) 作者 Fernando López*\n", "\n", diff --git a/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb index f406c3b9..d2f7d542 100644 --- a/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/mo/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "為了捕捉文本序列的意義，我們將使用一種稱為**循環神經網路**（Recurrent Neural Network，簡稱 RNN）的神經網路架構。在使用 RNN 時，我們會將句子逐個標記（token）傳遞給網路，網路會生成某種**狀態**，然後我們將該狀態與下一個標記一起再次傳遞給網路。\n", "\n", - "![顯示循環神經網路生成示例的圖片。](../../../../../translated_images/rnn.27f5c29c53d727b5.mo.png)\n", + "![顯示循環神經網路生成示例的圖片。](../../../../../translated_images/mo/rnn.27f5c29c53d727b5.png)\n", "\n", "給定標記輸入序列 $X_0,\\dots,X_n$，RNN 會創建一個神經網路區塊的序列，並通過反向傳播對該序列進行端到端訓練。每個網路區塊接受一對 $(X_i,S_i)$ 作為輸入，並生成 $S_{i+1}$ 作為結果。最終狀態 $S_n$ 或輸出 $Y_n$ 會進入線性分類器以生成結果。所有網路區塊共享相同的權重，並通過一次反向傳播訓練完成端到端學習。\n", "\n", @@ -369,7 +369,7 @@ "\n", "無論是單向還是雙向的循環網絡，都能捕捉序列中的模式，並將其存儲到狀態向量中或作為輸出返回。與卷積網絡類似，我們可以在第一層循環層之後再構建另一層循環層，以捕捉更高層次的模式，這些模式是由第一層提取的低層次模式構建而成的。這引出了 **多層 RNN** 的概念，它由兩層或更多層循環網絡組成，其中前一層的輸出作為下一層的輸入。\n", "\n", - "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mo.jpg)\n", + "![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/mo/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*圖片來源：[這篇精彩的文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)，作者 Fernando López。*\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 2f491ff6..90ad201f 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "我們將以以下方式訓練 RNN 來生成文本。在每一步中，我們會取一段長度為 `nchars` 的字元序列，並讓網路為每個輸入字元生成下一個輸出字元：\n", "\n", - "![顯示 RNN 生成單詞 'HELLO' 的範例圖像。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mo.png)\n", + "![顯示 RNN 生成單詞 'HELLO' 的範例圖像。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png)\n", "\n", "根據實際情況，我們可能還需要加入一些特殊字元，例如 *序列結束符* ``。在我們的例子中，我們只希望訓練網路進行無限文本生成，因此我們會將每個序列的大小固定為 `nchars` 個標記。因此，每個訓練樣本將包含 `nchars` 個輸入和 `nchars` 個輸出（輸出是將輸入序列向左移動一個符號後的結果）。一個小批次（minibatch）將由多個這樣的序列組成。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 872f1a94..d6cdef61 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "我們將以以下方式訓練 RNN 來生成新聞標題。在每一步中，我們會取一個標題，將其輸入到 RNN 中，並對於每個輸入的字元，要求網路生成下一個輸出的字元：\n", "\n", - "![顯示 RNN 生成單詞 'HELLO' 的範例圖片。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mo.png)\n", + "![顯示 RNN 生成單詞 'HELLO' 的範例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png)\n", "\n", "對於序列中的最後一個字元，我們會要求網路生成 `` 標記。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md index 9d16303a..28cd11aa 100644 --- a/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/mo/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經網絡架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.mo.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 圖片來源：[Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mo.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/mo/rnn-generate.56c54afb52f9781d.png) 在生成文本（推理過程）時，我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/mo/lessons/5-NLP/18-Transformers/README.md b/translations/mo/lessons/5-NLP/18-Transformers/README.md index 6889202c..f5e2c167 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/README.md +++ b/translations/mo/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意力機制**提供了一種方法，能夠對每個輸入向量對 RNN 每個輸出預測的上下文影響進行加權。其實現方式是通過在輸入 RNN 的中間狀態與輸出 RNN 之間創建捷徑。這樣，在生成輸出符號 yt 時，我們會考慮所有輸入隱藏狀態 hi，並賦予不同的權重係數 αt,i。 -![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mo.png) +![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) 中的加性注意力機制編碼器-解碼器模型，圖片來源於[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩陣 {αi,j} 表示某些輸入詞在生成輸出序列中特定詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示 RNNsearch-50 發現的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mo.png) +![顯示 RNNsearch-50 發現的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png) > 圖片來自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (圖3) @@ -66,7 +66,7 @@ Transformer 的核心思想之一是避免 RNN 的序列性質，並創建一個 接下來，我們需要捕捉序列中的一些模式。為此，Transformer 使用了**自注意力**機制，這本質上是將注意力應用於相同的輸入和輸出序列。應用自注意力使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它可以幫助我們理解哪些詞是由代詞（如 *it*）指代的，並考慮上下文： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.mo.png) +![](../../../../../translated_images/mo/CoreferenceResolution.861924d6d384a7d6.png) > 圖片來自 [Google 博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer 的核心思想之一是避免 RNN 的序列性質，並創建一個 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層 Transformer 網路，*BERT-base* 有 12 層，*BERT-large* 有 24 層。該模型首先在大規模文本語料庫（維基百科 + 書籍）上進行無監督訓練（預測句子中的遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程稱為**遷移學習**。 -![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mo.png) +![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 圖片 [來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 4ef149b5..d19acfa3 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/mo/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**注意力機制**提供了一種方法，能夠對每個輸入向量對RNN每個輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間創建捷徑。這樣，在生成輸出符號 $y_t$ 時，我們會考慮所有輸入隱藏狀態 $h_i$，並賦予不同的權重係數 $\\alpha_{t,i}$。\n", "\n", - "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mo.png)\n", + "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png)\n", "*來自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) 的加性注意力機制編碼器-解碼器模型，圖片引用自[這篇部落格文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "注意力矩陣 $\\{\\alpha_{i,j}\\}$ 表示某些輸入詞在生成輸出序列中特定詞時所起的作用程度。以下是這樣一個矩陣的示例：\n", "\n", - "![顯示由 RNNsearch-50 找到的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mo.png)\n", + "![顯示由 RNNsearch-50 找到的樣本對齊的圖片，取自 Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*圖片取自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3）*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT**（Bidirectional Encoder Representations from Transformers，雙向編碼器表示）是一個非常大的多層Transformer網路，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據（維基百科+書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來加以利用。這個過程被稱為**遷移學習**。\n", "\n", - "![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mo.png)\n", + "![圖片來自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer架構有許多變體，包括BERT、DistilBERT、BigBird、OpenGPT3等，這些模型都可以進行微調。[HuggingFace套件](https://github.com/huggingface/) 提供了用PyTorch訓練這些架構的資源庫。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index f488e1c1..3ac0e346 100644 --- a/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/mo/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**注意力機制**提供了一種方法，能夠對每個輸入向量在RNN的每個輸出預測中的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間創建捷徑。在生成輸出符號$y_t$時，我們會考慮所有輸入隱藏狀態$h_i$，並使用不同的權重係數$\\alpha_{t,i}$。\n", "\n", - "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mo.png)\n", + "![顯示具有加性注意力層的編碼器/解碼器模型的圖片](../../../../../translated_images/mo/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意力機制編碼器-解碼器模型，圖片引用自[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "注意力矩陣$\\{\\alpha_{i,j}\\}$表示某些輸入詞語在生成輸出序列中的某個詞語時所起的作用程度。以下是這樣一個矩陣的示例：\n", "\n", - "![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mo.png)\n", + "![顯示RNNsearch-50找到的示例對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/mo/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*圖片取自[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3）*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT**（雙向編碼器表示來自 Transformer）是一個非常大型的多層 Transformer 網絡，*BERT-base* 有 12 層，*BERT-large* 則有 24 層。該模型首先在大量文本數據（維基百科 + 書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，之後可以通過微調與其他數據集結合使用。這個過程被稱為 **遷移學習**。\n", "\n", - "![圖片來源：http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mo.png)\n", + "![圖片來源：http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mo/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer 架構有許多變體，包括 BERT、DistilBERT、BigBird、OpenGPT3 等，它們都可以進行微調。\n", "\n", diff --git a/translations/mo/lessons/5-NLP/19-NER/README.md b/translations/mo/lessons/5-NLP/19-NER/README.md index 5a0484ea..144a8e0d 100644 --- a/translations/mo/lessons/5-NLP/19-NER/README.md +++ b/translations/mo/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入標記 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個右側的 **多對多** 神經網絡模型： -![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.mo.jpg) +![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/mo/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *圖片來自 [這篇部落格文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) 作者 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於此圖片中的最右側網絡架構。* diff --git a/translations/mo/lessons/5-NLP/README.md b/translations/mo/lessons/5-NLP/README.md index 3f2c3a9f..a2e85a7a 100644 --- a/translations/mo/lessons/5-NLP/README.md +++ b/translations/mo/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP 任務的手繪摘要](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.mo.png) +![NLP 任務的手繪摘要](../../../../translated_images/mo/ai-nlp.b22dcb8ca4707cea.png) 在本節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)** 相關的任務。我們希望計算機能夠解決許多 NLP 問題： diff --git a/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md b/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md index b739bf23..0cf2f730 100644 --- a/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/mo/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優勢是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主界面。以下是一個描述狼和羊在有限資源（草地）條件下的種群模型示例。 -![NetLogo主界面](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.mo.png) +![NetLogo主界面](../../../../../translated_images/mo/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov提供的截圖 diff --git a/translations/mo/lessons/README.md b/translations/mo/lessons/README.md index e183e6d6..040347d5 100644 --- a/translations/mo/lessons/README.md +++ b/translations/mo/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概述 -![概述的手繪圖](../../../translated_images/ai-overview.0857791951d19500.mo.png) +![概述的手繪圖](../../../translated_images/mo/ai-overview.0857791951d19500.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/mo/lessons/X-Extras/X1-MultiModal/README.md b/translations/mo/lessons/X-Extras/X1-MultiModal/README.md index 81f72045..d77457c4 100644 --- a/translations/mo/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/mo/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的核心思想是能夠比較文本提示與圖像，並判斷圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.mo.png) +![CLIP 架構](../../../../../translated_images/mo/clip-arch.b3dbf20b4e8ed8be.png) > *圖片來源於[這篇博客](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可以從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲取 假設我們需要在貓、狗和人之間對圖像進行分類。在這種情況下，我們可以將圖像和一系列文本提示輸入模型，例如：“*一張貓的照片*”、“*一張狗的照片*”、“*一張人的照片*”。在結果的 3 個概率向量中，我們只需選擇值最大的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/clip-class.3af42ef0b2b19369.mo.png) +![CLIP 用於圖像分類](../../../../../translated_images/mo/clip-class.3af42ef0b2b19369.png) > *圖片來源於[這篇博客](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別是，後者可以從任何輸入向量生成一個體面的圖像，而 VQGAN 可能生成不連貫的圖像。因此，我們需要進一步引導圖像創建過程，這可以通過 CLIP 完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/vqgan.5027fe05051dfa31.mo.png) +![VQGAN+CLIP 架構](../../../../../translated_images/mo/vqgan.5027fe05051dfa31.png) 為了生成與文本提示相符的圖像，我們從一些隨機編碼向量開始，將其通過 VQGAN 生成圖像。然後使用 CLIP 生成一個損失函數，該函數顯示圖像與文本提示的匹配程度。接下來的目標是最小化這個損失，通過反向傳播調整輸入向量參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray)。 -![Pixray 生成的圖片](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.mo.png) | ![Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.mo.png) | ![Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.mo.png) +![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray 生成的圖片](../../../../../translated_images/mo/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- 根據提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 根據提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 @@ -75,7 +75,7 @@ DALL-E 是一個基於 GPT-3 的模型，專門用於從文本提示生成圖像 DALL-E 1 和 DALL-E 2 的主要區別在於，後者能生成更真實的圖像和藝術作品。 以下是使用 DALL-E 生成圖像的示例： -![DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.mo.png) | ![DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.mo.png) | ![DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.mo.png) +![DALL-E 生成的圖片](../../../../../translated_images/mo/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![DALL-E 生成的圖片](../../../../../translated_images/mo/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![DALL-E 生成的圖片](../../../../../translated_images/mo/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- 根據提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 根據提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 根據提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/mr/README.md b/translations/mr/README.md index dbda7e49..582730d7 100644 --- a/translations/mr/README.md +++ b/translations/mr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # नवशिक्यांसाठी कृत्रिम बुद्धिमत्ता - एक अभ्यासक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.mr.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/mr/ai-overview.0857791951d19500.png)| |:---:| | नवशिक्यांसाठी कृत्रिम बुद्धिमत्ता - _स्केचनोट [@girlie_mac](https://twitter.com/girlie_mac) कडून_ | diff --git a/translations/mr/lessons/1-Intro/README.md b/translations/mr/lessons/1-Intro/README.md index f3cf15af..1f32974b 100644 --- a/translations/mr/lessons/1-Intro/README.md +++ b/translations/mr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ची ओळख -![AI ची ओळख यावरील सामग्रीचा डूडलमध्ये सारांश](../../../../translated_images/ai-intro.bf28d1ac4235881c.mr.png) +![AI ची ओळख यावरील सामग्रीचा डूडलमध्ये सारांश](../../../../translated_images/mr/ai-intro.bf28d1ac4235881c.png) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) यांच्याकडून @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: मूळतः, संगणक [चार्ल्स बॅबेज](https://en.wikipedia.org/wiki/Charles_Babbage) यांनी संख्यांवर कार्य करण्यासाठी आणि एक निश्चित प्रक्रिया - अल्गोरिदम अनुसरण करण्यासाठी शोधले होते. आधुनिक संगणक, जरी 19व्या शतकात प्रस्तावित केलेल्या मूळ मॉडेलपेक्षा लक्षणीय अधिक प्रगत असले तरी, नियंत्रित गणनांच्या त्याच कल्पनेचे अनुसरण करतात. त्यामुळे जर आपल्याला एखादे लक्ष्य साध्य करण्यासाठी आवश्यक असलेल्या अचूक चरणांची क्रमवारी माहित असेल तर संगणकाला काहीतरी करण्यासाठी प्रोग्राम करणे शक्य आहे. -![व्यक्तीचा फोटो](../../../../translated_images/dsh_age.d212a30d4e54fb5f.mr.png) +![व्यक्तीचा फोटो](../../../../translated_images/mr/dsh_age.d212a30d4e54fb5f.png) > फोटो [Vickie Soshnikova](http://twitter.com/vickievalerie) यांच्याकडून @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** या संज्ञेशी संबंधित असताना एक समस्या अशी आहे की या संज्ञेची स्पष्ट व्याख्या नाही. एखाद्याला वाटू शकते की बुद्धिमत्ता **गूढ विचारांशी** किंवा **आत्म-जाणिवेशी** संबंधित आहे, परंतु आपण याची योग्य व्याख्या करू शकत नाही. -![मांजराचा फोटो](../../../../translated_images/photo-cat.8c8e8fb760ffe457.mr.jpg) +![मांजराचा फोटो](../../../../translated_images/mr/photo-cat.8c8e8fb760ffe457.jpg) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [Amber Kipp](https://unsplash.com/@sadmax) यांच्याकडून Unsplash वरून @@ -98,13 +98,13 @@ AGI बद्दल बोलताना आपल्याला काही > | ML बद्दल काय? | | > |--------------|-----------| -> | संगणकाला काही डेटा आधारित समस्या सोडवण्यासाठी शिकवण्यावर आधारित कृत्रिम बुद्धिमत्तेचा भाग **मशीन लर्निंग** म्हणून ओळखला जातो. आम्ही या अभ्यासक्रमात पारंपरिक मशीन लर्निंगचा विचार करणार नाही - आम्ही तुम्हाला स्वतंत्र [Machine Learning for Beginners](http://aka.ms/ml-beginners) अभ्यासक्रमाकडे संदर्भित करतो. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.mr.png) | +> | संगणकाला काही डेटा आधारित समस्या सोडवण्यासाठी शिकवण्यावर आधारित कृत्रिम बुद्धिमत्तेचा भाग **मशीन लर्निंग** म्हणून ओळखला जातो. आम्ही या अभ्यासक्रमात पारंपरिक मशीन लर्निंगचा विचार करणार नाही - आम्ही तुम्हाला स्वतंत्र [Machine Learning for Beginners](http://aka.ms/ml-beginners) अभ्यासक्रमाकडे संदर्भित करतो. | ![ML for Beginners](../../../../translated_images/mr/ml-for-beginners.9e4fed176fd5817d.png) | ## AI चा थोडक्यात इतिहास कृत्रिम बुद्धिमत्ता ही एक शाखा म्हणून विसाव्या शतकाच्या मध्यात सुरू झाली. सुरुवातीला, प्रतीकात्मक तर्कसंगतता हा एक प्रचलित दृष्टिकोन होता आणि यामुळे काही महत्त्वाच्या यशस्वीतेसाठी, जसे की तज्ज्ञ प्रणाली - मर्यादित समस्या क्षेत्रांमध्ये तज्ज्ञ म्हणून कार्य करण्यास सक्षम संगणक प्रोग्राम्स. तथापि, लवकरच हे स्पष्ट झाले की अशा दृष्टिकोनाचा चांगला विस्तार होत नाही. तज्ज्ञाकडून ज्ञान काढणे, संगणकात सादर करणे आणि त्या ज्ञानाच्या अचूकतेची खात्री करणे हे एक अत्यंत जटिल कार्य आहे आणि अनेक प्रकरणांमध्ये व्यावहारिकदृष्ट्या खूप महाग आहे. यामुळे 1970 च्या दशकात तथाकथित [AI Winter](https://en.wikipedia.org/wiki/AI_winter) आले. -AI चा थोडक्यात इतिहास +AI चा थोडक्यात इतिहास > प्रतिमा [Dmitry Soshnikov](http://soshnikov.com) यांच्याकडून diff --git a/translations/mr/lessons/2-Symbolic/Animals.ipynb b/translations/mr/lessons/2-Symbolic/Animals.ipynb index 6f37087e..73b86246 100644 --- a/translations/mr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/mr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "या नमुन्यात, आपण काही शारीरिक वैशिष्ट्यांवर आधारित प्राणी ओळखण्यासाठी एक साधी ज्ञान-आधारित प्रणाली अंमलात आणू. ही प्रणाली खालील AND-OR झाडाद्वारे दर्शविली जाऊ शकते (हे संपूर्ण झाडाचा एक भाग आहे, आपण सहजपणे आणखी काही नियम जोडू शकतो):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.mr.png)\n" + "![](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/mr/lessons/2-Symbolic/README.md b/translations/mr/lessons/2-Symbolic/README.md index ef0ddbdc..9b00b9aa 100644 --- a/translations/mr/lessons/2-Symbolic/README.md +++ b/translations/mr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञानाचे प्रतिनिधित्व आणि तज्ज्ञ प्रणाली -![Symbolic AI content चा सारांश](../../../../translated_images/ai-symbolic.715a30cb610411a6.mr.png) +![Symbolic AI content चा सारांश](../../../../translated_images/mr/ai-symbolic.715a30cb610411a6.png) > [Tomomi Imura](https://twitter.com/girlie_mac) यांचे स्केच नोट @@ -41,7 +41,7 @@ Symbolic AI मधील एक महत्त्वाची संकल् म्हणून, **ज्ञानाचे प्रतिनिधित्व** करण्याची समस्या म्हणजे संगणकाच्या आत डेटा स्वरूपात ज्ञानाचे प्रतिनिधित्व करण्याचा काही प्रभावी मार्ग शोधणे, जेणेकरून ते स्वयंचलितपणे वापरता येईल. याकडे एक स्पेक्ट्रम म्हणून पाहिले जाऊ शकते: -![ज्ञानाचे प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/knowledge-spectrum.b60df631852c0217.mr.png) +![ज्ञानाचे प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/mr/knowledge-spectrum.b60df631852c0217.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Symbolic AI च्या सुरुवातीच्या यशांपैकी एक म्हणजे **तज्ज्ञ प्रणाली** - संगणक प्रणाली जी मर्यादित समस्या क्षेत्रात तज्ज्ञ म्हणून कार्य करण्यासाठी डिझाइन केली गेली होती. त्या **ज्ञान बेस** वर आधारित होत्या, जे एका किंवा अधिक मानवी तज्ज्ञांकडून काढले गेले होते, आणि त्यामध्ये **तर्क इंजिन** होते जे त्यावर काही तर्कशक्ती अंमलात आणत होते. -![मानवी आर्किटेक्चर](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.mr.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.mr.png) +![मानवी आर्किटेक्चर](../../../../translated_images/mr/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/mr/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ मानवी न्यूरल प्रणालीची साधी रचना | ज्ञान-आधारित प्रणालीची आर्किटेक्चर @@ -106,7 +106,7 @@ Symbolic AI च्या सुरुवातीच्या यशांपै उदाहरण म्हणून, खालील तज्ज्ञ प्रणाली विचार करूया जी प्राण्याचे शारीरिक वैशिष्ट्यांवर आधारित निर्धारण करते: -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.mr.png) +![AND-OR Tree](../../../../translated_images/mr/AND-OR-Tree.5592d2c70187f283.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 839e267e..8a6d7dbc 100644 --- a/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/mr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "जर आपल्याकडे 2 पेक्षा जास्त वर्ग असतील, तर softmax सर्व वर्गांमध्ये संभाव्यता सामान्य करेल. येथे MNIST अंक वर्गीकरण करणाऱ्या नेटवर्क आर्किटेक्चरचा एक आकृती आहे:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.mr.png)\n" + "![MNIST Classifier](../../../../../translated_images/mr/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* कमी प्रशिक्षण नुकसान - मॉडेल प्रशिक्षण डेटा चांगल्या प्रकारे अंदाज करू शकते, कारण त्याच्याकडे पुरेशी अभिव्यक्ती क्षमता असते.\n", "* व्हॅलिडेशन नुकसान प्रशिक्षण नुकसानापेक्षा खूप जास्त असू शकते आणि प्रशिक्षणादरम्यान वाढू शकते - कारण मॉडेल \"प्रशिक्षण बिंदू\" लक्षात ठेवते आणि \"संपूर्ण चित्र\" गमावते.\n", "\n", - "![ओव्हरफिटिंग](../../../../../translated_images/overfit.a0bd57f717c15769.mr.png)\n", + "![ओव्हरफिटिंग](../../../../../translated_images/mr/overfit.a0bd57f717c15769.png)\n", "\n", "> या चित्रात, `x` प्रशिक्षण डेटा दर्शवतो, `o` - व्हॅलिडेशन डेटा. डावीकडे - रेखीय मॉडेल (एक-स्तरीय), ते डेटाच्या स्वरूपाचा चांगल्या प्रकारे अंदाज करते. उजवीकडे - ओव्हरफिटेड मॉडेल, मॉडेल प्रशिक्षण डेटा उत्तम प्रकारे अंदाज करते, पण इतर कोणत्याही डेटासह अर्थपूर्ण राहात नाही (व्हॅलिडेशन त्रुटी खूप जास्त आहे).\n" ] diff --git a/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 13015200..31f90065 100644 --- a/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/mr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: खालीलप्रमाणे 5 डॉट्स (ग्राफ्सवर `x` ने दर्शविलेले) अंदाज लावण्याच्या समस्येचा विचार करा: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.mr.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.mr.jpg) +![linear](../../../../../translated_images/mr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/mr/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **रेखीय मॉडेल, 2 पॅरामिटर्स** | **नॉन-रेखीय मॉडेल, 7 पॅरामिटर्स** प्रशिक्षण त्रुटी = 5.3 | प्रशिक्षण त्रुटी = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: वरील ग्राफवरून आपण पाहू शकतो की, ओव्हरफिटिंग खूप कमी प्रशिक्षण त्रुटी आणि जास्त व्हॅलिडेशन त्रुटीने ओळखले जाऊ शकते. सामान्यतः प्रशिक्षणादरम्यान आपण पाहतो की प्रशिक्षण आणि व्हॅलिडेशन त्रुटी कमी होऊ लागतात, आणि नंतर काही टप्प्यावर व्हॅलिडेशन त्रुटी कमी होणे थांबवते आणि वाढू लागते. हे ओव्हरफिटिंगचे चिन्ह असेल, आणि यावेळी प्रशिक्षण थांबवावे (किंवा किमान मॉडेलचा स्नॅपशॉट घ्यावा) याचा संकेत असेल. -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.mr.png) +![overfitting](../../../../../translated_images/mr/Overfitting.408ad91cd90b4371.png) ## ओव्हरफिटिंग कसे टाळावे diff --git a/translations/mr/lessons/3-NeuralNetworks/README.md b/translations/mr/lessons/3-NeuralNetworks/README.md index 516d7ff0..732083c2 100644 --- a/translations/mr/lessons/3-NeuralNetworks/README.md +++ b/translations/mr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्यूरल नेटवर्क्सची ओळख -![न्यूरल नेटवर्क्सच्या परिचयाचा सारांश एका चित्रात](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.mr.png) +![न्यूरल नेटवर्क्सच्या परिचयाचा सारांश एका चित्रात](../../../../translated_images/mr/ai-neuralnetworks.1c687ae40bc86e83.png) जसे आपण परिचयात चर्चा केली, बुद्धिमत्ता मिळवण्याचा एक मार्ग म्हणजे **कंप्यूटर मॉडेल** किंवा **कृत्रिम मेंदू** तयार करणे. विसाव्या शतकाच्या मध्यापासून संशोधकांनी विविध गणितीय मॉडेल्स वापरून पाहिले, आणि अलीकडच्या वर्षांत हा दृष्टिकोन अत्यंत यशस्वी ठरला. मेंदूचे असे गणितीय मॉडेल्स **न्यूरल नेटवर्क्स** म्हणून ओळखले जातात. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जीवशास्त्रानुसार, आपला मेंदू न्यूरल पेशींनी (न्यूरॉन्स) बनलेला असतो, ज्यामध्ये अनेक "इनपुट्स" (डेंड्राइट्स) आणि एक "आउटपुट" (अॅक्सॉन) असतो. डेंड्राइट्स आणि अॅक्सॉन्स दोन्ही विद्युत संकेत वाहून नेऊ शकतात, आणि त्यांच्यातील कनेक्शन्स — ज्यांना सायनॅप्स म्हणतात — विविध प्रकारच्या चालकतेचे प्रदर्शन करू शकतात, जे न्यूरोट्रान्समीटरद्वारे नियंत्रित केले जातात. -![न्यूरॉनचे मॉडेल](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.mr.jpg) | ![न्यूरॉनचे मॉडेल](../../../../translated_images/artneuron.1a5daa88d20ebe6f.mr.png) +![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्यूरॉनचे मॉडेल](../../../../translated_images/mr/artneuron.1a5daa88d20ebe6f.png) ----|---- वास्तविक न्यूरॉन *([Wikipedia](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) मधील प्रतिमा)* | कृत्रिम न्यूरॉन *(लेखकाने तयार केलेली प्रतिमा)* त्यामुळे, न्यूरॉनचे सर्वात सोपे गणितीय मॉडेलमध्ये अनेक इनपुट्स X1, ..., XN आणि एक आउटपुट Y, तसेच वजनांची मालिका W1, ..., WN असते. आउटपुट खालीलप्रमाणे गणना केली जाते: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जिथे f ही काही नॉन-लिनियर **अॅक्टिवेशन फंक्शन** आहे. diff --git a/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md index 72ecc5aa..638ecc39 100644 --- a/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/mr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तकाच्या छायाचित्राची पूर्व-प्रक्रिया**. आम्ही थ्रेशोल्डिंग, वैशिष्ट्य शोध, परिप्रेक्ष्य रूपांतरण आणि NumPy हेरफेर कसे वापरून ब्रेल चिन्हे वेगळी करू शकतो यावर लक्ष केंद्रित करतो, जेणेकरून न्यूरल नेटवर्कद्वारे पुढील वर्गीकरण करता येईल. -![ब्रेल प्रतिमा](../../../../../translated_images/braille.341962ff76b1bd70.mr.jpeg) | ![पूर्व-प्रक्रिया केलेली ब्रेल प्रतिमा](../../../../../translated_images/braille-result.46530fea020b03c7.mr.png) | ![ब्रेल चिन्हे](../../../../../translated_images/braille-symbols.0159185ab69d5339.mr.png) +![ब्रेल प्रतिमा](../../../../../translated_images/mr/braille.341962ff76b1bd70.jpeg) | ![पूर्व-प्रक्रिया केलेली ब्रेल प्रतिमा](../../../../../translated_images/mr/braille-result.46530fea020b03c7.png) | ![ब्रेल चिन्हे](../../../../../translated_images/mr/braille-symbols.0159185ab69d5339.png) ----|-----|----- > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून * **फ्रेम फरक वापरून व्हिडिओमध्ये हालचाल शोधणे**. जर कॅमेरा स्थिर असेल, तर कॅमेरा फीडमधील फ्रेम्स एकमेकांशी खूप समान असाव्यात. फ्रेम्स arrays म्हणून दर्शविल्या जात असल्याने, दोन अनुक्रमिक फ्रेम्ससाठी त्या arrays वजा करून आम्हाला पिक्सेल फरक मिळेल, जो स्थिर फ्रेम्ससाठी कमी असावा, आणि प्रतिमेमध्ये लक्षणीय हालचाल झाल्यावर जास्त होईल. -![व्हिडिओ फ्रेम्स आणि फ्रेम फरक प्रतिमा](../../../../../translated_images/frame-difference.706f805491a0883c.mr.png) +![व्हिडिओ फ्रेम्स आणि फ्रेम फरक प्रतिमा](../../../../../translated_images/mr/frame-difference.706f805491a0883c.png) > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **घन ऑप्टिकल फ्लो** प्रत्येक पिक्सेलसाठी तो कुठे हलतो हे दर्शविणारा वेक्टर फील्ड गणना करतो. - **दुर्मिळ ऑप्टिकल फ्लो** प्रतिमेतील काही वेगळ्या वैशिष्ट्यांवर आधारित असतो (उदा. कडा), आणि फ्रेम ते फ्रेम त्यांचा मार्ग तयार करतो. -![ऑप्टिकल फ्लो प्रतिमा](../../../../../translated_images/optical.1f4a94464579a83a.mr.png) +![ऑप्टिकल फ्लो प्रतिमा](../../../../../translated_images/mr/optical.1f4a94464579a83a.png) > प्रतिमा [OpenCV.ipynb](OpenCV.ipynb) मधून diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index e28bcc3d..d492a1b3 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 हा एक नेटवर्क आहे ज्याने 2014 मध्ये ImageNet टॉप-5 वर्गीकरणात 92.7% अचूकता मिळवली. यामध्ये खालील स्तर रचना आहे: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.mr.jpg) +![ImageNet Layers](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.jpg) जसे तुम्ही पाहू शकता, VGG पारंपरिक पिरॅमिड आर्किटेक्चरचे अनुसरण करते, जे कन्व्होल्यूशन-पूलिंग स्तरांचा क्रम आहे. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.mr.jpg) +![ImageNet Pyramid](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.jpg) > प्रतिमा [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) वरून घेतलेली आहे. diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b0f3ffae..432fdb4c 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "म्हणून, एका सामान्य CNN मध्ये अनेक कॉनव्होल्यूशन लेयर्स असतात, ज्यांच्या दरम्यान पूलिंग लेयर्स असतात जे प्रतिमेचे परिमाण कमी करतात. आपण फिल्टर्सची संख्या देखील वाढवतो, कारण पॅटर्न्स अधिक प्रगत होत जातात - आपल्याला शोधायच्या असलेल्या संभाव्य रचनांची संख्या वाढते.\n", "\n", - "![काही कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स दाखवणारी प्रतिमा.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.mr.png)\n", + "![काही कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स दाखवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "स्थानिक परिमाण कमी होणे आणि वैशिष्ट्ये/फिल्टर्सचे परिमाण वाढणे यामुळे, या आर्किटेक्चरला **पिरॅमिड आर्किटेक्चर** असेही म्हणतात.\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 5039c61b..a1d7cf1a 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "म्हणून, एका सामान्य CNN मध्ये अनेक कॉनव्होल्यूशन लेयर्स असतात, ज्यामध्ये प्रतिमेचे परिमाण कमी करण्यासाठी पूलिंग लेयर्स असतात. तसेच, आम्ही फिल्टर्सची संख्या वाढवतो, कारण नमुने अधिक प्रगत होत जातात - आपल्याला शोधण्यासाठी अधिक संभाव्य मनोरंजक संयोजन असतात.\n", "\n", - "![कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स असलेला पिरॅमिड आर्किटेक्चर दर्शवणारी प्रतिमा.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.mr.png)\n", + "![कॉनव्होल्यूशन लेयर्स आणि पूलिंग लेयर्स असलेला पिरॅमिड आर्किटेक्चर दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "जागात्मक परिमाण कमी होणे आणि वैशिष्ट्य/फिल्टर्स परिमाण वाढणे यामुळे, या आर्किटेक्चरला **पिरॅमिड आर्किटेक्चर** असेही म्हणतात.\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md index ea4668ce..0b3e862e 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: पॅटर्न्स काढण्यासाठी, आपण **कॉनव्होल्यूशनल फिल्टर्स** चा उपयोग करू. तुम्हाला माहीत आहेच की, प्रतिमा 2D-मॅट्रिक्स किंवा रंग खोलीसह 3D-टेंसरद्वारे दर्शवली जाते. फिल्टर लागू करणे म्हणजे आपण तुलनेने लहान **फिल्टर कर्नल** मॅट्रिक्स घेतो, आणि मूळ प्रतिमेतील प्रत्येक पिक्सेलसाठी शेजारील बिंदूंसह भारित सरासरीची गणना करतो. आपण याला असे पाहू शकतो की एक छोटी विंडो संपूर्ण प्रतिमेवर सरकत आहे, आणि फिल्टर कर्नल मॅट्रिक्समधील वजनांनुसार सर्व पिक्सेल्स सरासरी करत आहे. -![आडव्या कडांचा फिल्टर](../../../../../translated_images/filter-vert.b7148390ca0bc356.mr.png) | ![उभ्या कडांचा फिल्टर](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.mr.png) +![आडव्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-vert.b7148390ca0bc356.png) | ![उभ्या कडांचा फिल्टर](../../../../../translated_images/mr/filter-horiz.59b80ed4feb946ef.png) ----|---- > प्रतिमा: दिमित्री सोश्निकोव्ह @@ -38,7 +38,7 @@ CNN कसे कार्य करते यामागील महत्त * आपण नेटवर्क अशा प्रकारे डिझाइन करू शकतो की फिल्टर्स आपोआप प्रशिक्षित होतील * आपण मूळ प्रतिमेतील पॅटर्न्स शोधण्यासाठीच नव्हे तर उच्च-स्तरीय वैशिष्ट्यांमध्ये पॅटर्न्स शोधण्यासाठीही याच पद्धतीचा उपयोग करू शकतो. त्यामुळे CNN वैशिष्ट्य काढणे वैशिष्ट्यांच्या श्रेणीवर कार्य करते, कमी-स्तरीय पिक्सेल संयोजनांपासून ते प्रतिमेच्या भागांच्या उच्च-स्तरीय संयोजनांपर्यंत. -![हायरार्किकल वैशिष्ट्य काढणे](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.mr.png) +![हायरार्किकल वैशिष्ट्य काढणे](../../../../../translated_images/mr/FeatureExtractionCNN.d9b456cbdae7cb64.png) > प्रतिमा: [हिस्लॉप-लिंच यांच्या पेपरमधून](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), त्यांच्या [संशोधनावर आधारित](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN कसे कार्य करते यामागील महत्त उदाहरण म्हणून, VGG-16 च्या आर्किटेक्चरकडे पाहूया, ज्याने 2014 मध्ये ImageNet च्या टॉप-5 वर्गीकरणात 92.7% अचूकता मिळवली: -![ImageNet स्तर](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.mr.jpg) +![ImageNet स्तर](../../../../../translated_images/mr/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet पिरॅमिड](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.mr.jpg) +![ImageNet पिरॅमिड](../../../../../translated_images/mr/vgg-16-arch.64ff2137f50dd49f.jpg) > प्रतिमा: [रिसर्चगेट](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 314d3ed3..2e0180ff 100644 --- a/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/mr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) वापरणार आहोत, ज्यामध्ये 37 वेगवेगळ्या जातींच्या कुत्र्यांच्या आणि मांजरींच्या प्रतिमा आहेत. -![आपण हाताळत असलेला डेटासेट](../../../../../../translated_images/data.50b2a9d5484bdbf0.mr.png) +![आपण हाताळत असलेला डेटासेट](../../../../../../translated_images/mr/data.50b2a9d5484bdbf0.png) डेटासेट डाउनलोड करण्यासाठी, हा कोड स्निपेट वापरा: diff --git a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 47abaee8..a14ed80a 100644 --- a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "आदर्श मांजर पाहण्यासाठी, आपण एका यादृच्छिक आवाजाच्या प्रतिमेसह सुरुवात करू आणि ग्रेडियंट डिसेंट ऑप्टिमायझेशन तंत्राचा वापर करून प्रतिमा समायोजित करण्याचा प्रयत्न करू, जेणेकरून नेटवर्कला मांजर ओळखता येईल.\n", "\n", - "![ऑप्टिमायझेशन लूप](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.mr.png)\n", + "![ऑप्टिमायझेशन लूप](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ही आहे आपली सुरुवातीची प्रतिमा:\n" ] diff --git a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md index a6b2e329..834ac9d1 100644 --- a/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/mr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras आणि PyTorch मध्ये काही सामान्य आ VGG-16 नेटवर्कद्वारे मांजराच्या प्रतिमेतून काढलेली नमुना वैशिष्ट्ये येथे आहेत: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.mr.png) +![Features extracted by VGG-16](../../../../../translated_images/mr/features.6291f9c7ba3a0b95.png) ## मांजरे विरुद्ध कुत्रे डेटासेट @@ -48,19 +48,19 @@ VGG-16 नेटवर्कद्वारे मांजराच्या आपण एक पद्धत वापरू शकतो, जिथे आपण एका रँडम प्रतिमेसह सुरुवात करतो आणि नंतर **ग्रेडियंट डिसेंट ऑप्टिमायझेशन** तंत्र वापरून ती प्रतिमा समायोजित करण्याचा प्रयत्न करतो, ज्यामुळे नेटवर्कला वाटते की ती मांजर आहे. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.mr.png) +![Image Optimization Loop](../../../../../translated_images/mr/ideal-cat-loop.999fbb8ff306e044.png) मात्र, जर आपण असे केले, तर आपल्याला रँडम नॉइजसारखे काहीतरी मिळेल. कारण *नेटवर्कला वाटावे की इनपुट प्रतिमा मांजर आहे असे करण्याचे अनेक मार्ग आहेत*, ज्यामध्ये काही दृश्यदृष्ट्या अर्थपूर्ण नाहीत. जरी त्या प्रतिमांमध्ये मांजरीसाठी विशिष्ट नमुने असले तरी, त्यांना दृश्यदृष्ट्या वेगळे करण्यासाठी काहीही बंधन नाही. परिणाम सुधारण्यासाठी, आपण लॉस फंक्शनमध्ये आणखी एक टर्म जोडू शकतो, ज्याला **व्हेरिएशन लॉस** म्हणतात. हे एक मेट्रिक आहे जे प्रतिमेचे शेजारी असलेले पिक्सेल किती समान आहेत हे दर्शवते. व्हेरिएशन लॉस कमी केल्याने प्रतिमा गुळगुळीत होते आणि नॉइज दूर होते - त्यामुळे अधिक दृश्यदृष्ट्या आकर्षक नमुने उलगडतात. येथे अशा "आदर्श" प्रतिमांचे उदाहरण आहे, ज्यांना उच्च संभाव्यतेसह मांजर आणि झेब्रा म्हणून वर्गीकृत केले जाते: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.mr.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.mr.png) +![Ideal Cat](../../../../../translated_images/mr/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/mr/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *आदर्श मांजर* | *आदर्श झेब्रा* समान पद्धत वापरून तथाकथित **अड्व्हर्सेरियल हल्ले** न्यूरल नेटवर्कवर करता येतात. समजा आपण न्यूरल नेटवर्कला मूर्ख बनवायचे आहे आणि कुत्र्याला मांजरासारखे बनवायचे आहे. जर आपण कुत्र्याची प्रतिमा घेतली, जी नेटवर्कद्वारे कुत्रा म्हणून ओळखली जाते, तर आपण ती थोडीशी समायोजित करू शकतो, ग्रेडियंट डिसेंट ऑप्टिमायझेशन वापरून, जोपर्यंत नेटवर्क ती मांजर म्हणून वर्गीकृत करत नाही: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.mr.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.mr.png) +![Picture of a Dog](../../../../../translated_images/mr/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/mr/adversarial-dog.d9fc7773b0142b89.png) -----|----- *कुत्र्याची मूळ प्रतिमा* | *कुत्र्याची प्रतिमा जी मांजर म्हणून वर्गीकृत केली जाते* diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index fa149b22..a2158273 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ऑटोएनकोडरला मूळ प्रतिमेतील जास्तीत जास्त माहिती अचूक पुनर्रचना करण्यासाठी कॅप्चर करण्यासाठी प्रशिक्षण दिले जात असल्याने, नेटवर्क इनपुट प्रतिमांचे अर्थ कॅप्चर करण्यासाठी सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते.\n", "\n", - "![ऑटोएनकोडर आकृती](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mr.jpg)\n", + "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) वरून\n", "\n", diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 0092c13b..d3dfa684 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "ऑटोएनकोडरला मूळ प्रतिमेतील जास्तीत जास्त माहिती अचूक पुनर्रचना करण्यासाठी कॅप्चर करण्यासाठी प्रशिक्षण दिले जात असल्याने, नेटवर्क इनपुट प्रतिमांचे अर्थ कॅप्चर करण्यासाठी सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते.\n", "\n", - "![ऑटोएनकोडर आकृती](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mr.jpg)\n", + "![ऑटोएनकोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) मधून*\n", "\n", diff --git a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md index 1ba8c09f..767c546c 100644 --- a/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/mr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही ऑटोएन्कोडरला मूळ प्रतिमेतील माहिती अचूक पुनर्रचना करण्यासाठी शक्य तितकी माहिती कॅप्चर करण्यासाठी प्रशिक्षण देत असल्याने, नेटवर्क इनपुट प्रतिमांचे सर्वोत्तम **एम्बेडिंग** शोधण्याचा प्रयत्न करते. -![ऑटोएन्कोडर आकृती](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.mr.jpg) +![ऑटोएन्कोडर आकृती](../../../../../translated_images/mr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > प्रतिमा [Keras ब्लॉग](https://blog.keras.io/building-autoencoders-in-keras.html) मधून diff --git a/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md index 89cf3bd9..086a3f85 100644 --- a/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/mr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [पूर्व-व्याख्यान क्विझ](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.mr.png) +![ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > प्रतिमा [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) वरून @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइलवर इमेज क्लासिफिकेशन चालवा. 3. ज्या टाइल्समध्ये पुरेसे उच्च सक्रियता दिसते, त्या टाइल्समध्ये संबंधित वस्तू असल्याचे मानले जाऊ शकते. -![साधा ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.mr.png) +![साधा ऑब्जेक्ट डिटेक्शन](../../../../../translated_images/mr/naive-detection.e7f1ba220ccd08c6.png) > *प्रतिमा [व्यायाम नोटबुक](ObjectDetection-TF.ipynb) मधून* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 वर्ग * [COCO](http://cocodataset.org/#home) - कॉमन ऑब्जेक्ट्स इन कॉन्टेक्स्ट. 80 वर्ग, बॉक्सेस आणि सेगमेंटेशन मास्क -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.mr.jpg) +![COCO](../../../../../translated_images/mr/coco-examples.71bc60380fa6cceb.jpg) ## ऑब्जेक्ट डिटेक्शन मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: इमेज क्लासिफिकेशनसाठी अल्गोरिदम किती चांगले कार्य करते हे मोजणे सोपे आहे, परंतु ऑब्जेक्ट डिटेक्शनसाठी आपल्याला वर्गाची अचूकता आणि बॉक्सच्या स्थानाची अचूकता दोन्ही मोजावी लागते. यासाठी **इंटरसेक्शन ओव्हर युनियन** (IoU) वापरले जाते, जे दोन बॉक्सेस (किंवा दोन क्षेत्रे) किती चांगले ओव्हरलॅप होतात हे मोजते. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.mr.png) +![IoU](../../../../../translated_images/mr/iou_equation.9a4751d40fff4e11.png) > *[IoU वर उत्कृष्ट ब्लॉग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) मधून आकृती 2* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) वापरते ROI क्षेत्रांची संरचना तयार करण्यासाठी, जी नंतर CNN फीचर एक्स्ट्रॅक्टर्स आणि SVM-क्लासिफायर्सद्वारे वस्तूचा वर्ग निश्चित करण्यासाठी आणि *बॉक्स* समन्वय निश्चित करण्यासाठी रेषीय रेग्रेशनद्वारे प्रक्रिया केली जाते. [अधिकृत पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.mr.png) +![RCNN](../../../../../translated_images/mr/rcnn1.cae407020dfb1d1f.png) > *van de Sande et al. ICCV’11 मधून प्रतिमा* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.mr.png) +![RCNN-1](../../../../../translated_images/mr/rcnn2.2d9530bb83516484.png) > *[या ब्लॉग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) मधून प्रतिमा* @@ -109,7 +109,7 @@ $$ हा दृष्टिकोन R-CNN सारखाच आहे, परंतु क्षेत्रे कॉन्व्होल्यूशन लेयर्स लागू केल्यानंतर निश्चित केली जातात. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.mr.png) +![FRCNN](../../../../../translated_images/mr/f-rcnn.3cda6d9bb4188875.png) > प्रतिमा [अधिकृत पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 मधून @@ -117,7 +117,7 @@ $$ या दृष्टिकोनाची मुख्य कल्पना म्हणजे ROIs अंदाज लावण्यासाठी न्यूरल नेटवर्क वापरणे - ज्याला *Region Proposal Network* म्हणतात. [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.mr.png) +![FasterRCNN](../../../../../translated_images/mr/faster-rcnn.8d46c099b87ef30a.png) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/pdf/1506.01497.pdf) मधून @@ -129,7 +129,7 @@ $$ 2. फीचर्स **Position-Sensitive Score Map** द्वारे प्रक्रिया केली जातात. $C$ वर्गातील प्रत्येक वस्तू $k\times k$ क्षेत्रांमध्ये विभागली जाते, आणि वस्तूंचे भाग अंदाज लावण्यासाठी प्रशिक्षण दिले जाते. 3. $k\times k$ क्षेत्रांमधील प्रत्येक भागासाठी सर्व नेटवर्क्स वस्तू वर्गांसाठी मतदान करतात, आणि जास्तीत जास्त मत असलेला वर्ग निवडला जातो. -![r-fcn प्रतिमा](../../../../../translated_images/r-fcn.13eb88158b99a3da.mr.png) +![r-fcn प्रतिमा](../../../../../translated_images/mr/r-fcn.13eb88158b99a3da.png) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/abs/1605.06409) मधून @@ -140,7 +140,7 @@ YOLO हा एक रिअलटाइम वन-पास अल्गोर * प्रतिमा $S\times S$ क्षेत्रांमध्ये विभागली जाते. * प्रत्येक क्षेत्रासाठी, **CNN** $n$ शक्य वस्तू, *बॉक्स* समन्वय आणि *कॉन्फिडन्स*=*प्रोबॅबिलिटी* * IoU अंदाज लावते. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.mr.png) + ![YOLO](../../../../../translated_images/mr/yolo.a2648ec82ee8bb4e.png) > प्रतिमा [अधिकृत पेपर](https://arxiv.org/abs/1506.02640) मधून diff --git a/translations/mr/lessons/4-ComputerVision/README.md b/translations/mr/lessons/4-ComputerVision/README.md index 311548e6..1e41ab36 100644 --- a/translations/mr/lessons/4-ComputerVision/README.md +++ b/translations/mr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # संगणकीय दृष्टिकोन -![संगणकीय दृष्टिकोन विषयाची रेखाचित्रात संक्षिप्त माहिती](../../../../translated_images/ai-computervision.6506ebebac3fbf76.mr.png) +![संगणकीय दृष्टिकोन विषयाची रेखाचित्रात संक्षिप्त माहिती](../../../../translated_images/mr/ai-computervision.6506ebebac3fbf76.png) या विभागात आपण शिकणार आहोत: diff --git a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 886adea9..767a0f0a 100644 --- a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**शब्दांची पिशवी** (BoW) वेक्टर प्रतिनिधित्व हे पारंपरिक वेक्टर प्रतिनिधित्वांपैकी सर्वात जास्त वापरले जाणारे आहे. प्रत्येक शब्द एका वेक्टर निर्देशांकाशी जोडलेला असतो, आणि वेक्टर घटक दिलेल्या दस्तऐवजात त्या शब्दाच्या उपस्थितीची संख्या दर्शवतो.\n", "\n", - "![शब्दांची पिशवी वेक्टर प्रतिनिधित्व स्मृतीत कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.mr.png)\n", + "![शब्दांची पिशवी वेक्टर प्रतिनिधित्व स्मृतीत कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **टीप**: तुम्ही BoW ला मजकूरातील स्वतंत्र शब्दांसाठी असलेल्या सर्व एक-हॉट-एन्कोडेड वेक्टरच्या बेरीजप्रमाणे देखील विचार करू शकता.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index d18a00e7..815694aa 100644 --- a/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/mr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**बॅग-ऑफ-वर्ड्स** (BoW) वेक्टर प्रतिनिधित्व हे पारंपरिक वेक्टर प्रतिनिधित्वांपैकी सर्वात सोपे आणि समजण्यास सोपे आहे. प्रत्येक शब्द एका वेक्टर निर्देशांकाशी जोडलेला असतो, आणि वेक्टर घटक दिलेल्या दस्तऐवजात प्रत्येक शब्द किती वेळा आढळतो ते दर्शवतो.\n", "\n", - "![बॅग-ऑफ-वर्ड्स वेक्टर प्रतिनिधित्व मेमरीमध्ये कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.mr.png) \n", + "![बॅग-ऑफ-वर्ड्स वेक्टर प्रतिनिधित्व मेमरीमध्ये कसे सादर केले जाते हे दाखवणारी प्रतिमा.](../../../../../translated_images/mr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW चा विचार आपण मजकूरातील स्वतंत्र शब्दांसाठी एकत्रित केलेल्या सर्व वन-हॉट-एन्कोडेड वेक्टरच्या बेरीज म्हणून करू शकतो.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 8cc6b774..acd0f886 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "आपल्या नेटवर्कमध्ये एम्बेडिंग लेयर प्रथम लेयर म्हणून वापरल्याने, आपण बॅग-ऑफ-वर्ड्स मॉडेलवरून **एम्बेडिंग बॅग** मॉडेलकडे स्विच करू शकतो, जिथे आपण प्रथम आपल्या मजकुरातील प्रत्येक शब्द संबंधित एम्बेडिंगमध्ये रूपांतरित करतो आणि नंतर त्या सर्व एम्बेडिंगवर काही एकत्रित फंक्शन गणना करतो, जसे की `sum`, `average` किंवा `max`.\n", "\n", - "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mr.png)\n", + "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "आपले वर्गीकरणकर्ता न्यूरल नेटवर्क एम्बेडिंग लेयरने सुरू होईल, त्यानंतर एकत्रीकरण लेयर आणि त्यावर लीनियर वर्गीकरणकर्ता असेल:\n" ] @@ -176,7 +176,7 @@ "\n", "मागील आर्किटेक्चरमध्ये, सर्व अनुक्रम समान लांबीचे करण्यासाठी त्यांना पॅड करणे आवश्यक होते, जेणेकरून ते मिनीबॅचमध्ये बसतील. बदलत्या लांबीच्या अनुक्रमांचे प्रतिनिधित्व करण्याचा हा सर्वात कार्यक्षम मार्ग नाही - दुसरा दृष्टिकोन म्हणजे **ऑफसेट** व्हेक्टर वापरणे, जो एका मोठ्या व्हेक्टरमध्ये संग्रहित केलेल्या सर्व अनुक्रमांचे ऑफसेट ठेवेल.\n", "\n", - "![ऑफसेट अनुक्रमाचे प्रतिनिधित्व दर्शवणारी प्रतिमा](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.mr.png)\n", + "![ऑफसेट अनुक्रमाचे प्रतिनिधित्व दर्शवणारी प्रतिमा](../../../../../translated_images/mr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: वरील चित्रात, आम्ही अक्षरांच्या अनुक्रमाचे प्रदर्शन केले आहे, परंतु आमच्या उदाहरणात आम्ही शब्दांच्या अनुक्रमांवर काम करत आहोत. तथापि, ऑफसेट व्हेक्टरसह अनुक्रमांचे प्रतिनिधित्व करण्याचा सामान्य तत्त्व समान राहतो.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW जलद आहे, तर स्किप-ग्राम थोडा धीमा आहे, परंतु दुर्मिळ शब्दांचे प्रतिनिधित्व करण्याचे काम अधिक चांगल्या प्रकारे करतो.\n", "\n", - "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mr.png)\n", + "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News डेटासेटवर प्री-ट्रेन केलेल्या word2vec एम्बेडिंगसह प्रयोग करण्यासाठी, आपण **gensim** लायब्ररीचा वापर करू शकतो. खाली 'neural' या शब्दाशी सर्वाधिक समान शब्द शोधले आहेत:\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 5f2ce659..6027a907 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/mr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "आपल्या नेटवर्कमध्ये पहिल्या लेयर म्हणून एम्बेडिंग लेयर वापरल्याने, आपण बॅग-ऑफ-वर्ड्स मॉडेलऐवजी **एम्बेडिंग बॅग** मॉडेलकडे स्विच करू शकतो, जिथे आपण प्रथम आपल्या मजकुरातील प्रत्येक शब्द त्याच्या संबंधित एम्बेडिंगमध्ये रूपांतरित करतो, आणि नंतर त्या सर्व एम्बेडिंग्सवर काही एकत्रित फंक्शन (जसे की `sum`, `average` किंवा `max`) गणना करतो.\n", "\n", - "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरण दाखवणारी प्रतिमा.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mr.png)\n", + "![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "आपल्या वर्गीकरण न्यूरल नेटवर्कमध्ये खालील लेयर्स असतात:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW जलद आहे, आणि स्किप-ग्राम जरी हळू असला तरी, तो दुर्मिळ शब्दांचे प्रतिनिधित्व अधिक चांगल्या प्रकारे करतो.\n", "\n", - "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mr.png)\n", + "![CBoW आणि स्किप-ग्राम अल्गोरिदम्स शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी दाखवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News डेटासेटवर प्रीट्रेन केलेल्या Word2Vec एम्बेडिंगसह प्रयोग करण्यासाठी, आपण **gensim** लायब्ररीचा वापर करू शकतो. खाली 'neural' शब्दाशी सर्वाधिक समान असलेले शब्द शोधले आहेत.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/14-Embeddings/README.md b/translations/mr/lessons/5-NLP/14-Embeddings/README.md index 9cf42fa2..03b7ddc4 100644 --- a/translations/mr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/mr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW किंवा TF/IDF आधारित वर्गीकरण प्र आमच्या वर्गीकरण नेटवर्कमध्ये पहिल्या लेयर म्हणून एम्बेडिंग लेयर वापरून, आपण बॅग-ऑफ-वर्ड्स मॉडेलवरून **एम्बेडिंग बॅग** मॉडेलवर स्विच करू शकतो, जिथे आपण प्रथम आमच्या मजकुरातील प्रत्येक शब्द संबंधित एम्बेडिंगमध्ये रूपांतरित करतो आणि नंतर त्या सर्व एम्बेडिंग्सवर काही एकत्रित फंक्शन गणना करतो, जसे की `sum`, `average` किंवा `max`. -![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.mr.png) +![पाच अनुक्रम शब्दांसाठी एम्बेडिंग वर्गीकरणकर्ता दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/embedding-classifier-example.b77f021a7ee67eee.png) > लेखकाने तयार केलेली प्रतिमा @@ -40,7 +40,7 @@ BoW किंवा TF/IDF आधारित वर्गीकरण प्र CBoW जलद आहे, तर स्किप-ग्राम हळू आहे, पण दुर्मिळ शब्दांचे प्रतिनिधित्व चांगल्या प्रकारे करते. -![शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी CBoW आणि स्किप-ग्राम अल्गोरिदम दर्शवणारी प्रतिमा.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mr.png) +![शब्दांना व्हेक्टरमध्ये रूपांतरित करण्यासाठी CBoW आणि स्किप-ग्राम अल्गोरिदम दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [या पेपरमधून](https://arxiv.org/pdf/1301.3781.pdf) घेतलेली प्रतिमा diff --git a/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md index a029d085..68c921fd 100644 --- a/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/mr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **कंटिन्युअस बॅग-ऑफ-वर्ड्स** (CBoW), जिथे आपण टोकन अनुक्रमातील मध्यवर्ती टोकन $W_0$ ची भविष्यवाणी करतो $W_{-N}$, ..., $W_N$. * **स्किप-ग्राम**, जिथे आपण मध्यवर्ती टोकन $W_0$ पासून शेजारील टोकनांचा संच {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ची भविष्यवाणी करतो. -![शब्दांना वेक्टरमध्ये रूपांतरित करण्यासाठी वापरलेले अल्गोरिदम](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.mr.png) +![शब्दांना वेक्टरमध्ये रूपांतरित करण्यासाठी वापरलेले अल्गोरिदम](../../../../../translated_images/mr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [या पेपरमधून](https://arxiv.org/pdf/1301.3781.pdf) घेतलेली प्रतिमा diff --git a/translations/mr/lessons/5-NLP/16-RNN/README.md b/translations/mr/lessons/5-NLP/16-RNN/README.md index 58e81881..92aece0c 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/README.md +++ b/translations/mr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: मजकूर अनुक्रमाचा अर्थ पकडण्यासाठी, आपल्याला एक वेगळी तंत्रिका नेटवर्क रचना वापरावी लागते, ज्याला **पुनरावृत्ती तंत्रिका नेटवर्क** किंवा RNN म्हणतात. RNN मध्ये, आपण आपले वाक्य नेटवर्कमधून एकावेळी एक चिन्ह पाठवतो, आणि नेटवर्क काही **स्थिती** तयार करते, जी आपण पुढील चिन्हासह पुन्हा नेटवर्कमध्ये पाठवतो. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.mr.png) +![RNN](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.png) > लेखकाने तयार केलेले चित्र @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN प्रमाणेच आयोजित क पुनरावृत्ती नेटवर्क, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील विशिष्ट नमुने पकडतो आणि त्यांना स्थिती वेक्टरमध्ये संग्रहित करतो किंवा आउटपुटमध्ये पास करतो. जसे की कॉनव्होल्यूशन नेटवर्क्ससह, आपण पहिल्या स्तराद्वारे काढलेल्या कमी-स्तरीय नमुन्यांपासून उच्च-स्तरीय नमुने पकडण्यासाठी पहिल्या स्तरावर आणखी एक पुनरावृत्ती स्तर तयार करू शकतो. यामुळे **बहुस्तरीय RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील स्तराचा आउटपुट पुढील स्तराला इनपुट म्हणून पास केला जातो. -![बहुस्तरीय लांब-आवधी स्मृती RNN दर्शवणारे चित्र](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mr.jpg) +![बहुस्तरीय लांब-आवधी स्मृती RNN दर्शवणारे चित्र](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg) *चित्र [या उत्कृष्ट पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) फर्नांडो लोपेझ यांनी* diff --git a/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 67602873..f9214702 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "पुनरावृत्ती नेटवर्क, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील विशिष्ट नमुने कॅप्चर करते आणि त्यांना स्टेट व्हेक्टरमध्ये साठवते किंवा आउटपुटमध्ये पास करते. जसे कॉनव्होल्यूशनल नेटवर्क्समध्ये होते, तसेच आपण पहिल्या लेयरने काढलेल्या कमी-स्तरीय नमुन्यांवर आधारित उच्च-स्तरीय नमुने कॅप्चर करण्यासाठी पहिल्या लेयरच्या वर आणखी एक पुनरावृत्ती लेयर तयार करू शकतो. यामुळे **बहुपरत RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील लेयरचा आउटपुट पुढील लेयरला इनपुट म्हणून दिला जातो.\n", "\n", - "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mr.jpg)\n", + "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*फर्नांडो लोपेझ यांच्या [या अप्रतिम पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) घेतलेली प्रतिमा*\n", "\n", diff --git a/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2573183e..5302ccba 100644 --- a/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/mr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "मजकूर अनुक्रमाचा अर्थ पकडण्यासाठी, आपण **पुनरावृत्ती तंत्रिका नेटवर्क** किंवा RNN नावाची तंत्रिका नेटवर्क आर्किटेक्चर वापरणार आहोत. RNN वापरताना, आपण आपले वाक्य नेटवर्कमधून एकावेळी एक टोकन पास करतो, आणि नेटवर्क काही **स्थिती** तयार करते, जी आपण पुढील टोकनसह पुन्हा नेटवर्कमध्ये पास करतो.\n", "\n", - "![पुनरावृत्ती तंत्रिका नेटवर्क निर्मितीचे उदाहरण दर्शवणारी प्रतिमा.](../../../../../translated_images/rnn.27f5c29c53d727b5.mr.png)\n", + "![पुनरावृत्ती तंत्रिका नेटवर्क निर्मितीचे उदाहरण दर्शवणारी प्रतिमा.](../../../../../translated_images/mr/rnn.27f5c29c53d727b5.png)\n", "\n", "टोकनच्या इनपुट अनुक्रम $X_0,\\dots,X_n$ दिल्यास, RNN तंत्रिका नेटवर्क ब्लॉक्सची एक अनुक्रम तयार करते आणि बॅकप्रोपोगेशन वापरून हे अनुक्रम एंड-टू-एंड प्रशिक्षित करते. प्रत्येक नेटवर्क ब्लॉक $(X_i,S_i)$ ही जोडी इनपुट म्हणून घेतो आणि $S_{i+1}$ परिणाम म्हणून तयार करतो. अंतिम स्थिती $S_n$ किंवा आउटपुट $Y_n$ रेषीय वर्गीकरणामध्ये जाते जेणेकरून परिणाम तयार होतो. सर्व नेटवर्क ब्लॉक्स समान वजन सामायिक करतात आणि एकाच बॅकप्रोपोगेशन पासद्वारे एंड-टू-एंड प्रशिक्षित केले जातात.\n", "\n", @@ -369,7 +369,7 @@ "\n", "पुनरावृत्ती नेटवर्क्स, एकदिशात्मक किंवा द्विदिशात्मक, अनुक्रमातील नमुने पकडतात आणि त्यांना स्टेट व्हेक्टरमध्ये साठवतात किंवा आउटपुट म्हणून परत करतात. जसे कॉनव्होल्यूशन नेटवर्क्समध्ये होते, तसेच आपण पहिल्या लेयरने काढलेल्या कमी स्तरातील नमुन्यांमधून उच्च स्तराचे नमुने पकडण्यासाठी पहिल्या लेयरनंतर आणखी एक पुनरावृत्ती लेयर तयार करू शकतो. यामुळे **बहुपरत RNN** ची संकल्पना तयार होते, ज्यामध्ये दोन किंवा अधिक पुनरावृत्ती नेटवर्क्स असतात, जिथे मागील लेयरचे आउटपुट पुढील लेयरला इनपुट म्हणून दिले जाते.\n", "\n", - "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.mr.jpg)\n", + "![मल्टीलेयर लाँग-शॉर्ट-टर्म-मेमरी RNN दर्शवणारी प्रतिमा](../../../../../translated_images/mr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*फर्नांडो लोपेझ यांनी लिहिलेल्या [या अप्रतिम पोस्टमधून](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) घेतलेली प्रतिमा.*\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 91a39323..38b227a6 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNNला मजकूर तयार करण्यासाठी प्रशिक्षण देण्याची पद्धत पुढीलप्रमाणे आहे. प्रत्येक चरणावर, आम्ही `nchars` लांबीचा अक्षरांचा क्रम घेऊ आणि नेटवर्कला प्रत्येक इनपुट अक्षरासाठी पुढील आउटपुट अक्षर तयार करण्यास सांगू:\n", "\n", - "!['HELLO' शब्द तयार करणाऱ्या RNNचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mr.png)\n", + "!['HELLO' शब्द तयार करणाऱ्या RNNचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "खऱ्या परिस्थितीनुसार, आपल्याला काही विशेष अक्षरे समाविष्ट करायची असू शकतात, जसे की *end-of-sequence* ``. आपल्या बाबतीत, आम्हाला फक्त अखंड मजकूर तयार करण्यासाठी नेटवर्कला प्रशिक्षण द्यायचे आहे, त्यामुळे आम्ही प्रत्येक क्रमाची लांबी `nchars` टोकन इतकी निश्चित करू. परिणामी, प्रत्येक प्रशिक्षण उदाहरणामध्ये `nchars` इनपुट्स आणि `nchars` आउटपुट्स (जे इनपुट क्रम एका चिन्हाने डावीकडे सरकवलेले असतील) असतील. मिनीबॅचमध्ये अशा अनेक क्रमांचा समावेश असेल.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index d5cf54dd..83c1fa6c 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "आपण RNN कसे प्रशिक्षण देणार आहोत जेणेकरून ती बातम्यांची शीर्षके तयार करू शकेल, ते पुढीलप्रमाणे आहे. प्रत्येक टप्प्यावर, आपण एक शीर्षक घेऊ, जे RNN मध्ये दिले जाईल, आणि प्रत्येक इनपुट अक्षरासाठी आपण नेटवर्कला पुढील आउटपुट अक्षर तयार करण्यास सांगू:\n", "\n", - "!['HELLO' शब्दाच्या RNN जनरेशनचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mr.png)\n", + "!['HELLO' शब्दाच्या RNN जनरेशनचे उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "आमच्या अनुक्रमाच्या शेवटच्या अक्षरासाठी, आम्ही नेटवर्कला `` टोकन तयार करण्यास सांगू.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md index 1ec55694..2c4aa5d1 100644 --- a/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/mr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: यामुळे खालील चित्रात दाखवलेल्या विविध न्यूरल आर्किटेक्चर्स शक्य होतात: -![रीकरंट न्यूरल नेटवर्क्सचे सामान्य नमुने दाखवणारी प्रतिमा.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.mr.jpg) +![रीकरंट न्यूरल नेटवर्क्सचे सामान्य नमुने दाखवणारी प्रतिमा.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > प्रतिमा ब्लॉग पोस्ट [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) मधून [Andrej Karpaty](http://karpathy.github.io/) यांनी तयार केलेली. @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: आम्ही या RNN ला टप्प्याटप्प्याने मजकूर तयार करण्यासाठी प्रशिक्षित करू. प्रत्येक टप्प्यावर, आम्ही `nchars` लांबीचा कॅरेक्टर अनुक्रम घेऊ आणि नेटवर्कला प्रत्येक इनपुट कॅरेक्टरसाठी पुढील आउटपुट कॅरेक्टर तयार करण्यास सांगू: -!['HELLO' शब्द तयार करण्यासाठी RNN चा उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.mr.png) +!['HELLO' शब्द तयार करण्यासाठी RNN चा उदाहरण दाखवणारी प्रतिमा.](../../../../../translated_images/mr/rnn-generate.56c54afb52f9781d.png) मजकूर तयार करताना (इन्फरन्स दरम्यान), आम्ही काही **प्रॉम्प्ट** पासून सुरुवात करतो, जे RNN सेल्समधून पास होते आणि त्याचा इंटरमीडिएट स्टेट तयार होतो, आणि नंतर त्या स्टेटमधून निर्मिती सुरू होते. आम्ही एकावेळी एक कॅरेक्टर तयार करतो आणि स्टेट आणि तयार केलेला कॅरेक्टर पुढील RNN सेलला पास करतो, जोपर्यंत पुरेसे कॅरेक्टर्स तयार होत नाहीत. diff --git a/translations/mr/lessons/5-NLP/18-Transformers/README.md b/translations/mr/lessons/5-NLP/18-Transformers/README.md index f3bbf198..ab3cd005 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/mr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर **लक्षवेध यंत्रणा** प्रत्येक इनपुट व्हेक्टरच्या संदर्भात्मक प्रभावाचे वजन प्रत्येक RNN च्या आउटपुट अंदाजावर देण्याचा मार्ग प्रदान करते. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या मध्यवर्ती स्थिती आणि आउटपुट RNN दरम्यान शॉर्टकट तयार करणे. अशा प्रकारे, आउटपुट चिन्ह yt तयार करताना, आपण सर्व इनपुट लपवलेल्या स्थिती hi विचारात घेऊ, वेगवेगळ्या वजन गुणांक αt,i सह. -![एन्कोडर/डिकोडर मॉडेलसह अ‍ॅडिटिव्ह लक्षवेध स्तर दर्शवणारी प्रतिमा](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mr.png) +![एन्कोडर/डिकोडर मॉडेलसह अ‍ॅडिटिव्ह लक्षवेध स्तर दर्शवणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह लक्षवेध यंत्रणा असलेले एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) उद्धृत. लक्षवेध मॅट्रिक्स {αi,j} आउटपुट क्रमातील दिलेल्या शब्दाच्या निर्मितीत विशिष्ट इनपुट शब्द किती महत्त्वाची भूमिका बजावतात हे दर्शवेल. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे: -![Bahdanau - arviz.org मधून घेतलेले RNNsearch-50 द्वारे आढळलेले नमुना संरेखन दर्शवणारी प्रतिमा](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mr.png) +![Bahdanau - arviz.org मधून घेतलेले RNNsearch-50 द्वारे आढळलेले नमुना संरेखन दर्शवणारी प्रतिमा](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील आकृती (Fig.3) @@ -66,7 +66,7 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर पुढे, आपल्याला आपल्या क्रमातील काही नमुने पकडणे आवश्यक आहे. हे करण्यासाठी, ट्रान्सफॉर्मर्स **स्व-लक्षवेध** यंत्रणा वापरतात, जी इनपुट आणि आउटपुट म्हणून समान क्रमावर लागू केलेले लक्षवेध आहे. स्व-लक्षवेध लागू केल्याने आपल्याला वाक्याच्या **संदर्भ** विचारात घेता येतो, आणि कोणते शब्द परस्पर संबंधित आहेत हे पाहता येते. उदाहरणार्थ, हे आपल्याला *it* सारख्या कोरफेरन्सद्वारे संदर्भित केलेले शब्द पाहण्यास अनुमती देते, तसेच संदर्भ विचारात घेण्यास मदत करते: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.mr.png) +![](../../../../../translated_images/mr/CoreferenceResolution.861924d6d384a7d6.png) > [Google ब्लॉग](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) मधून प्रतिमा @@ -91,7 +91,7 @@ RNNs वापरून, क्रम-ते-क्रम दोन पुनर **BERT** (Bidirectional Encoder Representations from Transformers) हे 12 स्तरांसाठी *BERT-base* आणि 24 स्तरांसाठी *BERT-large* असलेले एक मोठे मल्टी-लेयर ट्रान्सफॉर्मर नेटवर्क आहे. मॉडेल प्रथम मोठ्या मजकूर डेटाच्या संग्रहावर (WikiPedia + पुस्तके) असंरचित प्रशिक्षण वापरून (वाक्यातील लपवलेले शब्द अंदाज करणे) पूर्व-प्रशिक्षित केले जाते. पूर्व-प्रशिक्षणादरम्यान मॉडेल महत्त्वपूर्ण स्तरांचा भाषा समज आत्मसात करते, ज्याचा नंतर इतर डेटासेटसह सूक्ष्म-ट्यूनिंगद्वारे लाभ घेतला जाऊ शकतो. या प्रक्रियेला **ट्रान्सफर लर्निंग** म्हणतात. -![http://jalammar.github.io/illustrated-bert/ मधून प्रतिमा](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mr.png) +![http://jalammar.github.io/illustrated-bert/ मधून प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > प्रतिमा [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 67aaeb39..5be26fc7 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/mr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**लक्षवेधी यंत्रणा (Attention Mechanisms)** RNN च्या प्रत्येक आउटपुट अंदाजावर प्रत्येक इनपुट वेक्टरच्या संदर्भात्मक प्रभावाचे वजन करण्याचा एक मार्ग प्रदान करतात. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या मध्यवर्ती स्थिती आणि आउटपुट RNN यांच्यात शॉर्टकट तयार करणे. अशा प्रकारे, आउटपुट चिन्ह $y_t$ तयार करताना, आपण सर्व इनपुट लपलेल्या स्थिती $h_i$ विचारात घेऊ, वेगवेगळ्या वजन गुणांक $\\alpha_{t,i}$ सह.\n", "\n", - "![एन्कोडर/डिकोडर मॉडेल आणि अ‍ॅडिटिव्ह लक्षवेधी स्तर दर्शविणारी प्रतिमा](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mr.png)\n", + "![एन्कोडर/डिकोडर मॉडेल आणि अ‍ॅडिटिव्ह लक्षवेधी स्तर दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह लक्षवेधी यंत्रणेसह एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) उद्धृत]*\n", "\n", "लक्षवेधी मॅट्रिक्स $\\{\\alpha_{i,j}\\}$ हे दर्शवेल की आउटपुट अनुक्रमातील विशिष्ट शब्द तयार करण्यात कोणत्या इनपुट शब्दांचा किती प्रभाव आहे. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे:\n", "\n", - "![RNNsearch-50 ने सापडलेले नमुना संरेखन दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mr.png)\n", + "![RNNsearch-50 ने सापडलेले नमुना संरेखन दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (आकृती 3) मधून घेतलेली आकृती]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) हे 12 स्तरांसाठी *BERT-base* आणि 24 स्तरांसाठी *BERT-large* असलेले खूप मोठे बहुस्तरीय ट्रान्सफॉर्मर नेटवर्क आहे. हे मॉडेल मोठ्या प्रमाणावर मजकूर डेटावर (विकिपीडिया + पुस्तके) असंरचित प्रशिक्षण (unsupervised training) वापरून आधी प्रशिक्षणित केले जाते (वाक्यातील लपवलेले शब्द ओळखणे). पूर्व-प्रशिक्षणादरम्यान मॉडेल महत्त्वपूर्ण भाषिक समज आत्मसात करते, ज्याचा नंतर इतर डेटासेट्ससह सूक्ष्म-ट्यूनिंगद्वारे उपयोग केला जाऊ शकतो. या प्रक्रियेला **स्थानांतरण शिक्षण (transfer learning)** म्हणतात.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ येथून घेतलेली प्रतिमा](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mr.png)\n", + "![http://jalammar.github.io/illustrated-bert/ येथून घेतलेली प्रतिमा](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 आणि इतर अनेक ट्रान्सफॉर्मर आर्किटेक्चर्सच्या विविध आवृत्त्या आहेत, ज्या सूक्ष्म-ट्यूनिंगसाठी वापरता येतात. [HuggingFace पॅकेज](https://github.com/huggingface/) PyTorch सह या आर्किटेक्चर्सपैकी अनेकांचे प्रशिक्षण घेण्यासाठी रिपॉझिटरी प्रदान करते.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 33b90717..285041f9 100644 --- a/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/mr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**अटेन्शन मेकॅनिझम्स** RNN च्या प्रत्येक आउटपुट प्रेडिक्शनवर प्रत्येक इनपुट व्हेक्टरच्या संदर्भात्मक प्रभावाचे वजन करण्याचा एक मार्ग प्रदान करतात. हे अंमलात आणण्याचा मार्ग म्हणजे इनपुट RNN च्या इंटरमिजिएट स्टेट्स आणि आउटपुट RNN दरम्यान शॉर्टकट तयार करणे. अशा प्रकारे, जेव्हा आउटपुट चिन्ह $y_t$ तयार करतो, तेव्हा आपण सर्व इनपुट हिडन स्टेट्स $h_i$ विचारात घेऊ, वेगवेगळ्या वजन गुणांक $\\alpha_{t,i}$ सह.\n", "\n", - "![एन्कोडर/डिकोडर मॉडेल अ‍ॅडिटिव्ह अटेन्शन लेयरसह दर्शविणारी प्रतिमा](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.mr.png)\n", + "![एन्कोडर/डिकोडर मॉडेल अ‍ॅडिटिव्ह अटेन्शन लेयरसह दर्शविणारी प्रतिमा](../../../../../translated_images/mr/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मधील अ‍ॅडिटिव्ह अटेन्शन मेकॅनिझमसह एन्कोडर-डिकोडर मॉडेल, [या ब्लॉग पोस्टमधून](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) घेतलेले*\n", "\n", "अटेन्शन मॅट्रिक्स $\\{\\alpha_{i,j}\\}$ हे दर्शवते की आउटपुट अनुक्रमातील विशिष्ट शब्द तयार करण्यात कोणत्या इनपुट शब्दांचा किती प्रभाव आहे. खाली अशा मॅट्रिक्सचे उदाहरण दिले आहे:\n", "\n", - "![RNNsearch-50 ने सापडलेले नमुना अलाइनमेंट दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.mr.png)\n", + "![RNNsearch-50 ने सापडलेले नमुना अलाइनमेंट दर्शविणारी प्रतिमा, Bahdanau - arviz.org कडून घेतलेली](../../../../../translated_images/mr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) मधून घेतलेली आकृती]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) हा एक मोठा मल्टी लेयर ट्रान्सफॉर्मर नेटवर्क आहे ज्यामध्ये *BERT-base* साठी 12 स्तर आणि *BERT-large* साठी 24 स्तर आहेत. हा मॉडेल प्रथम मोठ्या प्रमाणावर मजकूर डेटावर (WikiPedia + पुस्तके) असुपरवाइज्ड ट्रेनिंगचा वापर करून (वाक्यातील मास्क केलेल्या शब्दांची भविष्यवाणी करणे) प्री-ट्रेन केला जातो. प्री-ट्रेनिंग दरम्यान मॉडेल महत्त्वपूर्ण भाषिक समज आत्मसात करते, ज्याचा नंतर इतर डेटासेटसह फाइन ट्यूनिंगद्वारे उपयोग केला जाऊ शकतो. या प्रक्रियेला **ट्रान्सफर लर्निंग** म्हणतात.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.mr.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/mr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 आणि इतर अनेक ट्रान्सफॉर्मर आर्किटेक्चरचे प्रकार आहेत, जे फाइन ट्यून केले जाऊ शकतात.\n", "\n", diff --git a/translations/mr/lessons/5-NLP/19-NER/README.md b/translations/mr/lessons/5-NLP/19-NER/README.md index baa89a21..3e59d364 100644 --- a/translations/mr/lessons/5-NLP/19-NER/README.md +++ b/translations/mr/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O टोकन्स आणि वर्गांमध्ये एक-ते-एक संबंध तयार करणे आवश्यक असल्याने, आपण या चित्रातून उजव्या बाजूला असलेल्या **अनेक-ते-अनेक** न्यूरल नेटवर्क मॉडेलला प्रशिक्षण देऊ शकतो: -![सामान्य पुनरावृत्तीशील न्यूरल नेटवर्क नमुन्यांचे चित्र.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.mr.jpg) +![सामान्य पुनरावृत्तीशील न्यूरल नेटवर्क नमुन्यांचे चित्र.](../../../../../translated_images/mr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *[Andrej Karpathy](http://karpathy.github.io/) यांच्या [या ब्लॉग पोस्टमधून](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) घेतलेले चित्र. NER टोकन वर्गीकरण मॉडेल्स या चित्रातील उजव्या बाजूच्या नेटवर्क आर्किटेक्चरशी संबंधित आहेत.* diff --git a/translations/mr/lessons/5-NLP/README.md b/translations/mr/lessons/5-NLP/README.md index 6bb0db39..5c376603 100644 --- a/translations/mr/lessons/5-NLP/README.md +++ b/translations/mr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # नैसर्गिक भाषा प्रक्रिया -![NLP कार्यांचा संक्षेप एका चित्रात](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.mr.png) +![NLP कार्यांचा संक्षेप एका चित्रात](../../../../translated_images/mr/ai-nlp.b22dcb8ca4707cea.png) या विभागात, आपण **नैसर्गिक भाषा प्रक्रिया (NLP)** संबंधित कार्ये हाताळण्यासाठी न्यूरल नेटवर्क्सचा वापर कसा करायचा यावर लक्ष केंद्रित करू. अनेक NLP समस्या आहेत ज्या संगणकांनी सोडवाव्या अशी आपली इच्छा आहे: diff --git a/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md index 00e24b53..025cc88e 100644 --- a/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/mr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo ची एक उत्तम गोष्ट म्हणजे त् मॉडेल उघडल्यानंतर, तुम्हाला मुख्य NetLogo स्क्रीनवर नेले जाते. येथे एक नमुना मॉडेल आहे जो मर्यादित संसाधने (गवत) दिल्यास लांडगा आणि मेंढ्यांच्या लोकसंख्येचे वर्णन करतो. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.mr.png) +![NetLogo Main Screen](../../../../../translated_images/mr/NetLogo-Main.32653711ec1a01b3.png) > स्क्रीनशॉट - Dmitry Soshnikov diff --git a/translations/mr/lessons/README.md b/translations/mr/lessons/README.md index 61b40c0a..c4aac771 100644 --- a/translations/mr/lessons/README.md +++ b/translations/mr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # आढावा -![डूडलमधील आढावा](../../../translated_images/ai-overview.0857791951d19500.mr.png) +![डूडलमधील आढावा](../../../translated_images/mr/ai-overview.0857791951d19500.png) > स्केच नोट: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/mr/lessons/X-Extras/X1-MultiModal/README.md b/translations/mr/lessons/X-Extras/X1-MultiModal/README.md index cfb85427..c47287fe 100644 --- a/translations/mr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/mr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP कार्यांसाठी ट्रान्सफॉर्मर CLIP ची मुख्य कल्पना म्हणजे टेक्स्ट प्रॉम्प्ट्सची प्रतिमा सोबत तुलना करणे आणि प्रतिमा प्रॉम्प्टशी किती चांगली जुळते हे ठरवणे. -![CLIP आर्किटेक्चर](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.mr.png) +![CLIP आर्किटेक्चर](../../../../../translated_images/mr/clip-arch.b3dbf20b4e8ed8be.png) > *[या ब्लॉग पोस्ट](https://openai.com/blog/clip/) मधून चित्र* @@ -31,7 +31,7 @@ CLIP मॉडेल/लायब्ररी [OpenAI GitHub](https://github.com समजा आपल्याला प्रतिमा वर्गीकृत करायच्या आहेत, जसे की मांजरी, कुत्रे आणि माणसे. अशा परिस्थितीत, आपण मॉडेलला एक प्रतिमा आणि टेक्स्ट प्रॉम्प्ट्सची मालिका देऊ शकतो: "*मांजरीचे चित्र*", "*कुत्र्याचे चित्र*", "*माणसाचे चित्र*". परिणामी 3 संभाव्यतेच्या व्हेक्टरमध्ये आपल्याला फक्त सर्वाधिक मूल्य असलेल्या निर्देशांक निवडायचा आहे. -![प्रतिमा वर्गीकरणासाठी CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.mr.png) +![प्रतिमा वर्गीकरणासाठी CLIP](../../../../../translated_images/mr/clip-class.3af42ef0b2b19369.png) > *[या ब्लॉग पोस्ट](https://openai.com/blog/clip/) मधून चित्र* @@ -55,13 +55,13 @@ VQGAN बद्दल अधिक जाणून घेण्यासाठ VQGAN आणि पारंपरिक GAN मधील एक महत्त्वाचा फरक म्हणजे पारंपरिक GAN कोणत्याही इनपुट व्हेक्टरवरून चांगली प्रतिमा तयार करू शकते, तर VQGAN कदाचित सुसंगत प्रतिमा तयार करणार नाही. त्यामुळे, प्रतिमा निर्मिती प्रक्रियेला पुढे मार्गदर्शन करणे आवश्यक आहे, आणि ते CLIP वापरून केले जाऊ शकते. -![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/vqgan.5027fe05051dfa31.mr.png) +![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/mr/vqgan.5027fe05051dfa31.png) टेक्स्ट प्रॉम्प्टशी संबंधित प्रतिमा तयार करण्यासाठी, आपण काही रँडम एन्कोडिंग व्हेक्टरसह सुरुवात करतो जो VQGAN द्वारे प्रतिमा तयार करण्यासाठी पास केला जातो. नंतर CLIP चा वापर लॉस फंक्शन तयार करण्यासाठी केला जातो जो प्रतिमा टेक्स्ट प्रॉम्प्टशी किती चांगली जुळते हे दर्शवतो. त्यानंतर उद्दिष्ट म्हणजे हा लॉस कमी करणे, बॅक प्रोपोगेशन वापरून इनपुट व्हेक्टर पॅरामीटर्स समायोजित करणे. VQGAN+CLIP लागू करणारी एक उत्कृष्ट लायब्ररी [Pixray](http://github.com/pixray/pixray) आहे. -![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.mr.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.mr.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.mr.png) +![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- प्रॉम्प्ट *साहित्याच्या तरुण पुरुष शिक्षकाचा जलरंग पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *संगणक विज्ञानाच्या तरुण महिला शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *गणिताच्या वृद्ध पुरुष शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र @@ -77,7 +77,7 @@ CLIP च्या विपरीत, DALL-E टेक्स्ट आणि प DALL.E 1 आणि 2 मधील मुख्य फरक म्हणजे DALL-E 2 अधिक वास्तववादी प्रतिमा आणि कला तयार करते. DALL-E सह प्रतिमा निर्मितीचे उदाहरण: -![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.mr.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.mr.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.mr.png) +![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray द्वारे तयार केलेले चित्र](../../../../../translated_images/mr/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- प्रॉम्प्ट *साहित्याच्या तरुण पुरुष शिक्षकाचा जलरंग पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *संगणक विज्ञानाच्या तरुण महिला शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र | प्रॉम्प्ट *गणिताच्या वृद्ध पुरुष शिक्षकाचा तेल पोर्ट्रेट जवळून* वरून तयार केलेले चित्र diff --git a/translations/ms/README.md b/translations/ms/README.md index bee65bc7..20a6c39e 100644 --- a/translations/ms/README.md +++ b/translations/ms/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kecerdasan Buatan untuk Pemula - Kurikulum -|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ms.png)| +|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ms/ai-overview.0857791951d19500.png)| |:---:| | AI Untuk Pemula - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ms/lessons/1-Intro/README.md b/translations/ms/lessons/1-Intro/README.md index c82fb565..9d3c1018 100644 --- a/translations/ms/lessons/1-Intro/README.md +++ b/translations/ms/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengenalan kepada AI -![Ringkasan kandungan Pengenalan AI dalam bentuk doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c.ms.png) +![Ringkasan kandungan Pengenalan AI dalam bentuk doodle](../../../../translated_images/ms/ai-intro.bf28d1ac4235881c.png) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pada asalnya, komputer dicipta oleh [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) untuk beroperasi pada nombor mengikut prosedur yang jelas - iaitu algoritma. Komputer moden, walaupun jauh lebih maju daripada model asal yang dicadangkan pada abad ke-19, masih mengikuti idea yang sama iaitu pengiraan terkawal. Oleh itu, adalah mungkin untuk memprogram komputer untuk melakukan sesuatu jika kita tahu urutan langkah yang tepat yang perlu dilakukan untuk mencapai matlamat tersebut. -![Foto seorang individu](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ms.png) +![Foto seorang individu](../../../../translated_images/ms/dsh_age.d212a30d4e54fb5f.png) > Foto oleh [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Untuk maklumat lanjut, rujuk **[Artificial General Intelligence](https://en.wiki Salah satu masalah apabila berurusan dengan istilah **[Kecerdasan](https://en.wikipedia.org/wiki/Intelligence)** ialah tiada definisi yang jelas untuk istilah ini. Ada yang berpendapat bahawa kecerdasan berkaitan dengan **pemikiran abstrak**, atau **kesedaran diri**, tetapi kita tidak dapat mendefinisikannya dengan tepat. -![Foto seekor kucing](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ms.jpg) +![Foto seekor kucing](../../../../translated_images/ms/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) oleh [Amber Kipp](https://unsplash.com/@sadmax) dari Unsplash @@ -98,13 +98,13 @@ Sebaliknya, kita boleh cuba memodelkan elemen paling mudah dalam otak kita – i > | Bagaimana dengan ML? | | > |--------------|-----------| -> | Bahagian Kecerdasan Buatan yang berdasarkan komputer belajar untuk menyelesaikan masalah berdasarkan beberapa data dipanggil **Pembelajaran Mesin**. Kita tidak akan mempertimbangkan pembelajaran mesin klasik dalam kursus ini - kita merujuk anda kepada kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners) yang berasingan. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ms.png) | +> | Bahagian Kecerdasan Buatan yang berdasarkan komputer belajar untuk menyelesaikan masalah berdasarkan beberapa data dipanggil **Pembelajaran Mesin**. Kita tidak akan mempertimbangkan pembelajaran mesin klasik dalam kursus ini - kita merujuk anda kepada kurikulum [Machine Learning for Beginners](http://aka.ms/ml-beginners) yang berasingan. | ![ML for Beginners](../../../../translated_images/ms/ml-for-beginners.9e4fed176fd5817d.png) | ## Sejarah Ringkas AI Kecerdasan Buatan bermula sebagai satu bidang pada pertengahan abad ke-20. Pada mulanya, pendekatan penaakulan simbolik adalah pendekatan yang dominan, dan ia membawa kepada beberapa kejayaan penting, seperti sistem pakar – program komputer yang mampu bertindak sebagai pakar dalam beberapa domain masalah yang terhad. Walau bagaimanapun, tidak lama kemudian menjadi jelas bahawa pendekatan sedemikian tidak berskala dengan baik. Mengekstrak pengetahuan daripada pakar, mewakilkannya dalam komputer, dan memastikan pangkalan pengetahuan itu tepat ternyata menjadi tugas yang sangat kompleks, dan terlalu mahal untuk praktikal dalam banyak kes. Ini membawa kepada apa yang dipanggil [AI Winter](https://en.wikipedia.org/wiki/AI_winter) pada tahun 1970-an. -Sejarah Ringkas AI +Sejarah Ringkas AI > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Begitu juga, kita boleh melihat bagaimana pendekatan terhadap mencipta “progra * Pembantu moden, seperti Cortana, Siri atau Google Assistant semuanya adalah sistem hibrid yang menggunakan rangkaian neural untuk menukar pertuturan kepada teks dan mengenali niat kita, dan kemudian menggunakan beberapa penaakulan atau algoritma eksplisit untuk melaksanakan tindakan yang diperlukan. * Pada masa depan, kita mungkin menjangkakan model berasaskan neural sepenuhnya untuk mengendalikan dialog dengan sendirinya. Keluarga GPT dan [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rangkaian neural baru-baru ini menunjukkan kejayaan besar dalam hal ini. -evolusi ujian Turing +evolusi ujian Turing > Gambar oleh Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) oleh [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Penyelidikan AI Terkini diff --git a/translations/ms/lessons/2-Symbolic/Animals.ipynb b/translations/ms/lessons/2-Symbolic/Animals.ipynb index 96e5e150..48da5bc4 100644 --- a/translations/ms/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ms/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Dalam contoh ini, kita akan melaksanakan sistem berasaskan pengetahuan yang mudah untuk menentukan haiwan berdasarkan beberapa ciri fizikal. Sistem ini boleh diwakili oleh pokok AND-OR berikut (ini adalah sebahagian daripada keseluruhan pokok, kita boleh menambah lebih banyak peraturan dengan mudah):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ms.png)\n" + "![](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ms/lessons/2-Symbolic/README.md b/translations/ms/lessons/2-Symbolic/README.md index 1bac132e..8073d031 100644 --- a/translations/ms/lessons/2-Symbolic/README.md +++ b/translations/ms/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Perwakilan Pengetahuan dan Sistem Pakar -![Ringkasan kandungan AI Simbolik](../../../../translated_images/ai-symbolic.715a30cb610411a6.ms.png) +![Ringkasan kandungan AI Simbolik](../../../../translated_images/ms/ai-symbolic.715a30cb610411a6.png) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Kebiasaannya, kita tidak mentakrifkan pengetahuan secara ketat, tetapi kita meny Oleh itu, masalah **perwakilan pengetahuan** adalah untuk mencari cara yang berkesan untuk mewakili pengetahuan dalam komputer dalam bentuk data, supaya ia boleh digunakan secara automatik. Ini boleh dilihat sebagai spektrum: -![Spektrum perwakilan pengetahuan](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ms.png) +![Spektrum perwakilan pengetahuan](../../../../translated_images/ms/knowledge-spectrum.b60df631852c0217.png) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaks Blok | Indentasi | | | Salah satu kejayaan awal AI simbolik ialah **sistem pakar** - sistem komputer yang direka untuk bertindak sebagai pakar dalam domain masalah yang terhad. Ia berdasarkan **pangkalan pengetahuan** yang diekstrak daripada satu atau lebih pakar manusia, dan mengandungi **enjin inferens** yang melakukan beberapa penaakulan di atasnya. -![Seni Bina Manusia](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ms.png) | ![Sistem Berasaskan Pengetahuan](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ms.png) +![Seni Bina Manusia](../../../../translated_images/ms/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem Berasaskan Pengetahuan](../../../../translated_images/ms/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Struktur ringkas sistem saraf manusia | Seni bina sistem berasaskan pengetahuan @@ -106,7 +106,7 @@ Sistem pakar dibina seperti sistem penaakulan manusia, yang mengandungi **memori Sebagai contoh, mari kita pertimbangkan sistem pakar berikut untuk menentukan haiwan berdasarkan ciri fizikalnya: -![Pokok AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ms.png) +![Pokok AND-OR](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.png) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index a57659a1..6211a2d5 100644 --- a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Sekiranya kita mempunyai lebih daripada 2 kelas, softmax akan menormalkan kebarangkalian merentasi kesemuanya. Berikut adalah diagram seni bina rangkaian yang melakukan klasifikasi digit MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ms.png)\n" + "![MNIST Classifier](../../../../../translated_images/ms/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Kehilangan latihan yang rendah - model dapat menghampiri data latihan dengan baik kerana ia mempunyai kuasa ekspresi yang mencukupi.\n", "* Kehilangan validasi boleh menjadi jauh lebih tinggi daripada kehilangan latihan dan boleh mula meningkat semasa latihan - ini kerana model \"mengingati\" titik-titik latihan, dan kehilangan \"gambaran keseluruhan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.ms.png)\n", + "![Overfitting](../../../../../translated_images/ms/overfit.a0bd57f717c15769.png)\n", "\n", "> Dalam gambar ini, `x` mewakili data latihan, `o` - data validasi. Kiri - model linear (satu lapisan), ia menghampiri sifat data dengan baik. Kanan - model yang overfitting, model ini menghampiri data latihan dengan sempurna, tetapi tidak lagi masuk akal untuk data lain (kesalahan validasi sangat tinggi).\n" ] diff --git a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md index f7f22481..3c4936af 100644 --- a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting adalah konsep yang sangat penting dalam pembelajaran mesin, dan sang Pertimbangkan masalah berikut untuk menghampiri 5 titik (diwakili oleh `x` pada graf di bawah): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ms.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ms.jpg) +![linear](../../../../../translated_images/ms/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ms/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Model Linear, 2 parameter** | **Model Tidak Linear, 7 parameter** Ralat latihan = 5.3 | Ralat latihan = 0 @@ -79,7 +79,7 @@ Adalah sangat penting untuk mencapai keseimbangan yang betul antara kekayaan mod Seperti yang anda lihat daripada graf di atas, overfitting boleh dikesan melalui ralat latihan yang sangat rendah, dan ralat validasi yang tinggi. Biasanya semasa latihan kita akan melihat kedua-dua ralat latihan dan validasi mula berkurangan, dan kemudian pada satu ketika ralat validasi mungkin berhenti berkurangan dan mula meningkat. Ini akan menjadi tanda overfitting, dan petunjuk bahawa kita mungkin perlu menghentikan latihan pada ketika ini (atau sekurang-kurangnya membuat snapshot model). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ms.png) +![overfitting](../../../../../translated_images/ms/Overfitting.408ad91cd90b4371.png) ## Cara mencegah overfitting diff --git a/translations/ms/lessons/3-NeuralNetworks/README.md b/translations/ms/lessons/3-NeuralNetworks/README.md index 332e432a..9daed6ac 100644 --- a/translations/ms/lessons/3-NeuralNetworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengenalan kepada Rangkaian Neural -![Ringkasan kandungan Pengenalan Rangkaian Neural dalam bentuk doodle](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ms.png) +![Ringkasan kandungan Pengenalan Rangkaian Neural dalam bentuk doodle](../../../../translated_images/ms/ai-neuralnetworks.1c687ae40bc86e83.png) Seperti yang telah kita bincangkan dalam pengenalan, salah satu cara untuk mencapai kecerdasan adalah dengan melatih **model komputer** atau **otak tiruan**. Sejak pertengahan abad ke-20, para penyelidik telah mencuba pelbagai model matematik, sehingga beberapa tahun kebelakangan ini arah ini terbukti sangat berjaya. Model matematik otak seperti ini dipanggil **rangkaian neural**. @@ -36,13 +36,13 @@ Dalam kurikulum ini, kita hanya akan memberi tumpuan kepada model rangkaian neur Daripada biologi, kita tahu bahawa otak kita terdiri daripada sel-sel neural (neuron), setiap satunya mempunyai pelbagai "input" (dendrit) dan satu "output" (akson). Kedua-dua dendrit dan akson boleh menghantar isyarat elektrik, dan sambungan di antara mereka — dikenali sebagai sinaps — boleh menunjukkan tahap kekonduksian yang berbeza-beza, yang dikawal oleh neurotransmitter. -![Model Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ms.jpg) | ![Model Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ms.png) +![Model Neuron](../../../../translated_images/ms/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model Neuron](../../../../translated_images/ms/artneuron.1a5daa88d20ebe6f.png) ----|---- Neuron Sebenar *([Imej](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) dari Wikipedia)* | Neuron Tiruan *(Imej oleh Penulis)* Oleh itu, model matematik paling mudah bagi neuron mengandungi beberapa input X1, ..., XN dan satu output Y, serta satu siri pemberat W1, ..., WN. Output dikira sebagai: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) di mana f adalah beberapa **fungsi pengaktifan** bukan linear. diff --git a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md index 05c40496..f3cd9bb1 100644 --- a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Dalam [OpenCV Notebook](OpenCV.ipynb) kami, kami memberikan beberapa contoh di m * **Pra-pemprosesan gambar buku Braille**. Kami memberi tumpuan kepada bagaimana kami boleh menggunakan thresholding, pengesanan ciri, transformasi perspektif dan manipulasi NumPy untuk memisahkan simbol Braille individu untuk pengelasan selanjutnya oleh rangkaian neural. -![Imej Braille](../../../../../translated_images/braille.341962ff76b1bd70.ms.jpeg) | ![Imej Braille Pra-pemprosesan](../../../../../translated_images/braille-result.46530fea020b03c7.ms.png) | ![Simbol Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.ms.png) +![Imej Braille](../../../../../translated_images/ms/braille.341962ff76b1bd70.jpeg) | ![Imej Braille Pra-pemprosesan](../../../../../translated_images/ms/braille-result.46530fea020b03c7.png) | ![Simbol Braille](../../../../../translated_images/ms/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Imej dari [OpenCV.ipynb](OpenCV.ipynb) * **Mengesan pergerakan dalam video menggunakan perbezaan bingkai**. Jika kamera tetap, maka bingkai dari suapan kamera seharusnya agak serupa antara satu sama lain. Oleh kerana bingkai diwakili sebagai array, hanya dengan menolak array tersebut untuk dua bingkai berturut-turut kita akan mendapat perbezaan piksel, yang seharusnya rendah untuk bingkai statik, dan menjadi lebih tinggi apabila terdapat pergerakan yang ketara dalam imej. -![Imej bingkai video dan perbezaan bingkai](../../../../../translated_images/frame-difference.706f805491a0883c.ms.png) +![Imej bingkai video dan perbezaan bingkai](../../../../../translated_images/ms/frame-difference.706f805491a0883c.png) > Imej dari [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Dalam [OpenCV Notebook](OpenCV.ipynb) kami, kami memberikan beberapa contoh di m - **Dense Optical Flow** mengira medan vektor yang menunjukkan untuk setiap piksel ke mana ia bergerak - **Sparse Optical Flow** berdasarkan mengambil beberapa ciri yang jelas dalam imej (contohnya, tepi), dan membina trajektori mereka dari bingkai ke bingkai. -![Imej Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.ms.png) +![Imej Optical Flow](../../../../../translated_images/ms/optical.1f4a94464579a83a.png) > Imej dari [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 5062fb96..ac791498 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 adalah rangkaian yang mencapai ketepatan 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014. Ia mempunyai struktur lapisan seperti berikut: -![Lapisan ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ms.jpg) +![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.jpg) Seperti yang anda lihat, VGG mengikuti senibina piramid tradisional, iaitu urutan lapisan penumpuan dan pengumpulan. -![Piramid ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ms.jpg) +![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.jpg) > Imej daripada [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 67023f61..5c4382d2 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Oleh itu, dalam CNN biasa, akan terdapat beberapa lapisan pengenalan, dengan lapisan pengumpulan di antara mereka untuk mengurangkan dimensi gambar. Kita juga akan meningkatkan bilangan penapis, kerana apabila corak menjadi lebih kompleks - terdapat lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Imej menunjukkan beberapa lapisan pengenalan dengan lapisan pengumpulan.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ms.png)\n", + "![Imej menunjukkan beberapa lapisan pengenalan dengan lapisan pengumpulan.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Disebabkan oleh pengurangan dimensi ruang dan peningkatan dimensi ciri/penapis, seni bina ini juga dipanggil **seni bina piramid**.\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 3e8b57bf..08ba0e2c 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Oleh itu, dalam CNN tipikal, akan terdapat beberapa lapisan convolutional, dengan lapisan pooling di antara mereka untuk mengurangkan dimensi imej. Kita juga akan meningkatkan bilangan penapis, kerana apabila corak menjadi lebih kompleks - terdapat lebih banyak kombinasi menarik yang perlu kita cari.\n", "\n", - "![Imej menunjukkan beberapa lapisan convolutional dengan lapisan pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ms.png)\n", + "![Imej menunjukkan beberapa lapisan convolutional dengan lapisan pooling.](../../../../../translated_images/ms/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Disebabkan oleh pengurangan dimensi ruang dan peningkatan dimensi ciri/penapis, seni bina ini juga dipanggil **seni bina piramid**.\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md index 8841f0c6..b365a6a9 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Dalam kehidupan sebenar, kita ingin dapat mengenali objek dalam gambar tanpa men Untuk mengekstrak pola, kita akan menggunakan konsep **penapis konvolusi**. Seperti yang anda tahu, imej diwakili oleh matriks 2D, atau tensor 3D dengan kedalaman warna. Menggunakan penapis bermaksud kita mengambil matriks **kernel penapis** yang agak kecil, dan untuk setiap piksel dalam imej asal, kita mengira purata berwajaran dengan titik-titik jiran. Kita boleh melihat ini seperti tingkap kecil yang meluncur di seluruh imej, dan meratakan semua piksel mengikut berat dalam matriks kernel penapis. -![Penapis Tepi Menegak](../../../../../translated_images/filter-vert.b7148390ca0bc356.ms.png) | ![Penapis Tepi Mendatar](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ms.png) +![Penapis Tepi Menegak](../../../../../translated_images/ms/filter-vert.b7148390ca0bc356.png) | ![Penapis Tepi Mendatar](../../../../../translated_images/ms/filter-horiz.59b80ed4feb946ef.png) ----|---- > Imej oleh Dmitry Soshnikov @@ -38,7 +38,7 @@ Cara CNN berfungsi berdasarkan idea penting berikut: * Kita boleh mereka bentuk rangkaian sedemikian rupa sehingga penapis dilatih secara automatik * Kita boleh menggunakan pendekatan yang sama untuk mencari pola dalam ciri tahap tinggi, bukan hanya dalam imej asal. Oleh itu, pengekstrakan ciri CNN berfungsi pada hierarki ciri, bermula daripada gabungan piksel tahap rendah, sehingga gabungan tahap tinggi bahagian gambar. -![Pengekstrakan Ciri Hierarki](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ms.png) +![Pengekstrakan Ciri Hierarki](../../../../../translated_images/ms/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Imej daripada [kertas kerja oleh Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), berdasarkan [penyelidikan mereka](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Kebanyakan CNN yang digunakan untuk pemprosesan imej mengikuti seni bina yang di Sebagai contoh, mari kita lihat seni bina VGG-16, rangkaian yang mencapai ketepatan 92.7% dalam klasifikasi top-5 ImageNet pada tahun 2014: -![Lapisan ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ms.jpg) +![Lapisan ImageNet](../../../../../translated_images/ms/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramid ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ms.jpg) +![Piramid ImageNet](../../../../../translated_images/ms/vgg-16-arch.64ff2137f50dd49f.jpg) > Imej daripada [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2d27fbef..c7fe3722 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Anda perlu melatih rangkaian neural konvolusi untuk mengklasifikasikan pelbagai Kita akan menggunakan [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), yang mengandungi imej 37 baka anjing dan kucing yang berbeza. -![Dataset yang akan kita gunakan](../../../../../../translated_images/data.50b2a9d5484bdbf0.ms.png) +![Dataset yang akan kita gunakan](../../../../../../translated_images/ms/data.50b2a9d5484bdbf0.png) Untuk memuat turun dataset, gunakan kod berikut: diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index f92dac18..261d4655 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Untuk menggambarkan kucing yang ideal, kita akan bermula dengan imej bunyi rawak, dan akan cuba menggunakan teknik pengoptimuman penurunan kecerunan untuk melaraskan imej supaya rangkaian dapat mengenali kucing.\n", "\n", - "![Gelung Pengoptimuman](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ms.png)\n", + "![Gelung Pengoptimuman](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Berikut adalah imej permulaan kita:\n" ] diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md index f495cd93..b0e947cf 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Kedua-dua Keras dan PyTorch mengandungi fungsi untuk memuatkan berat rangkaian n Berikut adalah contoh ciri-ciri yang diekstrak daripada gambar kucing oleh rangkaian VGG-16: -![Ciri-ciri yang diekstrak oleh VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ms.png) +![Ciri-ciri yang diekstrak oleh VGG-16](../../../../../translated_images/ms/features.6291f9c7ba3a0b95.png) ## Dataset Kucing vs. Anjing @@ -48,19 +48,19 @@ Rangkaian neural pra-latih mengandungi pelbagai corak dalam "otaknya", termasuk Satu pendekatan yang boleh kita ambil adalah bermula dengan imej rawak, dan kemudian cuba menggunakan teknik **pengoptimuman penurunan kecerunan** untuk menyesuaikan imej tersebut sedemikian rupa sehingga rangkaian mula berfikir bahawa ia adalah kucing. -![Gelung Pengoptimuman Imej](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ms.png) +![Gelung Pengoptimuman Imej](../../../../../translated_images/ms/ideal-cat-loop.999fbb8ff306e044.png) Walau bagaimanapun, jika kita melakukan ini, kita akan mendapat sesuatu yang sangat mirip dengan bunyi rawak. Ini kerana *terdapat banyak cara untuk membuat rangkaian berfikir imej input adalah kucing*, termasuk beberapa yang tidak masuk akal secara visual. Walaupun imej-imej tersebut mengandungi banyak corak yang tipikal untuk kucing, tiada apa yang menghalang mereka daripada menjadi jelas secara visual. Untuk memperbaiki hasilnya, kita boleh menambah satu lagi istilah ke dalam fungsi kehilangan, yang dipanggil **kehilangan variasi**. Ia adalah metrik yang menunjukkan betapa serupa piksel-piksel yang bersebelahan dalam imej. Meminimumkan kehilangan variasi menjadikan imej lebih licin, dan menghilangkan bunyi - dengan itu mendedahkan corak yang lebih menarik secara visual. Berikut adalah contoh imej "ideal" seperti itu, yang diklasifikasikan sebagai kucing dan zebra dengan kebarangkalian tinggi: -![Kucing Ideal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ms.png) | ![Zebra Ideal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ms.png) +![Kucing Ideal](../../../../../translated_images/ms/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/ms/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Kucing Ideal* | *Zebra Ideal* Pendekatan serupa boleh digunakan untuk melakukan apa yang dipanggil **serangan adversarial** pada rangkaian neural. Katakan kita ingin mengelirukan rangkaian neural dan membuat anjing kelihatan seperti kucing. Jika kita mengambil imej anjing, yang dikenali oleh rangkaian sebagai anjing, kita kemudian boleh mengubahnya sedikit menggunakan pengoptimuman penurunan kecerunan, sehingga rangkaian mula mengklasifikasikannya sebagai kucing: -![Gambar Anjing](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ms.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ms.png) +![Gambar Anjing](../../../../../translated_images/ms/original-dog.8f68a67d2fe0911f.png) | ![Gambar anjing yang diklasifikasikan sebagai kucing](../../../../../translated_images/ms/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Gambar asal anjing* | *Gambar anjing yang diklasifikasikan sebagai kucing* diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 0e5ed09e..1b97784a 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Memandangkan kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi tujuan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maksudnya.\n", "\n", - "![Rajah AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ms.jpg)\n", + "![Rajah AutoEncoder](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Imej daripada [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index b7044ab2..dc9e3f0c 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Oleh kerana kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi menghasilkan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maksudnya.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ms.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Imej daripada [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md index f6449f77..42ff1072 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Namun, kita mungkin ingin menggunakan data mentah (tidak berlabel) untuk melatih Oleh kerana kita melatih autoencoder untuk menangkap sebanyak mungkin maklumat daripada imej asal bagi tujuan pembinaan semula yang tepat, rangkaian cuba mencari **embedding** terbaik bagi imej input untuk menangkap maknanya. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ms.jpg) +![AutoEncoder Diagram](../../../../../translated_images/ms/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Imej daripada [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md index b247b1ea..6e7e6925 100644 --- a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Model klasifikasi imej yang telah kita pelajari sebelum ini mengambil imej dan m ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Pengesanan Objek](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ms.png) +![Pengesanan Objek](../../../../../translated_images/ms/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Imej dari [laman web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Jika kita ingin mencari seekor kucing dalam gambar, pendekatan naif untuk penges 2. Jalankan klasifikasi imej pada setiap jubin. 3. Jubin yang menghasilkan pengaktifan yang cukup tinggi boleh dianggap mengandungi objek yang dicari. -![Pengesanan Objek Naif](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ms.png) +![Pengesanan Objek Naif](../../../../../translated_images/ms/naive-detection.e7f1ba220ccd08c6.png) > *Imej dari [Buku Latihan](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Anda mungkin akan menemui dataset berikut untuk tugas ini: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 kelas * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 kelas, kotak sempadan dan topeng segmentasi -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ms.jpg) +![COCO](../../../../../translated_images/ms/coco-examples.71bc60380fa6cceb.jpg) ## Metrik Pengesanan Objek @@ -50,7 +50,7 @@ Anda mungkin akan menemui dataset berikut untuk tugas ini: Untuk klasifikasi imej, mudah untuk mengukur sejauh mana algoritma berfungsi, tetapi untuk pengesanan objek kita perlu mengukur kedua-dua ketepatan kelas dan ketepatan lokasi kotak sempadan yang diramalkan. Untuk yang terakhir, kita menggunakan **Intersection over Union** (IoU), yang mengukur sejauh mana dua kotak (atau dua kawasan arbitrari) bertindih. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ms.png) +![IoU](../../../../../translated_images/ms/iou_equation.9a4751d40fff4e11.png) > *Rajah 2 dari [blog post yang sangat baik tentang IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Terdapat dua kelas utama algoritma pengesanan objek: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) menggunakan [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) untuk menghasilkan struktur hierarki kawasan ROI, yang kemudian dilalui oleh pengekstrak ciri CNN dan pengklasifikasi SVM untuk menentukan kelas objek, dan regresi linear untuk menentukan koordinat *kotak sempadan*. [Kertas Rasmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ms.png) +![RCNN](../../../../../translated_images/ms/rcnn1.cae407020dfb1d1f.png) > *Imej dari van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ms.png) +![RCNN-1](../../../../../translated_images/ms/rcnn2.2d9530bb83516484.png) > *Imej dari [blog ini](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Terdapat dua kelas utama algoritma pengesanan objek: Pendekatan ini serupa dengan R-CNN, tetapi kawasan ditentukan selepas lapisan konvolusi diterapkan. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ms.png) +![FRCNN](../../../../../translated_images/ms/f-rcnn.3cda6d9bb4188875.png) > Imej dari [Kertas Rasmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Pendekatan ini serupa dengan R-CNN, tetapi kawasan ditentukan selepas lapisan ko Idea utama pendekatan ini adalah menggunakan rangkaian neural untuk meramalkan ROI - yang dipanggil *Region Proposal Network*. [Kertas](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ms.png) +![FasterRCNN](../../../../../translated_images/ms/faster-rcnn.8d46c099b87ef30a.png) > Imej dari [kertas rasmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algoritma ini lebih pantas daripada Faster R-CNN. Idea utama adalah seperti beri 1. Ciri-ciri diproses oleh **Position-Sensitive Score Map**. Setiap objek dari $C$ kelas dibahagikan kepada $k\times k$ kawasan, dan kita melatih untuk meramalkan bahagian objek. 1. Untuk setiap bahagian dari $k\times k$ kawasan, semua rangkaian mengundi untuk kelas objek, dan kelas objek dengan undian maksimum dipilih. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ms.png) +![r-fcn image](../../../../../translated_images/ms/r-fcn.13eb88158b99a3da.png) > Imej dari [kertas rasmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO adalah algoritma satu-laluan masa nyata. Idea utama adalah seperti berikut: * Imej dibahagikan kepada $S\times S$ kawasan. * Untuk setiap kawasan, **CNN** meramalkan $n$ objek yang mungkin, koordinat *kotak sempadan* dan *confidence*=*probability* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ms.png) + ![YOLO](../../../../../translated_images/ms/yolo.a2648ec82ee8bb4e.png) > Imej dari [kertas rasmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/ms/lessons/4-ComputerVision/README.md b/translations/ms/lessons/4-ComputerVision/README.md index 772ddd02..dda84866 100644 --- a/translations/ms/lessons/4-ComputerVision/README.md +++ b/translations/ms/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Penglihatan Komputer -![Ringkasan kandungan Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ms.png) +![Ringkasan kandungan Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ms/ai-computervision.6506ebebac3fbf76.png) Dalam bahagian ini, kita akan mempelajari tentang: diff --git a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index affbbb6a..985a2ea3 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) adalah representasi vektor yang paling biasa digunakan dalam kaedah tradisional. Setiap perkataan dikaitkan dengan indeks vektor, dan elemen vektor mengandungi bilangan kemunculan sesuatu perkataan dalam dokumen tertentu.\n", "\n", - "![Imej menunjukkan bagaimana representasi vektor bag of words diwakili dalam memori.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ms.png) \n", + "![Imej menunjukkan bagaimana representasi vektor bag of words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Anda juga boleh menganggap BoW sebagai jumlah semua vektor satu-hot-encoded untuk setiap perkataan individu dalam teks.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 06aaddf2..7319a400 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ms/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) adalah representasi vektor tradisional yang paling mudah difahami. Setiap perkataan dihubungkan dengan indeks vektor, dan elemen vektor mengandungi bilangan kemunculan setiap perkataan dalam dokumen tertentu.\n", "\n", - "![Imej menunjukkan bagaimana representasi vektor bag-of-words diwakili dalam memori.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ms.png) \n", + "![Imej menunjukkan bagaimana representasi vektor bag-of-words diwakili dalam memori.](../../../../../translated_images/ms/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Anda juga boleh menganggap BoW sebagai jumlah semua vektor satu-hot-encoded untuk setiap perkataan dalam teks.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index feae99f3..060e1c79 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam rangkaian kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada embedding yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua embedding tersebut, seperti `sum`, `average` atau `max`.\n", "\n", - "![Imej menunjukkan pengklasifikasi embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ms.png)\n", + "![Imej menunjukkan pengklasifikasi embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Rangkaian neural pengklasifikasi kita akan bermula dengan lapisan embedding, kemudian lapisan agregasi, dan pengklasifikasi linear di atasnya:\n" ] @@ -176,7 +176,7 @@ "\n", "Dalam seni bina sebelumnya, kami perlu menambah semua jujukan kepada panjang yang sama untuk dimuatkan ke dalam minibatch. Ini bukan cara yang paling efisien untuk mewakili jujukan panjang berubah - pendekatan lain adalah menggunakan vektor **offset**, yang akan menyimpan offset semua jujukan yang disimpan dalam satu vektor besar.\n", "\n", - "![Imej menunjukkan representasi jujukan offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ms.png)\n", + "![Imej menunjukkan representasi jujukan offset](../../../../../translated_images/ms/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Nota**: Dalam gambar di atas, kami menunjukkan jujukan watak, tetapi dalam contoh kami, kami bekerja dengan jujukan perkataan. Walau bagaimanapun, prinsip umum mewakili jujukan dengan vektor offset tetap sama.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW lebih pantas, manakala skip-gram lebih perlahan, tetapi lebih baik dalam mewakili perkataan yang jarang digunakan.\n", "\n", - "![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ms.png)\n", + "![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Untuk mencuba embedding word2vec yang telah dilatih terlebih dahulu pada dataset Google News, kita boleh menggunakan perpustakaan **gensim**. Di bawah ini, kita mencari perkataan yang paling serupa dengan 'neural'\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 7c5f6955..f8e360ea 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ms/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Dengan menggunakan lapisan embedding sebagai lapisan pertama dalam rangkaian kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada embedding yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua embedding tersebut, seperti `sum`, `average` atau `max`.\n", "\n", - "![Imej menunjukkan pengelasan embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ms.png)\n", + "![Imej menunjukkan pengelasan embedding untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Rangkaian neural pengelasan kita terdiri daripada lapisan-lapisan berikut:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW lebih pantas, manakala skip-gram lebih perlahan tetapi lebih baik dalam mewakili perkataan yang jarang digunakan.\n", "\n", - "![Imej menunjukkan algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ms.png)\n", + "![Imej menunjukkan algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Untuk mencuba embedding Word2Vec yang telah dilatih terlebih dahulu pada dataset Google News, kita boleh menggunakan pustaka **gensim**. Di bawah ini, kita mencari perkataan yang paling serupa dengan 'neural'.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/README.md b/translations/ms/lessons/5-NLP/14-Embeddings/README.md index c8746a45..d1a851de 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ms/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Jadi, lapisan pembenaman akan mengambil perkataan sebagai input, dan menghasilka Dengan menggunakan lapisan pembenaman sebagai lapisan pertama dalam rangkaian pengklasifikasi kita, kita boleh beralih daripada model bag-of-words kepada model **embedding bag**, di mana kita mula-mula menukar setiap perkataan dalam teks kita kepada pembenaman yang sepadan, dan kemudian mengira beberapa fungsi agregat ke atas semua pembenaman tersebut, seperti `sum`, `average` atau `max`. -![Imej menunjukkan pengklasifikasi pembenaman untuk lima perkataan dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ms.png) +![Imej menunjukkan pengklasifikasi pembenaman untuk lima perkataan dalam urutan.](../../../../../translated_images/ms/embedding-classifier-example.b77f021a7ee67eee.png) > Imej oleh penulis @@ -40,7 +40,7 @@ Untuk mencapai itu, kita perlu melatih model pembenaman kita terlebih dahulu pad CBoW lebih pantas, manakala skip-gram lebih perlahan tetapi lebih baik dalam mewakili perkataan yang jarang digunakan. -![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ms.png) +![Imej menunjukkan kedua-dua algoritma CBoW dan Skip-Gram untuk menukar perkataan kepada vektor.](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imej daripada [kertas ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md index 220cd07b..935e4134 100644 --- a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dalam contoh sebelumnya, kita menggunakan pemerangkapan semantik yang telah dila * **Continuous Bag-of-Words** (CBoW), di mana kita meramalkan token tengah $W_0$ dalam urutan token $W_{-N}$, ..., $W_N$. * **Skip-gram**, di mana kita meramalkan satu set token berdekatan {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} daripada token tengah $W_0$. -![imej daripada kertas kerja tentang menukar perkataan kepada vektor](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ms.png) +![imej daripada kertas kerja tentang menukar perkataan kepada vektor](../../../../../translated_images/ms/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imej daripada [kertas kerja ini](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ms/lessons/5-NLP/16-RNN/README.md b/translations/ms/lessons/5-NLP/16-RNN/README.md index c6d96dcf..f03a0199 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/README.md +++ b/translations/ms/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Dalam bahagian sebelum ini, kita telah menggunakan representasi semantik teks ya Untuk menangkap makna urutan teks, kita perlu menggunakan seni bina rangkaian neural lain, yang dipanggil **rangkaian neural berulang**, atau RNN. Dalam RNN, kita lalui ayat kita melalui rangkaian satu simbol pada satu masa, dan rangkaian menghasilkan beberapa **keadaan**, yang kemudian kita lalui semula ke rangkaian bersama simbol seterusnya. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ms.png) +![RNN](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.png) > Gambar oleh penulis @@ -61,7 +61,7 @@ Kita telah membincangkan rangkaian berulang yang beroperasi dalam satu arah, dar Rangkaian berulang, sama ada satu arah atau dua arah, menangkap corak tertentu dalam urutan, dan boleh menyimpannya ke dalam vektor keadaan atau menghantarnya ke output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain di atas yang pertama untuk menangkap corak tahap lebih tinggi dan membina daripada corak tahap rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis** yang terdiri daripada dua atau lebih rangkaian berulang, di mana output lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input. -![Gambar menunjukkan RNN LSTM berlapis pelbagai](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ms.jpg) +![Gambar menunjukkan RNN LSTM berlapis pelbagai](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López* diff --git a/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 01858787..053f5e25 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rangkaian berulang, sama ada satu hala atau dwihala, menangkap corak tertentu dalam urutan, dan boleh menyimpannya ke dalam vektor keadaan atau menghantarnya ke output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain di atas lapisan pertama untuk menangkap corak tahap lebih tinggi, yang dibina daripada corak tahap rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis**, yang terdiri daripada dua atau lebih rangkaian berulang, di mana output daripada lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input.\n", "\n", - "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ms.jpg)\n", + "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López*\n", "\n", diff --git a/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb index 99cd5402..f040a620 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ms/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Untuk menangkap makna urutan teks, kita akan menggunakan seni bina rangkaian neural yang dipanggil **rangkaian neural berulang**, atau RNN. Apabila menggunakan RNN, kita menghantar ayat kita melalui rangkaian satu token pada satu masa, dan rangkaian menghasilkan beberapa **keadaan**, yang kemudian kita hantar semula ke rangkaian bersama token seterusnya.\n", "\n", - "![Imej menunjukkan contoh penjanaan rangkaian neural berulang.](../../../../../translated_images/rnn.27f5c29c53d727b5.ms.png)\n", + "![Imej menunjukkan contoh penjanaan rangkaian neural berulang.](../../../../../translated_images/ms/rnn.27f5c29c53d727b5.png)\n", "\n", "Diberikan urutan input token $X_0,\\dots,X_n$, RNN mencipta urutan blok rangkaian neural, dan melatih urutan ini secara hujung ke hujung menggunakan backpropagation. Setiap blok rangkaian mengambil pasangan $(X_i,S_i)$ sebagai input, dan menghasilkan $S_{i+1}$ sebagai hasil. Keadaan akhir $S_n$ atau output $Y_n$ dimasukkan ke dalam pengelas linear untuk menghasilkan keputusan. Semua blok rangkaian berkongsi berat yang sama, dan dilatih secara hujung ke hujung menggunakan satu laluan backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rangkaian berulang, sama ada sehala atau dwihala, menangkap corak dalam urutan, dan menyimpannya ke dalam vektor keadaan atau mengembalikannya sebagai output. Seperti rangkaian konvolusi, kita boleh membina lapisan berulang lain selepas yang pertama untuk menangkap corak tahap lebih tinggi, yang dibina daripada corak tahap lebih rendah yang diekstrak oleh lapisan pertama. Ini membawa kita kepada konsep **RNN berlapis**, yang terdiri daripada dua atau lebih rangkaian berulang, di mana output lapisan sebelumnya dihantar ke lapisan seterusnya sebagai input.\n", "\n", - "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ms.jpg)\n", + "![Imej menunjukkan RNN LSTM berlapis panjang-pendek](../../../../../translated_images/ms/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Gambar daripada [post yang hebat ini](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) oleh Fernando López.*\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index f0d03cde..92fa43ab 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cara kita akan melatih RNN untuk menghasilkan teks adalah seperti berikut. Pada setiap langkah, kita akan mengambil satu urutan watak dengan panjang `nchars`, dan meminta rangkaian untuk menghasilkan watak output seterusnya bagi setiap watak input:\n", "\n", - "![Imej menunjukkan contoh RNN menghasilkan perkataan 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ms.png)\n", + "![Imej menunjukkan contoh RNN menghasilkan perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Bergantung kepada senario sebenar, kita mungkin juga ingin memasukkan beberapa watak khas, seperti *end-of-sequence* ``. Dalam kes kita, kita hanya ingin melatih rangkaian untuk menghasilkan teks tanpa henti, oleh itu kita akan menetapkan saiz setiap urutan sama dengan token `nchars`. Akibatnya, setiap contoh latihan akan terdiri daripada `nchars` input dan `nchars` output (iaitu urutan input yang dialihkan satu simbol ke kiri). Minibatch akan terdiri daripada beberapa urutan seperti ini.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 43dd94e7..6d860091 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Cara kita akan melatih RNN untuk menjana tajuk berita adalah seperti berikut. Pada setiap langkah, kita akan mengambil satu tajuk, yang akan dimasukkan ke dalam RNN, dan untuk setiap aksara input, kita akan meminta rangkaian untuk menjana aksara output seterusnya:\n", "\n", - "![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ms.png)\n", + "![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Untuk aksara terakhir dalam urutan kita, kita akan meminta rangkaian untuk menjana token ``.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md index 0382f7b5..6049d046 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Dalam seni bina RNN yang kita bincangkan dalam unit sebelumnya, setiap unit RNN Ini membolehkan pelbagai seni bina neural yang ditunjukkan dalam gambar di bawah: -![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ms.jpg) +![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Imej daripada blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Dalam unit ini, kita akan memberi tumpuan kepada model generatif mudah yang memb Kita akan melatih RNN ini untuk menjana teks langkah demi langkah. Pada setiap langkah, kita akan mengambil urutan aksara sepanjang `nchars`, dan meminta rangkaian untuk menghasilkan aksara output seterusnya untuk setiap aksara input: -![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ms.png) +![Imej menunjukkan contoh penjanaan RNN untuk perkataan 'HELLO'.](../../../../../translated_images/ms/rnn-generate.56c54afb52f9781d.png) Semasa menjana teks (semasa inferens), kita bermula dengan beberapa **prompt**, yang dilalui melalui sel RNN untuk menghasilkan keadaan perantaraannya, dan kemudian daripada keadaan ini penjanaan bermula. Kita menjana satu aksara pada satu masa, dan menghantar keadaan dan aksara yang dijana kepada sel RNN lain untuk menjana aksara seterusnya, sehingga kita menjana aksara yang mencukupi. diff --git a/translations/ms/lessons/5-NLP/18-Transformers/README.md b/translations/ms/lessons/5-NLP/18-Transformers/README.md index 428e2d88..8d0b2a6d 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Dengan RNN, urutan-ke-urutan dilaksanakan oleh dua rangkaian berulang, di mana s **Mekanisme Perhatian** menyediakan cara untuk memberi berat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta pintasan antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output yt, kita akan mengambil kira semua keadaan tersembunyi input hi, dengan pekali berat yang berbeza αt,i. -![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ms.png) +![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png) > Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [blog post ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Berikut adalah contoh matriks sedemikian: -![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ms.png) +![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png) > Rajah daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3) @@ -66,7 +66,7 @@ Hasil yang kita peroleh dengan pemadanan kedudukan menggabungkan kedua-dua token Seterusnya, kita perlu menangkap beberapa corak dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian kendiri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai input dan output. Menerapkan perhatian kendiri membolehkan kita mengambil kira **konteks** dalam ayat, dan melihat perkataan mana yang saling berkaitan. Sebagai contoh, ia membolehkan kita melihat perkataan mana yang dirujuk oleh koreferensi, seperti *ia*, dan juga mengambil kira konteks: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ms.png) +![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.png) > Imej daripada [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Oleh kerana setiap kedudukan input dipetakan secara bebas ke setiap kedudukan ou **BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berlapis besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini mula-mula dilatih awal pada korpus teks yang besar (WikiPedia + buku) menggunakan latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam ayat). Semasa latihan awal, model menyerap tahap pemahaman bahasa yang ketara yang kemudiannya boleh dimanfaatkan dengan set data lain menggunakan penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ms.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Sumber imej [di sini](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md index db465ed5..6b3886a2 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Dengan RNN, urutan-ke-urutan diimplementasikan oleh dua jaringan berulang, di ma **Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor masukan terhadap setiap prediksi keluaran dari RNN. Cara ini diimplementasikan dengan membuat jalur pendek antara keadaan sementara dari RNN masukan dan RNN keluaran. Dengan cara ini, saat menghasilkan simbol keluaran yt, kita akan mempertimbangkan semua keadaan tersembunyi masukan hi, dengan koefisien bobot yang berbeda αt,i. -![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ms.png) +![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png) > Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [posting blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriks perhatian {αi,j} akan mewakili sejauh mana kata-kata masukan tertentu berperan dalam penghasilan kata tertentu dalam urutan keluaran. Di bawah ini adalah contoh matriks semacam itu: -![Gambar menunjukkan contoh keselarasan yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ms.png) +![Gambar menunjukkan contoh keselarasan yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png) > Gambar dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Hasil yang kita dapatkan dengan embedding posisi menggabungkan baik token asli m Selanjutnya, kita perlu menangkap beberapa pola dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian diri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai masukan dan keluaran. Menerapkan perhatian diri memungkinkan kita untuk mempertimbangkan **konteks** dalam kalimat, dan melihat kata-kata mana yang saling terkait. Misalnya, ini memungkinkan kita untuk melihat kata-kata mana yang dirujuk oleh ko-referensi, seperti *itu*, dan juga mempertimbangkan konteks: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ms.png) +![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.png) > Gambar dari [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Karena setiap posisi masukan dipetakan secara independen ke setiap posisi keluar **BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus data teks yang besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata-kata yang disembunyikan dalam kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan penyempurnaan. Proses ini disebut **transfer learning**. -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ms.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Gambar [sumber](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 0442ce74..e946704f 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ms/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberi pemberat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta pintasan antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output $y_t$, kita akan mengambil kira semua keadaan tersembunyi input $h_i$, dengan pekali pemberat yang berbeza $\\alpha_{t,i}$.\n", "\n", - "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ms.png)\n", + "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [catatan blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Di bawah adalah contoh matriks seperti itu:\n", "\n", - "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ms.png)\n", + "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Rajah diambil daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berlapis besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini mula-mula dilatih awal pada korpus teks yang besar (WikiPedia + buku) menggunakan latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam satu ayat). Semasa latihan awal, model menyerap tahap pemahaman bahasa yang signifikan yang kemudiannya boleh dimanfaatkan dengan set data lain menggunakan penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ms.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Terdapat banyak variasi seni bina Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3 dan banyak lagi yang boleh ditala halus. Pakej [HuggingFace](https://github.com/huggingface/) menyediakan repositori untuk melatih banyak seni bina ini dengan PyTorch.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 4d0df09e..a2e7fb30 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ms/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mekanisme Perhatian** menyediakan cara untuk memberi pemberat kepada kesan kontekstual setiap vektor input terhadap setiap ramalan output RNN. Cara ia dilaksanakan adalah dengan mencipta jalan pintas antara keadaan perantaraan RNN input dan RNN output. Dengan cara ini, apabila menghasilkan simbol output $y_t$, kita akan mengambil kira semua keadaan tersembunyi input $h_i$, dengan pekali pemberat yang berbeza $\\alpha_{t,i}$. \n", "\n", - "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ms.png)\n", + "![Imej menunjukkan model encoder/decoder dengan lapisan perhatian tambahan](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder dengan mekanisme perhatian tambahan dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dipetik daripada [catatan blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriks perhatian $\\{\\alpha_{i,j}\\}$ akan mewakili tahap di mana perkataan input tertentu memainkan peranan dalam penjanaan perkataan tertentu dalam urutan output. Di bawah adalah contoh matriks seperti itu:\n", "\n", - "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ms.png)\n", + "![Imej menunjukkan penjajaran sampel yang ditemui oleh RNNsearch-50, diambil daripada Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Rajah diambil daripada [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Rajah 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ialah rangkaian transformer berbilang lapisan yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 lapisan untuk *BERT-large*. Model ini mula-mula dilatih awal menggunakan korpus teks yang besar (WikiPedia + buku) melalui latihan tanpa pengawasan (meramalkan perkataan yang disembunyikan dalam ayat). Semasa latihan awal, model ini menyerap tahap pemahaman bahasa yang signifikan yang kemudiannya boleh dimanfaatkan dengan dataset lain melalui penalaan halus. Proses ini dipanggil **pembelajaran pemindahan**.\n", "\n", - "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ms.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Terdapat banyak variasi seni bina Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3 dan banyak lagi yang boleh ditala halus.\n", "\n", diff --git a/translations/ms/lessons/5-NLP/19-NER/README.md b/translations/ms/lessons/5-NLP/19-NER/README.md index a2b9d9f6..8cdfc24c 100644 --- a/translations/ms/lessons/5-NLP/19-NER/README.md +++ b/translations/ms/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Oleh kerana kita perlu membina korespondensi satu-ke-satu antara token dan kelas, kita boleh melatih model rangkaian neural **banyak-ke-banyak** paling kanan daripada gambar ini: -![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ms.jpg) +![Imej menunjukkan corak rangkaian neural berulang yang biasa.](../../../../../translated_images/ms/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Imej daripada [blog post ini](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) oleh [Andrej Karpathy](http://karpathy.github.io/). Model klasifikasi token NER sepadan dengan seni bina rangkaian paling kanan dalam gambar ini.* diff --git a/translations/ms/lessons/5-NLP/README.md b/translations/ms/lessons/5-NLP/README.md index c1076331..4e95b5f8 100644 --- a/translations/ms/lessons/5-NLP/README.md +++ b/translations/ms/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pemprosesan Bahasa Semula Jadi -![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ms.png) +![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ms/ai-nlp.b22dcb8ca4707cea.png) Dalam bahagian ini, kita akan memberi tumpuan kepada penggunaan Rangkaian Neural untuk menangani tugas-tugas berkaitan dengan **Pemprosesan Bahasa Semula Jadi (NLP)**. Terdapat banyak masalah NLP yang kita mahu komputer dapat selesaikan: diff --git a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md index 7dafad00..9a2bc9a1 100644 --- a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Anda boleh membuka salah satu model, contohnya **Biology → Flocking** Selepas membuka model, anda akan dibawa ke skrin utama NetLogo. Berikut adalah contoh model yang menerangkan populasi serigala dan kambing, dengan sumber yang terhad (rumput). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ms.png) +![NetLogo Main Screen](../../../../../translated_images/ms/NetLogo-Main.32653711ec1a01b3.png) > Tangkapan skrin oleh Dmitry Soshnikov diff --git a/translations/ms/lessons/README.md b/translations/ms/lessons/README.md index 8d966af3..c892436f 100644 --- a/translations/ms/lessons/README.md +++ b/translations/ms/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Gambaran Keseluruhan -![Gambaran Keseluruhan dalam bentuk lakaran](../../../translated_images/ai-overview.0857791951d19500.ms.png) +![Gambaran Keseluruhan dalam bentuk lakaran](../../../translated_images/ms/ai-overview.0857791951d19500.png) > Lakaran oleh [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md index 2a2b3649..72296480 100644 --- a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Selepas kejayaan model transformer dalam menyelesaikan tugas NLP, seni bina yang Idea utama CLIP adalah untuk membandingkan arahan teks dengan imej dan menentukan sejauh mana imej tersebut sesuai dengan arahan. -![Seni Bina CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ms.png) +![Seni Bina CLIP](../../../../../translated_images/ms/clip-arch.b3dbf20b4e8ed8be.png) > *Gambar dari [catatan blog ini](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Setelah model ini dilatih awal, kita boleh memberikannya sekumpulan imej dan sek Katakan kita perlu mengklasifikasikan imej antara, contohnya, kucing, anjing dan manusia. Dalam kes ini, kita boleh memberikan model imej, dan satu siri arahan teks: "*gambar seekor kucing*", "*gambar seekor anjing*", "*gambar seorang manusia*". Dalam vektor kebarangkalian yang dihasilkan, kita hanya perlu memilih indeks dengan nilai tertinggi. -![CLIP untuk Klasifikasi Imej](../../../../../translated_images/clip-class.3af42ef0b2b19369.ms.png) +![CLIP untuk Klasifikasi Imej](../../../../../translated_images/ms/clip-class.3af42ef0b2b19369.png) > *Gambar dari [catatan blog ini](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Ketahui lebih lanjut tentang VQGAN di laman web [Taming Transformers](https://co Salah satu perbezaan penting antara VQGAN dan GAN tradisional ialah yang terakhir boleh menghasilkan imej yang baik daripada sebarang vektor input, manakala VQGAN cenderung menghasilkan imej yang tidak koheren. Oleh itu, kita perlu membimbing proses penciptaan imej dengan lebih lanjut, dan itu boleh dilakukan menggunakan CLIP. -![Seni Bina VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.ms.png) +![Seni Bina VQGAN+CLIP](../../../../../translated_images/ms/vqgan.5027fe05051dfa31.png) Untuk menghasilkan imej yang sepadan dengan arahan teks, kita bermula dengan beberapa vektor pengekodan rawak yang dihantar melalui VQGAN untuk menghasilkan imej. Kemudian CLIP digunakan untuk menghasilkan fungsi kehilangan yang menunjukkan sejauh mana imej tersebut sesuai dengan arahan teks. Matlamatnya kemudian adalah untuk meminimumkan kehilangan ini, menggunakan propagasi balik untuk menyesuaikan parameter vektor input. Pustaka hebat yang melaksanakan VQGAN+CLIP ialah [Pixray](http://github.com/pixray/pixray). -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ms.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ms.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ms.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Gambar yang dihasilkan daripada arahan *potret closeup cat air guru lelaki muda dalam bidang kesusasteraan dengan sebuah buku* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru wanita muda dalam bidang sains komputer dengan sebuah komputer* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru lelaki tua dalam bidang matematik di hadapan papan hitam* @@ -75,7 +75,7 @@ Berbeza dengan CLIP, DALL-E menerima kedua-dua teks dan imej sebagai satu aliran Perbezaan utama antara DALL.E 1 dan 2 ialah ia menghasilkan imej dan seni yang lebih realistik. Contoh penjanaan imej dengan DALL-E: -![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ms.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ms.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ms.png) +![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/ms/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Gambar yang dihasilkan daripada arahan *potret closeup cat air guru lelaki muda dalam bidang kesusasteraan dengan sebuah buku* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru wanita muda dalam bidang sains komputer dengan sebuah komputer* | Gambar yang dihasilkan daripada arahan *potret closeup minyak guru lelaki tua dalam bidang matematik di hadapan papan hitam* diff --git a/translations/my/README.md b/translations/my/README.md index 0b75d637..37716775 100644 --- a/translations/my/README.md +++ b/translations/my/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # စက်မှုအသိပညာစနစ် အတွက် ရှေ့မီသူများ - သင်ရိုးညွှန်းတမ်း -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.my.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/my/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/my/lessons/1-Intro/README.md b/translations/my/lessons/1-Intro/README.md index 96ee69f1..55685708 100644 --- a/translations/my/lessons/1-Intro/README.md +++ b/translations/my/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI အကြောင်းအကျဉ်း -![AI အကြောင်းအကျဉ်းအကြောင်းအရာကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/ai-intro.bf28d1ac4235881c.my.png) +![AI အကြောင်းအကျဉ်းအကြောင်းအရာကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-intro.bf28d1ac4235881c.png) > [Tomomi Imura](https://twitter.com/girlie_mac) မှ Sketchnote @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: မူလကတော့ ကွန်ပျူတာတွေကို [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) က အလွန်သေချာတဲ့ နည်းလမ်းတစ်ခုဖြင့် နံပါတ်တွေကို လုပ်ဆောင်နိုင်ဖို့ တီထွင်ခဲ့တာပါ။ ယနေ့ခေတ်ကွန်ပျူတာတွေဟာ ၁၉ ရာစုက မူလပုံစံထက် အလွန်တိုးတက်လာပြီးသားဖြစ်ပေမယ့်လည်း ထိန်းချုပ်ထားတဲ့တွက်ချက်မှုတွေကို အခြေခံထားတဲ့ အယူအဆကိုပဲ ဆက်လက်လိုက်နာနေပါတယ်။ ဒါကြောင့် ရည်မှန်းချက်ကို ရရှိဖို့ လိုအပ်တဲ့ အဆင့်ဆင့်လုပ်ဆောင်မှုတွေကို သိထားရင် ကွန်ပျူတာကို အစီအစဉ်ရေးသားပြီး လုပ်ဆောင်နိုင်ပါတယ်။ -![လူတစ်ဦး၏ ဓာတ်ပုံ](../../../../translated_images/dsh_age.d212a30d4e54fb5f.my.png) +![လူတစ်ဦး၏ ဓာတ်ပုံ](../../../../translated_images/my/dsh_age.d212a30d4e54fb5f.png) > [Vickie Soshnikova](http://twitter.com/vickievalerie) မှ ဓာတ်ပုံ @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** ဆိုတဲ့ စကားလုံးကို သုံးတဲ့အခါမှာ အဓိပ္ပါယ်ရှင်းလင်းမှုမရှိတာက ပြဿနာတစ်ခုဖြစ်ပါတယ်။ ဉာဏ်ရည်ဆိုတာ **အထွေထွေစဉ်းစားနိုင်စွမ်း** သို့မဟုတ် **ကိုယ့်ကိုယ်ကိုသိမြင်မှု** နဲ့ ဆက်စပ်နေတယ်လို့ ဆိုနိုင်ပေမယ့် အတိအကျ သတ်မှတ်လို့မရပါဘူး။ -![ကြောင်တစ်ကောင်ရဲ့ ဓာတ်ပုံ](../../../../translated_images/photo-cat.8c8e8fb760ffe457.my.jpg) +![ကြောင်တစ်ကောင်ရဲ့ ဓာတ်ပုံ](../../../../translated_images/my/photo-cat.8c8e8fb760ffe457.jpg) > [Amber Kipp](https://unsplash.com/@sadmax) မှ [ဓာတ်ပုံ](https://unsplash.com/photos/75715CVEJhI) (Unsplash မှ) diff --git a/translations/my/lessons/2-Symbolic/Animals.ipynb b/translations/my/lessons/2-Symbolic/Animals.ipynb index 4e5656a9..d1b307a2 100644 --- a/translations/my/lessons/2-Symbolic/Animals.ipynb +++ b/translations/my/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ဒီနမူနာမှာတော့ ရုပ်ပိုင်းဆိုင်ရာ အင်္ဂါရပ်အချို့အပေါ် မူတည်ပြီး တိရစ္ဆာန်ကို သတ်မှတ်နိုင်ရန် ရိုးရှင်းတဲ့ အသိပညာအခြေပြု စနစ်တစ်ခုကို တည်ဆောက်သွားမှာ ဖြစ်ပါတယ်။ ဒီစနစ်ကို အောက်ပါ AND-OR သစ်ပင်ဖြင့် ကိုယ်စားပြုနိုင်ပါတယ် (ဒီဟာက သစ်ပင်တစ်ခုလုံးရဲ့ အစိတ်အပိုင်းတစ်ခုသာဖြစ်ပြီး နောက်ထပ် စည်းမျဉ်းများကို လွယ်ကူစွာ ထပ်ထည့်နိုင်ပါတယ်)။\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.my.png)\n" + "![](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/my/lessons/2-Symbolic/README.md b/translations/my/lessons/2-Symbolic/README.md index 688bc568..ba206b37 100644 --- a/translations/my/lessons/2-Symbolic/README.md +++ b/translations/my/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Knowledge Representation and Expert Systems -![Summary of Symbolic AI content](../../../../translated_images/ai-symbolic.715a30cb610411a6.my.png) +![Summary of Symbolic AI content](../../../../translated_images/my/ai-symbolic.715a30cb610411a6.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Symbolic AI ရဲ့ အရေးကြီးတဲ့အယူအဆတစ် ဒါကြောင့် **အသိပညာကိုဖော်ပြခြင်း** ဆိုတာ စက်ထဲမှာ ဒေတာအဖြစ် အသိပညာကို ထိရောက်စွာ ဖော်ပြနိုင်တဲ့ နည်းလမ်းတစ်ခုကို ရှာဖွေဖို့ ပြဿနာဖြစ်ပါတယ်၊ အလိုအလျောက် အသုံးပြုနိုင်စေဖို့ပါ။ ဒါကို အောက်ပါအတိုင်း စက်ရုပ်နည်းလမ်းအဖြစ် မြင်နိုင်ပါတယ်- -![Knowledge representation spectrum](../../../../translated_images/knowledge-spectrum.b60df631852c0217.my.png) +![Knowledge representation spectrum](../../../../translated_images/my/knowledge-spectrum.b60df631852c0217.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Symbolic AI ရဲ့ အစောပိုင်းအောင်မြင်မှုတွေထဲမှာ **expert systems** လို့ခေါ်တဲ့ စနစ်တွေ ပါဝင်ပါတယ်။ ဒီစနစ်တွေက အကန့်အသတ်ရှိတဲ့ ပြဿနာဒေသတစ်ခုမှာ ကျွမ်းကျင်သူတစ်ဦးအဖြစ် လုပ်ဆောင်ဖို့ ဖန်တီးထားတဲ့ computer systems တွေဖြစ်ပါတယ်။ ဒီစနစ်တွေမှာ **knowledge base** ကို လူသားကျွမ်းကျင်သူတစ်ဦး သို့မဟုတ် အများအပြားကနေ ထုတ်ယူပြီး **inference engine** ကို အသုံးပြုပြီး reasoning လုပ်ပါတယ်။ -![Human Architecture](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.my.png) | ![Knowledge-Based System](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.my.png) +![Human Architecture](../../../../translated_images/my/arch-human.5d4d35f1bba3ab1c.png) | ![Knowledge-Based System](../../../../translated_images/my/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ လူသားရဲ့ ဦးနှောက်စနစ်ရဲ့ ရိုးရှင်းတဲ့ဖွဲ့စည်းမှု | အသိပညာအခြေခံစနစ်ရဲ့ ဖွဲ့စည်းမှု @@ -106,7 +106,7 @@ Expert systems တွေကို လူသားရဲ့ reasoning စနစ ဥပမာအားဖြင့် အောက်ပါ expert system ကို သက်ရှိတစ်ခုရဲ့ ရုပ်ပိုင်းဆိုင်ရာလက္ခဏာအပေါ် အခြေခံပြီး သတ်မှတ်ခြင်းလုပ်ငန်းစဉ်အဖြစ် တွေးဆနိုင်ပါတယ်- -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.my.png) +![AND-OR Tree](../../../../translated_images/my/AND-OR-Tree.5592d2c70187f283.png) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 2ec30b5c..44648bf1 100644 --- a/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/my/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -489,7 +489,7 @@ "\n", "အကယ်၍ class ၂ ခုထက်ပိုရှိခဲ့ရင် softmax က class အားလုံးအတွက် probability တွေကို normalize လုပ်ပေးပါမယ်။ ဒီမှာ MNIST digit classification လုပ်ဆောင်တဲ့ network architecture ရဲ့ diagram ကိုဖော်ပြထားပါတယ်:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.my.png)\n" + "![MNIST Classifier](../../../../../translated_images/my/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1265,7 +1265,7 @@ "* သင်ကြားမှုအရှုံး (training loss) နည်းတယ် - မော်ဒယ်မှာ အစွမ်းထက်တဲ့ ဖော်ပြနိုင်စွမ်းရှိလို့ သင်ကြားမှုဒေတာကို မှန်ကန်စွာ ခန့်မှန်းနိုင်တယ်။\n", "* Validation loss က training loss ထက် ပိုမြင့်တတ်တယ်၊ သင်ကြားမှုအတွင်းမှာတောင် မြင့်တက်လာတတ်တယ် - ဒီအခြေအနေက မော်ဒယ်က သင်ကြားမှုဒေတာကို \"မှတ်မိ\" သွားပြီး \"အထွေထွေသဘော\" ကို ပျောက်ဆုံးသွားတာကြောင့် ဖြစ်တယ်။\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.my.png)\n", + "![Overfitting](../../../../../translated_images/my/overfit.a0bd57f717c15769.png)\n", "\n", "> ဒီပုံမှာ `x` က သင်ကြားမှုဒေတာကို ဆိုလိုတာဖြစ်ပြီး၊ `o` က စစ်ဆေးမှုဒေတာ (validation data) ကို ဆိုလိုတယ်။ ဘယ်ဘက်မှာ - linear မော်ဒယ် (တစ်လွှာမော်ဒယ်) က ဒေတာရဲ့ သဘာဝကို ကောင်းမွန်စွာ ခန့်မှန်းနိုင်တယ်။ ညာဘက်မှာ - overfitted မော်ဒယ်က သင်ကြားမှုဒေတာကို ပြည့်စုံစွာ ခန့်မှန်းနိုင်ပေမယ့် အခြားဒေတာတွေနဲ့တော့ အဓိပ္ပာယ်မရှိတော့ဘူး (validation error အလွန်မြင့်တက်တယ်)။\n" ] diff --git a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md index dd977230..46e90b7c 100644 --- a/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting သည် machine learning တွင် အလွန်အရေး အောက်ပါ 5 dots (graph ပေါ်တွင် `x` ဖြင့် ဖော်ပြထားသည်) ကို approximation လုပ်ရန် ပြဿနာကို စဉ်းစားပါ- -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.my.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.my.jpg) +![linear](../../../../../translated_images/my/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/my/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linear model, 2 parameters** | **Non-linear model, 7 parameters** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Model ၏ richness (parameter အရေအတွက်) နှင့် training အထက်ပါ graph မှာမြင်နိုင်သည့်အတိုင်း overfitting ကို training error အလွန်နည်းပြီး validation error အလွန်မြင့်ခြင်းဖြင့် ရှာဖွေနိုင်သည်။ Training အတွင်း training error နှင့် validation error နှစ်ခုစလုံး လျော့နည်းလာပြီး validation error သည် တစ်ချိန်တွင် လျော့နည်းမှုရပ်ပြီး မြင့်တက်လာနိုင်သည်။ ဤအချိန်သည် overfitting ဖြစ်နေသည်ဟု သက်သေပြသည်။ Training ကို ရပ်တန့်ရန် သို့မဟုတ် model ၏ snapshot ကို သိမ်းဆည်းရန် အချိန်ဖြစ်သည်။ -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.my.png) +![overfitting](../../../../../translated_images/my/Overfitting.408ad91cd90b4371.png) ## How to prevent overfitting diff --git a/translations/my/lessons/3-NeuralNetworks/README.md b/translations/my/lessons/3-NeuralNetworks/README.md index d1f0bde6..5c4b6fe5 100644 --- a/translations/my/lessons/3-NeuralNetworks/README.md +++ b/translations/my/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ် -![နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.my.png) +![နယူးရယ်နက်ဝါ့ခ်များအကြောင်း အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-neuralnetworks.1c687ae40bc86e83.png) ကျွန်ုပ်တို့ အကျဉ်းချုပ်တွင် ဆွေးနွေးခဲ့သည့်အတိုင်း၊ ဉာဏ်ရည်ကို ရရှိစေရန် နည်းလမ်းတစ်ခုမှာ **ကွန်ပျူတာမော်ဒယ်** သို့မဟုတ် **အတုဉာဏ်ရည်** တစ်ခုကို လေ့ကျင့်ခြင်းဖြစ်သည်။ ၂၀ ရာစု အလယ်ပိုင်းမှစ၍ သုတေသနပြုသူများသည် သင်္ချာမော်ဒယ်အမျိုးမျိုးကို စမ်းသပ်ခဲ့ကြပြီး၊ မကြာသေးမီနှစ်များတွင် ဤလမ်းကြောင်းသည် အလွန်အောင်မြင်ကြောင်း သက်သေပြခဲ့သည်။ ဉာဏ်ရည်၏ သင်္ချာမော်ဒယ်များကို **နယူးရယ်နက်ဝါ့ခ်များ** ဟု ခေါ်သည်။ @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ဇီဝဗေဒမှ ကျွန်ုပ်တို့သိရှိထားသည်မှာ၊ ကျွန်ုပ်တို့၏ ဦးနှောက်သည် နယူးရွန်ဆဲလ်များ (neurons) ဖြင့် ဖွဲ့စည်းထားပြီး၊ ၎င်းတို့တွင် "ထည့်သွင်းမှုများ" (dendrites) အများအပြားနှင့် "ထွက်ရှိမှု" (axon) တစ်ခုရှိသည်။ Dendrites နှင့် Axons နှစ်ခုစလုံးသည် လျှပ်စစ်သံကို သယ်ဆောင်နိုင်ပြီး၊ ၎င်းတို့အကြားရှိ ဆက်သွယ်မှုများ — synapses ဟုခေါ်သည် — သည် လျှပ်စစ်သံသယ်ဆောင်နိုင်စွမ်းအမျိုးမျိုးကို ပြသနိုင်ပြီး၊ ၎င်းတို့ကို neurotransmitters များက ထိန်းညှိသည်။ -![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.my.jpg) | ![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/artneuron.1a5daa88d20ebe6f.my.png) +![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![နယူးရွန်၏ မော်ဒယ်](../../../../translated_images/my/artneuron.1a5daa88d20ebe6f.png) ----|---- အမှန်တကယ် နယူးရွန် *([ပုံ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipedia မှ)* | အတုနယူးရွန် *(ရေးသားသူမှ ပုံဆွဲသည်)* ထို့ကြောင့်၊ နယူးရွန်၏ အလွယ်ဆုံး သင်္ချာမော်ဒယ်တွင် ထည့်သွင်းမှုများ X1, ..., XN နှင့် ထွက်ရှိမှု Y တို့နှင့် အလေးချိန်များ W1, ..., WN တို့ပါဝင်သည်။ ထွက်ရှိမှုကို အောက်ပါအတိုင်းတွက်ချက်သည်- -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ဤတွင် f သည် အချို့သော မလိုက်လျောသော **activation function** ဖြစ်သည်။ diff --git a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md index 8cbd2743..c7e58ee8 100644 --- a/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/my/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ကို အသုံးပြုပြီး ဗီဒီယို fram * **Braille စာအုပ်ရဲ့ ဓာတ်ပုံကို Pre-processing လုပ်ခြင်း**။ thresholding, feature detection, perspective transformation နဲ့ NumPy ကို ကိုင်တွယ်ခြင်းတို့ကို အသုံးပြုပြီး Braille အက္ခရာတစ်ခုချင်းစီကို neural network နဲ့ classification လုပ်ဖို့ ခွဲထုတ်ပေးနိုင်ပါတယ်။ -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.my.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.my.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.my.png) +![Braille Image](../../../../../translated_images/my/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/my/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/my/braille-symbols.0159185ab69d5339.png) ----|-----|----- > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် * **Frame difference ကို အသုံးပြုပြီး ဗီဒီယိုထဲမှာ လှုပ်ရှားမှုကို ရှာဖွေခြင်း**။ ကင်မရာက တည်နေရာမှာရှိရင် ကင်မရာ feed ရဲ့ frame တွေဟာ တူညီနေတတ်ပါတယ်။ Frame တွေကို array အနေနဲ့ ကိုယ်စားပြုထားတဲ့အတွက် frame 2 ခုကို လျော့ချက်လုပ်လိုက်ရင် pixel difference ကို ရရှိမှာဖြစ်ပြီး static frame တွေမှာ pixel difference နည်းနည်းရှိပြီး ပုံရိပ်ထဲမှာ လှုပ်ရှားမှုများလာတဲ့အခါ pixel difference ပိုများလာတတ်ပါတယ်။ -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.my.png) +![Image of video frames and frame differences](../../../../../translated_images/my/frame-difference.706f805491a0883c.png) > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် @@ -91,7 +91,7 @@ OpenCV ကို အသုံးပြုပြီး ဗီဒီယို fram - **Dense Optical Flow** က pixel တစ်ခုချင်းစီ ဘယ်နေရာကိုရွေ့လျားနေတယ်ဆိုတာကို ပြသတဲ့ vector field ကို တွက်ချက်ပေးပါတယ်။ - **Sparse Optical Flow** က ပုံရိပ်ထဲမှာ အထူးသတ်မှတ်ချက်တွေ (ဥပမာ - အနားသတ်) ကို ရွေးပြီး frame-to-frame trajectory ကို တည်ဆောက်ပေးပါတယ်။ -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.my.png) +![Image of Optical Flow](../../../../../translated_images/my/optical.1f4a94464579a83a.png) > [OpenCV.ipynb](OpenCV.ipynb) မှ ပုံရိပ် diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 553cff53..9c26ba71 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 သည် 2014 ခုနှစ်တွင် ImageNet top-5 classification တွင် 92.7% တိကျမှုရရှိခဲ့သော network တစ်ခုဖြစ်သည်။ ၎င်းတွင် အောက်ပါ layer အဆောက်အအုံပါရှိသည်- -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.my.jpg) +![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.jpg) VGG သည် convolution-pooling layers များ၏ အစဉ်အတိုင်း pyramid architecture ကို လိုက်နာသည်ကို သင်တွေ့နိုင်ပါသည်။ -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.my.jpg) +![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) မှရရှိသော ပုံ diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b81783d2..8044639f 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -258,7 +258,7 @@ "\n", "ထို့ကြောင့်၊ ပုံမှန် CNN တစ်ခုတွင် convolutional layer အတော်များများ ရှိပြီး၊ ၎င်းတို့အကြား dimension ကို လျှော့ချရန် pooling layer များပါဝင်လေ့ရှိသည်။ ထို့အပြင်၊ pattern များ ပိုမိုရှုပ်ထွေးလာသည့်အခါ - ရှာဖွေရမည့် စိတ်ဝင်စားဖွယ် ပုံစံပေါင်းစပ်မှုများ ပိုမိုများလာသည့်အတွက် filter များ၏ အရေအတွက်ကိုလည်း တိုးမြှင့်လေ့ရှိသည်။\n", "\n", - "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.my.png)\n", + "![An image showing several convolutional layers with pooling layers.](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Spatial dimensions ကို လျှော့ချခြင်းနှင့် feature/filter dimensions ကို တိုးမြှင့်ခြင်းကြောင့်၊ ဒီ architecture ကို **pyramid architecture** ဟုလည်း ခေါ်ဆိုကြသည်။\n" ] diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index e2e335c5..7c6be664 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -362,7 +362,7 @@ "\n", "ထို့ကြောင့်၊ အခြားသော CNN architecture များတွင် convolutional layer များစွာရှိပြီး၊ pooling layer များကို အကြားတွင် ထည့်သွင်းထားသည်။ ထိုကဲ့သို့ dimension များကို လျှော့ချခြင်းနှင့် filter များ၏ အရေအတွက်ကို တိုးမြှင့်ခြင်းဖြင့် pattern များသည် ပိုမိုအဆင့်မြင့်လာသည်နှင့်အမျှ - ရှာဖွေရမည့် combination များ ပိုမိုများလာသည်။\n", "\n", - "![Convolutional layer များနှင့် pooling layer များပါဝင်သော ပုံတစ်ပုံကို ပြသထားသည်။](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.my.png)\n", + "![Convolutional layer များနှင့် pooling layer များပါဝင်သော ပုံတစ်ပုံကို ပြသထားသည်။](../../../../../translated_images/my/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Spatial dimension များကို လျှော့ချခြင်းနှင့် feature/filter dimension များကို တိုးမြှင့်ခြင်းကြောင့်၊ architecture ကို **pyramid architecture** ဟုလည်း ခေါ်ဆိုပါသည်။\n" ] diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md index f96acccf..f0fd1772 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Patterns တွေကို ရှာဖွေဖို့ **convolutional filters** ဆိုတဲ့ အယူအဆကို အသုံးပြုပါမယ်။ သင်သိပြီးသားဖြစ်တဲ့အတိုင်း၊ ပုံတစ်ပုံဟာ 2D-matrix, ဒါမှမဟုတ် color depth ပါတဲ့ 3D-tensor အနေနဲ့ ဖော်ပြထားပါတယ်။ Filter ကို အသုံးပြုတဲ့အခါမှာ **filter kernel** matrix လေးတစ်ခုကို ယူပြီး၊ မူရင်းပုံထဲက pixel တစ်ခုစီအတွက် အနီးအနားမှာရှိတဲ့ point တွေနဲ့ weighted average ကိုတွက်ချက်ပါတယ်။ ဒါကို ပုံတစ်ပုံလုံးကို sliding လုပ်ပြီး၊ filter kernel matrix ထဲက weight တွေအတိုင်း pixel တွေကို averaging လုပ်နေတဲ့ window လေးတစ်ခုလိုမျိုး မြင်နိုင်ပါတယ်။ -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.my.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.my.png) +![Vertical Edge Filter](../../../../../translated_images/my/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/my/filter-horiz.59b80ed4feb946ef.png) ----|---- > Image by Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN တွေဟာ အောက်ပါ အရေးကြီးတဲ့ အ * Filters တွေကို အလိုအလျောက် သင်ယူနိုင်အောင် network ကို design လုပ်နိုင်တယ် * မူရင်းပုံထဲမှာသာမက၊ high-level features တွေထဲမှာ patterns တွေကို ရှာဖွေနိုင်ဖို့ အတူတူပုံစံကို အသုံးပြုနိုင်တယ်။ ဒါကြောင့် CNN feature extraction ဟာ low-level pixel combinations တွေကနေ စပြီး၊ ပုံရဲ့ အပိုင်းပိုင်းတွေကို ပေါင်းစပ်ထားတဲ့ higher-level features တွေထိ hierarchy အတိုင်း အလုပ်လုပ်ပါတယ်။ -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.my.png) +![Hierarchical Feature Extraction](../../../../../translated_images/my/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Image from [a paper by Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), based on [their research](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Convolutional neural networks တွေ ဘယ်လိုအလုပ်လု ဥပမာအနေနဲ့၊ VGG-16 ရဲ့ architecture ကို ကြည့်ကြမယ်၊ ဒီ network ဟာ 2014 မှာ ImageNet ရဲ့ top-5 classification မှာ 92.7% accuracy ရရှိခဲ့ပါတယ်- -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.my.jpg) +![ImageNet Layers](../../../../../translated_images/my/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.my.jpg) +![ImageNet Pyramid](../../../../../translated_images/my/vgg-16-arch.64ff2137f50dd49f.jpg) > Image from [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 6ee9b668..0560d54a 100644 --- a/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/my/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ကျွန်ုပ်တို့သည် [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ကို အသုံးပြုမည်ဖြစ်ပြီး၊ ၎င်းတွင် ခွေးနှင့် ကြောင်အမျိုးအစား ၃၇ မျိုး၏ ဓာတ်ပုံများ ပါဝင်ပါသည်။ -![ကျွန်ုပ်တို့ကို ကိုင်တွယ်ရမည့် Dataset](../../../../../../translated_images/data.50b2a9d5484bdbf0.my.png) +![ကျွန်ုပ်တို့ကို ကိုင်တွယ်ရမည့် Dataset](../../../../../../translated_images/my/data.50b2a9d5484bdbf0.png) Dataset ကို download လုပ်ရန် အောက်ပါ code snippet ကို အသုံးပြုပါ: diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index bd7a50a9..70f75609 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ကြိုက်နှစ်သက်သောကြောင်ပုံကို ရှင်းလင်းမြင်သာစေရန်အတွက် ကျွန်ုပ်တို့ random noise ပုံတစ်ပုံကို စတင်အသုံးပြုမည်ဖြစ်ပြီး၊ gradient descent optimization နည်းလမ်းကို အသုံးပြု၍ ပုံကို ပြင်ဆင်ကာ network တစ်ခုက ကြောင်ကို အသိအမှတ်ပြုနိုင်အောင် လုပ်ဆောင်မည်ဖြစ်သည်။\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.my.png)\n", + "![Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ဤသည်မှာ ကျွန်ုပ်တို့၏ စတင်ပုံဖြစ်သည်:\n" ] diff --git a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md index a49957b4..1cb05cd2 100644 --- a/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/my/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras နှင့် PyTorch တို့တွင် ImageNet ပုံမျ VGG-16 network မှ ကြောင်ပုံတစ်ပုံမှ ထုတ်ယူထားသော sample feature များကို အောက်တွင် ဖော်ပြထားသည်- -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.my.png) +![Features extracted by VGG-16](../../../../../translated_images/my/features.6291f9c7ba3a0b95.png) ## Cats vs. Dogs Dataset @@ -48,19 +48,19 @@ Pre-trained neural network တွင် ၎င်း၏ *brain* အတွင် တစ်ခုသော နည်းလမ်းကို အသုံးပြုနိုင်သည်မှာ random image တစ်ပုံကို စတင်ပြီး၊ **gradient descent optimization** နည်းလမ်းကို အသုံးပြု၍ ၎င်းပုံကို network သည် ၎င်းကို ကြောင်ဟု ထင်ရအောင် ပြောင်းလဲရန် ကြိုးစားခြင်းဖြစ်သည်။ -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.my.png) +![Image Optimization Loop](../../../../../translated_images/my/ideal-cat-loop.999fbb8ff306e044.png) သို့သော် ဤနည်းလမ်းကို အသုံးပြုပါက random noise ကဲ့သို့သော အရာတစ်ခုကို ရရှိမည်ဖြစ်သည်။ အကြောင်းမှာ *network ကို input image ကို ကြောင်ဟု ထင်ရအောင် ပြုလုပ်ရန် နည်းလမ်းများစွာရှိသည်*၊ ၎င်းတို့အနက် visual sense မရှိသော အရာများပါဝင်သည်။ ဤပုံများတွင် ကြောင်အတွက် အထူးသက်သက် pattern များစွာပါဝင်သော်လည်း၊ visually distinctive ဖြစ်ရန် အကန့်အသတ်မရှိပါ။ ရလဒ်ကို တိုးတက်စေရန် loss function တွင် **variation loss** ဟုခေါ်သော term တစ်ခုကို ထည့်သွင်းနိုင်သည်။ ၎င်းသည် ပုံ၏ အနီးအနား pixel များ၏ တူညီမှုကို ပြသသော metric ဖြစ်သည်။ Variation loss ကို လျှော့ချခြင်းဖြင့် ပုံကို ပိုမိုချောမွေ့စေပြီး၊ noise ကို ဖယ်ရှားနိုင်သည် - visual pattern များကို ပိုမိုရှင်းလင်းစွာ ဖော်ပြနိုင်သည်။ အောက်တွင် ကြောင်နှင့် zebra အဖြစ် classified ဖြစ်သော "ideal" ပုံများကို ဖော်ပြထားသည်- -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.my.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.my.png) +![Ideal Cat](../../../../../translated_images/my/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/my/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideal Cat* | *Ideal Zebra* အလားတူနည်းလမ်းကို neural network တွင် **adversarial attacks** ပြုလုပ်ရန် အသုံးပြုနိုင်သည်။ ကြောင်ကဲ့သို့သော ပုံကို ဖန်တီးရန် ကြိုးစားပါက၊ ကြောင်ဟု network မှ ခွဲခြားထားသော ကြောင်ပုံကို gradient descent optimization အသုံးပြု၍ network သည် ၎င်းကို ကြောင်ဟု ထင်ရအောင် ပြောင်းလဲနိုင်သည်- -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.my.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.my.png) +![Picture of a Dog](../../../../../translated_images/my/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/my/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Original picture of a dog* | *Picture of a dog classified as a cat* diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index eb77442b..3dff4b37 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Autoencoder ကို မူရင်းပုံမှ အချက်အလက်များကို အတတ်နိုင်ဆုံး ဖမ်းယူပြီး တိကျစွာ ပြန်လည်ဖန်တီးနိုင်ရန် လေ့ကျင့်နေသောကြောင့်၊ network သည် input ပုံများ၏ အဓိပ္ပာယ်ကို ဖမ်းယူနိုင်ရန် အကောင်းဆုံးသော **embedding** ကို ရှာဖွေကြိုးစားသည်။\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.my.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ပုံကို [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) မှ ယူဆောင်ထားသည်။\n", "\n", diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 05c00549..c901d049 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Autoencoder ကို မူရင်းပုံရိပ်မှ အချက်အလက်များကို အတိအကျ ပြန်လည်ဖော်ထုတ်နိုင်ရန် အများဆုံး ဖမ်းယူနိုင်ရန် သင်ကြားနေသောကြောင့်၊ network သည် input ပုံရိပ်များ၏ အဓိပ္ပါယ်ကို ဖမ်းယူနိုင်ရန် အကောင်းဆုံး **embedding** ကို ရှာဖွေကြိုးစားသည်။\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.my.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ပုံရင်း [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) မှ*\n", "\n", diff --git a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md index 9ea50070..ce5f518c 100644 --- a/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/my/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNN များကို လေ့ကျင့်ရာတွင် တစ် Autoencoder ကို မူရင်းပုံရိပ်မှ အချက်အလက်များကို အများဆုံး ဖမ်းယူရန် လေ့ကျင့်နေသောကြောင့်၊ ကွန်ယက်သည် input ပုံရိပ်များ၏ အဓိပ္ပါယ်ကို ဖမ်းယူရန် အကောင်းဆုံး **embedding** ကို ရှာဖွေသည်။ -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.my.jpg) +![AutoEncoder Diagram](../../../../../translated_images/my/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ပုံရိပ် - [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md index b31e9cfa..1e0c5e35 100644 --- a/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/my/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.my.png) +![Object Detection](../../../../../translated_images/my/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ပုံကို [YOLO v2 web site](https://pjreddie.com/darknet/yolov2/) မှရယူထားသည်။ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. တစ်ခုချင်းစီ tile တွေမှာ image classification ကို run လုပ်ပါ။ 3. activation အဆင့်မြင့်တဲ့ tiles တွေကို object ရှိတယ်လို့ယူဆနိုင်ပါတယ်။ -![Naive Object Detection](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.my.png) +![Naive Object Detection](../../../../../translated_images/my/naive-detection.e7f1ba220ccd08c6.png) > *ပုံကို [Exercise Notebook](ObjectDetection-TF.ipynb) မှရယူထားသည်* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 classes, bounding boxes နှင့် segmentation masks -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.my.jpg) +![COCO](../../../../../translated_images/my/coco-examples.71bc60380fa6cceb.jpg) ## Object Detection Metrics @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Image classification အတွက် algorithm ရဲ့ performance ကိုတိုင်းတာရလွယ်ကူသလို၊ object detection အတွက် class ရဲ့တိကျမှုနှင့် bounding box ရဲ့တိကျမှုကိုတိုင်းတာဖို့လိုအပ်ပါတယ်။ Bounding box ရဲ့တိကျမှုကိုတိုင်းတာဖို့ **Intersection over Union** (IoU) ကိုသုံးပါတယ်၊ ဒါဟာ box နှစ်ခု (သို့မဟုတ် arbitrary areas နှစ်ခု) overlap ဖြစ်ပုံကိုတိုင်းတာပေးပါတယ်။ -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.my.png) +![IoU](../../../../../translated_images/my/iou_equation.9a4751d40fff4e11.png) > *ပုံကို [IoU အကြောင်း blog post](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) မှရယူထားသည်* @@ -97,11 +97,11 @@ Object detection algorithms တွေကို broad classes နှစ်ခု [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) က [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ကိုသုံးပြီး ROI regions တွေကို hierarchical structure အဖြစ် generate လုပ်ပါတယ်၊ အဲ့ဒီ regions တွေကို CNN feature extractors နှင့် SVM-classifiers တွေက object class ကိုသတ်မှတ်ဖို့၊ linear regression က *bounding box* coordinates ကိုသတ်မှတ်ဖို့သုံးပါတယ်။ [Official Paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.my.png) +![RCNN](../../../../../translated_images/my/rcnn1.cae407020dfb1d1f.png) > *ပုံကို van de Sande et al. ICCV’11 မှရယူထားသည်* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.my.png) +![RCNN-1](../../../../../translated_images/my/rcnn2.2d9530bb83516484.png) > *ပုံကို [ဒီ blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) မှရယူထားသည်* @@ -109,7 +109,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု ဒီနည်းလမ်းက R-CNN နဲ့တူပေမယ့် regions တွေကို convolution layers apply လုပ်ပြီးမှသတ်မှတ်ပါတယ်။ -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.my.png) +![FRCNN](../../../../../translated_images/my/f-rcnn.3cda6d9bb4188875.png) > ပုံကို [Official Paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 မှရယူထားသည်။ @@ -117,7 +117,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု ဒီနည်းလမ်းရဲ့အဓိကအကြောင်းအရာက neural network ကိုသုံးပြီး ROIs ကို predict လုပ်တာဖြစ်ပါတယ် - *Region Proposal Network* လို့ခေါ်ပါတယ်။ [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.my.png) +![FasterRCNN](../../../../../translated_images/my/faster-rcnn.8d46c099b87ef30a.png) > ပုံကို [Official Paper](https://arxiv.org/pdf/1506.01497.pdf) မှရယူထားသည်။ @@ -129,7 +129,7 @@ Object detection algorithms တွေကို broad classes နှစ်ခု 2. Features တွေကို **Position-Sensitive Score Map** မှာ process လုပ်ပါ။ $C$ classes ထဲက object တစ်ခုစီကို $k\times k$ regions တွေခွဲပြီး၊ object parts တွေကို predict လုပ်ဖို့ training လုပ်ပါတယ်။ 3. $k\times k$ regions ထဲက part တစ်ခုစီအတွက် networks အားလုံးက object classes အတွက် vote လုပ်ပြီး၊ maximum vote ရတဲ့ object class ကိုရွေးချယ်ပါတယ်။ -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.my.png) +![r-fcn image](../../../../../translated_images/my/r-fcn.13eb88158b99a3da.png) > ပုံကို [Official Paper](https://arxiv.org/abs/1605.06409) မှရယူထားသည်။ @@ -140,7 +140,7 @@ YOLO က realtime one-pass algorithm ဖြစ်ပါတယ်။ အဓိက * ပုံကို $S\times S$ regions တွေခွဲပါ။ * Region တစ်ခုစီအတွက် **CNN** က $n$ possible objects, *bounding box* coordinates နှင့် *confidence*=*probability* * IoU ကို predict လုပ်ပါတယ်။ - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.my.png) + ![YOLO](../../../../../translated_images/my/yolo.a2648ec82ee8bb4e.png) > ပုံကို [Official Paper](https://arxiv.org/abs/1506.02640) မှရယူထားသည်။ diff --git a/translations/my/lessons/4-ComputerVision/README.md b/translations/my/lessons/4-ComputerVision/README.md index 7a7e0c64..aa3a7b52 100644 --- a/translations/my/lessons/4-ComputerVision/README.md +++ b/translations/my/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ကွန်ပျူတာမြင်ကြည့်မှု -![ကွန်ပျူတာမြင်ကြည့်မှုအကြောင်းအရာကို ရေးဆွဲထားသောပုံ](../../../../translated_images/ai-computervision.6506ebebac3fbf76.my.png) +![ကွန်ပျူတာမြင်ကြည့်မှုအကြောင်းအရာကို ရေးဆွဲထားသောပုံ](../../../../translated_images/my/ai-computervision.6506ebebac3fbf76.png) ဤအပိုင်းတွင် ကျွန်ုပ်တို့ သင်ယူမည့်အကြောင်းအရာများမှာ - diff --git a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 817af0b8..09a83af1 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -197,7 +197,7 @@ "\n", "**စကားလုံးအိတ်** (BoW) ဗက်တာကိုယ်စားပြုမှုဟာ အရင်က အသုံးများခဲ့တဲ့ ဗက်တာကိုယ်စားပြုမှုအနက် အများဆုံး အသုံးပြုတဲ့နည်းလမ်းဖြစ်ပါတယ်။ စကားလုံးတစ်လုံးချင်းစီကို ဗက်တာအညွှန်းနဲ့ ချိတ်ဆက်ထားပြီး၊ ဗက်တာအခန်းက စာရွက်တစ်ခုထဲမှာ စကားလုံးတစ်လုံးရဲ့ ဖြစ်ပေါ်မှုအရေအတွက်ကို ထည့်သွင်းထားပါတယ်။\n", "\n", - "![စကားလုံးအိတ် ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်ထဲမှာ ဘယ်လို ကိုယ်စားပြုထားတယ်ဆိုတာ ဖော်ပြတဲ့ ပုံ။](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.my.png)\n", + "![စကားလုံးအိတ် ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်ထဲမှာ ဘယ်လို ကိုယ်စားပြုထားတယ်ဆိုတာ ဖော်ပြတဲ့ ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: BoW ကို စာသားထဲမှာ စကားလုံးတစ်လုံးချင်းစီအတွက် one-hot-encoded ဗက်တာတွေကို စုပေါင်းထားတဲ့အနေနဲ့လည်း စဉ်းစားနိုင်ပါတယ်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index d072e20b..ca2bdcfe 100644 --- a/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/my/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**စကားလုံးအထုပ်** (BoW) ဗက်တာကိုယ်စားပြုမှုသည် နားလည်ရန် အလွယ်ဆုံးသော ရိုးရာဗက်တာကိုယ်စားပြုမှုဖြစ်သည်။ စကားလုံးတစ်ခုချင်းစီကို ဗက်တာအညွှန်းနှင့် ချိတ်ဆက်ထားပြီး၊ ဗက်တာအခန်းကဏ္ဍတစ်ခုတွင် သတ်မှတ်ထားသော စာရွက်စာတမ်းအတွင်း စကားလုံးတစ်ခုချင်းစီ၏ ဖြစ်ပေါ်မှုအရေအတွက်ကို ပါဝင်ထားသည်။\n", "\n", - "![စကားလုံးအထုပ်ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်တွင် ဘယ်လိုကိုယ်စားပြုထားသည်ကို ဖော်ပြထားသော ပုံ။](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.my.png)\n", + "![စကားလုံးအထုပ်ဗက်တာကိုယ်စားပြုမှုကို မှတ်ဉာဏ်တွင် ဘယ်လိုကိုယ်စားပြုထားသည်ကို ဖော်ပြထားသော ပုံ။](../../../../../translated_images/my/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: BoW ကို စာသားအတွင်း စကားလုံးတစ်ခုချင်းစီအတွက် တစ်ခုချင်းစီ *one-hot-encoded* ဗက်တာများ၏ စုစုပေါင်းအဖြစ်လည်း စဉ်းစားနိုင်ပါသည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 27c939e1..d1cd5d2d 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ကျွန်တော်တို့ network ရဲ့ ပထမဆုံး layer အနေနဲ့ embedding layer ကို သုံးခြင်းအားဖြင့်၊ bag-of-words မှ **embedding bag** မော်ဒယ်ဆီကို ပြောင်းနိုင်ပါတယ်။ ဒီမှာ ကျွန်တော်တို့ရဲ့ စာသားထဲက စကားလုံးတစ်လုံးစီကို သက်ဆိုင်ရာ embedding ကို ပြောင်းပြီး၊ ထို embedding တွေကို `sum`၊ `average` သို့မဟုတ် `max` ကဲ့သို့သော aggregate function တစ်ခုခုကို တွက်ချက်ပေးပါမယ်။\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.my.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ကျွန်တော်တို့ရဲ့ classifier neural network က embedding layer နဲ့ စပြီး၊ aggregation layer နဲ့ linear classifier ကို အပေါ်မှာ ထည့်သွင်းထားပါမယ်။\n" ] @@ -176,7 +176,7 @@ "\n", "ယခင် အဆောက်အအုံတွင်၊ minibatch ထဲသို့ ထည့်သွင်းနိုင်ရန် အစီအစဉ်အားလုံးကို အရှည်တူအောင် pad လုပ်ရန် လိုအပ်ခဲ့သည်။ သို့သော်၊ အရှည်မတူညီသော အစီအစဉ်များကို ကိုယ်စားပြုရန်အတွက် ဤနည်းလမ်းသည် အကျိုးရှိဆုံးမဟုတ်ပါ။ အခြားနည်းလမ်းတစ်ခုမှာ **offset** vector ကို အသုံးပြုခြင်းဖြစ်ပြီး၊ ၎င်းသည် အစီအစဉ်အားလုံး၏ offsets ကို တစ်ခုတည်းသော vector အကြီးထဲတွင် သိမ်းဆည်းထားမည်ဖြစ်သည်။\n", "\n", - "![Offset sequence ကို ကိုယ်စားပြုထားသော ပုံ](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.my.png)\n", + "![Offset sequence ကို ကိုယ်စားပြုထားသော ပုံ](../../../../../translated_images/my/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: အထက်ပါ ပုံတွင် အက္ခရာများ၏ အစီအစဉ်ကို ပြထားသော်လည်း၊ ကျွန်ုပ်တို့၏ ဥပမာတွင် စကားလုံးများ၏ အစီအစဉ်များနှင့် အလုပ်လုပ်နေပါသည်။ သို့သော်၊ offset vector ဖြင့် အစီအစဉ်များကို ကိုယ်စားပြုခြင်း၏ အခြေခံသဘောတရားမှာ မပြောင်းလဲပါ။\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW က ပိုမြန်ပါတယ်၊ skip-gram က ပိုနှေးပေမယ့် မကြာခဏ မတွေ့ရတဲ့ စကားလုံးများကို ပိုကောင်းစွာ ဖော်ပြနိုင်ပါတယ်။\n", "\n", - "![CBoW နဲ့ Skip-Gram algorithm နှစ်ခုစလုံးကို စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲဖို့ အသုံးပြုနေတဲ့ ပုံ။](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.my.png)\n", + "![CBoW နဲ့ Skip-Gram algorithm နှစ်ခုစလုံးကို စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲဖို့ အသုံးပြုနေတဲ့ ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News dataset ပေါ်မှာ ကြိုတင်သင်ယူထားတဲ့ word2vec embedding ကို စမ်းသပ်ဖို့၊ **gensim** library ကို အသုံးပြုနိုင်ပါတယ်။ အောက်မှာ 'neural' နဲ့ အနီးဆုံးသော စကားလုံးများကို ရှာဖွေထားပါတယ်-\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 2079eefb..26c24f51 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/my/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ကျွန်တော်တို့ network ရဲ့ ပထမဆုံး layer အနေနဲ့ embedding layer ကို အသုံးပြုခြင်းအားဖြင့် bag-of-words နည်းလမ်းကနေ **embedding bag** မော်ဒယ်ဆီကို ပြောင်းနိုင်ပါတယ်။ ဒီမှာ ကျွန်တော်တို့ စာသားထဲက စကားလုံးတစ်လုံးချင်းစီကို သက်ဆိုင်ရာ embedding ကို ပြောင်းပြီး၊ အဲဒီ embedding တွေကို `sum`, `average`, `max` စတဲ့ aggregate function တစ်ခုခုနဲ့ တွက်ချက်ပေးနိုင်ပါတယ်။\n", "\n", - "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.my.png)\n", + "![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ကျွန်တော်တို့ classifier neural network ရဲ့ layer တွေက အောက်ပါအတိုင်းဖြစ်ပါတယ်-\n", "\n", @@ -279,7 +279,7 @@ "\n", "CBoW သည် မြန်ဆန်သော်လည်း၊ skip-gram သည် နှေးကွေးသော်လည်း မကြာခဏမတွေ့ရသော စကားလုံးများကို ပိုမိုကောင်းမွန်စွာ ဖော်ပြနိုင်သည်။\n", "\n", - "![CBoW နှင့် Skip-Gram algorithm များကို အသုံးပြု၍ စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲနေသော ပုံ။](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.my.png)\n", + "![CBoW နှင့် Skip-Gram algorithm များကို အသုံးပြု၍ စကားလုံးများကို ဗက်တာအဖြစ် ပြောင်းလဲနေသော ပုံ။](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News dataset ပေါ်တွင် ကြိုတင်သင်ကြားထားသော Word2Vec embedding ကို စမ်းသပ်ရန် **gensim** library ကို အသုံးပြုနိုင်သည်။ အောက်တွင် 'neural' နှင့် နီးစပ်သော စကားလုံးများကို ရှာဖွေထားသည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/14-Embeddings/README.md b/translations/my/lessons/5-NLP/14-Embeddings/README.md index e9b373ac..c3388f2b 100644 --- a/translations/my/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/my/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Embedding layer က စကားလုံးကို input အနေနဲ့ Embedding layer ကို classifier network ရဲ့ ပထမဆုံး layer အနေနဲ့ အသုံးပြုခြင်းအားဖြင့်၊ bag-of-words model ကနေ **embedding bag** model သို့ ပြောင်းလဲနိုင်ပါတယ်။ ဒီမှာ စကားလုံးတစ်လုံးချင်းစီကို သက်ဆိုင်ရာ embedding သို့ ပြောင်းပြီး၊ အဲဒီ embeddings အားလုံးအပေါ်မှာ `sum`, `average` သို့မဟုတ် `max` ကဲ့သို့သော aggregate function တစ်ခုခုကို တွက်ချက်ပါမယ်။ -![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.my.png) +![Image showing an embedding classifier for five sequence words.](../../../../../translated_images/my/embedding-classifier-example.b77f021a7ee67eee.png) > ပုံကို စာရေးသူမှ ဖန်တီးထားသည် @@ -40,7 +40,7 @@ Embedding layer က စကားလုံးတွေကို vector representa CBoW က ပိုမြန်ပြီး၊ skip-gram က ပိုနှေးပေမယ့်၊ မကြာခဏ မတွေ့ရတဲ့ စကားလုံးတွေကို ပိုကောင်းစွာ ကိုယ်စားပြုနိုင်ပါတယ်။ -![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.my.png) +![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ပုံကို [ဒီစာတမ်း](https://arxiv.org/pdf/1301.3781.pdf) မှာ ရရှိခဲ့သည် diff --git a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md index a25b5860..627429a2 100644 --- a/translations/my/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/my/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Semantic embeddings, Word2Vec နှင့် GloVe က **ဘာသာစကာ * **Continuous Bag-of-Words** (CBoW)၊ token အစဉ် $W_{-N}$, ..., $W_N$ တွင် အလယ် token $W_0$ ကို ခန့်မှန်းခြင်း။ * **Skip-gram**၊ အလယ် token $W_0$ မှ အနီးအနား token များ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ကို ခန့်မှန်းခြင်း။ -![image from paper on converting words to vectors](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.my.png) +![image from paper on converting words to vectors](../../../../../translated_images/my/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Image from [this paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/my/lessons/5-NLP/16-RNN/README.md b/translations/my/lessons/5-NLP/16-RNN/README.md index 80bc3ab7..a9e56820 100644 --- a/translations/my/lessons/5-NLP/16-RNN/README.md +++ b/translations/my/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: စာသားအစီအစဉ်၏ အဓိပ္ပါယ်ကို ဖမ်းဆီးရန် **recurrent neural network** (RNN) ဟုခေါ်သော neural network architecture တစ်ခုကို အသုံးပြုရန် လိုအပ်သည်။ RNN တွင် ကျွန်ုပ်တို့၏ စာကြောင်းကို network အတွင်းသို့ သင်္ကေတတစ်ခုစီဖြင့် ဖြတ်သွားပြီး network သည် **state** တစ်ခုကို ထုတ်လုပ်သည်၊ ထို့နောက် ကျွန်ုပ်တို့သည် နောက်ထပ်သင်္ကေတနှင့်အတူ network သို့ ပြန်လည်ပေးပို့သည်။ -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.my.png) +![RNN](../../../../../translated_images/my/rnn.27f5c29c53d727b5.png) > ပုံကို စာရေးသူမှ ဖန်တီးသည် @@ -61,7 +61,7 @@ State C ၏ components များကို flags အဖြစ် switch on န Recurrent network တစ်ခုသည် direction တစ်ခုဖြစ်စေ bidirectional ဖြစ်စေ sequence အတွင်း certain patterns များကို ဖမ်းဆီးပြီး state vector သို့မဟုတ် output သို့ ပေးပို့နိုင်သည်။ Convolutional networks များနှင့်တူပင်၊ ပထမ layer မှ low-level patterns များကို extract လုပ်ပြီး အဆင့်မြင့် patterns များကို ဖမ်းဆီးရန် ပထမ layer အပေါ်တွင် recurrent layer တစ်ခုတိုးတက်စေပြီး **multi-layer RNN** ကို ဖန်တီးနိုင်သည်။ Multi-layer RNN သည် recurrent networks နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး ယခင် layer ၏ output ကို နောက်တစ်ခု layer ၏ input အဖြစ် ပေးပို့သည်။ -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.my.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Fernando López ရေးသားသော [this wonderful post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှ ပုံ* diff --git a/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index c8c33a9f..7aa14157 100644 --- a/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "Recurrent network, one-directional ဖြစ်စေ bidirectional ဖြစ်စေ၊ sequence တစ်ခုအတွင်းမှာ pattern အချို့ကို capture လုပ်နိုင်ပြီး၊ state vector ထဲမှာ သိမ်းဆည်းနိုင်သလို output ကို pass လုပ်နိုင်ပါတယ်။ Convolutional networks တွေနဲ့တူတူပဲ၊ ပထမ layer က low-level patterns တွေကို extract လုပ်ပြီး၊ အဲ့ဒီ patterns တွေကို အသုံးပြုပြီး higher level patterns တွေကို capture လုပ်ဖို့ ပထမ layer ရဲ့အပေါ်မှာ recurrent layer တစ်ခုတိုးဖွဲ့နိုင်ပါတယ်။ ဒီအရာက **multi-layer RNN** ဆိုတဲ့အယူအဆကို ရောက်လာစေပြီး၊ ဒါဟာ recurrent networks နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး၊ အရင် layer ရဲ့ output ကို နောက် layer ရဲ့ input အဖြစ် pass လုပ်ပေးတဲ့ network ဖြစ်ပါတယ်။\n", "\n", - "![Multilayer long-short-term-memory- RNN ကို ပြသတဲ့ပုံ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.my.jpg)\n", + "![Multilayer long-short-term-memory- RNN ကို ပြသတဲ့ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando López ရဲ့ [ဒီ post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှာရရှိတဲ့ပုံ*\n", "\n", diff --git a/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb index bfe57cc0..b11e47cd 100644 --- a/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/my/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "စာသားအစီအစဉ်၏ အဓိပ္ပါယ်ကို ဖမ်းဆီးရန်၊ **recurrent neural network** (RNN) ဟုခေါ်သော နယူးရယ်နက်ဝက် architecture ကို အသုံးပြုမည်ဖြစ်သည်။ RNN ကို အသုံးပြုသောအခါ၊ ကျွန်ုပ်တို့၏ စာကြောင်းကို network အတွင်းသို့ token တစ်ခုစီဖြင့် အဆင့်ဆင့် ဖြတ်သန်းပြီး၊ network သည် **state** တစ်ခုကို ထုတ်ပေးမည်ဖြစ်သည်။ ထို state ကို နောက် token နှင့်အတူ network သို့ ပြန်လည်ထည့်သွင်းမည်ဖြစ်သည်။\n", "\n", - "![Recurrent neural network ထုတ်လုပ်မှု၏ ဥပမာကို ပြသသော ပုံ။](../../../../../translated_images/rnn.27f5c29c53d727b5.my.png)\n", + "![Recurrent neural network ထုတ်လုပ်မှု၏ ဥပမာကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn.27f5c29c53d727b5.png)\n", "\n", "tokens များ၏ input အစီအစဉ် $X_0,\\dots,X_n$ ကို ပေးသောအခါ၊ RNN သည် နယူးရယ်နက်ဝက် block များ၏ အစီအစဉ်တစ်ခုကို ဖန်တီးပြီး၊ ထိုအစီအစဉ်ကို backpropagation အသုံးပြု၍ အဆုံးမှ အဆုံးသို့ လေ့ကျင့်သည်။ network block တစ်ခုစီသည် $(X_i,S_i)$ ကို input အနေဖြင့် လက်ခံပြီး၊ $S_{i+1}$ ကို ရလဒ်အဖြစ် ထုတ်ပေးသည်။ နောက်ဆုံး state $S_n$ သို့မဟုတ် output $Y_n$ ကို linear classifier သို့ ပေးပို့ပြီး ရလဒ်ကို ထုတ်ယူသည်။ network block အားလုံးသည် တူညီသော weight များကို မျှဝေထားပြီး၊ တစ်ကြိမ်တည်းသော backpropagation pass ဖြင့် အဆုံးမှ အဆုံးသို့ လေ့ကျင့်သည်။\n", "\n", @@ -369,7 +369,7 @@ "\n", "Recurrent network များ၊ unidirectional ဖြစ်စေ bidirectional ဖြစ်စေ၊ sequence အတွင်းရှိ pattern များကို ဖမ်းဆီးပြီး state vector များအဖြစ် သိမ်းဆည်းသို့မဟုတ် output အဖြစ် ပြန်လည်ပေးသည်။ Convolutional network များနှင့်တူပင်၊ ပထမ layer မှ အနိမ့်ဆုံး level pattern များကို ဖမ်းဆီးပြီး၊ အမြင့်ဆုံး level pattern များကို ဖမ်းဆီးရန် ပိုမိုမြင့်မားသော recurrent layer တစ်ခုကို တည်ဆောက်နိုင်သည်။ ၎င်းသည် **multi-layer RNN** ၏ အယူအဆသို့ ဦးတည်ပြီး၊ ၎င်းသည် recurrent network နှစ်ခု သို့မဟုတ် အများကြီးပါဝင်ပြီး၊ ယခင် layer ၏ output ကို နောက် layer ၏ input အဖြစ် ပေးပို့သည်။\n", "\n", - "![Multilayer long-short-term-memory- RNN ကို ပြသထားသော ပုံ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.my.jpg)\n", + "![Multilayer long-short-term-memory- RNN ကို ပြသထားသော ပုံ](../../../../../translated_images/my/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando López ရေးသားထားသော [ဤအလွန်အမိုက်ဆုံး post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) မှ ရိုက်ယူထားသော ပုံ။*\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 845b70fd..7c0400ea 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ကို စာသားထုတ်လုပ်ရန် လေ့ကျင့်ပုံမှာ အောက်ပါအတိုင်းဖြစ်ပါမည်။ အဆင့်တစ်ခုစီတွင် `nchars` အရှည်ရှိသော စာလုံးများ၏ အစဉ်အတိုင်းယူပြီး၊ နောက်ထွက်စာလုံးကို အဝင်စာလုံးတစ်ခုစီအတွက် ကွန်ယက်အားဖြင့် ထုတ်လုပ်ရန် တောင်းဆိုပါမည်။\n", "\n", - "![RNN သုံး၍ 'HELLO' စကားလုံးကို ထုတ်လုပ်နေသော ဥပမာပုံ။](../../../../../translated_images/rnn-generate.56c54afb52f9781d.my.png)\n", + "![RNN သုံး၍ 'HELLO' စကားလုံးကို ထုတ်လုပ်နေသော ဥပမာပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png)\n", "\n", "အခြေအနေအလိုက်၊ *အဆုံးအမှတ်အသား* `` ကဲ့သို့သော အထူးအက္ခရာများကိုလည်း ထည့်သွင်းလိုတတ်ပါသည်။ သို့သော် ကျွန်ုပ်တို့အနေဖြင့် အဆုံးမရှိသော စာသားထုတ်လုပ်မှုအတွက် ကွန်ယက်ကိုသာ လေ့ကျင့်လိုသောကြောင့်၊ အစဉ်တစ်ခုစီ၏ အရွယ်အစားကို `nchars` အက္ခရာများအဖြစ် သတ်မှတ်ထားမည်ဖြစ်သည်။ ထို့ကြောင့်၊ လေ့ကျင့်မှုဥပမာတစ်ခုစီတွင် `nchars` အဝင်များနှင့် `nchars` အထွက်များ (အဝင်အစဉ်ကို ဘယ်ဘက်သို့ အက္ခရာတစ်လုံးရွှေ့ထားသောအတိုင်း) ပါဝင်မည်ဖြစ်သည်။ Minibatch တစ်ခုတွင် ဤအစဉ်များစွာ ပါဝင်မည်ဖြစ်သည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 1001deb9..edb84d8b 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "RNN ကို သတင်းခေါင်းစဉ်များ ဖန်တီးရန် သင်ကြားပုံမှာ အောက်ပါအတိုင်း ဖြစ်ပါတယ်။ တစ်ခုချင်းစီအဆင့်မှာ RNN ထဲသို့ ထည့်မည့် ခေါင်းစဉ်တစ်ခုကို ရွေးပြီး၊ အင်ပွတ်အက္ခရာတစ်ခုစီအတွက် နောက်ထွက်အက္ခရာကို ဖန်တီးရန် ကွန်ယက်ကို မေးမြန်းပါမည်။\n", "\n", - "![RNN က 'HELLO' စကားလုံးကို ဖန်တီးနေသည်ကို ပြသသော ပုံ။](../../../../../translated_images/rnn-generate.56c54afb52f9781d.my.png)\n", + "![RNN က 'HELLO' စကားလုံးကို ဖန်တီးနေသည်ကို ပြသသော ပုံ။](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png)\n", "\n", "အကြောင်းအရာ၏ နောက်ဆုံးအက္ခရာအတွက် `` token ကို ဖန်တီးရန် ကွန်ယက်ကို မေးမြန်းပါမည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md index d8e6af49..825b0480 100644 --- a/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/my/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) နှင့် Long Short Term Memory Cells (L ဤအရာသည် အောက်ပါပုံတွင် ဖော်ပြထားသော neural architectures များကို ဖန်တီးနိုင်စေသည်- -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.my.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > ပုံကို [Andrej Karpaty](http://karpathy.github.io/) ရဲ့ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ဆိုတဲ့ blog post မှရယူထားသည်။ @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) နှင့် Long Short Term Memory Cells (L ဤ RNN ကို စာသားကို အဆင့်ဆင့် ထုတ်လုပ်ရန် training လုပ်မည်။ အဆင့်တစ်ခုစီတွင် `nchars` အရှည်ရှိသော စာလုံးများ၏ sequence ကိုယူပြီး၊ input character တစ်ခုစီအတွက် နောက်ထွက် character ကို network မှ ထုတ်ပေးရန် မေးမြန်းမည်- -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.my.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/my/rnn-generate.56c54afb52f9781d.png) စာသားထုတ်လုပ်ခြင်း (inference အတွင်း) တွင် **prompt** တစ်ခုကို RNN cells မှတဆင့် hidden state ကို ရယူပြီး၊ ထို့နောက် generation ကို စတင်မည်။ စာလုံးတစ်လုံးစီကို အဆင့်ဆင့် ထုတ်လုပ်ပြီး၊ state နှင့် ထုတ်လုပ်ထားသော စာလုံးကို နောက် RNN cell သို့ ပေးပို့ကာ နောက် character ကို ထုတ်လုပ်မည်။ ထိုနောက် လိုအပ်သော စာလုံးများကို ထုတ်လုပ်ပြီးမှ ရပ်မည်။ diff --git a/translations/my/lessons/5-NLP/18-Transformers/README.md b/translations/my/lessons/5-NLP/18-Transformers/README.md index 09cd338b..f1fe1d29 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/README.md +++ b/translations/my/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs အသုံးပြု၍ sequence-to-sequence ကို **encoder** န **Attention Mechanisms** သည် RNN ၏ output prediction အပေါ် input vector တစ်ခုချင်းစီ၏ context သက်ရောက်မှုကို အလေးပေးရန် နည်းလမ်းတစ်ခုဖြစ်သည်။ ဒီနည်းလမ်းကို input RNN ၏ intermediate states နှင့် output RNN အကြား shortcut များဖန်တီးခြင်းဖြင့် အကောင်အထည်ဖော်သည်။ ထို့ကြောင့် output symbol yt ကို ဖန်တီးသောအခါ input hidden states hi အားလုံးကို အလေးပေး coefficient αt,i များဖြင့် ထည့်သွင်းစဉ်းစားမည်။ -![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.my.png) +![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png) > Encoder-decoder မော်ဒယ်နှင့် additive attention mechanism ကို [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ ရယူထားသည်။ [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှာလည်း ရှင်းပြထားသည်။ Attention matrix {αi,j} သည် output sequence အတွင်း စကားလုံးတစ်ခုကို ဖန်တီးရာတွင် input စကားလုံးတစ်ခုချင်းစီ၏ သက်ရောက်မှုကို ကိုယ်စားပြုသည်။ အောက်တွင် matrix ၏ ဥပမာတစ်ခုကို ဖော်ပြထားသည်- -![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.my.png) +![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှာပါရှိသော (Fig.3) ပုံ @@ -66,7 +66,7 @@ Positional embedding ရလဒ်သည် original token နှင့် sequen ထို့နောက် sequence အတွင်း pattern များကို ဖမ်းဆီးရန်လိုအပ်သည်။ Transformers တွင် **self-attention** mechanism ကို အသုံးပြုသည်။ Self-attention သည် input နှင့် output အဖြစ် တူညီသော sequence အပေါ် attention ကို အသုံးပြုခြင်းဖြစ်သည်။ Self-attention ကို အသုံးပြုခြင်းဖြင့် sentence အတွင်း context ကို စဉ်းစားနိုင်ပြီး၊ စကားလုံးများ၏ inter-relationship ကို တွေ့နိုင်သည်။ ဥပမာ- *it* ကဲ့သို့သော coreferences ကို ရှာဖွေနိုင်သည်။ -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.my.png) +![](../../../../../translated_images/my/CoreferenceResolution.861924d6d384a7d6.png) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) မှ ပုံ @@ -91,7 +91,7 @@ Input position တစ်ခုချင်းစီကို output position တ **BERT** (Bidirectional Encoder Representations from Transformers) သည် *BERT-base* အတွက် 12 layers နှင့် *BERT-large* အတွက် 24 layers ပါဝင်သော အလွန်ကြီးမားသော multi-layer transformer network ဖြစ်သည်။ မော်ဒယ်ကို WikiPedia နှင့် books ကဲ့သို့သော text data အကြီးအကျယ်ကို unsupervised training (sentence အတွင်း masked words များကို ခန့်မှန်းခြင်း) ဖြင့် ပထမဦးဆုံး pre-train လုပ်သည်။ Pre-training အတွင်း မော်ဒယ်သည် language understanding အဆင့်များကို စွမ်းဆောင်နိုင်ပြီး၊ အခြား datasets များနှင့် fine-tuning ဖြင့် အသုံးပြုနိုင်သည်။ ဒီလုပ်ငန်းစဉ်ကို **transfer learning** ဟု ခေါ်သည်။ -![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.my.png) +![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > ပုံ [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 7a37b3d0..e224588d 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/my/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Attention Mechanisms (အာရုံစူးစိုက်မှုစနစ်များ)** ကတော့ RNN ရဲ့ output prediction (အထွက်ခန့်မှန်းမှု) တစ်ခုစီအပေါ် input vector (အဝင်ဗက်တာ) တစ်ခုစီရဲ့ အကြောင်းအရာသက်ရောက်မှုကို အလေးပေးနိုင်တဲ့ နည်းလမ်းတစ်ခုကို ပံ့ပိုးပေးပါတယ်။ ဒီစနစ်ကို အကောင်အထည်ဖော်တဲ့နည်းလမ်းက input RNN ရဲ့ အလယ်အလတ်အခြေအနေတွေနဲ့ output RNN အကြား shortcut (တိုက်ရိုက်လမ်းကြောင်း) တွေ ဖန်တီးခြင်းဖြစ်ပါတယ်။ ဒီနည်းလမ်းနဲ့ $y_t$ output symbol ကို ဖန်တီးတဲ့အခါမှာ input hidden states $h_i$ အားလုံးကို အလေးချိန်ကွဲပြားမှု $\\alpha_{t,i}$ နဲ့အတူ စဉ်းစားပါမယ်။\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.my.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ additive attention mechanism ပါတဲ့ encoder-decoder မော်ဒယ်ကို [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှ ရယူထားသည်။*\n", "\n", "Attention matrix $\\{\\alpha_{i,j}\\}$ က output sequence (အထွက်အဆက်မပြတ်အချက်အလက်) ရဲ့ စကားလုံးတစ်လုံးကို ဖန်တီးရာမှာ input words (အဝင်စကားလုံး) တစ်ချို့ရဲ့ သက်ရောက်မှုအဆင့်ကို ကိုယ်စားပြုပါတယ်။ အောက်မှာ ဒီလို matrix ရဲ့ ဥပမာကို ကြည့်နိုင်ပါတယ်။\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.my.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) မှ (ပုံ ၃) ကို ရယူထားသည်။*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) က အလွှာ ၁၂ လွှာပါတဲ့ *BERT-base* နဲ့ အလွှာ ၂၄ လွှာပါတဲ့ *BERT-large* တို့ဖြင့် ဖွဲ့စည်းထားတဲ့ အလွန်ကြီးမားတဲ့ multi-layer transformer network တစ်ခုဖြစ်ပါတယ်။ ဒီမော်ဒယ်ကို စာကြောင်းထဲက masked words (ဖုံးထားသောစကားလုံးများ) ကို ခန့်မှန်းတဲ့ unsupervised training (မကြီးကြပ်သောလေ့ကျင့်မှု) နည်းလမ်းနဲ့ စာကြောင်းအများအပြား (WikiPedia + စာအုပ်များ) အပေါ်မှာ အရင်ဆုံး pre-train လုပ်ထားပါတယ်။ Pre-training လုပ်စဉ်မှာ မော်ဒယ်က ဘာသာစကားနားလည်မှုအဆင့်မြင့်တစ်ခုကို စုပ်ယူထားပြီး၊ အဲ့ဒီနားလည်မှုကို အခြား dataset တွေနဲ့ fine-tuning (အသေးစိတ်ချိန်ညှိမှု) လုပ်နိုင်ပါတယ်။ ဒီလုပ်ငန်းစဉ်ကို **transfer learning** လို့ ခေါ်ပါတယ်။\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.my.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer architecture တွေမှာ BERT, DistilBERT, BigBird, OpenGPT3 စတဲ့ မော်ဒယ်အမျိုးအစားအများအပြားရှိပြီး၊ အဲ့ဒီမော်ဒယ်တွေကို fine-tune လုပ်နိုင်ပါတယ်။ [HuggingFace package](https://github.com/huggingface/) က PyTorch နဲ့ အဲ့ဒီ architecture တွေကို training လုပ်ဖို့ repository တစ်ခုကို ပံ့ပိုးပေးထားပါတယ်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 3b008ed0..9555ee27 100644 --- a/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/my/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**အာရုံစူးစိုက်မှု မော်ကွန်းများ** သည် RNN ၏ output များကို ခန့်မှန်းရာတွင် input vector တစ်ခုချင်းစီ၏ context သက်ရောက်မှုကို အလေးပေးနိုင်စေသော နည်းလမ်းတစ်ခုဖြစ်သည်။ ၎င်းကို အကောင်အထည်ဖော်ရာတွင် input RNN ၏ အလယ်အလတ် states များနှင့် output RNN အကြား shortcut များ ဖန်တီးခြင်းဖြင့် ပြုလုပ်သည်။ ဒီနည်းလမ်းဖြင့် output symbol $y_t$ ကို ထုတ်လုပ်စဉ်တွင် input hidden states $h_i$ အားလုံးကို အလေးချိန် coefficient များ $\\alpha_{t,i}$ ဖြင့် သက်ဆိုင်စွာ ထည့်သွင်းစဉ်းစားမည်ဖြစ်သည်။\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.my.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/my/encoder-decoder-attention.7a726296894fb567.png)\n", "*Additive attention mechanism ပါဝင်သည့် encoder-decoder မော်ဒယ် ([Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)) ကို [ဒီ blog post](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) မှ ရယူထားသည်*\n", "\n", "Attention matrix $\\{\\alpha_{i,j}\\}$ သည် output စာကြောင်းအတွင်း စကားလုံးတစ်လုံးကို ဖန်တီးရာတွင် သက်ဆိုင်သော input စကားလုံးများ၏ သက်ရောက်မှုအဆင့်ကို ကိုယ်စားပြုသည်။ အောက်တွင် ထို matrix ၏ ဥပမာကို ဖော်ပြထားသည်-\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.my.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/my/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) မှ ရယူထားသော ပုံ]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) သည် *BERT-base* အတွက် အလွှာ 12 လွှာနှင့် *BERT-large* အတွက် အလွှာ 24 လွှာပါဝင်သော အလွန်ကြီးမားသော multi-layer transformer network တစ်ခုဖြစ်သည်။ ဤမော်ဒယ်ကို ပထမဦးစွာ အကြီးမားသော စာသားဒေတာများ (WikiPedia + စာအုပ်များ) ကို အသုံးပြု၍ unsupervised training (ဝါကျအတွင်းရှိ masked စကားလုံးများကို ခန့်မှန်းခြင်း) ဖြင့် pre-training ပြုလုပ်သည်။ Pre-training လုပ်စဉ်အတွင်း မော်ဒယ်သည် ဘာသာစကားနားလည်မှုအဆင့်အတန်းများကို အလွန်အမင်း စွမ်းဆောင်နိုင်စွမ်း ရရှိလာပြီး၊ ထို့နောက် အခြားသောဒေတာများနှင့်အတူ fine tuning ဖြင့် အသုံးချနိုင်သည်။ ဤလုပ်ငန်းစဉ်ကို **transfer learning** ဟုခေါ်သည်။\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.my.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/my/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 စသည်တို့အပါအဝင် Transformer architecture များ၏ အမျိုးအစားများစွာရှိပြီး၊ ထိုမော်ဒယ်များကို fine tuning ပြုလုပ်နိုင်သည်။\n", "\n", diff --git a/translations/my/lessons/5-NLP/19-NER/README.md b/translations/my/lessons/5-NLP/19-NER/README.md index fc057248..ae652a40 100644 --- a/translations/my/lessons/5-NLP/19-NER/README.md +++ b/translations/my/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Token နှင့် class များအကြား တစ်ခုချင်းစီကို တိုက်ရိုက်ဆက်စပ်မှုတစ်ခုတည်ဆောက်ရန် လိုအပ်သဖြင့်၊ ဒီပုံစံမှ **many-to-many** neural network model တစ်ခုကို တည်ဆောက်နိုင်ပါသည်။ -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.my.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/my/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *ပုံကို [ဒီ blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) မှ [Andrej Karpathy](http://karpathy.github.io/) ရေးသားထားသည်။ NER token classification model များသည် ဒီပုံရဲ့ ညာဘက်ဆုံး network architecture ကို ကိုယ်စားပြုသည်။* diff --git a/translations/my/lessons/5-NLP/README.md b/translations/my/lessons/5-NLP/README.md index 3fdc7b44..5db04d23 100644 --- a/translations/my/lessons/5-NLP/README.md +++ b/translations/my/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # သဘာဝဘာသာစကားလုပ်ငန်းဆောင်တာ -![NLP လုပ်ငန်းဆောင်တာများကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.my.png) +![NLP လုပ်ငန်းဆောင်တာများကို ရေးဆွဲထားသော ပုံ](../../../../translated_images/my/ai-nlp.b22dcb8ca4707cea.png) ဤအပိုင်းတွင် **သဘာဝဘာသာစကားလုပ်ငန်းဆောင်တာ (NLP)** နှင့်ဆက်စပ်သောအလုပ်များကို Neural Networks အသုံးပြု၍ ဖြေရှင်းပုံကို အဓိကထားဆွေးနွေးသွားမည်ဖြစ်သည်။ ကွန်ပျူတာများကို ဖြေရှင်းစေလိုသော NLP ပြဿနာများစွာရှိသည်။ diff --git a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md index 1f764805..c7fcfe80 100644 --- a/translations/my/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/my/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ models တစ်ခုကို ဖွင့်နိုင်သည်၊ ဥ model ကို ဖွင့်ပြီးနောက် NetLogo ၏ အဓိက screen သို့ ရောက်ရှိသည်။ ဤနေရာတွင် finite resources (grass) ရှိသော wolves နှင့် sheep ၏ population ကို ဖော်ပြသော sample model တစ်ခုကို တွေ့နိုင်သည်။ -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.my.png) +![NetLogo Main Screen](../../../../../translated_images/my/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov ၏ screenshot diff --git a/translations/my/lessons/README.md b/translations/my/lessons/README.md index 7bf69526..c209f7bf 100644 --- a/translations/my/lessons/README.md +++ b/translations/my/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # အကျဉ်းချုပ် -![အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../translated_images/ai-overview.0857791951d19500.my.png) +![အကျဉ်းချုပ်ကို ရေးဆွဲထားသော ပုံ](../../../translated_images/my/ai-overview.0857791951d19500.png) > [Tomomi Imura](https://twitter.com/girlie_mac) မှ ရေးဆွဲထားသော Sketchnote diff --git a/translations/my/lessons/X-Extras/X1-MultiModal/README.md b/translations/my/lessons/X-Extras/X1-MultiModal/README.md index 790097f5..621d4395 100644 --- a/translations/my/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/my/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP အလုပ်များကို ဖြေရှင်းရန် trans CLIP ၏ အဓိကအကြောင်းအရာမှာ စာသား prompt များနှင့် ပုံတစ်ပုံကို နှိုင်းယှဉ်ပြီး ပုံသည် prompt နှင့် ဘယ်လောက်တိကျမှုရှိသည်ကို သတ်မှတ်နိုင်ရန်ဖြစ်သည်။ -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.my.png) +![CLIP Architecture](../../../../../translated_images/my/clip-arch.b3dbf20b4e8ed8be.png) > *ဤ blog post မှ ပုံ [ဒီမှာ](https://openai.com/blog/clip/)* @@ -31,7 +31,7 @@ CLIP မော်ဒယ်/စာကြည့်တိုက်ကို [OpenAI ဥပမာအားဖြင့် ပုံများကို ကြောင်၊ ခွေး၊ လူတို့အကြား ခွဲခြားရန် လိုအပ်သည်ဟု ယူဆပါစို့။ ဤအခါတွင် မော်ဒယ်ကို ပုံတစ်ပုံနှင့် စာသား prompt များ "*a picture of a cat*", "*a picture of a dog*", "*a picture of a human*" တို့ကို ပေးပါ။ 3 probabilities ရလဒ် vector တွင် အမြင့်ဆုံးတန်ဖိုးရှိသော index ကို ရွေးချယ်ရမည်ဖြစ်သည်။ -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.my.png) +![CLIP for Image Classification](../../../../../translated_images/my/clip-class.3af42ef0b2b19369.png) > *ဤ blog post မှ ပုံ [ဒီမှာ](https://openai.com/blog/clip/)* @@ -55,13 +55,13 @@ VQGAN အကြောင်းကို [Taming Transformers](https://compvis.gi VQGAN နှင့် ရိုးရာ GAN အကြား အရေးကြီးသော ကွာခြားချက်တစ်ခုမှာ၊ နောက်ဆုံးပုံကို မည်သည့် input vector မှမဆို ရိုးရာ GAN သည် သင့်တော်သောပုံကို ဖန်တီးနိုင်သော်လည်း၊ VQGAN သည် coherence မရှိသောပုံကို ဖန်တီးနိုင်သည်။ ထို့ကြောင့် ပုံဖန်တီးမှုလုပ်ငန်းစဉ်ကို CLIP ကို အသုံးပြု၍ ထပ်မံလမ်းညွှန်ရန် လိုအပ်သည်။ -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.my.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/my/vqgan.5027fe05051dfa31.png) စာသား prompt နှင့် ကိုက်ညီသော ပုံတစ်ပုံကို ဖန်တီးရန်၊ random encoding vector တစ်ခုဖြင့် စတင်ပြီး၊ ၎င်းကို VQGAN မှတဆင့် ပုံတစ်ပုံထုတ်လုပ်သည်။ ထို့နောက် CLIP ကို loss function တစ်ခုဖန်တီးရန် အသုံးပြုသည်။ ၎င်းသည် ပုံသည် စာသား prompt နှင့် ဘယ်လောက်ကိုက်ညီသည်ကို ပြသသည်။ ထို့နောက် loss ကို အနည်းဆုံးဖြစ်အောင်လုပ်ရန်၊ back propagation ကို အသုံးပြု၍ input vector parameters များကို ပြင်ဆင်သည်။ VQGAN+CLIP ကို အကောင်အထည်ဖော်ထားသော စာကြည့်တိုက်တစ်ခုမှာ [Pixray](http://github.com/pixray/pixray) ဖြစ်သည်။ -![Picture produced by Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.my.png) | ![Picture produced by pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.my.png) | ![Picture produced by Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.my.png) +![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/my/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Prompt *a closeup watercolor portrait of young male teacher of literature with a book* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of young female teacher of computer science with a computer* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of old male teacher of mathematics in front of blackboard* မှ ဖန်တီးထားသော ပုံ @@ -77,7 +77,7 @@ CLIP နှင့် မတူကွဲပြားသည်မှာ၊ DALL-E DALL.E 1 နှင့် 2 အကြား အဓိကကွာခြားချက်မှာ၊ ၎င်းသည် ပိုမိုလက်တွေ့ကျသော ပုံများနှင့် အနုပညာကို ဖန်တီးနိုင်ခြင်းဖြစ်သည်။ DALL-E ဖြင့် ဖန်တီးထားသော ပုံများ၏ ဥပမာများ: -![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.my.png) | ![Picture produced by pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.my.png) | ![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.my.png) +![Picture produced by Pixray](../../../../../translated_images/my/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Picture produced by pixray](../../../../../translated_images/my/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Picture produced by Pixray](../../../../../translated_images/my/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Prompt *a closeup watercolor portrait of young male teacher of literature with a book* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of young female teacher of computer science with a computer* မှ ဖန်တီးထားသော ပုံ | Prompt *a closeup oil portrait of old male teacher of mathematics in front of blackboard* မှ ဖန်တီးထားသော ပုံ diff --git a/translations/ne/README.md b/translations/ne/README.md index f6d79dd7..90f97dfe 100644 --- a/translations/ne/README.md +++ b/translations/ne/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # शुरुवातीहरूको लागि कृत्रिम बुद्धिमत्ता - एक पाठ्यक्रम -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ne.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ne/ai-overview.0857791951d19500.png)| |:---:| | शुरुवातीहरूको लागि कृत्रिम बुद्धिमत्ता - _स्केन्चोटो द्वारा [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ne/lessons/1-Intro/README.md b/translations/ne/lessons/1-Intro/README.md index 9f4fa3b4..4252d651 100644 --- a/translations/ne/lessons/1-Intro/README.md +++ b/translations/ne/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # एआईको परिचय -![एआईको परिचयको सामग्रीको डूडलमा सारांश](../../../../translated_images/ai-intro.bf28d1ac4235881c.ne.png) +![एआईको परिचयको सामग्रीको डूडलमा सारांश](../../../../translated_images/ne/ai-intro.bf28d1ac4235881c.png) > स्केच नोट [टोमोमी इमुरा](https://twitter.com/girlie_mac) द्वारा @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: सुरुमा, कम्प्युटरहरू [चार्ल्स बैबेज](https://en.wikipedia.org/wiki/Charles_Babbage) द्वारा संख्याहरूमा काम गर्न र एक राम्रो परिभाषित प्रक्रिया - एल्गोरिदम - अनुसरण गर्न आविष्कार गरिएको थियो। आधुनिक कम्प्युटरहरू, यद्यपि १९औं शताब्दीमा प्रस्तावित मूल मोडेलभन्दा धेरै उन्नत छन्, अझै पनि नियन्त्रित गणनाको उही विचारलाई अनुसरण गर्छन्। त्यसैले, यदि हामीलाई लक्ष्य प्राप्त गर्न आवश्यक चरणहरूको ठ्याक्कै क्रम थाहा छ भने, कम्प्युटरलाई कुनै काम गर्न प्रोग्राम गर्न सम्भव छ। -![व्यक्तिको फोटो](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ne.png) +![व्यक्तिको फोटो](../../../../translated_images/ne/dsh_age.d212a30d4e54fb5f.png) > फोटो [भिक्की सोश्निकोभा](http://twitter.com/vickievalerie) द्वारा @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[बुद्धिमत्ता](https://en.wikipedia.org/wiki/Intelligence)** शब्दसँग व्यवहार गर्दा एउटा समस्या यो हो कि यस शब्दको कुनै स्पष्ट परिभाषा छैन। कसैले तर्क गर्न सक्छ कि बुद्धिमत्ता **सार सोचाइ** वा **आत्म-जागरूकता**सँग सम्बन्धित छ, तर हामी यसलाई ठीकसँग परिभाषित गर्न सक्दैनौं। -![बिरालोको फोटो](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ne.jpg) +![बिरालोको फोटो](../../../../translated_images/ne/photo-cat.8c8e8fb760ffe457.jpg) > [फोटो](https://unsplash.com/photos/75715CVEJhI) [एम्बर किप](https://unsplash.com/@sadmax) द्वारा अनस्प्लाशबाट @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | एमएलको बारेमा के? | | > |--------------|-----------| -> | केही डेटा आधारित समस्याहरू समाधान गर्न कम्प्युटर सिकाइमा आधारित कृत्रिम बुद्धिमत्ताको भागलाई **मेसिन लर्निङ** भनिन्छ। हामी यस पाठ्यक्रममा शास्त्रीय मेसिन लर्निङलाई विचार गर्ने छैनौं - हामी तपाईंलाई छुट्टै [मेसिन लर्निङका लागि शुरुआती](http://aka.ms/ml-beginners) पाठ्यक्रममा सन्दर्भ दिन्छौं। | ![मेसिन लर्निङका लागि शुरुआती](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ne.png) | +> | केही डेटा आधारित समस्याहरू समाधान गर्न कम्प्युटर सिकाइमा आधारित कृत्रिम बुद्धिमत्ताको भागलाई **मेसिन लर्निङ** भनिन्छ। हामी यस पाठ्यक्रममा शास्त्रीय मेसिन लर्निङलाई विचार गर्ने छैनौं - हामी तपाईंलाई छुट्टै [मेसिन लर्निङका लागि शुरुआती](http://aka.ms/ml-beginners) पाठ्यक्रममा सन्दर्भ दिन्छौं। | ![मेसिन लर्निङका लागि शुरुआती](../../../../translated_images/ne/ml-for-beginners.9e4fed176fd5817d.png) | ## एआईको संक्षिप्त इतिहास कृत्रिम बुद्धिमत्ता २०औं शताब्दीको मध्यमा एउटा क्षेत्रको रूपमा सुरु भएको थियो। सुरुमा, प्रतीकात्मक तर्क एक प्रमुख दृष्टिकोण थियो, र यसले विशेषज्ञ प्रणालीहरू जस्ता केही महत्त्वपूर्ण सफलताहरू ल्यायो - कम्प्युटर प्रोग्रामहरू जसले केही सिमित समस्या क्षेत्रहरूमा विशेषज्ञको रूपमा काम गर्न सक्षम थिए। तर, चाँडै यो स्पष्ट भयो कि यस्तो दृष्टिकोण राम्रोसँग स्केल हुँदैन। विशेषज्ञबाट ज्ञान निकाल्नु, यसलाई कम्प्युटरमा प्रतिनिधित्व गर्नु, र त्यो ज्ञान आधारलाई सटीक राख्नु धेरै जटिल कार्य हो, र धेरै अवस्थामा व्यावहारिक हुन धेरै महँगो छ। यसले १९७० को दशकमा तथाकथित [एआई जाडो](https://en.wikipedia.org/wiki/AI_winter) ल्यायो। -एआईको संक्षिप्त इतिहास +एआईको संक्षिप्त इतिहास > छवि [दिमित्री सोश्निकोभ](http://soshnikov.com) द्वारा diff --git a/translations/ne/lessons/2-Symbolic/Animals.ipynb b/translations/ne/lessons/2-Symbolic/Animals.ipynb index 3eaef660..88e81db1 100644 --- a/translations/ne/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ne/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "यस नमूनामा, हामी केही भौतिक विशेषताहरूको आधारमा जनावर पहिचान गर्नको लागि एक साधारण ज्ञान-आधारित प्रणाली कार्यान्वयन गर्नेछौं। प्रणालीलाई निम्न AND-OR रूखद्वारा प्रतिनिधित्व गर्न सकिन्छ (यो सम्पूर्ण रूखको एक भाग हो, हामी सजिलै थप नियमहरू थप्न सक्छौं):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ne.png)\n" + "![](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ne/lessons/2-Symbolic/README.md b/translations/ne/lessons/2-Symbolic/README.md index c3d56f7a..1666833e 100644 --- a/translations/ne/lessons/2-Symbolic/README.md +++ b/translations/ne/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ज्ञान प्रतिनिधित्व र विशेषज्ञ प्रणाली -![सिम्बोलिक AI सामग्रीको सारांश](../../../../translated_images/ai-symbolic.715a30cb610411a6.ne.png) +![सिम्बोलिक AI सामग्रीको सारांश](../../../../translated_images/ne/ai-symbolic.715a30cb610411a6.png) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा @@ -41,7 +41,7 @@ AI को सुरुवाती दिनहरूमा, बुद्धि त्यसैले, **ज्ञान प्रतिनिधित्व** को समस्या भनेको कम्प्युटर भित्र ज्ञानलाई डाटाको रूपमा प्रतिनिधित्व गर्ने केही प्रभावकारी तरिका पत्ता लगाउनु हो, ताकि यसलाई स्वचालित रूपमा प्रयोग गर्न सकियोस्। यसलाई एक स्पेक्ट्रमको रूपमा हेर्न सकिन्छ: -![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ne.png) +![ज्ञान प्रतिनिधित्व स्पेक्ट्रम](../../../../translated_images/ne/knowledge-spectrum.b60df631852c0217.png) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा @@ -94,7 +94,7 @@ Untyped-Language | छैन | प्रकार परिभाषा सिम्बोलिक AI को प्रारम्भिक सफलताहरू मध्ये एक **विशेषज्ञ प्रणालीहरू** थिए - कम्प्युटर प्रणालीहरू जसलाई सीमित समस्या क्षेत्रमा विशेषज्ञको रूपमा कार्य गर्न डिजाइन गरिएको थियो। तिनीहरू **ज्ञान आधार** मा आधारित थिए, जुन एक वा बढी मानव विशेषज्ञहरूबाट निकालिएको थियो, र तिनीहरूमा **तर्क इन्जिन** समावेश थियो जसले यसमा केही तर्क प्रदर्शन गर्थ्यो। -![मानव वास्तुकला](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ne.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ne.png) +![मानव वास्तुकला](../../../../translated_images/ne/arch-human.5d4d35f1bba3ab1c.png) | ![ज्ञान-आधारित प्रणाली](../../../../translated_images/ne/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ मानव न्युरल प्रणालीको सरलीकृत संरचना | ज्ञान-आधारित प्रणालीको वास्तुकला @@ -106,7 +106,7 @@ Untyped-Language | छैन | प्रकार परिभाषा उदाहरणका लागि, निम्न विशेषज्ञ प्रणालीलाई विचार गरौं जसले शारीरिक विशेषताहरूको आधारमा जनावर निर्धारण गर्दछ: -![AND-OR ट्री](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ne.png) +![AND-OR ट्री](../../../../translated_images/ne/AND-OR-Tree.5592d2c70187f283.png) > छवि [Dmitry Soshnikov](http://soshnikov.com) द्वारा diff --git a/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 31c355e0..7cb301ca 100644 --- a/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ne/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "यदि हामीसँग २ भन्दा बढी वर्गहरू छन् भने, softmax ले ती सबैमा सम्भावनाहरूलाई सामान्यीकरण गर्नेछ। यहाँ MNIST अंक वर्गीकरण गर्ने नेटवर्क आर्किटेक्चरको एक चित्र छ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ne.png)\n" + "![MNIST Classifier](../../../../../translated_images/ne/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* कम प्रशिक्षण हानि - मोडेलले प्रशिक्षण डेटा राम्रोसँग अनुमान गर्न सक्छ, किनभने यससँग पर्याप्त अभिव्यक्तिपूर्ण शक्ति छ। \n", "* मान्यकरण हानि प्रशिक्षण हानिभन्दा धेरै उच्च हुन सक्छ र प्रशिक्षणको क्रममा बढ्न थाल्न सक्छ - यसको कारण मोडेलले \"प्रशिक्षण बिन्दुहरू\" सम्झिन्छ, र \"समग्र चित्र\" गुमाउँछ।\n", "\n", - "![ओभरफिटिङ](../../../../../translated_images/overfit.a0bd57f717c15769.ne.png)\n", + "![ओभरफिटिङ](../../../../../translated_images/ne/overfit.a0bd57f717c15769.png)\n", "\n", "> यस चित्रमा, `x` ले प्रशिक्षण डेटा जनाउँछ, `o` ले मान्यकरण डेटा। बायाँ - रेखीय मोडेल (एक-स्तरीय), यसले डेटा प्रकृतिलाई राम्रोसँग अनुमान गर्छ। दायाँ - ओभरफिट गरिएको मोडेल, यसले प्रशिक्षण डेटा पूर्ण रूपमा अनुमान गर्छ, तर अन्य कुनै पनि डेटा (मान्यकरण त्रुटि धेरै उच्च छ) सँग अर्थपूर्ण हुँदैन।\n" ] diff --git a/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md index bde44bd4..781b6026 100644 --- a/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ne/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* तलको समस्या विचार गर्नुहोस् जहाँ ५ बिन्दुहरूलाई (ग्राफमा `x` ले प्रतिनिधित्व गरिएको) अनुमान गर्नुपर्छ: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ne.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ne.jpg) +![linear](../../../../../translated_images/ne/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ne/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **रेखीय मोडेल, २ प्यारामिटरहरू** | **गैर-रेखीय मोडेल, ७ प्यारामिटरहरू** प्रशिक्षण त्रुटि = ५.३ | प्रशिक्षण त्रुटि = ० @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* जसरी माथिको ग्राफबाट देख्न सकिन्छ, ओभरफिटिङलाई धेरै कम प्रशिक्षण त्रुटि र उच्च मान्यकरण त्रुटिबाट पत्ता लगाउन सकिन्छ। सामान्यतया प्रशिक्षणको क्रममा हामीले प्रशिक्षण र मान्यकरण त्रुटिहरू दुवै घट्न थालेको देख्छौं, र त्यसपछि कुनै बिन्दुमा मान्यकरण त्रुटि घट्न रोक्न सक्छ र बढ्न थाल्न सक्छ। यो ओभरफिटिङको संकेत हुनेछ, र यो बिन्दुमा प्रशिक्षण रोक्नुपर्छ (वा कम्तीमा मोडेलको स्न्यापशट लिनुपर्छ) भन्ने सूचक हुनेछ। -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ne.png) +![overfitting](../../../../../translated_images/ne/Overfitting.408ad91cd90b4371.png) ## ओभरफिटिङ रोक्न कसरी diff --git a/translations/ne/lessons/3-NeuralNetworks/README.md b/translations/ne/lessons/3-NeuralNetworks/README.md index 2f921d7a..7030fb93 100644 --- a/translations/ne/lessons/3-NeuralNetworks/README.md +++ b/translations/ne/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # न्युरल नेटवर्कको परिचय -![न्युरल नेटवर्कको सामग्रीको सारांश एक चित्रमा](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ne.png) +![न्युरल नेटवर्कको सामग्रीको सारांश एक चित्रमा](../../../../translated_images/ne/ai-neuralnetworks.1c687ae40bc86e83.png) जसरी हामीले परिचयमा चर्चा गर्यौं, बुद्धिमत्ता प्राप्त गर्ने एक तरिका भनेको **कम्प्युटर मोडेल** वा **कृत्रिम मस्तिष्क**लाई प्रशिक्षण दिनु हो। २०औं शताब्दीको मध्यदेखि, अनुसन्धानकर्ताहरूले विभिन्न गणितीय मोडेलहरू प्रयास गरे, र पछिल्लो केही वर्षमा यो दिशा अत्यन्त सफल साबित भयो। मस्तिष्कको यस्ता गणितीय मोडेलहरूलाई **न्युरल नेटवर्क** भनिन्छ। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: जैविक विज्ञानबाट, हामीलाई थाहा छ कि हाम्रो मस्तिष्क न्युरल कोषहरू (न्युरोनहरू) बाट बनेको छ, जसको प्रत्येकमा धेरै "इनपुट" (डेंड्राइटहरू) र एक "आउटपुट" (एक्सन) हुन्छ। डेंड्राइटहरू र एक्सनहरू दुवैले विद्युतीय संकेतहरू प्रवाह गर्न सक्छन्, र तिनीहरू बीचको जडानहरू — जसलाई सिन्याप्स भनिन्छ — विभिन्न स्तरको प्रवाहशीलता देखाउन सक्छन्, जुन न्युरोट्रान्समिटरहरूले नियमन गर्छन्। -![न्युरोनको मोडेल](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ne.jpg) | ![न्युरोनको मोडेल](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ne.png) +![न्युरोनको मोडेल](../../../../translated_images/ne/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![न्युरोनको मोडेल](../../../../translated_images/ne/artneuron.1a5daa88d20ebe6f.png) ----|---- वास्तविक न्युरोन *([छवि](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) विकिपीडियाबाट)* | कृत्रिम न्युरोन *(लेखकद्वारा बनाइएको छवि)* त्यसैले, न्युरोनको सबैभन्दा सरल गणितीय मोडेलमा धेरै इनपुटहरू X1, ..., XN र एक आउटपुट Y हुन्छ, र तौलहरूको श्रृंखला W1, ..., WN हुन्छ। आउटपुट यसरी गणना गरिन्छ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) जहाँ f भनेको केही गैर-रेखीय **सक्रियता फलन** हो। diff --git a/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md index 49c05c8f..b1da174a 100644 --- a/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ne/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ब्रेल पुस्तकको तस्बिर पूर्व-प्रशोधन गर्ने**। हामी थ्रेसहोल्डिङ, फिचर डिटेक्सन, परिप्रेक्ष्य रूपान्तरण र NumPy हेरफेर प्रयोग गरेर व्यक्तिगत ब्रेल प्रतीकहरू अलग गर्नेमा केन्द्रित छौं, जसलाई न्यूरल नेटवर्कद्वारा थप वर्गीकरण गर्न सकिन्छ। -![ब्रेल छवि](../../../../../translated_images/braille.341962ff76b1bd70.ne.jpeg) | ![ब्रेल छवि पूर्व-प्रशोधित](../../../../../translated_images/braille-result.46530fea020b03c7.ne.png) | ![ब्रेल प्रतीकहरू](../../../../../translated_images/braille-symbols.0159185ab69d5339.ne.png) +![ब्रेल छवि](../../../../../translated_images/ne/braille.341962ff76b1bd70.jpeg) | ![ब्रेल छवि पूर्व-प्रशोधित](../../../../../translated_images/ne/braille-result.46530fea020b03c7.png) | ![ब्रेल प्रतीकहरू](../../../../../translated_images/ne/braille-symbols.0159185ab69d5339.png) ----|-----|----- > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट * **फ्रेम भिन्नता प्रयोग गरेर भिडियोमा गति पत्ता लगाउने**। यदि क्यामेरा स्थिर छ भने, क्यामेरा फिडका फ्रेमहरू एकअर्कासँग धेरै समान हुनुपर्छ। किनभने फ्रेमहरू एरेको रूपमा प्रतिनिधित्व गरिन्छ, दुई लगातार फ्रेमहरूको लागि ती एरेहरू घटाएर मात्र हामी पिक्सेल भिन्नता प्राप्त गर्नेछौं, जुन स्थिर फ्रेमहरूको लागि कम हुनुपर्छ, र छविमा पर्याप्त गति हुँदा उच्च हुन्छ। -![भिडियो फ्रेम र फ्रेम भिन्नताको छवि](../../../../../translated_images/frame-difference.706f805491a0883c.ne.png) +![भिडियो फ्रेम र फ्रेम भिन्नताको छवि](../../../../../translated_images/ne/frame-difference.706f805491a0883c.png) > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **डेंस अप्टिकल फ्लो** प्रत्येक पिक्सेलको लागि भेक्टर फिल्ड गणना गर्दछ जसले देखाउँछ कि यो कहाँ सर्दैछ। - **स्पार्स अप्टिकल फ्लो** छविमा केही विशिष्ट फिचरहरू (जस्तै किनाराहरू) लिनेमा आधारित छ, र फ्रेमबाट फ्रेममा तिनीहरूको ट्राजेक्टोरी निर्माण गर्ने। -![अप्टिकल फ्लोको छवि](../../../../../translated_images/optical.1f4a94464579a83a.ne.png) +![अप्टिकल फ्लोको छवि](../../../../../translated_images/ne/optical.1f4a94464579a83a.png) > छवि [OpenCV.ipynb](OpenCV.ipynb) बाट diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 4d1e23df..1951e9fa 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 एक नेटवर्क हो जसले २०१४ मा ImageNet टप-५ वर्गीकरणमा ९२.७% शुद्धता प्राप्त गर्यो। यसको लेयर संरचना निम्नानुसार छ: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ne.jpg) +![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.jpg) जस्तो कि तपाईं देख्न सक्नुहुन्छ, VGG ले परम्परागत पिरामिड आर्किटेक्चर अनुसरण गर्दछ, जुन कनभोल्युसन-पूलिङ लेयरहरूको क्रम हो। -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ne.jpg) +![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) बाट छवि diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8f2a9903..448ac48e 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "त्यसैले, एउटा सामान्य CNN मा धेरै कनभोल्युसनल तहहरू हुन्छन्, जसको बीचमा पूलिङ तहहरू हुन्छन् तस्बिरको आयाम घटाउनका लागि। हामी फिल्टरहरूको संख्या पनि बढाउँछौं, किनकि ढाँचाहरू जति जटिल बन्दै जान्छन् - त्यति नै धेरै सम्भावित रोचक संयोजनहरू हुन्छन् जसलाई हामी खोज्नुपर्ने हुन्छ।\n", "\n", - "![कनभोल्युसनल तहहरू र पूलिङ तहहरूको पिरामिड देखाउने तस्बिर।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ne.png)\n", + "![कनभोल्युसनल तहहरू र पूलिङ तहहरूको पिरामिड देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "स्थानिक आयाम घटाउने र विशेषता/फिल्टर आयाम बढाउने कारणले गर्दा, यो आर्किटेक्चरलाई **पिरामिड आर्किटेक्चर** पनि भनिन्छ।\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 5cbd5695..b0d7b4b2 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "त्यसैले, एउटा सामान्य CNN मा धेरै कन्भोल्युसनल तहहरू हुनेछन्, जसको बीचमा पूलिङ तहहरू तस्बिरको आयाम घटाउन प्रयोग गरिन्छ। हामी फिल्टरहरूको संख्या पनि बढाउँछौं, किनभने ढाँचाहरू जति जटिल बन्दै जान्छन् - त्यति नै धेरै सम्भावित चाखलाग्दा संयोजनहरू हुन्छन् जसलाई हामी खोज्न आवश्यक हुन्छ।\n", "\n", - "![कन्भोल्युसनल तहहरू र पूलिङ तहहरूको साथमा पिरामिड आर्किटेक्चर देखाउने तस्बिर।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ne.png)\n", + "![कन्भोल्युसनल तहहरू र पूलिङ तहहरूको साथमा पिरामिड आर्किटेक्चर देखाउने तस्बिर।](../../../../../translated_images/ne/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "स्थानिक आयाम घट्दै र विशेषता/फिल्टर आयाम बढ्दै जाने भएकाले, यस आर्किटेक्चरलाई **पिरामिड आर्किटेक्चर** पनि भनिन्छ।\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md index d64b5aa4..e32ad037 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ढाँचाहरू निकाल्न, हामी **कन्भोल्युसनल फिल्टरहरू** को धारणा प्रयोग गर्नेछौं। तपाईंलाई थाहा छ, छवि 2D-म्याट्रिक्स वा रंग गहिराइ भएको 3D-टेन्सरद्वारा प्रतिनिधित्व गरिन्छ। फिल्टर लागू गर्नुको मतलब हामी सानो **फिल्टर कर्नेल** म्याट्रिक्स लिन्छौं, र मूल छविको प्रत्येक पिक्सेलको लागि हामी छिमेकी बिन्दुहरूसँग तौलित औसत गणना गर्छौं। हामी यसलाई सानो झ्यालले सम्पूर्ण छविमा स्लाइड गर्दै, र फिल्टर कर्नेल म्याट्रिक्समा तौलहरू अनुसार सबै पिक्सेलहरू औसत गर्दै हेर्न सक्छौं। -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.ne.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ne.png) +![Vertical Edge Filter](../../../../../translated_images/ne/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/ne/filter-horiz.59b80ed4feb946ef.png) ----|---- > छवि: दिमित्री सोश्निकोभ @@ -38,7 +38,7 @@ CNN काम गर्ने तरिका निम्न महत्त् * हामी नेटवर्कलाई यसरी डिजाइन गर्न सक्छौं कि फिल्टरहरू स्वचालित रूपमा प्रशिक्षित हुन्छन् * हामी मूल छविमा मात्र होइन, उच्च-स्तरका विशेषताहरूमा ढाँचाहरू फेला पार्न उस्तै दृष्टिकोण प्रयोग गर्न सक्छौं। यसरी CNN विशेषता निकाल्ने काम विशेषताको पदानुक्रममा हुन्छ, तल्लो-स्तरका पिक्सेल संयोजनबाट सुरु गर्दै, चित्रका भागहरूको उच्च-स्तर संयोजनसम्म। -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ne.png) +![Hierarchical Feature Extraction](../../../../../translated_images/ne/FeatureExtractionCNN.d9b456cbdae7cb64.png) > छवि: [Hislop-Lynch को पेपर](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) बाट, [तिनीहरूको अनुसन्धान](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) मा आधारित @@ -55,9 +55,9 @@ CNN काम गर्ने तरिका निम्न महत्त् उदाहरणका लागि, VGG-16 को आर्किटेक्चरलाई हेरौं, जसले 2014 मा ImageNet को शीर्ष-5 वर्गीकरणमा 92.7% शुद्धता प्राप्त गर्यो: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ne.jpg) +![ImageNet Layers](../../../../../translated_images/ne/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ne.jpg) +![ImageNet Pyramid](../../../../../translated_images/ne/vgg-16-arch.64ff2137f50dd49f.jpg) > छवि: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) बाट diff --git a/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 83df5641..7f3bb440 100644 --- a/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ne/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: हामी [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) प्रयोग गर्नेछौं, जसमा कुकुर र बिरालोका ३७ विभिन्न प्रजातिहरूका छविहरू समावेश छन्। -![हामीले प्रयोग गर्ने डेटासेट](../../../../../../translated_images/data.50b2a9d5484bdbf0.ne.png) +![हामीले प्रयोग गर्ने डेटासेट](../../../../../../translated_images/ne/data.50b2a9d5484bdbf0.png) डेटासेट डाउनलोड गर्न, यो कोड स्निपेट प्रयोग गर्नुहोस्: diff --git a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index b3ccaf89..ca413930 100644 --- a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "आदर्श बिरालोलाई देखाउनका लागि, हामी एउटा जथाभावी आवाज भएको तस्बिरबाट सुरु गर्नेछौं, र ग्रेडियन्ट डिसेन्ट अनुकूलन प्रविधि प्रयोग गरेर तस्बिरलाई समायोजन गर्ने प्रयास गर्नेछौं ताकि नेटवर्कले बिरालोलाई चिन्न सकोस्।\n", "\n", - "![अनुकूलन लूप](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ne.png)\n", + "![अनुकूलन लूप](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "यहाँ हाम्रो सुरुवाती तस्बिर छ:\n" ] diff --git a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md index f28db26c..5362a888 100644 --- a/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ne/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras र PyTorch दुवैमा सामान्य आर्किटे यहाँ VGG-16 नेटवर्कले बिरालोको तस्बिरबाट निकालेका विशेषताहरूको उदाहरण छ: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ne.png) +![Features extracted by VGG-16](../../../../../translated_images/ne/features.6291f9c7ba3a0b95.png) ## बिरालो र कुकुर डेटासेट @@ -48,19 +48,19 @@ Keras र PyTorch दुवैमा सामान्य आर्किटे हामी एउटा विधि लिन सक्छौं, जहाँ हामी एउटा र्यान्डम छविबाट सुरु गर्छौं, र त्यसपछि **ग्रेडियन्ट डिसेन्ट अप्टिमाइजेसन** प्रविधि प्रयोग गरेर त्यो छवि समायोजन गर्ने प्रयास गर्छौं, ताकि नेटवर्कले सोच्न थाल्छ कि यो बिरालो हो। -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ne.png) +![Image Optimization Loop](../../../../../translated_images/ne/ideal-cat-loop.999fbb8ff306e044.png) तर, यदि हामी यसो गर्छौं भने, हामीले र्यान्डम आवाजसँग धेरै मिल्दोजुल्दो केही प्राप्त गर्नेछौं। यसको कारण हो कि *नेटवर्कलाई इनपुट छवि बिरालो हो भनेर सोच्न बनाउने धेरै तरिकाहरू छन्*, जसमा केही दृश्य रूपमा अर्थपूर्ण छैनन्। ती छविहरूमा बिरालोको लागि सामान्य ढाँचाहरू धेरै हुन्छन्, तर तिनीहरूलाई दृश्य रूपमा विशिष्ट बनाउने कुनै बाध्यता छैन। नतिजा सुधार गर्न, हामी हानि कार्यमा अर्को पद थप्न सक्छौं, जसलाई **भेरिएसन हानि** भनिन्छ। यो एउटा मेट्रिक हो, जसले छविको छेउछाउका पिक्सेलहरू कति समान छन् भनेर देखाउँछ। भेरिएसन हानि न्यूनतम गर्दा छवि चिल्लो हुन्छ, र आवाज हट्छ - जसले दृश्य रूपमा आकर्षक ढाँचाहरू प्रकट गर्छ। यहाँ उच्च सम्भावनाका साथ बिरालो र जेब्रा भनेर वर्गीकृत गरिएका "आदर्श" छविहरूको उदाहरण छ: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ne.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ne.png) +![Ideal Cat](../../../../../translated_images/ne/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/ne/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *आदर्श बिरालो* | *आदर्श जेब्रा* यस्तै विधि प्रयोग गरेर न्यूरल नेटवर्कमा **adversarial attacks** गर्न सकिन्छ। मानौं हामी न्यूरल नेटवर्कलाई मूर्ख बनाउन चाहन्छौं र कुकुरलाई बिरालो जस्तो देखाउन चाहन्छौं। यदि हामी कुकुरको छवि लिन्छौं, जुन नेटवर्कले कुकुर भनेर पहिचान गर्छ, हामी त्यसलाई थोरै समायोजन गर्न सक्छौं ग्रेडियन्ट डिसेन्ट अप्टिमाइजेसन प्रयोग गरेर, जबसम्म नेटवर्कले यसलाई बिरालो भनेर वर्गीकृत गर्न थाल्दैन: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ne.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ne.png) +![Picture of a Dog](../../../../../translated_images/ne/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/ne/adversarial-dog.d9fc7773b0142b89.png) -----|----- *कुकुरको मूल छवि* | *कुकुरको छवि बिरालो भनेर वर्गीकृत गरिएको* diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 12d8d728..92144c8c 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "किनभने हामी ऑटोएन्कोडरलाई मूल छविबाट सकेसम्म धेरै जानकारी समात्न प्रशिक्षण दिइरहेका छौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको अर्थ समात्नको लागि उत्तम **एम्बेडिङ** खोज्ने प्रयास गर्छ।\n", "\n", - "![ऑटोएन्कोडर चित्र](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ne.jpg)\n", + "![ऑटोएन्कोडर चित्र](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट\n", "\n", diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 52e7c2de..1d064ac5 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "किनकि हामी ऑटोएन्कोडरलाई मूल छविबाट यथासम्भव धेरै जानकारी समात्न प्रशिक्षण गरिरहेका छौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको अर्थ समात्नको लागि उत्तम **एम्बेडिङ** खोज्ने प्रयास गर्छ।\n", "\n", - "![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ne.jpg)\n", + "![ऑटोएन्कोडर डायग्राम](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट*\n", "\n", diff --git a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md index 9036ec7f..953c043d 100644 --- a/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ne/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNs प्रशिक्षण गर्दा, एउटा समस्य किनकि हामी अटोएनकोडरलाई मूल छविबाट सकेसम्म धेरै जानकारी समात्न प्रशिक्षण गर्दैछौं ताकि सही पुनर्निर्माण गर्न सकियोस्, नेटवर्कले इनपुट छविहरूको उत्तम **एम्बेडिङ** पत्ता लगाउन प्रयास गर्छ। -![अटोएनकोडर आरेख](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ne.jpg) +![अटोएनकोडर आरेख](../../../../../translated_images/ne/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > छवि [Keras ब्लग](https://blog.keras.io/building-autoencoders-in-keras.html) बाट diff --git a/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md index 5ec8e485..20853f09 100644 --- a/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ne/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [पूर्व-व्याख्यान क्विज](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![वस्तु पहिचान](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ne.png) +![वस्तु पहिचान](../../../../../translated_images/ne/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > छवि [YOLO v2 वेबसाइट](https://pjreddie.com/darknet/yolov2/) बाट @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. प्रत्येक टाइलमा छवि वर्गीकरण चलाउनुहोस्। 3. ती टाइलहरू, जसले पर्याप्त उच्च सक्रियता देखाउँछन्, तिनीहरूमा खोजिएको वस्तु भएको मान्न सकिन्छ। -![साधारण वस्तु पहिचान](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ne.png) +![साधारण वस्तु पहिचान](../../../../../translated_images/ne/naive-detection.e7f1ba220ccd08c6.png) > *छवि [व्यायाम नोटबुक](ObjectDetection-TF.ipynb) बाट* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - २० वर्गहरू * [COCO](http://cocodataset.org/#home) - सामान्य वस्तुहरू सन्दर्भमा। ८० वर्गहरू, सीमाना बक्सहरू र खण्डन मास्कहरू -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ne.jpg) +![COCO](../../../../../translated_images/ne/coco-examples.71bc60380fa6cceb.jpg) ## वस्तु पहिचान मेट्रिक्स @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: जहाँ छवि वर्गीकरणका लागि एल्गोरिदमको प्रदर्शन मापन गर्न सजिलो छ, वस्तु पहिचानका लागि वर्गको शुद्धता र अनुमानित सीमाना बक्स स्थानको सटीकता दुवै मापन गर्न आवश्यक छ। पछिल्लोका लागि, हामी **Intersection over Union** (IoU) प्रयोग गर्छौं, जसले दुई बक्सहरू (वा दुई मनमानी क्षेत्रहरू) कत्तिको ओभरल्याप गर्छन् भनेर मापन गर्छ। -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ne.png) +![IoU](../../../../../translated_images/ne/iou_equation.9a4751d40fff4e11.png) > *[IoU सम्बन्धी उत्कृष्ट ब्लग पोस्ट](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) बाट चित्र २* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ले [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) प्रयोग गरी ROI क्षेत्रहरूको पदानुक्रम संरचना उत्पन्न गर्छ, जसलाई CNN फिचर एक्स्ट्र्याक्टरहरू र SVM वर्गीकरणकर्ताहरू मार्फत पास गरिन्छ, वस्तु वर्ग निर्धारण गर्न, र *सीमाना बक्स* समन्वय निर्धारण गर्न रेखीय रिग्रेसन प्रयोग गरिन्छ। [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ne.png) +![RCNN](../../../../../translated_images/ne/rcnn1.cae407020dfb1d1f.png) > *van de Sande et al. ICCV’11 बाट छवि* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ne.png) +![RCNN-1](../../../../../translated_images/ne/rcnn2.2d9530bb83516484.png) > *[यो ब्लग](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) बाट छविहरू* @@ -109,7 +109,7 @@ $$ यो दृष्टिकोण R-CNN जस्तै हो, तर क्षेत्रहरू कन्भोल्युसन तहहरू लागू भएपछि परिभाषित गरिन्छन्। -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ne.png) +![FRCNN](../../../../../translated_images/ne/f-rcnn.3cda6d9bb4188875.png) > [आधिकारिक पेपर](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 बाट छवि @@ -117,7 +117,7 @@ $$ यस दृष्टिकोणको मुख्य विचार भनेको क्षेत्रहरू भविष्यवाणी गर्न न्यूरल नेटवर्क प्रयोग गर्नु हो - जसलाई *क्षेत्र प्रस्ताव नेटवर्क* भनिन्छ। [पेपर](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ne.png) +![FasterRCNN](../../../../../translated_images/ne/faster-rcnn.8d46c099b87ef30a.png) > [आधिकारिक पेपर](https://arxiv.org/pdf/1506.01497.pdf) बाट छवि @@ -129,7 +129,7 @@ $$ 2. फिचरहरू **पोजिसन-सेंसिटिभ स्कोर म्याप** द्वारा प्रशोधन गरिन्छ। $C$ वर्गका प्रत्येक वस्तु $k\times k$ क्षेत्रहरूमा विभाजन गरिन्छ, र हामी वस्तुका भागहरू भविष्यवाणी गर्न प्रशिक्षण गर्छौं। 3. $k\times k$ क्षेत्रका प्रत्येक भागका लागि सबै नेटवर्कहरूले वस्तु वर्गहरूको लागि मतदान गर्छन्, र अधिकतम भोट भएको वस्तु वर्ग चयन गरिन्छ। -![r-fcn छवि](../../../../../translated_images/r-fcn.13eb88158b99a3da.ne.png) +![r-fcn छवि](../../../../../translated_images/ne/r-fcn.13eb88158b99a3da.png) > [आधिकारिक पेपर](https://arxiv.org/abs/1605.06409) बाट छवि @@ -140,7 +140,7 @@ YOLO एक वास्तविक-समय एक-पास एल्गो * छवि $S\times S$ क्षेत्रहरूमा विभाजन गरिन्छ। * प्रत्येक क्षेत्रका लागि, **CNN** ले $n$ सम्भावित वस्तुहरू, *सीमाना बक्स* समन्वय र *विश्वास* = *संभाव्यता* * IoU भविष्यवाणी गर्छ। - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ne.png) + ![YOLO](../../../../../translated_images/ne/yolo.a2648ec82ee8bb4e.png) > [आधिकारिक पेपर](https://arxiv.org/abs/1506.02640) बाट छवि diff --git a/translations/ne/lessons/4-ComputerVision/README.md b/translations/ne/lessons/4-ComputerVision/README.md index e3ada9d7..00722a9d 100644 --- a/translations/ne/lessons/4-ComputerVision/README.md +++ b/translations/ne/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # कम्प्युटर भिजन -![कम्प्युटर भिजन सामग्रीको सारांश डुडलमा](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ne.png) +![कम्प्युटर भिजन सामग्रीको सारांश डुडलमा](../../../../translated_images/ne/ai-computervision.6506ebebac3fbf76.png) यस खण्डमा हामी सिक्नेछौं: diff --git a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 6034b8a1..03e3fa16 100644 --- a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**शब्दहरूको झोला** (BoW) भेक्टर प्रतिनिधित्व परम्परागत भेक्टर प्रतिनिधित्वमा सबैभन्दा धेरै प्रयोग गरिने विधि हो। प्रत्येक शब्दलाई भेक्टरको सूचकसँग जोडिन्छ, र भेक्टरको तत्त्वले कुनै विशेष दस्तावेजमा शब्दको उपस्थितिको सङ्ख्या समावेश गर्दछ।\n", "\n", - "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी देखिन्छ भन्ने देखाउने छवि।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ne.png)\n", + "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी देखिन्छ भन्ने देखाउने छवि।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: तपाईं BoW लाई पाठका व्यक्तिगत शब्दहरूको लागि सबै एक-तर्फ-कोड गरिएको भेक्टरहरूको योगको रूपमा पनि सोच्न सक्नुहुन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index b2c64a6e..764d5eb3 100644 --- a/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ne/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**शब्दहरूको झोला** (BoW) भेक्टर प्रतिनिधित्व परम्परागत भेक्टर प्रतिनिधित्वहरू मध्ये सबैभन्दा सरल र बुझ्न सजिलो हो। प्रत्येक शब्दलाई भेक्टरको सूचकसँग जोडिन्छ, र भेक्टरको तत्वले कुनै पनि दस्तावेजमा प्रत्येक शब्दको उपस्थितिको संख्या समावेश गर्दछ।\n", "\n", - "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी प्रतिनिधित्व गरिन्छ भन्ने देखाउने चित्र।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ne.png)\n", + "![शब्दहरूको झोला भेक्टर प्रतिनिधित्व मेमोरीमा कसरी प्रतिनिधित्व गरिन्छ भन्ने देखाउने चित्र।](../../../../../translated_images/ne/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: तपाईं BoW लाई पाठमा व्यक्तिगत शब्दहरूको लागि एक-हट-एन्कोड गरिएको भेक्टरहरूको योगको रूपमा पनि सोच्न सक्नुहुन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 4fd540e0..16f68fe4 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "हाम्रो नेटवर्कमा पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी ब्याग-अफ-वर्ड्स मोडेलबाट **एम्बेडिङ ब्याग** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी पहिलो पटक हाम्रो पाठका प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङहरूमा `sum`, `average` वा `max` जस्ता कुनै एग्रिगेट फङ्सन गणना गर्छौं। \n", "\n", - "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ क्लासिफायर देखाउने छवि।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ne.png)\n", + "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ क्लासिफायर देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "हाम्रो क्लासिफायर न्यूरल नेटवर्क एम्बेडिङ लेयरबाट सुरु हुनेछ, त्यसपछि एग्रिगेसन लेयर, र यसको माथि लीनियर क्लासिफायर हुनेछ:\n" ] @@ -176,7 +176,7 @@ "\n", "अघिल्लो आर्किटेक्चरमा, हामीले सबै अनुक्रमहरूलाई एउटै लम्बाइमा ल्याउनका लागि padding गर्नुपर्ने हुन्थ्यो ताकि तिनीहरूलाई मिनिब्याचमा फिट गर्न सकियोस्। यो भिन्न-लम्बाइ अनुक्रमहरूको प्रतिनिधित्व गर्ने सबैभन्दा प्रभावकारी तरिका होइन - अर्को तरिका भनेको **offset** भेक्टर प्रयोग गर्नु हो, जसले एउटै ठूलो भेक्टरमा भण्डारण गरिएका सबै अनुक्रमहरूको offsets राख्छ।\n", "\n", - "![Offset अनुक्रम प्रतिनिधित्व देखाउने चित्र](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ne.png)\n", + "![Offset अनुक्रम प्रतिनिधित्व देखाउने चित्र](../../../../../translated_images/ne/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: माथिको चित्रमा, हामीले क्यारेक्टरहरूको अनुक्रम देखाएका छौं, तर हाम्रो उदाहरणमा हामी शब्दहरूको अनुक्रमसँग काम गरिरहेका छौं। यद्यपि, offset भेक्टर प्रयोग गरेर अनुक्रमहरूको प्रतिनिधित्व गर्ने सामान्य सिद्धान्त उस्तै रहन्छ।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW छिटो हुन्छ, जबकि स्किप-ग्राम ढिलो हुन्छ, तर दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ।\n", "\n", - "![CBoW र स्किप-ग्राम एल्गोरिदमहरू शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने तरिका देखाउने चित्र।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ne.png)\n", + "![CBoW र स्किप-ग्राम एल्गोरिदमहरू शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने तरिका देखाउने चित्र।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News डेटासेटमा प्रि-ट्रेन गरिएको word2vec एम्बेडिङसँग प्रयोग गर्न, हामी **gensim** लाइब्रेरी प्रयोग गर्न सक्छौं। तल हामी 'neural' शब्दसँग सबैभन्दा मिल्दो शब्दहरू फेला पार्छौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index df99b3e4..b199245c 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ne/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "हाम्रो नेटवर्कको पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी ब्याग-ऑफ-वर्ड्स मोडेलबाट **एम्बेडिङ ब्याग** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी पहिलो पटक हाम्रो पाठको प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङहरूमा केही समग्र कार्य गणना गर्छौं, जस्तै `sum`, `average` वा `max`। \n", "\n", - "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरण देखाउने छवि।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ne.png)\n", + "![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरण देखाउने छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "हाम्रो वर्गीकरण न्युरल नेटवर्क निम्न लेयरहरू समावेश गर्दछ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW छिटो हुन्छ, जबकि स्किप-ग्राम ढिलो भए पनि यो दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ।\n", "\n", - "![CBoW र स्किप-ग्राम एल्गोरिदमहरूलाई शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न देखाउने छवि।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ne.png)\n", + "![CBoW र स्किप-ग्राम एल्गोरिदमहरूलाई शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न देखाउने छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News डेटासेटमा प्रि-ट्रेन गरिएको Word2Vec एम्बेडिङसँग प्रयोग गर्न, हामी **gensim** लाइब्रेरी प्रयोग गर्न सक्छौं। तल हामी 'neural' शब्दसँग सबैभन्दा मिल्दोजुल्दो शब्दहरू फेला पार्छौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/14-Embeddings/README.md b/translations/ne/lessons/5-NLP/14-Embeddings/README.md index 6f2e307a..d7aba910 100644 --- a/translations/ne/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ne/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: हाम्रो वर्गीकरणकर्ता नेटवर्कमा पहिलो लेयरको रूपमा एम्बेडिङ लेयर प्रयोग गरेर, हामी बाग-ऑफ-वर्ड्सबाट **embedding bag** मोडेलमा स्विच गर्न सक्छौं, जहाँ हामी हाम्रो पाठमा प्रत्येक शब्दलाई सम्बन्धित एम्बेडिङमा रूपान्तरण गर्छौं, र त्यसपछि ती सबै एम्बेडिङ्समा केही समग्र कार्य जस्तै `sum`, `average` वा `max` गणना गर्छौं। -![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरणकर्ताको छवि।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ne.png) +![पाँच अनुक्रम शब्दहरूको लागि एम्बेडिङ वर्गीकरणकर्ताको छवि।](../../../../../translated_images/ne/embedding-classifier-example.b77f021a7ee67eee.png) > लेखकद्वारा प्रदान गरिएको छवि @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW छिटो छ, जबकि स्किप-ग्राम ढिलो छ, तर दुर्लभ शब्दहरूको प्रतिनिधित्व गर्न राम्रो काम गर्छ। -![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न CBoW र स्किप-ग्राम एल्गोरिदमहरूको छवि।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ne.png) +![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्न CBoW र स्किप-ग्राम एल्गोरिदमहरूको छवि।](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [यस पेपर](https://arxiv.org/pdf/1301.3781.pdf) बाट लिइएको छवि diff --git a/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md index 94cbc18c..4cee2eca 100644 --- a/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ne/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec र GloVe जस्ता सेम्यान्टिक एम् * **Continuous Bag-of-Words** (CBoW), जहाँ हामी टोकन अनुक्रम $W_{-N}$, ..., $W_N$ को बीचको टोकन $W_0$ भविष्यवाणी गर्छौं। * **Skip-gram**, जहाँ हामी बीचको टोकन $W_0$ बाट छेउछाउका टोकनहरूको सेट {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} भविष्यवाणी गर्छौं। -![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने एल्गोरिदमको कागजबाट चित्र](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ne.png) +![शब्दहरूलाई भेक्टरमा रूपान्तरण गर्ने एल्गोरिदमको कागजबाट चित्र](../../../../../translated_images/ne/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > चित्र [यस कागज](https://arxiv.org/pdf/1301.3781.pdf) बाट diff --git a/translations/ne/lessons/5-NLP/16-RNN/README.md b/translations/ne/lessons/5-NLP/16-RNN/README.md index a793cb13..c61b0524 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/README.md +++ b/translations/ne/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: पाठ अनुक्रमको अर्थ समेट्नका लागि, हामीले अर्को न्यूरल नेटवर्क वास्तुकला प्रयोग गर्नुपर्छ, जसलाई **पुनरावर्ती न्यूरल नेटवर्क** (RNN) भनिन्छ। RNN मा, हामी हाम्रो वाक्यलाई नेटवर्कमार्फत एक पटकमा एउटा प्रतीक पठाउँछौं, र नेटवर्कले केही **स्थिति** उत्पादन गर्छ, जसलाई हामी अर्को प्रतीकसँग पुनः नेटवर्कमा पठाउँछौं। -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ne.png) +![RNN](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.png) > लेखकद्वारा तयार गरिएको छवि @@ -61,7 +61,7 @@ LSTM नेटवर्क RNN जस्तै व्यवस्थित छ, पुनरावर्ती नेटवर्क, चाहे एक-दिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका निश्चित ढाँचाहरू समात्छ, र तिनीहरूलाई स्थिति भेक्टरमा भण्डारण गर्न वा आउटपुटमा पास गर्न सक्छ। कन्भोल्युसनल नेटवर्कहरूको जस्तै, हामी पहिलो तहले निकालेका तल्लो-स्तरका ढाँचाहरूबाट उच्च-स्तरका ढाँचाहरू समात्न अर्को पुनरावर्ती तह निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-तह RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पास गरिन्छ। -![बहु-तह लामो-छोटो-समय-स्मृति RNN देखाउने छवि](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ne.jpg) +![बहु-तह लामो-छोटो-समय-स्मृति RNN देखाउने छवि](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg) *फर्नान्डो लोपेजको [यो अद्भुत पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट लिइएको चित्र* diff --git a/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 8e2c052e..21b30539 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "पुनरावर्ती नेटवर्क, एक-दिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका केही ढाँचाहरू समात्छ, र तिनीहरूलाई अवस्था भेक्टरमा भण्डारण गर्न वा आउटपुटमा पास गर्न सक्छ। जस्तै कनभोल्युसनल नेटवर्कहरूमा, हामी पहिलो तहले निकालेका तल्लो-स्तरीय ढाँचाहरूबाट उच्च-स्तरीय ढाँचाहरू समात्न अर्को पुनरावर्ती तह माथि निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-स्तरीय RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पास गरिन्छ।\n", "\n", - "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ne.jpg)\n", + "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*फर्नान्डो लोपेजको [यो उत्कृष्ट पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट चित्र*\n", "\n", diff --git a/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb index 8dd388e4..e0695a78 100644 --- a/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ne/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "पाठ अनुक्रमको अर्थ समेट्न, हामी **पुनरावर्ती न्युरल नेटवर्क** भनिने न्युरल नेटवर्क आर्किटेक्चर प्रयोग गर्नेछौं। RNN प्रयोग गर्दा, हामी हाम्रो वाक्यलाई नेटवर्कमा एक पटकमा एक टोकन पास गर्छौं, र नेटवर्कले केही **स्थिति** उत्पादन गर्छ, जुन हामी अर्को टोकनसँग फेरि नेटवर्कमा पास गर्छौं।\n", "\n", - "![पुनरावर्ती न्युरल नेटवर्क उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/rnn.27f5c29c53d727b5.ne.png)\n", + "![पुनरावर्ती न्युरल नेटवर्क उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn.27f5c29c53d727b5.png)\n", "\n", "टोकनहरूको इनपुट अनुक्रम $X_0,\\dots,X_n$ दिइएको अवस्थामा, RNN ले न्युरल नेटवर्क ब्लकहरूको अनुक्रम सिर्जना गर्छ, र यो अनुक्रमलाई ब्याकप्रोपोगेसन प्रयोग गरेर अन्त-देखि-अन्तसम्म प्रशिक्षण गर्छ। प्रत्येक नेटवर्क ब्लकले $(X_i,S_i)$ जोडीलाई इनपुटको रूपमा लिन्छ, र परिणामस्वरूप $S_{i+1}$ उत्पादन गर्छ। अन्तिम स्थिति $S_n$ वा आउटपुट $Y_n$ लाई रैखिक वर्गीकरणकर्तामा पठाइन्छ ताकि परिणाम उत्पादन गर्न सकियोस्। सबै नेटवर्क ब्लकहरूले समान तौलहरू साझा गर्छन्, र एक ब्याकप्रोपोगेसन पास प्रयोग गरेर अन्त-देखि-अन्तसम्म प्रशिक्षण गरिन्छ।\n", "\n", @@ -369,7 +369,7 @@ "\n", "पुनरावर्ती नेटवर्कहरूले, एकदिशात्मक होस् वा द्विदिशात्मक, अनुक्रमभित्रका ढाँचाहरूलाई समात्छन्, र तिनीहरूलाई अवस्था भेक्टरहरूमा भण्डारण गर्छन् वा तिनीहरूलाई आउटपुटको रूपमा फिर्ता दिन्छन्। कन्भोल्युसनल नेटवर्कहरूको जस्तै, हामी पहिलो तहले निकालेका तल्लो स्तरका ढाँचाहरूबाट उच्च स्तरका ढाँचाहरू समात्न अर्को पुनरावर्ती तह निर्माण गर्न सक्छौं। यसले हामीलाई **बहु-स्तरीय RNN** को अवधारणामा पुर्‍याउँछ, जसमा दुई वा बढी पुनरावर्ती नेटवर्कहरू हुन्छन्, जहाँ अघिल्लो तहको आउटपुटलाई अर्को तहमा इनपुटको रूपमा पठाइन्छ।\n", "\n", - "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ne.jpg)\n", + "![बहु-स्तरीय लामो-छोटो-अवधि-स्मृति RNN देखाउने चित्र](../../../../../translated_images/ne/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*फर्नान्डो लोपेजको [यो उत्कृष्ट पोस्ट](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) बाट लिइएको चित्र।*\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index e5418391..cf955d99 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "हामी RNN लाई पाठ उत्पन्न गर्न प्रशिक्षण दिने तरिका निम्नानुसार हुनेछ। प्रत्येक चरणमा, हामी `nchars` लम्बाइको अक्षरहरूको श्रृंखला लिनेछौं, र प्रत्येक इनपुट अक्षरको लागि नेटवर्कलाई अर्को आउटपुट अक्षर उत्पन्न गर्न अनुरोध गर्नेछौं:\n", "\n", - "![RNN ले 'HELLO' शब्द उत्पन्न गरिरहेको उदाहरण देखाउने छवि।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ne.png)\n", + "![RNN ले 'HELLO' शब्द उत्पन्न गरिरहेको उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png)\n", "\n", "वास्तविक परिदृश्यमा निर्भर गर्दै, हामीले केही विशेष अक्षरहरू पनि समावेश गर्न चाहन सक्छौं, जस्तै *end-of-sequence* ``। हाम्रो अवस्थामा, हामी केवल नेटवर्कलाई अन्तहीन पाठ उत्पन्न गर्न प्रशिक्षण दिन चाहन्छौं, त्यसैले हामी प्रत्येक श्रृंखलाको आकारलाई `nchars` टोकनहरूको बराबरमा स्थिर गर्नेछौं। तदनुसार, प्रत्येक प्रशिक्षण उदाहरण `nchars` इनपुटहरू र `nchars` आउटपुटहरू (जसले इनपुट श्रृंखलालाई एक प्रतीक बायाँ सिफ्ट गरेको हुन्छ) बाट बनेको हुनेछ। मिनिब्याचमा यस्ता धेरै श्रृंखलाहरू हुनेछन्।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 8ea0585e..f60c954e 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "हामीले RNN लाई समाचार शीर्षकहरू उत्पन्न गर्न प्रशिक्षण गर्ने तरिका यस प्रकार हुनेछ। प्रत्येक चरणमा, हामी एउटा शीर्षक लिनेछौं, जसलाई RNN मा खुवाइनेछ, र प्रत्येक इनपुट अक्षरको लागि, हामी नेटवर्कलाई अर्को आउटपुट अक्षर उत्पन्न गर्न अनुरोध गर्नेछौं:\n", "\n", - "![शब्द 'HELLO' को RNN द्वारा उत्पन्न गर्ने उदाहरण देखाउने छवि।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ne.png)\n", + "![शब्द 'HELLO' को RNN द्वारा उत्पन्न गर्ने उदाहरण देखाउने छवि।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png)\n", "\n", "हाम्रो अनुक्रमको अन्तिम अक्षरको लागि, हामी नेटवर्कलाई `` टोकन उत्पन्न गर्न अनुरोध गर्नेछौं।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md index c83ce9ee..c18b2875 100644 --- a/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ne/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) र तिनका गेटेड सेल यसले विभिन्न न्युरल आर्किटेक्चरहरूलाई अनुमति दिन्छ जुन तलको चित्रमा देखाइएको छ: -![सामान्य पुनरावर्ती न्युरल नेटवर्क ढाँचाहरू देखाउने चित्र।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ne.jpg) +![सामान्य पुनरावर्ती न्युरल नेटवर्क ढाँचाहरू देखाउने चित्र।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > [Andrej Karpaty](http://karpathy.github.io/) द्वारा [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ब्लग पोस्टबाट चित्र @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) र तिनका गेटेड सेल हामीले यो RNN लाई चरण-दर-चरण पाठ उत्पन्न गर्न तालिम दिनेछौं। प्रत्येक चरणमा, हामी `nchars` लम्बाइको क्यारेक्टरहरूको अनुक्रम लिनेछौं, र नेटवर्कलाई प्रत्येक इनपुट क्यारेक्टरको लागि अर्को आउटपुट क्यारेक्टर उत्पन्न गर्न सोध्नेछौं: -![शब्द 'HELLO' को RNN उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ne.png) +![शब्द 'HELLO' को RNN उत्पादनको उदाहरण देखाउने चित्र।](../../../../../translated_images/ne/rnn-generate.56c54afb52f9781d.png) पाठ उत्पन्न गर्दा (इनफरेन्सको समयमा), हामी केही **प्रम्प्ट** बाट सुरु गर्छौं, जुन RNN सेलहरू मार्फत पास गरिन्छ यसको मध्यवर्ती अवस्था उत्पन्न गर्न, र त्यसपछि यो अवस्थाबाट उत्पादन सुरु हुन्छ। हामी एक पटकमा एक क्यारेक्टर उत्पन्न गर्छौं, र अर्को RNN सेलमा अवस्था र उत्पन्न क्यारेक्टर पास गर्छौं अर्को क्यारेक्टर उत्पन्न गर्न, जबसम्म हामी पर्याप्त क्यारेक्टरहरू उत्पन्न गर्दैनौं। diff --git a/translations/ne/lessons/5-NLP/18-Transformers/README.md b/translations/ne/lessons/5-NLP/18-Transformers/README.md index bc99cb2b..a20335b2 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ne/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs प्रयोग गर्दा, sequence-to-sequence दुई पु **ध्यान मेकानिज्महरू** प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई प्रत्येक RNN को आउटपुट भविष्यवाणीमा तौल दिने माध्यम प्रदान गर्छ। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN र आउटपुट RNN का बीचमा छोटो मार्गहरू सिर्जना गर्नु हो। यस प्रकार, आउटपुट प्रतीक yt उत्पन्न गर्दा, हामी सबै इनपुट लुकाइएको अवस्थाहरू hi लाई विभिन्न तौल गुणांकहरू αt,i सहित विचार गर्नेछौं। -![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ne.png) +![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा additive attention मेकानिज्मसहितको encoder-decoder मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत। ध्यान म्याट्रिक्स {αi,j} ले इनपुट अनुक्रमका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्दको उत्पत्तिमा कति भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण छ: -![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ne.png) +![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट चित्र (Fig.3) @@ -66,7 +66,7 @@ Positional embedding को परिणामले मूल टोकन र अब, हामीले हाम्रो अनुक्रमभित्र केही ढाँचाहरू कब्जा गर्न आवश्यक छ। यो गर्न ट्रान्सफर्मरहरूले **self-attention** मेकानिज्म प्रयोग गर्छन्, जुन इनपुट र आउटपुटको रूपमा समान अनुक्रममा लागू गरिएको ध्यान हो। Self-attention लागू गर्दा हामी वाक्यभित्रको **सन्दर्भ**लाई विचार गर्न सक्छौं, र कुन शब्दहरू परस्पर सम्बन्धित छन् हेर्न सक्छौं। उदाहरणका लागि, यसले *it* जस्ता coreferences द्वारा उल्लेख गरिएका शब्दहरू हेर्न र सन्दर्भलाई विचार गर्न अनुमति दिन्छ: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ne.png) +![](../../../../../translated_images/ne/CoreferenceResolution.861924d6d384a7d6.png) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) बाट चित्र। @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs मा प्रयोग गरिएको ध **BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-लेयर ट्रान्सफर्मर नेटवर्क हो, *BERT-base* को लागि 12 लेयरहरू, र *BERT-large* को लागि 24। मोडेललाई पहिलो पटक ठूलो पाठ डाटाको संग्रह (WikiPedia + किताबहरू) मा unsupervised प्रशिक्षण (वाक्यमा masked शब्दहरूको भविष्यवाणी गर्दै) प्रयोग गरेर प्रि-ट्रेन गरिन्छ। प्रि-ट्रेनिङको क्रममा मोडेलले भाषा बुझ्ने महत्त्वपूर्ण स्तरहरू अवशोषित गर्छ, जसलाई अन्य डाटासेटहरूसँग fine tuning गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **transfer learning** भनिन्छ। -![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ne.png) +![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > [स्रोत](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 91de8068..decc2b06 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ne/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ध्यान केन्द्रित गर्ने प्रणालीहरू** (Attention Mechanisms) ले प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई RNN को प्रत्येक आउटपुट भविष्यवाणीमा तौल दिने माध्यम प्रदान गर्छ। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN का मध्यवर्ती अवस्थाहरू र आउटपुट RNN बीच छोटो मार्गहरू सिर्जना गर्नु हो। यसरी, आउटपुट प्रतीक $y_t$ उत्पन्न गर्दा, हामी सबै इनपुट लुकेका अवस्थाहरू $h_i$ लाई विभिन्न तौल गुणांक $\\alpha_{t,i}$ का साथ विचार गर्नेछौं।\n", "\n", - "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ne.png)\n", + "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा एडिटिभ ध्यान प्रणाली भएको एन्कोडर-डिकोडर मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत]*\n", "\n", "ध्यान म्याट्रिक्स $\\{\\alpha_{i,j}\\}$ ले इनपुटका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्द उत्पन्न गर्न कत्तिको भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण दिइएको छ:\n", "\n", - "![RNNsearch-50 द्वारा फेला परेको नमूना संरेखण देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ne.png)\n", + "![RNNsearch-50 द्वारा फेला परेको नमूना संरेखण देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट लिइएको चित्र (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-तह ट्रान्सफर्मर नेटवर्क हो, जसमा *BERT-base* का लागि १२ तहहरू र *BERT-large* का लागि २४ तहहरू छन्। मोडेललाई पहिलो पटक ठूलो पाठ डाटाको कर्पस (WikiPedia + पुस्तकहरू) मा असुपरभाइज्ड प्रशिक्षण (वाक्यमा लुकेका शब्दहरूको भविष्यवाणी) प्रयोग गरेर पूर्व-प्रशिक्षण गरिन्छ। पूर्व-प्रशिक्षणको क्रममा मोडेलले महत्त्वपूर्ण स्तरको भाषा बुझाइ प्राप्त गर्छ, जसलाई अन्य डाटासेटहरूसँग फाइन ट्युनिङ गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **ट्रान्सफर लर्निङ** भनिन्छ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ बाट चित्र](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ne.png)\n", + "![http://jalammar.github.io/illustrated-bert/ बाट चित्र](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 जस्ता ट्रान्सफर्मर आर्किटेक्चरका धेरै भेरिएसनहरू छन्, जसलाई फाइन ट्युन गर्न सकिन्छ। [HuggingFace package](https://github.com/huggingface/) ले यी आर्किटेक्चरहरूलाई PyTorch का साथ प्रशिक्षण गर्नका लागि रिपोजिटरी प्रदान गर्दछ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index e2470e45..593ed455 100644 --- a/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ne/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ध्यान मेकानिज्महरू** RNN को प्रत्येक आउटपुट भविष्यवाणीमा प्रत्येक इनपुट भेक्टरको सन्दर्भात्मक प्रभावलाई तौल दिने माध्यम प्रदान गर्छन्। यसलाई कार्यान्वयन गर्ने तरिका भनेको इनपुट RNN का मध्यवर्ती अवस्थाहरू र आउटपुट RNN बीच छोटो मार्गहरू सिर्जना गर्नु हो। यसरी, आउटपुट प्रतीक $y_t$ उत्पन्न गर्दा, हामी सबै इनपुट लुकेका अवस्थाहरू $h_i$ लाई विभिन्न तौल गुणांक $\\alpha_{t,i}$ का साथ विचार गर्नेछौं।\n", "\n", - "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ne.png)\n", + "![एन्कोडर/डिकोडर मोडेलमा एडिटिभ ध्यान तह देखाउने छवि](../../../../../translated_images/ne/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) मा एडिटिभ ध्यान मेकानिज्म भएको एन्कोडर-डिकोडर मोडेल, [यो ब्लग पोस्ट](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) बाट उद्धृत]*\n", "\n", "ध्यान म्याट्रिक्स $\\{\\alpha_{i,j}\\}$ ले इनपुटका निश्चित शब्दहरूले आउटपुट अनुक्रमको कुनै शब्द उत्पन्न गर्न कत्तिको भूमिका खेल्छन् भन्ने प्रतिनिधित्व गर्दछ। तल यस्तो म्याट्रिक्सको उदाहरण दिइएको छ:\n", "\n", - "![RNNsearch-50 द्वारा फेला परेको नमूना एलाइनमेन्ट देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ne.png)\n", + "![RNNsearch-50 द्वारा फेला परेको नमूना एलाइनमेन्ट देखाउने छवि, Bahdanau - arviz.org बाट लिइएको](../../../../../translated_images/ne/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) बाट लिइएको चित्र (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) एक धेरै ठूलो बहु-स्तरीय ट्रान्सफर्मर नेटवर्क हो जसमा *BERT-base* का लागि १२ तहहरू र *BERT-large* का लागि २४ तहहरू छन्। यो मोडेललाई पहिलो चरणमा ठूलो पाठ डाटाको संग्रह (WikiPedia + किताबहरू) मा असुपरभाइज्ड तालिम (वाक्यमा लुकेका शब्दहरूको भविष्यवाणी गर्दै) प्रयोग गरेर प्रि-ट्रेन गरिन्छ। प्रि-ट्रेनिङको क्रममा मोडेलले भाषाको गहिरो समझ हासिल गर्छ, जसलाई अन्य डाटासेटहरूसँग फाइन ट्युनिङ गरेर उपयोग गर्न सकिन्छ। यस प्रक्रियालाई **ट्रान्सफर लर्निङ** भनिन्छ।\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ne.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ne/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 लगायतका ट्रान्सफर्मर आर्किटेक्चरका धेरै भेरिएसनहरू छन् जसलाई फाइन ट्युन गर्न सकिन्छ।\n", "\n", diff --git a/translations/ne/lessons/5-NLP/19-NER/README.md b/translations/ne/lessons/5-NLP/19-NER/README.md index 2dd25021..792c8881 100644 --- a/translations/ne/lessons/5-NLP/19-NER/README.md +++ b/translations/ne/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O किनकि हामीले टोकनहरू र वर्गहरू बीच एक-देखि-एक सम्बन्ध निर्माण गर्नुपर्छ, हामी यस चित्रबाट सही **many-to-many** न्यूरल नेटवर्क मोडेल प्रशिक्षण गर्न सक्छौं: -![सामान्य पुनरावर्ती न्यूरल नेटवर्क ढाँचाहरू देखाउने छवि।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ne.jpg) +![सामान्य पुनरावर्ती न्यूरल नेटवर्क ढाँचाहरू देखाउने छवि।](../../../../../translated_images/ne/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *[Andrej Karpathy](http://karpathy.github.io/) द्वारा [यो ब्लग पोस्ट](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) बाट छवि। NER टोकन वर्गीकरण मोडेलहरू यस चित्रको दायाँ-तर्फको नेटवर्क आर्किटेक्चरसँग मेल खान्छ।* diff --git a/translations/ne/lessons/5-NLP/README.md b/translations/ne/lessons/5-NLP/README.md index d7dca438..efc23b7a 100644 --- a/translations/ne/lessons/5-NLP/README.md +++ b/translations/ne/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # प्राकृतिक भाषा प्रशोधन -![NLP कार्यहरूको सारांश](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ne.png) +![NLP कार्यहरूको सारांश](../../../../translated_images/ne/ai-nlp.b22dcb8ca4707cea.png) यस खण्डमा, हामी **प्राकृतिक भाषा प्रशोधन (NLP)** सम्बन्धित कार्यहरू समाधान गर्न न्युरल नेटवर्कहरू प्रयोग गर्ने कुरामा ध्यान केन्द्रित गर्नेछौं। कम्प्युटरले समाधान गर्नुपर्ने धेरै NLP समस्याहरू छन्: diff --git a/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md index 63732728..d8705553 100644 --- a/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ne/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo को एक उत्कृष्ट पक्ष यो हो कि मोडेल खोलिसकेपछि, तपाईंलाई मुख्य NetLogo स्क्रिनमा लगिन्छ। यहाँ सीमित स्रोतहरू (घाँस) दिइएको भेडा र ब्वाँसोको जनसंख्यालाई वर्णन गर्ने नमूना मोडेल छ। -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ne.png) +![NetLogo Main Screen](../../../../../translated_images/ne/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov द्वारा स्क्रिनशट diff --git a/translations/ne/lessons/README.md b/translations/ne/lessons/README.md index a1de382e..0b49b1f5 100644 --- a/translations/ne/lessons/README.md +++ b/translations/ne/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # अवलोकन -![डुडलमा अवलोकन](../../../translated_images/ai-overview.0857791951d19500.ne.png) +![डुडलमा अवलोकन](../../../translated_images/ne/ai-overview.0857791951d19500.png) > स्केच नोट [Tomomi Imura](https://twitter.com/girlie_mac) द्वारा diff --git a/translations/ne/lessons/X-Extras/X1-MultiModal/README.md b/translations/ne/lessons/X-Extras/X1-MultiModal/README.md index 16f1aa32..8c40bfa7 100644 --- a/translations/ne/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ne/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP कार्यहरू समाधान गर्न ट्रान् CLIP को मुख्य विचार भनेको टेक्स्ट प्रम्प्टहरूलाई तस्बिरसँग तुलना गर्न र तस्बिरले प्रम्प्टसँग कत्तिको मेल खान्छ भनेर निर्धारण गर्न सक्षम हुनु हो। -![CLIP आर्किटेक्चर](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ne.png) +![CLIP आर्किटेक्चर](../../../../../translated_images/ne/clip-arch.b3dbf20b4e8ed8be.png) > *[यो ब्लग पोस्ट](https://openai.com/blog/clip/) बाट तस्बिर* @@ -31,7 +31,7 @@ CLIP मोडेल/लाइब्रेरी [OpenAI GitHub](https://github. मानौं हामीलाई तस्बिरहरूलाई बिरालो, कुकुर र मानिसहरू बीच वर्गीकरण गर्नुपर्छ। यस अवस्थामा, हामी मोडेललाई तस्बिर र टेक्स्ट प्रम्प्टहरूको श्रृंखला दिन सक्छौं: "*बिरालोको तस्बिर*", "*कुकुरको तस्बिर*", "*मानिसको तस्बिर*"। परिणामस्वरूप 3 सम्भावनाहरूको भेक्टरमा हामीले सबैभन्दा उच्च मान भएको इन्डेक्स चयन गर्नुपर्छ। -![इमेज क्लासिफिकेशनको लागि CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.ne.png) +![इमेज क्लासिफिकेशनको लागि CLIP](../../../../../translated_images/ne/clip-class.3af42ef0b2b19369.png) > *[यो ब्लग पोस्ट](https://openai.com/blog/clip/) बाट तस्बिर* @@ -55,13 +55,13 @@ VQGAN को बारेमा थप जान्न [Taming Transformers](htt VQGAN र पारम्परिक GAN बीचको महत्त्वपूर्ण भिन्नता भनेको पछिल्लोले कुनै पनि इनपुट भेक्टरबाट राम्रो तस्बिर उत्पादन गर्न सक्छ, जबकि VQGAN ले सुसंगत तस्बिर उत्पादन गर्न सक्दैन। त्यसैले, हामीले तस्बिर निर्माण प्रक्रियालाई थप मार्गदर्शन गर्न आवश्यक छ, र त्यो CLIP प्रयोग गरेर गर्न सकिन्छ। -![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/vqgan.5027fe05051dfa31.ne.png) +![VQGAN+CLIP आर्किटेक्चर](../../../../../translated_images/ne/vqgan.5027fe05051dfa31.png) टेक्स्ट प्रम्प्टसँग मेल खाने तस्बिर निर्माण गर्न, हामी केही र्यान्डम इन्कोडिङ भेक्टरबाट सुरु गर्छौं जुन VQGAN मार्फत पास गरिन्छ र तस्बिर उत्पादन गरिन्छ। त्यसपछि CLIP प्रयोग गरेर लस फङ्क्सन उत्पादन गरिन्छ जसले तस्बिर टेक्स्ट प्रम्प्टसँग कत्तिको मेल खान्छ भनेर देखाउँछ। त्यसपछि यो लसलाई न्यूनतम बनाउने लक्ष्य राखिन्छ, ब्याक प्रोपोगेसन प्रयोग गरेर इनपुट भेक्टर प्यारामिटरहरू समायोजन गरिन्छ। VQGAN+CLIP कार्यान्वयन गर्ने उत्कृष्ट लाइब्रेरी [Pixray](http://github.com/pixray/pixray) हो। -![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ne.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ne.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ne.png) +![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- प्रम्प्ट *साहित्यको युवा पुरुष शिक्षकको पुस्तकसहितको नजिकको वाटरकलर पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *कम्प्युटर विज्ञानको युवा महिला शिक्षकको कम्प्युटरसहितको नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *गणितको वृद्ध पुरुष शिक्षकको ब्ल्याकबोर्ड अगाडि नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर @@ -77,7 +77,7 @@ CLIP भन्दा फरक, DALL-E ले टेक्स्ट र तस DALL-E 1 र 2 बीचको मुख्य भिन्नता भनेको यसले थप यथार्थपरक तस्बिरहरू र कला निर्माण गर्दछ। DALL-E द्वारा तस्बिर निर्माणका उदाहरणहरू: -![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ne.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ne.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ne.png) +![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray द्वारा उत्पादन गरिएको तस्बिर](../../../../../translated_images/ne/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- प्रम्प्ट *साहित्यको युवा पुरुष शिक्षकको पुस्तकसहितको नजिकको वाटरकलर पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *कम्प्युटर विज्ञानको युवा महिला शिक्षकको कम्प्युटरसहितको नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर | प्रम्प्ट *गणितको वृद्ध पुरुष शिक्षकको ब्ल्याकबोर्ड अगाडि नजिकको तेल पोर्ट्रेट* बाट उत्पन्न तस्बिर diff --git a/translations/nl/README.md b/translations/nl/README.md index e00b3e28..a3a790dd 100644 --- a/translations/nl/README.md +++ b/translations/nl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstmatige Intelligentie voor Beginners - Een Curriculum -|![Sketchnote door @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.nl.png)| +|![Sketchnote door @girlie_mac https://twitter.com/girlie_mac](../../translated_images/nl/ai-overview.0857791951d19500.png)| |:---:| | AI Voor Beginners - _Sketchnote door [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/nl/lessons/1-Intro/README.md b/translations/nl/lessons/1-Intro/README.md index b4894e04..e7e530d7 100644 --- a/translations/nl/lessons/1-Intro/README.md +++ b/translations/nl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introductie tot AI -![Samenvatting van de inhoud van de introductie van AI in een schets](../../../../translated_images/ai-intro.bf28d1ac4235881c.nl.png) +![Samenvatting van de inhoud van de introductie van AI in een schets](../../../../translated_images/nl/ai-intro.bf28d1ac4235881c.png) > Sketchnote door [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Oorspronkelijk werden computers uitgevonden door [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) om met cijfers te werken volgens een goed gedefinieerde procedure - een algoritme. Moderne computers, hoewel aanzienlijk geavanceerder dan het oorspronkelijke model dat in de 19e eeuw werd voorgesteld, volgen nog steeds hetzelfde idee van gecontroleerde berekeningen. Het is dus mogelijk om een computer te programmeren om iets te doen als we de exacte reeks stappen kennen die nodig zijn om het doel te bereiken. -![Foto van een persoon](../../../../translated_images/dsh_age.d212a30d4e54fb5f.nl.png) +![Foto van een persoon](../../../../translated_images/nl/dsh_age.d212a30d4e54fb5f.png) > Foto door [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Voor meer informatie, zie **[Algemene Kunstmatige Intelligentie](https://en.wiki Een van de problemen bij het omgaan met de term **[Intelligentie](https://en.wikipedia.org/wiki/Intelligence)** is dat er geen duidelijke definitie van deze term is. Men kan stellen dat intelligentie verbonden is met **abstract denken**, of met **zelfbewustzijn**, maar we kunnen het niet goed definiëren. -![Foto van een kat](../../../../translated_images/photo-cat.8c8e8fb760ffe457.nl.jpg) +![Foto van een kat](../../../../translated_images/nl/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) door [Amber Kipp](https://unsplash.com/@sadmax) van Unsplash @@ -98,13 +98,13 @@ Alternatief kunnen we proberen de eenvoudigste elementen in ons brein te modelle > | Wat betreft ML? | | > |--------------|-----------| -> | Een onderdeel van Kunstmatige Intelligentie dat gebaseerd is op het leren van een computer om een probleem op te lossen op basis van bepaalde data wordt **Machine Learning** genoemd. We zullen klassieke machine learning niet behandelen in deze cursus - we verwijzen je naar een aparte [Machine Learning voor Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML voor Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.nl.png) | +> | Een onderdeel van Kunstmatige Intelligentie dat gebaseerd is op het leren van een computer om een probleem op te lossen op basis van bepaalde data wordt **Machine Learning** genoemd. We zullen klassieke machine learning niet behandelen in deze cursus - we verwijzen je naar een aparte [Machine Learning voor Beginners](http://aka.ms/ml-beginners) curriculum. | ![ML voor Beginners](../../../../translated_images/nl/ml-for-beginners.9e4fed176fd5817d.png) | ## Een korte geschiedenis van AI Kunstmatige Intelligentie begon als een vakgebied in het midden van de twintigste eeuw. Aanvankelijk was symbolisch redeneren een veelgebruikte benadering, en dit leidde tot een aantal belangrijke successen, zoals expertsystemen – computerprogramma's die als expert konden optreden in enkele beperkte probleemdomeinen. Het werd echter al snel duidelijk dat deze benadering niet goed schaalbaar is. Het extraheren van kennis van een expert, het representeren ervan in een computer en het nauwkeurig houden van die kennisbank blijkt een zeer complexe taak te zijn, en te duur om in veel gevallen praktisch te zijn. Dit leidde tot de zogenaamde [AI Winter](https://en.wikipedia.org/wiki/AI_winter) in de jaren '70. -Korte geschiedenis van AI +Korte geschiedenis van AI > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Evenzo kunnen we zien hoe de benadering van het creëren van "sprekende programm * Moderne assistenten, zoals Cortana, Siri of Google Assistant, zijn allemaal hybride systemen die neurale netwerken gebruiken om spraak om te zetten in tekst en onze intentie te herkennen, en vervolgens enige redenering of expliciete algoritmen gebruiken om de vereiste acties uit te voeren. * In de toekomst kunnen we een volledig neurale model verwachten dat zelf een dialoog kan afhandelen. De recente GPT- en [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) familie van neurale netwerken tonen hierin grote successen. -de evolutie van de Turing-test +de evolutie van de Turing-test > Afbeelding door Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) door [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Recente AI-onderzoeken diff --git a/translations/nl/lessons/2-Symbolic/Animals.ipynb b/translations/nl/lessons/2-Symbolic/Animals.ipynb index a4a75b1d..6cbd7621 100644 --- a/translations/nl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/nl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "In dit voorbeeld implementeren we een eenvoudig kennisgebaseerd systeem om een dier te bepalen op basis van enkele fysieke kenmerken. Het systeem kan worden weergegeven door de volgende AND-OR-boom (dit is een deel van de volledige boom, we kunnen eenvoudig meer regels toevoegen):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.nl.png)\n" + "![](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/nl/lessons/2-Symbolic/README.md b/translations/nl/lessons/2-Symbolic/README.md index f41f9ac1..781a0d29 100644 --- a/translations/nl/lessons/2-Symbolic/README.md +++ b/translations/nl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kennisrepresentatie en Expertsystemen -![Samenvatting van Symbolische AI-inhoud](../../../../translated_images/ai-symbolic.715a30cb610411a6.nl.png) +![Samenvatting van Symbolische AI-inhoud](../../../../translated_images/nl/ai-symbolic.715a30cb610411a6.png) > Sketchnote door [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Meestal definiëren we kennis niet strikt, maar brengen we het in lijn met ander Het probleem van **kennisrepresentatie** is dus om een effectieve manier te vinden om kennis binnen een computer in de vorm van data te representeren, zodat het automatisch bruikbaar is. Dit kan worden gezien als een spectrum: -![Spectrum van kennisrepresentatie](../../../../translated_images/knowledge-spectrum.b60df631852c0217.nl.png) +![Spectrum van kennisrepresentatie](../../../../translated_images/nl/knowledge-spectrum.b60df631852c0217.png) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-syntaxis | Inspringing | | | Een van de vroege successen van symbolische AI waren de zogenaamde **expertsystemen** - computersystemen die waren ontworpen om als expert te functioneren in een beperkt probleemgebied. Ze waren gebaseerd op een **kennisbasis** die was geëxtraheerd van een of meer menselijke experts, en ze bevatten een **inferentie-engine** die enige redenering uitvoerde bovenop deze basis. -![Menselijke Architectuur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.nl.png) | ![Kennisgebaseerd Systeem](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.nl.png) +![Menselijke Architectuur](../../../../translated_images/nl/arch-human.5d4d35f1bba3ab1c.png) | ![Kennisgebaseerd Systeem](../../../../translated_images/nl/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Vereenvoudigde structuur van een menselijk neuraal systeem | Architectuur van een kennisgebaseerd systeem @@ -106,7 +106,7 @@ Expertsystemen zijn gebouwd zoals het menselijke redeneersysteem, dat **korteter Als voorbeeld bekijken we het volgende expertsysteem om een dier te bepalen op basis van zijn fysieke kenmerken: -![AND-OR Boom](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.nl.png) +![AND-OR Boom](../../../../translated_images/nl/AND-OR-Tree.5592d2c70187f283.png) > Afbeelding door [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index a19bd918..8b50aeb2 100644 --- a/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/nl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "In het geval dat we meer dan 2 klassen hebben, zal softmax de waarschijnlijkheden over alle klassen normaliseren. Hier is een diagram van de netwerkarchitectuur die MNIST-cijferclassificatie uitvoert:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.nl.png)\n" + "![MNIST Classifier](../../../../../translated_images/nl/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Lage trainingsfout - het model kan trainingsdata goed benaderen, omdat het voldoende expressieve kracht heeft.\n", "* Validatiefout kan veel hoger zijn dan de trainingsfout en kan tijdens het trainen beginnen te stijgen - dit komt doordat het model de trainingspunten \"onthoudt\" en het \"overzicht\" verliest.\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.nl.png)\n", + "![Overfitting](../../../../../translated_images/nl/overfit.a0bd57f717c15769.png)\n", "\n", "> Op deze afbeelding staat `x` voor trainingsdata, `o` voor validatiedata. Links - lineair model (één laag), het benadert de aard van de data redelijk goed. Rechts - overfitted model, het model benadert de trainingsdata perfect, maar verliest betekenis bij andere data (validatiefout is erg hoog).\n" ] diff --git a/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 53b838f1..58c79ecc 100644 --- a/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/nl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting is een uiterst belangrijk concept in machine learning, en het is ess Bekijk het volgende probleem van het benaderen van 5 punten (weergegeven door `x` op de grafieken hieronder): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.nl.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.nl.jpg) +![linear](../../../../../translated_images/nl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/nl/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineair model, 2 parameters** | **Niet-lineair model, 7 parameters** Trainingsfout = 5.3 | Trainingsfout = 0 @@ -79,7 +79,7 @@ Het is erg belangrijk om een juiste balans te vinden tussen de rijkdom van het m Zoals je kunt zien op de bovenstaande grafiek, kan overfitting worden gedetecteerd door een zeer lage trainingsfout en een hoge validatiefout. Normaal gesproken zien we tijdens het trainen zowel de trainings- als validatiefouten afnemen, en op een gegeven moment kan de validatiefout stoppen met afnemen en beginnen te stijgen. Dit is een teken van overfitting en een indicatie dat we waarschijnlijk op dit punt moeten stoppen met trainen (of op zijn minst een snapshot van het model moeten maken). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.nl.png) +![overfitting](../../../../../translated_images/nl/Overfitting.408ad91cd90b4371.png) ## Hoe overfitting te voorkomen diff --git a/translations/nl/lessons/3-NeuralNetworks/README.md b/translations/nl/lessons/3-NeuralNetworks/README.md index f064ce52..fcc92212 100644 --- a/translations/nl/lessons/3-NeuralNetworks/README.md +++ b/translations/nl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introductie tot Neurale Netwerken -![Samenvatting van de inhoud van Intro Neural Networks in een schets](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.nl.png) +![Samenvatting van de inhoud van Intro Neural Networks in een schets](../../../../translated_images/nl/ai-neuralnetworks.1c687ae40bc86e83.png) Zoals we in de introductie hebben besproken, is een van de manieren om intelligentie te bereiken het trainen van een **computermodel** of een **kunstmatig brein**. Sinds het midden van de 20e eeuw hebben onderzoekers verschillende wiskundige modellen geprobeerd, totdat deze richting in de afgelopen jaren enorm succesvol bleek te zijn. Dergelijke wiskundige modellen van het brein worden **neurale netwerken** genoemd. @@ -36,13 +36,13 @@ In dit curriculum richten we ons uitsluitend op neurale netwerkmodellen. Uit de biologie weten we dat ons brein bestaat uit neurale cellen (neuronen), die elk meerdere "inputs" (dendrieten) en een enkele "output" (axon) hebben. Zowel dendrieten als axonen kunnen elektrische signalen geleiden, en de verbindingen daartussen — bekend als synapsen — kunnen verschillende graden van geleiding vertonen, die worden gereguleerd door neurotransmitters. -![Model van een Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.nl.jpg) | ![Model van een Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.nl.png) +![Model van een Neuron](../../../../translated_images/nl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model van een Neuron](../../../../translated_images/nl/artneuron.1a5daa88d20ebe6f.png) ----|---- Echt Neuron *([Afbeelding](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) van Wikipedia)* | Kunstmatig Neuron *(Afbeelding door Auteur)* Het eenvoudigste wiskundige model van een neuron bevat dus meerdere inputs X1, ..., XN en een output Y, en een reeks gewichten W1, ..., WN. Een output wordt berekend als: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) waarbij f een niet-lineaire **activatiefunctie** is. diff --git a/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md index e946ee8b..de813e01 100644 --- a/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/nl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ In ons [OpenCV Notebook](OpenCV.ipynb) geven we enkele voorbeelden van wanneer c * **Voorbewerking van een foto van een Braille-boek**. We richten ons op hoe we thresholding, kenmerkdetectie, perspectieftransformatie en NumPy-manipulaties kunnen gebruiken om individuele Braille-symbolen te scheiden voor verdere classificatie door een neuraal netwerk. -![Braille Afbeelding](../../../../../translated_images/braille.341962ff76b1bd70.nl.jpeg) | ![Braille Afbeelding Voorbewerkt](../../../../../translated_images/braille-result.46530fea020b03c7.nl.png) | ![Braille Symbolen](../../../../../translated_images/braille-symbols.0159185ab69d5339.nl.png) +![Braille Afbeelding](../../../../../translated_images/nl/braille.341962ff76b1bd70.jpeg) | ![Braille Afbeelding Voorbewerkt](../../../../../translated_images/nl/braille-result.46530fea020b03c7.png) | ![Braille Symbolen](../../../../../translated_images/nl/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) * **Beweging detecteren in video met behulp van frameverschillen**. Als de camera vast staat, zouden frames van de camerafeed vrij vergelijkbaar met elkaar moeten zijn. Omdat frames worden weergegeven als arrays, krijg je door die arrays van twee opeenvolgende frames van elkaar af te trekken het pixelverschil, dat laag zou moeten zijn voor statische frames, en hoger wordt zodra er aanzienlijke beweging in de afbeelding is. -![Afbeelding van videoframes en frameverschillen](../../../../../translated_images/frame-difference.706f805491a0883c.nl.png) +![Afbeelding van videoframes en frameverschillen](../../../../../translated_images/nl/frame-difference.706f805491a0883c.png) > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ In ons [OpenCV Notebook](OpenCV.ipynb) geven we enkele voorbeelden van wanneer c - **Dense Optical Flow** berekent het vectorveld dat voor elke pixel laat zien waar deze naartoe beweegt. - **Sparse Optical Flow** is gebaseerd op het nemen van enkele onderscheidende kenmerken in de afbeelding (bijv. randen) en het opbouwen van hun traject van frame tot frame. -![Afbeelding van Optische Stroom](../../../../../translated_images/optical.1f4a94464579a83a.nl.png) +![Afbeelding van Optische Stroom](../../../../../translated_images/nl/optical.1f4a94464579a83a.png) > Afbeelding uit [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 24e49509..d2f94bc8 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 is een netwerk dat in 2014 een nauwkeurigheid van 92,7% behaalde in de ImageNet top-5 classificatie. Het heeft de volgende laagstructuur: -![ImageNet Lagen](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.nl.jpg) +![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.jpg) Zoals je kunt zien, volgt VGG een traditionele piramide-architectuur, wat een opeenvolging is van convolutie- en poolinglagen. -![ImageNet Piramide](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.nl.jpg) +![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.jpg) > Afbeelding van [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index a96d3eb9..dc9a2ef3 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "In een typische CNN zijn er dus meerdere convolutionele lagen, met pooling-lagen ertussen om de afmetingen van de afbeelding te verkleinen. We zouden ook het aantal filters verhogen, omdat er, naarmate patronen complexer worden, meer mogelijke interessante combinaties zijn waar we naar moeten zoeken.\n", "\n", - "![Een afbeelding die meerdere convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.nl.png)\n", + "![Een afbeelding die meerdere convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Vanwege de afnemende ruimtelijke afmetingen en toenemende kenmerken/filters wordt deze architectuur ook wel **piramide-architectuur** genoemd.\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 1dccf55b..2028c605 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "In een typische CNN zijn er dus meerdere convolutionele lagen, met pooling-lagen ertussen om de afmetingen van de afbeelding te verkleinen. We zouden ook het aantal filters verhogen, omdat er naarmate patronen geavanceerder worden, meer mogelijke interessante combinaties zijn waar we naar moeten zoeken.\n", "\n", - "![Een afbeelding die verschillende convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.nl.png)\n", + "![Een afbeelding die verschillende convolutionele lagen met pooling-lagen toont.](../../../../../translated_images/nl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Vanwege de afnemende ruimtelijke dimensies en toenemende feature-/filterdimensies wordt deze architectuur ook wel **piramide-architectuur** genoemd.\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md index aa4838a9..886cb6e4 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ In het echte leven willen we objecten op een afbeelding kunnen herkennen, ongeac Om patronen te extraheren, maken we gebruik van het concept van **convolutionele filters**. Zoals je weet, wordt een afbeelding weergegeven door een 2D-matrix, of een 3D-tensor met kleurendiepte. Het toepassen van een filter betekent dat we een relatief kleine **filterkernel**-matrix nemen, en voor elke pixel in de originele afbeelding het gewogen gemiddelde berekenen met naburige punten. Dit kun je zien als een klein venster dat over de hele afbeelding schuift en alle pixels gemiddeld volgens de gewichten in de filterkernel-matrix. -![Verticaal Randfilter](../../../../../translated_images/filter-vert.b7148390ca0bc356.nl.png) | ![Horizontaal Randfilter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.nl.png) +![Verticaal Randfilter](../../../../../translated_images/nl/filter-vert.b7148390ca0bc356.png) | ![Horizontaal Randfilter](../../../../../translated_images/nl/filter-horiz.59b80ed4feb946ef.png) ----|---- > Afbeelding door Dmitry Soshnikov @@ -38,7 +38,7 @@ De werking van CNN's is gebaseerd op de volgende belangrijke ideeën: * We kunnen het netwerk zo ontwerpen dat de filters automatisch worden getraind. * We kunnen dezelfde aanpak gebruiken om patronen te vinden in hoog-niveau kenmerken, niet alleen in de originele afbeelding. Hierdoor werkt de CNN-functie-extractie op een hiërarchie van kenmerken, beginnend bij laag-niveau pixelcombinaties tot hoog-niveau combinaties van afbeeldingsonderdelen. -![Hiërarchische Functie-extractie](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.nl.png) +![Hiërarchische Functie-extractie](../../../../../translated_images/nl/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Afbeelding uit [een paper van Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), gebaseerd op [hun onderzoek](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De meeste CNN's die worden gebruikt voor beeldverwerking volgen een zogenaamde p Als voorbeeld bekijken we de architectuur van VGG-16, een netwerk dat in 2014 een nauwkeurigheid van 92,7% behaalde in de top-5 classificatie van ImageNet: -![ImageNet Lagen](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.nl.jpg) +![ImageNet Lagen](../../../../../translated_images/nl/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Piramide](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.nl.jpg) +![ImageNet Piramide](../../../../../translated_images/nl/vgg-16-arch.64ff2137f50dd49f.jpg) > Afbeelding van [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 4a939959..69f276a5 100644 --- a/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/nl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Je moet een convolutioneel neuraal netwerk trainen om verschillende rassen van k We gebruiken de [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), die afbeeldingen bevat van 37 verschillende rassen van honden en katten. -![Dataset waarmee we werken](../../../../../../translated_images/data.50b2a9d5484bdbf0.nl.png) +![Dataset waarmee we werken](../../../../../../translated_images/nl/data.50b2a9d5484bdbf0.png) Om de dataset te downloaden, gebruik deze codefragment: diff --git a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e219a290..a48e795c 100644 --- a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Om de ideale kat te visualiseren, beginnen we met een willekeurige ruisafbeelding en proberen we de optimalisatietechniek van gradient descent te gebruiken om de afbeelding aan te passen zodat een netwerk een kat herkent.\n", "\n", - "![Optimalisatie Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.nl.png)\n", + "![Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Hier is onze startafbeelding:\n" ] diff --git a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md index 3a11023c..751a36ea 100644 --- a/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/nl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Zowel Keras als PyTorch bevatten functies om eenvoudig voorgetrainde neurale net Hier zijn voorbeeldkenmerken die door een VGG-16 netwerk uit een afbeelding van een kat zijn gehaald: -![Kenmerken geëxtraheerd door VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.nl.png) +![Kenmerken geëxtraheerd door VGG-16](../../../../../translated_images/nl/features.6291f9c7ba3a0b95.png) ## Cats vs. Dogs Dataset @@ -48,19 +48,19 @@ Een voorgetraind neuraal netwerk bevat verschillende patronen in zijn *brein*, w Een aanpak die we kunnen nemen is om te beginnen met een willekeurige afbeelding en vervolgens de techniek van **gradient descent optimalisatie** te gebruiken om die afbeelding zo aan te passen dat het netwerk begint te denken dat het een kat is. -![Afbeelding Optimalisatie Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.nl.png) +![Afbeelding Optimalisatie Loop](../../../../../translated_images/nl/ideal-cat-loop.999fbb8ff306e044.png) Als we dit doen, krijgen we echter iets dat erg lijkt op willekeurige ruis. Dit komt omdat *er veel manieren zijn om een netwerk te laten denken dat de invoerafbeelding een kat is*, inclusief enkele die visueel geen zin hebben. Hoewel deze afbeeldingen veel patronen bevatten die typisch zijn voor een kat, is er niets dat hen dwingt visueel onderscheidend te zijn. Om het resultaat te verbeteren, kunnen we een andere term toevoegen aan de verliesfunctie, genaamd **variation loss**. Dit is een maatstaf die aangeeft hoe vergelijkbaar naburige pixels van de afbeelding zijn. Het minimaliseren van variation loss maakt de afbeelding gladder en verwijdert ruis, waardoor meer visueel aantrekkelijke patronen zichtbaar worden. Hier is een voorbeeld van dergelijke "ideale" afbeeldingen, die met hoge waarschijnlijkheid als kat en als zebra worden geclassificeerd: -![Ideale Kat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.nl.png) | ![Ideale Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.nl.png) +![Ideale Kat](../../../../../translated_images/nl/ideal-cat.203dd4597643d6b0.png) | ![Ideale Zebra](../../../../../translated_images/nl/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideale Kat* | *Ideale Zebra* Een soortgelijke aanpak kan worden gebruikt om zogenaamde **adversarial attacks** op een neuraal netwerk uit te voeren. Stel dat we een neuraal netwerk willen misleiden en een hond eruit willen laten zien als een kat. Als we een afbeelding van een hond nemen, die door een netwerk wordt herkend als een hond, kunnen we deze vervolgens een beetje aanpassen met behulp van gradient descent optimalisatie, totdat het netwerk deze begint te classificeren als een kat: -![Afbeelding van een Hond](../../../../../translated_images/original-dog.8f68a67d2fe0911f.nl.png) | ![Afbeelding van een hond geclassificeerd als een kat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.nl.png) +![Afbeelding van een Hond](../../../../../translated_images/nl/original-dog.8f68a67d2fe0911f.png) | ![Afbeelding van een hond geclassificeerd als een kat](../../../../../translated_images/nl/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originele afbeelding van een hond* | *Afbeelding van een hond geclassificeerd als een kat* diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 82f7e0a8..2c199387 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Omdat we de autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van invoerafbeeldingen te vinden om de betekenis vast te leggen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.nl.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Afbeelding afkomstig van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 09766a5c..de310e27 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Omdat we de autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van inputafbeeldingen te vinden om de betekenis te vatten.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.nl.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Afbeelding afkomstig van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md index 23e9285d..e4bc5900 100644 --- a/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/nl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ We willen echter mogelijk ruwe (ongelabelde) data gebruiken om CNN-feature extra Omdat we een autoencoder trainen om zoveel mogelijk informatie uit de originele afbeelding vast te leggen voor een nauwkeurige reconstructie, probeert het netwerk de beste **embedding** van invoerafbeeldingen te vinden om de betekenis vast te leggen. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.nl.jpg) +![AutoEncoder Diagram](../../../../../translated_images/nl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Afbeelding van [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md index e86f0539..1c44bfbc 100644 --- a/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/nl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De beeldclassificatiemodellen die we tot nu toe hebben behandeld, namen een afbe ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objectdetectie](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.nl.png) +![Objectdetectie](../../../../../translated_images/nl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Afbeelding van [YOLO v2 website](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Stel dat we een kat op een afbeelding willen vinden, een zeer eenvoudige aanpak 2. Voer beeldclassificatie uit op elke tegel. 3. De tegels die een voldoende hoge activatie opleveren, kunnen worden beschouwd als tegels die het betreffende object bevatten. -![Eenvoudige objectdetectie](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.nl.png) +![Eenvoudige objectdetectie](../../../../../translated_images/nl/naive-detection.e7f1ba220ccd08c6.png) > *Afbeelding uit [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Je kunt de volgende datasets tegenkomen voor deze taak: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klassen * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klassen, begrenzingskaders en segmentatiemaskers -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.nl.jpg) +![COCO](../../../../../translated_images/nl/coco-examples.71bc60380fa6cceb.jpg) ## Objectdetectie-metrics @@ -50,7 +50,7 @@ Je kunt de volgende datasets tegenkomen voor deze taak: Bij beeldclassificatie is het eenvoudig om te meten hoe goed het algoritme presteert, maar bij objectdetectie moeten we zowel de juistheid van de klasse als de precisie van de voorspelde locatie van het begrenzingskader meten. Voor dat laatste gebruiken we de zogenaamde **Intersection over Union** (IoU), die meet hoe goed twee kaders (of twee willekeurige gebieden) overlappen. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.nl.png) +![IoU](../../../../../translated_images/nl/iou_equation.9a4751d40fff4e11.png) > *Figuur 2 uit [deze uitstekende blogpost over IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Er zijn twee brede categorieën van objectdetectie-algoritmen: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) gebruikt [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) om een hiërarchische structuur van ROI-regio's te genereren, die vervolgens door CNN-feature extractors en SVM-classificators worden geleid om de objectklasse te bepalen, en lineaire regressie om de coördinaten van *begrenzingskaders* te bepalen. [Officiële paper](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.nl.png) +![RCNN](../../../../../translated_images/nl/rcnn1.cae407020dfb1d1f.png) > *Afbeelding van van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.nl.png) +![RCNN-1](../../../../../translated_images/nl/rcnn2.2d9530bb83516484.png) > *Afbeeldingen uit [deze blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Er zijn twee brede categorieën van objectdetectie-algoritmen: Deze aanpak lijkt op R-CNN, maar regio's worden gedefinieerd nadat convolutielagen zijn toegepast. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.nl.png) +![FRCNN](../../../../../translated_images/nl/f-rcnn.3cda6d9bb4188875.png) > Afbeelding uit [de officiële paper](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Deze aanpak lijkt op R-CNN, maar regio's worden gedefinieerd nadat convolutielag Het belangrijkste idee van deze aanpak is om een neuraal netwerk te gebruiken om ROI's te voorspellen - de zogenaamde *Region Proposal Network*. [Paper](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.nl.png) +![FasterRCNN](../../../../../translated_images/nl/faster-rcnn.8d46c099b87ef30a.png) > Afbeelding uit [de officiële paper](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Dit algoritme is zelfs sneller dan Faster R-CNN. Het belangrijkste idee is als v 2. Features worden verwerkt door **Position-Sensitive Score Map**. Elk object uit $C$ klassen wordt verdeeld in $k\times k$ regio's, en we trainen om delen van objecten te voorspellen. 3. Voor elk deel uit $k\times k$ regio's stemmen alle netwerken op objectklassen, en de objectklasse met de meeste stemmen wordt geselecteerd. -![r-fcn afbeelding](../../../../../translated_images/r-fcn.13eb88158b99a3da.nl.png) +![r-fcn afbeelding](../../../../../translated_images/nl/r-fcn.13eb88158b99a3da.png) > Afbeelding uit [officiële paper](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO is een realtime one-pass algoritme. Het belangrijkste idee is als volgt: * De afbeelding wordt verdeeld in $S\times S$ regio's. * Voor elke regio voorspelt **CNN** $n$ mogelijke objecten, *begrenzingskader*-coördinaten en *vertrouwen*=*waarschijnlijkheid* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.nl.png) + ![YOLO](../../../../../translated_images/nl/yolo.a2648ec82ee8bb4e.png) > Afbeelding uit [officiële paper](https://arxiv.org/abs/1506.02640) diff --git a/translations/nl/lessons/4-ComputerVision/README.md b/translations/nl/lessons/4-ComputerVision/README.md index e947acce..b61451cb 100644 --- a/translations/nl/lessons/4-ComputerVision/README.md +++ b/translations/nl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Samenvatting van Computer Vision-inhoud in een schets](../../../../translated_images/ai-computervision.6506ebebac3fbf76.nl.png) +![Samenvatting van Computer Vision-inhoud in een schets](../../../../translated_images/nl/ai-computervision.6506ebebac3fbf76.png) In deze sectie leren we over: diff --git a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 78a30d2a..62031c99 100644 --- a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Zak met Woorden** (BoW) vectorrepresentatie is de meest gebruikte traditionele vectorrepresentatie. Elk woord is gekoppeld aan een vectorindex, en het vectorelement bevat het aantal keren dat een woord voorkomt in een bepaald document.\n", "\n", - "![Afbeelding die laat zien hoe een zak met woorden vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.nl.png) \n", + "![Afbeelding die laat zien hoe een zak met woorden vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Je kunt BoW ook zien als de som van alle one-hot-gecodeerde vectoren voor individuele woorden in de tekst.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 16b78961..536b3115 100644 --- a/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/nl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vectorrepresentatie is de meest eenvoudige traditionele vectorrepresentatie om te begrijpen. Elk woord is gekoppeld aan een vectorindex, en een element in de vector bevat het aantal keren dat elk woord voorkomt in een bepaald document.\n", "\n", - "![Afbeelding die laat zien hoe een bag-of-words vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.nl.png) \n", + "![Afbeelding die laat zien hoe een bag-of-words vectorrepresentatie in het geheugen wordt weergegeven.](../../../../../translated_images/nl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Je kunt BoW ook zien als de som van alle one-hot-gecodeerde vectoren voor individuele woorden in de tekst.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index ff22c84b..e345e5c7 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Door een embedding-laag als eerste laag in ons netwerk te gebruiken, kunnen we overschakelen van een bag-of-words naar een **embedding bag**-model, waarbij we eerst elk woord in onze tekst omzetten naar de bijbehorende embedding en vervolgens een aggregatiefunctie toepassen op al deze embeddings, zoals `sum`, `average` of `max`.\n", "\n", - "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.nl.png)\n", + "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Ons classifier-neuraal netwerk zal beginnen met een embedding-laag, gevolgd door een aggregatielaag en een lineaire classifier bovenop:\n" ] @@ -176,7 +176,7 @@ "\n", "In de vorige architectuur moesten we alle sequenties op dezelfde lengte opvullen om ze in een minibatch te passen. Dit is niet de meest efficiënte manier om sequenties met variabele lengte te representeren - een andere aanpak zou zijn om een **offset**-vector te gebruiken, die de offsets van alle sequenties in één grote vector bevat.\n", "\n", - "![Afbeelding die een offset-sequentierepresentatie toont](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.nl.png)\n", + "![Afbeelding die een offset-sequentierepresentatie toont](../../../../../translated_images/nl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Op de afbeelding hierboven tonen we een sequentie van karakters, maar in ons voorbeeld werken we met sequenties van woorden. Het algemene principe van het representeren van sequenties met een offset-vector blijft echter hetzelfde.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden.\n", "\n", - "![Afbeelding die zowel CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.nl.png)\n", + "![Afbeelding die zowel CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Om te experimenteren met Word2Vec-embedding die is voorgetraind op de Google News dataset, kunnen we de **gensim**-bibliotheek gebruiken. Hieronder vinden we de woorden die het meest lijken op 'neural'.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 6236f2e3..3b91f611 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/nl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Door een embedding-laag als de eerste laag in ons netwerk te gebruiken, kunnen we overschakelen van een bag-of-words-model naar een **embedding bag**-model, waarbij we eerst elk woord in onze tekst omzetten naar de bijbehorende embedding en vervolgens een aggregatiefunctie toepassen op al deze embeddings, zoals `sum`, `average` of `max`.\n", "\n", - "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.nl.png)\n", + "![Afbeelding die een embedding-classificator toont voor vijf sequentiewoorden.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Ons classifier-neuraal netwerk bestaat uit de volgende lagen:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden.\n", "\n", - "![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren om te zetten.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.nl.png)\n", + "![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren om te zetten.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Om te experimenteren met de Word2Vec-embedding die vooraf is getraind op de Google News-dataset, kunnen we de **gensim**-bibliotheek gebruiken. Hieronder vinden we de woorden die het meest lijken op 'neural'.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/14-Embeddings/README.md b/translations/nl/lessons/5-NLP/14-Embeddings/README.md index 3af06c56..13d21956 100644 --- a/translations/nl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/nl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ De embeddinglaag neemt een woord als invoer en produceert een uitvoervector met Door een embeddinglaag als eerste laag in ons classifier-netwerk te gebruiken, kunnen we overschakelen van een bag-of-words naar een **embedding bag** model, waarbij we eerst elk woord in onze tekst omzetten in de bijbehorende embedding, en vervolgens een aggregatiefunctie berekenen over al deze embeddings, zoals `sum`, `average` of `max`. -![Afbeelding die een embedding classifier toont voor vijf woorden in een reeks.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.nl.png) +![Afbeelding die een embedding classifier toont voor vijf woorden in een reeks.](../../../../../translated_images/nl/embedding-classifier-example.b77f021a7ee67eee.png) > Afbeelding door de auteur @@ -40,7 +40,7 @@ Om dit te bereiken, moeten we ons embeddingmodel op een grote tekstcollectie op CBoW is sneller, terwijl skip-gram langzamer is, maar beter presteert bij het representeren van zeldzame woorden. -![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.nl.png) +![Afbeelding die zowel de CBoW- als Skip-Gram-algoritmen toont om woorden naar vectoren te converteren.](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Afbeelding uit [dit artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md index 1b9482f4..6a7527ab 100644 --- a/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/nl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ In onze eerdere voorbeelden hebben we gebruik gemaakt van vooraf getrainde seman * **Continuous Bag-of-Words** (CBoW), waarbij we het middelste token $W_0$ voorspellen in een reeks tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, waarbij we een set van naburige tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} voorspellen vanuit het middelste token $W_0$. -![afbeelding uit paper over het converteren van woorden naar vectoren](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.nl.png) +![afbeelding uit paper over het converteren van woorden naar vectoren](../../../../../translated_images/nl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Afbeelding uit [deze paper](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/nl/lessons/5-NLP/16-RNN/README.md b/translations/nl/lessons/5-NLP/16-RNN/README.md index d7e704b8..23a685e6 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/README.md +++ b/translations/nl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ In de vorige secties hebben we gebruik gemaakt van rijke semantische representat Om de betekenis van een tekstsequentie vast te leggen, moeten we een andere neurale netwerkarchitectuur gebruiken, genaamd een **recurrent neural network**, of RNN. In een RNN sturen we onze zin één symbool tegelijk door het netwerk, en het netwerk produceert een **toestand**, die we vervolgens weer doorgeven aan het netwerk samen met het volgende symbool. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.nl.png) +![RNN](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.png) > Afbeelding door de auteur @@ -61,7 +61,7 @@ We hebben recurrente netwerken besproken die in één richting werken, van het b Een recurrent netwerk, of het nu éénrichtings of bidirectioneel is, legt bepaalde patronen binnen een sequentie vast en kan deze opslaan in een toestandsvector of doorgeven aan de uitvoer. Net zoals bij convolutionele netwerken, kunnen we een andere recurrente laag bovenop de eerste bouwen om hogere niveau patronen vast te leggen en te bouwen op laag-niveau patronen die door de eerste laag zijn geëxtraheerd. Dit leidt ons naar het concept van een **multi-layer RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de uitvoer van de vorige laag wordt doorgegeven aan de volgende laag als invoer. -![Afbeelding van een multilayer long-short-term-memory RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.nl.jpg) +![Afbeelding van een multilayer long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Afbeelding uit [dit geweldige artikel](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) door Fernando López* diff --git a/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 88e2b61e..763cd800 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Een recurrent netwerk, of het nu eendirectioneel of bidirectioneel is, legt bepaalde patronen binnen een reeks vast en kan deze opslaan in de toestandsvector of doorgeven aan de uitvoer. Net als bij convolutionele netwerken kunnen we een andere recurrente laag bovenop de eerste bouwen om patronen van een hoger niveau vast te leggen, opgebouwd uit de laag-niveau patronen die door de eerste laag zijn geëxtraheerd. Dit brengt ons bij het concept van een **meerlaagse RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de uitvoer van de vorige laag wordt doorgegeven aan de volgende laag als invoer.\n", "\n", - "![Afbeelding van een meerlaagse long-short-term-memory-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.nl.jpg)\n", + "![Afbeelding van een meerlaagse long-short-term-memory-RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Afbeelding afkomstig uit [deze geweldige post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) van Fernando López*\n", "\n", diff --git a/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb index d4ba6839..b777f148 100644 --- a/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/nl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Om de betekenis van een tekstsequentie vast te leggen, gebruiken we een neurale netwerkarchitectuur genaamd **recurrente neurale netwerken**, of RNN. Bij het gebruik van een RNN voeren we onze zin één token tegelijk door het netwerk, en het netwerk produceert een bepaalde **toestand**, die we vervolgens weer doorgeven aan het netwerk met het volgende token.\n", "\n", - "![Afbeelding die een voorbeeld van generatie door een recurrent neuraal netwerk toont.](../../../../../translated_images/rnn.27f5c29c53d727b5.nl.png)\n", + "![Afbeelding die een voorbeeld van generatie door een recurrent neuraal netwerk toont.](../../../../../translated_images/nl/rnn.27f5c29c53d727b5.png)\n", "\n", "Gegeven de invoersequentie van tokens $X_0,\\dots,X_n$, creëert de RNN een reeks neurale netwerkblokken en traint deze reeks end-to-end met behulp van backpropagation. Elk netwerkblok neemt een paar $(X_i,S_i)$ als invoer en produceert $S_{i+1}$ als resultaat. De uiteindelijke toestand $S_n$ of uitvoer $Y_n$ gaat naar een lineaire classifier om het resultaat te produceren. Alle netwerkblokken delen dezelfde gewichten en worden end-to-end getraind met één backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Recurrente netwerken, unidirectioneel of bidirectioneel, leggen patronen binnen een reeks vast en slaan deze op in toestandsvectoren of geven ze terug als output. Net zoals bij convolutionele netwerken kunnen we een andere recurrente laag bouwen na de eerste om hogere-orde patronen vast te leggen, opgebouwd uit lagere-orde patronen die door de eerste laag zijn geëxtraheerd. Dit leidt ons naar het concept van een **meerlaagse RNN**, die bestaat uit twee of meer recurrente netwerken, waarbij de output van de vorige laag als invoer wordt doorgegeven aan de volgende laag.\n", "\n", - "![Afbeelding van een meerlaagse long-short-term-memory RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.nl.jpg)\n", + "![Afbeelding van een meerlaagse long-short-term-memory RNN](../../../../../translated_images/nl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Afbeelding afkomstig uit [deze geweldige post](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) van Fernando López.*\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index ac7a7110..2a3c60c0 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "De manier waarop we een RNN zullen trainen om tekst te genereren, is als volgt. Bij elke stap nemen we een reeks karakters van lengte `nchars` en vragen we het netwerk om het volgende uitvoerkarakter te genereren voor elk invoerkarakter:\n", "\n", - "![Afbeelding die een voorbeeld toont van een RNN die het woord 'HELLO' genereert.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.nl.png)\n", + "![Afbeelding die een voorbeeld toont van een RNN die het woord 'HELLO' genereert.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Afhankelijk van het specifieke scenario willen we mogelijk ook enkele speciale karakters opnemen, zoals *einde-van-sequentie* ``. In ons geval willen we het netwerk gewoon trainen voor eindeloze tekstgeneratie, dus stellen we de grootte van elke sequentie vast op `nchars` tokens. Bijgevolg zal elk trainingsvoorbeeld bestaan uit `nchars` invoer en `nchars` uitvoer (wat de invoersequentie is, verschoven met één symbool naar links). Een minibatch zal bestaan uit meerdere van dergelijke sequenties.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 89f429e5..23c6fbb5 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "De manier waarop we een RNN zullen trainen om nieuwstitels te genereren is als volgt. Bij elke stap nemen we één titel, die wordt ingevoerd in een RNN, en voor elk invoerkarakter vragen we het netwerk om het volgende uitvoerkarakter te genereren:\n", "\n", - "![Afbeelding die een voorbeeld toont van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.nl.png)\n", + "![Afbeelding die een voorbeeld toont van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Voor het laatste karakter van onze reeks vragen we het netwerk om een ``-token te genereren.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md index 1090441c..fc6b22ff 100644 --- a/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/nl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ In de RNN-architectuur die we in de vorige eenheid hebben besproken, produceerde Dit maakt verschillende neurale architecturen mogelijk, zoals weergegeven in de onderstaande afbeelding: -![Afbeelding met veelvoorkomende patronen van recurrente neurale netwerken.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.nl.jpg) +![Afbeelding met veelvoorkomende patronen van recurrente neurale netwerken.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Afbeelding uit de blogpost [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) door [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ In deze eenheid richten we ons op eenvoudige generatieve modellen die ons helpen We trainen deze RNN om tekst stap voor stap te genereren. Bij elke stap nemen we een reeks karakters van lengte `nchars` en vragen we het netwerk om het volgende uitvoerkarakter te genereren voor elk invoerkarakter: -![Afbeelding met een voorbeeld van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.nl.png) +![Afbeelding met een voorbeeld van RNN-generatie van het woord 'HELLO'.](../../../../../translated_images/nl/rnn-generate.56c54afb52f9781d.png) Bij het genereren van tekst (tijdens inferentie) beginnen we met een **prompt**, die door de RNN-cellen wordt doorgegeven om de tussenliggende toestand te genereren. Vanuit deze toestand begint de generatie. We genereren één karakter tegelijk en geven de toestand en het gegenereerde karakter door aan een andere RNN-cel om het volgende te genereren, totdat we genoeg karakters hebben gegenereerd. diff --git a/translations/nl/lessons/5-NLP/18-Transformers/README.md b/translations/nl/lessons/5-NLP/18-Transformers/README.md index 280fc9d7..478b405b 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/nl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Met RNNs wordt sequence-to-sequence geïmplementeerd door twee recurrente netwer **Aandachtsmechanismen** bieden een manier om het contextuele effect van elke invoervector op elke outputvoorspelling van de RNN te wegen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van de input-RNN en de output-RNN. Op deze manier nemen we bij het genereren van outputsymbool yt alle verborgen toestanden hi van de input in aanmerking, met verschillende gewichtcoëfficiënten αt,i. -![Afbeelding van een encoder/decoder-model met een additieve aandachtlaag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.nl.png) +![Afbeelding van een encoder/decoder-model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png) > Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) De aandachtmatrix {αi,j} vertegenwoordigt de mate waarin bepaalde invoerwoorden een rol spelen bij het genereren van een bepaald woord in de outputsequentie. Hieronder staat een voorbeeld van zo'n matrix: -![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.nl.png) +![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png) > Afbeelding uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Het resultaat dat we krijgen met positionele embedding embedt zowel het oorspron Vervolgens moeten we enkele patronen binnen onze sequentie vastleggen. Om dit te doen, gebruiken transformers een **zelf-aandachtsmechanisme**, wat in wezen aandacht is toegepast op dezelfde sequentie als input en output. Het toepassen van zelf-aandacht stelt ons in staat om **context** binnen de zin in aanmerking te nemen en te zien welke woorden met elkaar verbonden zijn. Bijvoorbeeld, het stelt ons in staat om te zien welke woorden worden verwezen door coreferenties, zoals *het*, en ook de context in aanmerking te nemen: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.nl.png) +![](../../../../../translated_images/nl/CoreferenceResolution.861924d6d384a7d6.png) > Afbeelding van de [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Omdat elke invoerpositie onafhankelijk wordt gekoppeld aan elke uitvoerpositie, **BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformernetwerk met 12 lagen voor *BERT-base*, en 24 voor *BERT-large*. Het model wordt eerst voorgetraind op een grote corpus van tekstdata (WikiPedia + boeken) met behulp van ongesuperviseerde training (voorspellen van gemaskeerde woorden in een zin). Tijdens het voortrainen absorbeert het model aanzienlijke niveaus van taalbegrip, die vervolgens kunnen worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd. -![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.nl.png) +![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Afbeelding [bron](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index cfe6533f..7a841441 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/nl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Aandachtsmechanismen** bieden een manier om het contextuele effect van elke invoervector op elke uitvoervoorspelling van het RNN te wegen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van het invoer-RNN en het uitvoer-RNN. Op deze manier houden we bij het genereren van het uitvoersymbool $y_t$ rekening met alle verborgen toestanden van de invoer $h_i$, met verschillende gewichtscoëfficiënten $\\alpha_{t,i}$.\n", "\n", - "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.nl.png)\n", + "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png)\n", "*Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "De aandachtsmatrix $\\{\\alpha_{i,j}\\}$ vertegenwoordigt de mate waarin bepaalde invoerwoorden een rol spelen bij het genereren van een bepaald woord in de uitvoerreeks. Hieronder staat een voorbeeld van zo'n matrix:\n", "\n", - "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.nl.png)\n", + "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig van Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Afbeelding afkomstig uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformer-netwerk met 12 lagen voor *BERT-base* en 24 voor *BERT-large*. Het model wordt eerst voorgetraind op een grote corpus van tekstdata (Wikipedia + boeken) met behulp van ongesuperviseerde training (voorspellen van gemaskeerde woorden in een zin). Tijdens het voortrainen absorbeert het model een significant niveau van taalbegrip, dat vervolgens kan worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd.\n", "\n", - "![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.nl.png)\n", + "![Afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Er zijn veel variaties van Transformer-architecturen, waaronder BERT, DistilBERT, BigBird, OpenGPT3 en meer, die kunnen worden gefinetuned. Het [HuggingFace-pakket](https://github.com/huggingface/) biedt een repository voor het trainen van veel van deze architecturen met PyTorch.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 90410cd1..cbe8bf7a 100644 --- a/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/nl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Aandachtsmechanismen** bieden een manier om het contextuele gewicht van elke invoervector op elke uitvoervoorspelling van het RNN te bepalen. Dit wordt geïmplementeerd door shortcuts te creëren tussen de tussenliggende toestanden van het invoer-RNN en het uitvoer-RNN. Op deze manier houden we bij het genereren van het uitvoersymbool $y_t$ rekening met alle verborgen toestanden van de invoer $h_i$, met verschillende gewichtscoëfficiënten $\\alpha_{t,i}$. \n", "\n", - "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.nl.png)\n", + "![Afbeelding van een encoder/decoder model met een additieve aandachtlaag](../../../../../translated_images/nl/encoder-decoder-attention.7a726296894fb567.png)\n", "*Het encoder-decoder model met additief aandachtsmechanisme in [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), geciteerd uit [deze blogpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "De aandachtsmatrix $\\{\\alpha_{i,j}\\}$ vertegenwoordigt de mate waarin bepaalde invoerwoorden bijdragen aan de generatie van een specifiek woord in de uitvoerreeks. Hieronder staat een voorbeeld van zo'n matrix:\n", "\n", - "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig uit Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.nl.png)\n", + "![Afbeelding van een voorbeelduitlijning gevonden door RNNsearch-50, afkomstig uit Bahdanau - arviz.org](../../../../../translated_images/nl/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Afbeelding afkomstig uit [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) is een zeer groot meerlagig transformernetwerk met 12 lagen voor *BERT-base* en 24 lagen voor *BERT-large*. Het model wordt eerst voorgetraind op een grote hoeveelheid tekstdata (WikiPedia + boeken) met behulp van ongesuperviseerd leren (voorspellen van gemaskeerde woorden in een zin). Tijdens de voortraining absorbeert het model een aanzienlijk niveau van taalbegrip, wat vervolgens kan worden benut met andere datasets door middel van fine-tuning. Dit proces wordt **transfer learning** genoemd.\n", "\n", - "![afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.nl.png)\n", + "![afbeelding van http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/nl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Er zijn veel variaties van Transformer-architecturen, waaronder BERT, DistilBERT, BigBird, OpenGPT3 en meer, die kunnen worden fijn afgestemd.\n", "\n", diff --git a/translations/nl/lessons/5-NLP/19-NER/README.md b/translations/nl/lessons/5-NLP/19-NER/README.md index d6b1f83f..482766f8 100644 --- a/translations/nl/lessons/5-NLP/19-NER/README.md +++ b/translations/nl/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Omdat we een één-op-één-correspondentie tussen tokens en klassen moeten opbouwen, kunnen we een rechtse **veel-op-veel** neurale netwerkmodel trainen zoals in deze afbeelding: -![Afbeelding die veelvoorkomende patronen van recurrente neurale netwerken toont.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.nl.jpg) +![Afbeelding die veelvoorkomende patronen van recurrente neurale netwerken toont.](../../../../../translated_images/nl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Afbeelding uit [deze blogpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) door [Andrej Karpathy](http://karpathy.github.io/). NER-tokenclassificatiemodellen komen overeen met de rechtse netwerkarchitectuur op deze afbeelding.* diff --git a/translations/nl/lessons/5-NLP/README.md b/translations/nl/lessons/5-NLP/README.md index abf41235..0e4c7417 100644 --- a/translations/nl/lessons/5-NLP/README.md +++ b/translations/nl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natuurlijke Taalverwerking -![Samenvatting van NLP-taken in een schets](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.nl.png) +![Samenvatting van NLP-taken in een schets](../../../../translated_images/nl/ai-nlp.b22dcb8ca4707cea.png) In deze sectie richten we ons op het gebruik van neurale netwerken om taken met betrekking tot **Natuurlijke Taalverwerking (NLP)** uit te voeren. Er zijn veel NLP-problemen die we willen dat computers kunnen oplossen: diff --git a/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md index af068cd4..b20f26ce 100644 --- a/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/nl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Je kunt een van de modellen openen, bijvoorbeeld **Biology → Flocking Na het openen van het model kom je op het hoofdscherm van NetLogo. Hier is een voorbeeldmodel dat de populatie van wolven en schapen beschrijft, gegeven eindige middelen (gras). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.nl.png) +![NetLogo Main Screen](../../../../../translated_images/nl/NetLogo-Main.32653711ec1a01b3.png) > Screenshot door Dmitry Soshnikov diff --git a/translations/nl/lessons/README.md b/translations/nl/lessons/README.md index a1ff3acf..97dc79ec 100644 --- a/translations/nl/lessons/README.md +++ b/translations/nl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Overzicht -![Overzicht in een schets](../../../translated_images/ai-overview.0857791951d19500.nl.png) +![Overzicht in een schets](../../../translated_images/nl/ai-overview.0857791951d19500.png) > Schetsnotitie door [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/nl/lessons/X-Extras/X1-MultiModal/README.md b/translations/nl/lessons/X-Extras/X1-MultiModal/README.md index 0dedd512..202855d0 100644 --- a/translations/nl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/nl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Na het succes van transformer-modellen voor het oplossen van NLP-taken, zijn dez Het belangrijkste idee van CLIP is om tekstprompts te kunnen vergelijken met een afbeelding en te bepalen hoe goed de afbeelding overeenkomt met de prompt. -![CLIP Architectuur](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.nl.png) +![CLIP Architectuur](../../../../../translated_images/nl/clip-arch.b3dbf20b4e8ed8be.png) > *Afbeelding uit [deze blogpost](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Zodra dit model is voorgetraind, kunnen we het een batch afbeeldingen en een bat Stel dat we afbeeldingen moeten classificeren tussen bijvoorbeeld katten, honden en mensen. In dit geval kunnen we het model een afbeelding geven en een reeks tekstprompts: "*een afbeelding van een kat*", "*een afbeelding van een hond*", "*een afbeelding van een mens*". In de resulterende vector van 3 waarschijnlijkheden hoeven we alleen de index met de hoogste waarde te selecteren. -![CLIP voor Afbeeldingsclassificatie](../../../../../translated_images/clip-class.3af42ef0b2b19369.nl.png) +![CLIP voor Afbeeldingsclassificatie](../../../../../translated_images/nl/clip-class.3af42ef0b2b19369.png) > *Afbeelding uit [deze blogpost](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Meer informatie over VQGAN vind je op de [Taming Transformers](https://compvis.g Een belangrijk verschil tussen VQGAN en traditionele GAN is dat de laatste een behoorlijke afbeelding kan produceren vanuit elke invoervector, terwijl VQGAN waarschijnlijk een afbeelding produceert die niet coherent is. Daarom moeten we het proces van afbeeldingcreatie verder sturen, en dat kan worden gedaan met behulp van CLIP. -![VQGAN+CLIP Architectuur](../../../../../translated_images/vqgan.5027fe05051dfa31.nl.png) +![VQGAN+CLIP Architectuur](../../../../../translated_images/nl/vqgan.5027fe05051dfa31.png) Om een afbeelding te genereren die overeenkomt met een tekstprompt, beginnen we met een willekeurige coderingsvector die door VQGAN wordt doorgegeven om een afbeelding te produceren. Vervolgens wordt CLIP gebruikt om een verliesfunctie te produceren die aangeeft hoe goed de afbeelding overeenkomt met de tekstprompt. Het doel is dan om dit verlies te minimaliseren, door middel van backpropagation om de parameters van de invoervector aan te passen. Een geweldige bibliotheek die VQGAN+CLIP implementeert is [Pixray](http://github.com/pixray/pixray). -![Afbeelding gegenereerd door Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.nl.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.nl.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.nl.png) +![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Afbeelding gegenereerd vanuit prompt *een close-up aquarelportret van een jonge mannelijke leraar literatuur met een boek* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een jonge vrouwelijke leraar informatica met een computer* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een oude mannelijke leraar wiskunde voor een schoolbord* @@ -75,7 +75,7 @@ In tegenstelling tot CLIP ontvangt DALL-E zowel tekst als afbeelding als een enk Het belangrijkste verschil tussen DALL.E 1 en 2 is dat het meer realistische afbeeldingen en kunst genereert. Voorbeelden van afbeeldingsgeneraties met DALL-E: -![Afbeelding gegenereerd door Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.nl.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.nl.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.nl.png) +![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Afbeelding gegenereerd door Pixray](../../../../../translated_images/nl/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Afbeelding gegenereerd vanuit prompt *een close-up aquarelportret van een jonge mannelijke leraar literatuur met een boek* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een jonge vrouwelijke leraar informatica met een computer* | Afbeelding gegenereerd vanuit prompt *een close-up olieverfportret van een oude mannelijke leraar wiskunde voor een schoolbord* diff --git a/translations/no/README.md b/translations/no/README.md index d4b84f0a..405edf5c 100644 --- a/translations/no/README.md +++ b/translations/no/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Kunstig intelligens for nybegynnere - Et undervisningsopplegg -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.no.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/no/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote av [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/no/lessons/1-Intro/README.md b/translations/no/lessons/1-Intro/README.md index 560ea5f3..ef9cbd2b 100644 --- a/translations/no/lessons/1-Intro/README.md +++ b/translations/no/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduksjon til AI -![Oppsummering av innholdet i Introduksjon til AI i en tegning](../../../../translated_images/ai-intro.bf28d1ac4235881c.no.png) +![Oppsummering av innholdet i Introduksjon til AI i en tegning](../../../../translated_images/no/ai-intro.bf28d1ac4235881c.png) > Tegning av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Opprinnelig ble datamaskiner oppfunnet av [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) for å operere på tall ved å følge en veldefinert prosedyre – en algoritme. Moderne datamaskiner, selv om de er betydelig mer avanserte enn den opprinnelige modellen foreslått på 1800-tallet, følger fortsatt samme idé om kontrollerte beregninger. Dermed er det mulig å programmere en datamaskin til å gjøre noe hvis vi kjenner den nøyaktige sekvensen av trinn som må utføres for å oppnå målet. -![Bilde av en person](../../../../translated_images/dsh_age.d212a30d4e54fb5f.no.png) +![Bilde av en person](../../../../translated_images/no/dsh_age.d212a30d4e54fb5f.png) > Foto av [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ For mer informasjon, se **[Artificial General Intelligence](https://en.wikipedia Et av problemene med begrepet **[intelligens](https://en.wikipedia.org/wiki/Intelligence)** er at det ikke finnes noen klar definisjon av dette begrepet. Man kan argumentere for at intelligens er knyttet til **abstrakt tenkning** eller **selvbevissthet**, men vi kan ikke definere det ordentlig. -![Bilde av en katt](../../../../translated_images/photo-cat.8c8e8fb760ffe457.no.jpg) +![Bilde av en katt](../../../../translated_images/no/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) av [Amber Kipp](https://unsplash.com/@sadmax) fra Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi prøve å modellere de enkleste elementene i hjernen vår – > | Hva med ML? | | > |--------------|-----------| -> | En del av kunstig intelligens som er basert på at datamaskiner lærer å løse et problem basert på noen data, kalles **maskinlæring**. Vi vil ikke gå inn på klassisk maskinlæring i dette kurset – vi henviser deg til et eget [Maskinlæring for nybegynnere](http://aka.ms/ml-beginners)-pensum. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.no.png) | +> | En del av kunstig intelligens som er basert på at datamaskiner lærer å løse et problem basert på noen data, kalles **maskinlæring**. Vi vil ikke gå inn på klassisk maskinlæring i dette kurset – vi henviser deg til et eget [Maskinlæring for nybegynnere](http://aka.ms/ml-beginners)-pensum. | ![ML for Beginners](../../../../translated_images/no/ml-for-beginners.9e4fed176fd5817d.png) | ## En Kort Historie om AI Kunstig intelligens startet som et felt på midten av det tjuende århundre. Opprinnelig var symbolsk resonnering en dominerende tilnærming, og det førte til en rekke viktige suksesser, som ekspertsystemer – dataprogrammer som kunne opptre som en ekspert innenfor noen begrensede problemområder. Det ble imidlertid raskt klart at en slik tilnærming ikke skalerer godt. Å trekke ut kunnskap fra en ekspert, representere det i en datamaskin og holde kunnskapsbasen nøyaktig viste seg å være en svært kompleks oppgave, og for kostbar til å være praktisk i mange tilfeller. Dette førte til den såkalte [AI-vinteren](https://en.wikipedia.org/wiki/AI_winter) på 1970-tallet. -Kort Historie om AI +Kort Historie om AI > Bilde av [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samme måte kan vi se hvordan tilnærmingen til å lage "snakkende programme * Moderne assistenter, som Cortana, Siri eller Google Assistant, er alle hybridsystemer som bruker nevrale nettverk for å konvertere tale til tekst og gjenkjenne vår intensjon, og deretter benytter noe resonnering eller eksplisitte algoritmer for å utføre nødvendige handlinger. * I fremtiden kan vi forvente en komplett nevrale-basert modell som håndterer dialog helt på egen hånd. De nylige GPT- og [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)-familiene av nevrale nettverk viser stor suksess i dette. -Turing-testens utvikling +Turing-testens utvikling > Bilde av Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) av [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nyere AI-forskning diff --git a/translations/no/lessons/2-Symbolic/Animals.ipynb b/translations/no/lessons/2-Symbolic/Animals.ipynb index 16d422d3..62d67b14 100644 --- a/translations/no/lessons/2-Symbolic/Animals.ipynb +++ b/translations/no/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I dette eksempelet skal vi implementere et enkelt kunnskapsbasert system for å identifisere et dyr basert på noen fysiske egenskaper. Systemet kan representeres av følgende AND-OR-tre (dette er en del av hele treet, vi kan enkelt legge til flere regler):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.no.png)\n" + "![](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/no/lessons/2-Symbolic/README.md b/translations/no/lessons/2-Symbolic/README.md index 8ce641bc..e04c4edc 100644 --- a/translations/no/lessons/2-Symbolic/README.md +++ b/translations/no/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kunnskapsrepresentasjon og ekspertsystemer -![Oppsummering av Symbolic AI-innhold](../../../../translated_images/ai-symbolic.715a30cb610411a6.no.png) +![Oppsummering av Symbolic AI-innhold](../../../../translated_images/no/ai-symbolic.715a30cb610411a6.png) > Sketchnote av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Ofte definerer vi ikke kunnskap strengt, men vi knytter det til andre relaterte Dermed er problemet med **kunnskapsrepresentasjon** å finne en effektiv måte å representere kunnskap inne i en datamaskin i form av data, slik at den kan brukes automatisk. Dette kan sees som et spektrum: -![Spektrum for kunnskapsrepresentasjon](../../../../translated_images/knowledge-spectrum.b60df631852c0217.no.png) +![Spektrum for kunnskapsrepresentasjon](../../../../translated_images/no/knowledge-spectrum.b60df631852c0217.png) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blokk-syntaks | Innrykk | | | En av de tidlige suksessene til symbolsk AI var de såkalte **ekspertsystemene** - datasystemer som var designet for å fungere som en ekspert innenfor et begrenset problemområde. De var basert på en **kunnskapsbase** hentet fra en eller flere menneskelige eksperter, og de inneholdt en **slutningsmotor** som utførte resonnering basert på denne kunnskapen. -![Menneskelig arkitektur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.no.png) | ![Kunnskapsbasert system](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.no.png) +![Menneskelig arkitektur](../../../../translated_images/no/arch-human.5d4d35f1bba3ab1c.png) | ![Kunnskapsbasert system](../../../../translated_images/no/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Forenklet struktur av et menneskelig nervesystem | Arkitektur av et kunnskapsbasert system @@ -106,7 +106,7 @@ Ekspertsystemer er bygget som det menneskelige resonnanssystemet, som inneholder Som et eksempel, la oss se på følgende ekspertsystem for å bestemme et dyr basert på dets fysiske egenskaper: -![AND-OR-tre](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.no.png) +![AND-OR-tre](../../../../translated_images/no/AND-OR-Tree.5592d2c70187f283.png) > Bilde av [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index d6a0f901..fcbd3b1d 100644 --- a/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/no/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Hvis vi har mer enn 2 klasser, vil softmax normalisere sannsynlighetene på tvers av alle. Her er et diagram over nettverksarkitekturen som utfører MNIST-sifferklassifisering:\n", "\n", - "![MNIST Klassifiserer](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.no.png)\n" + "![MNIST Klassifiserer](../../../../../translated_images/no/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1257,7 +1257,7 @@ "* Lav treningsfeil - modellen kan tilpasse seg treningsdata godt, fordi den har nok uttrykkskraft.\n", "* Valideringsfeilen kan være mye høyere enn treningsfeilen og kan begynne å øke under treningen - dette skjer fordi modellen \"husker\" treningspunktene og mister \"helhetsbildet\".\n", "\n", - "![Overtilpasning](../../../../../translated_images/overfit.a0bd57f717c15769.no.png)\n", + "![Overtilpasning](../../../../../translated_images/no/overfit.a0bd57f717c15769.png)\n", "\n", "> På dette bildet står `x` for treningsdata, `o` for valideringsdata. Til venstre - lineær modell (ett lag), den tilpasser seg naturen til dataen ganske bra. Til høyre - overtilpasset modell, modellen tilpasser seg treningsdata perfekt, men gir ingen mening for annen data (valideringsfeilen er veldig høy).\n" ] diff --git a/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md index be893e68..fa6e75b9 100644 --- a/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/no/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overtilpasning er et ekstremt viktig konsept innen maskinlæring, og det er veld Tenk på følgende problem med å tilnærme 5 punkter (representert med `x` på grafene nedenfor): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.no.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.no.jpg) +![linear](../../../../../translated_images/no/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/no/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineær modell, 2 parametere** | **Ikke-lineær modell, 7 parametere** Treningsfeil = 5.3 | Treningsfeil = 0 @@ -79,7 +79,7 @@ Det er veldig viktig å finne en riktig balanse mellom modellens kompleksitet (a Som du kan se fra grafen ovenfor, kan overtilpasning oppdages ved en veldig lav treningsfeil og en høy valideringsfeil. Normalt under trening vil vi se både trenings- og valideringsfeil begynne å avta, og deretter på et tidspunkt kan valideringsfeilen slutte å avta og begynne å stige. Dette vil være et tegn på overtilpasning og en indikator på at vi sannsynligvis bør stoppe treningen på dette punktet (eller i det minste ta et øyeblikksbilde av modellen). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.no.png) +![overfitting](../../../../../translated_images/no/Overfitting.408ad91cd90b4371.png) ## Hvordan forhindre overtilpasning diff --git a/translations/no/lessons/3-NeuralNetworks/README.md b/translations/no/lessons/3-NeuralNetworks/README.md index f1e4e0c1..b41109db 100644 --- a/translations/no/lessons/3-NeuralNetworks/README.md +++ b/translations/no/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduksjon til nevrale nettverk -![Oppsummering av innholdet i Intro Neural Networks i en tegning](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.no.png) +![Oppsummering av innholdet i Intro Neural Networks i en tegning](../../../../translated_images/no/ai-neuralnetworks.1c687ae40bc86e83.png) Som vi diskuterte i introduksjonen, er en av måtene å oppnå intelligens på å trene en **datamodell** eller en **kunstig hjerne**. Siden midten av 1900-tallet har forskere prøvd ulike matematiske modeller, og i de senere år har denne retningen vist seg å være svært vellykket. Slike matematiske modeller av hjernen kalles **nevrale nettverk**. @@ -36,13 +36,13 @@ I dette pensumet vil vi kun fokusere på modeller for nevrale nettverk. Fra biologien vet vi at hjernen vår består av nerveceller (nevroner), som hver har flere "innganger" (dendritter) og en enkelt "utgang" (akson). Både dendritter og aksoner kan lede elektriske signaler, og forbindelsene mellom dem — kjent som synapser — kan ha varierende grad av ledningsevne, som reguleres av nevrotransmittere. -![Modell av et nevron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.no.jpg) | ![Modell av et nevron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.no.png) +![Modell av et nevron](../../../../translated_images/no/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modell av et nevron](../../../../translated_images/no/artneuron.1a5daa88d20ebe6f.png) ----|---- Ekte nevron *([Bilde](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) fra Wikipedia)* | Kunstig nevron *(Bilde av forfatteren)* Dermed inneholder den enkleste matematiske modellen av et nevron flere innganger X1, ..., XN og en utgang Y, samt en serie vekter W1, ..., WN. En utgang beregnes som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) der f er en ikke-lineær **aktiveringsfunksjon**. diff --git a/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md index 3896836b..9cc3410c 100644 --- a/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/no/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vår [OpenCV Notebook](OpenCV.ipynb) gir vi noen eksempler på når datamaskin * **Forhåndsbehandling av et fotografi av en Braille-bok**. Vi fokuserer på hvordan vi kan bruke terskling, funksjonsdeteksjon, perspektivtransformasjon og NumPy-manipulasjoner for å separere individuelle Braille-symboler for videre klassifisering av et nevralt nettverk. -![Braille-bilde](../../../../../translated_images/braille.341962ff76b1bd70.no.jpeg) | ![Forhåndsbehandlet Braille-bilde](../../../../../translated_images/braille-result.46530fea020b03c7.no.png) | ![Braille-symboler](../../../../../translated_images/braille-symbols.0159185ab69d5339.no.png) +![Braille-bilde](../../../../../translated_images/no/braille.341962ff76b1bd70.jpeg) | ![Forhåndsbehandlet Braille-bilde](../../../../../translated_images/no/braille-result.46530fea020b03c7.png) | ![Braille-symboler](../../../../../translated_images/no/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) * **Deteksjon av bevegelse i video ved hjelp av rammeforskjell**. Hvis kameraet er fast, bør rammer fra kamerafeeden være ganske like hverandre. Siden rammer er representert som arrays, vil vi ved å trekke fra disse arrayene for to påfølgende rammer få pikselforskjellen, som bør være lav for statiske rammer, og bli høyere når det er betydelig bevegelse i bildet. -![Bilde av videorammer og rammeforskjeller](../../../../../translated_images/frame-difference.706f805491a0883c.no.png) +![Bilde av videorammer og rammeforskjeller](../../../../../translated_images/no/frame-difference.706f805491a0883c.png) > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vår [OpenCV Notebook](OpenCV.ipynb) gir vi noen eksempler på når datamaskin - **Tett optisk flyt** beregner vektorfeltet som viser hvor hver piksel beveger seg. - **Sparsom optisk flyt** er basert på å ta noen distinkte funksjoner i bildet (f.eks. kanter) og bygge deres bane fra ramme til ramme. -![Bilde av optisk flyt](../../../../../translated_images/optical.1f4a94464579a83a.no.png) +![Bilde av optisk flyt](../../../../../translated_images/no/optical.1f4a94464579a83a.png) > Bilde fra [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 9eee29c4..ec019299 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 er et nettverk som oppnådde 92,7 % nøyaktighet i ImageNet top-5 klassifisering i 2014. Det har følgende lagstruktur: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.no.jpg) +![ImageNet Layers](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.jpg) Som du kan se, følger VGG en tradisjonell pyramidearkitektur, som er en sekvens av konvolusjons- og pooling-lag. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.no.jpg) +![ImageNet Pyramid](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.jpg) > Bilde fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index a111c829..4c04b11c 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Dermed vil en typisk CNN ha flere konvolusjonslag, med pooling-lag mellom dem for å redusere dimensjonene på bildet. Vi vil også øke antallet filtre, fordi når mønstrene blir mer avanserte, er det flere mulige interessante kombinasjoner vi må se etter.\n", "\n", - "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.no.png)\n", + "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grunn av reduserte romlige dimensjoner og økte funksjons-/filterdimensjoner, kalles denne arkitekturen også **pyramidearkitektur**.\n" ] diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index d5d45912..eb511605 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Dermed vil en typisk CNN ha flere konvolusjonslag, med pooling-lag mellom dem for å redusere dimensjonene på bildet. Vi vil også øke antallet filtre, fordi når mønstrene blir mer avanserte, er det flere mulige interessante kombinasjoner vi må se etter.\n", "\n", - "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.no.png)\n", + "![Et bilde som viser flere konvolusjonslag med pooling-lag.](../../../../../translated_images/no/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grunn av reduserte romlige dimensjoner og økte funksjons-/filterdimensjoner, kalles denne arkitekturen også **pyramidearkitektur**.\n" ] diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md index 30711fbc..ada5232a 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I virkeligheten ønsker vi å kunne gjenkjenne objekter på et bilde uavhengig a For å trekke ut mønstre, vil vi bruke begrepet **konvolusjonsfiltre**. Som du vet, er et bilde representert av en 2D-matrise, eller en 3D-tensor med fargedybde. Å bruke et filter betyr at vi tar en relativt liten **filterkjerne**-matrise, og for hver piksel i det originale bildet beregner vi det vektede gjennomsnittet med nabopunktene. Vi kan se på dette som et lite vindu som glir over hele bildet og jevner ut alle pikslene i henhold til vektene i filterkjernematrisen. -![Vertikalt kantfilter](../../../../../translated_images/filter-vert.b7148390ca0bc356.no.png) | ![Horisontalt kantfilter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.no.png) +![Vertikalt kantfilter](../../../../../translated_images/no/filter-vert.b7148390ca0bc356.png) | ![Horisontalt kantfilter](../../../../../translated_images/no/filter-horiz.59b80ed4feb946ef.png) ----|---- > Bilde av Dmitry Soshnikov @@ -38,7 +38,7 @@ Måten CNN-er fungerer på er basert på følgende viktige ideer: * Vi kan designe nettverket slik at filtrene trenes automatisk * Vi kan bruke samme tilnærming for å finne mønstre i høyere nivå-funksjoner, ikke bare i det originale bildet. Dermed fungerer CNN-funksjonsekstraksjon på en hierarki av funksjoner, fra lavnivå pikselkombinasjoner til høyere nivå kombinasjoner av bildeelementer. -![Hierarkisk funksjonsekstraksjon](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.no.png) +![Hierarkisk funksjonsekstraksjon](../../../../../translated_images/no/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Bilde fra [en artikkel av Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), basert på [deres forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De fleste CNN-er som brukes til bildebehandling følger en såkalt pyramidearkit Som et eksempel, la oss se på arkitekturen til VGG-16, et nettverk som oppnådde 92,7 % nøyaktighet i ImageNet's topp-5 klassifisering i 2014: -![ImageNet-lag](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.no.jpg) +![ImageNet-lag](../../../../../translated_images/no/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet-pyramide](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.no.jpg) +![ImageNet-pyramide](../../../../../translated_images/no/vgg-16-arch.64ff2137f50dd49f.jpg) > Bilde fra [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e98bedf8..97001f75 100644 --- a/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/no/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du må trene et konvolusjonsnevralt nettverk for å klassifisere forskjellige ra Vi skal bruke [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som inneholder bilder av 37 forskjellige raser av hunder og katter. -![Datasettet vi skal jobbe med](../../../../../../translated_images/data.50b2a9d5484bdbf0.no.png) +![Datasettet vi skal jobbe med](../../../../../../translated_images/no/data.50b2a9d5484bdbf0.png) For å laste ned datasettet, bruk denne kodebiten: diff --git a/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 2b0c6153..face15ad 100644 --- a/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/no/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "For å visualisere den ideelle katten, starter vi med et tilfeldig støybilde og bruker gradientnedstigning som optimaliseringsteknikk for å justere bildet slik at nettverket gjenkjenner en katt.\n", "\n", - "![Optimaliseringssløyfe](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.no.png)\n", + "![Optimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Her er vårt startbilde:\n" ] diff --git a/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md index bba366c5..66e8296a 100644 --- a/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/no/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras og PyTorch inneholder funksjoner for enkelt å laste inn forhåndstr Her er eksempler på funksjoner som er trukket ut fra et bilde av en katt av VGG-16-nettverket: -![Funksjoner trukket ut av VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.no.png) +![Funksjoner trukket ut av VGG-16](../../../../../translated_images/no/features.6291f9c7ba3a0b95.png) ## Datasett: Katter vs. Hunder @@ -48,19 +48,19 @@ Et forhåndstrent nevralt nettverk inneholder ulike mønstre i sin *hjerne*, ink En tilnærming vi kan bruke, er å starte med et tilfeldig bilde og deretter bruke **gradient descent-optimalisering** for å justere bildet slik at nettverket begynner å tro at det er en katt. -![Bildeoptimaliseringssløyfe](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.no.png) +![Bildeoptimaliseringssløyfe](../../../../../translated_images/no/ideal-cat-loop.999fbb8ff306e044.png) Men hvis vi gjør dette, vil vi få noe som ligner veldig på tilfeldig støy. Dette er fordi *det finnes mange måter å få nettverket til å tro at inngangsbilde er en katt*, inkludert noen som ikke gir mening visuelt. Selv om disse bildene inneholder mange mønstre som er typiske for en katt, er det ingenting som begrenser dem til å være visuelt distinkte. For å forbedre resultatet kan vi legge til et annet ledd i tapsfunksjonen, som kalles **variasjonstap**. Dette er en metrikk som viser hvor like nabopikslene i bildet er. Ved å minimere variasjonstap blir bildet jevnere og støy fjernes, noe som avslører mer visuelt tiltalende mønstre. Her er et eksempel på slike "ideelle" bilder, som klassifiseres som katt og som sebra med høy sannsynlighet: -![Ideell Katt](../../../../../translated_images/ideal-cat.203dd4597643d6b0.no.png) | ![Ideell Sebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.no.png) +![Ideell Katt](../../../../../translated_images/no/ideal-cat.203dd4597643d6b0.png) | ![Ideell Sebra](../../../../../translated_images/no/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideell Katt* | *Ideell Sebra* En lignende tilnærming kan brukes til å utføre såkalte **adversarielle angrep** på et nevralt nettverk. Anta at vi ønsker å lure et nevralt nettverk og få en hund til å se ut som en katt. Hvis vi tar et bilde av en hund, som nettverket gjenkjenner som en hund, kan vi justere det litt ved hjelp av gradient descent-optimalisering, til nettverket begynner å klassifisere det som en katt: -![Bilde av en Hund](../../../../../translated_images/original-dog.8f68a67d2fe0911f.no.png) | ![Bilde av en hund klassifisert som en katt](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.no.png) +![Bilde av en Hund](../../../../../translated_images/no/original-dog.8f68a67d2fe0911f.png) | ![Bilde av en hund klassifisert som en katt](../../../../../translated_images/no/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originalbilde av en hund* | *Bilde av en hund klassifisert som en katt* diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 86def571..288bac64 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Siden vi trener autoencoderen til å fange så mye informasjon som mulig fra det originale bildet for nøyaktig rekonstruksjon, prøver nettverket å finne den beste **innkapslingen** av inngangsbildene for å fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.no.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Bilde fra [Keras-bloggen](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 4bdb1cd6..40dbdd9a 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Siden vi trener autoencoderen til å fange opp så mye informasjon som mulig fra det originale bildet for å oppnå nøyaktig rekonstruksjon, prøver nettverket å finne den beste **innkapslingen** av inngangsbilder for å fange meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.no.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Bilde fra [Keras-bloggen](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md index 806279e2..3a3de3c0 100644 --- a/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/no/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vi kan imidlertid ønske å bruke rå (umerkede) data for å trene CNN-funksjons Siden vi trener en autoencoder til å fange så mye informasjon som mulig fra det originale bildet for nøyaktig rekonstruksjon, prøver nettverket å finne den beste **embedding** av input-bilder for å fange meningen. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.no.jpg) +![AutoEncoder Diagram](../../../../../translated_images/no/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Bilde fra [Keras-blogg](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md index 751dbbda..36f43b5d 100644 --- a/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/no/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Bildklassifiseringsmodellene vi har jobbet med så langt tar et bilde og gir et ## [Quiz før forelesning](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektgjenkjenning](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.no.png) +![Objektgjenkjenning](../../../../../translated_images/no/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Bilde fra [YOLO v2 nettside](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Anta at vi ønsket å finne en katt på et bilde. En veldig naiv tilnærming til 2. Kjør bildklassifisering på hver flis. 3. De flisene som gir tilstrekkelig høy aktivering kan anses å inneholde det aktuelle objektet. -![Naiv objektgjenkjenning](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.no.png) +![Naiv objektgjenkjenning](../../../../../translated_images/no/naive-detection.e7f1ba220ccd08c6.png) > *Bilde fra [Øvingsnotatbok](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan komme over følgende datasett for denne oppgaven: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klasser * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 klasser, avgrensningsbokser og segmenteringsmasker -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.no.jpg) +![COCO](../../../../../translated_images/no/coco-examples.71bc60380fa6cceb.jpg) ## Objektgjenkjenningsmetrikker @@ -50,7 +50,7 @@ Du kan komme over følgende datasett for denne oppgaven: Mens det er enkelt å måle hvor godt algoritmen presterer for bildklassifisering, må vi for objektgjenkjenning måle både korrektheten av klassen og presisjonen til den utledede plasseringen av avgrensningsboksen. For sistnevnte bruker vi den såkalte **Intersection over Union** (IoU), som måler hvor godt to bokser (eller to vilkårlige områder) overlapper. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.no.png) +![IoU](../../../../../translated_images/no/iou_equation.9a4751d40fff4e11.png) > *Figur 2 fra [dette utmerkede blogginnlegget om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Det finnes to brede klasser av algoritmer for objektgjenkjenning: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) bruker [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) for å generere en hierarkisk struktur av ROI-regioner, som deretter sendes gjennom CNN-funksjonsekstraktorer og SVM-klassifisatorer for å bestemme objektklassen, og lineær regresjon for å bestemme *avgrensningsboks*-koordinater. [Offisiell artikkel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.no.png) +![RCNN](../../../../../translated_images/no/rcnn1.cae407020dfb1d1f.png) > *Bilde fra van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.no.png) +![RCNN-1](../../../../../translated_images/no/rcnn2.2d9530bb83516484.png) > *Bilder fra [denne bloggen](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Det finnes to brede klasser av algoritmer for objektgjenkjenning: Denne tilnærmingen ligner på R-CNN, men regioner defineres etter at konvolusjonslagene er blitt brukt. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.no.png) +![FRCNN](../../../../../translated_images/no/f-rcnn.3cda6d9bb4188875.png) > Bilde fra [den offisielle artikkelen](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denne tilnærmingen ligner på R-CNN, men regioner defineres etter at konvolusjo Hovedideen med denne tilnærmingen er å bruke et nevralt nettverk til å forutsi ROI-er – såkalte *Region Proposal Network*. [Artikkel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.no.png) +![FasterRCNN](../../../../../translated_images/no/faster-rcnn.8d46c099b87ef30a.png) > Bilde fra [den offisielle artikkelen](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denne algoritmen er enda raskere enn Faster R-CNN. Hovedideen er følgende: 2. Funksjonene behandles av **Position-Sensitive Score Map**. Hvert objekt fra $C$ klasser deles inn i $k\times k$ regioner, og vi trener på å forutsi deler av objekter. 3. For hver del fra $k\times k$ regioner stemmer alle nettverkene på objektklasser, og objektklassen med flest stemmer blir valgt. -![r-fcn bilde](../../../../../translated_images/r-fcn.13eb88158b99a3da.no.png) +![r-fcn bilde](../../../../../translated_images/no/r-fcn.13eb88158b99a3da.png) > Bilde fra [offisiell artikkel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO er en sanntids én-pass-algoritme. Hovedideen er følgende: * Bildet deles inn i $S\times S$ regioner. * For hver region forutsier **CNN** $n$ mulige objekter, *avgrensningsboks*-koordinater og *confidence*=*sannsynlighet* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.no.png) + ![YOLO](../../../../../translated_images/no/yolo.a2648ec82ee8bb4e.png) > Bilde fra [offisiell artikkel](https://arxiv.org/abs/1506.02640) diff --git a/translations/no/lessons/4-ComputerVision/README.md b/translations/no/lessons/4-ComputerVision/README.md index 9402e890..8dee9d04 100644 --- a/translations/no/lessons/4-ComputerVision/README.md +++ b/translations/no/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Datamaskinsyn -![Sammendrag av innholdet om datamaskinsyn i en skisse](../../../../translated_images/ai-computervision.6506ebebac3fbf76.no.png) +![Sammendrag av innholdet om datamaskinsyn i en skisse](../../../../translated_images/no/ai-computervision.6506ebebac3fbf76.png) I denne delen skal vi lære om: diff --git a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 8e6d69ce..77ee0cac 100644 --- a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -197,7 +197,7 @@ "\n", "**Bag of Words** (BoW) vektorrepresentasjon er den mest brukte tradisjonelle vektorrepresentasjonen. Hvert ord er knyttet til en vektorindeks, og hvert element i vektoren inneholder antall forekomster av et ord i et gitt dokument.\n", "\n", - "![Bilde som viser hvordan en bag of words-vektorrepresentasjon lagres i minnet.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.no.png) \n", + "![Bilde som viser hvordan en bag of words-vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Merk**: Du kan også tenke på BoW som en sum av alle én-hot-kodede vektorer for de individuelle ordene i teksten.\n", "\n", diff --git a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index d0470b7a..48990a9f 100644 --- a/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/no/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepresentasjon er den enkleste tradisjonelle vektorrepresentasjonen å forstå. Hvert ord er knyttet til en vektorindeks, og et vektorelement inneholder antall forekomster av hvert ord i et gitt dokument.\n", "\n", - "![Bilde som viser hvordan en bag-of-words vektorrepresentasjon lagres i minnet.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.no.png) \n", + "![Bilde som viser hvordan en bag-of-words vektorrepresentasjon lagres i minnet.](../../../../../translated_images/no/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Du kan også tenke på BoW som en sum av alle én-hot-kodede vektorer for individuelle ord i teksten.\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 202cbb3a..9efe9fe4 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ved å bruke embedding-laget som det første laget i vårt nettverk, kan vi gå fra bag-of-words til **embedding bag**-modellen, hvor vi først konverterer hvert ord i teksten vår til tilsvarende embedding, og deretter beregner en aggregatfunksjon over alle disse embeddingene, som for eksempel `sum`, `average` eller `max`. \n", "\n", - "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.no.png)\n", + "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vårt klassifiserings-nevrale nettverk vil starte med embedding-lag, deretter et aggregasjonslag, og en lineær klassifiserer på toppen av det:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidligere arkitekturen måtte vi fylle alle sekvenser til samme lengde for å passe dem inn i en minibatch. Dette er ikke den mest effektive måten å representere sekvenser med variabel lengde på - en annen tilnærming ville være å bruke en **offset**-vektor, som holder offsetene til alle sekvenser lagret i én stor vektor.\n", "\n", - "![Bilde som viser en offset-sekvensrepresentasjon](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.no.png)\n", + "![Bilde som viser en offset-sekvensrepresentasjon](../../../../../translated_images/no/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: På bildet ovenfor viser vi en sekvens av tegn, men i vårt eksempel jobber vi med sekvenser av ord. Prinsippet for å representere sekvenser med en offset-vektor forblir imidlertid det samme.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW er raskere, mens skip-gram er tregere, men gjør en bedre jobb med å representere sjeldne ord.\n", "\n", - "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.no.png)\n", + "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "For å eksperimentere med word2vec-embedding forhåndstrent på Google News-datasettet, kan vi bruke **gensim**-biblioteket. Nedenfor finner vi ordene som ligner mest på 'neural'\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index bd5bf336..cb54e2e0 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/no/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ved å bruke et embedding-lag som det første laget i nettverket vårt, kan vi gå fra bag-of-words til en **embedding bag**-modell, der vi først konverterer hvert ord i teksten vår til den tilsvarende embedding, og deretter beregner en aggregasjonsfunksjon over alle disse embeddingene, som for eksempel `sum`, `average` eller `max`. \n", "\n", - "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.no.png)\n", + "![Bilde som viser en embedding-klassifiserer for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vårt klassifiserings-nevrale nettverk består av følgende lag:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW er raskere, mens skip-gram er tregere, men det gjør en bedre jobb med å representere sjeldne ord.\n", "\n", - "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.no.png)\n", + "![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "For å eksperimentere med Word2Vec-embedding forhåndstrent på Google News-datasettet, kan vi bruke **gensim**-biblioteket. Nedenfor finner vi ordene som ligner mest på 'neural'.\n", "\n", diff --git a/translations/no/lessons/5-NLP/14-Embeddings/README.md b/translations/no/lessons/5-NLP/14-Embeddings/README.md index 969fc533..f293e235 100644 --- a/translations/no/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/no/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så, innebyggingslaget vil ta et ord som input og produsere en output-vektor med Ved å bruke et innebyggingslag som første lag i vårt klassifikatornettverk, kan vi bytte fra en bag-of-words til **embedding bag**-modell, hvor vi først konverterer hvert ord i teksten vår til tilsvarende innebygging, og deretter beregner en aggregatfunksjon over alle disse innebyggingene, som `sum`, `average` eller `max`. -![Bilde som viser en innebyggingsklassifikator for fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.no.png) +![Bilde som viser en innebyggingsklassifikator for fem sekvensord.](../../../../../translated_images/no/embedding-classifier-example.b77f021a7ee67eee.png) > Bilde av forfatteren @@ -40,7 +40,7 @@ For å oppnå dette må vi forhåndstrene innebyggingsmodellen vår på en stor CBoW er raskere, mens skip-gram er tregere, men gjør en bedre jobb med å representere sjeldne ord. -![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.no.png) +![Bilde som viser både CBoW- og Skip-Gram-algoritmer for å konvertere ord til vektorer.](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Bilde fra [denne artikkelen](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/no/lessons/5-NLP/15-LanguageModeling/README.md b/translations/no/lessons/5-NLP/15-LanguageModeling/README.md index 4727c8e9..fd3b951a 100644 --- a/translations/no/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/no/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I våre tidligere eksempler brukte vi forhåndstrente semantiske embeddinger, me * **Continuous Bag-of-Words** (CBoW), der vi forutsier det midterste tokenet $W_0$ i en sekvens av token $W_{-N}$, ..., $W_N$. * **Skip-gram**, der vi forutsier et sett av nabotoken {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} fra det midterste tokenet $W_0$. -![bilde fra artikkel om konvertering av ord til vektorer](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.no.png) +![bilde fra artikkel om konvertering av ord til vektorer](../../../../../translated_images/no/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Bilde fra [denne artikkelen](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/no/lessons/5-NLP/16-RNN/README.md b/translations/no/lessons/5-NLP/16-RNN/README.md index 778c465d..18297499 100644 --- a/translations/no/lessons/5-NLP/16-RNN/README.md +++ b/translations/no/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I tidligere seksjoner har vi brukt rike semantiske representasjoner av tekst og For å fange betydningen av tekstsekvenser, må vi bruke en annen nevralt nettverksarkitektur, som kalles et **rekurrent nevralt nettverk**, eller RNN. I RNN sender vi setningen vår gjennom nettverket én symbol om gangen, og nettverket produserer en **tilstand**, som vi deretter sender tilbake til nettverket sammen med neste symbol. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.no.png) +![RNN](../../../../../translated_images/no/rnn.27f5c29c53d727b5.png) > Bilde av forfatteren @@ -61,7 +61,7 @@ Vi har diskutert rekurrente nettverk som opererer i én retning, fra begynnelsen Et rekurrent nettverk, enten én-retning eller bidireksjonalt, fanger visse mønstre innen en sekvens og kan lagre dem i en tilstandsvektor eller sende dem til output. Som med konvolusjonsnettverk, kan vi bygge et annet rekurrent lag oppå det første for å fange høyere nivå mønstre og bygge fra lavnivå mønstre som er hentet ut av det første laget. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der output fra det forrige laget sendes til neste lag som input. -![Bilde som viser et flerlags long-short-term-memory-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.no.jpg) +![Bilde som viser et flerlags long-short-term-memory-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Bilde fra [denne fantastiske posten](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López* diff --git a/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 724eae66..fe4b8acf 100644 --- a/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "Et rekurrent nettverk, enten det er énveis eller toveis, fanger visse mønstre innen en sekvens og kan lagre dem i tilstandsvektoren eller sende dem til utgangen. Som med konvolusjonsnettverk kan vi bygge et annet rekurrent lag oppå det første for å fange mønstre på høyere nivå, bygget fra lavnivåmønstre som det første laget har hentet ut. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der utgangen fra det forrige laget sendes til det neste laget som inngang.\n", "\n", - "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.no.jpg)\n", + "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Bilde fra [denne fantastiske artikkelen](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López*\n", "\n", diff --git a/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb index 4eb80c0a..971d122a 100644 --- a/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/no/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "For å fange meningen av en tekstsekvens, vil vi bruke en nevralt nettverksarkitektur kalt **rekurrente nevrale nettverk**, eller RNN. Når vi bruker en RNN, sender vi setningen vår gjennom nettverket én token om gangen, og nettverket produserer en **tilstand**, som vi deretter sender tilbake til nettverket sammen med neste token.\n", "\n", - "![Bilde som viser et eksempel på generering med rekurrente nevrale nettverk.](../../../../../translated_images/rnn.27f5c29c53d727b5.no.png)\n", + "![Bilde som viser et eksempel på generering med rekurrente nevrale nettverk.](../../../../../translated_images/no/rnn.27f5c29c53d727b5.png)\n", "\n", "Gitt en inngangssekvens av tokenene $X_0,\\dots,X_n$, lager RNN-en en sekvens av nevrale nettverksblokker og trener denne sekvensen ende-til-ende ved hjelp av backpropagation. Hver nettverksblokk tar et par $(X_i,S_i)$ som input og produserer $S_{i+1}$ som resultat. Den endelige tilstanden $S_n$ eller utgangen $Y_n$ går inn i en lineær klassifiserer for å produsere resultatet. Alle nettverksblokker deler de samme vektene og trenes ende-til-ende med én backpropagation-passering.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrente nettverk, enten de er enveis eller toveis, fanger opp mønstre innen en sekvens og lagrer dem i tilstandsvektorer eller returnerer dem som output. Akkurat som med konvolusjonsnettverk, kan vi bygge et annet rekurrent lag etter det første for å fange opp mønstre på et høyere nivå, bygget fra mønstre på lavere nivå som det første laget har hentet ut. Dette leder oss til begrepet **flerlags RNN**, som består av to eller flere rekurrente nettverk, der output fra det forrige laget sendes videre til det neste laget som input.\n", "\n", - "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.no.jpg)\n", + "![Bilde som viser et flerlags lang-korttidsminne-RNN](../../../../../translated_images/no/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Bilde fra [denne fantastiske artikkelen](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López.*\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 33d52a24..6b09b2ad 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Måten vi skal trene en RNN til å generere tekst på, er som følger. For hvert steg tar vi en sekvens av tegn med lengde `nchars`, og ber nettverket generere neste utgangstegn for hvert inngangstegn:\n", "\n", - "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.no.png)\n", + "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Avhengig av det faktiske scenariet, kan vi også ønske å inkludere noen spesialtegn, som *slutt-på-sekvens* ``. I vårt tilfelle ønsker vi bare å trene nettverket for uendelig tekstgenerering, så vi vil fastsette størrelsen på hver sekvens til å være lik `nchars` tokens. Følgelig vil hvert treningseksempel bestå av `nchars` innganger og `nchars` utganger (som er inngangssekvensen forskjøvet én symbol til venstre). Minibatcher vil bestå av flere slike sekvenser.\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index f9e10795..63d11999 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Måten vi skal trene RNN til å generere nyhetstitler på er som følger. For hvert steg tar vi én tittel, som mates inn i en RNN, og for hvert inndata-tegn ber vi nettverket om å generere neste utdata-tegn:\n", "\n", - "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.no.png)\n", + "![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png)\n", "\n", "For det siste tegnet i sekvensen vår ber vi nettverket om å generere ``-token.\n", "\n", diff --git a/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md index e569dcbf..41b2fd58 100644 --- a/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/no/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen vi diskuterte i forrige enhet, produserte hver RNN-enhet den Dette åpner for ulike nevrale arkitekturer som vist i bildet nedenfor: -![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.no.jpg) +![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Bilde fra blogginnlegget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denne enheten vil vi fokusere på enkle generative modeller som hjelper oss me Vi vil trene denne RNN-en til å generere tekst steg for steg. På hvert steg tar vi en sekvens av tegn med lengde `nchars` og ber nettverket generere neste output-tegn for hvert input-tegn: -![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.no.png) +![Bilde som viser et eksempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/no/rnn-generate.56c54afb52f9781d.png) Når vi genererer tekst (under inferens), starter vi med en **prompt**, som sendes gjennom RNN-celler for å generere dens mellomliggende tilstand, og deretter starter genereringen fra denne tilstanden. Vi genererer ett tegn om gangen og sender tilstanden og det genererte tegnet til en annen RNN-celle for å generere det neste, helt til vi har generert nok tegn. diff --git a/translations/no/lessons/5-NLP/18-Transformers/README.md b/translations/no/lessons/5-NLP/18-Transformers/README.md index 0a1a808c..54fcc808 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/README.md +++ b/translations/no/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNN-er implementeres sekvens-til-sekvens med to rekurrente nettverk, hvor et **Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle innvirkningen av hver inngangsvektor på hver utgangsprediksjon av RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet yt, tar vi hensyn til alle skjulte inngangstilstander hi, med forskjellige vektkoeffisienter αt,i. -![Bilde som viser en enkoder/dekoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.no.png) +![Bilde som viser en enkoder/dekoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png) > Enkoder-dekoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Oppmerksomhetsmatrisen {αi,j} representerer graden av innflytelse visse inngangsord har på genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise: -![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.no.png) +![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png) > Figur fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet vi får med posisjonsembedding inkluderer både det originale tokenet Deretter må vi fange noen mønstre i vår sekvens. For å gjøre dette bruker transformere en **selvoppmerksomhetsmekanisme**, som i hovedsak er oppmerksomhet anvendt på samme sekvens som inngang og utgang. Å bruke selvoppmerksomhet lar oss ta hensyn til **kontekst** i setningen og se hvilke ord som er relaterte. For eksempel lar det oss se hvilke ord som refereres til av korreferanser, som *det*, og også ta konteksten i betraktning: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.no.png) +![](../../../../../translated_images/no/CoreferenceResolution.861924d6d384a7d6.png) > Bilde fra [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Siden hver inngangsposisjon kartlegges uavhengig til hver utgangsposisjon, kan t **BERT** (Bidirectional Encoder Representations from Transformers) er et veldig stort flerlags transformernettverk med 12 lag for *BERT-base*, og 24 for *BERT-large*. Modellen er først forhåndstrent på en stor tekstkorpus (Wikipedia + bøker) ved hjelp av usupervisert trening (predikere maskerte ord i en setning). Under forhåndstreningen absorberer modellen betydelige nivåer av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **transfer learning**. -![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.no.png) +![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Bilde [kilde](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index b1e4b4bd..0c66ea82 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/no/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle innvirkningen av hver inngangsvektor på hver utgangsprediksjon av RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet $y_t$, tar vi hensyn til alle skjulte inngangstilstander $h_i$, med forskjellige vektkoeffisienter $\\alpha_{t,i}$.\n", "\n", - "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.no.png)\n", + "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Oppmerksomhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerer graden til hvilken visse inngangsord spiller en rolle i genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise:\n", "\n", - "![Bilde som viser en eksempeljustering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.no.png)\n", + "![Bilde som viser en eksempeljustering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur hentet fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et svært stort flerlags transformatornettverk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen blir først forhåndstrent på en stor tekstkorpus (Wikipedia + bøker) ved hjelp av usupervisert trening (predikere maskerte ord i en setning). Under forhåndstreningen absorberer modellen et betydelig nivå av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **overføringslæring**.\n", "\n", - "![Bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.no.png)\n", + "![Bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Det finnes mange varianter av transformator-arkitekturer, inkludert BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres. [HuggingFace-pakken](https://github.com/huggingface/) gir et bibliotek for å trene mange av disse arkitekturene med PyTorch.\n", "\n", diff --git a/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index bcd8c472..247bdf9e 100644 --- a/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/no/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Oppmerksomhetsmekanismer** gir en måte å vekte den kontekstuelle påvirkningen av hver inngangsvektor på hver utgangsprediksjon i RNN. Dette implementeres ved å lage snarveier mellom mellomliggende tilstander i inngangs-RNN og utgangs-RNN. På denne måten, når vi genererer utgangssymbolet $y_t$, tar vi hensyn til alle skjulte inngangstilstander $h_i$, med forskjellige vektkoeffisienter $\\alpha_{t,i}$. \n", "\n", - "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.no.png)\n", + "![Bilde som viser en encoder/decoder-modell med et additivt oppmerksomhetslag](../../../../../translated_images/no/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv oppmerksomhetsmekanisme i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), sitert fra [denne bloggposten](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Oppmerksomhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerer graden til hvilken visse inngangsord spiller en rolle i genereringen av et gitt ord i utgangssekvensen. Nedenfor er et eksempel på en slik matrise:\n", "\n", - "![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.no.png)\n", + "![Bilde som viser et eksempel på justering funnet av RNNsearch-50, hentet fra Bahdanau - arviz.org](../../../../../translated_images/no/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur hentet fra [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) er et svært stort flerlags transformatornettverk med 12 lag for *BERT-base* og 24 for *BERT-large*. Modellen blir først forhåndstrent på en stor mengde tekstdata (Wikipedia + bøker) ved hjelp av usupervisert trening (forutsi maskerte ord i en setning). Under forhåndstreningen absorberer modellen et betydelig nivå av språkforståelse som deretter kan utnyttes med andre datasett ved hjelp av finjustering. Denne prosessen kalles **transfer learning**.\n", "\n", - "![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.no.png)\n", + "![bilde fra http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/no/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Det finnes mange varianter av transformatorarkitekturer, inkludert BERT, DistilBERT, BigBird, OpenGPT3 og flere, som kan finjusteres.\n", "\n", diff --git a/translations/no/lessons/5-NLP/19-NER/README.md b/translations/no/lessons/5-NLP/19-NER/README.md index f3b523d8..f6c71eab 100644 --- a/translations/no/lessons/5-NLP/19-NER/README.md +++ b/translations/no/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ barn | O Siden vi må bygge en én-til-én korrespondanse mellom tokens og klasser, kan vi trene en høyreorientert **mange-til-mange** nevralt nettverksmodell fra dette bildet: -![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.no.jpg) +![Bilde som viser vanlige mønstre for rekurrente nevrale nettverk.](../../../../../translated_images/no/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Bilde fra [denne bloggposten](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpathy](http://karpathy.github.io/). NER-tokenklassifiseringsmodeller tilsvarer nettverksarkitekturen lengst til høyre på dette bildet.* diff --git a/translations/no/lessons/5-NLP/README.md b/translations/no/lessons/5-NLP/README.md index e1a99406..e44d12ea 100644 --- a/translations/no/lessons/5-NLP/README.md +++ b/translations/no/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Språkbehandling -![Oppsummering av NLP-oppgaver i en skisse](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.no.png) +![Oppsummering av NLP-oppgaver i en skisse](../../../../translated_images/no/ai-nlp.b22dcb8ca4707cea.png) I denne delen vil vi fokusere på å bruke nevrale nettverk for å håndtere oppgaver relatert til **Naturlig Språkbehandling (NLP)**. Det finnes mange NLP-problemer vi ønsker at datamaskiner skal kunne løse: diff --git a/translations/no/lessons/6-Other/23-MultiagentSystems/README.md b/translations/no/lessons/6-Other/23-MultiagentSystems/README.md index 3e2e0761..7e933fe2 100644 --- a/translations/no/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/no/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan åpne en av modellene, for eksempel **Biology → Flocking**. Etter å ha åpnet modellen, kommer du til hovedskjermen i NetLogo. Her er et eksempel på en modell som beskriver populasjonen av ulver og sauer, gitt begrensede ressurser (gress). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.no.png) +![NetLogo Main Screen](../../../../../translated_images/no/NetLogo-Main.32653711ec1a01b3.png) > Skjermbilde av Dmitry Soshnikov diff --git a/translations/no/lessons/README.md b/translations/no/lessons/README.md index 05f7eb74..a6aada5c 100644 --- a/translations/no/lessons/README.md +++ b/translations/no/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Oversikt -![Oversikt i en skisse](../../../translated_images/ai-overview.0857791951d19500.no.png) +![Oversikt i en skisse](../../../translated_images/no/ai-overview.0857791951d19500.png) > Skisse laget av [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/no/lessons/X-Extras/X1-MultiModal/README.md b/translations/no/lessons/X-Extras/X1-MultiModal/README.md index f549b6fb..30a4f94b 100644 --- a/translations/no/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/no/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Etter suksessen med transformer-modeller for å løse NLP-oppgaver, har de samme Hovedideen med CLIP er å kunne sammenligne tekstbeskrivelser med et bilde og avgjøre hvor godt bildet samsvarer med beskrivelsen. -![CLIP Arkitektur](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.no.png) +![CLIP Arkitektur](../../../../../translated_images/no/clip-arch.b3dbf20b4e8ed8be.png) > *Bilde fra [denne bloggposten](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Når denne modellen er forhåndstrent, kan vi gi den en batch med bilder og en b Anta at vi må klassifisere bilder mellom for eksempel katter, hunder og mennesker. I dette tilfellet kan vi gi modellen et bilde og en serie tekstbeskrivelser: "*et bilde av en katt*", "*et bilde av en hund*", "*et bilde av et menneske*". I den resulterende vektoren med 3 sannsynligheter trenger vi bare å velge indeksen med høyest verdi. -![CLIP for Bildeklassifisering](../../../../../translated_images/clip-class.3af42ef0b2b19369.no.png) +![CLIP for Bildeklassifisering](../../../../../translated_images/no/clip-class.3af42ef0b2b19369.png) > *Bilde fra [denne bloggposten](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lær mer om VQGAN på [Taming Transformers](https://compvis.github.io/taming-tra En av de viktige forskjellene mellom VQGAN og tradisjonelle GAN er at sistnevnte kan produsere et anstendig bilde fra hvilken som helst inputvektor, mens VQGAN sannsynligvis vil produsere et bilde som ikke er sammenhengende. Derfor må vi videre veilede bildeopprettingsprosessen, og det kan gjøres ved hjelp av CLIP. -![VQGAN+CLIP Arkitektur](../../../../../translated_images/vqgan.5027fe05051dfa31.no.png) +![VQGAN+CLIP Arkitektur](../../../../../translated_images/no/vqgan.5027fe05051dfa31.png) For å generere et bilde som samsvarer med en tekstbeskrivelse, starter vi med en tilfeldig kodingsvektor som sendes gjennom VQGAN for å produsere et bilde. Deretter brukes CLIP til å produsere en tapfunksjon som viser hvor godt bildet samsvarer med tekstbeskrivelsen. Målet er da å minimere dette tapet, ved hjelp av backpropagation for å justere inputvektorens parametere. Et flott bibliotek som implementerer VQGAN+CLIP er [Pixray](http://github.com/pixray/pixray). -![Bilde produsert av Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.no.png) | ![Bilde produsert av Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.no.png) | ![Bilde produsert av Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.no.png) +![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Bilde generert fra beskrivelsen *et nærbilde akvarellportrett av ung mannlig lærer i litteratur med en bok* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av ung kvinnelig lærer i informatikk med en datamaskin* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av eldre mannlig lærer i matematikk foran en tavle* @@ -75,7 +75,7 @@ I motsetning til CLIP mottar DALL-E både tekst og bilde som en enkelt strøm av Hovedforskjellen mellom DALL.E 1 og 2 er at den genererer mer realistiske bilder og kunst. Eksempler på bildegenerering med DALL-E: -![Bilde produsert av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.no.png) | ![Bilde produsert av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.no.png) | ![Bilde produsert av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.no.png) +![Bilde produsert av Pixray](../../../../../translated_images/no/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Bilde produsert av Pixray](../../../../../translated_images/no/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Bilde generert fra beskrivelsen *et nærbilde akvarellportrett av ung mannlig lærer i litteratur med en bok* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av ung kvinnelig lærer i informatikk med en datamaskin* | Bilde generert fra beskrivelsen *et nærbilde oljemaleriportrett av eldre mannlig lærer i matematikk foran en tavle* diff --git a/translations/pa/README.md b/translations/pa/README.md index c4fc1dfd..359c6b59 100644 --- a/translations/pa/README.md +++ b/translations/pa/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ਸ਼ੁਰੂਆਤੀ ਲਈ ਕৃত੍ਰਿਮ ਬੁੱਧੀ - ਇੱਕ ਕੋਰਸ -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.pa.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pa/ai-overview.0857791951d19500.png)| |:---:| |ਹੋਰ ਜਾਣਕਾਰੀ ਲਈ [@girlie_mac](https://twitter.com/girlie_mac) ਵੱਲੋਂ ਕਿਰਿਆ ਗਿਆ AI For Beginners - _ਸਕੈਚਨੋਟ_ | diff --git a/translations/pa/lessons/1-Intro/README.md b/translations/pa/lessons/1-Intro/README.md index 9fc2b779..e8d8f1dd 100644 --- a/translations/pa/lessons/1-Intro/README.md +++ b/translations/pa/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI ਦਾ ਪਰਿਚਯ -![AI ਸਮੱਗਰੀ ਦੇ ਪਰਿਚਯ ਦਾ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/ai-intro.bf28d1ac4235881c.pa.png) +![AI ਸਮੱਗਰੀ ਦੇ ਪਰਿਚਯ ਦਾ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-intro.bf28d1ac4235881c.png) > ਸਕੈਚਨੋਟ [ਟੋਮੋਮੀ ਇਮੁਰਾ](https://twitter.com/girlie_mac) ਦੁਆਰਾ @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਮੂਲ ਰੂਪ ਵਿੱਚ, ਕੰਪਿਊਟਰਾਂ ਨੂੰ [ਚਾਰਲਸ ਬੈਬੇਜ](https://en.wikipedia.org/wiki/Charles_Babbage) ਦੁਆਰਾ ਸੰਖਿਆਵਾਂ 'ਤੇ ਕੰਮ ਕਰਨ ਲਈ ਬਣਾਇਆ ਗਿਆ ਸੀ, ਇੱਕ ਸਪਸ਼ਟ ਤਰੀਕੇ - ਇੱਕ ਐਲਗੋਰਿਦਮ ਦੀ ਪਾਲਣਾ ਕਰਦੇ ਹੋਏ। ਆਧੁਨਿਕ ਕੰਪਿਊਟਰ, ਹਾਲਾਂਕਿ 19ਵੀਂ ਸਦੀ ਵਿੱਚ ਪ੍ਰਸਤਾਵਿਤ ਮੂਲ ਮਾਡਲ ਨਾਲੋਂ ਕਾਫ਼ੀ ਅਗਰਗਾਮੀ ਹਨ, ਫਿਰ ਵੀ ਨਿਯੰਤਰਿਤ ਗਣਨਾਵਾਂ ਦੇ ਇੱਕੋ ਹੀ ਵਿਚਾਰ ਦੀ ਪਾਲਣਾ ਕਰਦੇ ਹਨ। ਇਸ ਲਈ, ਇਹ ਸੰਭਵ ਹੈ ਕਿ ਕੰਪਿਊਟਰ ਨੂੰ ਕੁਝ ਕਰਨ ਲਈ ਪ੍ਰੋਗਰਾਮ ਕੀਤਾ ਜਾ ਸਕੇ ਜੇਕਰ ਅਸੀਂ ਉਹ ਸਪਸ਼ਟ ਕਦਮਾਂ ਦੀ ਲੜੀ ਜਾਣਦੇ ਹਾਂ ਜੋ ਲਕਸ਼ ਪ੍ਰਾਪਤ ਕਰਨ ਲਈ ਕਰਨੇ ਹਨ। -![ਇੱਕ ਵਿਅਕਤੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/dsh_age.d212a30d4e54fb5f.pa.png) +![ਇੱਕ ਵਿਅਕਤੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/dsh_age.d212a30d4e54fb5f.png) > ਤਸਵੀਰ [ਵਿਕੀ ਸੋਸ਼ਨਿਕੋਵਾ](http://twitter.com/vickievalerie) ਦੁਆਰਾ @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** ਸ਼ਬਦ ਨਾਲ ਨਜਿੱਠਣ ਵੇਲੇ ਇੱਕ ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਇਸ ਸ਼ਬਦ ਦੀ ਕੋਈ ਸਪਸ਼ਟ ਪਰਿਭਾਸ਼ਾ ਨਹੀਂ ਹੈ। ਕੋਈ ਦਲੀਲ ਕਰ ਸਕਦਾ ਹੈ ਕਿ ਬੁੱਧੀ **ਅਮੂਰ ਚਿੰਤਨ** ਜਾਂ **ਸਵੈ-ਜਾਗਰੂਕਤਾ** ਨਾਲ ਜੁੜੀ ਹੋਈ ਹੈ, ਪਰ ਅਸੀਂ ਇਸਨੂੰ ਢੰਗ ਨਾਲ ਪਰਿਭਾਸ਼ਿਤ ਨਹੀਂ ਕਰ ਸਕਦੇ। -![ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/photo-cat.8c8e8fb760ffe457.pa.jpg) +![ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ](../../../../translated_images/pa/photo-cat.8c8e8fb760ffe457.jpg) > [ਤਸਵੀਰ](https://unsplash.com/photos/75715CVEJhI) [ਐਂਬਰ ਕਿਪ](https://unsplash.com/@sadmax) ਦੁਆਰਾ Unsplash ਤੋਂ diff --git a/translations/pa/lessons/2-Symbolic/Animals.ipynb b/translations/pa/lessons/2-Symbolic/Animals.ipynb index 10a886fc..0d8c9921 100644 --- a/translations/pa/lessons/2-Symbolic/Animals.ipynb +++ b/translations/pa/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ਇਸ ਉਦਾਹਰਨ ਵਿੱਚ, ਅਸੀਂ ਕੁਝ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਜਾਨਵਰ ਦੀ ਪਛਾਣ ਕਰਨ ਲਈ ਇੱਕ ਸਧਾਰਣ ਗਿਆਨ-ਅਧਾਰਿਤ ਪ੍ਰਣਾਲੀ ਲਾਗੂ ਕਰਾਂਗੇ। ਇਸ ਪ੍ਰਣਾਲੀ ਨੂੰ ਹੇਠਾਂ ਦਿੱਤੇ AND-OR ਟ੍ਰੀ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾ ਸਕਦਾ ਹੈ (ਇਹ ਪੂਰੇ ਟ੍ਰੀ ਦਾ ਇੱਕ ਹਿੱਸਾ ਹੈ, ਅਸੀਂ ਆਸਾਨੀ ਨਾਲ ਹੋਰ ਨਿਯਮ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.pa.png)\n" + "![](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/pa/lessons/2-Symbolic/README.md b/translations/pa/lessons/2-Symbolic/README.md index b6986516..59bf97dc 100644 --- a/translations/pa/lessons/2-Symbolic/README.md +++ b/translations/pa/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਅਤੇ ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ -![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/ai-symbolic.715a30cb610411a6.pa.png) +![ਸੰਕੇਤਕ AI ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-symbolic.715a30cb610411a6.png) > ਸਕੈਚਨੋਟ [Tomomi Imura](https://twitter.com/girlie_mac) ਦੁਆਰਾ @@ -41,7 +41,7 @@ AI ਦੇ ਸ਼ੁਰੂਆਤੀ ਦਿਨਾਂ ਵਿੱਚ, ਬੁੱਧੀ ਇਸ ਤਰ੍ਹਾਂ, **ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ** ਦੀ ਸਮੱਸਿਆ ਇਹ ਹੈ ਕਿ ਕੰਪਿਊਟਰ ਵਿੱਚ ਡਾਟਾ ਦੇ ਰੂਪ ਵਿੱਚ ਗਿਆਨ ਨੂੰ ਦਰਸਾਉਣ ਦਾ ਕੁਝ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਤਰੀਕਾ ਲੱਭਿਆ ਜਾਵੇ, ਤਾਂ ਜੋ ਇਹ ਸਵੈਚਾਲਿਤ ਤਰੀਕੇ ਨਾਲ ਵਰਤਿਆ ਜਾ ਸਕੇ। ਇਸਨੂੰ ਇੱਕ ਸਪੈਕਟ੍ਰਮ ਵਜੋਂ ਦੇਖਿਆ ਜਾ ਸਕਦਾ ਹੈ: -![ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਸਪੈਕਟ੍ਰਮ](../../../../translated_images/knowledge-spectrum.b60df631852c0217.pa.png) +![ਗਿਆਨ ਪ੍ਰਸਤੁਤੀ ਸਪੈਕਟ੍ਰਮ](../../../../translated_images/pa/knowledge-spectrum.b60df631852c0217.png) > ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ @@ -94,7 +94,7 @@ Block Syntax | Indent | | | ਸੰਕੇਤਕ AI ਦੀਆਂ ਸ਼ੁਰੂਆਤੀ ਸਫਲਤਾਵਾਂ ਵਿੱਚੋਂ ਇੱਕ **ਮਾਹਰ ਪ੍ਰਣਾਲੀਆਂ** ਸਨ - ਕੰਪਿਊਟਰ ਪ੍ਰਣਾਲੀਆਂ ਜੋ ਕੁਝ ਸੀਮਿਤ ਸਮੱਸਿਆ ਖੇਤਰ ਵਿੱਚ ਮਾਹਰ ਵਜੋਂ ਕੰਮ ਕਰਨ ਲਈ ਡਿਜ਼ਾਈਨ ਕੀਤੀਆਂ ਗਈਆਂ ਸਨ। ਇਹਨਾਂ ਦਾ ਆਧਾਰ **ਗਿਆਨ ਅਧਾਰ** ਸੀ ਜੋ ਇੱਕ ਜਾਂ ਵੱਧ ਮਨੁੱਖੀ ਮਾਹਰਾਂ ਤੋਂ ਕੱਢਿਆ ਗਿਆ ਸੀ, ਅਤੇ ਇਹਨਾਂ ਵਿੱਚ ਇੱਕ **ਤਰਕ ਇੰਜਣ** ਸੀ ਜੋ ਇਸ 'ਤੇ ਕੁਝ ਤਰਕ ਕਰਦਾ ਸੀ। -![ਮਨੁੱਖੀ ਆਰਕੀਟੈਕਚਰ](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.pa.png) | ![ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.pa.png) +![ਮਨੁੱਖੀ ਆਰਕੀਟੈਕਚਰ](../../../../translated_images/pa/arch-human.5d4d35f1bba3ab1c.png) | ![ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ](../../../../translated_images/pa/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ ਮਨੁੱਖੀ ਨਰਵ ਪ੍ਰਣਾਲੀ ਦੀ ਸਰਲ ਰਚਨਾ | ਗਿਆਨ-ਅਧਾਰਤ ਪ੍ਰਣਾਲੀ ਦੀ ਆਰਕੀਟੈਕਚਰ @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ਉਦਾਹਰਣ ਵਜੋਂ, ਆਓ ਹੇਠਾਂ ਦਿੱਤੀ ਮਾਹਰ ਪ੍ਰਣਾਲੀ ਨੂੰ ਵੇਖੀਏ ਜੋ ਕਿਸੇ ਜਾਨਵਰ ਨੂੰ ਇਸਦੇ ਭੌਤਿਕ ਲੱਛਣਾਂ ਦੇ ਆਧਾਰ 'ਤੇ ਨਿਰਧਾਰਤ ਕਰਦੀ ਹੈ: -![AND-OR ਟ੍ਰੀ](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.pa.png) +![AND-OR ਟ੍ਰੀ](../../../../translated_images/pa/AND-OR-Tree.5592d2c70187f283.png) > ਚਿੱਤਰ [Dmitry Soshnikov](http://soshnikov.com) ਦੁਆਰਾ diff --git a/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 1e627f16..72f6d293 100644 --- a/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/pa/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "ਜੇਕਰ ਸਾਡੇ ਕੋਲ 2 ਤੋਂ ਵੱਧ ਕਲਾਸਾਂ ਹਨ, ਤਾਂ ਸੌਫਟਮੈਕਸ ਸਾਰੀਆਂ ਕਲਾਸਾਂ ਵਿੱਚ ਸੰਭਾਵਨਾਵਾਂ ਨੂੰ ਨਾਰਮਲਾਈਜ਼ ਕਰੇਗਾ। ਇੱਥੇ MNIST ਅੰਕ ਵਰਗੀਕਰਨ ਕਰਨ ਵਾਲੇ ਨੈਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਦਾ ਇੱਕ ਡਾਇਗ੍ਰਾਮ ਦਿੱਤਾ ਗਿਆ ਹੈ:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.pa.png)\n" + "![MNIST Classifier](../../../../../translated_images/pa/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* ਘੱਟ ਸਿਖਲਾਈ ਨੁਕਸਾਨ - ਮਾਡਲ ਸਿਖਲਾਈ ਡਾਟਾ ਨੂੰ ਚੰਗੀ ਤਰ੍ਹਾਂ ਅਨੁਕੂਲ ਕਰ ਸਕਦਾ ਹੈ, ਕਿਉਂਕਿ ਇਸਦੇ ਕੋਲ ਕਾਫ਼ੀ ਅਭਿਵੈਕਤਮਕ ਸ਼ਕਤੀ ਹੁੰਦੀ ਹੈ।\n", "* ਵੈਲੀਡੇਸ਼ਨ ਨੁਕਸਾਨ ਸਿਖਲਾਈ ਨੁਕਸਾਨ ਨਾਲੋਂ ਕਾਫ਼ੀ ਵੱਧ ਹੋ ਸਕਦਾ ਹੈ ਅਤੇ ਸਿਖਲਾਈ ਦੌਰਾਨ ਵਧਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦਾ ਹੈ - ਇਹ ਇਸ ਲਈ ਹੁੰਦਾ ਹੈ ਕਿਉਂਕਿ ਮਾਡਲ \"ਸਿਖਲਾਈ ਬਿੰਦੂਆਂ\" ਨੂੰ ਯਾਦ ਕਰ ਲੈਂਦਾ ਹੈ ਅਤੇ \"ਕੁੱਲ ਤਸਵੀਰ\" ਨੂੰ ਗੁਆ ਲੈਂਦਾ ਹੈ।\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.pa.png)\n", + "![Overfitting](../../../../../translated_images/pa/overfit.a0bd57f717c15769.png)\n", "\n", "> ਇਸ ਤਸਵੀਰ ਵਿੱਚ, `x` ਸਿਖਲਾਈ ਡਾਟਾ ਲਈ ਹੈ, `o` - ਵੈਲੀਡੇਸ਼ਨ ਡਾਟਾ ਲਈ। ਖੱਬੇ ਪਾਸੇ - ਰੇਖੀ ਮਾਡਲ (ਇੱਕ-ਪਰਤ), ਇਹ ਡਾਟਾ ਦੀ ਕੁਦਰਤ ਨੂੰ ਕਾਫ਼ੀ ਚੰਗੇ ਤਰੀਕੇ ਨਾਲ ਅਨੁਕੂਲ ਕਰਦਾ ਹੈ। ਸੱਜੇ ਪਾਸੇ - ਓਵਰਫਿਟ ਮਾਡਲ, ਮਾਡਲ ਸਿਖਲਾਈ ਡਾਟਾ ਨੂੰ ਬਿਲਕੁਲ ਸਹੀ ਤਰੀਕੇ ਨਾਲ ਅਨੁਕੂਲ ਕਰਦਾ ਹੈ, ਪਰ ਕਿਸੇ ਹੋਰ ਡਾਟਾ ਨਾਲ (ਵੈਲੀਡੇਸ਼ਨ ਗਲਤੀ ਬਹੁਤ ਜ਼ਿਆਦਾ ਹੈ) ਸਮਝ ਨਹੀਂ ਬਣਾਉਂਦਾ।\n" ] diff --git a/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md index 0fd273f0..76771641 100644 --- a/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pa/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ਹੇਠਾਂ ਦਿੱਤੇ ਸਮੱਸਿਆ ਨੂੰ ਵਿਚਾਰੋ ਜਿਸ ਵਿੱਚ 5 ਬਿੰਦੂਆਂ ਨੂੰ ਅਨੁਮਾਨਿਤ ਕਰਨਾ ਹੈ (ਗ੍ਰਾਫ ਵਿੱਚ `x` ਨਾਲ ਦਰਸਾਇਆ ਗਿਆ ਹੈ): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.pa.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.pa.jpg) +![linear](../../../../../translated_images/pa/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pa/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **ਲਿਨੀਅਰ ਮਾਡਲ, 2 ਪੈਰਾਮੀਟਰ** | **ਨਾਨ-ਲਿਨੀਅਰ ਮਾਡਲ, 7 ਪੈਰਾਮੀਟਰ** ਟ੍ਰੇਨਿੰਗ ਐਰਰ = 5.3 | ਟ੍ਰੇਨਿੰਗ ਐਰਰ = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ਜਿਵੇਂ ਕਿ ਉੱਪਰ ਦਿੱਤੇ ਗ੍ਰਾਫ ਤੋਂ ਦਿਖਾਈ ਦਿੰਦਾ ਹੈ, ਓਵਰਫਿਟਿੰਗ ਦੀ ਪਛਾਣ ਬਹੁਤ ਘੱਟ ਟ੍ਰੇਨਿੰਗ ਐਰਰ ਅਤੇ ਉੱਚੇ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਦੁਆਰਾ ਕੀਤੀ ਜਾ ਸਕਦੀ ਹੈ। ਆਮ ਤੌਰ 'ਤੇ ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ ਅਸੀਂ ਦੋਵੇਂ ਟ੍ਰੇਨਿੰਗ ਅਤੇ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਨੂੰ ਘਟਦੇ ਹੋਏ ਦੇਖਾਂਗੇ, ਅਤੇ ਫਿਰ ਕਿਸੇ ਸਮੇਂ ਵੈਲੀਡੇਸ਼ਨ ਐਰਰ ਘਟਣਾ ਬੰਦ ਕਰ ਸਕਦਾ ਹੈ ਅਤੇ ਵਧਣਾ ਸ਼ੁਰੂ ਕਰ ਸਕਦਾ ਹੈ। ਇਹ ਓਵਰਫਿਟਿੰਗ ਦਾ ਸੰਕੇਤ ਹੋਵੇਗਾ, ਅਤੇ ਇਹ ਦਰਸਾਵੇਗਾ ਕਿ ਸਾਨੂੰ ਇਸ ਸਮੇਂ ਟ੍ਰੇਨਿੰਗ ਰੋਕ ਦੇਣੀ ਚਾਹੀਦੀ ਹੈ (ਜਾਂ ਘੱਟੋ-ਘੱਟ ਮਾਡਲ ਦਾ ਸਨੈਪਸ਼ਾਟ ਲੈਣਾ ਚਾਹੀਦਾ ਹੈ)। -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.pa.png) +![overfitting](../../../../../translated_images/pa/Overfitting.408ad91cd90b4371.png) ## ਓਵਰਫਿਟਿੰਗ ਨੂੰ ਰੋਕਣ ਦੇ ਤਰੀਕੇ diff --git a/translations/pa/lessons/3-NeuralNetworks/README.md b/translations/pa/lessons/3-NeuralNetworks/README.md index c5d54e88..e2673646 100644 --- a/translations/pa/lessons/3-NeuralNetworks/README.md +++ b/translations/pa/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਦਾ ਪਰਿਚਯ -![ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.pa.png) +![ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ ਸਮੱਗਰੀ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-neuralnetworks.1c687ae40bc86e83.png) ਜਿਵੇਂ ਕਿ ਅਸੀਂ ਪਰਿਚਯ ਵਿੱਚ ਚਰਚਾ ਕੀਤੀ ਸੀ, ਬੁੱਧੀ ਪ੍ਰਾਪਤ ਕਰਨ ਦੇ ਤਰੀਕਿਆਂ ਵਿੱਚੋਂ ਇੱਕ ਤਰੀਕਾ **ਕੰਪਿਊਟਰ ਮਾਡਲ** ਜਾਂ ਇੱਕ **ਕ੍ਰਿਤ੍ਰਿਮ ਦਿਮਾਗ** ਨੂੰ ਸਿਖਲਾਈ ਦੇਣਾ ਹੈ। 20ਵੀਂ ਸਦੀ ਦੇ ਮੱਧ ਤੋਂ, ਖੋਜਕਰਤਾਵਾਂ ਨੇ ਵੱਖ-ਵੱਖ ਗਣਿਤ ਮਾਡਲਾਂ ਦੀ ਕੋਸ਼ਿਸ਼ ਕੀਤੀ, ਜਦੋਂ ਤੱਕ ਕਿ ਹਾਲੀਆ ਸਾਲਾਂ ਵਿੱਚ ਇਹ ਦਿਸ਼ਾ ਬਹੁਤ ਸਫਲ ਸਾਬਤ ਨਹੀਂ ਹੋਈ। ਦਿਮਾਗ ਦੇ ਅਜਿਹੇ ਗਣਿਤ ਮਾਡਲਾਂ ਨੂੰ **ਨਿਊਰਲ ਨੈੱਟਵਰਕਸ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: ਜੀਵ ਵਿਗਿਆਨ ਤੋਂ, ਅਸੀਂ ਜਾਣਦੇ ਹਾਂ ਕਿ ਸਾਡਾ ਦਿਮਾਗ ਨਿਊਰਲ ਸੈਲ (ਨਿਊਰੋਨ) ਤੋਂ ਬਣਿਆ ਹੁੰਦਾ ਹੈ, ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਹਰ ਇੱਕ ਦੇ ਕਈ "ਇਨਪੁੱਟ" (ਡੈਂਡਰਾਈਟਸ) ਅਤੇ ਇੱਕ "ਆਉਟਪੁੱਟ" (ਐਕਸੋਨ) ਹੁੰਦੇ ਹਨ। ਡੈਂਡਰਾਈਟਸ ਅਤੇ ਐਕਸੋਨ ਦੋਵੇਂ ਬਿਜਲਈ ਸੰਕੇਤਾਂ ਨੂੰ ਚਲਾਉਣ ਦੇ ਯੋਗ ਹੁੰਦੇ ਹਨ, ਅਤੇ ਉਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਦੇ ਸੰਪਰਕ — ਜਿਨ੍ਹਾਂ ਨੂੰ ਸਿਨੈਪਸ ਕਿਹਾ ਜਾਂਦਾ ਹੈ — ਵੱਖ-ਵੱਖ ਪੱਧਰ ਦੀ ਚਾਲਕਤਾ ਦਿਖਾ ਸਕਦੇ ਹਨ, ਜੋ ਨਿਊਰੋਟ੍ਰਾਂਸਮੀਟਰਾਂ ਦੁਆਰਾ ਨਿਯੰਤਰਿਤ ਹੁੰਦੀ ਹੈ। -![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.pa.jpg) | ![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/artneuron.1a5daa88d20ebe6f.pa.png) +![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![ਨਿਊਰੋਨ ਦਾ ਮਾਡਲ](../../../../translated_images/pa/artneuron.1a5daa88d20ebe6f.png) ----|---- ਅਸਲੀ ਨਿਊਰੋਨ *([ਵਿਕੀਪੀਡੀਆ ਤੋਂ ਚਿੱਤਰ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg))* | ਕ੍ਰਿਤ੍ਰਿਮ ਨਿਊਰੋਨ *(ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ)* ਇਸ ਤਰ੍ਹਾਂ, ਨਿਊਰੋਨ ਦਾ ਸਭ ਤੋਂ ਸਧਾਰਨ ਗਣਿਤ ਮਾਡਲ ਕਈ ਇਨਪੁੱਟ X1, ..., XN ਅਤੇ ਇੱਕ ਆਉਟਪੁੱਟ Y, ਅਤੇ ਕਈ ਵਜ਼ਨ W1, ..., WN ਸ਼ਾਮਲ ਕਰਦਾ ਹੈ। ਆਉਟਪੁੱਟ ਇਸ ਤਰ੍ਹਾਂ ਗਣਨਾ ਕੀਤੀ ਜਾਂਦੀ ਹੈ: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ਜਿੱਥੇ f ਕੁਝ ਗੈਰ-ਰੇਖਿਕ **ਐਕਟੀਵੇਸ਼ਨ ਫੰਕਸ਼ਨ** ਹੈ। diff --git a/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md index a9e581b3..ff424971 100644 --- a/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pa/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **ਬ੍ਰੇਲ ਬੁੱਕ ਦੀ ਤਸਵੀਰ ਦੀ ਪ੍ਰੀ-ਪ੍ਰੋਸੈਸਿੰਗ**। ਅਸੀਂ ਧਿਆਨ ਦਿੰਦੇ ਹਾਂ ਕਿ ਕਿਵੇਂ ਅਸੀਂ thresholding, feature detection, perspective transformation ਅਤੇ NumPy ਮੈਨਿਪੂਲੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਵਿਅਕਤੀਗਤ ਬ੍ਰੇਲ ਚਿੰਨ੍ਹਾਂ ਨੂੰ neural network ਦੁਆਰਾ ਹੋਰ ਵਰਗੀਕਰਨ ਲਈ ਵੱਖ ਕਰ ਸਕਦੇ ਹਾਂ। -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.pa.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.pa.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.pa.png) +![Braille Image](../../../../../translated_images/pa/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/pa/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/pa/braille-symbols.0159185ab69d5339.png) ----|-----|----- > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ * **ਫਰੇਮ ਡਿਫਰੈਂਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਵੀਡੀਓ ਵਿੱਚ ਗਤੀ ਪਛਾਣਣਾ**। ਜੇਕਰ ਕੈਮਰਾ ਸਥਿਰ ਹੈ, ਤਾਂ ਕੈਮਰੇ ਫੀਡ ਤੋਂ ਫਰੇਮ ਇੱਕ ਦੂਜੇ ਨਾਲ ਕਾਫ਼ੀ ਮਿਲਦੇ-ਜੁਲਦੇ ਹੋਣੇ ਚਾਹੀਦੇ ਹਨ। ਕਿਉਂਕਿ ਫਰੇਮ ਐਰੇ ਵਜੋਂ ਦਰਸਾਏ ਜਾਂਦੇ ਹਨ, ਸਿਰਫ਼ ਉਹਨਾਂ ਐਰੇਜ਼ ਨੂੰ ਦੋ ਲਗਾਤਾਰ ਫਰੇਮਾਂ ਲਈ ਘਟਾ ਕੇ ਅਸੀਂ ਪਿਕਸਲ ਡਿਫਰੈਂਸ ਪ੍ਰਾਪਤ ਕਰਾਂਗੇ, ਜੋ ਸਥਿਰ ਫਰੇਮਾਂ ਲਈ ਘੱਟ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ, ਅਤੇ ਚਿੱਤਰ ਵਿੱਚ ਮਹੱਤਵਪੂਰਨ ਗਤੀ ਹੋਣ 'ਤੇ ਵਧ ਜਾਵੇਗਾ। -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.pa.png) +![Image of video frames and frame differences](../../../../../translated_images/pa/frame-difference.706f805491a0883c.png) > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Dense Optical Flow** ਹਰ ਪਿਕਸਲ ਲਈ ਵੇਕਟਰ ਫੀਲਡ ਦੀ ਗਣਨਾ ਕਰਦਾ ਹੈ ਜੋ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਇਹ ਕਿੱਥੇ ਹਿਲ ਰਿਹਾ ਹੈ। - **Sparse Optical Flow** ਚਿੱਤਰ ਵਿੱਚ ਕੁਝ ਵਿਸ਼ੇਸ਼ ਲੱਛਣ (ਜਿਵੇਂ ਕਿ edges) ਲੈਣ 'ਤੇ ਅਧਾਰਿਤ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਫਰੇਮ ਤੋਂ ਫਰੇਮ ਤੱਕ ਉਹਨਾਂ ਦੀ ਰਾਹ ਬਣਾਉਂਦਾ ਹੈ। -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.pa.png) +![Image of Optical Flow](../../../../../translated_images/pa/optical.1f4a94464579a83a.png) > ਚਿੱਤਰ [OpenCV.ipynb](OpenCV.ipynb) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d087099e..04e6eea9 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ਇੱਕ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਨੇ 2014 ਵਿੱਚ ImageNet top-5 ਵਰਗੀਕਰਨ ਵਿੱਚ 92.7% ਸਹੀਤਾ ਹਾਸਲ ਕੀਤੀ। ਇਸ ਵਿੱਚ ਹੇਠਾਂ ਦਿੱਤੇ ਲੇਅਰ ਸਟ੍ਰਕਚਰ ਹਨ: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pa.jpg) +![ImageNet Layers](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.jpg) ਜਿਵੇਂ ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ, VGG ਇੱਕ ਪਰੰਪਰਾਗਤ ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ ਦੀ ਪਾਲਣਾ ਕਰਦਾ ਹੈ, ਜੋ ਕਿ ਕਨਵੋਲੂਸ਼ਨ-ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਦੀ ਲੜੀ ਹੈ। -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pa.jpg) +![ImageNet Pyramid](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.jpg) > ਚਿੱਤਰ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 23404492..046f81ab 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "ਇਸ ਤਰ੍ਹਾਂ, ਇੱਕ ਆਮ CNN ਵਿੱਚ ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰ ਹੁੰਦੇ ਹਨ, ਜਿਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਪੂਲਿੰਗ ਲੇਅਰ ਹੁੰਦੇ ਹਨ ਜੋ ਤਸਵੀਰ ਦੇ ਆਕਾਰ ਨੂੰ ਘਟਾਉਂਦੇ ਹਨ। ਅਸੀਂ ਫਿਲਟਰਾਂ ਦੀ ਗਿਣਤੀ ਵੀ ਵਧਾਉਂਦੇ ਹਾਂ, ਕਿਉਂਕਿ ਜਿਵੇਂ ਪੈਟਰਨ ਹੋਰ ਅਡਵਾਂਸਡ ਹੁੰਦੇ ਹਨ - ਸਾਨੂੰ ਵੇਖਣ ਲਈ ਹੋਰ ਸੰਭਾਵਤ ਦਿਲਚਸਪ ਕੌਂਬੀਨੇਸ਼ਨ ਹੁੰਦੇ ਹਨ।\n", "\n", - "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਜ਼ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਜ਼ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.pa.png)\n", + "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਜ਼ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਜ਼ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "ਸਪੇਸ਼ਲ ਡਾਈਮੇੰਸ਼ਨ ਘਟਣ ਅਤੇ ਫੀਚਰ/ਫਿਲਟਰ ਡਾਈਮੇੰਸ਼ਨ ਵਧਣ ਦੇ ਕਾਰਨ, ਇਸ ਆਰਕੀਟੈਕਚਰ ਨੂੰ **ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index d0a93994..bb5627ad 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "ਇਸ ਤਰ੍ਹਾਂ, ਇੱਕ ਆਮ CNN ਵਿੱਚ ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰ ਹੁੰਦੇ ਹਨ, ਜਿਨ੍ਹਾਂ ਦੇ ਵਿਚਕਾਰ ਪੂਲਿੰਗ ਲੇਅਰ ਹੁੰਦੇ ਹਨ ਜੋ ਤਸਵੀਰ ਦੇ ਆਕਾਰ ਨੂੰ ਘਟਾਉਂਦੇ ਹਨ। ਅਸੀਂ ਫਿਲਟਰਾਂ ਦੀ ਗਿਣਤੀ ਵੀ ਵਧਾਉਂਦੇ ਹਾਂ, ਕਿਉਂਕਿ ਜਿਵੇਂ ਪੈਟਰਨ ਹੋਰ ਅਡਵਾਂਸਡ ਬਣਦੇ ਹਨ - ਸਾਨੂੰ ਭਾਲਣ ਲਈ ਹੋਰ ਸੰਭਾਵਿਤ ਦਿਲਚਸਪ ਸੰਯੋਜਨ ਮਿਲਦੇ ਹਨ।\n", "\n", - "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਾਂ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.pa.png)\n", + "![ਕਈ ਕਨਵੋਲੂਸ਼ਨਲ ਲੇਅਰਾਂ ਨੂੰ ਪੂਲਿੰਗ ਲੇਅਰਾਂ ਨਾਲ ਦਿਖਾਉਂਦੀ ਇੱਕ ਤਸਵੀਰ।](../../../../../translated_images/pa/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "ਸਪੇਸ਼ਲ ਡਾਈਮੇੰਸ਼ਨ ਘਟਣ ਅਤੇ ਫੀਚਰ/ਫਿਲਟਰ ਡਾਈਮੇੰਸ਼ਨ ਵਧਣ ਦੇ ਕਾਰਨ, ਇਸ ਆਰਕੀਟੈਕਚਰ ਨੂੰ **ਪਿਰਾਮਿਡ ਆਰਕੀਟੈਕਚਰ** ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md index 3a778241..030fccd2 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: ਪੈਟਰਨਜ਼ ਨੂੰ ਕੱਢਣ ਲਈ, ਅਸੀਂ **ਕਨਵੋਲੂਸ਼ਨਲ ਫਿਲਟਰਜ਼** ਦੀ ਧਾਰਨਾ ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ। ਜਿਵੇਂ ਕਿ ਤੁਸੀਂ ਜਾਣਦੇ ਹੋ, ਇੱਕ ਚਿੱਤਰ ਨੂੰ 2D-ਮੈਟ੍ਰਿਕਸ ਜਾਂ ਰੰਗ ਦੀ ਗਹਿਰਾਈ ਵਾਲੇ 3D-ਟੈਂਸਰ ਦੁਆਰਾ ਦਰਸਾਇਆ ਜਾਂਦਾ ਹੈ। ਫਿਲਟਰ ਲਾਗੂ ਕਰਨ ਦਾ ਮਤਲਬ ਹੈ ਕਿ ਅਸੀਂ ਇੱਕ ਛੋਟੀ **ਫਿਲਟਰ ਕਰਨਲ** ਮੈਟ੍ਰਿਕਸ ਲੈਂਦੇ ਹਾਂ, ਅਤੇ ਮੂਲ ਚਿੱਤਰ ਵਿੱਚ ਹਰ ਪਿਕਸਲ ਲਈ ਅਸੀਂ ਪੜੋਸੀ ਬਿੰਦੂਆਂ ਨਾਲ ਵਜ਼ਨੀ ਔਸਤ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ। ਅਸੀਂ ਇਸਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਦੇਖ ਸਕਦੇ ਹਾਂ ਕਿ ਇੱਕ ਛੋਟੀ ਵਿੰਡੋ ਸਾਰੇ ਚਿੱਤਰ 'ਤੇ ਸਲਾਈਡ ਕਰ ਰਹੀ ਹੈ, ਅਤੇ ਫਿਲਟਰ ਕਰਨਲ ਮੈਟ੍ਰਿਕਸ ਵਿੱਚ ਵਜ਼ਨਾਂ ਦੇ ਅਨੁਸਾਰ ਸਾਰੇ ਪਿਕਸਲਾਂ ਨੂੰ ਔਸਤ ਕਰ ਰਹੀ ਹੈ। -![ਵਰਟਿਕਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/filter-vert.b7148390ca0bc356.pa.png) | ![ਹੋਰਿਜ਼ਾਂਟਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.pa.png) +![ਵਰਟਿਕਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-vert.b7148390ca0bc356.png) | ![ਹੋਰਿਜ਼ਾਂਟਲ ਐਜ ਫਿਲਟਰ](../../../../../translated_images/pa/filter-horiz.59b80ed4feb946ef.png) ----|---- > ਚਿੱਤਰ: ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ @@ -38,7 +38,7 @@ CNNs ਦੇ ਕੰਮ ਕਰਨ ਦਾ ਤਰੀਕਾ ਹੇਠਾਂ ਦਿੱ * ਅਸੀਂ ਨੈਟਵਰਕ ਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਡਿਜ਼ਾਈਨ ਕਰ ਸਕਦੇ ਹਾਂ ਕਿ ਫਿਲਟਰਜ਼ ਆਪਣੇ ਆਪ ਸਿੱਖੇ ਜਾਣ * ਅਸੀਂ ਇਹੀ ਤਰੀਕਾ ਉੱਚ-ਸਤਰ ਦੇ ਫੀਚਰਜ਼ ਵਿੱਚ ਪੈਟਰਨਜ਼ ਲੱਭਣ ਲਈ ਵਰਤ ਸਕਦੇ ਹਾਂ, ਨਾ ਕਿ ਸਿਰਫ਼ ਮੂਲ ਚਿੱਤਰ ਵਿੱਚ। ਇਸ ਤਰ੍ਹਾਂ CNN ਫੀਚਰ ਕੱਢਣ ਦਾ ਕੰਮ ਫੀਚਰਜ਼ ਦੀ ਹਾਇਰਾਰਕੀ 'ਤੇ ਹੁੰਦਾ ਹੈ, ਜੋ ਨੀਚਲੇ-ਸਤਰ ਦੇ ਪਿਕਸਲ ਸੰਯੋਜਨਾਂ ਤੋਂ ਸ਼ੁਰੂ ਹੁੰਦਾ ਹੈ, ਅਤੇ ਚਿੱਤਰ ਦੇ ਹਿੱਸਿਆਂ ਦੇ ਉੱਚ-ਸਤਰ ਦੇ ਸੰਯੋਜਨ ਤੱਕ ਪਹੁੰਚਦਾ ਹੈ। -![ਹਾਇਰਾਰਕਲ ਫੀਚਰ ਕੱਢਣਾ](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.pa.png) +![ਹਾਇਰਾਰਕਲ ਫੀਚਰ ਕੱਢਣਾ](../../../../../translated_images/pa/FeatureExtractionCNN.d9b456cbdae7cb64.png) > ਚਿੱਤਰ: [ਹਿਸਲੋਪ-ਲਿੰਚ ਦੇ ਪੇਪਰ](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ਤੋਂ, [ਉਨ੍ਹਾਂ ਦੇ ਰਿਸਰਚ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ਦੇ ਆਧਾਰ 'ਤੇ @@ -55,9 +55,9 @@ CNNs ਦੇ ਕੰਮ ਕਰਨ ਦਾ ਤਰੀਕਾ ਹੇਠਾਂ ਦਿੱ ਉਦਾਹਰਣ ਵਜੋਂ, ਆਓ VGG-16 ਦੀ ਆਰਕੀਟੈਕਚਰ ਨੂੰ ਦੇਖੀਏ, ਇੱਕ ਨੈਟਵਰਕ ਜਿਸ ਨੇ 2014 ਵਿੱਚ ImageNet ਦੇ ਟਾਪ-5 ਵਰਗੀਕਰਨ ਵਿੱਚ 92.7% ਸਹੀਤਾ ਪ੍ਰਾਪਤ ਕੀਤੀ: -![ImageNet ਲੇਅਰਜ਼](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pa.jpg) +![ImageNet ਲੇਅਰਜ਼](../../../../../translated_images/pa/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet ਪਿਰਾਮਿਡ](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pa.jpg) +![ImageNet ਪਿਰਾਮਿਡ](../../../../../translated_images/pa/vgg-16-arch.64ff2137f50dd49f.jpg) > ਚਿੱਤਰ: [ਰਿਸਰਚਗੇਟ](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5a33decb..46ec0e57 100644 --- a/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pa/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ਅਸੀਂ [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ਦੀ ਵਰਤੋਂ ਕਰਾਂਗੇ, ਜਿਸ ਵਿੱਚ ਕੁੱਤਿਆਂ ਅਤੇ ਬਿੱਲੀਆਂ ਦੀਆਂ 37 ਵੱਖ-ਵੱਖ ਬਰੀਡਾਂ ਦੀਆਂ ਤਸਵੀਰਾਂ ਸ਼ਾਮਲ ਹਨ। -![ਜਿਸ ਡੇਟਾ ਨਾਲ ਅਸੀਂ ਕੰਮ ਕਰਨ ਜਾ ਰਹੇ ਹਾਂ](../../../../../../translated_images/data.50b2a9d5484bdbf0.pa.png) +![ਜਿਸ ਡੇਟਾ ਨਾਲ ਅਸੀਂ ਕੰਮ ਕਰਨ ਜਾ ਰਹੇ ਹਾਂ](../../../../../../translated_images/pa/data.50b2a9d5484bdbf0.png) ਡੇਟਾਸੈਟ ਡਾਊਨਲੋਡ ਕਰਨ ਲਈ, ਇਸ ਕੋਡ ਸਨਿੱਪਟ ਦੀ ਵਰਤੋਂ ਕਰੋ: diff --git a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 0f456680..59148368 100644 --- a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "ਆਦਰਸ਼ ਬਿੱਲੀ ਨੂੰ ਦਿਖਾਉਣ ਲਈ, ਅਸੀਂ ਇੱਕ ਰੈਂਡਮ ਸ਼ੋਰ ਵਾਲੀ ਤਸਵੀਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਾਂਗੇ ਅਤੇ ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਅਨੁਕੂਲਤਾ ਤਕਨੀਕ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਤਸਵੀਰ ਨੂੰ ਇਸ ਤਰ੍ਹਾਂ ਬਦਲਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ ਕਿ ਨੈੱਟਵਰਕ ਬਿੱਲੀ ਨੂੰ ਪਛਾਣ ਸਕੇ।\n", "\n", - "![ਅਨੁਕੂਲਤਾ ਲੂਪ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.pa.png)\n", + "![ਅਨੁਕੂਲਤਾ ਲੂਪ](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ਇਹ ਸਾਡੀ ਸ਼ੁਰੂਆਤੀ ਤਸਵੀਰ ਹੈ:\n" ] diff --git a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md index 3660ee77..66dcb8f1 100644 --- a/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pa/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras ਅਤੇ PyTorch ਵਿੱਚ ਕੁਝ ਆਮ ਆਰਕੀਟੈਕਚ ਇੱਥੇ VGG-16 ਨੈਟਵਰਕ ਦੁਆਰਾ ਇੱਕ ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ ਤੋਂ ਕਾਢੇ ਗਏ ਫੀਚਰਾਂ ਦੇ ਨਮੂਨੇ ਹਨ: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.pa.png) +![Features extracted by VGG-16](../../../../../translated_images/pa/features.6291f9c7ba3a0b95.png) ## ਬਿੱਲੀਆਂ ਵਸੋਂ ਕੁੱਤੇ ਡਾਟਾਸੈਟ @@ -48,19 +48,19 @@ Keras ਅਤੇ PyTorch ਵਿੱਚ ਕੁਝ ਆਮ ਆਰਕੀਟੈਕਚ ਇੱਕ ਪਹੁੰਚ ਜੋ ਅਸੀਂ ਅਪਣਾਉ ਸਕਦੇ ਹਾਂ ਉਹ ਹੈ ਇੱਕ ਰੈਂਡਮ ਚਿੱਤਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਨਾ, ਅਤੇ ਫਿਰ **ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਓਪਟੀਮਾਈਜ਼ੇਸ਼ਨ** ਤਕਨੀਕ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਉਸ ਚਿੱਤਰ ਨੂੰ ਇਸ ਤਰੀਕੇ ਨਾਲ ਢਾਲਣਾ, ਕਿ ਨੈਟਵਰਕ ਇਹ ਸੋਚਣ ਲੱਗੇ ਕਿ ਇਹ ਬਿੱਲੀ ਹੈ। -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.pa.png) +![Image Optimization Loop](../../../../../translated_images/pa/ideal-cat-loop.999fbb8ff306e044.png) ਹਾਲਾਂਕਿ, ਜੇ ਅਸੀਂ ਇਹ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਸਾਨੂੰ ਕੁਝ ਬਹੁਤ ਹੀ ਰੈਂਡਮ ਸ਼ੋਰ ਵਰਗਾ ਮਿਲੇਗਾ। ਇਹ ਇਸ ਲਈ ਹੈ ਕਿ *ਨੈਟਵਰਕ ਨੂੰ ਇਹ ਸੋਚਣ ਲਈ ਬਹੁਤ ਸਾਰੇ ਤਰੀਕੇ ਹਨ ਕਿ ਇਨਪੁਟ ਚਿੱਤਰ ਬਿੱਲੀ ਹੈ*, ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਕੁਝ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਸਮਝਦਾਰ ਨਹੀਂ ਹਨ। ਜਦੋਂ ਕਿ ਉਹ ਚਿੱਤਰ ਬਿੱਲੀ ਲਈ ਆਮ ਪੈਟਰਨਾਂ ਨੂੰ ਸ਼ਾਮਲ ਕਰਦੇ ਹਨ, ਉਨ੍ਹਾਂ ਨੂੰ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਵੱਖਰੇ ਹੋਣ ਲਈ ਕੁਝ ਵੀ ਬਾਧਾ ਨਹੀਂ ਹੈ। ਨਤੀਜੇ ਨੂੰ ਸੁਧਾਰਨ ਲਈ, ਅਸੀਂ ਲਾਸ ਫੰਕਸ਼ਨ ਵਿੱਚ ਇੱਕ ਹੋਰ ਟਰਮ ਸ਼ਾਮਲ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿਸ ਨੂੰ **ਵੈਰੀਏਸ਼ਨ ਲਾਸ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। ਇਹ ਇੱਕ ਮੈਟ੍ਰਿਕ ਹੈ ਜੋ ਦਿਖਾਉਂਦੀ ਹੈ ਕਿ ਚਿੱਤਰ ਦੇ ਪੜੋਸੀ ਪਿਕਸਲ ਕਿੰਨੇ ਸਮਾਨ ਹਨ। ਵੈਰੀਏਸ਼ਨ ਲਾਸ ਨੂੰ ਘਟਾਉਣ ਨਾਲ ਚਿੱਤਰ ਸਮੂਥ ਬਣਦਾ ਹੈ, ਅਤੇ ਸ਼ੋਰ ਨੂੰ ਦੂਰ ਕਰਦਾ ਹੈ - ਇਸ ਤਰ੍ਹਾਂ ਵਧੇਰੇ ਵਿਜ਼ੁਅਲ ਤੌਰ 'ਤੇ ਆਕਰਸ਼ਕ ਪੈਟਰਨਾਂ ਨੂੰ ਪ੍ਰਗਟ ਕਰਦਾ ਹੈ। ਇੱਥੇ ਉਹ "ਆਦਰਸ਼" ਚਿੱਤਰਾਂ ਦੇ ਉਦਾਹਰਨ ਹਨ, ਜੋ ਬਿੱਲੀ ਅਤੇ ਜ਼ੈਬਰਾ ਵਜੋਂ ਉੱਚ ਸੰਭਾਵਨਾ ਨਾਲ ਕਲਾਸੀਫਾਈ ਕੀਤੇ ਗਏ ਹਨ: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.pa.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.pa.png) +![Ideal Cat](../../../../../translated_images/pa/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/pa/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *ਆਦਰਸ਼ ਬਿੱਲੀ* | *ਆਦਰਸ਼ ਜ਼ੈਬਰਾ* ਇਹੀ ਪਹੁੰਚ **ਐਡਵਰਸਰੀਅਲ ਅਟੈਕਸ** ਕਰਨ ਲਈ ਵਰਤੀ ਜਾ ਸਕਦੀ ਹੈ। ਮੰਨ ਲਓ ਕਿ ਅਸੀਂ ਨਿਊਰਲ ਨੈਟਵਰਕ ਨੂੰ ਬੇਵਕੂਫ ਬਣਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ ਅਤੇ ਕੁੱਤੇ ਨੂੰ ਬਿੱਲੀ ਵਜੋਂ ਦਿਖਾਉਣਾ ਚਾਹੁੰਦੇ ਹਾਂ। ਜੇਕਰ ਅਸੀਂ ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ ਲੈਂਦੇ ਹਾਂ, ਜਿਸਨੂੰ ਨੈਟਵਰਕ ਦੁਆਰਾ ਕੁੱਤੇ ਵਜੋਂ ਪਛਾਣਿਆ ਜਾਂਦਾ ਹੈ, ਤਾਂ ਅਸੀਂ ਇਸਨੂੰ ਥੋੜ੍ਹਾ ਜਿਹਾ ਢਾਲ ਸਕਦੇ ਹਾਂ **ਗ੍ਰੇਡੀਅੰਟ ਡਿਸੈਂਟ ਓਪਟੀਮਾਈਜ਼ੇਸ਼ਨ** ਦੀ ਵਰਤੋਂ ਕਰਕੇ, ਜਦੋਂ ਤੱਕ ਨੈਟਵਰਕ ਇਸਨੂੰ ਬਿੱਲੀ ਵਜੋਂ ਕਲਾਸੀਫਾਈ ਕਰਨਾ ਸ਼ੁਰੂ ਨਹੀਂ ਕਰਦਾ: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.pa.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.pa.png) +![Picture of a Dog](../../../../../translated_images/pa/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/pa/adversarial-dog.d9fc7773b0142b89.png) -----|----- *ਕੁੱਤੇ ਦੀ ਮੂਲ ਤਸਵੀਰ* | *ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ ਜੋ ਬਿੱਲੀ ਵਜੋਂ ਕਲਾਸੀਫਾਈ ਕੀਤੀ ਗਈ ਹੈ* diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 6071758a..9b50292c 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ਕਿਉਂਕਿ ਅਸੀਂ ਆਟੋਇੰਕੋਡਰ ਨੂੰ ਮੂਲ ਤਸਵੀਰ ਤੋਂ ਜਿੰਨੀ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਲੈ ਸਕੇ, ਉਤਨੀ ਸਹੀ ਰੀਕੰਸਟਰਕਸ਼ਨ ਲਈ ਟ੍ਰੇਨ ਕਰ ਰਹੇ ਹਾਂ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਤਸਵੀਰਾਂ ਦੀ ਸਭ ਤੋਂ ਵਧੀਆ **ਐਮਬੈਡਿੰਗ** ਲੱਭਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ ਤਾਂ ਜੋ ਅਸਲ ਮਤਲਬ ਨੂੰ ਕੈਪਚਰ ਕੀਤਾ ਜਾ ਸਕੇ।\n", "\n", - "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.pa.jpg)\n", + "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ਤਸਵੀਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ\n", "\n", diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9a1835a3..27a455a8 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "ਜਿਵੇਂ ਕਿ ਅਸੀਂ ਆਟੋਇੰਕੋਡਰ ਨੂੰ ਮੂਲ ਚਿੱਤਰ ਤੋਂ ਜਿੰਨੀ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਪ੍ਰਾਪਤ ਕਰ ਸਕੀਏ, ਉਸਨੂੰ ਸਹੀ ਤਰੀਕੇ ਨਾਲ ਮੁੜ ਬਣਾਉਣ ਲਈ ਟ੍ਰੇਨ ਕਰ ਰਹੇ ਹਾਂ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਚਿੱਤਰਾਂ ਦੀ ਸਭ ਤੋਂ ਵਧੀਆ **ਐਮਬੈਡਿੰਗ** ਲੱਭਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ ਤਾਂ ਜੋ ਅਸਲ ਮਤਲਬ ਨੂੰ ਕੈਪਚਰ ਕੀਤਾ ਜਾ ਸਕੇ।\n", "\n", - "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.pa.jpg)\n", + "![ਆਟੋਇੰਕੋਡਰ ਡਾਇਗ੍ਰਾਮ](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ਚਿੱਤਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ*\n", "\n", diff --git a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md index 0f3d39b2..94936915 100644 --- a/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pa/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਜਦੋਂ ਅਸੀਂ ਆਟੋਇਨਕੋਡਰ ਨੂੰ ਮੂਲ ਚਿੱਤਰ ਤੋਂ ਜਿੰਨਾ ਜ਼ਿਆਦਾ ਜਾਣਕਾਰੀ ਕੈਪਚਰ ਕਰ ਸਕਦੇ ਹਾਂ ਉਸ ਲਈ ਟ੍ਰੇਨ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਕਿ ਸਹੀ ਰੀਕੰਸਟ੍ਰਕਸ਼ਨ ਹੋ ਸਕੇ, ਨੈਟਵਰਕ ਇਨਪੁਟ ਚਿੱਤਰਾਂ ਦੀ **embedding** ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਸਭ ਤੋਂ ਵਧੀਆ ਕੋਸ਼ਿਸ਼ ਕਰਦਾ ਹੈ। -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.pa.jpg) +![AutoEncoder Diagram](../../../../../translated_images/pa/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ਚਿੱਤਰ [Keras ਬਲੌਗ](https://blog.keras.io/building-autoencoders-in-keras.html) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md index 042a6abc..30742e96 100644 --- a/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pa/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ਪ੍ਰੀ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.pa.png) +![Object Detection](../../../../../translated_images/pa/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ਤਸਵੀਰ [YOLO v2 ਵੈਬਸਾਈਟ](https://pjreddie.com/darknet/yolov2/) ਤੋਂ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ਹਰ ਟਾਈਲ 'ਤੇ ਇਮੇਜ ਕਲਾਸੀਫਿਕੇਸ਼ਨ ਚਲਾਓ। 3. ਉਹ ਟਾਈਲਾਂ ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਕਾਫ਼ੀ ਉੱਚੀ ਐਕਟੀਵੇਸ਼ਨ ਹੁੰਦੀ ਹੈ, ਉਹਨਾਂ ਨੂੰ ਉਹ ਵਸਤੂ ਸ਼ਾਮਲ ਕਰਨ ਵਾਲੇ ਮੰਨਿਆ ਜਾ ਸਕਦਾ ਹੈ। -![Naive Object Detection](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.pa.png) +![Naive Object Detection](../../../../../translated_images/pa/naive-detection.e7f1ba220ccd08c6.png) > *ਤਸਵੀਰ [ਐਕਸਰਸਾਈਜ਼ ਨੋਟਬੁੱਕ](ObjectDetection-TF.ipynb) ਤੋਂ* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 ਕਲਾਸਾਂ * [COCO](http://cocodataset.org/#home) - ਸਧਾਰਨ ਵਸਤੂਆਂ ਸੰਦਰਭ ਵਿੱਚ। 80 ਕਲਾਸਾਂ, ਬਾਊਂਡਿੰਗ ਬਾਕਸ ਅਤੇ ਸੈਗਮੈਂਟੇਸ਼ਨ ਮਾਸਕ -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.pa.jpg) +![COCO](../../../../../translated_images/pa/coco-examples.71bc60380fa6cceb.jpg) ## ਆਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ ਮੈਟ੍ਰਿਕਸ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ਜਦੋਂ ਕਿ ਇਮੇਜ ਕਲਾਸੀਫਿਕੇਸ਼ਨ ਲਈ ਇਹ ਮਾਪਣਾ ਆਸਾਨ ਹੈ ਕਿ ਐਲਗੋਰਿਦਮ ਕਿੰਨਾ ਚੰਗਾ ਕੰਮ ਕਰਦਾ ਹੈ, ਆਬਜੈਕਟ ਡਿਟੈਕਸ਼ਨ ਲਈ ਸਾਨੂੰ ਕਲਾਸ ਦੀ ਸਹੀਤਾ ਦੇ ਨਾਲ-साथ ਅਨੁਮਾਨਿਤ ਬਾਊਂਡਿੰਗ ਬਾਕਸ ਸਥਿਤੀ ਦੀ ਸ਼ੁੱਧਤਾ ਨੂੰ ਮਾਪਣਾ ਪੈਂਦਾ ਹੈ। ਇਸ ਲਈ, ਅਸੀਂ **ਇੰਟਰਸੈਕਸ਼ਨ ਓਵਰ ਯੂਨੀਅਨ** (IoU) ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਜੋ ਮਾਪਦਾ ਹੈ ਕਿ ਦੋ ਬਾਕਸ (ਜਾਂ ਦੋ ਮਨਮਾਨੇ ਖੇਤਰ) ਕਿੰਨੇ ਚੰਗੇ ਤਰੀਕੇ ਨਾਲ ਓਵਰਲੈਪ ਕਰਦੇ ਹਨ। -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.pa.png) +![IoU](../../../../../translated_images/pa/iou_equation.9a4751d40fff4e11.png) > *ਫਿਗਰ 2 [ਇਹ ਸ਼ਾਨਦਾਰ ਬਲੌਗ ਪੋਸਟ IoU 'ਤੇ](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) ਤੋਂ* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ਦੀ ਵਰਤੋਂ ਕਰਦਾ ਹੈ ROI ਖੇਤਰਾਂ ਦੀ ਹਾਇਰਾਰਕੀਕਲ ਸਟ੍ਰਕਚਰ ਪੈਦਾ ਕਰਨ ਲਈ, ਜੋ ਫਿਰ CNN ਫੀਚਰ ਐਕਸਟ੍ਰੈਕਟਰ ਅਤੇ SVM-ਕਲਾਸੀਫਾਇਰਾਂ ਦੁਆਰਾ ਪਾਸ ਕੀਤੇ ਜਾਂਦੇ ਹਨ ਵਸਤੂਆਂ ਦੀ ਸ਼੍ਰੇਣੀ ਨੂੰ ਨਿਰਧਾਰਤ ਕਰਨ ਲਈ, ਅਤੇ *ਬਾਊਂਡਿੰਗ ਬਾਕਸ* ਕੋਆਰਡੀਨੇਟਸ ਨੂੰ ਨਿਰਧਾਰਤ ਕਰਨ ਲਈ ਲੀਨੀਅਰ ਰੇਗ੍ਰੈਸ਼ਨ। [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.pa.png) +![RCNN](../../../../../translated_images/pa/rcnn1.cae407020dfb1d1f.png) > *ਤਸਵੀਰ ਵੈਨ ਡੇ ਸੈਂਡ ਆਦਿ ICCV’11 ਤੋਂ* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.pa.png) +![RCNN-1](../../../../../translated_images/pa/rcnn2.2d9530bb83516484.png) > *ਤਸਵੀਰਾਂ [ਇਸ ਬਲੌਗ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) ਤੋਂ* @@ -109,7 +109,7 @@ $$ ਇਹ ਪਹੁੰਚ R-CNN ਦੇ ਸਮਾਨ ਹੈ, ਪਰ ਖੇਤਰ ਕਨਵੋਲੂਸ਼ਨ ਲੇਅਰਾਂ ਦੇ ਲਾਗੂ ਹੋਣ ਤੋਂ ਬਾਅਦ ਨਿਰਧਾਰਤ ਕੀਤੇ ਜਾਂਦੇ ਹਨ। -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.pa.png) +![FRCNN](../../../../../translated_images/pa/f-rcnn.3cda6d9bb4188875.png) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 ਤੋਂ @@ -117,7 +117,7 @@ $$ ਇਸ ਪਹੁੰਚ ਦਾ ਮੁੱਖ ਵਿਚਾਰ ROI ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਨ ਲਈ ਨਿਊਰਲ ਨੈਟਵਰਕ ਦੀ ਵਰਤੋਂ ਕਰਨਾ ਹੈ - ਜਿਸਨੂੰ *Region Proposal Network* ਕਿਹਾ ਜਾਂਦਾ ਹੈ। [ਪੇਪਰ](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.pa.png) +![FasterRCNN](../../../../../translated_images/pa/faster-rcnn.8d46c099b87ef30a.png) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/pdf/1506.01497.pdf) ਤੋਂ @@ -129,7 +129,7 @@ $$ 1. ਫੀਚਰ **ਪੋਜ਼ੀਸ਼ਨ-ਸੈਂਸਿਟਿਵ ਸਕੋਰ ਮੈਪ** ਦੁਆਰਾ ਪ੍ਰੋਸੈਸ ਕੀਤੇ ਜਾਂਦੇ ਹਨ। $C$ ਕਲਾਸਾਂ ਵਿੱਚੋਂ ਹਰ ਵਸਤੂ ਨੂੰ $k\times k$ ਖੇਤਰਾਂ ਦੁਆਰਾ ਵੰਡਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਅਸੀਂ ਵਸਤੂਆਂ ਦੇ ਹਿੱਸਿਆਂ ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਨ ਲਈ ਟ੍ਰੇਨਿੰਗ ਕਰਦੇ ਹਾਂ। 1. $k\times k$ ਖੇਤਰਾਂ ਵਿੱਚੋਂ ਹਰ ਹਿੱਸੇ ਲਈ ਸਾਰੇ ਨੈਟਵਰਕ ਵਸਤੂਆਂ ਦੀਆਂ ਕਲਾਸਾਂ ਲਈ ਵੋਟ ਕਰਦੇ ਹਨ, ਅਤੇ ਵਧੇਰੇ ਵੋਟ ਵਾਲੀ ਵਸਤੂ ਦੀ ਕਲਾਸ ਚੁਣੀ ਜਾਂਦੀ ਹੈ। -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.pa.png) +![r-fcn image](../../../../../translated_images/pa/r-fcn.13eb88158b99a3da.png) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/abs/1605.06409) ਤੋਂ @@ -140,7 +140,7 @@ YOLO ਇੱਕ ਰੀਅਲਟਾਈਮ ਵਨ-ਪਾਸ ਐਲਗੋਰਿਦ * ਤਸਵੀਰ ਨੂੰ $S\times S$ ਖੇਤਰਾਂ ਵਿੱਚ ਵੰਡਿਆ ਜਾਂਦਾ ਹੈ। * ਹਰ ਖੇਤਰ ਲਈ, **CNN** $n$ ਸੰਭਾਵਿਤ ਵਸਤੂਆਂ, *ਬਾਊਂਡਿੰਗ ਬਾਕਸ* ਕੋਆਰਡੀਨੇਟਸ ਅਤੇ *ਕਾਨਫਿਡੈਂਸ*=*ਪ੍ਰੋਬੈਬਿਲਿਟੀ* * IoU ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦਾ ਹੈ। - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.pa.png) + ![YOLO](../../../../../translated_images/pa/yolo.a2648ec82ee8bb4e.png) > ਤਸਵੀਰ [ਆਧਿਕਾਰਿਕ ਪੇਪਰ](https://arxiv.org/abs/1506.02640) ਤੋਂ diff --git a/translations/pa/lessons/4-ComputerVision/README.md b/translations/pa/lessons/4-ComputerVision/README.md index fa7e6c48..956afa73 100644 --- a/translations/pa/lessons/4-ComputerVision/README.md +++ b/translations/pa/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ -![ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਸਮੱਗਰੀ ਦਾ ਇੱਕ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/ai-computervision.6506ebebac3fbf76.pa.png) +![ਕੰਪਿਊਟਰ ਵਿਜ਼ਨ ਸਮੱਗਰੀ ਦਾ ਇੱਕ ਡੂਡਲ ਵਿੱਚ ਸਾਰ](../../../../translated_images/pa/ai-computervision.6506ebebac3fbf76.png) ਇਸ ਭਾਗ ਵਿੱਚ ਅਸੀਂ ਸਿੱਖਾਂਗੇ: diff --git a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 4e211e41..9e77d46a 100644 --- a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ** (BoW) ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਸਭ ਤੋਂ ਆਮ ਤੌਰ 'ਤੇ ਵਰਤੀ ਜਾਣ ਵਾਲੀ ਰਵਾਇਤੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਹੈ। ਹਰ ਸ਼ਬਦ ਨੂੰ ਇੱਕ ਵੇਕਟਰ ਇੰਡੈਕਸ ਨਾਲ ਜੋੜਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਵੇਕਟਰ ਤੱਤ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਦਸਤਾਵੇਜ਼ ਵਿੱਚ ਸ਼ਬਦ ਦੀ ਘਟਨਾ ਦੀ ਗਿਣਤੀ ਹੁੰਦੀ ਹੈ।\n", "\n", - "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਮੈਮਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.pa.png) \n", + "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਸ਼ਬਦਾਂ ਦੀ ਥੈਲੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧੀ ਮੈਮਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: ਤੁਸੀਂ BoW ਨੂੰ ਟੈਕਸਟ ਵਿੱਚ ਵੱਖ-ਵੱਖ ਸ਼ਬਦਾਂ ਲਈ ਇੱਕ-ਹਾਟ-ਐਨਕੋਡ ਕੀਤੇ ਵੇਕਟਰਾਂ ਦੇ ਜੋੜ ਵਜੋਂ ਵੀ ਸੋਚ ਸਕਦੇ ਹੋ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 488ed3f9..8f63410f 100644 --- a/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/pa/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**ਬੈਗ-ਆਫ-ਵਰਡਸ** (BoW) ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਸਭ ਤੋਂ ਸਧਾਰਨ ਅਤੇ ਸਮਝਣ ਵਿੱਚ ਆਸਾਨ ਰਵਾਇਤੀ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਹੈ। ਹਰ ਸ਼ਬਦ ਨੂੰ ਇੱਕ ਵੇਕਟਰ ਇੰਡੈਕਸ ਨਾਲ ਜੋੜਿਆ ਜਾਂਦਾ ਹੈ, ਅਤੇ ਇੱਕ ਵੇਕਟਰ ਤੱਤ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਦਸਤਾਵੇਜ਼ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਦੇ ਆਵਿਰਤੀ ਦੀ ਗਿਣਤੀ ਹੁੰਦੀ ਹੈ।\n", "\n", - "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਬੈਗ-ਆਫ-ਵਰਡਸ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਮੈਮੋਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.pa.png)\n", + "![ਇਮੇਜ ਦਿਖਾ ਰਿਹਾ ਹੈ ਕਿ ਬੈਗ-ਆਫ-ਵਰਡਸ ਵੇਕਟਰ ਪ੍ਰਤੀਨਿਧਤਾ ਮੈਮੋਰੀ ਵਿੱਚ ਕਿਵੇਂ ਦਰਸਾਈ ਜਾਂਦੀ ਹੈ।](../../../../../translated_images/pa/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: ਤੁਸੀਂ BoW ਨੂੰ ਟੈਕਸਟ ਵਿੱਚ ਵੱਖ-ਵੱਖ ਸ਼ਬਦਾਂ ਲਈ ਇੱਕ-ਹਾਟ-ਐਨਕੋਡਡ ਵੇਕਟਰਾਂ ਦੇ ਜੋੜ ਵਜੋਂ ਵੀ ਸੋਚ ਸਕਦੇ ਹੋ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 5b5315fb..73fe0c7a 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "ਜਦੋਂ ਅਸੀਂ ਆਪਣੇ ਨੈੱਟਵਰਕ ਵਿੱਚ ਪਹਿਲੇ ਲੇਅਰ ਵਜੋਂ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਜ਼ ਤੋਂ **ਐਮਬੈਡਿੰਗ ਬੈਗ** ਮਾਡਲ ਵਿੱਚ ਬਦਲ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਪਾਠ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਉਸਦੇ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਸਾਰੇ ਐਮਬੈਡਿੰਗਜ਼ 'ਤੇ ਕੁਝ ਸਮੂਹ ਫੰਕਸ਼ਨ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`।\n", "\n", - "![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਂਦੀ ਇੱਕ ਚਿੱਤਰ।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.pa.png)\n", + "![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਂਦੀ ਇੱਕ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ਸਾਡਾ ਕਲਾਸੀਫਾਇਰ ਨਿਊਰਲ ਨੈੱਟਵਰਕ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਨਾਲ ਸ਼ੁਰੂ ਹੋਵੇਗਾ, ਫਿਰ ਐਗਰੀਗੇਸ਼ਨ ਲੇਅਰ, ਅਤੇ ਇਸਦੇ ਉੱਪਰ ਲੀਨਿਅਰ ਕਲਾਸੀਫਾਇਰ:\n" ] @@ -176,7 +176,7 @@ "\n", "ਪਿਛਲੀ ਆਰਕੀਟੈਕਚਰ ਵਿੱਚ, ਸਾਨੂੰ ਸਾਰੇ ਕ੍ਰਮਾਂ ਨੂੰ ਇੱਕੋ ਲੰਬਾਈ ਵਿੱਚ ਪੈਡ ਕਰਨਾ ਪੈਂਦਾ ਸੀ ਤਾਂ ਜੋ ਉਹਨਾਂ ਨੂੰ ਇੱਕ ਮਿਨੀਬੈਚ ਵਿੱਚ ਫਿੱਟ ਕੀਤਾ ਜਾ ਸਕੇ। ਇਹ ਵੱਖ-ਵੱਖ ਲੰਬਾਈ ਵਾਲੇ ਕ੍ਰਮਾਂ ਨੂੰ ਪ੍ਰਤੀਨਿਧਤ ਕਰਨ ਦਾ ਸਭ ਤੋਂ ਕੁਸ਼ਲ ਤਰੀਕਾ ਨਹੀਂ ਹੈ - ਇੱਕ ਹੋਰ ਤਰੀਕਾ **ਆਫਸੈਟ** ਵੇਕਟਰ ਦੀ ਵਰਤੋਂ ਕਰਨਾ ਹੋਵੇਗਾ, ਜੋ ਇੱਕ ਵੱਡੇ ਵੇਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕੀਤੇ ਸਾਰੇ ਕ੍ਰਮਾਂ ਦੇ ਆਫਸੈਟ ਨੂੰ ਰੱਖੇਗਾ।\n", "\n", - "![ਆਫਸੈਟ ਕ੍ਰਮ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.pa.png)\n", + "![ਆਫਸੈਟ ਕ੍ਰਮ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ](../../../../../translated_images/pa/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: ਉੱਪਰ ਦਿੱਤੀ ਤਸਵੀਰ ਵਿੱਚ, ਅਸੀਂ ਅੱਖਰਾਂ ਦੇ ਕ੍ਰਮ ਨੂੰ ਦਿਖਾਇਆ ਹੈ, ਪਰ ਸਾਡੇ ਉਦਾਹਰਨ ਵਿੱਚ ਅਸੀਂ ਸ਼ਬਦਾਂ ਦੇ ਕ੍ਰਮਾਂ ਨਾਲ ਕੰਮ ਕਰ ਰਹੇ ਹਾਂ। ਹਾਲਾਂਕਿ, ਆਫਸੈਟ ਵੇਕਟਰ ਨਾਲ ਕ੍ਰਮਾਂ ਦੀ ਪ੍ਰਤੀਨਿਧਤਾ ਕਰਨ ਦਾ ਆਮ ਸਿਧਾਂਤ ਇੱਕੋ ਜਿਹਾ ਰਹਿੰਦਾ ਹੈ।\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਇਹ ਅਲਭ ਸ਼ਬਦਾਂ ਦੀ ਵਧੀਆ ਪ੍ਰਤੀਨਿਧੀ ਕਰਦਾ ਹੈ।\n", "\n", - "![ਦੋਵੇਂ CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮਾਂ ਨੂੰ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pa.png)\n", + "![ਦੋਵੇਂ CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮਾਂ ਨੂੰ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "ਗੂਗਲ ਨਿਊਜ਼ ਡੇਟਾਸੈਟ 'ਤੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤੇ ਵਰਡ2ਵੈਕ ਐਮਬੈਡਿੰਗ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਲਈ, ਅਸੀਂ **gensim** ਲਾਇਬ੍ਰੇਰੀ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਹੇਠਾਂ ਅਸੀਂ 'neural' ਦੇ ਸਭ ਤੋਂ ਸਮਾਨ ਸ਼ਬਦਾਂ ਨੂੰ ਲੱਭਦੇ ਹਾਂ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 3eb867e8..027b78fb 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/pa/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "ਜਦੋਂ ਅਸੀਂ ਆਪਣੇ ਨੈਟਵਰਕ ਵਿੱਚ ਪਹਿਲੀ ਲੇਅਰ ਵਜੋਂ ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ, ਤਾਂ ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਸ ਮਾਡਲ ਤੋਂ **ਐਮਬੈਡਿੰਗ ਬੈਗ** ਮਾਡਲ ਵਿੱਚ ਸਵਿੱਚ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਟੈਕਸਟ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਉਸ ਦੇ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਉਹਨਾਂ ਸਾਰੀਆਂ ਐਮਬੈਡਿੰਗਜ਼ 'ਤੇ ਕੁਝ ਸਮੁੱਚੇ ਫੰਕਸ਼ਨ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`। \n", "\n", - "![ਪੰਜ ਕ੍ਰਮਵਾਰ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.pa.png)\n", + "![ਪੰਜ ਕ੍ਰਮਵਾਰ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ਸਾਡੇ ਕਲਾਸੀਫਾਇਰ ਨਿਊਰਲ ਨੈਟਵਰਕ ਵਿੱਚ ਹੇਠਾਂ ਦਿੱਤੀਆਂ ਲੇਅਰਾਂ ਸ਼ਾਮਲ ਹਨ:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਇਹ ਅਲਪ-ਵਰਤੋਂ ਵਾਲੇ ਸ਼ਬਦਾਂ ਦੀ ਵਧੀਆ ਪ੍ਰਤੀਨਿਧੀ ਕਰਦਾ ਹੈ।\n", "\n", - "![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰਕਾਰੀ ਜੋ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਦੀ ਹੈ।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pa.png)\n", + "![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਐਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰਕਾਰੀ ਜੋ ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਦੀ ਹੈ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News ਡੇਟਾਸੈਟ 'ਤੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤੇ ਵਰਡ2ਵੈਕ ਐਮਬੈਡਿੰਗ ਨਾਲ ਪ੍ਰਯੋਗ ਕਰਨ ਲਈ, ਅਸੀਂ **gensim** ਲਾਇਬ੍ਰੇਰੀ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹਾਂ। ਹੇਠਾਂ ਅਸੀਂ 'neural' ਦੇ ਸਭ ਤੋਂ ਸਮਾਨ ਸ਼ਬਦਾਂ ਨੂੰ ਲੱਭਦੇ ਹਾਂ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/14-Embeddings/README.md b/translations/pa/lessons/5-NLP/14-Embeddings/README.md index 24b10d8f..1c9e5923 100644 --- a/translations/pa/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pa/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਐਮਬੈਡਿੰਗ ਲੇਅਰ ਨੂੰ ਸਾਡੇ ਕਲਾਸੀਫਾਇਰ ਨੈਟਵਰਕ ਵਿੱਚ ਪਹਿਲੇ ਲੇਅਰ ਵਜੋਂ ਵਰਤ ਕੇ, ਅਸੀਂ ਬੈਗ-ਆਫ-ਵਰਡਸ ਤੋਂ **embedding bag** ਮਾਡਲ ਵਿੱਚ ਸਵਿੱਚ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿੱਥੇ ਅਸੀਂ ਪਹਿਲਾਂ ਆਪਣੇ ਟੈਕਸਟ ਵਿੱਚ ਹਰ ਸ਼ਬਦ ਨੂੰ ਸੰਬੰਧਿਤ ਐਮਬੈਡਿੰਗ ਵਿੱਚ ਬਦਲਦੇ ਹਾਂ, ਅਤੇ ਫਿਰ ਸਾਰੇ ਐਮਬੈਡਿੰਗਸ 'ਤੇ ਕੁਝ ਸਮੁੱਚੇ ਫੰਕਸ਼ਨ ਦੀ ਗਣਨਾ ਕਰਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ `sum`, `average` ਜਾਂ `max`। -![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.pa.png) +![ਪੰਜ ਕ੍ਰਮ ਸ਼ਬਦਾਂ ਲਈ ਐਮਬੈਡਿੰਗ ਕਲਾਸੀਫਾਇਰ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/embedding-classifier-example.b77f021a7ee67eee.png) > ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW ਤੇਜ਼ ਹੈ, ਜਦਕਿ ਸਕਿਪ-ਗ੍ਰਾਮ ਹੌਲੀ ਹੈ, ਪਰ ਅਲਭ ਸ਼ਬਦਾਂ ਦੀ ਪ੍ਰਤੀਨਿਧੀ ਕਰਨ ਵਿੱਚ ਵਧੀਆ ਕੰਮ ਕਰਦਾ ਹੈ। -![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਅਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pa.png) +![CBoW ਅਤੇ ਸਕਿਪ-ਗ੍ਰਾਮ ਅਲਗੋਰਿਥਮ ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ।](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [ਇਸ ਪੇਪਰ](https://arxiv.org/pdf/1301.3781.pdf) ਤੋਂ ਚਿੱਤਰ diff --git a/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md index 2cee6097..06f8c700 100644 --- a/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pa/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **ਕੰਟਿਨਿਊਅਸ ਬੈਗ-ਆਫ-ਵਰਡਸ** (CBoW), ਜਦੋਂ ਅਸੀਂ ਟੋਕਨ ਸੀਕਵੈਂਸ $W_{-N}$, ..., $W_N$ ਵਿੱਚ ਮੱਧਲੇ ਟੋਕਨ $W_0$ ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦੇ ਹਾਂ। * **ਸਕਿਪ-ਗ੍ਰਾਮ**, ਜਿੱਥੇ ਅਸੀਂ ਮੱਧਲੇ ਟੋਕਨ $W_0$ ਤੋਂ ਨੇੜਲੇ ਟੋਕਨਜ਼ {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ਦੀ ਪੇਸ਼ਗੂਈ ਕਰਦੇ ਹਾਂ। -![ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਪੇਪਰ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pa.png) +![ਸ਼ਬਦਾਂ ਨੂੰ ਵੈਕਟਰ ਵਿੱਚ ਬਦਲਣ ਲਈ ਪੇਪਰ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ਚਿੱਤਰ [ਇਸ ਪੇਪਰ](https://arxiv.org/pdf/1301.3781.pdf) ਤੋਂ diff --git a/translations/pa/lessons/5-NLP/16-RNN/README.md b/translations/pa/lessons/5-NLP/16-RNN/README.md index b5ccd9ee..1680cf98 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/README.md +++ b/translations/pa/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ਟੈਕਸਟ ਕ੍ਰਮ ਦੇ ਅਰਥ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ, ਸਾਨੂੰ ਇੱਕ ਹੋਰ ਨਿਊਰਲ ਨੈਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਦੀ ਵਰਤੋਂ ਕਰਨ ਦੀ ਲੋੜ ਹੈ, ਜਿਸਨੂੰ **ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ** ਜਾਂ RNN ਕਿਹਾ ਜਾਂਦਾ ਹੈ। RNN ਵਿੱਚ, ਅਸੀਂ ਆਪਣੇ ਵਾਕਾਂਸ਼ ਨੂੰ ਨੈਟਵਰਕ ਵਿੱਚ ਇੱਕ ਸਮੇਂ ਵਿੱਚ ਇੱਕ ਚਿੰਨ੍ਹ ਦੇ ਰਾਹੀਂ ਪਾਸ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਨੈਟਵਰਕ ਕੁਝ **ਸਟੇਟ** ਪੈਦਾ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਅਗਲੇ ਚਿੰਨ੍ਹ ਦੇ ਨਾਲ ਮੁੜ ਨੈਟਵਰਕ ਵਿੱਚ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.pa.png) +![RNN](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.png) > ਲੇਖਕ ਦੁਆਰਾ ਚਿੱਤਰ @@ -61,7 +61,7 @@ LSTM ਨੈਟਵਰਕ RNN ਦੇ ਸਮਾਨ ਸੰਗਠਿਤ ਹੈ, ਪ ਇੱਕ ਰਿਕਰੰਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾ ਵਾਲਾ ਹੋਵੇ ਜਾਂ ਬਾਈਡਾਇਰੈਕਸ਼ਨਲ, ਲੜੀ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ ਅਤੇ ਇਸਨੂੰ ਸਟੇਟ ਵੈਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕਰਦਾ ਹੈ ਜਾਂ ਆਉਟਪੁਟ ਵਿੱਚ ਪਾਸ ਕਰਦਾ ਹੈ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕਸ ਦੇ ਨਾਲ, ਅਸੀਂ ਪਹਿਲੇ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਗਏ ਨੀਵਾਂ-ਸਤਹ ਪੈਟਰਨਾਂ ਤੋਂ ਉੱਚ-ਸਤਹ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਪਹਿਲੇ ਲੇਅਰ ਦੇ ਉੱਪਰ ਇੱਕ ਹੋਰ ਰਿਕਰੰਟ ਲੇਅਰ ਬਣਾਉਣ ਲਈ ਬਣਾਉਣ ਕਰ ਸਕਦੇ ਹਾਂ। ਇਸ ਨਾਲ ਸਾਨੂੰ **ਮਲਟੀ-ਲੇਅਰ RNN** ਦੀ ਧਾਰਨਾ ਮਿਲਦੀ ਹੈ ਜੋ ਦੋ ਜਾਂ ਵੱਧ ਰਿਕਰੰਟ ਨੈਟਵਰਕਸ ਤੋਂ ਬਣਦੀ ਹੈ, ਜਿੱਥੇ ਪਿਛਲੇ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੇ ਲੇਅਰ ਵਿੱਚ ਇਨਪੁਟ ਵਜੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। -![ਮਲਟੀਲੇਅਰ ਲਾਂਗ-ਸ਼ਾਰਟ-ਟਰਮ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਂਦਾ ਚਿੱਤਰ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.pa.jpg) +![ਮਲਟੀਲੇਅਰ ਲਾਂਗ-ਸ਼ਾਰਟ-ਟਰਮ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਂਦਾ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg) *ਚਿੱਤਰ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ* diff --git a/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 8f06dfd9..699c02cf 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "ਰੀਕਰਨਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾ ਹੋਵੇ ਜਾਂ ਦੋ-ਦਿਸ਼ਾ, ਕ੍ਰਮ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ, ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਸਟੇਟ ਵੈਕਟਰ ਵਿੱਚ ਸਟੋਰ ਕਰਦਾ ਹੈ ਜਾਂ ਆਉਟਪੁਟ ਵਿੱਚ ਪਾਸ ਕਰਦਾ ਹੈ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕਸ ਵਿੱਚ, ਅਸੀਂ ਪਹਿਲੀ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਨੀਵੀਂ-ਪੱਧਰੀ ਪੈਟਰਨਾਂ ਤੋਂ ਉੱਚ-ਪੱਧਰੀ ਪੈਟਰਨਾਂ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ ਪਹਿਲੀ ਲੇਅਰ ਦੇ ਉੱਪਰ ਇੱਕ ਹੋਰ ਰੀਕਰਨਟ ਲੇਅਰ ਬਣਾਉਣ ਦੀ ਯੋਜਨਾ ਬਣਾ ਸਕਦੇ ਹਾਂ। ਇਸ ਨਾਲ **ਬਹੁ-ਪੱਧਰੀ RNN** ਦੀ ਧਾਰਨਾ ਬਣਦੀ ਹੈ, ਜਿਸ ਵਿੱਚ ਦੋ ਜਾਂ ਵੱਧ ਰੀਕਰਨਟ ਨੈਟਵਰਕਸ ਹੁੰਦੇ ਹਨ, ਜਿੱਥੇ ਪਿਛਲੀ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੀ ਲੇਅਰ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![ਬਹੁ-ਪੱਧਰੀ ਲੰਬੇ-ਛੋਟੇ-ਅਵਧੀ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.pa.jpg)\n", + "![ਬਹੁ-ਪੱਧਰੀ ਲੰਬੇ-ਛੋਟੇ-ਅਵਧੀ-ਮੈਮੋਰੀ RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਤੋਂ ਚਿੱਤਰ*\n", "\n", diff --git a/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb index 9fa28316..042e066c 100644 --- a/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/pa/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ਪਾਠ ਕ੍ਰਮ ਦੇ ਅਰਥ ਨੂੰ ਕੈਪਚਰ ਕਰਨ ਲਈ, ਅਸੀਂ ਇੱਕ ਨਿਊਰਲ ਨੈਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਵਰਤਾਂਗੇ ਜਿਸਨੂੰ **ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ** ਜਾਂ RNN ਕਿਹਾ ਜਾਂਦਾ ਹੈ। RNN ਵਰਤਦੇ ਸਮੇਂ, ਅਸੀਂ ਆਪਣੀ ਵਾਕ ਨੂੰ ਨੈਟਵਰਕ ਵਿੱਚ ਇੱਕ ਟੋਕਨ ਇੱਕ ਵਾਰ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਨੈਟਵਰਕ ਕੁਝ **ਸਟੇਟ** ਤਿਆਰ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਅਸੀਂ ਅਗਲੇ ਟੋਕਨ ਦੇ ਨਾਲ ਫਿਰ ਨੈਟਵਰਕ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ।\n", "\n", - "![ਇੱਕ ਉਦਾਹਰਣ ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/rnn.27f5c29c53d727b5.pa.png)\n", + "![ਇੱਕ ਉਦਾਹਰਣ ਰਿਕਰੰਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn.27f5c29c53d727b5.png)\n", "\n", "ਜਦੋਂ ਟੋਕਨ ਦਾ ਇਨਪੁਟ ਕ੍ਰਮ $X_0,\\dots,X_n$ ਦਿੱਤਾ ਜਾਂਦਾ ਹੈ, RNN ਨਿਊਰਲ ਨੈਟਵਰਕ ਬਲਾਕਾਂ ਦੀ ਇੱਕ ਲੜੀ ਬਣਾਉਂਦਾ ਹੈ ਅਤੇ ਇਸ ਲੜੀ ਨੂੰ ਬੈਕਪ੍ਰੋਪਾਗੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਐਂਡ-ਟੂ-ਐਂਡ ਟ੍ਰੇਨ ਕਰਦਾ ਹੈ। ਹਰ ਨੈਟਵਰਕ ਬਲਾਕ ਇੱਕ ਜੋੜੇ $(X_i,S_i)$ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਲੈਂਦਾ ਹੈ ਅਤੇ ਨਤੀਜੇ ਵਜੋਂ $S_{i+1}$ ਤਿਆਰ ਕਰਦਾ ਹੈ। ਅੰਤਿਮ ਸਟੇਟ $S_n$ ਜਾਂ ਆਉਟਪੁਟ $Y_n$ ਨੂੰ ਨਤੀਜਾ ਤਿਆਰ ਕਰਨ ਲਈ ਇੱਕ ਲੀਨੀਅਰ ਕਲਾਸੀਫਾਇਰ ਵਿੱਚ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਸਾਰੇ ਨੈਟਵਰਕ ਬਲਾਕ ਇੱਕੋ ਜਿਹੇ ਵਜ਼ਨ ਸਾਂਝੇ ਕਰਦੇ ਹਨ ਅਤੇ ਇੱਕੋ ਬੈਕਪ੍ਰੋਪਾਗੇਸ਼ਨ ਪਾਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਐਂਡ-ਟੂ-ਐਂਡ ਟ੍ਰੇਨ ਕੀਤੇ ਜਾਂਦੇ ਹਨ।\n", "\n", @@ -369,7 +369,7 @@ "\n", "ਰਿਕਰੰਟ ਨੈਟਵਰਕ, ਚਾਹੇ ਇੱਕ-ਦਿਸ਼ਾਵਾਂ ਹੋਣ ਜਾਂ ਦੋ-ਦਿਸ਼ਾਵਾਂ, ਸ਼੍ਰੇਣੀ ਦੇ ਅੰਦਰ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦੇ ਹਨ ਅਤੇ ਉਨ੍ਹਾਂ ਨੂੰ ਸਟੇਟ ਵੈਕਟਰਾਂ ਵਿੱਚ ਸਟੋਰ ਕਰਦੇ ਹਨ ਜਾਂ ਉਨ੍ਹਾਂ ਨੂੰ ਆਉਟਪੁਟ ਵਜੋਂ ਵਾਪਸ ਕਰਦੇ ਹਨ। ਜਿਵੇਂ ਕਿ ਕਨਵੋਲੂਸ਼ਨਲ ਨੈਟਵਰਕ ਵਿੱਚ ਹੁੰਦਾ ਹੈ, ਅਸੀਂ ਪਹਿਲੇ ਲੇਅਰ ਦੇ ਬਾਅਦ ਇੱਕ ਹੋਰ ਰਿਕਰੰਟ ਲੇਅਰ ਬਣਾਉਣ ਦੇ ਯੋਗ ਹੋ ਸਕਦੇ ਹਾਂ, ਜੋ ਉੱਚ ਪੱਧਰ ਦੇ ਪੈਟਰਨ ਨੂੰ ਕੈਪਚਰ ਕਰਦਾ ਹੈ, ਜੋ ਪਹਿਲੇ ਲੇਅਰ ਦੁਆਰਾ ਕੈਪਚਰ ਕੀਤੇ ਨੀਵੇਂ ਪੱਧਰ ਦੇ ਪੈਟਰਨ ਤੋਂ ਬਣੇ ਹੁੰਦੇ ਹਨ। ਇਸ ਨਾਲ ਸਾਨੂੰ **ਬਹੁ-ਪਤਰੀ RNN** ਦਾ ਧਾਰਨਾ ਮਿਲਦੀ ਹੈ, ਜਿਸ ਵਿੱਚ ਦੋ ਜਾਂ ਵੱਧ ਰਿਕਰੰਟ ਨੈਟਵਰਕ ਹੁੰਦੇ ਹਨ, ਜਿੱਥੇ ਪਿਛਲੇ ਲੇਅਰ ਦਾ ਆਉਟਪੁਟ ਅਗਲੇ ਲੇਅਰ ਨੂੰ ਇਨਪੁਟ ਵਜੋਂ ਦਿੱਤਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![ਬਹੁ-ਪਤਰੀ ਲੰਬੇ-ਛੋਟੇ-ਸਮੇਂ-ਯਾਦاشت RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.pa.jpg)\n", + "![ਬਹੁ-ਪਤਰੀ ਲੰਬੇ-ਛੋਟੇ-ਸਮੇਂ-ਯਾਦاشت RNN ਦਿਖਾਉਣ ਵਾਲੀ ਚਿੱਤਰ](../../../../../translated_images/pa/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ਫਰਨਾਂਡੋ ਲੋਪੇਜ਼ ਦੁਆਰਾ [ਇਸ ਸ਼ਾਨਦਾਰ ਪੋਸਟ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ਤੋਂ ਚਿੱਤਰ।*\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b7502d26..2343638b 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ਅਸੀਂ RNN ਨੂੰ ਟੈਕਸਟ ਜਨਰੇਟ ਕਰਨ ਲਈ ਇਸ ਤਰੀਕੇ ਨਾਲ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ ਅੱਖਰਾਂ ਦੀ `nchars` ਲੰਬਾਈ ਦੀ ਲੜੀ ਲਵਾਂਗੇ ਅਤੇ ਨੈਟਵਰਕ ਨੂੰ ਹਰ ਇਨਪੁਟ ਅੱਖਰ ਲਈ ਅਗਲਾ ਆਉਟਪੁੱਟ ਅੱਖਰ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ:\n", "\n", - "![ਇਮੇਜ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦੀ ਹੈ।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.pa.png)\n", + "![ਇਮੇਜ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦੀ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ਅਸਲ ਸਥਿਤੀ ਦੇ ਅਨੁਸਾਰ, ਅਸੀਂ ਕੁਝ ਖਾਸ ਅੱਖਰ ਵੀ ਸ਼ਾਮਲ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ ਸਕਦੇ ਹਾਂ, ਜਿਵੇਂ ਕਿ *end-of-sequence* ``। ਸਾਡੇ ਕੇਸ ਵਿੱਚ, ਅਸੀਂ ਨੈਟਵਰਕ ਨੂੰ ਅਨੰਤ ਟੈਕਸਟ ਜਨਰੇਸ਼ਨ ਲਈ ਟ੍ਰੇਨ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਇਸ ਲਈ ਅਸੀਂ ਹਰ ਲੜੀ ਦਾ ਆਕਾਰ `nchars` ਟੋਕਨ ਦੇ ਬਰਾਬਰ ਰੱਖਾਂਗੇ। ਇਸ ਤਰ੍ਹਾਂ, ਹਰ ਟ੍ਰੇਨਿੰਗ ਉਦਾਹਰਨ `nchars` ਇਨਪੁਟ ਅਤੇ `nchars` ਆਉਟਪੁੱਟ (ਜੋ ਇਨਪੁਟ ਲੜੀ ਨੂੰ ਇੱਕ ਚਿੰਨ੍ਹ ਖੱਬੇ ਵੱਲ ਸ਼ਿਫਟ ਕਰਕੇ ਬਣਦੀ ਹੈ) 'ਤੇ ਮੁਸ਼ਤਮਿਲ ਹੋਵੇਗੀ। ਮਿਨੀਬੈਚ ਕਈ ਇਸ ਤਰ੍ਹਾਂ ਦੀਆਂ ਲੜੀਆਂ 'ਤੇ ਮੁਸ਼ਤਮਿਲ ਹੋਵੇਗਾ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 56e6d376..2fb37509 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "ਅਸੀਂ RNN ਨੂੰ ਖ਼ਬਰਾਂ ਦੇ ਸਿਰਲੇਖ ਬਣਾਉਣ ਲਈ ਇਸ ਤਰੀਕੇ ਨਾਲ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ ਇੱਕ ਸਿਰਲੇਖ ਲਵਾਂਗੇ, ਜਿਸਨੂੰ RNN ਵਿੱਚ ਫੀਡ ਕੀਤਾ ਜਾਵੇਗਾ, ਅਤੇ ਹਰ ਇਨਪੁਟ ਅੱਖਰ ਲਈ ਅਸੀਂ ਨੈੱਟਵਰਕ ਨੂੰ ਅਗਲਾ ਆਉਟਪੁਟ ਅੱਖਰ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ:\n", "\n", - "![ਇੱਕ ਉਦਾਹਰਣ RNN 'HELLO' ਸ਼ਬਦ ਦੀ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.pa.png)\n", + "![ਇੱਕ ਉਦਾਹਰਣ RNN 'HELLO' ਸ਼ਬਦ ਦੀ ਜਨਰੇਸ਼ਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ਸਾਡੇ ਕ੍ਰਮ ਦੇ ਆਖਰੀ ਅੱਖਰ ਲਈ, ਅਸੀਂ ਨੈੱਟਵਰਕ ਨੂੰ `` ਟੋਕਨ ਜਨਰੇਟ ਕਰਨ ਲਈ ਕਹਾਂਗੇ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md index 4d68fe0b..4c20e085 100644 --- a/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pa/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ਇਸ ਨਾਲ ਵੱਖ-ਵੱਖ ਨਿਊਰਲ ਆਰਕੀਟੈਕਚਰਸ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ ਜੋ ਹੇਠਾਂ ਦਿੱਤੇ ਚਿੱਤਰ ਵਿੱਚ ਦਿਖਾਏ ਗਏ ਹਨ: -![ਚਿੱਤਰ ਜੋ ਆਮ ਰੀਕਰਨਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pa.jpg) +![ਚਿੱਤਰ ਜੋ ਆਮ ਰੀਕਰਨਟ ਨਿਊਰਲ ਨੈਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > ਚਿੱਤਰ ਬਲੌਗ ਪੋਸਟ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ਤੋਂ [Andrej Karpaty](http://karpathy.github.io/) ਦੁਆਰਾ @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: ਅਸੀਂ ਇਸ RNN ਨੂੰ ਕਦਮ-ਦਰ-ਕਦਮ ਟੈਕਸਟ ਪੈਦਾ ਕਰਨ ਲਈ ਟ੍ਰੇਨ ਕਰਾਂਗੇ। ਹਰ ਕਦਮ 'ਤੇ, ਅਸੀਂ `nchars` ਦੀ ਲੰਬਾਈ ਦੇ ਕਿਰਦਾਰਾਂ ਦੇ ਕ੍ਰਮ ਨੂੰ ਲਵਾਂਗੇ, ਅਤੇ ਨੈਟਵਰਕ ਤੋਂ ਹਰ ਇਨਪੁਟ ਕਿਰਦਾਰ ਲਈ ਅਗਲਾ ਆਉਟਪੁਟ ਕਿਰਦਾਰ ਪੈਦਾ ਕਰਨ ਲਈ ਕਹਾਂਗੇ: -![ਚਿੱਤਰ ਜੋ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/rnn-generate.56c54afb52f9781d.pa.png) +![ਚਿੱਤਰ ਜੋ 'HELLO' ਸ਼ਬਦ ਦੇ RNN ਜਨਰੇਸ਼ਨ ਦਾ ਉਦਾਹਰਨ ਦਿਖਾਉਂਦਾ ਹੈ।](../../../../../translated_images/pa/rnn-generate.56c54afb52f9781d.png) ਜਦੋਂ ਟੈਕਸਟ ਪੈਦਾ ਕਰਨਾ (ਇਨਫਰੈਂਸ ਦੌਰਾਨ), ਅਸੀਂ ਕੁਝ **ਪ੍ਰਾਂਪਟ** ਨਾਲ ਸ਼ੁਰੂ ਕਰਦੇ ਹਾਂ, ਜਿਸਨੂੰ RNN ਸੈਲਸ ਵਿੱਚੋਂ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਤਾਂ ਜੋ ਇਸਦਾ ਮੱਧਵਰਤੀ ਸਟੇਟ ਪੈਦਾ ਕੀਤਾ ਜਾ ਸਕੇ, ਅਤੇ ਫਿਰ ਇਸ ਸਟੇਟ ਤੋਂ ਜਨਰੇਸ਼ਨ ਸ਼ੁਰੂ ਹੁੰਦੀ ਹੈ। ਅਸੀਂ ਇੱਕ ਸਮੇਂ ਵਿੱਚ ਇੱਕ ਕਿਰਦਾਰ ਪੈਦਾ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਸਟੇਟ ਅਤੇ ਪੈਦਾ ਕੀਤੇ ਕਿਰਦਾਰ ਨੂੰ ਅਗਲੇ RNN ਸੈਲ ਵਿੱਚ ਪਾਸ ਕਰਦੇ ਹਾਂ ਤਾਂ ਜੋ ਅਗਲਾ ਪੈਦਾ ਕੀਤਾ ਜਾ ਸਕੇ, ਜਦੋਂ ਤੱਕ ਅਸੀਂ ਕਾਫ਼ੀ ਕਿਰਦਾਰ ਪੈਦਾ ਨਹੀਂ ਕਰ ਲੈਂਦੇ। diff --git a/translations/pa/lessons/5-NLP/18-Transformers/README.md b/translations/pa/lessons/5-NLP/18-Transformers/README.md index a903dee2..6a2fafbb 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pa/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs ਨਾਲ, ਸੀਕਵੈਂਸ-ਟੂ-ਸੀਕਵੈਂਸ ਦੋ ਰਿ **ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਾਂ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ yt ਬਣਾਇਆ ਜਾਂਦਾ ਹੈ, ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟ hi ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂਕ αt,i ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ। -![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pa.png) +![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਨਾਲ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ। Attention ਮੈਟ੍ਰਿਕਸ {αi,j} ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦਾਂ ਦਾ ਇੱਕ ਦਿੱਤੇ ਗਏ ਆਉਟਪੁੱਟ ਸੀਕਵੈਂਸ ਵਿੱਚ ਸ਼ਬਦ ਬਣਾਉਣ ਵਿੱਚ ਕਿੰਨਾ ਯੋਗਦਾਨ ਹੈ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ: -![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pa.png) +![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਚਿੱਤਰ @@ -66,7 +66,7 @@ Positional encoding ਦਾ ਵਿਚਾਰ ਹੇਠਾਂ ਦਿੱਤਾ ਗ ਅਗਲੇ, ਸਾਨੂੰ ਆਪਣੇ ਸੀਕਵੈਂਸ ਵਿੱਚ ਕੁਝ ਪੈਟਰਨ ਕੈਪਚਰ ਕਰਨ ਦੀ ਲੋੜ ਹੈ। ਇਹ ਕਰਨ ਲਈ, ਟ੍ਰਾਂਸਫਾਰਮਰ **self-attention** ਮਕੈਨਿਜ਼ਮ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਨ, ਜੋ ਮੂਲ ਤੌਰ 'ਤੇ attention ਹੈ ਜੋ ਇਨਪੁਟ ਅਤੇ ਆਉਟਪੁੱਟ ਦੇ ਤੌਰ 'ਤੇ ਇੱਕੋ ਸੀਕਵੈਂਸ 'ਤੇ ਲਾਗੂ ਹੁੰਦੀ ਹੈ। Self-attention ਲਾਗੂ ਕਰਨ ਨਾਲ ਸਾਨੂੰ **context** ਨੂੰ ਵਾਕ ਵਿੱਚ ਧਿਆਨ ਵਿੱਚ ਲੈਣ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ, ਅਤੇ ਵੇਖਣ ਦੀ ਆਗਿਆ ਮਿਲਦੀ ਹੈ ਕਿ ਕਿਹੜੇ ਸ਼ਬਦ ਆਪਸ ਵਿੱਚ ਜੁੜੇ ਹੋਏ ਹਨ। ਉਦਾਹਰਨ ਲਈ, ਇਹ ਸਾਨੂੰ ਇਹ ਵੇਖਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ ਕਿ ਕਿਹੜੇ ਸ਼ਬਦ coreferences ਦੁਆਰਾ ਦਰਸਾਏ ਜਾਂਦੇ ਹਨ, ਜਿਵੇਂ ਕਿ *it*, ਅਤੇ context ਨੂੰ ਵੀ ਧਿਆਨ ਵਿੱਚ ਲੈਣ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.pa.png) +![](../../../../../translated_images/pa/CoreferenceResolution.861924d6d384a7d6.png) > [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) ਤੋਂ ਚਿੱਤਰ @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs ਵਿੱਚ ਵਰਤੇ attention ਮਕੈਨ **BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ multi-layer ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਲੇਅਰ ਹਨ, ਅਤੇ *BERT-large* ਲਈ 24। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਇੱਕ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ ਕੋਰਪਸ (WikiPedia + ਕਿਤਾਬਾਂ) 'ਤੇ unsupervised training (ਵਾਕ ਵਿੱਚ masked ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ pre-train ਕੀਤਾ ਜਾਂਦਾ ਹੈ। Pre-training ਦੌਰਾਨ ਮਾਡਲ ਭਾਸ਼ਾ ਸਮਝਣ ਦੇ ਮਹੱਤਵਪੂਰਨ ਪੱਧਰਾਂ ਨੂੰ ਅਪਣਾਉਂਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਹੋਰ ਡਾਟਾਸੈਟਾਂ ਨਾਲ fine-tuning ਦੁਆਰਾ leveraged ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **transfer learning** ਕਿਹਾ ਜਾਂਦਾ ਹੈ। -![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pa.png) +![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > ਚਿੱਤਰ [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 37ea1f89..6e2099c8 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/pa/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਸ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ $y_t$ ਬਣਾਇਆ ਜਾ ਰਿਹਾ ਹੈ, ਤਾਂ ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟਸ $h_i$ ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂਕ $\\alpha_{t,i}$ ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ।\n", "\n", - "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pa.png)\n", + "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਵਾਲਾ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ।]*\n", "\n", "Attention ਮੈਟ੍ਰਿਕਸ $\\{\\alpha_{i,j}\\}$ ਇਹ ਦਰਸਾਉਂਦੀ ਹੈ ਕਿ ਕਿਸ ਹੱਦ ਤੱਕ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦ ਆਉਟਪੁੱਟ ਕ੍ਰਮ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਸ਼ਬਦ ਦੇ ਜਨਰੇਸ਼ਨ ਵਿੱਚ ਭੂਮਿਕਾ ਨਿਭਾਉਂਦੇ ਹਨ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ:\n", "\n", - "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pa.png)\n", + "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਲਿਆ ਗਿਆ ਚਿੱਤਰ]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ ਮਲਟੀ ਲੇਅਰ ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ, ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਲੇਅਰ ਹਨ, ਅਤੇ *BERT-large* ਲਈ 24। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ (WikiPedia + ਕਿਤਾਬਾਂ) 'ਤੇ ਅਨਸੁਪਰਵਾਈਜ਼ਡ ਟ੍ਰੇਨਿੰਗ (ਵਾਕ ਵਿੱਚ ਮਾਸਕ ਕੀਤੇ ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਪ੍ਰੀ-ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ ਮਾਡਲ ਭਾਸ਼ਾ ਦੀ ਮਹੱਤਵਪੂਰਨ ਸਮਝ ਹਾਸਲ ਕਰਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਹੋਰ ਡਾਟਾਸੈਟਸ ਨਾਲ ਫਾਈਨ ਟਿਊਨਿੰਗ ਦੁਆਰਾ ਲਾਭਕਾਰੀ ਬਣਾਇਆ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਟ੍ਰਾਂਸਫਰ ਲਰਨਿੰਗ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pa.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਚਿੱਤਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ਟ੍ਰਾਂਸਫਾਰਮਰ ਆਰਕੀਟੈਕਚਰਾਂ ਦੇ ਕਈ ਰੂਪ ਹਨ, ਜਿਵੇਂ ਕਿ BERT, DistilBERT, BigBird, OpenGPT3 ਅਤੇ ਹੋਰ, ਜਿਨ੍ਹਾਂ ਨੂੰ ਫਾਈਨ ਟਿਊਨ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। [HuggingFace ਪੈਕੇਜ](https://github.com/huggingface/) PyTorch ਨਾਲ ਇਨ੍ਹਾਂ ਆਰਕੀਟੈਕਚਰਾਂ ਨੂੰ ਟ੍ਰੇਨ ਕਰਨ ਲਈ ਰਿਪੋਜ਼ਟਰੀ ਪ੍ਰਦਾਨ ਕਰਦਾ ਹੈ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 7e46ab47..5d70ac38 100644 --- a/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/pa/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**ਧਿਆਨ ਮਕੈਨਿਜ਼ਮ** RNN ਦੇ ਹਰ ਆਉਟਪੁੱਟ ਅਨੁਮਾਨ 'ਤੇ ਹਰ ਇਨਪੁਟ ਵੇਕਟਰ ਦੇ ਸੰਦਰਭਕ ਪ੍ਰਭਾਵ ਨੂੰ ਵਜਨ ਦੇਣ ਦਾ ਇੱਕ ਢੰਗ ਪ੍ਰਦਾਨ ਕਰਦੇ ਹਨ। ਇਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਕਿ ਇਨਪੁਟ RNN ਦੇ ਮੱਧਵਰਤੀ ਸਟੇਟਸ ਅਤੇ ਆਉਟਪੁੱਟ RNN ਦੇ ਵਿਚਕਾਰ ਸ਼ਾਰਟਕਟ ਬਣਾਏ ਜਾਂਦੇ ਹਨ। ਇਸ ਤਰੀਕੇ ਨਾਲ, ਜਦੋਂ ਆਉਟਪੁੱਟ ਚਿੰਨ੍ਹ $y_t$ ਬਣਾਇਆ ਜਾ ਰਿਹਾ ਹੈ, ਅਸੀਂ ਸਾਰੇ ਇਨਪੁਟ ਹਿਡਨ ਸਟੇਟਸ $h_i$ ਨੂੰ ਵੱਖ-ਵੱਖ ਵਜਨ ਗੁਣਾਂ $\\alpha_{t,i}$ ਦੇ ਨਾਲ ਧਿਆਨ ਵਿੱਚ ਲਵਾਂਗੇ।\n", "\n", - "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਦੇ ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pa.png)\n", + "![ਇੱਕ ਐਨਕੋਡਰ/ਡਿਕੋਡਰ ਮਾਡਲ ਨੂੰ additive attention layer ਦੇ ਨਾਲ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) ਵਿੱਚ additive attention ਮਕੈਨਿਜ਼ਮ ਵਾਲਾ ਐਨਕੋਡਰ-ਡਿਕੋਡਰ ਮਾਡਲ, [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) ਤੋਂ ਲਿਆ ਗਿਆ।]*\n", "\n", "Attention ਮੈਟ੍ਰਿਕਸ $\\{\\alpha_{i,j}\\}$ ਇਹ ਦਰਸਾਏਗੀ ਕਿ ਕਿਸ ਹੱਦ ਤੱਕ ਕੁਝ ਇਨਪੁਟ ਸ਼ਬਦ ਆਉਟਪੁੱਟ ਕ੍ਰਮ ਵਿੱਚ ਦਿੱਤੇ ਗਏ ਸ਼ਬਦ ਦੇ ਜਨਰੇਸ਼ਨ ਵਿੱਚ ਭੂਮਿਕਾ ਨਿਭਾਉਂਦੇ ਹਨ। ਹੇਠਾਂ ਇਸ ਮੈਟ੍ਰਿਕਸ ਦਾ ਇੱਕ ਉਦਾਹਰਨ ਦਿੱਤਾ ਗਿਆ ਹੈ:\n", "\n", - "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pa.png)\n", + "![Bahdanau - arviz.org ਤੋਂ ਲਿਆ ਗਿਆ RNNsearch-50 ਦੁਆਰਾ ਪਾਈ ਗਈ ਇੱਕ ਨਮੂਨਾ ਅਲਾਈਨਮੈਂਟ ਦਿਖਾਉਂਦੀ ਚਿੱਤਰ](../../../../../translated_images/pa/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) ਤੋਂ ਲਿਆ ਗਿਆ ਚਿੱਤਰ]*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ਇੱਕ ਬਹੁਤ ਵੱਡਾ ਬਹੁ-ਪਰਤ ਟ੍ਰਾਂਸਫਾਰਮਰ ਨੈਟਵਰਕ ਹੈ ਜਿਸ ਵਿੱਚ *BERT-base* ਲਈ 12 ਪਰਤਾਂ ਹਨ ਅਤੇ *BERT-large* ਲਈ 24 ਪਰਤਾਂ। ਮਾਡਲ ਨੂੰ ਪਹਿਲਾਂ ਵੱਡੇ ਟੈਕਸਟ ਡਾਟਾ (WikiPedia + ਕਿਤਾਬਾਂ) ਦੇ ਕੋਰਪਸ 'ਤੇ ਅਨਸੁਪਰਵਾਈਜ਼ਡ ਟ੍ਰੇਨਿੰਗ (ਵਾਕ ਵਿੱਚ ਮਾਸਕ ਕੀਤੇ ਸ਼ਬਦਾਂ ਦੀ ਪੇਸ਼ਕਸ਼) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਪ੍ਰੀ-ਟ੍ਰੇਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਪ੍ਰੀ-ਟ੍ਰੇਨਿੰਗ ਦੌਰਾਨ, ਮਾਡਲ ਭਾਸ਼ਾ ਦੀ ਸਮਝ ਦੇ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਪੱਧਰ ਨੂੰ ਅਪਣਾਉਂਦਾ ਹੈ, ਜਿਸਨੂੰ ਫਿਰ ਫਾਈਨ ਟਿਊਨਿੰਗ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਹੋਰ ਡਾਟਾਸੈਟਾਂ ਨਾਲ ਲਾਗੂ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਇਸ ਪ੍ਰਕਿਰਿਆ ਨੂੰ **ਟ੍ਰਾਂਸਫਰ ਲਰਨਿੰਗ** ਕਿਹਾ ਜਾਂਦਾ ਹੈ।\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਤਸਵੀਰ](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pa.png)\n", + "![http://jalammar.github.io/illustrated-bert/ ਤੋਂ ਤਸਵੀਰ](../../../../../translated_images/pa/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ਟ੍ਰਾਂਸਫਾਰਮਰ ਆਰਕੀਟੈਕਚਰ ਦੇ ਕਈ ਰੂਪ ਹਨ ਜਿਵੇਂ ਕਿ BERT, DistilBERT, BigBird, OpenGPT3 ਅਤੇ ਹੋਰ, ਜਿਨ੍ਹਾਂ ਨੂੰ ਫਾਈਨ ਟਿਊਨ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ।\n", "\n", diff --git a/translations/pa/lessons/5-NLP/19-NER/README.md b/translations/pa/lessons/5-NLP/19-NER/README.md index 3a670b30..9a44ec40 100644 --- a/translations/pa/lessons/5-NLP/19-NER/README.md +++ b/translations/pa/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O ਕਿਉਂਕਿ ਸਾਨੂੰ ਟੋਕਨਾਂ ਅਤੇ ਵਰਗਾਂ ਦੇ ਵਿਚਕਾਰ ਇੱਕ-ਤੋਂ-ਇੱਕ ਸੰਬੰਧ ਬਣਾਉਣਾ ਹੁੰਦਾ ਹੈ, ਅਸੀਂ ਇਸ ਤਸਵੀਰ ਤੋਂ ਇੱਕ ਸਹੀ **ਬਹੁਤ-ਤੋਂ-ਬਹੁਤ** ਨਰਲ ਨੈੱਟਵਰਕ ਮਾਡਲ ਨੂੰ ਟ੍ਰੇਨ ਕਰ ਸਕਦੇ ਹਾਂ: -![ਸਧਾਰਨ ਰਿਕਰੰਟ ਨਰਲ ਨੈੱਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pa.jpg) +![ਸਧਾਰਨ ਰਿਕਰੰਟ ਨਰਲ ਨੈੱਟਵਰਕ ਪੈਟਰਨ ਦਿਖਾਉਂਦੀ ਤਸਵੀਰ।](../../../../../translated_images/pa/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ਤੋਂ [Andrej Karpathy](http://karpathy.github.io/) ਦੁਆਰਾ। NER ਟੋਕਨ ਵਰਗੀਕਰਨ ਮਾਡਲ ਇਸ ਤਸਵੀਰ ਦੇ ਸੱਜੇ ਪਾਸੇ ਵਾਲੇ ਨੈੱਟਵਰਕ ਆਰਕੀਟੈਕਚਰ ਨਾਲ ਮਿਲਦੇ ਹਨ।* diff --git a/translations/pa/lessons/5-NLP/README.md b/translations/pa/lessons/5-NLP/README.md index 8cb8d12d..233bf2cd 100644 --- a/translations/pa/lessons/5-NLP/README.md +++ b/translations/pa/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਨੈਚਰਲ ਲੈਂਗਵੇਜ ਪ੍ਰੋਸੈਸਿੰਗ -![NLP ਟਾਸਕਾਂ ਦਾ ਸਾਰ](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.pa.png) +![NLP ਟਾਸਕਾਂ ਦਾ ਸਾਰ](../../../../translated_images/pa/ai-nlp.b22dcb8ca4707cea.png) ਇਸ ਸੈਕਸ਼ਨ ਵਿੱਚ, ਅਸੀਂ **ਨੈਚਰਲ ਲੈਂਗਵੇਜ ਪ੍ਰੋਸੈਸਿੰਗ (NLP)** ਨਾਲ ਸੰਬੰਧਿਤ ਟਾਸਕਾਂ ਨੂੰ ਹੱਲ ਕਰਨ ਲਈ ਨਿਊਰਲ ਨੈਟਵਰਕਸ ਦੀ ਵਰਤੋਂ 'ਤੇ ਧਿਆਨ ਦੇਵਾਂਗੇ। ਬਹੁਤ ਸਾਰੇ NLP ਸਮੱਸਿਆਵਾਂ ਹਨ ਜਿਨ੍ਹਾਂ ਨੂੰ ਅਸੀਂ ਚਾਹੁੰਦੇ ਹਾਂ ਕਿ ਕੰਪਿਊਟਰ ਹੱਲ ਕਰ ਸਕਣ: diff --git a/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md index c6ac4dfe..d652126f 100644 --- a/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pa/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ ਮਾਡਲ ਖੋਲ੍ਹਣ ਤੋਂ ਬਾਅਦ, ਤੁਹਾਨੂੰ ਨੈਟਲੋਗੋ ਦੇ ਮੁੱਖ ਸਕ੍ਰੀਨ 'ਤੇ ਲਿਆਂਦਾ ਜਾਂਦਾ ਹੈ। ਇੱਥੇ ਇੱਕ ਨਮੂਨਾ ਮਾਡਲ ਹੈ ਜੋ ਵੁਲਫ਼ ਅਤੇ ਭੇਡਾਂ ਦੀ ਆਬਾਦੀ ਦਾ ਵਰਣਨ ਕਰਦਾ ਹੈ, ਜਦੋਂ ਕਿ ਸੰਸਾਧਨ ਸੀਮਤ ਹਨ (ਘਾਹ)। -![ਨੈਟਲੋਗੋ ਮੁੱਖ ਸਕ੍ਰੀਨ](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.pa.png) +![ਨੈਟਲੋਗੋ ਮੁੱਖ ਸਕ੍ਰੀਨ](../../../../../translated_images/pa/NetLogo-Main.32653711ec1a01b3.png) > ਦਿਮਿਤਰੀ ਸੋਸ਼ਨਿਕੋਵ ਦੁਆਰਾ ਸਕ੍ਰੀਨਸ਼ਾਟ diff --git a/translations/pa/lessons/README.md b/translations/pa/lessons/README.md index 65ee85ed..9a161824 100644 --- a/translations/pa/lessons/README.md +++ b/translations/pa/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਝਲਕ -![ਝਲਕ ਇੱਕ ਡੂਡਲ ਵਿੱਚ](../../../translated_images/ai-overview.0857791951d19500.pa.png) +![ਝਲਕ ਇੱਕ ਡੂਡਲ ਵਿੱਚ](../../../translated_images/pa/ai-overview.0857791951d19500.png) > ਸਕੈਚਨੋਟ [Tomomi Imura](https://twitter.com/girlie_mac) ਦੁਆਰਾ diff --git a/translations/pa/lessons/X-Extras/X1-MultiModal/README.md b/translations/pa/lessons/X-Extras/X1-MultiModal/README.md index 4a7e74cf..f25790d2 100644 --- a/translations/pa/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/pa/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP ਟਾਸਕਾਂ ਨੂੰ ਹੱਲ ਕਰਨ ਲਈ ਟ੍ਰਾਂਸ CLIP ਦਾ ਮੁੱਖ ਵਿਚਾਰ ਇਹ ਹੈ ਕਿ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟਸ ਨੂੰ ਇੱਕ ਚਿੱਤਰ ਨਾਲ ਤੁਲਨਾ ਕਰਨ ਅਤੇ ਇਹ ਨਿਰਧਾਰਤ ਕਰਨ ਦੀ ਸਮਰਥਾ ਹੋਵੇ ਕਿ ਚਿੱਤਰ ਪ੍ਰੌਮਪਟ ਨਾਲ ਕਿੰਨਾ ਚੰਗਾ ਮੇਲ ਖਾਂਦਾ ਹੈ। -![CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.pa.png) +![CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/clip-arch.b3dbf20b4e8ed8be.png) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://openai.com/blog/clip/) ਤੋਂ* @@ -31,7 +31,7 @@ CLIP ਮਾਡਲ/ਲਾਇਬ੍ਰੇਰੀ [OpenAI GitHub](https://github.com ਮੰਨ ਲਓ ਕਿ ਸਾਨੂੰ ਚਿੱਤਰਾਂ ਨੂੰ ਬਿੱਲੀਆਂ, ਕੁੱਤੇ ਅਤੇ ਮਨੁੱਖਾਂ ਵਿੱਚ ਵਰਗੀਕਰਣ ਦੀ ਲੋੜ ਹੈ। ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਅਸੀਂ ਮਾਡਲ ਨੂੰ ਇੱਕ ਚਿੱਤਰ ਅਤੇ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟਸ ਦੀ ਲੜੀ ਦੇ ਸਕਦੇ ਹਾਂ: "*ਬਿੱਲੀ ਦੀ ਤਸਵੀਰ*", "*ਕੁੱਤੇ ਦੀ ਤਸਵੀਰ*", "*ਮਨੁੱਖ ਦੀ ਤਸਵੀਰ*"। 3 ਸੰਭਾਵਨਾਵਾਂ ਦੇ ਨਤੀਜੇ ਵਾਲੇ ਵੈਕਟਰ ਵਿੱਚ ਸਿਰਫ ਸਭ ਤੋਂ ਉੱਚੇ ਮੁੱਲ ਵਾਲੇ ਇੰਡੈਕਸ ਨੂੰ ਚੁਣਨ ਦੀ ਲੋੜ ਹੈ। -![ਚਿੱਤਰ ਵਰਗੀਕਰਨ ਲਈ CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.pa.png) +![ਚਿੱਤਰ ਵਰਗੀਕਰਨ ਲਈ CLIP](../../../../../translated_images/pa/clip-class.3af42ef0b2b19369.png) > *ਤਸਵੀਰ [ਇਸ ਬਲੌਗ ਪੋਸਟ](https://openai.com/blog/clip/) ਤੋਂ* @@ -55,13 +55,13 @@ VQGAN ਬਾਰੇ ਹੋਰ ਜਾਣਕਾਰੀ [Taming Transformers](https:/ VQGAN ਅਤੇ ਪ੍ਰੰਪਰਾਗਤ GAN ਦੇ ਵਿਚਕਾਰ ਇੱਕ ਮਹੱਤਵਪੂਰਨ ਅੰਤਰ ਇਹ ਹੈ ਕਿ ਪੁਰਾਣਾ ਕਿਸੇ ਵੀ ਇਨਪੁਟ ਵੈਕਟਰ ਤੋਂ ਇੱਕ ਢੰਗ ਦਾ ਚਿੱਤਰ ਪੈਦਾ ਕਰ ਸਕਦਾ ਹੈ, ਜਦਕਿ VQGAN ਸੰਭਾਵਨਾ ਹੈ ਕਿ ਇੱਕ ਚਿੱਤਰ ਪੈਦਾ ਕਰੇ ਜੋ ਸੰਗਤਿ ਵਾਲਾ ਨਾ ਹੋਵੇ। ਇਸ ਲਈ, ਸਾਨੂੰ ਚਿੱਤਰ ਬਣਾਉਣ ਦੀ ਪ੍ਰਕਿਰਿਆ ਨੂੰ ਹੋਰ ਮਾਰਗਦਰਸ਼ਨ ਕਰਨ ਦੀ ਲੋੜ ਹੈ, ਅਤੇ ਇਹ CLIP ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। -![VQGAN+CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/vqgan.5027fe05051dfa31.pa.png) +![VQGAN+CLIP ਆਰਕੀਟੈਕਚਰ](../../../../../translated_images/pa/vqgan.5027fe05051dfa31.png) ਟੈਕਸਟ ਪ੍ਰੌਮਪਟ ਦੇ ਅਨੁਕੂਲ ਚਿੱਤਰ ਬਣਾਉਣ ਲਈ, ਅਸੀਂ ਕੁਝ ਰੈਂਡਮ ਐਨਕੋਡਿੰਗ ਵੈਕਟਰ ਨਾਲ ਸ਼ੁਰੂ ਕਰਦੇ ਹਾਂ ਜੋ VQGAN ਦੁਆਰਾ ਚਿੱਤਰ ਪੈਦਾ ਕਰਨ ਲਈ ਪਾਸ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। ਫਿਰ CLIP ਨੂੰ ਇੱਕ ਲੌਸ ਫੰਕਸ਼ਨ ਪੈਦਾ ਕਰਨ ਲਈ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ ਜੋ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਚਿੱਤਰ ਟੈਕਸਟ ਪ੍ਰੌਮਪਟ ਨਾਲ ਕਿੰਨਾ ਚੰਗਾ ਮੇਲ ਖਾਂਦਾ ਹੈ। ਫਿਰ ਉਦੇਸ਼ ਇਸ ਲੌਸ ਨੂੰ ਘਟਾਉਣਾ ਹੈ, ਬੈਕ ਪ੍ਰੋਪਾਗੇਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇਨਪੁਟ ਵੈਕਟਰ ਪੈਰਾਮੀਟਰਾਂ ਨੂੰ ਢਾਲਣਾ। VQGAN+CLIP ਨੂੰ ਲਾਗੂ ਕਰਨ ਵਾਲੀ ਇੱਕ ਸ਼ਾਨਦਾਰ ਲਾਇਬ੍ਰੇਰੀ [Pixray](http://github.com/pixray/pixray) ਹੈ। -![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.pa.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.pa.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.pa.png) +![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਸਾਹਿਤ ਦੇ ਇੱਕ ਨੌਜਵਾਨ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਜਲਰੰਗ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕਿਤਾਬ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਕੰਪਿਊਟਰ ਵਿਗਿਆਨ ਦੀ ਇੱਕ ਨੌਜਵਾਨ ਮਹਿਲਾ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕੰਪਿਊਟਰ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਗਣਿਤ ਦੇ ਇੱਕ ਬੁਜ਼ੁਰਗ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜੋ ਬਲੈਕਬੋਰਡ ਦੇ ਸਾਹਮਣੇ ਹੈ* @@ -77,7 +77,7 @@ CLIP ਦੇ ਉਲਟ, DALL-E ਟੈਕਸਟ ਅਤੇ ਚਿੱਤਰ ਨੂ DALL.E 1 ਅਤੇ 2 ਦੇ ਵਿਚਕਾਰ ਮੁੱਖ ਅੰਤਰ ਇਹ ਹੈ ਕਿ ਇਹ ਹੋਰ ਹਕੀਕਤਵਾਦੀ ਚਿੱਤਰ ਅਤੇ ਕਲਾ ਪੈਦਾ ਕਰਦਾ ਹੈ। DALL-E ਨਾਲ ਚਿੱਤਰ ਜਨਰੇਸ਼ਨ ਦੇ ਉਦਾਹਰਨ: -![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.pa.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.pa.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.pa.png) +![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਤਸਵੀਰ](../../../../../translated_images/pa/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਸਾਹਿਤ ਦੇ ਇੱਕ ਨੌਜਵਾਨ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਜਲਰੰਗ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕਿਤਾਬ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਕੰਪਿਊਟਰ ਵਿਗਿਆਨ ਦੀ ਇੱਕ ਨੌਜਵਾਨ ਮਹਿਲਾ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜਿਸਦੇ ਕੋਲ ਇੱਕ ਕੰਪਿਊਟਰ ਹੈ* | ਪ੍ਰੌਮਪਟ ਤੋਂ ਬਣਾਈ ਗਈ ਤਸਵੀਰ *ਗਣਿਤ ਦੇ ਇੱਕ ਬੁਜ਼ੁਰਗ ਪੁਰਸ਼ ਅਧਿਆਪਕ ਦਾ ਇੱਕ ਤੇਲ ਪੋਰਟਰੇਟ ਜੋ ਬਲੈਕਬੋਰਡ ਦੇ ਸਾਹਮਣੇ ਹੈ* diff --git a/translations/pl/README.md b/translations/pl/README.md index 8fe72cd1..b5e395cb 100644 --- a/translations/pl/README.md +++ b/translations/pl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificial Intelligence for Beginners - Program nauczania -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.pl.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pl/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnotka autorstwa [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/pl/lessons/1-Intro/README.md b/translations/pl/lessons/1-Intro/README.md index 06026424..723394c8 100644 --- a/translations/pl/lessons/1-Intro/README.md +++ b/translations/pl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wprowadzenie do AI -![Podsumowanie treści wprowadzenia do AI w formie rysunku](../../../../translated_images/ai-intro.bf28d1ac4235881c.pl.png) +![Podsumowanie treści wprowadzenia do AI w formie rysunku](../../../../translated_images/pl/ai-intro.bf28d1ac4235881c.png) > Rysunek odręczny autorstwa [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pierwotnie komputery zostały wynalezione przez [Charlesa Babbage'a](https://en.wikipedia.org/wiki/Charles_Babbage), aby operować na liczbach zgodnie z dobrze zdefiniowaną procedurą – algorytmem. Współczesne komputery, choć znacznie bardziej zaawansowane niż pierwotny model zaproponowany w XIX wieku, nadal opierają się na tej samej idei kontrolowanych obliczeń. Dlatego możliwe jest zaprogramowanie komputera do wykonania czegoś, jeśli znamy dokładną sekwencję kroków potrzebnych do osiągnięcia celu. -![Zdjęcie osoby](../../../../translated_images/dsh_age.d212a30d4e54fb5f.pl.png) +![Zdjęcie osoby](../../../../translated_images/pl/dsh_age.d212a30d4e54fb5f.png) > Zdjęcie autorstwa [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Więcej informacji znajdziesz w **[Ogólnej Sztucznej Inteligencji](https://en.w Jednym z problemów związanych z terminem **[Inteligencja](https://en.wikipedia.org/wiki/Intelligence)** jest brak jasnej definicji tego pojęcia. Można argumentować, że inteligencja jest związana z **myśleniem abstrakcyjnym** lub **samoświadomością**, ale nie potrafimy jej właściwie zdefiniować. -![Zdjęcie kota](../../../../translated_images/photo-cat.8c8e8fb760ffe457.pl.jpg) +![Zdjęcie kota](../../../../translated_images/pl/photo-cat.8c8e8fb760ffe457.jpg) > [Zdjęcie](https://unsplash.com/photos/75715CVEJhI) autorstwa [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternatywnie, możemy spróbować modelować najprostsze elementy w naszym móz > | A co z ML? | | > |--------------|-----------| -> | Część Sztucznej Inteligencji, która opiera się na uczeniu komputera rozwiązywania problemu na podstawie danych, nazywa się **Uczeniem Maszynowym**. Nie będziemy rozważać klasycznego uczenia maszynowego w tym kursie – odsyłamy Cię do osobnego programu [Uczenie Maszynowe dla Początkujących](http://aka.ms/ml-beginners). | ![ML dla Początkujących](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.pl.png) | +> | Część Sztucznej Inteligencji, która opiera się na uczeniu komputera rozwiązywania problemu na podstawie danych, nazywa się **Uczeniem Maszynowym**. Nie będziemy rozważać klasycznego uczenia maszynowego w tym kursie – odsyłamy Cię do osobnego programu [Uczenie Maszynowe dla Początkujących](http://aka.ms/ml-beginners). | ![ML dla Początkujących](../../../../translated_images/pl/ml-for-beginners.9e4fed176fd5817d.png) | ## Krótka historia AI Sztuczna Inteligencja jako dziedzina rozpoczęła się w połowie XX wieku. Początkowo podejście symboliczne było dominujące i doprowadziło do wielu ważnych sukcesów, takich jak systemy ekspertowe – programy komputerowe, które były w stanie działać jako ekspert w ograniczonych dziedzinach problemowych. Jednak szybko stało się jasne, że takie podejście nie jest skalowalne. Wydobycie wiedzy od eksperta, reprezentowanie jej w komputerze i utrzymanie tej bazy wiedzy w aktualności okazuje się bardzo złożonym zadaniem i zbyt kosztownym, aby było praktyczne w wielu przypadkach. Doprowadziło to do tzw. [Zimy AI](https://en.wikipedia.org/wiki/AI_winter) w latach 70. -Krótka historia AI +Krótka historia AI > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobnie możemy zobaczyć, jak zmieniało się podejście do tworzenia „progr * Współczesne asystenty, takie jak Cortana, Siri czy Google Assistant, to hybrydowe systemy, które wykorzystują sieci neuronowe do konwersji mowy na tekst i rozpoznawania naszych intencji, a następnie stosują pewne rozumowanie lub explicite algorytmy do wykonywania wymaganych działań. * W przyszłości możemy oczekiwać pełnego modelu opartego na sieciach neuronowych, który samodzielnie obsłuży dialog. Ostatnie sieci neuronowe GPT i [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) wykazują wielkie sukcesy w tym zakresie. -Ewolucja testu Turinga +Ewolucja testu Turinga > Obraz autorstwa Dmitry Soshnikov, [zdjęcie](https://unsplash.com/photos/r8LmVbUKgns) autorstwa [Mariny Abrosimovej](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Ostatnie badania nad sztuczną inteligencją diff --git a/translations/pl/lessons/2-Symbolic/README.md b/translations/pl/lessons/2-Symbolic/README.md index 988e01c3..ce553e3a 100644 --- a/translations/pl/lessons/2-Symbolic/README.md +++ b/translations/pl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentacja wiedzy i systemy ekspertowe -![Podsumowanie treści o Symbolicznym AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.pl.png) +![Podsumowanie treści o Symbolicznym AI](../../../../translated_images/pl/ai-symbolic.715a30cb610411a6.png) > Sketchnote autorstwa [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najczęściej nie definiujemy wiedzy w sposób ścisły, ale zestawiamy ją z in Problem **reprezentacji wiedzy** polega więc na znalezieniu skutecznego sposobu reprezentowania wiedzy w komputerze w formie danych, aby była automatycznie użyteczna. Można to postrzegać jako spektrum: -![Spektrum reprezentacji wiedzy](../../../../translated_images/knowledge-spectrum.b60df631852c0217.pl.png) +![Spektrum reprezentacji wiedzy](../../../../translated_images/pl/knowledge-spectrum.b60df631852c0217.png) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Składnia blokowa | Wcięcia | | | Jednym z wczesnych sukcesów symbolicznego AI były tzw. **systemy ekspertowe** - systemy komputerowe zaprojektowane do działania jako ekspert w ograniczonej dziedzinie problemowej. Opierały się na **bazie wiedzy** wydobytej od jednego lub więcej ludzkich ekspertów i zawierały **silnik wnioskowania**, który wykonywał wnioskowanie na jej podstawie. -![Architektura człowieka](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.pl.png) | ![System oparty na wiedzy](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.pl.png) +![Architektura człowieka](../../../../translated_images/pl/arch-human.5d4d35f1bba3ab1c.png) | ![System oparty na wiedzy](../../../../translated_images/pl/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Uproszczona struktura ludzkiego układu nerwowego | Architektura systemu opartego na wiedzy @@ -106,7 +106,7 @@ Systemy ekspertowe są zbudowane podobnie jak system wnioskowania człowieka, kt Na przykład rozważmy następujący system ekspertowy do określania zwierzęcia na podstawie jego cech fizycznych: -![Drzewo AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.pl.png) +![Drzewo AND-OR](../../../../translated_images/pl/AND-OR-Tree.5592d2c70187f283.png) > Obraz autorstwa [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md index b431ed11..9758aaab 100644 --- a/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Nadmierne dopasowanie to niezwykle ważne pojęcie w uczeniu maszynowym i bardzo Rozważmy następujący problem aproksymacji 5 punktów (reprezentowanych przez `x` na poniższych wykresach): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.pl.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.pl.jpg) +![linear](../../../../../translated_images/pl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pl/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Model liniowy, 2 parametry** | **Model nieliniowy, 7 parametrów** Błąd treningowy = 5.3 | Błąd treningowy = 0 @@ -79,7 +79,7 @@ Bardzo ważne jest znalezienie odpowiedniej równowagi między złożonością m Jak widać na powyższym wykresie, nadmierne dopasowanie można wykryć po bardzo niskim błędzie treningowym i wysokim błędzie walidacyjnym. Zazwyczaj podczas treningu widzimy, że zarówno błędy treningowe, jak i walidacyjne zaczynają się zmniejszać, a następnie w pewnym momencie błąd walidacyjny może przestać się zmniejszać i zacząć rosnąć. To będzie oznaka nadmiernego dopasowania i wskazówka, że powinniśmy prawdopodobnie zatrzymać trening w tym momencie (lub przynajmniej zrobić migawkę modelu). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.pl.png) +![overfitting](../../../../../translated_images/pl/Overfitting.408ad91cd90b4371.png) ## Jak zapobiegać nadmiernemu dopasowaniu diff --git a/translations/pl/lessons/3-NeuralNetworks/README.md b/translations/pl/lessons/3-NeuralNetworks/README.md index 4092140b..5fc804d2 100644 --- a/translations/pl/lessons/3-NeuralNetworks/README.md +++ b/translations/pl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wprowadzenie do sieci neuronowych -![Podsumowanie treści wprowadzających do sieci neuronowych w formie rysunku](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.pl.png) +![Podsumowanie treści wprowadzających do sieci neuronowych w formie rysunku](../../../../translated_images/pl/ai-neuralnetworks.1c687ae40bc86e83.png) Jak omówiliśmy we wstępie, jednym ze sposobów osiągnięcia inteligencji jest trenowanie **modelu komputerowego** lub **sztucznego mózgu**. Od połowy XX wieku badacze próbowali różnych modeli matematycznych, aż w ostatnich latach ten kierunek okazał się niezwykle skuteczny. Takie matematyczne modele mózgu nazywane są **sieciami neuronowymi**. @@ -36,13 +36,13 @@ W tym programie nauczania skupimy się wyłącznie na modelach sieci neuronowych Z biologii wiemy, że nasz mózg składa się z komórek nerwowych (neuronów), z których każda ma wiele "wejść" (dendrytów) i jedno "wyjście" (akson). Zarówno dendryty, jak i aksony mogą przewodzić sygnały elektryczne, a połączenia między nimi — znane jako synapsy — mogą wykazywać różne stopnie przewodnictwa, które są regulowane przez neuroprzekaźniki. -![Model neuronu](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.pl.jpg) | ![Model neuronu](../../../../translated_images/artneuron.1a5daa88d20ebe6f.pl.png) +![Model neuronu](../../../../translated_images/pl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neuronu](../../../../translated_images/pl/artneuron.1a5daa88d20ebe6f.png) ----|---- Prawdziwy neuron *([Obraz](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipedii)* | Sztuczny neuron *(Obraz autora)* Najprostszy matematyczny model neuronu zawiera kilka wejść X1, ..., XN oraz jedno wyjście Y, a także serię wag W1, ..., WN. Wyjście obliczane jest jako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) gdzie f jest pewną nieliniową **funkcją aktywacji**. diff --git a/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md index a93e8a6f..b35e112a 100644 --- a/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ W naszym [OpenCV Notebook](OpenCV.ipynb) przedstawiamy kilka przykładów, kiedy * **Wstępne przetwarzanie fotografii książki Braille'a**. Skupiamy się na tym, jak można użyć progowania, detekcji cech, transformacji perspektywicznej i manipulacji NumPy, aby oddzielić pojedyncze symbole Braille'a do dalszej klasyfikacji przez sieć neuronową. -![Obraz Braille'a](../../../../../translated_images/braille.341962ff76b1bd70.pl.jpeg) | ![Obraz Braille'a po przetworzeniu](../../../../../translated_images/braille-result.46530fea020b03c7.pl.png) | ![Symbole Braille'a](../../../../../translated_images/braille-symbols.0159185ab69d5339.pl.png) +![Obraz Braille'a](../../../../../translated_images/pl/braille.341962ff76b1bd70.jpeg) | ![Obraz Braille'a po przetworzeniu](../../../../../translated_images/pl/braille-result.46530fea020b03c7.png) | ![Symbole Braille'a](../../../../../translated_images/pl/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Obraz z [OpenCV.ipynb](OpenCV.ipynb) * **Detekcja ruchu w wideo za pomocą różnicy klatek**. Jeśli kamera jest nieruchoma, klatki z jej strumienia powinny być dość podobne do siebie. Ponieważ klatki są reprezentowane jako tablice, wystarczy odjąć te tablice dla dwóch kolejnych klatek, aby uzyskać różnicę pikseli, która powinna być niska dla statycznych klatek, a wyższa, gdy w obrazie występuje znaczący ruch. -![Obraz klatek wideo i różnic klatek](../../../../../translated_images/frame-difference.706f805491a0883c.pl.png) +![Obraz klatek wideo i różnic klatek](../../../../../translated_images/pl/frame-difference.706f805491a0883c.png) > Obraz z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ W naszym [OpenCV Notebook](OpenCV.ipynb) przedstawiamy kilka przykładów, kiedy - **Gęsty optyczny przepływ** oblicza pole wektorowe, które pokazuje, gdzie każdy piksel się porusza. - **Rzadki optyczny przepływ** opiera się na wybraniu charakterystycznych cech obrazu (np. krawędzi) i budowaniu ich trajektorii od klatki do klatki. -![Obraz optycznego przepływu](../../../../../translated_images/optical.1f4a94464579a83a.pl.png) +![Obraz optycznego przepływu](../../../../../translated_images/pl/optical.1f4a94464579a83a.png) > Obraz z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d1afc1ae..8d7d47ef 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 to sieć, która osiągnęła 92,7% dokładności w klasyfikacji top-5 ImageNet w 2014 roku. Ma następującą strukturę warstw: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pl.jpg) +![ImageNet Layers](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.jpg) Jak widać, VGG stosuje tradycyjną architekturę piramidy, czyli sekwencję warstw konwolucyjnych i poolingowych. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pl.jpg) +![ImageNet Pyramid](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.jpg) > Obraz z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md index 95a2afd3..7956119e 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ W rzeczywistości chcemy być w stanie rozpoznawać obiekty na zdjęciu niezale Aby wyodrębnić wzory, użyjemy pojęcia **filtrów konwolucyjnych**. Jak wiadomo, obraz jest reprezentowany jako macierz 2D lub tensor 3D z głębią kolorów. Zastosowanie filtra oznacza, że bierzemy stosunkowo małą macierz **jądra filtra** i dla każdego piksela w oryginalnym obrazie obliczamy średnią ważoną z sąsiednich punktów. Możemy to sobie wyobrazić jako małe okno przesuwające się po całym obrazie, uśredniające wszystkie piksele zgodnie z wagami w macierzy jądra filtra. -![Filtr krawędzi pionowych](../../../../../translated_images/filter-vert.b7148390ca0bc356.pl.png) | ![Filtr krawędzi poziomych](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.pl.png) +![Filtr krawędzi pionowych](../../../../../translated_images/pl/filter-vert.b7148390ca0bc356.png) | ![Filtr krawędzi poziomych](../../../../../translated_images/pl/filter-horiz.59b80ed4feb946ef.png) ----|---- > Obraz autorstwa Dmitry Soshnikov @@ -38,7 +38,7 @@ Działanie CNN opiera się na następujących ważnych założeniach: * Możemy zaprojektować sieć w taki sposób, aby filtry były trenowane automatycznie * Możemy użyć tego samego podejścia do znajdowania wzorów w cechach wysokiego poziomu, a nie tylko w oryginalnym obrazie. W ten sposób ekstrakcja cech w CNN działa na hierarchii cech, zaczynając od kombinacji pikseli niskiego poziomu, aż do kombinacji części obrazu na wyższym poziomie. -![Hierarchiczna Ekstrakcja Cech](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.pl.png) +![Hierarchiczna Ekstrakcja Cech](../../../../../translated_images/pl/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Obraz z [artykułu Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), opartego na [ich badaniach](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Większość CNN używanych do przetwarzania obrazów stosuje tzw. architekturę Na przykład, spójrzmy na architekturę VGG-16, sieci, która osiągnęła 92,7% dokładności w klasyfikacji top-5 ImageNet w 2014 roku: -![Warstwy ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pl.jpg) +![Warstwy ImageNet](../../../../../translated_images/pl/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pl.jpg) +![Piramida ImageNet](../../../../../translated_images/pl/vgg-16-arch.64ff2137f50dd49f.jpg) > Obraz z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 93c630e6..ad58007f 100644 --- a/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Twoim zadaniem jest wytrenowanie konwolucyjnej sieci neuronowej do klasyfikacji Użyjemy [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), który zawiera obrazy 37 różnych ras psów i kotów. -![Zestaw danych, z którym będziemy pracować](../../../../../../translated_images/data.50b2a9d5484bdbf0.pl.png) +![Zestaw danych, z którym będziemy pracować](../../../../../../translated_images/pl/data.50b2a9d5484bdbf0.png) Aby pobrać zestaw danych, użyj tego fragmentu kodu: diff --git a/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md index 872cd689..14d610a8 100644 --- a/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Zarówno Keras, jak i PyTorch zawierają funkcje umożliwiające łatwe ładowan Oto przykładowe cechy wyodrębnione z obrazu kota przez sieć VGG-16: -![Cechy wyodrębnione przez VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.pl.png) +![Cechy wyodrębnione przez VGG-16](../../../../../translated_images/pl/features.6291f9c7ba3a0b95.png) ## Zbiór danych Koty vs. Psy @@ -48,19 +48,19 @@ Wstępnie wytrenowana sieć neuronowa zawiera różne wzorce w swoim *mózgu*, w Jednym z podejść, które możemy zastosować, jest rozpoczęcie od losowego obrazu, a następnie próba użycia techniki **optymalizacji metodą gradientu** w celu dostosowania tego obrazu w taki sposób, aby sieć zaczęła myśleć, że to kot. -![Pętla optymalizacji obrazu](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.pl.png) +![Pętla optymalizacji obrazu](../../../../../translated_images/pl/ideal-cat-loop.999fbb8ff306e044.png) Jednak jeśli to zrobimy, otrzymamy coś bardzo podobnego do losowego szumu. Dzieje się tak, ponieważ *istnieje wiele sposobów, aby sieć myślała, że obraz wejściowy to kot*, w tym takie, które nie mają sensu wizualnie. Chociaż te obrazy zawierają wiele wzorców typowych dla kota, nic nie zmusza ich do bycia wizualnie wyraźnymi. Aby poprawić wynik, możemy dodać kolejny składnik do funkcji straty, który nazywa się **stratą wariacji**. Jest to metryka pokazująca, jak podobne są sąsiadujące piksele obrazu. Minimalizowanie straty wariacji sprawia, że obraz staje się bardziej gładki i pozbywa się szumu - ujawniając bardziej atrakcyjne wizualnie wzorce. Oto przykład takich "idealnych" obrazów, które są klasyfikowane jako kot i jako zebra z dużym prawdopodobieństwem: -![Idealny kot](../../../../../translated_images/ideal-cat.203dd4597643d6b0.pl.png) | ![Idealna zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.pl.png) +![Idealny kot](../../../../../translated_images/pl/ideal-cat.203dd4597643d6b0.png) | ![Idealna zebra](../../../../../translated_images/pl/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Idealny kot* | *Idealna zebra* Podobne podejście można zastosować do przeprowadzania tzw. **ataków adversarialnych** na sieć neuronową. Załóżmy, że chcemy oszukać sieć neuronową i sprawić, by pies wyglądał jak kot. Jeśli weźmiemy obraz psa, który jest rozpoznawany przez sieć jako pies, możemy go nieco zmodyfikować za pomocą optymalizacji metodą gradientu, aż sieć zacznie klasyfikować go jako kota: -![Obraz psa](../../../../../translated_images/original-dog.8f68a67d2fe0911f.pl.png) | ![Obraz psa klasyfikowany jako kot](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.pl.png) +![Obraz psa](../../../../../translated_images/pl/original-dog.8f68a67d2fe0911f.png) | ![Obraz psa klasyfikowany jako kot](../../../../../translated_images/pl/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Oryginalny obraz psa* | *Obraz psa klasyfikowany jako kot* diff --git a/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md index 9b9722e0..c60240a9 100644 --- a/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Możemy jednak chcieć wykorzystać surowe (nieoznaczone) dane do trenowania eks Ponieważ trenujemy autoenkoder, aby uchwycić jak najwięcej informacji z oryginalnego obrazu w celu dokładnej rekonstrukcji, sieć stara się znaleźć najlepsze **osadzenie** obrazów wejściowych, aby uchwycić ich znaczenie. -![Schemat Autoenkodera](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.pl.jpg) +![Schemat Autoenkodera](../../../../../translated_images/pl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Obraz z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md index 76110cbb..dc93e14d 100644 --- a/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modele klasyfikacji obrazów, które omawialiśmy do tej pory, przyjmowały obra ## [Quiz przed wykładem](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Wykrywanie Obiektów](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.pl.png) +![Wykrywanie Obiektów](../../../../../translated_images/pl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Obraz z [witryny YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Załóżmy, że chcemy znaleźć kota na zdjęciu. Bardzo naiwne podejście do w 2. Uruchom klasyfikację obrazu na każdym kafelku. 3. Kafelki, które dają wystarczająco wysoką aktywację, można uznać za zawierające poszukiwany obiekt. -![Naiwne Wykrywanie Obiektów](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.pl.png) +![Naiwne Wykrywanie Obiektów](../../../../../translated_images/pl/naive-detection.e7f1ba220ccd08c6.png) > *Obraz z [notatnika ćwiczeniowego](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Możesz natknąć się na następujące zbiory danych do tego zadania: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 klas * [COCO](http://cocodataset.org/#home) – Common Objects in Context. 80 klas, ramki ograniczające i maski segmentacji -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.pl.jpg) +![COCO](../../../../../translated_images/pl/coco-examples.71bc60380fa6cceb.jpg) ## Metryki wykrywania obiektów @@ -50,7 +50,7 @@ Możesz natknąć się na następujące zbiory danych do tego zadania: Podczas gdy w klasyfikacji obrazów łatwo jest zmierzyć, jak dobrze działa algorytm, w wykrywaniu obiektów musimy ocenić zarówno poprawność klasy, jak i precyzję lokalizacji przewidywanej ramki ograniczającej. Do tego ostatniego używamy tzw. **Intersection over Union** (IoU), które mierzy, jak dobrze dwie ramki (lub dwa dowolne obszary) się pokrywają. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.pl.png) +![IoU](../../../../../translated_images/pl/iou_equation.9a4751d40fff4e11.png) > *Rysunek 2 z [tego doskonałego wpisu na blogu o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Istnieją dwie główne klasy algorytmów wykrywania obiektów: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) wykorzystuje [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) do generowania hierarchicznej struktury regionów ROI, które następnie są przetwarzane przez ekstraktory cech CNN i klasyfikatory SVM w celu określenia klasy obiektu oraz regresję liniową w celu określenia współrzędnych *ramki ograniczającej*. [Oficjalny artykuł](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.pl.png) +![RCNN](../../../../../translated_images/pl/rcnn1.cae407020dfb1d1f.png) > *Obraz z van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.pl.png) +![RCNN-1](../../../../../translated_images/pl/rcnn2.2d9530bb83516484.png) > *Obrazy z [tego bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Istnieją dwie główne klasy algorytmów wykrywania obiektów: To podejście jest podobne do R-CNN, ale regiony są definiowane po zastosowaniu warstw konwolucyjnych. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.pl.png) +![FRCNN](../../../../../translated_images/pl/f-rcnn.3cda6d9bb4188875.png) > Obraz z [oficjalnego artykułu](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ To podejście jest podobne do R-CNN, ale regiony są definiowane po zastosowaniu Główna idea tego podejścia polega na użyciu sieci neuronowej do przewidywania ROI – tzw. *Region Proposal Network*. [Artykuł](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.pl.png) +![FasterRCNN](../../../../../translated_images/pl/faster-rcnn.8d46c099b87ef30a.png) > Obraz z [oficjalnego artykułu](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ten algorytm jest jeszcze szybszy niż Faster R-CNN. Główna idea jest następu 2. Cechy są przetwarzane przez **Position-Sensitive Score Map**. Każdy obiekt z $C$ klas jest dzielony na $k\times k$ regiony, a sieć jest trenowana do przewidywania części obiektów. 3. Dla każdej części z $k\times k$ regionów wszystkie sieci głosują na klasy obiektów, a klasa z największą liczbą głosów jest wybierana. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.pl.png) +![r-fcn image](../../../../../translated_images/pl/r-fcn.13eb88158b99a3da.png) > Obraz z [oficjalnego artykułu](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO to algorytm jednoprzebiegowy w czasie rzeczywistym. Główna idea jest nast * Obraz jest dzielony na $S\times S$ regiony. * Dla każdego regionu **CNN** przewiduje $n$ możliwych obiektów, współrzędne *ramki ograniczającej* oraz *pewność* = *prawdopodobieństwo* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.pl.png) + ![YOLO](../../../../../translated_images/pl/yolo.a2648ec82ee8bb4e.png) > Obraz z [oficjalnego artykułu](https://arxiv.org/abs/1506.02640) diff --git a/translations/pl/lessons/5-NLP/14-Embeddings/README.md b/translations/pl/lessons/5-NLP/14-Embeddings/README.md index 2e4d404f..dd3a600b 100644 --- a/translations/pl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Warstwa osadzenia przyjmuje słowo jako wejście i generuje wektor wyjściowy o Używając warstwy osadzenia jako pierwszej warstwy w naszej sieci klasyfikatora, możemy przejść od modelu bag-of-words do modelu **embedding bag**, gdzie najpierw konwertujemy każde słowo w naszym tekście na odpowiadające mu osadzenie, a następnie obliczamy pewną funkcję agregującą dla wszystkich tych osadzeń, taką jak `sum`, `average` lub `max`. -![Obraz przedstawiający klasyfikator osadzeń dla pięciu słów w sekwencji.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.pl.png) +![Obraz przedstawiający klasyfikator osadzeń dla pięciu słów w sekwencji.](../../../../../translated_images/pl/embedding-classifier-example.b77f021a7ee67eee.png) > Obraz autorstwa autora @@ -40,7 +40,7 @@ Aby to osiągnąć, musimy wstępnie wytrenować nasz model osadzenia na dużym CBoW działa szybciej, podczas gdy skip-gram jest wolniejszy, ale lepiej reprezentuje rzadkie słowa. -![Obraz przedstawiający algorytmy CBoW i Skip-Gram do konwersji słów na wektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pl.png) +![Obraz przedstawiający algorytmy CBoW i Skip-Gram do konwersji słów na wektory.](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obraz z [tego artykułu](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md index d259b37a..c3df220c 100644 --- a/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ W naszych wcześniejszych przykładach korzystaliśmy z wstępnie wytrenowanych * **Continuous Bag-of-Words** (CBoW), gdzie przewidujemy środkowy token $W_0$ w sekwencji tokenów $W_{-N}$, ..., $W_N$. * **Skip-gram**, gdzie przewidujemy zestaw sąsiednich tokenów {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} na podstawie środkowego tokena $W_0$. -![obraz z artykułu o konwersji słów na wektory](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pl.png) +![obraz z artykułu o konwersji słów na wektory](../../../../../translated_images/pl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obraz z [tego artykułu](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pl/lessons/5-NLP/16-RNN/README.md b/translations/pl/lessons/5-NLP/16-RNN/README.md index 3828c8f3..b9394854 100644 --- a/translations/pl/lessons/5-NLP/16-RNN/README.md +++ b/translations/pl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ W poprzednich sekcjach korzystaliśmy z bogatych semantycznych reprezentacji tek Aby uchwycić znaczenie sekwencji tekstu, musimy użyć innej architektury sieci neuronowej, zwanej **siecią neuronową rekurencyjną** (RNN). W RNN przekazujemy nasze zdanie przez sieć symbol po symbolu, a sieć generuje pewien **stan**, który następnie przekazujemy z kolejnym symbolem. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.pl.png) +![RNN](../../../../../translated_images/pl/rnn.27f5c29c53d727b5.png) > Obraz autorstwa autora @@ -61,7 +61,7 @@ Omówiliśmy sieci rekurencyjne, które działają w jednym kierunku, od począt Sieć rekurencyjna, czy to jednokierunkowa, czy dwukierunkowa, wychwytuje pewne wzorce w sekwencji i może je przechowywać w wektorze stanu lub przekazywać na wyjście. Podobnie jak w przypadku sieci konwolucyjnych, możemy zbudować kolejną warstwę rekurencyjną na szczycie pierwszej, aby uchwycić wzorce wyższego poziomu i budować na bazie wzorców niskiego poziomu wyodrębnionych przez pierwszą warstwę. Prowadzi to do pojęcia **wielowarstwowego RNN**, który składa się z dwóch lub więcej sieci rekurencyjnych, gdzie wyjście poprzedniej warstwy jest przekazywane jako wejście do następnej warstwy. -![Obraz przedstawiający wielowarstwowy LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.pl.jpg) +![Obraz przedstawiający wielowarstwowy LSTM RNN](../../../../../translated_images/pl/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Obraz z [tego wspaniałego artykułu](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) autorstwa Fernando Lópeza* diff --git a/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md index bd73b1df..f623a0e5 100644 --- a/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ W architekturze RNN, którą omawialiśmy w poprzedniej jednostce, każda jednos To umożliwia różne architektury sieci neuronowych, które przedstawiono na poniższym obrazku: -![Obraz przedstawiający typowe wzorce rekurencyjnych sieci neuronowych.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pl.jpg) +![Obraz przedstawiający typowe wzorce rekurencyjnych sieci neuronowych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Obraz z wpisu na blogu [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorstwa [Andreja Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ W tej jednostce skupimy się na prostych modelach generatywnych, które pomagaj Wytrenujemy tę RNN do generowania tekstu krok po kroku. Na każdym kroku weźmiemy sekwencję znaków o długości `nchars` i poprosimy sieć o wygenerowanie kolejnego znaku wyjściowego dla każdego znaku wejściowego: -![Obraz przedstawiający przykład generowania słowa 'HELLO' przez RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.pl.png) +![Obraz przedstawiający przykład generowania słowa 'HELLO' przez RNN.](../../../../../translated_images/pl/rnn-generate.56c54afb52f9781d.png) Podczas generowania tekstu (w trakcie inferencji) zaczynamy od jakiegoś **podpowiedzi** (prompt), która jest przepuszczana przez komórki RNN, aby wygenerować jej stan pośredni, a następnie z tego stanu rozpoczyna się generowanie. Generujemy jeden znak na raz, przekazujemy stan i wygenerowany znak do kolejnej komórki RNN, aby wygenerować następny znak, aż wygenerujemy wystarczającą liczbę znaków. diff --git a/translations/pl/lessons/5-NLP/18-Transformers/README.md b/translations/pl/lessons/5-NLP/18-Transformers/README.md index 529389b7..897dd868 100644 --- a/translations/pl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ W przypadku RNN, zadania sequence-to-sequence są realizowane za pomocą dwóch **Mechanizmy uwagi** umożliwiają ważenie kontekstowego wpływu każdego wektora wejściowego na każdą prognozę wyjściową RNN. Implementuje się to poprzez tworzenie skrótów między stanami pośrednimi wejściowego RNN a wyjściowego RNN. W ten sposób, generując symbol wyjściowy yt, uwzględniamy wszystkie stany ukryte wejścia hi, z różnymi współczynnikami wagowymi αt,i. -![Obraz przedstawiający model enkoder/dekoder z warstwą uwagi addytywnej](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pl.png) +![Obraz przedstawiający model enkoder/dekoder z warstwą uwagi addytywnej](../../../../../translated_images/pl/encoder-decoder-attention.7a726296894fb567.png) > Model enkoder-dekoder z mechanizmem uwagi addytywnej w [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), cytowany z [tego wpisu na blogu](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Macierz uwagi {αi,j} reprezentuje stopień, w jakim określone słowa wejściowe wpływają na generowanie danego słowa w sekwencji wyjściowej. Poniżej znajduje się przykład takiej macierzy: -![Obraz przedstawiający przykładowe wyrównanie znalezione przez RNNsearch-50, zaczerpnięte z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pl.png) +![Obraz przedstawiający przykładowe wyrównanie znalezione przez RNNsearch-50, zaczerpnięte z Bahdanau - arviz.org](../../../../../translated_images/pl/bahdanau-fig3.09ba2d37f202a6af.png) > Rysunek z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Wynik, który uzyskujemy dzięki osadzaniu pozycji, osadza zarówno oryginalny t Następnie musimy wychwycić pewne wzorce w naszej sekwencji. Aby to zrobić, modele Transformer używają mechanizmu **samo-uwagi**, który w zasadzie jest uwagą zastosowaną do tej samej sekwencji jako wejście i wyjście. Zastosowanie samo-uwagi pozwala nam uwzględnić **kontekst** w zdaniu i zobaczyć, które słowa są ze sobą powiązane. Na przykład pozwala nam zobaczyć, które słowa są odniesieniami do innych, takich jak *to*, oraz uwzględnić kontekst: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.pl.png) +![](../../../../../translated_images/pl/CoreferenceResolution.861924d6d384a7d6.png) > Obraz z [Bloga Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Ponieważ każda pozycja wejściowa jest mapowana niezależnie na każdą pozycj **BERT** (Bidirectional Encoder Representations from Transformers) to bardzo duża wielowarstwowa sieć Transformer z 12 warstwami dla *BERT-base* i 24 dla *BERT-large*. Model jest najpierw wstępnie trenowany na dużym korpusie danych tekstowych (Wikipedia + książki) za pomocą treningu niesuperwizowanego (przewidywanie zamaskowanych słów w zdaniu). Podczas wstępnego treningu model przyswaja znaczące poziomy zrozumienia języka, które można następnie wykorzystać z innymi zestawami danych za pomocą dostrajania. Ten proces nazywa się **transfer learning**. -![obrazek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pl.png) +![obrazek z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Źródło obrazu [tutaj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pl/lessons/5-NLP/19-NER/README.md b/translations/pl/lessons/5-NLP/19-NER/README.md index 9f9be15d..a4284f3c 100644 --- a/translations/pl/lessons/5-NLP/19-NER/README.md +++ b/translations/pl/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ noworodka | O Ponieważ musimy zbudować jednoznaczną korespondencję między tokenami a klasami, możemy wytrenować odpowiedni model sieci neuronowej **wielu-do-wielu** z tego obrazu: -![Obraz przedstawiający typowe wzorce sieci neuronowych rekurencyjnych.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pl.jpg) +![Obraz przedstawiający typowe wzorce sieci neuronowych rekurencyjnych.](../../../../../translated_images/pl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Obraz z [tego wpisu na blogu](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) autorstwa [Andreja Karpathy'ego](http://karpathy.github.io/). Modele klasyfikacji tokenów NER odpowiadają architekturze sieci po prawej stronie tego obrazu.* diff --git a/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md index e8404cee..6140a782 100644 --- a/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Możesz otworzyć jeden z modeli, na przykład **Biology → Flocking** Po otwarciu modelu zostaniesz przeniesiony na główny ekran NetLogo. Oto przykładowy model opisujący populację wilków i owiec, biorąc pod uwagę ograniczone zasoby (trawę). -![Główny ekran NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.pl.png) +![Główny ekran NetLogo](../../../../../translated_images/pl/NetLogo-Main.32653711ec1a01b3.png) > Zrzut ekranu autorstwa Dmitry Soshnikov diff --git a/translations/pt/README.md b/translations/pt/README.md index 024dcf8e..24d913b2 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligência Artificial para Iniciantes - Um Currículo -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.pt.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pt/ai-overview.0857791951d19500.png)| |:---:| | Inteligência Artificial para Iniciantes - _Sketchnote por [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/pt/lessons/1-Intro/README.md b/translations/pt/lessons/1-Intro/README.md index c5661756..b4296a65 100644 --- a/translations/pt/lessons/1-Intro/README.md +++ b/translations/pt/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à IA -![Resumo do conteúdo de Introdução à IA em um desenho](../../../../translated_images/ai-intro.bf28d1ac4235881c.pt.png) +![Resumo do conteúdo de Introdução à IA em um desenho](../../../../translated_images/pt/ai-intro.bf28d1ac4235881c.png) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Originalmente, os computadores foram inventados por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar com números seguindo um procedimento bem definido - um algoritmo. Os computadores modernos, embora significativamente mais avançados do que o modelo original proposto no século XIX, ainda seguem a mesma ideia de cálculos controlados. Assim, é possível programar um computador para fazer algo se soubermos a sequência exata de passos necessários para alcançar o objetivo. -![Foto de uma pessoa](../../../../translated_images/dsh_age.d212a30d4e54fb5f.pt.png) +![Foto de uma pessoa](../../../../translated_images/pt/dsh_age.d212a30d4e54fb5f.png) > Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para mais informações, consulte **[Inteligência Artificial Geral](https://en. Um dos problemas ao lidar com o termo **[Inteligência](https://en.wikipedia.org/wiki/Intelligence)** é que não há uma definição clara para este termo. Pode-se argumentar que inteligência está conectada ao **pensamento abstrato** ou à **autoconsciência**, mas não conseguimos defini-la adequadamente. -![Foto de um gato](../../../../translated_images/photo-cat.8c8e8fb760ffe457.pt.jpg) +![Foto de um gato](../../../../translated_images/pt/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) do Unsplash @@ -98,13 +98,13 @@ Alternativamente, podemos tentar modelar os elementos mais simples dentro do nos > | E o ML? | | > |--------------|-----------| -> | Parte da Inteligência Artificial que se baseia no computador aprendendo a resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.pt.png) | +> | Parte da Inteligência Artificial que se baseia no computador aprendendo a resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/pt/ml-for-beginners.9e4fed176fd5817d.png) | ## Um Breve Histórico da IA A Inteligência Artificial começou como um campo no meio do século XX. Inicialmente, o raciocínio simbólico era a abordagem predominante, e isso levou a uma série de sucessos importantes, como sistemas especialistas – programas de computador que eram capazes de agir como especialistas em alguns domínios de problemas limitados. No entanto, logo ficou claro que essa abordagem não escala bem. Extrair o conhecimento de um especialista, representá-lo em um computador e manter essa base de conhecimento precisa acaba sendo uma tarefa muito complexa e cara demais para ser prática em muitos casos. Isso levou ao chamado [Inverno da IA](https://en.wikipedia.org/wiki/AI_winter) na década de 1970. -Breve Histórico da IA +Breve Histórico da IA > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Da mesma forma, podemos ver como a abordagem para criar “programas falantes” * Assistentes modernos, como Cortana, Siri ou Google Assistant, são todos sistemas híbridos que usam redes neurais para converter fala em texto e reconhecer nossa intenção, e depois empregam algum raciocínio ou algoritmos explícitos para realizar as ações necessárias. * No futuro, podemos esperar um modelo completamente baseado em redes neurais para lidar com diálogos por conta própria. As recentes redes neurais da família GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostram grande sucesso nisso. -a evolução do Teste de Turing +a evolução do Teste de Turing > Imagem de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) de [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Investigação Recente em IA diff --git a/translations/pt/lessons/2-Symbolic/README.md b/translations/pt/lessons/2-Symbolic/README.md index 940dd6fa..6a6a33e5 100644 --- a/translations/pt/lessons/2-Symbolic/README.md +++ b/translations/pt/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representação de Conhecimento e Sistemas Especialistas -![Resumo do conteúdo de IA Simbólica](../../../../translated_images/ai-symbolic.715a30cb610411a6.pt.png) +![Resumo do conteúdo de IA Simbólica](../../../../translated_images/pt/ai-symbolic.715a30cb610411a6.png) > Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Na maioria das vezes, não definimos estritamente o conhecimento, mas alinhamos Assim, o problema da **representação de conhecimento** é encontrar uma forma eficaz de representar o conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: -![Espectro de representação de conhecimento](../../../../translated_images/knowledge-spectrum.b60df631852c0217.pt.png) +![Espectro de representação de conhecimento](../../../../translated_images/pt/knowledge-spectrum.b60df631852c0217.png) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxe de Bloco | Indentação | | Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais projetados para atuar como especialistas em um domínio de problema limitado. Eles baseavam-se em uma **base de conhecimento** extraída de um ou mais especialistas humanos e continham um **motor de inferência** que realizava algum raciocínio sobre ela. -![Arquitetura Humana](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.pt.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.pt.png) +![Arquitetura Humana](../../../../translated_images/pt/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema Baseado em Conhecimento](../../../../translated_images/pt/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento @@ -106,7 +106,7 @@ Os sistemas especialistas são construídos como o sistema de raciocínio humano Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base nas suas características físicas: -![Árvore AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.pt.png) +![Árvore AND-OR](../../../../translated_images/pt/AND-OR-Tree.5592d2c70187f283.png) > Imagem por [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md index 6ff5e2ad..36f25468 100644 --- a/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/pt/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting é um conceito extremamente importante em aprendizagem automática, Considere o seguinte problema de aproximar 5 pontos (representados por `x` nos gráficos abaixo): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.pt.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.pt.jpg) +![linear](../../../../../translated_images/pt/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/pt/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Modelo linear, 2 parâmetros** | **Modelo não-linear, 7 parâmetros** Erro de treino = 5.3 | Erro de treino = 0 @@ -79,7 +79,7 @@ Erro de validação = 5.1 | Erro de validação = 20 Como pode ver no gráfico acima, o overfitting pode ser detetado por um erro de treino muito baixo e um erro de validação elevado. Normalmente, durante o treino, vemos tanto os erros de treino quanto de validação começarem a diminuir, e então, em algum momento, o erro de validação pode parar de diminuir e começar a aumentar. Este será um sinal de overfitting e um indicador de que provavelmente devemos parar o treino nesse ponto (ou pelo menos fazer um snapshot do modelo). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.pt.png) +![overfitting](../../../../../translated_images/pt/Overfitting.408ad91cd90b4371.png) ## Como prevenir o overfitting diff --git a/translations/pt/lessons/3-NeuralNetworks/README.md b/translations/pt/lessons/3-NeuralNetworks/README.md index ad15f379..63f72bd0 100644 --- a/translations/pt/lessons/3-NeuralNetworks/README.md +++ b/translations/pt/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução às Redes Neuronais -![Resumo do conteúdo de Introdução às Redes Neuronais em um desenho](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.pt.png) +![Resumo do conteúdo de Introdução às Redes Neuronais em um desenho](../../../../translated_images/pt/ai-neuralnetworks.1c687ae40bc86e83.png) Como discutimos na introdução, uma das formas de alcançar inteligência é treinar um **modelo computacional** ou um **cérebro artificial**. Desde meados do século XX, os investigadores experimentaram diferentes modelos matemáticos, até que, nos últimos anos, esta abordagem provou ser extremamente bem-sucedida. Esses modelos matemáticos do cérebro são chamados de **redes neuronais**. @@ -36,13 +36,13 @@ Neste currículo, focar-nos-emos apenas em modelos de redes neuronais. Na biologia, sabemos que o nosso cérebro é composto por células neuronais (neurónios), cada uma delas com múltiplas "entradas" (dendritos) e uma única "saída" (axónio). Tanto os dendritos como os axónios podem conduzir sinais elétricos, e as conexões entre eles — conhecidas como sinapses — podem apresentar diferentes graus de condutividade, que são regulados por neurotransmissores. -![Modelo de um Neurónio](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.pt.jpg) | ![Modelo de um Neurónio](../../../../translated_images/artneuron.1a5daa88d20ebe6f.pt.png) +![Modelo de um Neurónio](../../../../translated_images/pt/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo de um Neurónio](../../../../translated_images/pt/artneuron.1a5daa88d20ebe6f.png) ----|---- Neurónio Real *([Imagem](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipédia)* | Neurónio Artificial *(Imagem do Autor)* Assim, o modelo matemático mais simples de um neurónio contém várias entradas X1, ..., XN e uma saída Y, e uma série de pesos W1, ..., WN. A saída é calculada como: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) onde **f** é uma **função de ativação** não linear. diff --git a/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md index f88c55d8..e677a250 100644 --- a/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/pt/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ No nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis * **Pré-processamento de uma fotografia de um livro em Braille**. Focamos em como podemos usar thresholding, deteção de características, transformação de perspetiva e manipulações NumPy para separar símbolos individuais em Braille para posterior classificação por uma rede neuronal. -![Imagem Braille](../../../../../translated_images/braille.341962ff76b1bd70.pt.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/braille-result.46530fea020b03c7.pt.png) | ![Símbolos Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.pt.png) +![Imagem Braille](../../../../../translated_images/pt/braille.341962ff76b1bd70.jpeg) | ![Imagem Braille Pré-processada](../../../../../translated_images/pt/braille-result.46530fea020b03c7.png) | ![Símbolos Braille](../../../../../translated_images/pt/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Imagem de [OpenCV.ipynb](OpenCV.ipynb) * **Deteção de movimento em vídeo usando diferença de frames**. Se a câmara estiver fixa, os frames do feed da câmara devem ser bastante semelhantes entre si. Como os frames são representados como arrays, apenas subtraindo esses arrays de dois frames subsequentes obteremos a diferença de pixels, que deve ser baixa para frames estáticos e tornar-se maior quando houver movimento substancial na imagem. -![Imagem de frames de vídeo e diferenças de frames](../../../../../translated_images/frame-difference.706f805491a0883c.pt.png) +![Imagem de frames de vídeo e diferenças de frames](../../../../../translated_images/pt/frame-difference.706f805491a0883c.png) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ No nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a vis - **Fluxo Ótico Denso** calcula o campo vetorial que mostra para cada pixel onde ele está a mover-se. - **Fluxo Ótico Esparso** baseia-se em tomar algumas características distintivas na imagem (por exemplo, bordas) e construir a sua trajetória de frame para frame. -![Imagem de Fluxo Ótico](../../../../../translated_images/optical.1f4a94464579a83a.pt.png) +![Imagem de Fluxo Ótico](../../../../../translated_images/pt/optical.1f4a94464579a83a.png) > Imagem de [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 1b54a463..cf88e7d7 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 é uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014. Tem a seguinte estrutura de camadas: -![Camadas do ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pt.jpg) +![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.jpg) Como pode ver, a VGG segue uma arquitetura tradicional em pirâmide, que consiste numa sequência de camadas de convolução e pooling. -![Pirâmide do ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pt.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md index e8a40fe0..9af82b03 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Na vida real, queremos ser capazes de reconhecer objetos numa imagem independent Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Como sabe, uma imagem é representada por uma matriz 2D ou um tensor 3D com profundidade de cor. Aplicar um filtro significa que utilizamos uma matriz relativamente pequena chamada **kernel do filtro**, e para cada pixel na imagem original calculamos a média ponderada com os pontos vizinhos. Podemos imaginar isto como uma pequena janela que desliza sobre toda a imagem, e que calcula a média de todos os pixels de acordo com os pesos na matriz kernel do filtro. -![Filtro de Borda Vertical](../../../../../translated_images/filter-vert.b7148390ca0bc356.pt.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.pt.png) +![Filtro de Borda Vertical](../../../../../translated_images/pt/filter-vert.b7148390ca0bc356.png) | ![Filtro de Borda Horizontal](../../../../../translated_images/pt/filter-horiz.59b80ed4feb946ef.png) ----|---- > Imagem por Dmitry Soshnikov @@ -38,7 +38,7 @@ O funcionamento das CNN baseia-se nas seguintes ideias importantes: * Podemos projetar a rede de forma a que os filtros sejam treinados automaticamente * Podemos usar a mesma abordagem para encontrar padrões em características de alto nível, não apenas na imagem original. Assim, a extração de características pelas CNN funciona numa hierarquia de características, começando por combinações de pixels de baixo nível até combinações de partes da imagem de nível mais alto. -![Extração Hierárquica de Características](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.pt.png) +![Extração Hierárquica de Características](../../../../../translated_images/pt/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Imagem de [um artigo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseado na [sua pesquisa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ A maioria das CNN utilizadas para processamento de imagens segue uma arquitetura Como exemplo, vejamos a arquitetura da VGG-16, uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014: -![Camadas do ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.pt.jpg) +![Camadas do ImageNet](../../../../../translated_images/pt/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Pirâmide do ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.pt.jpg) +![Pirâmide do ImageNet](../../../../../translated_images/pt/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2ed8d4e8..5a636f18 100644 --- a/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/pt/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Precisa treinar uma rede neuronal convolucional para classificar diferentes raç Vamos utilizar o [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), que contém imagens de 37 raças diferentes de cães e gatos. -![Dataset com que iremos trabalhar](../../../../../../translated_images/data.50b2a9d5484bdbf0.pt.png) +![Dataset com que iremos trabalhar](../../../../../../translated_images/pt/data.50b2a9d5484bdbf0.png) Para descarregar o dataset, utilize este trecho de código: diff --git a/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md index e866a157..ad22f76f 100644 --- a/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/pt/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tanto o Keras como o PyTorch possuem funções para carregar facilmente pesos de Aqui estão características extraídas de uma imagem de um gato pela rede VGG-16: -![Características extraídas pela VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.pt.png) +![Características extraídas pela VGG-16](../../../../../translated_images/pt/features.6291f9c7ba3a0b95.png) ## Conjunto de Dados de Gatos vs. Cães @@ -48,19 +48,19 @@ Uma rede neural pré-treinada contém diferentes padrões no seu *cérebro*, inc Uma abordagem que podemos adotar é começar com uma imagem aleatória e tentar usar a técnica de **otimização por descida de gradiente** para ajustar essa imagem de forma que a rede comece a pensar que é um gato. -![Ciclo de Otimização de Imagem](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.pt.png) +![Ciclo de Otimização de Imagem](../../../../../translated_images/pt/ideal-cat-loop.999fbb8ff306e044.png) No entanto, se fizermos isso, obteremos algo muito semelhante a um ruído aleatório. Isso acontece porque *existem muitas maneiras de fazer a rede pensar que a imagem de entrada é um gato*, incluindo algumas que não fazem sentido visualmente. Embora essas imagens contenham muitos padrões típicos de um gato, não há nada que as obrigue a serem visualmente distintas. Para melhorar o resultado, podemos adicionar outro termo à função de perda, chamado **perda de variação**. É uma métrica que mostra quão semelhantes são os pixels vizinhos da imagem. Minimizar a perda de variação torna a imagem mais suave e elimina o ruído, revelando padrões mais visualmente apelativos. Aqui está um exemplo de imagens "ideais", classificadas como gato e zebra com alta probabilidade: -![Gato Ideal](../../../../../translated_images/ideal-cat.203dd4597643d6b0.pt.png) | ![Zebra Ideal](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.pt.png) +![Gato Ideal](../../../../../translated_images/pt/ideal-cat.203dd4597643d6b0.png) | ![Zebra Ideal](../../../../../translated_images/pt/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Gato Ideal* | *Zebra Ideal* Uma abordagem semelhante pode ser usada para realizar os chamados **ataques adversariais** numa rede neural. Suponha que queremos enganar uma rede neural e fazer com que um cão pareça um gato. Se pegarmos na imagem de um cão, que é reconhecida pela rede como um cão, podemos ajustá-la ligeiramente usando otimização por descida de gradiente até que a rede comece a classificá-la como um gato: -![Imagem de um Cão](../../../../../translated_images/original-dog.8f68a67d2fe0911f.pt.png) | ![Imagem de um cão classificada como gato](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.pt.png) +![Imagem de um Cão](../../../../../translated_images/pt/original-dog.8f68a67d2fe0911f.png) | ![Imagem de um cão classificada como gato](../../../../../translated_images/pt/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Imagem original de um cão* | *Imagem de um cão classificada como gato* diff --git a/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md index 557f72ee..79621355 100644 --- a/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/pt/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ No entanto, podemos querer usar dados brutos (não etiquetados) para treinar ext Como estamos a treinar um autoencoder para capturar o máximo de informação possível da imagem original para uma reconstrução precisa, a rede tenta encontrar a melhor **representação** das imagens de entrada para captar o seu significado. -![Diagrama de Autoencoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.pt.jpg) +![Diagrama de Autoencoder](../../../../../translated_images/pt/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Imagem retirada do [blog da Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md index 0fd4a044..b2e8d7f6 100644 --- a/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/pt/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Os modelos de classificação de imagens que abordámos até agora recebiam uma ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detecção de Objetos](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.pt.png) +![Detecção de Objetos](../../../../../translated_images/pt/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Imagem do [site YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Supondo que queremos encontrar um gato numa imagem, uma abordagem muito ingénua 2. Executar a classificação de imagem em cada bloco. 3. Os blocos que resultarem numa ativação suficientemente alta podem ser considerados como contendo o objeto em questão. -![Detecção Ingénua de Objetos](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.pt.png) +![Detecção Ingénua de Objetos](../../../../../translated_images/pt/naive-detection.e7f1ba220ccd08c6.png) > *Imagem do [Caderno de Exercícios](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Poderá encontrar os seguintes conjuntos de dados para esta tarefa: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 classes * [COCO](http://cocodataset.org/#home) - Objetos Comuns em Contexto. 80 classes, caixas delimitadoras e máscaras de segmentação -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.pt.jpg) +![COCO](../../../../../translated_images/pt/coco-examples.71bc60380fa6cceb.jpg) ## Métricas de Detecção de Objetos @@ -50,7 +50,7 @@ Poderá encontrar os seguintes conjuntos de dados para esta tarefa: Enquanto na classificação de imagens é fácil medir o desempenho do algoritmo, na detecção de objetos precisamos de medir tanto a correção da classe como a precisão da localização da caixa delimitadora inferida. Para esta última, utilizamos a chamada **Interseção sobre União** (IoU), que mede o quão bem duas caixas (ou duas áreas arbitrárias) se sobrepõem. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.pt.png) +![IoU](../../../../../translated_images/pt/iou_equation.9a4751d40fff4e11.png) > *Figura 2 de [este excelente artigo sobre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existem duas grandes classes de algoritmos de detecção de objetos: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) utiliza [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) para gerar uma estrutura hierárquica de regiões ROI, que são então passadas por extratores de características CNN e classificadores SVM para determinar a classe do objeto, e regressão linear para determinar as coordenadas da *caixa delimitadora*. [Artigo Oficial](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.pt.png) +![RCNN](../../../../../translated_images/pt/rcnn1.cae407020dfb1d1f.png) > *Imagem de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.pt.png) +![RCNN-1](../../../../../translated_images/pt/rcnn2.2d9530bb83516484.png) > *Imagens de [este artigo](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existem duas grandes classes de algoritmos de detecção de objetos: Esta abordagem é semelhante à R-CNN, mas as regiões são definidas após as camadas de convolução terem sido aplicadas. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.pt.png) +![FRCNN](../../../../../translated_images/pt/f-rcnn.3cda6d9bb4188875.png) > Imagem do [Artigo Oficial](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Esta abordagem é semelhante à R-CNN, mas as regiões são definidas após as c A ideia principal desta abordagem é usar uma rede neural para prever ROIs - a chamada *Rede de Proposta de Região*. [Artigo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.pt.png) +![FasterRCNN](../../../../../translated_images/pt/faster-rcnn.8d46c099b87ef30a.png) > Imagem do [artigo oficial](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Este algoritmo é ainda mais rápido que o Faster R-CNN. A ideia principal é a 1. As características são processadas por **Position-Sensitive Score Map**. Cada objeto das classes $C$ é dividido em regiões $k\times k$, e treinamos para prever partes dos objetos. 1. Para cada parte das regiões $k\times k$, todas as redes votam pelas classes de objetos, e a classe de objeto com o voto máximo é selecionada. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.pt.png) +![r-fcn image](../../../../../translated_images/pt/r-fcn.13eb88158b99a3da.png) > Imagem do [artigo oficial](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO é um algoritmo de uma única passagem em tempo real. A ideia principal é * A imagem é dividida em regiões $S\times S$. * Para cada região, **CNN** prevê $n$ objetos possíveis, coordenadas da *caixa delimitadora* e *confiança*=*probabilidade* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.pt.png) + ![YOLO](../../../../../translated_images/pt/yolo.a2648ec82ee8bb4e.png) > Imagem do [artigo oficial](https://arxiv.org/abs/1506.02640) diff --git a/translations/pt/lessons/5-NLP/14-Embeddings/README.md b/translations/pt/lessons/5-NLP/14-Embeddings/README.md index 30c1ba80..969ee93a 100644 --- a/translations/pt/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/pt/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Assim, a camada de embedding receberia uma palavra como entrada e produziria um Ao usar uma camada de embedding como a primeira camada na nossa rede de classificação, podemos mudar de um modelo de bag-of-words para um modelo de **embedding bag**, onde primeiro convertemos cada palavra no nosso texto no embedding correspondente e, em seguida, calculamos alguma função agregada sobre todos esses embeddings, como `sum`, `average` ou `max`. -![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.pt.png) +![Imagem mostrando um classificador de embedding para cinco palavras de sequência.](../../../../../translated_images/pt/embedding-classifier-example.b77f021a7ee67eee.png) > Imagem do autor @@ -40,7 +40,7 @@ Para isso, precisamos de pré-treinar o nosso modelo de embedding numa grande co CBoW é mais rápido, enquanto skip-gram é mais lento, mas faz um trabalho melhor ao representar palavras menos frequentes. -![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pt.png) +![Imagem mostrando os algoritmos CBoW e Skip-Gram para converter palavras em vetores.](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md b/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md index 2cfaddd8..4d499a1d 100644 --- a/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/pt/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Nos nossos exemplos anteriores, utilizámos embeddings semânticos pré-treinado * **Continuous Bag-of-Words** (CBoW), onde prevemos o token central $W_0$ numa sequência de tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, onde prevemos um conjunto de tokens vizinhos {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} a partir do token central $W_0$. -![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.pt.png) +![imagem do artigo sobre conversão de palavras em vetores](../../../../../translated_images/pt/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagem retirada [deste artigo](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/pt/lessons/5-NLP/16-RNN/README.md b/translations/pt/lessons/5-NLP/16-RNN/README.md index 93e0437b..d84ae842 100644 --- a/translations/pt/lessons/5-NLP/16-RNN/README.md +++ b/translations/pt/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Nas secções anteriores, utilizámos representações semânticas ricas de text Para capturar o significado de uma sequência de texto, precisamos de usar outra arquitetura de rede neural, chamada **rede neural recorrente**, ou RNN. Numa RNN, passamos a nossa frase pela rede, um símbolo de cada vez, e a rede produz um **estado**, que depois passamos novamente à rede com o próximo símbolo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.pt.png) +![RNN](../../../../../translated_images/pt/rnn.27f5c29c53d727b5.png) > Imagem do autor @@ -61,7 +61,7 @@ Discutimos redes recorrentes que operam numa direção, do início de uma sequê Uma rede recorrente, seja unidirecional ou bidirecional, captura certos padrões dentro de uma sequência e pode armazená-los num vetor de estado ou passá-los para a saída. Tal como nas redes convolucionais, podemos construir outra camada recorrente sobre a primeira para capturar padrões de nível superior e construir a partir dos padrões de baixo nível extraídos pela primeira camada. Isto leva-nos à noção de uma **RNN multicamada**, que consiste em duas ou mais redes recorrentes, onde a saída da camada anterior é passada para a próxima camada como entrada. -![Imagem mostrando uma RNN multicamada com LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.pt.jpg) +![Imagem mostrando uma RNN multicamada com LSTM](../../../../../translated_images/pt/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Imagem retirada [deste excelente artigo](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md index c5bea678..959cc59e 100644 --- a/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/pt/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Na arquitetura de RNN discutida na unidade anterior, cada unidade RNN produzia o Isto permite diferentes arquiteturas neuronais, como mostrado na imagem abaixo: -![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pt.jpg) +![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Imagem do artigo [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Nesta unidade, vamos focar-nos em modelos generativos simples que nos ajudam a g Vamos treinar esta RNN para gerar texto passo a passo. Em cada passo, tomaremos uma sequência de caracteres de comprimento `nchars` e pediremos à rede que gere o próximo carácter de saída para cada carácter de entrada: -![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.pt.png) +![Imagem mostrando um exemplo de geração de RNN da palavra 'HELLO'.](../../../../../translated_images/pt/rnn-generate.56c54afb52f9781d.png) Ao gerar texto (durante a inferência), começamos com um **prompt**, que é passado pelas células RNN para gerar o estado intermédio, e a partir deste estado começa a geração. Geramos um carácter de cada vez e passamos o estado e o carácter gerado para outra célula RNN para gerar o próximo, até gerarmos caracteres suficientes. diff --git a/translations/pt/lessons/5-NLP/18-Transformers/README.md b/translations/pt/lessons/5-NLP/18-Transformers/README.md index 0d62aed7..37818f51 100644 --- a/translations/pt/lessons/5-NLP/18-Transformers/README.md +++ b/translations/pt/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Com RNNs, a tarefa de sequência para sequência é implementada por duas redes Os **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. Isto é implementado criando atalhos entre estados intermediários da RNN de entrada e a RNN de saída. Desta forma, ao gerar o símbolo de saída yt, consideramos todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/descodificador com uma camada de atenção aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pt.png) +![Imagem mostrando um modelo codificador/descodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.png) > O modelo codificador-descodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado deste [post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada influenciam a geração de uma determinada palavra na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pt.png) +![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, retirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.png) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ O resultado que obtemos com o embedding posicional incorpora tanto o token origi A seguir, precisamos capturar alguns padrões dentro da nossa sequência. Para isso, os transformers usam um mecanismo de **auto-atenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. Aplicar auto-atenção permite-nos levar em conta o **contexto** dentro da frase e ver quais palavras estão inter-relacionadas. Por exemplo, permite-nos ver quais palavras são referidas por correferências, como *it*, e também considerar o contexto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.pt.png) +![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.png) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Bidirectional Encoder Representations from Transformers) é uma rede transformer muito grande com várias camadas: 12 camadas para o *BERT-base* e 24 para o *BERT-large*. O modelo é primeiro pré-treinado num grande corpus de dados de texto (WikiPedia + livros) usando treino não supervisionado (prevendo palavras mascaradas numa frase). Durante o pré-treino, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Este processo é chamado de **aprendizagem por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pt.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md index d658f522..978f9cd5 100644 --- a/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/pt/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Com RNNs, a sequência-para-sequência é implementada por duas redes recorrente **Mecanismos de Atenção** fornecem um meio de ponderar o impacto contextual de cada vetor de entrada em cada previsão de saída da RNN. A forma como é implementado é criando atalhos entre estados intermediários da RNN de entrada e da RNN de saída. Dessa maneira, ao gerar o símbolo de saída yt, levaremos em conta todos os estados ocultos de entrada hi, com diferentes coeficientes de peso αt,i. -![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.pt.png) +![Imagem mostrando um modelo codificador/decodificador com uma camada de atenção aditiva](../../../../../translated_images/pt/encoder-decoder-attention.7a726296894fb567.png) > O modelo codificador-decodificador com mecanismo de atenção aditiva em [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citado a partir [deste post de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) A matriz de atenção {αi,j} representaria o grau em que certas palavras de entrada desempenham na geração de uma palavra específica na sequência de saída. Abaixo está um exemplo de tal matriz: -![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, tirada de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.pt.png) +![Imagem mostrando um alinhamento de exemplo encontrado por RNNsearch-50, tirada de Bahdanau - arviz.org](../../../../../translated_images/pt/bahdanau-fig3.09ba2d37f202a6af.png) > Figura de [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ O resultado que obtemos com o embutimento posicional incorpora tanto o token ori Em seguida, precisamos capturar alguns padrões dentro da nossa sequência. Para fazer isso, os transformers usam um mecanismo de **autoatenção**, que é essencialmente atenção aplicada à mesma sequência como entrada e saída. A aplicação de autoatenção nos permite levar em conta o **contexto** dentro da sentença e ver quais palavras estão inter-relacionadas. Por exemplo, isso nos permite ver quais palavras são referidas por co-referências, como *isso*, e também levar o contexto em consideração: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.pt.png) +![](../../../../../translated_images/pt/CoreferenceResolution.861924d6d384a7d6.png) > Imagem do [Blog do Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Como cada posição de entrada é mapeada independentemente para cada posição **BERT** (Representações de Codificador Bidirecional de Transformers) é uma rede transformer multilayer muito grande com 12 camadas para *BERT-base* e 24 para *BERT-large*. O modelo é primeiro pré-treinado em um grande corpus de dados textuais (WikiPedia + livros) usando treinamento não supervisionado (previsão de palavras mascaradas em uma sentença). Durante o pré-treinamento, o modelo absorve níveis significativos de compreensão da linguagem, que podem ser aproveitados com outros conjuntos de dados usando ajuste fino. Este processo é chamado de **aprendizado por transferência**. -![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.pt.png) +![imagem de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/pt/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Imagem [fonte](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/pt/lessons/5-NLP/19-NER/README.md b/translations/pt/lessons/5-NLP/19-NER/README.md index f544b43e..6e18c33d 100644 --- a/translations/pt/lessons/5-NLP/19-NER/README.md +++ b/translations/pt/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Como precisamos construir uma correspondência um-para-um entre tokens e classes, podemos treinar um modelo de rede neural **muitos-para-muitos** da seguinte forma: -![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.pt.jpg) +![Imagem mostrando padrões comuns de redes neuronais recorrentes.](../../../../../translated_images/pt/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Imagem retirada [deste artigo](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) por [Andrej Karpathy](http://karpathy.github.io/). Os modelos de classificação de tokens NER correspondem à arquitetura de rede mais à direita nesta imagem.* diff --git a/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md b/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md index a469b172..5dea4dbd 100644 --- a/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/pt/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Pode abrir um dos modelos, por exemplo **Biology → Flocking**. Depois de abrir o modelo, será levado ao ecrã principal do NetLogo. Aqui está um modelo de exemplo que descreve a população de lobos e ovelhas, dado recursos finitos (relva). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.pt.png) +![NetLogo Main Screen](../../../../../translated_images/pt/NetLogo-Main.32653711ec1a01b3.png) > Captura de ecrã por Dmitry Soshnikov diff --git a/translations/ro/README.md b/translations/ro/README.md index 65178b33..022e22d8 100644 --- a/translations/ro/README.md +++ b/translations/ro/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Inteligență Artificială pentru Începători - Un Curriculum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ro.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ro/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote de [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ro/lessons/1-Intro/README.md b/translations/ro/lessons/1-Intro/README.md index 718e720d..692c765a 100644 --- a/translations/ro/lessons/1-Intro/README.md +++ b/translations/ro/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducere în Inteligența Artificială -![Rezumat al conținutului Introducerii în AI într-un desen](../../../../translated_images/ai-intro.bf28d1ac4235881c.ro.png) +![Rezumat al conținutului Introducerii în AI într-un desen](../../../../translated_images/ro/ai-intro.bf28d1ac4235881c.png) > Schiță de [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Inițial, computerele au fost inventate de [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) pentru a opera pe numere urmând o procedură bine definită - un algoritm. Computerele moderne, deși semnificativ mai avansate decât modelul original propus în secolul al XIX-lea, încă urmează aceeași idee de calcule controlate. Astfel, este posibil să programăm un computer să facă ceva dacă știm exact secvența de pași necesară pentru a atinge scopul. -![Fotografie a unei persoane](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ro.png) +![Fotografie a unei persoane](../../../../translated_images/ro/dsh_age.d212a30d4e54fb5f.png) > Fotografie de [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Pentru mai multe informații, consultați **[Inteligența Artificială Generală Una dintre problemele legate de termenul **[Inteligență](https://en.wikipedia.org/wiki/Intelligence)** este că nu există o definiție clară a acestui termen. Se poate argumenta că inteligența este legată de **gândirea abstractă** sau de **auto-conștientizare**, dar nu o putem defini corect. -![Fotografie a unei pisici](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ro.jpg) +![Fotografie a unei pisici](../../../../translated_images/ro/photo-cat.8c8e8fb760ffe457.jpg) > [Fotografie](https://unsplash.com/photos/75715CVEJhI) de [Amber Kipp](https://unsplash.com/@sadmax) de pe Unsplash @@ -98,13 +98,13 @@ Alternativ, putem încerca să modelăm cele mai simple elemente din creierul no > | Ce ziceți de ML? | | > |--------------|-----------| -> | O parte a Inteligenței Artificiale care se bazează pe învățarea computerului să rezolve o problemă pe baza unor date se numește **Învățare Automată**. Nu vom analiza învățarea automată clasică în acest curs - vă recomandăm un curriculum separat [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML pentru Începători](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ro.png) | +> | O parte a Inteligenței Artificiale care se bazează pe învățarea computerului să rezolve o problemă pe baza unor date se numește **Învățare Automată**. Nu vom analiza învățarea automată clasică în acest curs - vă recomandăm un curriculum separat [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML pentru Începători](../../../../translated_images/ro/ml-for-beginners.9e4fed176fd5817d.png) | ## O Scurtă Istorie a AI Inteligența Artificială a început ca un domeniu la mijlocul secolului XX. Inițial, raționamentul simbolic a fost o abordare predominantă și a condus la o serie de succese importante, cum ar fi sistemele expert – programe de computer care puteau acționa ca un expert în unele domenii limitate de probleme. Cu toate acestea, a devenit curând clar că o astfel de abordare nu se scalează bine. Extragerea cunoștințelor de la un expert, reprezentarea lor într-un computer și menținerea bazei de cunoștințe exacte s-a dovedit a fi o sarcină foarte complexă și prea costisitoare pentru a fi practică în multe cazuri. Acest lucru a dus la așa-numita [Iarnă AI](https://en.wikipedia.org/wiki/AI_winter) în anii 1970. -Scurtă Istorie a AI +Scurtă Istorie a AI > Imagine de [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Putem observa cum s-au schimbat abordările, de exemplu, în crearea unui progra * Asistenții moderni, cum ar fi Cortana, Siri sau Google Assistant, sunt toate sisteme hibride care folosesc rețele neuronale pentru a converti vorbirea în text și a recunoaște intenția noastră, iar apoi utilizează un raționament sau algoritmi expliciți pentru a efectua acțiunile necesare. * În viitor, ne putem aștepta la un model complet bazat pe rețele neuronale care să gestioneze dialogul de unul singur. Familiile recente de rețele neuronale GPT și [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) arată un mare succes în acest sens. -evoluția testului Turing +evoluția testului Turing > Imagine de Dmitry Soshnikov, [fotografie](https://unsplash.com/photos/r8LmVbUKgns) de [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Cercetări recente în domeniul AI diff --git a/translations/ro/lessons/2-Symbolic/Animals.ipynb b/translations/ro/lessons/2-Symbolic/Animals.ipynb index ebe9b17d..c1b14c00 100644 --- a/translations/ro/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ro/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "În acest exemplu, vom implementa un sistem simplu bazat pe cunoștințe pentru a determina un animal pe baza unor caracteristici fizice. Sistemul poate fi reprezentat prin următorul arbore AND-OR (acesta este doar o parte din arborele complet, putem adăuga cu ușurință mai multe reguli):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ro.png)\n" + "![](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ro/lessons/2-Symbolic/README.md b/translations/ro/lessons/2-Symbolic/README.md index cb9b9c3a..cb6d509b 100644 --- a/translations/ro/lessons/2-Symbolic/README.md +++ b/translations/ro/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Reprezentarea Cunoașterii și Sisteme Expert -![Rezumat al conținutului AI simbolic](../../../../translated_images/ai-symbolic.715a30cb610411a6.ro.png) +![Rezumat al conținutului AI simbolic](../../../../translated_images/ro/ai-symbolic.715a30cb610411a6.png) > Sketchnote de [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ De cele mai multe ori, nu definim strict cunoașterea, ci o aliniem cu alte conc Astfel, problema **reprezentării cunoașterii** este de a găsi o modalitate eficientă de a reprezenta cunoașterea într-un computer sub formă de date, pentru a o face utilizabilă automat. Acest lucru poate fi văzut ca un spectru: -![Spectrul reprezentării cunoașterii](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ro.png) +![Spectrul reprezentării cunoașterii](../../../../translated_images/ro/knowledge-spectrum.b60df631852c0217.png) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Sintaxă Bloc | Indentare | | | Unul dintre succesele timpurii ale AI simbolic au fost așa-numitele **sisteme expert** - sisteme de computer concepute să acționeze ca un expert într-un domeniu limitat de probleme. Acestea se bazau pe o **bază de cunoaștere** extrasă de la unul sau mai mulți experți umani și conțineau un **motor de inferență** care efectua raționamente pe baza acesteia. -![Arhitectura umană](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ro.png) | ![Sistem bazat pe cunoaștere](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ro.png) +![Arhitectura umană](../../../../translated_images/ro/arch-human.5d4d35f1bba3ab1c.png) | ![Sistem bazat pe cunoaștere](../../../../translated_images/ro/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Structura simplificată a sistemului neural uman | Arhitectura unui sistem bazat pe cunoaștere @@ -106,7 +106,7 @@ Sistemele expert sunt construite similar cu sistemul de raționament uman, care Ca exemplu, să luăm în considerare următorul sistem expert de determinare a unui animal pe baza caracteristicilor sale fizice: -![Arbore AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ro.png) +![Arbore AND-OR](../../../../translated_images/ro/AND-OR-Tree.5592d2c70187f283.png) > Imagine de [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 73a1962a..1b08d2bb 100644 --- a/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ro/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "În cazul în care avem mai mult de 2 clase, softmax va normaliza probabilitățile pentru toate. Iată un diagram al arhitecturii rețelei care realizează clasificarea cifrelor MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ro.png)\n" + "![MNIST Classifier](../../../../../translated_images/ro/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Pierdere mică în antrenament - modelul poate aproxima bine datele de antrenament, deoarece are suficientă putere expresivă.\n", "* Pierderea pe validare poate fi mult mai mare decât pierderea pe antrenament și poate începe să crească în timpul antrenamentului - acest lucru se întâmplă deoarece modelul \"memorizează\" punctele de antrenament și pierde \"imaginea de ansamblu\".\n", "\n", - "![Supraînvățare](../../../../../translated_images/overfit.a0bd57f717c15769.ro.png)\n", + "![Supraînvățare](../../../../../translated_images/ro/overfit.a0bd57f717c15769.png)\n", "\n", "> În această imagine, `x` reprezintă datele de antrenament, iar `o` - datele de validare. Stânga - model liniar (cu un singur strat), care aproximează destul de bine natura datelor. Dreapta - model supraînvățat, care aproximează perfect datele de antrenament, dar nu mai are sens pentru alte date (eroarea pe validare este foarte mare).\n" ] diff --git a/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md index 3079edfd..34170c28 100644 --- a/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ro/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting este un concept extrem de important în învățarea automată și e Luați în considerare următoarea problemă de aproximare a 5 puncte (reprezentate de `x` pe graficele de mai jos): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ro.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ro.jpg) +![linear](../../../../../translated_images/ro/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ro/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Model liniar, 2 parametri** | **Model neliniar, 7 parametri** Eroare de antrenare = 5.3 | Eroare de antrenare = 0 @@ -79,7 +79,7 @@ Este foarte important să găsim un echilibru corect între complexitatea modelu Așa cum se vede din graficul de mai sus, overfitting poate fi detectat printr-o eroare de antrenare foarte mică și o eroare de validare mare. În mod normal, în timpul antrenării, vom vedea atât erorile de antrenare, cât și cele de validare începând să scadă, iar apoi, la un moment dat, eroarea de validare poate înceta să scadă și să înceapă să crească. Acesta va fi un semn de overfitting și un indicator că ar trebui să oprim antrenarea în acel moment (sau cel puțin să facem un snapshot al modelului). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ro.png) +![overfitting](../../../../../translated_images/ro/Overfitting.408ad91cd90b4371.png) ## Cum prevenim overfitting diff --git a/translations/ro/lessons/3-NeuralNetworks/README.md b/translations/ro/lessons/3-NeuralNetworks/README.md index 13c33b32..049f4a3b 100644 --- a/translations/ro/lessons/3-NeuralNetworks/README.md +++ b/translations/ro/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducere în Rețele Neuronale -![Rezumat al conținutului despre Introducerea în Rețele Neuronale într-un desen](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ro.png) +![Rezumat al conținutului despre Introducerea în Rețele Neuronale într-un desen](../../../../translated_images/ro/ai-neuralnetworks.1c687ae40bc86e83.png) Așa cum am discutat în introducere, una dintre modalitățile de a obține inteligență este să antrenăm un **model computerizat** sau un **creier artificial**. Începând cu mijlocul secolului al XX-lea, cercetătorii au încercat diferite modele matematice, până când, în ultimii ani, această direcție s-a dovedit a fi extrem de eficientă. Aceste modele matematice ale creierului sunt numite **rețele neuronale**. @@ -36,13 +36,13 @@ Vom analiza cele mai comune două probleme din Învățarea Automată: Din biologie, știm că creierul nostru este format din celule neuronale (neuroni), fiecare având multiple "intrări" (dendrite) și o singură "ieșire" (axon). Atât dendritele, cât și axonii pot conduce semnale electrice, iar conexiunile dintre ele — cunoscute sub numele de sinapse — pot prezenta grade variate de conductivitate, care sunt reglate de neurotransmițători. -![Model al unui Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ro.jpg) | ![Model al unui Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ro.png) +![Model al unui Neuron](../../../../translated_images/ro/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model al unui Neuron](../../../../translated_images/ro/artneuron.1a5daa88d20ebe6f.png) ----|---- Neuron Real *([Imagine](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) de pe Wikipedia)* | Neuron Artificial *(Imagine de Autor)* Astfel, cel mai simplu model matematic al unui neuron conține mai multe intrări X1, ..., XN și o ieșire Y, precum și o serie de ponderi W1, ..., WN. Ieșirea este calculată astfel: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) unde f este o **funcție de activare** neliniară. diff --git a/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md index 8632f931..ae6c4eb5 100644 --- a/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ro/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ De asemenea, poți folosi OpenCV pentru a încărca cadre video unul câte unul * **Preprocesarea unei fotografii a unei cărți Braille**. Ne concentrăm pe modul în care putem utiliza thresholding, detectarea caracteristicilor, transformarea de perspectivă și manipulările NumPy pentru a separa simbolurile individuale Braille pentru clasificarea ulterioară de către o rețea neuronală. -![Imagine Braille](../../../../../translated_images/braille.341962ff76b1bd70.ro.jpeg) | ![Imagine Braille Preprocesată](../../../../../translated_images/braille-result.46530fea020b03c7.ro.png) | ![Simboluri Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.ro.png) +![Imagine Braille](../../../../../translated_images/ro/braille.341962ff76b1bd70.jpeg) | ![Imagine Braille Preprocesată](../../../../../translated_images/ro/braille-result.46530fea020b03c7.png) | ![Simboluri Braille](../../../../../translated_images/ro/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Imagine din [OpenCV.ipynb](OpenCV.ipynb) * **Detectarea mișcării în video folosind diferența dintre cadre**. Dacă camera este fixă, atunci cadrele din fluxul camerei ar trebui să fie destul de similare între ele. Deoarece cadrele sunt reprezentate ca matrice, doar prin scăderea acestor matrice pentru două cadre consecutive vom obține diferența de pixeli, care ar trebui să fie mică pentru cadre statice și să devină mai mare odată ce există o mișcare semnificativă în imagine. -![Imagine a cadrelor video și diferențelor dintre cadre](../../../../../translated_images/frame-difference.706f805491a0883c.ro.png) +![Imagine a cadrelor video și diferențelor dintre cadre](../../../../../translated_images/ro/frame-difference.706f805491a0883c.png) > Imagine din [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ De asemenea, poți folosi OpenCV pentru a încărca cadre video unul câte unul - **Flux Optic Dens** calculează câmpul vectorial care arată pentru fiecare pixel unde se mișcă. - **Flux Optic Rar** se bazează pe luarea unor caracteristici distinctive din imagine (de exemplu, margini) și construirea traiectoriei lor de la un cadru la altul. -![Imagine a Fluxului Optic](../../../../../translated_images/optical.1f4a94464579a83a.ro.png) +![Imagine a Fluxului Optic](../../../../../translated_images/ro/optical.1f4a94464579a83a.png) > Imagine din [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8d3f82bb..2a5ba02e 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 este o rețea care a atins o acuratețe de 92.7% în clasificarea top-5 ImageNet în 2014. Structura sa de straturi este următoarea: -![Straturi ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ro.jpg) +![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.jpg) După cum se poate observa, VGG urmează o arhitectură tradițională de tip piramidă, care constă într-o secvență de straturi de convoluție și pooling. -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ro.jpg) +![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagine de la [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index d674a4b2..f643f148 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Astfel, într-un CNN tipic, ar exista mai multe straturi de convoluție, cu straturi de pooling între ele pentru a reduce dimensiunile imaginii. De asemenea, am crește numărul de filtre, deoarece, pe măsură ce modelele devin mai complexe, există mai multe combinații interesante pe care trebuie să le căutăm.\n", "\n", - "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ro.png)\n", + "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Datorită reducerii dimensiunilor spațiale și creșterii dimensiunilor caracteristicilor/filtrelor, această arhitectură este numită și **arhitectură piramidală**.\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 0c02169c..8040318e 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Astfel, într-un CNN tipic, ar exista mai multe straturi de convoluție, cu straturi de pooling între ele pentru a reduce dimensiunile imaginii. De asemenea, am crește numărul de filtre, deoarece, pe măsură ce modelele devin mai complexe, există mai multe combinații interesante pe care trebuie să le căutăm.\n", "\n", - "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ro.png)\n", + "![O imagine care arată mai multe straturi de convoluție cu straturi de pooling.](../../../../../translated_images/ro/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Datorită reducerii dimensiunilor spațiale și creșterii dimensiunilor caracteristicilor/filtrelor, această arhitectură este numită și **arhitectură piramidală**.\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md index e7c5c1d3..558c35f3 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Am văzut anterior că rețelele neuronale sunt destul de bune la procesarea ima Pentru a extrage tipare, vom folosi noțiunea de **filtre convoluționale**. După cum știți, o imagine este reprezentată printr-o matrice 2D sau un tensor 3D cu adâncime de culoare. Aplicarea unui filtru înseamnă că luăm o matrice relativ mică numită **kernel de filtru**, iar pentru fiecare pixel din imaginea originală calculăm media ponderată cu punctele vecine. Putem privi acest proces ca o fereastră mică care alunecă peste întreaga imagine și calculează media tuturor pixelilor conform greutăților din matricea kernelului de filtru. -![Filtru pentru margini verticale](../../../../../translated_images/filter-vert.b7148390ca0bc356.ro.png) | ![Filtru pentru margini orizontale](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ro.png) +![Filtru pentru margini verticale](../../../../../translated_images/ro/filter-vert.b7148390ca0bc356.png) | ![Filtru pentru margini orizontale](../../../../../translated_images/ro/filter-horiz.59b80ed4feb946ef.png) ----|---- > Imagine de Dmitry Soshnikov @@ -38,7 +38,7 @@ Modul în care funcționează CNN-urile se bazează pe următoarele idei importa * Putem proiecta rețeaua astfel încât filtrele să fie antrenate automat * Putem folosi aceeași abordare pentru a găsi tipare în caracteristici de nivel înalt, nu doar în imaginea originală. Astfel, extragerea caracteristicilor prin CNN funcționează pe o ierarhie de caracteristici, începând de la combinații de pixeli de nivel scăzut, până la combinații de nivel înalt ale părților imaginii. -![Extragerea ierarhică a caracteristicilor](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ro.png) +![Extragerea ierarhică a caracteristicilor](../../../../../translated_images/ro/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Imagine din [un articol de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), bazat pe [cercetarea lor](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Majoritatea CNN-urilor utilizate pentru procesarea imaginilor urmează o așa-nu Ca exemplu, să analizăm arhitectura VGG-16, o rețea care a obținut o acuratețe de 92.7% în clasificarea top-5 din ImageNet în 2014: -![Straturi ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ro.jpg) +![Straturi ImageNet](../../../../../translated_images/ro/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ro.jpg) +![Piramida ImageNet](../../../../../translated_images/ro/vgg-16-arch.64ff2137f50dd49f.jpg) > Imagine de pe [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md index c3502050..98a21093 100644 --- a/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ro/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Trebuie să antrenezi o rețea neuronală convoluțională pentru a clasifica di Vom folosi [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), care conține imagini ale 37 de rase diferite de câini și pisici. -![Dataset-ul cu care vom lucra](../../../../../../translated_images/data.50b2a9d5484bdbf0.ro.png) +![Dataset-ul cu care vom lucra](../../../../../../translated_images/ro/data.50b2a9d5484bdbf0.png) Pentru a descărca dataset-ul, folosește acest fragment de cod: diff --git a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 7a820129..d469a739 100644 --- a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Pentru a vizualiza pisica ideală, vom începe cu o imagine de zgomot aleatoriu și vom încerca să folosim tehnica de optimizare prin gradient descent pentru a ajusta imaginea astfel încât o rețea să recunoască o pisică.\n", "\n", - "![Bucla de Optimizare](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ro.png)\n", + "![Bucla de Optimizare](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Iată imaginea noastră de început:\n" ] diff --git a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md index 0f344d46..2e322342 100644 --- a/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ro/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Atât Keras, cât și PyTorch conțin funcții pentru a încărca cu ușurință Iată caracteristici extrase dintr-o imagine cu o pisică de către rețeaua VGG-16: -![Caracteristici extrase de VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ro.png) +![Caracteristici extrase de VGG-16](../../../../../translated_images/ro/features.6291f9c7ba3a0b95.png) ## Setul de Date Pisici vs. Câini @@ -48,19 +48,19 @@ Rețeaua neuronală pre-antrenată conține diferite modele în "creierul" său, O abordare pe care o putem adopta este să începem cu o imagine aleatorie și apoi să folosim tehnica de optimizare **gradient descent** pentru a ajusta acea imagine astfel încât rețeaua să înceapă să creadă că este o pisică. -![Buclă de Optimizare a Imaginilor](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ro.png) +![Buclă de Optimizare a Imaginilor](../../../../../translated_images/ro/ideal-cat-loop.999fbb8ff306e044.png) Totuși, dacă facem acest lucru, vom obține ceva foarte asemănător cu un zgomot aleatoriu. Acest lucru se întâmplă deoarece *există multe moduri prin care rețeaua poate crede că imaginea de intrare este o pisică*, inclusiv unele care nu au sens vizual. Deși aceste imagini conțin multe modele tipice pentru o pisică, nu există nimic care să le constrângă să fie distincte vizual. Pentru a îmbunătăți rezultatul, putem adăuga un alt termen în funcția de pierdere, numit **pierdere de variație**. Este o metrică care arată cât de similari sunt pixelii vecini ai imaginii. Minimizarea pierderii de variație face imaginea mai netedă și elimină zgomotul - dezvăluind astfel modele mai atractive vizual. Iată un exemplu de astfel de imagini "ideale", care sunt clasificate ca pisică și ca zebră cu o probabilitate mare: -![Pisică Ideală](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ro.png) | ![Zebră Ideală](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ro.png) +![Pisică Ideală](../../../../../translated_images/ro/ideal-cat.203dd4597643d6b0.png) | ![Zebră Ideală](../../../../../translated_images/ro/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Pisică Ideală* | *Zebră Ideală* O abordare similară poate fi utilizată pentru a efectua așa-numitele **atacuri adversariale** asupra unei rețele neuronale. Să presupunem că dorim să păcălim o rețea neuronală și să facem un câine să arate ca o pisică. Dacă luăm imaginea unui câine, care este recunoscută de rețea ca fiind un câine, putem apoi să o ajustăm puțin folosind optimizarea gradient descent, până când rețeaua începe să o clasifice ca fiind o pisică: -![Imaginea unui Câine](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ro.png) | ![Imaginea unui câine clasificat ca pisică](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ro.png) +![Imaginea unui Câine](../../../../../translated_images/ro/original-dog.8f68a67d2fe0911f.png) | ![Imaginea unui câine clasificat ca pisică](../../../../../translated_images/ro/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Imagine originală a unui câine* | *Imaginea unui câine clasificat ca pisică* diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3b1712d9..362b1386 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Deoarece antrenăm autoencoderul să capteze cât mai multe informații din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația.\n", "\n", - "![Diagrama AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ro.jpg)\n", + "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Imagine de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 16c12f1c..9c1c2ded 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Deoarece antrenăm autoencoderul să capteze cât mai multe informații din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația.\n", "\n", - "![Diagrama AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ro.jpg)\n", + "![Diagrama AutoEncoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Imagine preluată de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md index caa33037..cf74e4c1 100644 --- a/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ro/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Totuși, s-ar putea să dorim să folosim date brute (neetichetate) pentru a ant Deoarece antrenăm un autoencoder pentru a captura cât mai multă informație din imaginea originală pentru o reconstrucție precisă, rețeaua încearcă să găsească cea mai bună **reprezentare** a imaginilor de intrare pentru a surprinde semnificația acestora. -![Diagrama Autoencoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ro.jpg) +![Diagrama Autoencoder](../../../../../translated_images/ro/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Imagine de pe [blogul Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md index 137c620d..20adf2ef 100644 --- a/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ro/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modelele de clasificare a imaginilor pe care le-am abordat până acum au luat o ## [Chestionar înainte de lecție](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detectarea Obiectelor](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ro.png) +![Detectarea Obiectelor](../../../../../translated_images/ro/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Imagine de pe [site-ul YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Presupunând că dorim să găsim o pisică într-o imagine, o abordare foarte n 2. Aplicăm clasificarea imaginilor pe fiecare secțiune. 3. Secțiunile care generează o activare suficient de mare pot fi considerate ca conținând obiectul în cauză. -![Detectare Naivă a Obiectelor](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ro.png) +![Detectare Naivă a Obiectelor](../../../../../translated_images/ro/naive-detection.e7f1ba220ccd08c6.png) > *Imagine din [Notebook-ul de exerciții](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Este posibil să întâlniți următoarele seturi de date pentru această sarcin * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 clase * [COCO](http://cocodataset.org/#home) - Obiecte Comune în Context. 80 clase, casete de delimitare și măști de segmentare -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ro.jpg) +![COCO](../../../../../translated_images/ro/coco-examples.71bc60380fa6cceb.jpg) ## Metrice pentru Detectarea Obiectelor @@ -50,7 +50,7 @@ Este posibil să întâlniți următoarele seturi de date pentru această sarcin În timp ce pentru clasificarea imaginilor este ușor să măsurăm cât de bine performează algoritmul, pentru detectarea obiectelor trebuie să măsurăm atât corectitudinea clasei, cât și precizia locației casetei de delimitare inferate. Pentru aceasta din urmă, folosim așa-numita **Intersecția peste Uniune** (IoU), care măsoară cât de bine se suprapun două casete (sau două zone arbitrare). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ro.png) +![IoU](../../../../../translated_images/ro/iou_equation.9a4751d40fff4e11.png) > *Figura 2 din [acest articol excelent despre IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Există două clase largi de algoritmi de detectare a obiectelor: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) folosește [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) pentru a genera o structură ierarhică de regiuni ROI, care sunt apoi trecute prin extractoare de caracteristici CNN și clasificatoare SVM pentru a determina clasa obiectului, și regresie liniară pentru a determina coordonatele *casetei de delimitare*. [Lucrare oficială](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ro.png) +![RCNN](../../../../../translated_images/ro/rcnn1.cae407020dfb1d1f.png) > *Imagine de van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ro.png) +![RCNN-1](../../../../../translated_images/ro/rcnn2.2d9530bb83516484.png) > *Imagini din [acest blog](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Există două clase largi de algoritmi de detectare a obiectelor: Această abordare este similară cu R-CNN, dar regiunile sunt definite după ce straturile de convoluție au fost aplicate. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ro.png) +![FRCNN](../../../../../translated_images/ro/f-rcnn.3cda6d9bb4188875.png) > Imagine din [Lucrarea Oficială](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Această abordare este similară cu R-CNN, dar regiunile sunt definite după ce Ideea principală a acestei abordări este de a folosi o rețea neuronală pentru a prezice ROI-urile - așa-numita *Rețea de Propunere a Regiunilor*. [Lucrare](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ro.png) +![FasterRCNN](../../../../../translated_images/ro/faster-rcnn.8d46c099b87ef30a.png) > Imagine din [lucrarea oficială](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Acest algoritm este chiar mai rapid decât Faster R-CNN. Ideea principală este 2. Caracteristicile sunt procesate de **Position-Sensitive Score Map**. Fiecare obiect din $C$ clase este împărțit în regiuni $k\times k$, și antrenăm pentru a prezice părți ale obiectelor. 3. Pentru fiecare parte din regiunile $k\times k$, toate rețelele votează pentru clasele de obiecte, iar clasa de obiect cu votul maxim este selectată. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ro.png) +![r-fcn image](../../../../../translated_images/ro/r-fcn.13eb88158b99a3da.png) > Imagine din [lucrarea oficială](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO este un algoritm în timp real, cu o singură trecere. Ideea principală es * Imaginea este împărțită în regiuni $S\times S$. * Pentru fiecare regiune, **CNN** prezice $n$ obiecte posibile, coordonatele *casetei de delimitare* și *încrederea*=*probabilitatea* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ro.png) + ![YOLO](../../../../../translated_images/ro/yolo.a2648ec82ee8bb4e.png) > Imagine din [lucrarea oficială](https://arxiv.org/abs/1506.02640) diff --git a/translations/ro/lessons/4-ComputerVision/README.md b/translations/ro/lessons/4-ComputerVision/README.md index 535011b0..1a8b3b71 100644 --- a/translations/ro/lessons/4-ComputerVision/README.md +++ b/translations/ro/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Viziune Computerizată -![Rezumat al conținutului despre Viziune Computerizată într-un desen](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ro.png) +![Rezumat al conținutului despre Viziune Computerizată într-un desen](../../../../translated_images/ro/ai-computervision.6506ebebac3fbf76.png) În această secțiune vom învăța despre: diff --git a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 8d925ff9..1c6b2204 100644 --- a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Reprezentarea vectorială **Bag of Words** (BoW) este cea mai utilizată metodă tradițională de reprezentare vectorială. Fiecare cuvânt este asociat unui index vectorial, iar elementul vectorului conține numărul de apariții ale unui cuvânt într-un document dat.\n", "\n", - "![Imagine care arată cum este reprezentată în memorie metoda bag of words.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ro.png) \n", + "![Imagine care arată cum este reprezentată în memorie metoda bag of words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Poți să te gândești la BoW și ca la o sumă a tuturor vectorilor one-hot-encoded pentru cuvintele individuale din text.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 272791d8..c7f493ee 100644 --- a/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ro/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Reprezentarea vectorială **Bag-of-words** (BoW) este cea mai simplă de înțeles dintre reprezentările vectoriale tradiționale. Fiecare cuvânt este asociat unui index vectorial, iar un element al vectorului conține numărul de apariții ale fiecărui cuvânt într-un document dat.\n", "\n", - "![Imagine care arată cum este reprezentată în memorie o reprezentare vectorială bag-of-words.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ro.png) \n", + "![Imagine care arată cum este reprezentată în memorie o reprezentare vectorială bag-of-words.](../../../../../translated_images/ro/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Poți considera BoW și ca o sumă a tuturor vectorilor one-hot-encoded pentru cuvintele individuale din text.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 9d5e5b08..24a1ed3c 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Folosind stratul de embedding ca prim strat în rețeaua noastră, putem trece de la modelul bag-of-words la modelul **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în embedding-ul corespunzător, iar apoi calculăm o funcție de agregare peste toate aceste embedding-uri, cum ar fi `sum`, `average` sau `max`.\n", "\n", - "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ro.png)\n", + "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Rețeaua noastră neuronală de clasificare va începe cu un strat de embedding, urmat de un strat de agregare și un clasificator liniar deasupra acestuia:\n" ] @@ -176,7 +176,7 @@ "\n", "În arhitectura anterioară, a fost necesar să completăm toate secvențele la aceeași lungime pentru a le încadra într-un minibatch. Aceasta nu este cea mai eficientă metodă de a reprezenta secvențele de lungime variabilă - o altă abordare ar fi utilizarea unui vector de **offset**, care ar conține offset-urile tuturor secvențelor stocate într-un singur vector mare.\n", "\n", - "![Imagine care arată o reprezentare a secvențelor cu offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ro.png)\n", + "![Imagine care arată o reprezentare a secvențelor cu offset](../../../../../translated_images/ro/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: În imaginea de mai sus, este prezentată o secvență de caractere, dar în exemplul nostru lucrăm cu secvențe de cuvinte. Totuși, principiul general de reprezentare a secvențelor cu un vector de offset rămâne același.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW este mai rapid, în timp ce skip-gram este mai lent, dar oferă o reprezentare mai bună pentru cuvintele rare.\n", "\n", - "![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ro.png)\n", + "![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Pentru a experimenta cu încapsulările word2vec pre-antrenate pe setul de date Google News, putem folosi biblioteca **gensim**. Mai jos găsim cuvintele cele mai similare cu 'neural'.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index c6e0e007..178212d2 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ro/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Folosind un strat de embedding ca prim strat în rețeaua noastră, putem trece de la modelul bag-of-words la un model **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în embedding-ul corespunzător, iar apoi calculăm o funcție agregată pentru toate aceste embedding-uri, cum ar fi `sum`, `average` sau `max`.\n", "\n", - "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ro.png)\n", + "![Imagine care arată un clasificator embedding pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Rețeaua noastră neurală de clasificare constă din următoarele straturi:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW este mai rapid, iar skip-gram, deși mai lent, face o treabă mai bună în reprezentarea cuvintelor rare.\n", "\n", - "![Imagine care ilustrează algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ro.png)\n", + "![Imagine care ilustrează algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Pentru a experimenta cu încapsularea Word2Vec preantrenată pe setul de date Google News, putem folosi biblioteca **gensim**. Mai jos găsim cuvintele cele mai similare cu 'neural'.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/14-Embeddings/README.md b/translations/ro/lessons/5-NLP/14-Embeddings/README.md index 1e76f261..6d139e2a 100644 --- a/translations/ro/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ro/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Astfel, stratul de încapsulare ar lua un cuvânt ca intrare și ar produce un v Folosind un strat de încapsulare ca prim strat în rețeaua noastră de clasificare, putem trece de la un model bag-of-words la un model **embedding bag**, unde mai întâi convertim fiecare cuvânt din textul nostru în încapsularea corespunzătoare, și apoi calculăm o funcție agregată peste toate aceste încapsulări, cum ar fi `sum`, `average` sau `max`. -![Imagine care arată un clasificator bazat pe încapsulări pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ro.png) +![Imagine care arată un clasificator bazat pe încapsulări pentru cinci cuvinte dintr-o secvență.](../../../../../translated_images/ro/embedding-classifier-example.b77f021a7ee67eee.png) > Imagine realizată de autor @@ -40,7 +40,7 @@ Pentru a face acest lucru, trebuie să pre-antrenăm modelul de încapsulare pe CBoW este mai rapid, în timp ce skip-gram este mai lent, dar face o treabă mai bună în reprezentarea cuvintelor rare. -![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ro.png) +![Imagine care arată algoritmii CBoW și Skip-Gram pentru conversia cuvintelor în vectori.](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagine din [acest articol](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md index 5387dae2..ed66bec3 100644 --- a/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ro/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Ideea principală din spatele modelării limbajului este antrenarea acestora pe * **Continuous Bag-of-Words** (CBoW), când prezicem tokenul din mijloc $W_0$ într-o secvență de tokeni $W_{-N}$, ..., $W_N$. * **Skip-gram**, unde prezicem un set de tokeni vecini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} pornind de la tokenul din mijloc $W_0$. -![imagine dintr-un articol despre convertirea cuvintelor în vectori](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ro.png) +![imagine dintr-un articol despre convertirea cuvintelor în vectori](../../../../../translated_images/ro/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Imagine din [acest articol](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ro/lessons/5-NLP/16-RNN/README.md b/translations/ro/lessons/5-NLP/16-RNN/README.md index 54c0aba2..d1a2745a 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/README.md +++ b/translations/ro/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Pentru a capta sensul unei secvențe de text, trebuie să utilizăm o altă arhitectură de rețea neuronală, numită **rețea neuronală recurentă**, sau RNN. În RNN, trecem propoziția prin rețea, un simbol la un moment dat, iar rețeaua produce un **stare**, pe care o trecem din nou prin rețea împreună cu următorul simbol. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ro.png) +![RNN](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.png) > Imagine realizată de autor @@ -61,7 +61,7 @@ Am discutat despre rețelele recurente care operează într-o singură direcție O rețea recurentă, fie unidirecțională, fie bidirecțională, captează anumite modele dintr-o secvență și le poate stoca într-un vector de stare sau le poate transmite ca ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent deasupra primului pentru a capta modele de nivel superior și a construi din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la noțiunea de **RNN multistrat**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare. -![Imagine care arată un RNN LSTM multistrat](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ro.jpg) +![Imagine care arată un RNN LSTM multistrat](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Imagine din [acest articol minunat](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López* diff --git a/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index ee0e305e..fd1bac21 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "O rețea recurentă, fie unidirecțională, fie bidirecțională, captează anumite modele dintr-o secvență și le poate stoca în vectorul de stare sau le poate transmite în ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent deasupra primului pentru a capta modele de nivel superior, construite din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la conceptul de **RNN multilayer**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare.\n", "\n", - "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ro.jpg)\n", + "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Imagine din [această postare minunată](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López*\n", "\n", diff --git a/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb index 6424ae56..cb47e9d1 100644 --- a/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ro/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Pentru a capta semnificația unei secvențe de text, vom folosi o arhitectură de rețea neuronală numită **rețea neuronală recurentă**, sau RNN. Când utilizăm o RNN, trecem propoziția prin rețea, un token pe rând, iar rețeaua produce un **statut**, pe care îl transmitem din nou rețelei împreună cu următorul token.\n", "\n", - "![Imagine care arată un exemplu de generare a unei rețele neuronale recurente.](../../../../../translated_images/rnn.27f5c29c53d727b5.ro.png)\n", + "![Imagine care arată un exemplu de generare a unei rețele neuronale recurente.](../../../../../translated_images/ro/rnn.27f5c29c53d727b5.png)\n", "\n", "Având secvența de intrare de tokeni $X_0,\\dots,X_n$, RNN creează o secvență de blocuri de rețea neuronală și antrenează această secvență cap-coadă folosind retropropagarea. Fiecare bloc de rețea ia o pereche $(X_i,S_i)$ ca intrare și produce $S_{i+1}$ ca rezultat. Statutul final $S_n$ sau ieșirea $Y_n$ este transmisă unui clasificator liniar pentru a produce rezultatul. Toate blocurile de rețea împărtășesc aceleași greutăți și sunt antrenate cap-coadă folosind o singură trecere de retropropagare.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rețelele recurente, unidirecționale sau bidirecționale, captează modele dintr-o secvență și le stochează în vectori de stare sau le returnează ca ieșire. La fel ca în cazul rețelelor convoluționale, putem construi un alt strat recurent care urmează primului pentru a capta modele de nivel superior, construite din modelele de nivel inferior extrase de primul strat. Acest lucru ne conduce la noțiunea de **RNN multilayer**, care constă din două sau mai multe rețele recurente, unde ieșirea stratului anterior este transmisă stratului următor ca intrare.\n", "\n", - "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ro.jpg)\n", + "![Imagine care arată un RNN multilayer cu memorie pe termen lung și scurt](../../../../../translated_images/ro/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Imagine din [această postare minunată](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) de Fernando López.*\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 3a5b4a2b..5d5858b6 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Modul în care vom antrena RNN pentru a genera text este următorul. La fiecare pas, vom lua o secvență de caractere de lungime `nchars` și vom cere rețelei să genereze următorul caracter de ieșire pentru fiecare caracter de intrare:\n", "\n", - "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ro.png)\n", + "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png)\n", "\n", "În funcție de scenariul concret, este posibil să dorim să includem și câteva caractere speciale, cum ar fi *sfârșit-de-secvență* ``. În cazul nostru, dorim doar să antrenăm rețeaua pentru generarea continuă de text, așa că vom fixa dimensiunea fiecărei secvențe să fie egală cu `nchars` tokeni. Prin urmare, fiecare exemplu de antrenament va consta din `nchars` intrări și `nchars` ieșiri (care sunt secvența de intrare deplasată cu un simbol spre stânga). Un minibatch va consta din mai multe astfel de secvențe.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index a8997068..e237640d 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Modul în care vom antrena RNN pentru a genera titluri de știri este următorul. La fiecare pas, vom lua un titlu, care va fi introdus într-un RNN, iar pentru fiecare caracter de intrare vom cere rețelei să genereze următorul caracter de ieșire:\n", "\n", - "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ro.png)\n", + "![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Pentru ultimul caracter al secvenței noastre, vom cere rețelei să genereze token-ul ``.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md index 2a96397e..cda5ba64 100644 --- a/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ro/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Rețelele Neuronale Recurente (RNN) și variantele lor cu celule cu porți, cum Acest lucru permite diferite arhitecturi neuronale, așa cum sunt prezentate în imaginea de mai jos: -![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ro.jpg) +![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Imagine din articolul [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Acest lucru permite diferite arhitecturi neuronale, așa cum sunt prezentate în Vom antrena acest RNN pentru a genera text pas cu pas. La fiecare pas, vom lua o secvență de caractere de lungime `nchars` și vom cere rețelei să genereze următorul caracter de ieșire pentru fiecare caracter de intrare: -![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ro.png) +![Imagine care arată un exemplu de generare RNN a cuvântului 'HELLO'.](../../../../../translated_images/ro/rnn-generate.56c54afb52f9781d.png) Când generăm text (în timpul inferenței), începem cu un **prompt**, care este trecut prin celulele RNN pentru a genera starea intermediară, iar apoi, din această stare, începe generarea. Generăm un caracter pe rând și transmitem starea și caracterul generat unei alte celule RNN pentru a genera următorul, până când generăm suficiente caractere. diff --git a/translations/ro/lessons/5-NLP/18-Transformers/README.md b/translations/ro/lessons/5-NLP/18-Transformers/README.md index 36113c5b..8ef2f2f9 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ro/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Cu RNN-uri, sarcinile de tip sequence-to-sequence sunt implementate prin două r **Mecanismele de Atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Modul în care este implementat constă în crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ul de ieșire. Astfel, atunci când generăm simbolul de ieșire yt, vom lua în considerare toate stările ascunse de intrare hi, cu diferiți coeficienți de greutate αt,i. -![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ro.png) +![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png) > Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matricea de atenție {αi,j} ar reprezenta gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrice: -![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ro.png) +![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png) > Figură din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Rezultatul pe care îl obținem cu încorporarea pozițională încorporează at Următorul pas este capturarea unor tipare în cadrul secvenței noastre. Pentru a face acest lucru, transformerele folosesc un mecanism de **auto-atenție**, care este, în esență, atenție aplicată aceleași secvențe ca intrare și ieșire. Aplicarea auto-atenției ne permite să luăm în considerare **contextul** din propoziție și să vedem care cuvinte sunt inter-relaționate. De exemplu, ne permite să vedem care cuvinte sunt referite prin coreferințe, cum ar fi *it*, și să luăm contextul în considerare: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ro.png) +![](../../../../../translated_images/ro/CoreferenceResolution.861924d6d384a7d6.png) > Imagine din [Blogul Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Deoarece fiecare poziție de intrare este mapată independent la fiecare poziți **BERT** (Bidirectional Encoder Representations from Transformers) este o rețea transformer foarte mare, cu mai multe straturi: 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date text (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul dobândește niveluri semnificative de înțelegere a limbajului, care pot fi apoi utilizate cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**. -![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ro.png) +![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Imagine [sursă](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 5be7bb0b..bd1c9084 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ro/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismele de atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Acest lucru este implementat prin crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ului de ieșire. Astfel, atunci când generăm simbolul de ieșire $y_t$, vom lua în considerare toate stările ascunse de intrare $h_i$, cu coeficienți de greutate diferiți $\\alpha_{t,i}$.\n", "\n", - "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ro.png)\n", + "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png)\n", "*Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matricea de atenție $\\{\\alpha_{i,j}\\}$ ar reprezenta gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrice:\n", "\n", - "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ro.png)\n", + "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura preluată din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) este o rețea transformatoare foarte mare, cu mai multe straturi: 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date text (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul absoarbe un nivel semnificativ de înțelegere a limbajului, care poate fi apoi valorificat cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**.\n", "\n", - "![Imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ro.png)\n", + "![Imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Există multe variații ale arhitecturilor Transformatoare, inclusiv BERT, DistilBERT, BigBird, OpenGPT3 și altele, care pot fi ajustate fin. Pachetul [HuggingFace](https://github.com/huggingface/) oferă un depozit pentru antrenarea multora dintre aceste arhitecturi cu PyTorch.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 8cd5548d..dd106609 100644 --- a/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ro/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mecanismele de atenție** oferă o modalitate de a pondera impactul contextual al fiecărui vector de intrare asupra fiecărei predicții de ieșire a RNN-ului. Acest lucru este implementat prin crearea unor scurtături între stările intermediare ale RNN-ului de intrare și RNN-ului de ieșire. Astfel, atunci când generăm simbolul de ieșire $y_t$, vom lua în considerare toate stările ascunse de intrare $h_i$, cu coeficienți de greutate diferiți $\\alpha_{t,i}$. \n", "\n", - "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ro.png)\n", + "![Imagine care arată un model encoder/decoder cu un strat de atenție aditiv](../../../../../translated_images/ro/encoder-decoder-attention.7a726296894fb567.png)\n", "*Modelul encoder-decoder cu mecanism de atenție aditiv din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citat din [acest articol de blog](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matricea de atenție $\\{\\alpha_{i,j}\\}$ reprezintă gradul în care anumite cuvinte de intrare contribuie la generarea unui cuvânt dat în secvența de ieșire. Mai jos este un exemplu al unei astfel de matrici:\n", "\n", - "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ro.png)\n", + "![Imagine care arată o aliniere exemplară găsită de RNNsearch-50, preluată din Bahdanau - arviz.org](../../../../../translated_images/ro/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figura preluată din [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -229,7 +229,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) este o rețea de transformatoare foarte mare, cu mai multe straturi, având 12 straturi pentru *BERT-base* și 24 pentru *BERT-large*. Modelul este mai întâi pre-antrenat pe un corpus mare de date textuale (Wikipedia + cărți) folosind antrenare nesupravegheată (prezicerea cuvintelor mascate într-o propoziție). În timpul pre-antrenării, modelul dobândește un nivel semnificativ de înțelegere a limbajului, care poate fi apoi valorificat cu alte seturi de date prin ajustare fină. Acest proces se numește **învățare transferabilă**.\n", "\n", - "![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ro.png)\n", + "![imagine de pe http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ro/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Există multe variații ale arhitecturilor Transformer, inclusiv BERT, DistilBERT, BigBird, OpenGPT3 și altele, care pot fi ajustate fin.\n", "\n", diff --git a/translations/ro/lessons/5-NLP/19-NER/README.md b/translations/ro/lessons/5-NLP/19-NER/README.md index 7e136725..a389bf14 100644 --- a/translations/ro/lessons/5-NLP/19-NER/README.md +++ b/translations/ro/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Deoarece trebuie să construim o corespondență unu-la-unu între token-uri și clase, putem antrena un model neuronal **many-to-many** din această imagine: -![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ro.jpg) +![Imagine care arată modele comune de rețele neuronale recurente.](../../../../../translated_images/ro/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Imagine din [acest articol de blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) de [Andrej Karpathy](http://karpathy.github.io/). Modelele de clasificare de token-uri NER corespund arhitecturii de rețea din partea dreaptă a imaginii.* diff --git a/translations/ro/lessons/5-NLP/README.md b/translations/ro/lessons/5-NLP/README.md index 65858429..a8c4513a 100644 --- a/translations/ro/lessons/5-NLP/README.md +++ b/translations/ro/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Procesarea Limbajului Natural -![Rezumat al sarcinilor NLP într-un desen](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ro.png) +![Rezumat al sarcinilor NLP într-un desen](../../../../translated_images/ro/ai-nlp.b22dcb8ca4707cea.png) În această secțiune, ne vom concentra pe utilizarea Rețelelor Neuronale pentru a rezolva sarcini legate de **Procesarea Limbajului Natural (NLP)**. Există multe probleme NLP pe care ne dorim ca calculatoarele să le poată rezolva: diff --git a/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md index ad95d969..7e8b443c 100644 --- a/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ro/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Poți deschide unul dintre modele, de exemplu **Biology → Flocking**. După ce deschizi modelul, vei fi dus la ecranul principal NetLogo. Iată un model exemplu care descrie populația de lupi și oi, având resurse finite (iarbă). -![Ecranul principal NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ro.png) +![Ecranul principal NetLogo](../../../../../translated_images/ro/NetLogo-Main.32653711ec1a01b3.png) > Captură de ecran de Dmitry Soshnikov diff --git a/translations/ro/lessons/README.md b/translations/ro/lessons/README.md index f5a8af88..0fd97600 100644 --- a/translations/ro/lessons/README.md +++ b/translations/ro/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Prezentare generală -![Prezentare generală într-un desen](../../../translated_images/ai-overview.0857791951d19500.ro.png) +![Prezentare generală într-un desen](../../../translated_images/ro/ai-overview.0857791951d19500.png) > Notiță ilustrată de [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ro/lessons/X-Extras/X1-MultiModal/README.md b/translations/ro/lessons/X-Extras/X1-MultiModal/README.md index 8ba3ede2..a6f8b103 100644 --- a/translations/ro/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ro/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ După succesul modelelor transformer în rezolvarea sarcinilor NLP, aceleași sa Ideea principală a CLIP este de a putea compara descrieri textuale cu o imagine și de a determina cât de bine corespunde imaginea descrierii. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ro.png) +![CLIP Architecture](../../../../../translated_images/ro/clip-arch.b3dbf20b4e8ed8be.png) > *Imagine din [acest articol](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Odată ce acest model este pre-antrenat, putem să-i oferim un lot de imagini ș Să presupunem că trebuie să clasificăm imagini între, să zicem, pisici, câini și oameni. În acest caz, putem oferi modelului o imagine și o serie de descrieri textuale: "*o imagine cu o pisică*", "*o imagine cu un câine*", "*o imagine cu un om*". În vectorul rezultat de 3 probabilități, trebuie doar să selectăm indexul cu cea mai mare valoare. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.ro.png) +![CLIP for Image Classification](../../../../../translated_images/ro/clip-class.3af42ef0b2b19369.png) > *Imagine din [acest articol](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Aflați mai multe despre VQGAN pe site-ul [Taming Transformers](https://compvis. Una dintre diferențele importante între VQGAN și GAN-ul tradițional este că cel din urmă poate produce o imagine decentă din orice vector de intrare, în timp ce VQGAN este probabil să producă o imagine incoerentă. Astfel, trebuie să ghidăm procesul de creare a imaginii, iar acest lucru poate fi realizat folosind CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.ro.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/ro/vqgan.5027fe05051dfa31.png) Pentru a genera o imagine corespunzătoare unei descrieri textuale, începem cu un vector de codificare aleatoriu care este transmis prin VQGAN pentru a produce o imagine. Apoi, CLIP este utilizat pentru a produce o funcție de pierdere care arată cât de bine corespunde imaginea descrierii textuale. Scopul este de a minimiza această pierdere, utilizând backpropagation pentru a ajusta parametrii vectorului de intrare. O bibliotecă excelentă care implementează VQGAN+CLIP este [Pixray](http://github.com/pixray/pixray). -![Imagine produsă de Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ro.png) | ![Imagine produsă de Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ro.png) | ![Imagine produsă de Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ro.png) +![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Imagine generată din descrierea *un portret în acuarelă, prim-plan, al unui tânăr profesor de literatură cu o carte* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unei tinere profesoare de informatică cu un computer* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unui profesor bătrân de matematică în fața unei table negre* @@ -75,7 +75,7 @@ Spre deosebire de CLIP, DALL-E primește atât textul, cât și imaginea ca un f Principala diferență între DALL-E 1 și 2 este că aceasta generează imagini și artă mai realiste. Exemple de imagini generate cu DALL-E: -![Imagine produsă de Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ro.png) | ![Imagine produsă de Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ro.png) | ![Imagine produsă de Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ro.png) +![Imagine produsă de Pixray](../../../../../translated_images/ro/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Imagine produsă de Pixray](../../../../../translated_images/ro/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Imagine generată din descrierea *un portret în acuarelă, prim-plan, al unui tânăr profesor de literatură cu o carte* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unei tinere profesoare de informatică cu un computer* | Imagine generată din descrierea *un portret în ulei, prim-plan, al unui profesor bătrân de matematică în fața unei table negre* diff --git a/translations/ru/README.md b/translations/ru/README.md index 95538070..2ac52be7 100644 --- a/translations/ru/README.md +++ b/translations/ru/README.md @@ -1,21 +1,21 @@ -[![Лицензия GitHub](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) -[![Участники GitHub](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) -[![Проблемы GitHub](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) -[![Запросы на включение](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) +[![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) +[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) +[![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -[![Наблюдатели GitHub](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) -[![Форки GitHub](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) -[![Звезды GitHub](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) +[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) +[![GitHub forks](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) +[![GitHub stars](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) [![Gitter](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) @@ -23,23 +23,23 @@ CO_OP_TRANSLATOR_METADATA: # Искусственный интеллект для начинающих — учебная программа -|![Конспект от @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ru.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/ru/ai-overview.0857791951d19500.webp)| |:---:| -| Искусственный интеллект для начинающих - _Конспект от [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI For Beginners - _Скетчноут от [@girlie_mac](https://twitter.com/girlie_mac)_ | -Изучите мир **Искусственного интеллекта** (ИИ) с нашей учебной программой на 12 недель и 24 урока! Включает практические занятия, викторины и лабораторные работы. Учебная программа подходит для начинающих и охватывает такие инструменты, как TensorFlow и PyTorch, а также этику в ИИ. +Исследуйте мир **Искусственного интеллекта** (ИИ) с нашей 12-недельной, 24-урочной учебной программой! Включает практические занятия, викторины и лабораторные работы. Программа подходит для начинающих и охватывает инструменты, такие как TensorFlow и PyTorch, а также этику в ИИ -### 🌐 Многоязычная поддержка +### 🌐 Поддержка нескольких языков -#### Поддерживается через GitHub Action (автоматизированно и всегда актуально) +#### Поддерживается через GitHub Action (автоматически и всегда актуально) -[Арабский](../ar/README.md) | [Бенгальский](../bn/README.md) | [Болгарский](../bg/README.md) | [Бирманский (Мьянма)](../my/README.md) | [Китайский (упрощённый)](../zh/README.md) | [Китайский (традиционный, Гонконг)](../hk/README.md) | [Китайский (традиционный, Макао)](../mo/README.md) | [Китайский (традиционный, Тайвань)](../tw/README.md) | [Хорватский](../hr/README.md) | [Чешский](../cs/README.md) | [Датский](../da/README.md) | [Нидерландский](../nl/README.md) | [Эстонский](../et/README.md) | [Финский](../fi/README.md) | [Французский](../fr/README.md) | [Немецкий](../de/README.md) | [Греческий](../el/README.md) | [Иврит](../he/README.md) | [Хинди](../hi/README.md) | [Венгерский](../hu/README.md) | [Индонезийский](../id/README.md) | [Итальянский](../it/README.md) | [Японский](../ja/README.md) | [Каннада](../kn/README.md) | [Корейский](../ko/README.md) | [Литовский](../lt/README.md) | [Малайский](../ms/README.md) | [Малаялам](../ml/README.md) | [Маратхи](../mr/README.md) | [Непальский](../ne/README.md) | [Нигерийский Пиджин](../pcm/README.md) | [Норвежский](../no/README.md) | [Персидский (Фарси)](../fa/README.md) | [Польский](../pl/README.md) | [Португальский (Бразилия)](../br/README.md) | [Португальский (Португалия)](../pt/README.md) | [Панджаби (Гурмукхи)](../pa/README.md) | [Румынский](../ro/README.md) | [Русский](./README.md) | [Сербский (кириллица)](../sr/README.md) | [Словацкий](../sk/README.md) | [Словенский](../sl/README.md) | [Испанский](../es/README.md) | [Суахили](../sw/README.md) | [Шведский](../sv/README.md) | [Тагалог (филиппинский)](../tl/README.md) | [Тамильский](../ta/README.md) | [Телугу](../te/README.md) | [Тайский](../th/README.md) | [Турецкий](../tr/README.md) | [Украинский](../uk/README.md) | [Урду](../ur/README.md) | [Вьетнамский](../vi/README.md) +[Арабский](../ar/README.md) | [Бенгальский](../bn/README.md) | [Болгарский](../bg/README.md) | [Бирманский (Мьянма)](../my/README.md) | [Китайский (упрощённый)](../zh/README.md) | [Китайский (традиционный, Гонконг)](../hk/README.md) | [Китайский (традиционный, Макао)](../mo/README.md) | [Китайский (традиционный, Тайвань)](../tw/README.md) | [Хорватский](../hr/README.md) | [Чешский](../cs/README.md) | [Датский](../da/README.md) | [Голландский](../nl/README.md) | [Эстонский](../et/README.md) | [Финский](../fi/README.md) | [Французский](../fr/README.md) | [Немецкий](../de/README.md) | [Греческий](../el/README.md) | [Иврит](../he/README.md) | [Хинди](../hi/README.md) | [Венгерский](../hu/README.md) | [Индонезийский](../id/README.md) | [Итальянский](../it/README.md) | [Японский](../ja/README.md) | [Каннада](../kn/README.md) | [Корейский](../ko/README.md) | [Литовский](../lt/README.md) | [Малайский](../ms/README.md) | [Малаялам](../ml/README.md) | [Маратхи](../mr/README.md) | [Непальский](../ne/README.md) | [Нигерийский пиджин](../pcm/README.md) | [Норвежский](../no/README.md) | [Персидский (фарси)](../fa/README.md) | [Польский](../pl/README.md) | [Португальский (Бразилия)](../br/README.md) | [Португальский (Португалия)](../pt/README.md) | [Пенджабский (Гурмукхи)](../pa/README.md) | [Румынский](../ro/README.md) | [Русский](./README.md) | [Сербский (кириллица)](../sr/README.md) | [Словацкий](../sk/README.md) | [Словенский](../sl/README.md) | [Испанский](../es/README.md) | [Суахили](../sw/README.md) | [Шведский](../sv/README.md) | [Тагалог (филиппинский)](../tl/README.md) | [Тамильский](../ta/README.md) | [Телугу](../te/README.md) | [Тайский](../th/README.md) | [Турецкий](../tr/README.md) | [Украинский](../uk/README.md) | [Урду](../ur/README.md) | [Вьетнамский](../vi/README.md) > **Предпочитаете клонировать локально?** -> Этот репозиторий включает более 50 языковых переводов, что значительно увеличивает размер загрузки. Чтобы клонировать без переводов, используйте sparse checkout: +> Этот репозиторий содержит более 50 переводов на разные языки, что значительно увеличивает размер скачивания. Чтобы клонировать без переводов, используйте sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners @@ -48,183 +48,180 @@ CO_OP_TRANSLATOR_METADATA: > Это даст вам всё необходимое для прохождения курса с гораздо более быстрой загрузкой. -**Если вы хотите, чтобы были поддержаны дополнительные языки перевода, список которых приведён [здесь](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Если вы хотите добавить поддержку дополнительных языков, список поддерживаемых языков доступен [здесь](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Присоединяйтесь к сообществу [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Чему вы научитесь +## Что вы изучите -**[Ментальная карта курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Майндмэп курса](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -В этой учебной программе вы изучите: +В этой учебной программе вы узнаете: -* Различные подходы к Искусственному интеллекту, включая "старую добрую" символическую модель с **представлением знаний** и рассуждениями ([GOFAI](https://ru.wikipedia.org/wiki/%D0%A1%D0%B8%D0%BC%D0%B2%D0%BE%D0%BB%D1%8C%D0%BD%D1%8B%D0%B9_%D0%B8%D1%81%D0%BA%D1%83%D1%81%D1%81%D1%82%D0%B2%D0%B5%D0%BD%D0%BD%D1%8B%D0%B9_%D0%B8%D0%BD%D1%82%D0%B5%D0%BB%D0%BB%D0%B5%D0%BA%D1%82)). -* **Нейронные сети** и **Глубокое обучение**, являющиеся основой современного ИИ. Мы покажем концепции этих важных тем с использованием кода в двух самых популярных фреймворках — [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). -* **Нейронные архитектуры** для работы с изображениями и текстом. Мы рассмотрим недавние модели, хотя некоторые из них могут немного отставать от передового уровня. -* Менее популярные подходы в ИИ, такие как **Генетические алгоритмы** и **Мультиагентные системы**. +* Различные подходы к искусственному интеллекту, включая «старый добрый» символьный подход с **Представлением знаний** и рассуждениями ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Нейронные сети** и **глубокое обучение**, которые лежат в основе современного ИИ. Мы проиллюстрируем концепции этих важных тем с помощью кода в двух из самых популярных фреймворков — [TensorFlow](http://Tensorflow.org) и [PyTorch](http://pytorch.org). +* **Нейронные архитектуры** для работы с изображениями и текстом. Мы рассмотрим современные модели, хотя возможно будет не полностью освещён самый передовой уровень. +* Менее популярные подходы к ИИ, такие как **генетические алгоритмы** и **мультиагентные системы**. -Что мы не будем рассматривать в этой учебной программе: +Чего мы не будем охватывать в этой учебной программе: > [Найдите все дополнительные ресурсы для этого курса в нашей коллекции Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Бизнес-кейсы использования **ИИ в бизнесе**. Рассмотрите возможность прохождения пути обучения [Введение в ИИ для бизнес-пользователей](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn или [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), разработанного в сотрудничестве с [INSEAD](https://www.insead.edu/). -* **Классическое машинное обучение**, хорошо описанное в нашей учебной программе [Machine Learning for Beginners](http://github.com/Microsoft/ML-for-Beginners). -* Практические приложения ИИ, созданные с использованием **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Для этого рекомендуем начать с модулей Microsoft Learn по [машинному зрению](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обработке естественного языка](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративному ИИ с помощью Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и другим. -* Специфичные облачные фреймворки МЛ, такие как [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Рассмотрите возможность использования путей обучения [Создание и эксплуатация решений машинного обучения с Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Создание и эксплуатация решений машинного обучения с Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **Разговорный ИИ** и **Чат-боты**. Существует отдельный путь обучения [Создание решений разговорного ИИ](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), также можно ознакомиться с [этой статьёй в блоге](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) для подробностей. -* **Глубокую математику**, лежащую в основе глубокого обучения. Для этого рекомендуем книгу [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) Иэна Гудфеллоу, Йошуа Бенджио и Аарона Курвиля, которая также доступна онлайн по адресу [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Бизнес-кейсы использования **ИИ в бизнесе**. Рассмотрите возможность прохождения учебного пути [Введение в ИИ для бизнес-пользователей](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) на Microsoft Learn или [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), разработанного в сотрудничестве с [INSEAD](https://www.insead.edu/). +* **Классическое машинное обучение**, которое хорошо описано в нашей [Учебной программе по машинному обучению для начинающих](http://github.com/Microsoft/ML-for-Beginners). +* Практические приложения ИИ, построенные с использованием **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Для этого рекомендуем начать с модулей Microsoft Learn по [видению](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [обработке естественного языка](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Генеративному ИИ с Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** и другим. +* Конкретные облачные ML-фреймворки, такие как [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum) или [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Рассмотрите использование учебных путей [Создайте и управляйте решениями машинного обучения с Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) и [Создайте и управляйте решениями машинного обучения с Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **Разговорный ИИ** и **чат-боты**. Существует отдельный учебный путь [Создание решений разговорного ИИ](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), а также вы можете ознакомиться с [этой статьей в блоге](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) для получения дополнительной информации. +* **Глубокая математика** за глубоким обучением. Для этого рекомендуем книгу [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) Иэна Гудфеллоу, Йошуа Бенжио и Аарона Куравиля, которая также доступна онлайн на [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Для плавного введения в темы _ИИ в облаке_ вы можете пройти путь обучения [Начало работы с искусственным интеллектом на Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Для мягкого введения в темы _ИИ в облаке_ вы можете пройти учебный путь [Начало работы с искусственным интеллектом на Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Содержание -| | Ссылка на урок | PyTorch/Keras/TensorFlow | Лабораторная | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | --------------------------------------------------------- | -| 0 | [Настройка курса](./lessons/0-course-setup/setup.md) | [Настройте среду разработки](./lessons/0-course-setup/how-to-run.md) | | +| | Ссылка на урок | PyTorch/Keras/TensorFlow | Лабораторная работа | +| :-: | :--------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ---------------------------------------------------------------- | +| 0 | [Настройка курса](./lessons/0-course-setup/setup.md) | [Настройка среды разработки](./lessons/0-course-setup/how-to-run.md) | | | I | [**Введение в ИИ**](./lessons/1-Intro/README.md) | | | | 01 | [Введение и история ИИ](./lessons/1-Intro/README.md) | - | - | -| II | **Символический ИИ** | -| 02 | [Представление знаний и экспертные системы](./lessons/2-Symbolic/README.md) | [Экспертные системы](./lessons/2-Symbolic/Animals.ipynb) / [Онтология](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф понятий](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| II | **Символьный ИИ** | +| 02 | [Представление знаний и экспертные системы](./lessons/2-Symbolic/README.md) | [Экспертные системы](./lessons/2-Symbolic/Animals.ipynb) / [Онтология](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Граф концепций](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Введение в нейронные сети**](./lessons/3-NeuralNetworks/README.md) ||| -| 03 | [Перцептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Блокнот](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лабораторная работа](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Многослойный перцептрон и создание собственного фреймворка](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Блокнот](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лабораторная работа](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Введение в фреймворки (PyTorch/TensorFlow) и переобучение](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторная работа](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Компьютерное зрение**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Исследуйте компьютерное зрение на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Введение в компьютерное зрение. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Блокнот](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лабораторная работа](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Сверточные нейронные сети](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Архитектуры CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лабораторная работа](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Предобученные сети и перенос обучения](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Приемы обучения](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторная работа](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Автоэнкодеры и вариационные автоэнкодеры (VAE)](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Генеративные состязательные сети и перенос художественного стиля](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | -| 11 | [Обнаружение объектов](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Лабораторная работа](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 03 | [Персептрон](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Тетрадь](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Лабораторная](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Многослойный персептрон и создание собственной платформы](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Тетрадь](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Лабораторная](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Введение в платформы (PyTorch/TensorFlow) и переобучение](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторная](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Компьютерное зрение**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Изучите компьютерное зрение на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Введение в компьютерное зрение. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Тетрадь](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Лабораторная](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Сверточные нейронные сети](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Архитектуры CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Лабораторная](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Предобученные сети и перенос обучения](./lessons/4-ComputerVision/08-TransferLearning/README.md) и [Трюки при обучении](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Лабораторная](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Автокодировщики и VAE](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Генеративные состязательные сети и перенос стилистики](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Обнаружение объектов](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Лабораторная](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Семантическая сегментация. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | -| V | [**Обработка естественного языка**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Исследуйте обработку естественного языка на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Обработка естественного языка**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Изучите обработку естественного языка на Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Представление текста. BoW/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | | 14 | [Семантические векторные представления слов. Word2Vec и GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | -| 15 | [Языковое моделирование. Обучение собственных эмбеддингов](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Лабораторная работа](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 15 | [Моделирование языка. Обучение собственных векторных представлений](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Лабораторная](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Рекуррентные нейронные сети](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Генеративные рекуррентные сети](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Лабораторная работа](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 17 | [Генеративные рекуррентные сети](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Лабораторная](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Трансформеры. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Распознавание именованных сущностей](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лабораторная работа](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Крупные языковые модели, программирование подсказок и задачи с малым объемом данных](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Другие техники ИИ** || | -| 21 | [Генетические алгоритмы](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Блокнот](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Глубокое обучение с подкреплением](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Лабораторная работа](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Мультиагентные системы](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 19 | [Распознавание именованных сущностей](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Лабораторная](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Крупные языковые модели, программирование подсказок и задачи Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Другие методы ИИ** || | +| 21 | [Генетические алгоритмы](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Тетрадь](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Глубокое обучение с подкреплением](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Лабораторная](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Системы с несколькими агентами](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Этика ИИ** | | | -| 24 | [Этика ИИ и ответственное ИИ](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципы ответственного ИИ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Этика ИИ и ответственный ИИ](./lessons/7-Ethics/README.md) | [Microsoft Learn: Принципы ответственного ИИ](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Дополнительно** | | | -| 25 | [Мультимодальные сети, CLIP и VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Блокнот](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Мультимодальные сети, CLIP и VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Тетрадь](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | -## Каждая лекция содержит +## Каждый урок содержит * Материалы для предварительного чтения -* Выполнимые блокноты Jupyter, которые часто специфичны для фреймворка (**PyTorch** или **TensorFlow**). Выполнимый блокнот также содержит много теоретического материала, поэтому для понимания темы необходимо пройти по крайней мере одну версию блокнота (либо PyTorch, либо TensorFlow). -* **Лабораторные работы**, доступные для некоторых тем, которые дают возможность попробовать применить изученный материал к конкретной задаче. -* Некоторые разделы содержат ссылки на модули [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), которые охватывают связанные темы. +* Исполняемые Jupyter тетради, которые часто являются специфичными для платформы (**PyTorch** или **TensorFlow**). Исполняемая тетрадь также содержит много теоретического материала, поэтому для понимания темы нужно пройти хотя бы одну версию тетради (либо PyTorch, либо TensorFlow). +* **Лабораторные работы** для некоторых тем, которые дают возможность применить изученный материал к конкретной задаче. +* Некоторые разделы содержат ссылки на модули [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum), охватывающие связанные темы. ## Начало работы -### 🎯 Новичок в ИИ? Начните здесь! +### 🎯 Новый в ИИ? Начните здесь! -Если вы совершенно новичок в ИИ и хотите быстрые, практические примеры, ознакомьтесь с нашими [**Примерами для начинающих**](./examples/README.md)! Среди них: +Если вы совсем новичок в ИИ и хотите быстрые практические примеры, ознакомьтесь с нашими [**Примерами для начинающих**](./examples/README.md)! Среди них: -- 🌟 **Привет, ИИ мир** - ваша первая программа на ИИ (распознавание шаблонов) -- 🧠 **Простая нейронная сеть** - создание нейронной сети с нуля -- 🖼️ **Классификатор изображений** - классификация изображений с подробными комментариями -- 💬 **Анализ тональности текста** — Анализ положительного/отрицательного текста +- 🌟 **Hello AI World** - Ваша первая программа на ИИ (распознавание шаблонов) +- 🧠 **Простая нейронная сеть** - Создайте нейронную сеть с нуля +- 🖼️ **Классификатор изображений** - Классификация изображений с подробными комментариями +- 💬 **Анализ настроения текста** — Анализ положительного/отрицательного текста -Эти примеры созданы, чтобы помочь вам понять концепции ИИ перед тем, как приступить к полному курсу. +Эти примеры созданы, чтобы помочь вам понять концепции ИИ перед погружением в полный учебный курс. -### 📚 Настройка полного курса +### 📚 Настройка полного учебного курса -- Мы создали [урок по настройке](./lessons/0-course-setup/setup.md), чтобы помочь вам с настройкой вашей среды разработки. -- Для преподавателей мы также создали [урок по настройке учебных программ](./lessons/0-course-setup/for-teachers.md)! -- Как [запустить код в VSCode или Codepace](./lessons/0-course-setup/how-to-run.md) +- Мы создали [урок по настройке](./lessons/0-course-setup/setup.md), чтобы помочь вам с настройкой вашей среды разработки. - Для преподавателей мы также создали [урок по настройке учебных программ](./lessons/0-course-setup/for-teachers.md)! +- Как [запустить код в VSCode или Codespace](./lessons/0-course-setup/how-to-run.md) -Следуйте этим шагам: +Выполните следующие шаги: -Fork репозитория: Нажмите кнопку "Fork" в правом верхнем углу этой страницы. +Форкните репозиторий: нажмите кнопку «Fork» в правом верхнем углу этой страницы. Клонируйте репозиторий: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Не забудьте поставить звезду (🌟) этому репозиторию, чтобы позже было проще его найти. +Не забудьте поставить звезду (🌟) этому репозиторию, чтобы потом его легче было найти. -## Встречайтесь с другими учениками +## Встречайте других учеников -Присоединяйтесь к нашему [официальному Discord-серверу ИИ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), чтобы познакомиться и пообщаться с другими учащимися этого курса и получить поддержку. +Присоединяйтесь к нашему [официальному Discord-серверу по ИИ](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum), чтобы познакомиться и пообщаться с другими учениками этого курса и получить поддержку. Если у вас есть отзывы о продукте или вопросы во время разработки, посетите наш [форум разработчиков Azure AI Foundry](https://aka.ms/foundry/forum) ## Викторины -> **Примечание о викторинах**: Все викторины находятся в папке Quiz-app в etc\quiz-app, или [онлайн здесь](https://ff-quizzes.netlify.app/). -Они связаны с уроками, приложение викторины можно запускать локально или развертывать в Azure; следуйте инструкциям в папке `quiz-app`. -Викторины постепенно переводятся на другие языки. +> **Примечание о викторинах**: Все викторины находятся в папке Quiz-app в etc\quiz-app, или [Онлайн здесь](https://ff-quizzes.netlify.app/). Они связаны с уроками, приложение викторин можно запускать локально или развертывать в Azure; следуйте инструкциям в папке `quiz-app`. Викторины постепенно локализуются. -## Нужна помощь +## Требуется помощь -Есть предложения или нашли ошибки орфографии или кода? Создайте issue или pull request. +Есть предложения или нашли орфографические ошибки или ошибки в коде? Создайте issue или pull request. ## Особая благодарность -* **✍️ Основной автор:** [Дмитрий Сошников](http://soshnikov.com), PhD -* **🔥 Редактор:** [Джен Лупер](https://twitter.com/jenlooper), PhD -* **🎨 Иллюстратор скетчей:** [Томоми Имура](https://twitter.com/girlie_mac) -* **✅ Создатель викторин:** [Латифа Белло](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Главные участники:** [Евгений Пищик](https://github.com/Pe4enIks) +* **✍️ Главный автор:** [Дмитрий Сошников](http://soshnikov.com), PhD +* **🔥 Редактор:** [Джен Лупер](https://twitter.com/jenlooper), PhD +* **🎨 Иллюстратор скетчнотов:** [Томоми Имура](https://twitter.com/girlie_mac) +* **✅ Создатель викторин:** [Латифа Белло](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Основные участники:** [Евгений Пищик](https://github.com/Pe4enIks) ## Другие учебные программы -Наша команда создаёт и другие учебные программы! Ознакомьтесь: +Наша команда также выпускает другие учебные программы! Ознакомьтесь с: ### LangChain -[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j для новичков](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js для новичков](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- ### Azure / Edge / MCP / Агенты -[![AZD для начинающих](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Edge AI для начинающих](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![MCP для начинающих](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) -[![AI агенты для начинающих](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AZD для новичков](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI для новичков](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP для новичков](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents для новичков](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) --- - -### Серия по генеративному ИИ -[![Генеративный ИИ для начинающих](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) -[![Генеративный ИИ (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) -[![Генеративный ИИ (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) -[![Генеративный ИИ (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) + +### Серия Generative AI +[![Generative AI для новичков](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) --- - + ### Основное обучение -[![Машинное обучение для начинающих](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) -[![Data Science для начинающих](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) -[![ИИ для начинающих](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) -[![Кибербезопасность для начинающих](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) -[![Веб-разработка для начинающих](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) -[![IoT для начинающих](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) -[![XR разработка для начинающих](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML для новичков](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science для новичков](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![ИИ для новичков](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Кибербезопасность для новичков](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Веб-разработка для новичков](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT для новичков](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![XR разработка для новичков](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) --- - + ### Серия Copilot -[![Copilot для совместного программирования с ИИ](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot для парного программирования с ИИ](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) [![Copilot для C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) -[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![Приключения Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) ## Получение помощи -Если у вас возникли трудности или есть вопросы по созданию ИИ-приложений, присоединяйтесь к сообществу учащихся и опытных разработчиков для обсуждений MCP. Это поддерживающее сообщество, где вопросы приветствуются, а знания свободно делятся. +Если застряли или есть вопросы по созданию приложений с ИИ. Присоединяйтесь к другим ученикам и опытным разработчикам для обсуждений MCP. Это дружелюбное сообщество, где вы можете задавать вопросы и свободно обмениваться знаниями. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Если у вас есть отзывы о продукте или возникли ошибки во время разработки, посетите: +Если у вас есть отзывы о продукте или ошибки при разработке, посетите: [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) @@ -232,5 +229,5 @@ Fork репозитория: Нажмите кнопку "Fork" в правом **Отказ от ответственности**: -Этот документ был переведен с помощью AI-сервиса перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия по обеспечению точности, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода. +Этот документ был переведен с использованием сервиса машинного перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для критически важной информации рекомендуется обращаться к услугам профессионального человеческого перевода. Мы не несем ответственности за любые недоразумения или неверные толкования, возникшие в результате использования данного перевода. \ No newline at end of file diff --git a/translations/ru/lessons/0-course-setup/how-to-run.md b/translations/ru/lessons/0-course-setup/how-to-run.md index d7c95a5c..2008cb1e 100644 --- a/translations/ru/lessons/0-course-setup/how-to-run.md +++ b/translations/ru/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Как запустить код -Этот курс содержит множество исполняемых примеров и лабораторных работ, которые вы захотите запустить. Для этого вам нужно иметь возможность выполнять код Python в Jupyter Notebooks, предоставленных в рамках этого курса. У вас есть несколько вариантов запуска кода: +В этой программе содержится множество исполняемых примеров и лабораторных работ, которые вы захотите запустить. Для этого вам нужна возможность выполнять код на Python в Jupyter Notebook, предоставленных в рамках этой программы. У вас есть несколько вариантов запуска кода: ## Запуск локально на вашем компьютере -Чтобы запустить код локально на вашем компьютере, вам нужно установить какую-либо версию Python. Я лично рекомендую установить **[miniconda](https://conda.io/en/latest/miniconda.html)** — это довольно легковесная установка, которая поддерживает менеджер пакетов `conda` для различных **виртуальных окружений** Python. +Для запуска кода локально на вашем компьютере необходима установка Python. Одним из рекомендованных вариантов является установка **[miniconda](https://conda.io/en/latest/miniconda.html)** — это достаточно легковесная установка, которая поддерживает менеджер пакетов `conda` для создания различных **виртуальных окружений** Python. -После установки miniconda вам нужно клонировать репозиторий и создать виртуальное окружение для использования в этом курсе: +После установки miniconda склонируйте репозиторий и создайте виртуальное окружение, которое будет использоваться для этого курса: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,51 +26,55 @@ conda activate ai4beg ### Использование Visual Studio Code с расширением Python -Вероятно, лучший способ использовать этот курс — открыть его в [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) с [расширением Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Эта программа лучше всего используется при открытии в [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) с расширением [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: После клонирования и открытия директории в VS Code, программа автоматически предложит вам установить расширения для Python. Вам также нужно будет установить miniconda, как описано выше. +> **Примечание**: После клонирования и открытия каталога в VS Code, он автоматически предложит установить расширение Python. Также необходимо будет установить miniconda, как описано выше. -> **Note**: Если VS Code предложит вам открыть репозиторий в контейнере, откажитесь от этого, чтобы использовать локальную установку Python. +> **Примечание**: Если VS Code предлагает открыть репозиторий в контейнере, стоит отказаться от этого, чтобы использовать локальную установку Python. ### Использование Jupyter в браузере -Вы также можете использовать Jupyter прямо в браузере на вашем компьютере. На самом деле, как классический Jupyter, так и Jupyter Hub предоставляют удобную среду разработки с автодополнением, подсветкой кода и другими функциями. +Вы также можете использовать среду Jupyter из браузера на вашем компьютере. Как классический Jupyter, так и JupyterHub предоставляют удобную среду разработки с автодополнением, подсветкой кода и др. -Чтобы запустить Jupyter локально, перейдите в директорию курса и выполните: +Чтобы запустить Jupyter локально, перейдите в каталог курса и выполните: ```bash jupyter notebook -``` -или +``` +или ```bash jupyterhub -``` +``` После этого вы сможете перейти к любому из файлов `.ipynb`, открыть их и начать работу. ### Запуск в контейнере -Альтернативой установке Python может быть запуск кода в контейнере. Поскольку наш репозиторий содержит специальную папку `.devcontainer`, которая описывает, как создать контейнер для этого репозитория, VS Code предложит вам открыть код в контейнере. Это потребует установки Docker и будет более сложным, поэтому мы рекомендуем этот вариант более опытным пользователям. +Альтернативой установке Python может быть запуск кода в контейнере. Поскольку в нашем репозитории есть специальная папка `.devcontainer`, которая содержит инструкции по сборке контейнера для этого репозитория, VS Code дает возможность заново открыть код в контейнере. Это требует установки Docker и более сложной настройки, поэтому мы рекомендуем этот вариант для более опытных пользователей. ## Запуск в облаке -Если вы не хотите устанавливать Python локально и имеете доступ к облачным ресурсам, хорошей альтернативой будет запуск кода в облаке. Существует несколько способов сделать это: +Если вы не хотите устанавливать Python локально и у вас есть доступ к облачным ресурсам, хорошей альтернативой будет запуск кода в облаке. Есть несколько способов сделать это: -* Использование **[GitHub Codespaces](https://github.com/features/codespaces)** — это виртуальная среда, созданная для вас на GitHub, доступная через интерфейс браузера VS Code. Если у вас есть доступ к Codespaces, просто нажмите кнопку **Code** в репозитории, начните Codespace, и вы сможете быстро приступить к работе. -* Использование **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) предоставляет бесплатные вычислительные ресурсы в облаке, чтобы вы могли протестировать код на GitHub. На главной странице есть кнопка для открытия репозитория в Binder — это быстро перенесет вас на сайт Binder, который создаст контейнер и запустит веб-интерфейс Jupyter для вас. +* Использование **[GitHub Codespaces](https://github.com/features/codespaces)** — виртуальная среда, созданная для вас на GitHub, доступная через браузерный интерфейс VS Code. Если у вас есть доступ к Codespaces, просто нажмите кнопку **Code** в репозитории, запустите codespace и начните работу быстро. +* Использование **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) предлагает бесплатные вычислительные ресурсы в облаке для людей, как вы, чтобы тестировать код с GitHub. На главной странице есть кнопка для открытия репозитория в Binder — это быстро приведет вас на сайт Binder, который создаст контейнер и запустит веб-интерфейс Jupyter для вас. -> **Note**: Чтобы предотвратить злоупотребления, Binder блокирует доступ к некоторым веб-ресурсам. Это может помешать работе кода, который загружает модели и/или наборы данных из Интернета. Вам могут понадобиться обходные пути. Кроме того, вычислительные ресурсы, предоставляемые Binder, довольно ограничены, поэтому обучение будет медленным, особенно в более сложных уроках. +> **Примечание**: Для предотвращения злоупотреблений у Binder заблокирован доступ к некоторым веб-ресурсам. Это может помешать работе некоторых частей кода, которые загружают модели и/или наборы данных из интернета. Возможно, потребуется поискать обходные пути. Кроме того, предоставляемые Binder вычислительные ресурсы довольно ограничены, поэтому обучение будет медленным, особенно в более поздних, сложных уроках. -## Запуск в облаке с поддержкой GPU +## Запуск в облаке с GPU -Некоторые из более поздних уроков этого курса значительно выиграют от поддержки GPU, так как иначе обучение будет очень медленным. Есть несколько вариантов, которые вы можете использовать, особенно если у вас есть доступ к облаку через [Azure для студентов](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или через ваше учебное заведение: +Некоторые из поздних уроков этой программы значительно выиграют от поддержки GPU. Например, обучение моделей может быть чрезвычайно медленным без нее. Есть несколько вариантов, если у вас есть доступ к облаку, например, через [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) или через ваше учебное учреждение: -* Создайте [виртуальную машину для Data Science](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и подключитесь к ней через Jupyter. Вы можете клонировать репозиторий прямо на эту машину и начать обучение. Виртуальные машины серии NC поддерживают GPU. +* Создайте [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) и подключитесь к ней через Jupyter. Вы можете клонировать репозиторий непосредственно на машину и начать учиться. Виртуальные машины серии NC поддерживают GPU. -> **Note**: Некоторые подписки, включая Azure для студентов, не предоставляют поддержку GPU по умолчанию. Вам может понадобиться запросить дополнительные GPU-ядра через техническую поддержку. +> **Примечание**: Некоторые подписки, включая Azure for Students, изначально не предоставляют поддержку GPU. Возможно, потребуется подать запрос в техническую поддержку для получения дополнительных ядер GPU. -* Создайте [Workspace Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и используйте функцию Notebook там. [Это видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показывает, как клонировать репозиторий в Azure ML Notebook и начать его использование. +* Создайте [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) и используйте функцию Notebook там. [Это видео](https://azure-for-academics.github.io/quickstart/azureml-papers/) показывает, как клонировать репозиторий в Azure ML notebook и начать работу. -Вы также можете использовать Google Colab, который предоставляет некоторую бесплатную поддержку GPU, и загружать туда Jupyter Notebooks для выполнения их поочередно. +Вы также можете использовать Google Colab, который предоставляет некоторую бесплатную поддержку GPU, и загружать туда Jupyter Notebook для их последовательного выполнения. -**Отказ от ответственности**: -Этот документ был переведен с использованием сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные интерпретации, возникающие в результате использования данного перевода. \ No newline at end of file +--- + + +**Отказ от ответственности**: +Этот документ был переведен с помощью сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия по обеспечению точности, просим учитывать, что автоматический перевод может содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для критически важной информации рекомендуется использовать профессиональный перевод, выполненный человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода. + \ No newline at end of file diff --git a/translations/ru/lessons/1-Intro/README.md b/translations/ru/lessons/1-Intro/README.md index 85b9bb8d..76c76f7f 100644 --- a/translations/ru/lessons/1-Intro/README.md +++ b/translations/ru/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Введение в искусственный интеллект -![Резюме содержания введения в ИИ в виде рисунка](../../../../translated_images/ai-intro.bf28d1ac4235881c.ru.png) +![Резюме содержания введения в ИИ в виде рисунка](../../../../translated_images/ru/ai-intro.bf28d1ac4235881c.webp) > Рисунок от [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Изначально компьютеры были изобретены [Чарльзом Бэббиджем](https://en.wikipedia.org/wiki/Charles_Babbage) для работы с числами, следуя четко определенной процедуре — алгоритму. Современные компьютеры, хотя и значительно более продвинуты, чем оригинальная модель, предложенная в XIX веке, все еще основываются на той же идее управляемых вычислений. Таким образом, можно запрограммировать компьютер на выполнение задачи, если мы знаем точную последовательность шагов, необходимых для достижения цели. -![Фото человека](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ru.png) +![Фото человека](../../../../translated_images/ru/dsh_age.d212a30d4e54fb5f.webp) > Фото от [Викки Сошниковой](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Одна из проблем при работе с термином **[интеллект](https://en.wikipedia.org/wiki/Intelligence)** заключается в том, что нет четкого определения этого термина. Можно утверждать, что интеллект связан с **абстрактным мышлением** или **самосознанием**, но мы не можем точно его определить. -![Фото кота](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ru.jpg) +![Фото кота](../../../../translated_images/ru/photo-cat.8c8e8fb760ffe457.webp) > [Фото](https://unsplash.com/photos/75715CVEJhI) от [Amber Kipp](https://unsplash.com/@sadmax) с Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | А как насчет ML? | | > |--------------|-----------| -> | Часть искусственного интеллекта, основанная на обучении компьютера решать задачу на основе данных, называется **машинным обучением**. Мы не будем рассматривать классическое машинное обучение в этом курсе — мы рекомендуем вам отдельный учебный курс [Машинное обучение для начинающих](http://aka.ms/ml-beginners). | ![ML для начинающих](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ru.png) | +> | Часть искусственного интеллекта, основанная на обучении компьютера решать задачу на основе данных, называется **машинным обучением**. Мы не будем рассматривать классическое машинное обучение в этом курсе — мы рекомендуем вам отдельный учебный курс [Машинное обучение для начинающих](http://aka.ms/ml-beginners). | ![ML для начинающих](../../../../translated_images/ru/ml-for-beginners.9e4fed176fd5817d.webp) | ## Краткая история ИИ Искусственный интеллект как область начался в середине двадцатого века. Изначально подход символического рассуждения был преобладающим и привел к ряду важных успехов, таких как экспертные системы — компьютерные программы, способные действовать как эксперт в некоторых ограниченных областях. Однако вскоре стало ясно, что такой подход плохо масштабируется. Извлечение знаний от эксперта, представление их в компьютере и поддержание базы знаний в актуальном состоянии оказалось очень сложной задачей и слишком дорогой для практического применения во многих случаях. Это привело к так называемой [зиме ИИ](https://en.wikipedia.org/wiki/AI_winter) в 1970-х годах. -Краткая история ИИ +Краткая история ИИ > Изображение от [Дмитрия Сошникова](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * Современные помощники, такие как Cortana, Siri или Google Assistant, являются гибридными системами, которые используют нейронные сети для преобразования речи в текст и распознавания нашего намерения, а затем применяют некоторые рассуждения или явные алгоритмы для выполнения необходимых действий. * В будущем мы можем ожидать полной нейронной модели, способной самостоятельно вести диалог. Недавние нейронные сети семейства GPT и [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) демонстрируют большие успехи в этом. -эволюция теста Тьюринга +эволюция теста Тьюринга > Изображение Дмитрия Сошникова, [фото](https://unsplash.com/photos/r8LmVbUKgns) Марина Абросимова, [Unsplash](https://unsplash.com/@abrosimova_marina_foto) ## Недавние исследования в области ИИ diff --git a/translations/ru/lessons/2-Symbolic/Animals.ipynb b/translations/ru/lessons/2-Symbolic/Animals.ipynb index 2769d2d2..50aa4a5d 100644 --- a/translations/ru/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ru/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Реализация экспертной системы для определения животных\n", + "# Реализация экспертной системы для животных\n", "\n", - "Пример из [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", + "Пример из [Учебной программы по ИИ для начинающих](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "В этом примере мы реализуем простую систему на основе знаний, которая определяет животное на основе некоторых физических характеристик. Система может быть представлена следующим деревом И-ИЛИ (это часть всего дерева, к которому можно легко добавить дополнительные правила):\n", + "В этом примере мы реализуем простую систему на основе знаний для определения животного на основе некоторых физических характеристик. Система может быть представлена в виде следующего дерева И-ИЛИ (это часть всего дерева, мы легко можем добавить несколько дополнительных правил):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ru.png)\n" + "![](../../../../../../translated_images/ru/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Наша собственная оболочка экспертных систем с обратным выводом\n", + "## Собственная оболочка экспертных систем с обратным выводом\n", "\n", - "Давайте попробуем определить простой язык для представления знаний на основе продукционных правил. Мы будем использовать классы Python в качестве ключевых слов для определения правил. По сути, будет три типа классов:\n", - "* `Ask` представляет вопрос, который нужно задать пользователю. Он содержит набор возможных ответов.\n", - "* `If` представляет правило и является просто синтаксическим сахаром для хранения содержания правила.\n", - "* `AND`/`OR` — это классы для представления ветвей дерева И/ИЛИ. Они просто хранят список аргументов внутри. Чтобы упростить код, вся функциональность определена в родительском классе `Content`.\n" + "Давайте попробуем определить простой язык для представления знаний на основе продукционных правил. Мы будем использовать классы Python в качестве ключевых слов для определения правил. По сути, будет 3 типа классов:\n", + "* `Ask` представляет вопрос, который необходимо задать пользователю. Он содержит набор возможных ответов.\n", + "* `If` представляет правило и является просто синтаксическим сахаром для хранения содержимого правила.\n", + "* `AND`/`OR` — классы для представления ветвей И/ИЛИ дерева. Они просто хранят список аргументов внутри. Для упрощения кода вся функциональность определяется в родительском классе `Content`.\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "В нашей системе рабочая память будет содержать список **фактов** в виде **пар атрибут-значение**. База знаний может быть определена как один большой словарь, который сопоставляет действия (новые факты, которые должны быть добавлены в рабочую память) с условиями, выраженными в виде AND-OR выражений. Также некоторые факты могут быть `Запрошены`.\n" + "В нашей системе рабочая память будет содержать список **фактов** в виде **пар атрибут-значение**. Базу знаний можно определить как один большой словарь, который сопоставляет действия (новые факты, которые должны быть добавлены в рабочую память) с условиями, выраженными в виде выражений И-ИЛИ. Также некоторые факты могут быть `Ask`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Чтобы выполнить обратное выведение, мы определим класс `Knowledgebase`. Он будет содержать:\n", - "* Рабочую `память` - словарь, который сопоставляет атрибуты со значениями\n", - "* `Правила` базы знаний в формате, определенном выше\n", + "Чтобы выполнить обратный вывод, мы определим класс `Knowledgebase`. Он будет содержать:\n", + "* Рабочую `memory` — словарь, который сопоставляет атрибуты со значениями\n", + "* Правила базы знаний `rules` в формате, определённом выше\n", "\n", "Два основных метода:\n", - "* `get` для получения значения атрибута, выполняя выведение при необходимости. Например, `get('color')` получит значение слота цвета (при необходимости запросит его и сохранит значение для дальнейшего использования в рабочей памяти). Если мы запросим `get('color:blue')`, метод запросит цвет, а затем вернет значение `y`/`n` в зависимости от цвета.\n", - "* `eval` выполняет фактическое выведение, то есть проходит по дереву AND/OR, оценивает подцели и т.д.\n" + "* `get` для получения значения атрибута, выполняя вывод при необходимости. Например, `get('color')` получит значение цветового слота (при необходимости спросит и сохранит значение для дальнейшего использования в рабочей памяти). Если мы вызовем `get('color:blue')`, он спросит про цвет, а затем вернёт значение `y`/`n` в зависимости от цвета.\n", + "* `eval` выполняет сам вывод, то есть обходит дерево И/ИЛИ, оценивает подцели и т.д.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Теперь давайте определим нашу базу знаний о животных и проведем консультацию. Обратите внимание, что этот вызов будет задавать вам вопросы. Вы можете отвечать, вводя `y`/`n` для вопросов с ответами \"да-нет\" или указывая число (0..N) для вопросов с более длинными вариантами ответов.\n" + "Теперь давайте определим нашу базу знаний о животных и проведем консультацию. Обратите внимание, что этот вызов будет задавать вам вопросы. Вы можете отвечать, вводя `y`/`n` для вопросов с ответом да-нет или указывая номер (0..N) для вопросов с более длинными вариантами ответов.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Использование PyKnow для прямого вывода\n", + "## Использование Experta для прямого вывода\n", "\n", - "В следующем примере мы попробуем реализовать прямой вывод, используя одну из библиотек для представления знаний, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** — это библиотека для создания систем прямого вывода на Python, разработанная по аналогии с классической старой системой [CLIPS](http://www.clipsrules.net/index.html).\n", + "В следующем примере мы попробуем реализовать прямой вывод с использованием одной из библиотек для представления знаний, [Experta](https://github.com/nilp0inter/experta). **Experta** — это библиотека для создания систем прямого вывода на Python, которая разработана так, чтобы быть похожей на классическую старую систему [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Мы могли бы реализовать прямую цепочку вывода и самостоятельно без особых трудностей, но наивные реализации обычно не очень эффективны. Для более эффективного сопоставления правил используется специальный алгоритм [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Мы также могли бы реализовать прямое связывание сами без особых проблем, но наивные реализации, как правило, не очень эффективны. Для более эффективного сопоставления правил используется специальный алгоритм [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Мы определим нашу систему как класс, который является подклассом `KnowledgeEngine`. Каждое правило определяется отдельной функцией с аннотацией `@Rule`, которая указывает, когда правило должно сработать. Внутри правила мы можем добавлять новые факты с помощью функции `declare`, и добавление этих фактов приведет к вызову дополнительных правил движком прямого вывода.\n" + "Мы определим нашу систему как класс, который наследуется от `KnowledgeEngine`. Каждое правило определяется отдельной функцией с аннотацией `@Rule`, которая указывает, когда правило должно сработать. Внутри правила мы можем добавлять новые факты с помощью функции `declare`, и добавление этих фактов приведет к тому, что некоторый другие правила будут вызваны механизмом прямого вывода.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Как только мы определили базу знаний, мы заполняем рабочую память некоторыми исходными фактами, а затем вызываем метод `run()`, чтобы выполнить вывод. В результате вы можете увидеть, что новые выведенные факты добавляются в рабочую память, включая конечный факт о животном (если мы правильно задали все исходные факты).\n" + "Как только мы определили базу знаний, мы заполняем нашу рабочую память некоторыми начальными фактами, а затем вызываем метод `run()` для выполнения вывода. В результате вы можете видеть, что новые выведенные факты добавляются в рабочую память, включая окончательный факт об животном (если мы правильно настроили все начальные факты).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Отказ от ответственности**: \nЭтот документ был переведен с использованием сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода.\n" + "---\n\n\n**Отказ от ответственности**: \nЭтот документ был переведен с помощью автоматического сервиса перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия по обеспечению точности, имейте в виду, что машинный перевод может содержать ошибки или неточности. Оригинальный документ на исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется использовать профессиональный перевод, выполненный человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования данного перевода.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T11:30:36+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T10:39:07+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ru" } diff --git a/translations/ru/lessons/2-Symbolic/README.md b/translations/ru/lessons/2-Symbolic/README.md index 042349b8..3f8aa18f 100644 --- a/translations/ru/lessons/2-Symbolic/README.md +++ b/translations/ru/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ -# Представление знаний и экспертные системы +# Представление Знаний и Экспертные Системы -![Сводка по символическому ИИ](../../../../translated_images/ai-symbolic.715a30cb610411a6.ru.png) +![Резюме содержимого Символического ИИ](../../../../../../translated_images/ru/ai-symbolic.715a30cb610411a6.webp) -> Скетчноут от [Tomomi Imura](https://twitter.com/girlie_mac) +> Набросок от [Tomomi Imura](https://twitter.com/girlie_mac) -Поиск искусственного интеллекта основан на стремлении к знаниям, чтобы понимать мир так же, как это делают люди. Но как это можно осуществить? +Поиск искусственного интеллекта основан на поиске знаний, чтобы понять мир так же, как это делают люди. Но как можно это сделать? -## [Тест перед лекцией](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Викторина перед лекцией](https://ff-quizzes.netlify.app/en/ai/quiz/3) -В первые дни развития ИИ популярным был подход сверху вниз к созданию интеллектуальных систем (обсуждался в предыдущем уроке). Идея заключалась в том, чтобы извлечь знания от людей в машиночитаемую форму, а затем использовать их для автоматического решения задач. Этот подход основывался на двух ключевых идеях: +В ранние дни ИИ популярным был подход сверху вниз к созданию интеллектуальных систем (обсуждалось в предыдущем уроке). Идея состояла в том, чтобы извлечь знания от людей в некоторую машиночитаемую форму, а затем использовать их для автоматического решения задач. Этот подход основывался на двух больших идеях: * Представление знаний * Рассуждение ## Представление знаний -Одним из важных понятий в символическом ИИ являются **знания**. Важно отличать знания от *информации* или *данных*. Например, можно сказать, что книги содержат знания, потому что изучая их, можно стать экспертом. Однако то, что содержится в книгах, на самом деле называется *данными*, и, читая книги и интегрируя эти данные в нашу модель мира, мы превращаем данные в знания. +Одним из важных понятий в Символическом ИИ является **знание**. Важно отличать знание от *информации* или *данных*. Например, можно сказать, что книги содержат знания, потому что можно изучать книги и стать экспертом. Однако то, что содержат книги, на самом деле называется *данными*, и, читая книги и интегрируя эти данные в нашу модель мира, мы преобразуем их в знания. -> ✅ **Знания** — это то, что содержится в нашей голове и представляет наше понимание мира. Они приобретаются в процессе активного **обучения**, который интегрирует полученную информацию в нашу активную модель мира. +> ✅ **Знание** — это то, что содержится в нашей голове и представляет наше понимание мира. Оно получается в процессе активного **обучения**, которое интегрирует фрагменты информации, получаемой нами, в нашу активную модель мира. -Чаще всего мы не даем строгого определения знаниям, но связываем их с другими близкими понятиями, используя [пирамиду DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Она включает следующие понятия: +Как правило, мы не даём строгого определения знания, но согласуем его с другими связанными понятиями с помощью [пирамиды DIKW](https://ru.wikipedia.org/wiki/Пирамида_DIKW). Она включает в себя следующие концепции: -* **Данные** — это то, что представлено на физическом носителе, например, в виде текста или устной речи. Данные существуют независимо от человека и могут передаваться между людьми. +* **Данные** — это нечто, представленное в физической среде, например, в виде письменного текста или устных слов. Данные существуют независимо от людей и могут передаваться между ними. * **Информация** — это то, как мы интерпретируем данные в своей голове. Например, когда мы слышим слово *компьютер*, у нас есть некоторое представление о том, что это такое. -* **Знания** — это информация, интегрированная в нашу модель мира. Например, изучив, что такое компьютер, мы начинаем понимать, как он работает, сколько он стоит и для чего его можно использовать. Эта сеть взаимосвязанных понятий формирует наши знания. -* **Мудрость** — это еще один уровень нашего понимания мира, который представляет собой *метазнания*, например, представление о том, как и когда следует использовать знания. +* **Знание** — это информация, интегрированная в нашу модель мира. Например, когда мы узнаём, что такое компьютер, у нас появляются идеи о том, как он работает, сколько стоит и для чего используется. Эта сеть взаимосвязанных концепций формирует наше знание. +* **Мудрость** — это ещё один уровень нашего понимания мира, который представляет *метазнание*, например, представление о том, как и когда знания должны использоваться. - + -*Изображение [из Википедии](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0* +*Изображение [из Википедии](https://commons.wikimedia.org/w/index.php?curid=37705247), автор Longlivetheux - собственная работа, лицензия CC BY-SA 4.0* -Таким образом, задача **представления знаний** заключается в поиске эффективного способа представления знаний внутри компьютера в виде данных, чтобы они могли быть использованы автоматически. Это можно рассматривать как спектр: +Таким образом, задача **представления знаний** состоит в том, чтобы найти эффективный способ представления знаний внутри компьютера в виде данных, чтобы они могли использоваться автоматически. Это можно рассматривать как спектр: -![Спектр представления знаний](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ru.png) +![Спектр представления знаний](../../../../../../translated_images/ru/knowledge-spectrum.b60df631852c0217.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) -* Слева находятся очень простые типы представления знаний, которые могут быть эффективно использованы компьютерами. Самый простой — алгоритмический, когда знания представлены в виде компьютерной программы. Однако это не лучший способ представления знаний, так как он не гибкий. Знания в нашей голове часто не алгоритмичны. -* Справа находятся представления, такие как естественный текст. Это самый мощный способ, но он не может быть использован для автоматического рассуждения. +* Слева находятся очень простые типы представления знаний, которые могут быть эффективно использованы компьютерами. Самый простой — алгоритмический, когда знание представлено компьютерной программой. Однако это не самый лучший способ представления знаний, так как он не гибок. Знания в нашей голове часто неалгоритмичны. +* Справа — представления, такие как естественный текст. Это самый мощный способ, но он не может быть использован для автоматического рассуждения. -> ✅ Подумайте минуту о том, как вы представляете знания в своей голове и переводите их в заметки. Есть ли формат, который помогает вам лучше запоминать? +> ✅ Подумайте минуту, как вы представляете знания в своей голове и как преобразуете их в заметки. Есть ли какой-то формат, который помогает вам лучше запоминать? -## Классификация представлений знаний в компьютере +## Классификация методов представления знаний в компьютерах -Мы можем классифицировать различные методы представления знаний в компьютере по следующим категориям: +Мы можем классифицировать различные методы компьютерного представления знаний следующим образом: -* **Сетевые представления** основаны на том, что в нашей голове есть сеть взаимосвязанных понятий. Мы можем попытаться воспроизвести такие же сети в виде графа внутри компьютера — так называемую **семантическую сеть**. +* **Сетевые представления** основаны на том, что у нас в голове есть сеть взаимосвязанных понятий. Мы можем попытаться воспроизвести такие же сети в виде графа в компьютере — так называемую **семантическую сеть**. -1. **Триплеты объект-атрибут-значение** или **пары атрибут-значение**. Поскольку граф может быть представлен в компьютере как список узлов и ребер, мы можем представить семантическую сеть в виде списка триплетов, содержащих объекты, атрибуты и значения. Например, мы создаем следующие триплеты о языках программирования: +1. **Тройки "Объект-Атрибут-Значение"** или **пары атрибут-значение**. Поскольку граф в компьютере может быть представлен списком узлов и рёбер, семантическую сеть можно представить списком троек, содержащих объекты, атрибуты и значения. Например, можно построить такие тройки о языках программирования: Объект | Атрибут | Значение -------|---------|--------- -Python | является | Языком без типизации -Python | изобретен | Гвидо ван Россумом -Python | синтаксис блока | отступы -Язык без типизации | не имеет | определения типов +Python | является | необъявленным языком +Python | создан | Гвидо ван Россумом +Python | синтаксис блоков | отступы +Необъявленный язык | не имеет | определений типов -> ✅ Подумайте, как триплеты могут быть использованы для представления других типов знаний. +> ✅ Подумайте, как тройки можно использовать для представления других типов знаний. -2. **Иерархические представления** подчеркивают тот факт, что мы часто создаем иерархию объектов в своей голове. Например, мы знаем, что канарейка — это птица, и у всех птиц есть крылья. Мы также имеем представление о том, какого цвета обычно канарейки и какова их скорость полета. +2. **Иерархические представления** подчёркивают, что мы часто создаём иерархии объектов в голове. Например, мы знаем, что канарейка — это птица, а у всех птиц есть крылья. Мы также представляем, какого цвета обычно канарейка и какова их скорость полёта. - - **Представление в виде фреймов** основано на представлении каждого объекта или класса объектов в виде **фрейма**, который содержит **слоты**. Слоты могут иметь возможные значения по умолчанию, ограничения значений или хранимые процедуры, которые можно вызвать для получения значения слота. Все фреймы образуют иерархию, похожую на иерархию объектов в языках программирования, основанных на объектно-ориентированном подходе. - - **Сценарии** — это особый вид фреймов, которые представляют сложные ситуации, разворачивающиеся во времени. + - **Представление в виде фреймов** основано на том, что каждый объект или класс объектов представлен как **фрейм** с **слотами**. Слоты имеют возможные значения по умолчанию, ограничения или хранимые процедуры, которые можно вызвать для получения значения слота. Все фреймы формируют иерархию, похожую на иерархию объектов в объектно-ориентированных языках программирования. + - **Сценарии** — особый вид фреймов, которые представляют сложные ситуации, разворачивающиеся во времени. **Python** Слот | Значение | Значение по умолчанию | Интервал | ------|---------|-----------------------|----------| +-----|----------|-----------------------|----------| Имя | Python | | | -Является | Языком без типизации | | | -Регистр переменных | | CamelCase | | +Является | необъявленным языком | | | +Регистронезависимость переменных | | CamelCase | | Длина программы | | | 5-5000 строк | -Синтаксис блока | Отступ | | | +Синтаксис блоков | отступы | | | -3. **Процедурные представления** основаны на представлении знаний в виде списка действий, которые могут быть выполнены при возникновении определенного условия. - - Правила продукций — это конструкции вида "если-то", которые позволяют делать выводы. Например, у врача может быть правило, гласящее, что **ЕСЛИ** у пациента высокая температура **ИЛИ** высокий уровень С-реактивного белка в анализе крови, **ТО** у него воспаление. Как только мы сталкиваемся с одним из условий, мы можем сделать вывод о воспалении, а затем использовать его для дальнейших рассуждений. - - Алгоритмы можно считать другой формой процедурного представления, хотя они почти никогда не используются напрямую в системах, основанных на знаниях. +3. **Процедурные представления** основаны на представлении знаний в виде списка действий, которые могут быть выполнены при наступлении определённого условия. + - Правила продукций — это выражения типа "если-то", которые позволяют делать выводы. Например, у врача может быть правило: **ЕСЛИ** у пациента высокая температура **ИЛИ** высокий уровень С-реактивного белка в анализе крови, **ТО** у него воспаление. При появлении одного из условий можно сделать вывод о воспалении и использовать его в дальнейших рассуждениях. + - Алгоритмы можно рассматривать как другой вид процедурного представления, хотя их почти никогда не используют напрямую в системах на основе знаний. -4. **Логика** была изначально предложена Аристотелем как способ представления универсальных человеческих знаний. - - Логика предикатов как математическая теория слишком богата, чтобы быть вычислимой, поэтому обычно используется ее подмножество, например, Хорновы клаузы, применяемые в Prolog. - - Описательная логика — это семейство логических систем, используемых для представления и рассуждения о иерархиях объектов и распределенных представлениях знаний, таких как *семантическая сеть*. +4. **Логика** изначально была предложена Аристотелем как способ представления универсальных человеческих знаний. + - Предикатная логика как математическая теория слишком объёмна для вычисления, поэтому обычно используется её подмножество, например, роговые предложения, применяемые в Prolog. + - Описательная логика — это семейство логических систем, используемых для представления и рассуждения о иерархиях объектов и распределённых представлениях знаний, таких как *семантическая паутина*. ## Экспертные системы -Одним из ранних успехов символического ИИ стали так называемые **экспертные системы** — компьютерные системы, разработанные для выполнения роли эксперта в ограниченной области задач. Они основывались на **базе знаний**, извлеченной от одного или нескольких человеческих экспертов, и содержали **выводящий механизм**, который выполнял рассуждения на ее основе. +Одним из ранних успехов символического ИИ стали так называемые **экспертные системы** — компьютерные системы, предназначенные для работы в качестве эксперта в ограниченной предметной области. Они базировались на **базе знаний**, извлечённой от одного или нескольких человеческих экспертов, и содержали **выводящую машину**, выполняющую логическое рассуждение на её основе. -![Архитектура человека](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ru.png) | ![Система на основе знаний](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ru.png) +![Человеческая архитектура](../../../../../../translated_images/ru/arch-human.5d4d35f1bba3ab1c.webp) | ![Система на основе знаний](../../../../../../translated_images/ru/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Упрощенная структура нейронной системы человека | Архитектура системы на основе знаний +Упрощённая структура человеческой нервной системы | Архитектура системы на основе знаний -Экспертные системы строятся подобно системе рассуждений человека, которая содержит **кратковременную память** и **долговременную память**. Аналогично, в системах на основе знаний выделяются следующие компоненты: +Экспертные системы строятся по образцу системы рассуждения человека, которая содержит **кратковременную память** и **долговременную память**. Аналогично, в системах на основе знаний различают следующие компоненты: -* **Память задачи**: содержит знания о задаче, которая в данный момент решается, например, температура или давление пациента, есть ли у него воспаление и т.д. Эти знания также называются **статическими знаниями**, так как они представляют собой снимок того, что мы знаем о задаче в данный момент — так называемое *состояние задачи*. -* **База знаний**: представляет собой долговременные знания о предметной области. Она извлекается вручную от человеческих экспертов и не изменяется от консультации к консультации. Поскольку она позволяет переходить от одного состояния задачи к другому, ее также называют **динамическими знаниями**. -* **Выводящий механизм**: организует весь процесс поиска в пространстве состояний задачи, задавая вопросы пользователю, когда это необходимо. Он также отвечает за поиск подходящих правил для применения к каждому состоянию. +* **Память задачи**: содержит знания о текущей решаемой задаче, например, температуру или давление пациента, наличие у него воспаления и т. д. Эти знания называются **статическими**, так как содержат снимок того, что мы сейчас знаем о задаче — так называемое *состояние задачи*. +* **База знаний**: представляет долгосрочные знания о предметной области. Извлекается вручную у экспертов и не меняется во время консультаций. Поскольку она позволяет переходить от одного состояния задачи к другому, называют также **динамическими знаниями**. +* **Выводящая машина**: управляет всем процессом поиска в пространстве состояний задачи, задаёт вопросы пользователю при необходимости. Также отвечает за поиск правил, применимых в каждом состоянии. -В качестве примера рассмотрим следующую экспертную систему для определения животного на основе его физических характеристик: +В качестве примера рассмотрим следующую экспертную систему определения животного по его физическим характеристикам: -![AND-OR дерево](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ru.png) +![AND-OR дерево](../../../../../../translated_images/ru/AND-OR-Tree.5592d2c70187f283.webp) > Изображение от [Dmitry Soshnikov](http://soshnikov.com) -Эта диаграмма называется **AND-OR деревом**, и она представляет собой графическое отображение набора правил продукций. Рисование дерева полезно на начальном этапе извлечения знаний от эксперта. Для представления знаний внутри компьютера удобнее использовать правила: +Эта диаграмма называется **AND-OR деревом**, и представляет собой графическое изображение набора правил продукций. Рисование дерева полезно на начальном этапе извлечения знаний от эксперта. Для представления знаний внутри компьютера удобнее использовать правила: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Вы можете заметить, что каждое условие в левой части правила и действие фактически являются триплетами объект-атрибут-значение (OAV). **Рабочая память** содержит набор триплетов OAV, соответствующих задаче, которая в данный момент решается. **Механизм правил** ищет правила, для которых условие выполнено, и применяет их, добавляя новый триплет в рабочую память. +Вы можете заметить, что каждое условие слева от правила и действие по сути являются тройками "объект-атрибут-значение" (OAV). **Рабочая память** содержит набор OAV-троек, соответствующих решаемой в данный момент задаче. **Машина правил** ищет правила, для которых условие удовлетворено, и применяет их, добавляя новую тройку в рабочую память. -> ✅ Нарисуйте свое собственное AND-OR дерево на тему, которая вам интересна! +> ✅ Нарисуйте собственное AND-OR дерево на любую вам интересующую тему! ### Прямой и обратный вывод -Процесс, описанный выше, называется **прямым выводом**. Он начинается с некоторой начальной информации о задаче, доступной в рабочей памяти, и затем выполняет следующий цикл рассуждений: +Процесс, описанный выше, называется **прямым выводом**. Он начинается с некоторых исходных данных о задаче, содержащихся в рабочей памяти, и затем выполняет следующий цикл рассуждений: 1. Если целевой атрибут присутствует в рабочей памяти — остановиться и выдать результат. -2. Найти все правила, условия которых в данный момент выполнены — получить **конфликтный набор** правил. -3. Выполнить **разрешение конфликта** — выбрать одно правило, которое будет выполнено на этом шаге. Существуют разные стратегии разрешения конфликта: - - Выбрать первое применимое правило в базе знаний. - - Выбрать случайное правило. - - Выбрать *более специфичное* правило, то есть то, которое удовлетворяет наибольшему числу условий в левой части (LHS). -4. Применить выбранное правило и вставить новый кусок знаний в состояние задачи. -5. Повторить с шага 1. +2. Найти все правила, условие которых удовлетворено в данный момент — получить **множество конфликтных правил**. +3. Выполнить **разрешение конфликта** — выбрать одно правило для применения на этом шаге. Существуют разные стратегии разрешения конфликтов: + - Выбрать первое применимое правило из базы знаний + - Выбрать случайное правило + - Выбрать *более специфическое* правило, то есть правило, у которого условие (слева) удовлетворяет наибольшему числу критериев. +4. Применить выбранное правило и добавить новый фрагмент знаний в состояние задачи. +5. Повторять с шага 1. -Однако в некоторых случаях мы можем захотеть начать с пустых знаний о задаче и задавать вопросы, которые помогут нам прийти к выводу. Например, при медицинской диагностике мы обычно не проводим все медицинские анализы заранее, прежде чем начать диагностику пациента. Мы скорее хотим проводить анализы, когда нужно принять решение. +Однако в некоторых случаях нам может потребоваться начать с пустых знаний о проблеме и задавать вопросы, которые помогут прийти к заключению. Например, при медицинской диагностике обычно не проводят все анализы заранее, а проводят их по мере необходимости. -Этот процесс можно смоделировать с помощью **обратного вывода**. Он управляется **целью** — значением атрибута, которое мы пытаемся найти: +Этот процесс моделируется с помощью **обратного вывода**. Он управляется **целью** — значением атрибута, которое мы хотим найти: -1. Выбрать все правила, которые могут дать нам значение цели (то есть с целью в правой части (RHS)) — конфликтный набор. -1. Если для этого атрибута нет правил или есть правило, говорящее, что значение следует спросить у пользователя — спросить его, иначе: -1. Использовать стратегию разрешения конфликта, чтобы выбрать одно правило, которое мы будем использовать как *гипотезу* — мы попробуем доказать его. +1. Выбрать все правила, которые могут дать значение цели (т. е. с нужным значением в "правой части" — RHS) — получить множество конфликтных правил. +1. Если таких правил нет или есть правило, которое требует запроса значения у пользователя — запросить значение, иначе: +1. Применить стратегию разрешения конфликта для выбора одного правила, которое будет использовано как *гипотеза* — мы постараемся её доказать. 1. Рекурсивно повторить процесс для всех атрибутов в левой части правила, пытаясь доказать их как цели. -1. Если в какой-то момент процесс терпит неудачу — использовать другое правило на шаге 3. +1. Если в любой момент процесс завершится неудачей — использовать другое правило на шаге 3. -> ✅ В каких ситуациях прямой вывод более уместен? А как насчет обратного вывода? +> ✅ В каких ситуациях более уместен прямой вывод? А в каких — обратный? ### Реализация экспертных систем -Экспертные системы могут быть реализованы с использованием различных инструментов: +Экспертные системы можно реализовать с помощью разных инструментов: -* Программирование их напрямую на каком-либо языке высокого уровня. Это не лучший вариант, так как главное преимущество системы на основе знаний заключается в том, что знания отделены от вывода, и потенциально эксперт в предметной области должен иметь возможность писать правила, не понимая деталей процесса вывода. -* Использование **оболочки экспертной системы**, то есть системы, специально разработанной для заполнения знаниями с использованием языка представления знаний. +* Программирование их напрямую на каком-либо высокоуровневом языке. Это не лучшая идея, так как главное преимущество системы на основе знаний — разделение знаний и механизма вывода, и эксперт по предметной области потенциально может писать правила без понимания деталей процесса вывода. +* Использование **экспертных оболочек** — систем, специально созданных для наполнения знаниями с помощью языка представления знаний. -## ✍️ Упражнение: вывод о животных +## ✍️ Упражнение: Определение животного -См. [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) для примера реализации экспертной системы с прямым и обратным выводом. +Посмотрите [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) для примера реализации экспертной системы с прямым и обратным выводом. -> **Примечание**: Этот пример довольно прост и лишь дает представление о том, как выглядит экспертная система. Как только вы начнете создавать такую систему, вы заметите некоторое *интеллектуальное* поведение только после достижения определенного количества правил, около 200+. В какой-то момент правила становятся слишком сложными, чтобы держать их все в голове, и в этот момент вы можете начать задаваться вопросом, почему система принимает определенные решения. Однако важной характеристикой систем на основе знаний является то, что вы всегда можете *объяснить*, как было принято любое из решений. +> **Примечание**: этот пример достаточно простой и даёт лишь общее представление о том, как выглядит экспертная система. Когда вы начнёте создавать такую систему, вы заметите *интеллектуальное* поведение лишь при достижении определённого количества правил, примерно от 200 и выше. В какой-то момент правила становятся слишком сложными, чтобы удерживать их все в голове, и возникает вопрос, почему система принимает те или иные решения. Однако важным свойством систем на основе знаний является то, что вы всегда можете *объяснить* точно, как было принято любое решение. -## Онтологии и семантическая сеть +## Онтологии и Семантическая паутина -В конце XX века была инициатива использовать представление знаний для аннотирования интернет-ресурсов, чтобы можно было находить ресурсы, соответствующие очень специфическим запросам. Это движение называлось **семантической сетью**, и оно опиралось на несколько концепций: +В конце XX века появилась инициатива использовать представление знаний для аннотации интернет-ресурсов, чтобы можно было находить ресурсы, соответствующие очень специфическим запросам. Это движение получило название **Семантическая паутина** и основывалось на нескольких концепциях: -- Специальное представление знаний, основанное на **[описательной логике](https://en.wikipedia.org/wiki/Description_logic)** (DL). Оно похоже на представление знаний в виде фреймов, так как строит иерархию объектов с их свойствами, но имеет формальную логическую семантику и вывод. Существует целое семейство DL, которые балансируют между выразительностью и алгоритмической сложностью вывода. -- Распределенное представление знаний, где все понятия представлены глобальным идентификатором URI, что делает возможным создание иерархий знаний, охватывающих интернет. -- Семейство языков на основе XML для описания знаний: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Специальном представлении знаний, основанном на **[описательных логиках](https://en.wikipedia.org/wiki/Description_logic)** (DL). Оно похоже на фреймовое представление, так как строит иерархию объектов с их свойствами, но имеет формальную логическую семантику и вывод. Существует целое семейство описательных логик, балансирующих между выразительностью и алгоритмической сложностью вывода. +- Распределённом представлении знаний, при котором все концепты имеют глобальный URI-идентификатор, что позволяет создавать иерархии знаний, охватывающие интернет. +- Семейство XML-основанных языков для описания знаний: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Основное понятие в Семантической сети — это **Онтология**. Она представляет собой явное описание предметной области с использованием формального представления знаний. Самая простая онтология может быть просто иерархией объектов в предметной области, но более сложные онтологии включают правила, которые можно использовать для вывода. +Основное понятие в Семантической паутине — это концепция **Онтологии**. Она означает явное определение области предмета с использованием некоторого формального представления знаний. Самая простая онтология может быть просто иерархией объектов в области предмета, но более сложные онтологии будут включать правила, которые могут использоваться для логического вывода. -В семантической сети все представления основаны на триплетах. Каждый объект и каждая связь уникально идентифицируются с помощью URI. Например, если мы хотим указать факт, что этот учебный курс по искусственному интеллекту был разработан Дмитрием Сошниковым 1 января 2022 года, вот триплеты, которые мы можем использовать: +В семантической паутине все представления основаны на триплетах. Каждый объект и каждое отношение уникально идентифицируются URI. Например, если мы хотим зафиксировать факт, что этот курс по ИИ был разработан Дмитрием Сошниковым 1 января 2022 года — вот какие триплеты мы можем использовать: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Здесь `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` — это известные и общепринятые URI для выражения понятий *создатель* и *дата создания*. +> ✅ Здесь `http://www.example.com/terms/creation-date` и `http://purl.org/dc/elements/1.1/creator` — это хорошо известные и универсально принятые URI для выражения понятий *создатель* и *дата создания*. -В более сложном случае, если мы хотим определить список создателей, мы можем использовать некоторые структуры данных, определенные в RDF. +В более сложном случае, если мы хотим определить список создателей, мы можем использовать некоторые структуры данных, определённые в RDF. - + -> Диаграммы выше созданы [Дмитрием Сошниковым](http://soshnikov.com) +> Диаграммы выше — Дмитрий Сошников (http://soshnikov.com) -Развитие Семантической сети было несколько замедлено успехом поисковых систем и методов обработки естественного языка, которые позволяют извлекать структурированные данные из текста. Однако в некоторых областях все еще предпринимаются значительные усилия для поддержания онтологий и баз знаний. Несколько проектов, заслуживающих внимания: +Развитие Семантической паутины в некоторой степени замедлилось из-за успехов поисковых систем и технологий обработки естественного языка, которые позволяют извлекать структурированные данные из текста. Однако в некоторых областях всё ещё прилагаются значительные усилия для поддержки онтологий и баз знаний. Несколько проектов, заслуживающих внимания: -* [WikiData](https://wikidata.org/) — это коллекция машиночитаемых баз знаний, связанных с Википедией. Большая часть данных извлекается из *InfoBoxes* Википедии — структурированных элементов контента внутри страниц Википедии. Вы можете [запросить](https://query.wikidata.org/) данные WikiData с помощью SPARQL, специального языка запросов для Семантической сети. Вот пример запроса, который отображает самые популярные цвета глаз среди людей: +* [WikiData](https://wikidata.org/) — это коллекция машинно-читаемых баз знаний, связанных с Википедией. Большая часть данных собирается из *инфо-боксов* Википедии, фрагментов структурированного контента на страницах Википедии. Можно [запрашивать](https://query.wikidata.org/) wikidata с помощью SPARQL, специального языка запросов для Семантической паутины. Вот пример запроса, который показывает самые популярные цвета глаз среди людей: ```sparql #defaultView:BubbleChart @@ -206,47 +206,51 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) — еще один проект, похожий на WikiData. +* [DBpedia](https://www.dbpedia.org/) — ещё одна инициатива, подобная WikiData. -> ✅ Если вы хотите поэкспериментировать с созданием собственных онтологий или изучением существующих, есть отличный визуальный редактор онтологий [Protégé](https://protege.stanford.edu/). Скачайте его или используйте онлайн. +> ✅ Если вы хотите поэкспериментировать с созданием собственных онтологий или открыть существующие, существует отличный визуальный редактор онтологий под названием [Protégé](https://protege.stanford.edu/). Скачайте его или используйте онлайн. - + -*Веб-редактор Protégé открыт с онтологией семьи Романовых. Скриншот Дмитрия Сошникова* +*Web-редактор Protégé открыт с онтологией семьи Романовых. Скриншот Дмитрия Сошникова* ## ✍️ Упражнение: Онтология семьи -Смотрите [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) для примера использования методов Семантической сети для анализа семейных отношений. Мы возьмем генеалогическое дерево, представленное в формате GEDCOM, и онтологию семейных отношений, чтобы построить граф всех семейных связей для заданного набора людей. +См. [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) для примера использования техник Семантической паутины для рассуждений о семейных отношениях. Мы возьмем генеалогическое древо, представленное в распространённом формате GEDCOM, и онтологию семейных отношений, чтобы построить граф всех семейных связей для заданного набора лиц. ## Microsoft Concept Graph -В большинстве случаев онтологии создаются вручную. Однако также возможно **извлекать** онтологии из неструктурированных данных, например, из текстов на естественном языке. +В большинстве случаев онтологии тщательно создаются вручную. Однако возможно также **выделять** онтологии из неструктурированных данных, например, из текстов на естественном языке. -Один из таких проектов был реализован Microsoft Research и привел к созданию [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Одна из таких попыток была сделана исследователями Microsoft, и это привело к созданию [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Это большая коллекция сущностей, сгруппированных с использованием отношения наследования `is-a`. Она позволяет отвечать на вопросы вроде "Что такое Microsoft?" — ответ будет что-то вроде "компания с вероятностью 0.87 и бренд с вероятностью 0.75". +Это большая коллекция сущностей, сгруппированных с помощью отношения наследования `is-a`. Это позволяет отвечать на вопросы вроде «Что такое Microsoft?» — ответом будет что-то вроде «компания с вероятностью 0.87 и бренд с вероятностью 0.75». -Граф доступен либо через REST API, либо в виде большого текстового файла, содержащего все пары сущностей. +Граф доступен либо через REST API, либо в виде большого загружаемого текстового файла, в котором перечислены пары сущностей. ## ✍️ Упражнение: Граф концепций -Попробуйте блокнот [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), чтобы увидеть, как можно использовать Microsoft Concept Graph для группировки новостных статей по категориям. +Попробуйте блокнот [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb), чтобы увидеть, как можно использовать Microsoft Concept Graph для группировки новостных статей по нескольким категориям. ## Заключение -Сегодня искусственный интеллект часто ассоциируется с *машинным обучением* или *нейронными сетями*. Однако человек также демонстрирует явное рассуждение, что пока не реализовано в нейронных сетях. В реальных проектах явное рассуждение все еще используется для выполнения задач, требующих объяснений или возможности контролируемо изменять поведение системы. +Сегодня искусственный интеллект часто рассматривается как синоним *машинного обучения* или *нейронных сетей*. Однако человек также обладает явным рассуждением, и это то, что пока не обрабатывается нейронными сетями. В реальных проектах явное рассуждение всё ещё используется для выполнения задач, требующих объяснений или возможности управляемого изменения поведения системы. ## 🚀 Задание -В блокноте Family Ontology, связанном с этим уроком, есть возможность поэкспериментировать с другими семейными отношениями. Попробуйте найти новые связи между людьми в генеалогическом дереве. +В блокноте онтологии семьи, связанном с этим уроком, есть возможность поэкспериментировать с другими семейными отношениями. Попробуйте обнаружить новые связи между людьми в генеалогическом дереве. -## [Тест после лекции](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Викторина после лекции](https://ff-quizzes.netlify.app/en/ai/quiz/4) ## Обзор и самостоятельное изучение -Исследуйте в интернете области, где люди пытались количественно оценить и систематизировать знания. Ознакомьтесь с таксономией Блума и изучите историю, чтобы узнать, как люди пытались осмыслить окружающий мир. Изучите работу Линнея по созданию таксономии организмов и обратите внимание на то, как Дмитрий Менделеев разработал способ описания и группировки химических элементов. Какие еще интересные примеры вы можете найти? +Поищите информацию в интернете о сферах, в которых люди пытались количественно описать и кодифицировать знания. Ознакомьтесь с таксономией Блума и вернитесь к истории, чтобы узнать, как люди пытались понять свой мир. Изучите работу Линнея по созданию таксономии организмов, а также обратите внимание на способ, которым Дмитрий Менделеев создал способ описания и группировки химических элементов. Какие ещё интересные примеры вы можете найти? -**Задание**: [Создайте онтологию](assignment.md) +**Задание**: [Создать онтологию](assignment.md) --- + +**Отказ от ответственности**: +Данный документ был переведен с использованием автоматического сервиса перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечить точность, имейте в виду, что автоматический перевод может содержать ошибки или неточности. Оригинальный документ на исходном языке следует считать авторитетным источником. Для критически важной информации рекомендуется прибегать к профессиональному переводу человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникающие при использовании данного перевода. + \ No newline at end of file diff --git a/translations/ru/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ru/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 9488ee78..adbe37e9 100644 --- a/translations/ru/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ru/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Если у нас больше двух классов, softmax нормализует вероятности для всех них. Вот диаграмма архитектуры сети, которая выполняет классификацию цифр MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ru.png)\n" + "![MNIST Classifier](../../../../../translated_images/ru/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1248,7 +1248,7 @@ "* Низкий тренировочный loss — модель хорошо аппроксимирует обучающие данные, так как обладает достаточной выразительной мощностью.\n", "* Валидационный loss может быть значительно выше тренировочного и может начать увеличиваться во время обучения — это происходит потому, что модель \"запоминает\" тренировочные точки и теряет \"общую картину\".\n", "\n", - "![Переобучение](../../../../../translated_images/overfit.a0bd57f717c15769.ru.png)\n", + "![Переобучение](../../../../../translated_images/ru/overfit.a0bd57f717c15769.webp)\n", "\n", "> На этом изображении `x` обозначает тренировочные данные, `o` — валидационные данные. Слева — линейная модель (однослойная), она довольно хорошо аппроксимирует природу данных. Справа — переобученная модель, она идеально аппроксимирует тренировочные данные, но перестает быть полезной для любых других данных (валидационная ошибка очень высокая).\n" ] diff --git a/translations/ru/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ru/lessons/3-NeuralNetworks/05-Frameworks/README.md index bfbda4e5..f0c5c245 100644 --- a/translations/ru/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ru/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: Рассмотрим следующую задачу аппроксимации 5 точек (представленных как `x` на графиках ниже): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ru.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ru.jpg) +![linear](../../../../../translated_images/ru/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/ru/overfit2.131f5800ae10ca5e.webp) -------------------------|-------------------------- **Линейная модель, 2 параметра** | **Нелинейная модель, 7 параметров** Ошибка на обучении = 5.3 | Ошибка на обучении = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: Как видно из графика выше, переобучение можно обнаружить по очень низкой ошибке на обучении и высокой ошибке на валидации. Обычно во время обучения мы видим, как ошибки на обучении и валидации начинают уменьшаться, а затем в какой-то момент ошибка на валидации может перестать уменьшаться и начать расти. Это будет признаком переобучения и сигналом, что, вероятно, стоит остановить обучение (или хотя бы сохранить текущую версию модели). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ru.png) +![overfitting](../../../../../translated_images/ru/Overfitting.408ad91cd90b4371.webp) ## Как предотвратить переобучение diff --git a/translations/ru/lessons/3-NeuralNetworks/README.md b/translations/ru/lessons/3-NeuralNetworks/README.md index f08edb0e..5609ad9a 100644 --- a/translations/ru/lessons/3-NeuralNetworks/README.md +++ b/translations/ru/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Введение в нейронные сети -![Краткое содержание введения в нейронные сети в виде рисунка](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ru.png) +![Краткое содержание введения в нейронные сети в виде рисунка](../../../../translated_images/ru/ai-neuralnetworks.1c687ae40bc86e83.webp) Как мы обсуждали во введении, один из способов достижения интеллекта — это обучение **компьютерной модели** или **искусственного мозга**. С середины XX века исследователи пробовали различные математические модели, и в последние годы этот подход оказался чрезвычайно успешным. Такие математические модели мозга называются **нейронными сетями**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: Из биологии мы знаем, что наш мозг состоит из нейронных клеток (нейронов), каждая из которых имеет несколько "входов" (дендритов) и один "выход" (аксон). И дендриты, и аксоны могут проводить электрические сигналы, а соединения между ними — известные как синапсы — могут демонстрировать различные степени проводимости, которые регулируются нейромедиаторами. -![Модель нейрона](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ru.jpg) | ![Модель нейрона](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ru.png) +![Модель нейрона](../../../../translated_images/ru/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Модель нейрона](../../../../translated_images/ru/artneuron.1a5daa88d20ebe6f.webp) ----|---- Реальный нейрон *([Изображение](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) из Википедии)* | Искусственный нейрон *(Изображение автора)* Таким образом, самая простая математическая модель нейрона содержит несколько входов X1, ..., XN и один выход Y, а также ряд весов W1, ..., WN. Выход вычисляется как: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) где f — это некоторая нелинейная **функция активации**. diff --git a/translations/ru/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ru/lessons/4-ComputerVision/06-IntroCV/README.md index 4d1d5802..156df134 100644 --- a/translations/ru/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ru/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Предварительная обработка фотографии книги на шрифте Брайля**. Мы показываем, как можно использовать пороговую обработку, обнаружение признаков, перспективное преобразование и манипуляции с NumPy для разделения отдельных символов шрифта Брайля для дальнейшей классификации нейронной сетью. -![Изображение шрифта Брайля](../../../../../translated_images/braille.341962ff76b1bd70.ru.jpeg) | ![Обработанное изображение шрифта Брайля](../../../../../translated_images/braille-result.46530fea020b03c7.ru.png) | ![Символы шрифта Брайля](../../../../../translated_images/braille-symbols.0159185ab69d5339.ru.png) +![Изображение шрифта Брайля](../../../../../translated_images/ru/braille.341962ff76b1bd70.webp) | ![Обработанное изображение шрифта Брайля](../../../../../translated_images/ru/braille-result.46530fea020b03c7.webp) | ![Символы шрифта Брайля](../../../../../translated_images/ru/braille-symbols.0159185ab69d5339.webp) ----|-----|----- > Изображение из [OpenCV.ipynb](OpenCV.ipynb) * **Обнаружение движения на видео с помощью разницы кадров**. Если камера фиксирована, то кадры из видеопотока должны быть довольно похожи друг на друга. Так как кадры представлены в виде массивов, просто вычитая массивы двух последовательных кадров, мы получим разницу пикселей, которая будет небольшой для статичных кадров и увеличиваться при значительном движении на изображении. -![Изображение кадров видео и разницы кадров](../../../../../translated_images/frame-difference.706f805491a0883c.ru.png) +![Изображение кадров видео и разницы кадров](../../../../../translated_images/ru/frame-difference.706f805491a0883c.webp) > Изображение из [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Плотный оптический поток** вычисляет векторное поле, показывающее, куда перемещается каждый пиксель. - **Разреженный оптический поток** основывается на выделении характерных признаков изображения (например, краев) и построении их траектории от кадра к кадру. -![Изображение оптического потока](../../../../../translated_images/optical.1f4a94464579a83a.ru.png) +![Изображение оптического потока](../../../../../translated_images/ru/optical.1f4a94464579a83a.webp) > Изображение из [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/ru/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ru/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 2a785a38..61db3cfc 100644 --- a/translations/ru/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ru/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 — это сеть, которая достигла точности 92.7% в классификации ImageNet (top-5) в 2014 году. Она имеет следующую структуру слоев: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ru.jpg) +![ImageNet Layers](../../../../../translated_images/ru/vgg-16-arch1.d901a5583b3a51ba.webp) Как видно, VGG следует традиционной пирамидальной архитектуре, представляющей собой последовательность слоев свертки и пуллинга. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ru.jpg) +![ImageNet Pyramid](../../../../../translated_images/ru/vgg-16-arch.64ff2137f50dd49f.webp) > Изображение с [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index c6d7aa4c..ba7ec4fa 100644 --- a/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Таким образом, в типичной CNN будет несколько сверточных слоев, между которыми располагаются слои объединения для уменьшения размеров изображения. Мы также увеличиваем количество фильтров, поскольку по мере усложнения шаблонов появляется больше возможных интересных комбинаций, которые нужно искать.\n", "\n", - "![Изображение, показывающее несколько сверточных слоев с слоями объединения.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ru.png)\n", + "![Изображение, показывающее несколько сверточных слоев с слоями объединения.](../../../../../translated_images/ru/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Из-за уменьшения пространственных размеров и увеличения размеров признаков/фильтров эта архитектура также называется **пирамидальной архитектурой**.\n" ] diff --git a/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 674a95e7..4bc9b77e 100644 --- a/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ru/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Таким образом, в типичной CNN будет несколько сверточных слоев, между которыми располагаются слои пуллинга для уменьшения размеров изображения. Мы также увеличиваем количество фильтров, потому что по мере усложнения шаблонов появляется больше возможных интересных комбинаций, которые нужно искать.\n", "\n", - "![Изображение, показывающее несколько сверточных слоев с слоями пуллинга.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ru.png)\n", + "![Изображение, показывающее несколько сверточных слоев с слоями пуллинга.](../../../../../translated_images/ru/cnn-pyramid.85915455759ef0ce.webp)\n", "\n", "Из-за уменьшения пространственных размеров и увеличения размеров признаков/фильтров эту архитектуру также называют **пирамидальной архитектурой**.\n" ] diff --git a/translations/ru/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ru/lessons/4-ComputerVision/07-ConvNets/README.md index a20fe042..09668da6 100644 --- a/translations/ru/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ru/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Для извлечения шаблонов мы будем использовать понятие **свёрточных фильтров**. Как вы знаете, изображение представлено в виде двумерной матрицы или трёхмерного тензора с глубиной цвета. Применение фильтра означает, что мы берём относительно небольшую матрицу **ядра фильтра** и для каждого пикселя исходного изображения вычисляем взвешенное среднее с соседними точками. Это можно представить как небольшое окно, скользящее по всему изображению и усредняющее все пиксели в соответствии с весами в матрице ядра фильтра. -![Фильтр вертикальных краёв](../../../../../translated_images/filter-vert.b7148390ca0bc356.ru.png) | ![Фильтр горизонтальных краёв](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ru.png) +![Фильтр вертикальных краёв](../../../../../translated_images/ru/filter-vert.b7148390ca0bc356.webp) | ![Фильтр горизонтальных краёв](../../../../../translated_images/ru/filter-horiz.59b80ed4feb946ef.webp) ----|---- > Изображение Дмитрия Сошникова @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Мы можем спроектировать сеть так, чтобы фильтры обучались автоматически * Мы можем использовать тот же подход для поиска шаблонов в высокоуровневых признаках, а не только в исходном изображении. Таким образом, извлечение признаков в CNN работает на иерархии признаков, начиная с низкоуровневых комбинаций пикселей и заканчивая высокоуровневыми комбинациями частей изображения. -![Иерархическое извлечение признаков](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ru.png) +![Иерархическое извлечение признаков](../../../../../translated_images/ru/FeatureExtractionCNN.d9b456cbdae7cb64.webp) > Изображение из [статьи Хислопа-Линча](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), основанное на [их исследовании](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: В качестве примера давайте рассмотрим архитектуру VGG-16, сети, которая достигла точности 92.7% в топ-5 классификации ImageNet в 2014 году: -![Слои ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ru.jpg) +![Слои ImageNet](../../../../../translated_images/ru/vgg-16-arch1.d901a5583b3a51ba.webp) -![Пирамида ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ru.jpg) +![Пирамида ImageNet](../../../../../translated_images/ru/vgg-16-arch.64ff2137f50dd49f.webp) > Изображение с [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/ru/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ru/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 695425c4..d9a57064 100644 --- a/translations/ru/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ru/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Мы будем использовать [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), который содержит изображения 37 различных пород собак и кошек. -![Набор данных, с которым мы будем работать](../../../../../../translated_images/data.50b2a9d5484bdbf0.ru.png) +![Набор данных, с которым мы будем работать](../../../../../../translated_images/ru/data.50b2a9d5484bdbf0.webp) Чтобы скачать набор данных, используйте этот код: diff --git a/translations/ru/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ru/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index ffb30888..27f9bd0c 100644 --- a/translations/ru/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ru/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Чтобы визуализировать идеального кота, мы начнем с изображения случайного шума и попробуем использовать метод оптимизации градиентного спуска, чтобы изменить изображение так, чтобы сеть распознала кота.\n", "\n", - "![Цикл оптимизации](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ru.png)\n", + "![Цикл оптимизации](../../../../../translated_images/ru/ideal-cat-loop.999fbb8ff306e044.webp)\n", "\n", "Вот наше начальное изображение:\n" ] diff --git a/translations/ru/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ru/lessons/4-ComputerVision/08-TransferLearning/README.md index b2ac30e2..05380a14 100644 --- a/translations/ru/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ru/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Вот пример признаков, извлечённых из изображения кошки сетью VGG-16: -![Признаки, извлечённые VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.ru.png) +![Признаки, извлечённые VGG-16](../../../../../translated_images/ru/features.6291f9c7ba3a0b95.webp) ## Набор данных "Кошки против собак" @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Один из подходов, который мы можем использовать, — это начать с случайного изображения и затем попытаться применить технику **оптимизации градиентного спуска**, чтобы изменить это изображение так, чтобы сеть начала думать, что это кошка. -![Цикл оптимизации изображения](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ru.png) +![Цикл оптимизации изображения](../../../../../translated_images/ru/ideal-cat-loop.999fbb8ff306e044.webp) Однако, если мы сделаем это, мы получим что-то очень похожее на случайный шум. Это происходит потому, что *существует множество способов заставить сеть думать, что входное изображение — это кошка*, включая те, которые визуально не имеют смысла. Хотя эти изображения содержат множество паттернов, характерных для кошки, ничего не ограничивает их визуальную выразительность. Чтобы улучшить результат, мы можем добавить ещё один член в функцию потерь, который называется **потеря вариации**. Это метрика, показывающая, насколько похожи соседние пиксели изображения. Минимизация потери вариации делает изображение более гладким и избавляет от шума, раскрывая более визуально привлекательные паттерны. Вот пример таких "идеальных" изображений, которые классифицируются как кошка и как зебра с высокой вероятностью: -![Идеальная кошка](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ru.png) | ![Идеальная зебра](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ru.png) +![Идеальная кошка](../../../../../translated_images/ru/ideal-cat.203dd4597643d6b0.webp) | ![Идеальная зебра](../../../../../translated_images/ru/ideal-zebra.7f70e8b54ee15a7a.webp) -----|----- *Идеальная кошка* | *Идеальная зебра* Похожий подход можно использовать для выполнения так называемых **атак на нейронную сеть**. Предположим, мы хотим обмануть нейронную сеть и заставить собаку выглядеть как кошка. Если мы возьмём изображение собаки, которое сеть распознаёт как собаку, мы можем немного изменить его с помощью оптимизации градиентного спуска, пока сеть не начнёт классифицировать его как кошку: -![Изображение собаки](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ru.png) | ![Изображение собаки, классифицированное как кошка](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ru.png) +![Изображение собаки](../../../../../translated_images/ru/original-dog.8f68a67d2fe0911f.webp) | ![Изображение собаки, классифицированное как кошка](../../../../../translated_images/ru/adversarial-dog.d9fc7773b0142b89.webp) -----|----- *Оригинальное изображение собаки* | *Изображение собаки, классифицированное как кошка* diff --git a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 373e6c31..8506cdd4 100644 --- a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Поскольку мы обучаем автоэнкодер захватывать как можно больше информации из исходного изображения для точного восстановления, сеть пытается найти наилучшее **встраивание** входных изображений, чтобы уловить их смысл.\n", "\n", - "![Схема автоэнкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg)\n", + "![Схема автоэнкодера](../../../../../translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "> Изображение из [блога Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 96611d4e..f68eccdc 100644 --- a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Поскольку мы обучаем автоэнкодер захватывать как можно больше информации из исходного изображения для точного восстановления, сеть пытается найти наилучшее **встраивание** входных изображений, чтобы уловить их смысл.\n", "\n", - "![Схема автоэнкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg)\n", + "![Схема автоэнкодера](../../../../../translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp)\n", "\n", "*Изображение из [блога Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/README.md index 364d142e..10bed46c 100644 --- a/translations/ru/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ru/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Поскольку мы обучаем автоэнкодер захватывать как можно больше информации из исходного изображения для точного восстановления, сеть пытается найти наилучшее **встраивание** входных изображений, чтобы уловить их смысл. -![Схема автоэнкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ru.jpg) +![Схема автоэнкодера](../../../../../translated_images/ru/autoencoder_schema.5e6fc9ad98a5eb61.webp) > Изображение из [блога Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/ru/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ru/lessons/4-ComputerVision/11-ObjectDetection/README.md index aec9c051..337ee3bc 100644 --- a/translations/ru/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ru/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Квиз перед лекцией](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Обнаружение объектов](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ru.png) +![Обнаружение объектов](../../../../../translated_images/ru/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.webp) > Изображение с [веб-сайта YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Запустить классификацию изображений для каждой плитки. 3. Те плитки, которые дают достаточно высокую активацию, можно считать содержащими искомый объект. -![Простое обнаружение объектов](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ru.png) +![Простое обнаружение объектов](../../../../../translated_images/ru/naive-detection.e7f1ba220ccd08c6.webp) > *Изображение из [учебной тетради](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) — 20 классов. * [COCO](http://cocodataset.org/#home) — Common Objects in Context. 80 классов, ограничивающие рамки и маски сегментации. -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ru.jpg) +![COCO](../../../../../translated_images/ru/coco-examples.71bc60380fa6cceb.webp) ## Метрики для обнаружения объектов @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Если для классификации изображений легко измерить, насколько хорошо работает алгоритм, то для обнаружения объектов нужно оценивать как правильность класса, так и точность предсказанных координат ограничивающей рамки. Для последнего используется метрика **Пересечение над объединением** (IoU), которая измеряет, насколько хорошо две рамки (или две произвольные области) перекрываются. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ru.png) +![IoU](../../../../../translated_images/ru/iou_equation.9a4751d40fff4e11.webp) > *Рисунок 2 из [этого отличного блога о IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) использует [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) для генерации иерархической структуры регионов ROI, которые затем проходят через CNN для извлечения признаков, SVM-классификаторы для определения класса объекта и линейную регрессию для определения координат *ограничивающей рамки*. [Официальная статья](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ru.png) +![RCNN](../../../../../translated_images/ru/rcnn1.cae407020dfb1d1f.webp) > *Изображение из van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ru.png) +![RCNN-1](../../../../../translated_images/ru/rcnn2.2d9530bb83516484.webp) > *Изображения из [этого блога](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ $$ Этот подход похож на R-CNN, но регионы определяются после применения сверточных слоев. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ru.png) +![FRCNN](../../../../../translated_images/ru/f-rcnn.3cda6d9bb4188875.webp) > Изображение из [официальной статьи](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ $$ Основная идея этого подхода заключается в использовании нейронной сети для предсказания ROI — так называемой *сети предложений регионов* (Region Proposal Network). [Статья](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ru.png) +![FasterRCNN](../../../../../translated_images/ru/faster-rcnn.8d46c099b87ef30a.webp) > Изображение из [официальной статьи](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ $$ 2. Признаки обрабатываются **Position-Sensitive Score Map**. Каждый объект из $C$ классов делится на $k\times k$ регионов, и сеть обучается предсказывать части объектов. 3. Для каждой части из $k\times k$ регионов все сети голосуют за классы объектов, и выбирается класс объекта с максимальным количеством голосов. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ru.png) +![r-fcn image](../../../../../translated_images/ru/r-fcn.13eb88158b99a3da.webp) > Изображение из [официальной статьи](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO — это алгоритм реального времени с одним * Изображение делится на $S\times S$ регионы. * Для каждого региона **CNN** предсказывает $n$ возможных объектов, координаты *ограничивающей рамки* и *уверенность*=*вероятность* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ru.png) + ![YOLO](../../../../../translated_images/ru/yolo.a2648ec82ee8bb4e.webp) > Изображение из [официальной статьи](https://arxiv.org/abs/1506.02640) diff --git a/translations/ru/lessons/4-ComputerVision/README.md b/translations/ru/lessons/4-ComputerVision/README.md index 2244c8d3..20e5db60 100644 --- a/translations/ru/lessons/4-ComputerVision/README.md +++ b/translations/ru/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Компьютерное зрение -![Резюме по теме "Компьютерное зрение" в виде рисунка](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ru.png) +![Резюме по теме "Компьютерное зрение" в виде рисунка](../../../../translated_images/ru/ai-computervision.6506ebebac3fbf76.webp) В этом разделе мы изучим: diff --git a/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 5ff04732..894d2513 100644 --- a/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Мешок слов** (Bag of Words, BoW) — это наиболее часто используемое традиционное векторное представление. Каждое слово связано с индексом вектора, а элемент вектора содержит количество вхождений слова в данном документе.\n", "\n", - "![Изображение, показывающее, как представление \"мешка слов\" хранится в памяти.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ru.png)\n", + "![Изображение, показывающее, как представление \"мешка слов\" хранится в памяти.](../../../../../translated_images/ru/bag-of-words-example.606fc1738f1d7ba9.webp)\n", "\n", "> **Note**: Вы также можете представить BoW как сумму всех векторов с единичным кодированием (one-hot encoding) для отдельных слов в тексте.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 7eb88a0d..c5f5833e 100644 --- a/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ru/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Мешок слов** (BoW) — это самое простое для понимания традиционное представление в виде вектора. Каждое слово связано с индексом вектора, а элемент вектора содержит количество вхождений каждого слова в данном документе.\n", "\n", - "![Изображение, показывающее, как представление вектора методом \"мешка слов\" хранится в памяти.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ru.png) \n", + "![Изображение, показывающее, как представление вектора методом \"мешка слов\" хранится в памяти.](../../../../../translated_images/ru/bag-of-words-example.606fc1738f1d7ba9.webp) \n", "\n", "> **Note**: Метод BoW также можно рассматривать как сумму всех векторов с одним активным элементом (one-hot-encoded), соответствующих отдельным словам в тексте.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 9315f17f..244f631f 100644 --- a/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Используя слой эмбеддинга в качестве первого слоя нашей сети, мы можем перейти от модели bag-of-words к модели **embedding bag**, где мы сначала преобразуем каждое слово в тексте в соответствующий эмбеддинг, а затем вычисляем некоторую агрегирующую функцию для всех этих эмбеддингов, например, `sum`, `average` или `max`.\n", "\n", - "![Изображение, показывающее классификатор с использованием эмбеддинга для пяти слов последовательности.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png)\n", + "![Изображение, показывающее классификатор с использованием эмбеддинга для пяти слов последовательности.](../../../../../translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша нейронная сеть-классификатор начнется со слоя эмбеддинга, затем слоя агрегации и линейного классификатора поверх него:\n" ] @@ -176,7 +176,7 @@ "\n", "В предыдущей архитектуре нам приходилось дополнять все последовательности до одинаковой длины, чтобы они подходили для минибатча. Это не самый эффективный способ представления последовательностей переменной длины — другой подход заключается в использовании **вектора смещений**, который содержит смещения всех последовательностей, хранящихся в одном большом векторе.\n", "\n", - "![Изображение, показывающее представление последовательности со смещением](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ru.png)\n", + "![Изображение, показывающее представление последовательности со смещением](../../../../../translated_images/ru/offset-sequence-representation.eb73fcefb29b46ee.webp)\n", "\n", "> **Note**: На изображении выше показана последовательность символов, но в нашем примере мы работаем с последовательностями слов. Однако общий принцип представления последовательностей с помощью вектора смещений остается тем же.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW работает быстрее, тогда как skip-gram медленнее, но лучше справляется с представлением редких слов.\n", "\n", - "![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png)\n", + "![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Чтобы поэкспериментировать с эмбеддингами Word2Vec, предварительно обученными на наборе данных Google News, мы можем использовать библиотеку **gensim**. Ниже приведён пример поиска слов, наиболее похожих на 'neural'.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 793df0bb..45962586 100644 --- a/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ru/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Используя слой эмбеддинга в качестве первого слоя нашей сети, мы можем перейти от модели \"мешка слов\" к модели **мешка эмбеддингов**, где сначала каждое слово в тексте преобразуется в соответствующий эмбеддинг, а затем вычисляется некоторая агрегирующая функция для всех этих эмбеддингов, например, `sum`, `average` или `max`.\n", "\n", - "![Изображение, показывающее классификатор с использованием эмбеддингов для пяти слов последовательности.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png)\n", + "![Изображение, показывающее классификатор с использованием эмбеддингов для пяти слов последовательности.](../../../../../translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp)\n", "\n", "Наша нейронная сеть-классификатор состоит из следующих слоев:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW работает быстрее, а skip-gram, хотя и медленнее, лучше справляется с представлением редких слов.\n", "\n", - "![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png)\n", + "![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp)\n", "\n", "Чтобы поэкспериментировать с эмбеддингом Word2Vec, предварительно обученным на наборе данных Google News, мы можем использовать библиотеку **gensim**. Ниже мы находим слова, наиболее похожие на 'neural'.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/14-Embeddings/README.md b/translations/ru/lessons/5-NLP/14-Embeddings/README.md index 2b68bf72..070caa2a 100644 --- a/translations/ru/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ru/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Используя слой встраивания в качестве первого слоя в нашей сети классификатора, мы можем перейти от модели мешка слов к модели **мешка встраиваний**, где мы сначала преобразуем каждое слово в нашем тексте в соответствующее встраивание, а затем вычисляем некоторую агрегатную функцию для всех этих встраиваний, такую как `sum`, `average` или `max`. -![Изображение, показывающее классификатор на основе встраиваний для пяти слов последовательности.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ru.png) +![Изображение, показывающее классификатор на основе встраиваний для пяти слов последовательности.](../../../../../translated_images/ru/embedding-classifier-example.b77f021a7ee67eee.webp) > Изображение автора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW работает быстрее, а пропуск-грамм медленнее, но лучше справляется с представлением редких слов. -![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png) +![Изображение, показывающее алгоритмы CBoW и Skip-Gram для преобразования слов в векторы.](../../../../../translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Изображение из [этой статьи](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ru/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ru/lessons/5-NLP/15-LanguageModeling/README.md index a59405e4..b38888cd 100644 --- a/translations/ru/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ru/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Непрерывный мешок слов** (Continuous Bag-of-Words, CBoW), когда мы предсказываем центральный токен $W_0$ в последовательности токенов $W_{-N}$, ..., $W_N$. * **Skip-gram**, где мы предсказываем набор соседних токенов {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} на основе центрального токена $W_0$. -![изображение из статьи о преобразовании слов в векторы](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ru.png) +![изображение из статьи о преобразовании слов в векторы](../../../../../translated_images/ru/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.webp) > Изображение из [этой статьи](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ru/lessons/5-NLP/16-RNN/README.md b/translations/ru/lessons/5-NLP/16-RNN/README.md index 33baf02d..476fbfe0 100644 --- a/translations/ru/lessons/5-NLP/16-RNN/README.md +++ b/translations/ru/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Чтобы захватить смысл последовательности текста, необходимо использовать другую архитектуру нейронной сети, которая называется **рекуррентной нейронной сетью** или RNN. В RNN мы пропускаем предложение через сеть по одному символу за раз, и сеть производит некоторое **состояние**, которое затем передается обратно в сеть вместе со следующим символом. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ru.png) +![RNN](../../../../../translated_images/ru/rnn.27f5c29c53d727b5.webp) > Изображение автора @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: Рекуррентная сеть, будь то однонаправленная или двунаправленная, захватывает определенные шаблоны внутри последовательности и может сохранять их в векторе состояния или передавать в выход. Как и в случае с сверточными сетями, мы можем построить еще один рекуррентный слой поверх первого, чтобы захватывать шаблоны более высокого уровня и строить их из шаблонов низкого уровня, извлеченных первым слоем. Это приводит нас к понятию **многослойной RNN**, которая состоит из двух или более рекуррентных сетей, где выход предыдущего слоя передается следующему слою в качестве входа. -![Изображение многослойной LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg) +![Изображение многослойной LSTM RNN](../../../../../translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp) *Картинка из [этого замечательного поста](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса* diff --git a/translations/ru/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ru/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 6ccf4d0b..75f4555f 100644 --- a/translations/ru/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ru/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекуррентная сеть, будь то однонаправленная или двунаправленная, захватывает определённые паттерны внутри последовательности и может сохранять их во векторе состояния или передавать на выход. Как и в случае с сверточными сетями, мы можем построить ещё один рекуррентный слой поверх первого, чтобы захватывать паттерны более высокого уровня, сформированные из паттернов низкого уровня, извлечённых первым слоем. Это приводит нас к понятию **многослойной RNN**, которая состоит из двух или более рекуррентных сетей, где выход предыдущего слоя передаётся в следующий слой в качестве входа.\n", "\n", - "![Изображение, показывающее многослойную LSTM-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg)\n", + "![Изображение, показывающее многослойную LSTM-RNN](../../../../../translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Изображение из [этой замечательной статьи](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса*\n", "\n", diff --git a/translations/ru/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ru/lessons/5-NLP/16-RNN/RNNTF.ipynb index 53d755f7..30904729 100644 --- a/translations/ru/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ru/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Чтобы уловить смысл последовательности текста, мы будем использовать архитектуру нейронной сети, называемую **рекуррентной нейронной сетью** или RNN. При использовании RNN мы пропускаем наше предложение через сеть по одному токену за раз, и сеть создает некоторое **состояние**, которое затем передается в сеть вместе со следующим токеном.\n", "\n", - "![Изображение, показывающее пример генерации рекуррентной нейронной сети.](../../../../../translated_images/rnn.27f5c29c53d727b5.ru.png)\n", + "![Изображение, показывающее пример генерации рекуррентной нейронной сети.](../../../../../translated_images/ru/rnn.27f5c29c53d727b5.webp)\n", "\n", "Учитывая входную последовательность токенов $X_0,\\dots,X_n$, RNN создает последовательность блоков нейронной сети и обучает эту последовательность от начала до конца с использованием обратного распространения ошибки. Каждый блок сети принимает пару $(X_i,S_i)$ в качестве входных данных и производит $S_{i+1}$ в качестве результата. Финальное состояние $S_n$ или выход $Y_n$ передается в линейный классификатор для получения результата. Все блоки сети имеют одинаковые веса и обучаются от начала до конца за один проход обратного распространения.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекуррентные сети, будь то однонаправленные или двунаправленные, захватывают закономерности внутри последовательности и сохраняют их в векторах состояния или возвращают их в качестве результата. Как и в случае с сверточными сетями, мы можем добавить еще один рекуррентный слой после первого, чтобы захватывать закономерности более высокого уровня, построенные на основе закономерностей более низкого уровня, извлеченных первым слоем. Это приводит нас к понятию **многослойной RNN**, которая состоит из двух или более рекуррентных сетей, где выход предыдущего слоя передается в следующий слой в качестве входных данных.\n", "\n", - "![Изображение, показывающее многослойную LSTM-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ru.jpg)\n", + "![Изображение, показывающее многослойную LSTM-RNN](../../../../../translated_images/ru/multi-layer-lstm.dd975e29bb2a59fe.webp)\n", "\n", "*Изображение из [этой замечательной статьи](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса.*\n", "\n", diff --git a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 97511e27..5c1e8de4 100644 --- a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Метод, который мы будем использовать для обучения RNN генерации текста, следующий. На каждом шаге мы берем последовательность символов длиной `nchars` и просим сеть сгенерировать следующий выходной символ для каждого входного символа:\n", "\n", - "![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ru.png)\n", + "![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/ru/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "В зависимости от конкретного сценария, мы также можем включить специальные символы, такие как *конец последовательности* ``. В нашем случае мы хотим обучить сеть бесконечной генерации текста, поэтому фиксируем размер каждой последовательности равным `nchars` токенов. Таким образом, каждый обучающий пример будет состоять из `nchars` входов и `nchars` выходов (входная последовательность, сдвинутая на один символ влево). Минипакет будет состоять из нескольких таких последовательностей.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 696b6230..c8f21fa9 100644 --- a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Метод, который мы будем использовать для обучения RNN создавать заголовки новостей, следующий. На каждом шаге мы берем один заголовок, который подается в RNN, и для каждого входного символа сеть должна сгенерировать следующий выходной символ:\n", "\n", - "![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ru.png)\n", + "![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/ru/rnn-generate.56c54afb52f9781d.webp)\n", "\n", "Для последнего символа нашей последовательности мы попросим сеть сгенерировать токен ``.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/README.md index 7411a1bb..c01da5f6 100644 --- a/translations/ru/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ru/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Это позволяет создавать различные нейронные архитектуры, которые показаны на изображении ниже: -![Изображение, показывающее распространенные паттерны рекуррентных нейронных сетей.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ru.jpg) +![Изображение, показывающее распространенные паттерны рекуррентных нейронных сетей.](../../../../../translated_images/ru/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > Изображение из статьи [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) Андрея Карпати ([Andrej Karpathy](http://karpathy.github.io/)) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Мы будем обучать эту RNN генерировать текст шаг за шагом. На каждом шаге мы будем брать последовательность символов длиной `nchars` и просить сеть сгенерировать следующий символ для каждого входного символа: -![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ru.png) +![Изображение, показывающее пример генерации слова 'HELLO' с помощью RNN.](../../../../../translated_images/ru/rnn-generate.56c54afb52f9781d.webp) При генерации текста (во время инференса) мы начинаем с некоторого **запроса**, который передается через ячейки RNN для генерации промежуточного состояния, а затем из этого состояния начинается генерация. Мы генерируем по одному символу за раз, передаем состояние и сгенерированный символ в следующую ячейку RNN для генерации следующего символа, пока не будет сгенерировано достаточное количество символов. diff --git a/translations/ru/lessons/5-NLP/18-Transformers/README.md b/translations/ru/lessons/5-NLP/18-Transformers/README.md index 9e0c6b34..a7202140 100644 --- a/translations/ru/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ru/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механизмы внимания** предоставляют способ взвешивания контекстного влияния каждого входного вектора на каждое предсказание выхода RNN. Это реализуется путем создания "ярлыков" между промежуточными состояниями входной RNN и выходной RNN. Таким образом, при генерации выходного символа yt мы учитываем все скрытые состояния входа hi, с различными весовыми коэффициентами αt,i. -![Изображение, показывающее модель кодировщик/декодировщик с аддитивным слоем внимания](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ru.png) +![Изображение, показывающее модель кодировщик/декодировщик с аддитивным слоем внимания](../../../../../translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp) > Модель кодировщик-декодировщик с механизмом аддитивного внимания из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитируется из [этого блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрица внимания {αi,j} представляет степень, в которой определенные входные слова участвуют в генерации данного слова в выходной последовательности. Ниже приведен пример такой матрицы: -![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png) +![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp) > Рисунок из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Далее нам нужно захватить некоторые паттерны внутри нашей последовательности. Для этого трансформеры используют механизм **самовнимания**, который по сути является вниманием, применяемым к одной и той же последовательности как входу, так и выходу. Применение самовнимания позволяет учитывать **контекст** внутри предложения и видеть, какие слова взаимосвязаны. Например, это позволяет понять, к каким словам относятся местоимения, такие как *it*, и учитывать контекст: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ru.png) +![](../../../../../translated_images/ru/CoreferenceResolution.861924d6d384a7d6.webp) > Изображение из [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) — это очень большая многослойная сеть трансформеров с 12 слоями для *BERT-base* и 24 для *BERT-large*. Модель сначала предварительно обучается на большом корпусе текстовых данных (WikiPedia + книги) с использованием обучения без учителя (предсказание замаскированных слов в предложении). Во время предварительного обучения модель усваивает значительные уровни понимания языка, которые затем можно использовать с другими наборами данных с помощью тонкой настройки. Этот процесс называется **передача обучения**. -![картинка с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png) +![картинка с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Изображение [источник](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ru/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ru/lessons/5-NLP/18-Transformers/READMEtransformers.md index 3d359c81..5a4b350b 100644 --- a/translations/ru/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/ru/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ **Механизмы внимания** предоставляют способ взвешивания контекстного влияния каждого входного вектора на каждое предсказание выхода РСН. Это реализуется путем создания кратких путей между промежуточными состояниями входной РСН и выходной РСН. Таким образом, при генерации выходного символа yt мы будем учитывать все входные скрытые состояния hi с различными весовыми коэффициентами αt,i. -![Изображение, показывающее модель кодировщика/декодировщика с аддитивным слоем внимания](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ru.png) +![Изображение, показывающее модель кодировщика/декодировщика с аддитивным слоем внимания](../../../../../translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp) > Модель кодировщика-декодировщика с аддитивным механизмом внимания в [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), процитировано из [этого блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрица внимания {αi,j} будет представлять степень, в которой определенные входные слова участвуют в генерации данного слова в выходной последовательности. Ниже приведен пример такой матрицы: -![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png) +![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp) > Рисунок из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис. 3) @@ -57,7 +57,7 @@ Далее нам нужно захватить некоторые паттерны в нашей последовательности. Для этого трансформеры используют механизм **самовнимания**, который по сути представляет собой внимание, примененное к той же последовательности как входной и выходной. Применение самовнимания позволяет нам учитывать **контекст** в предложении и видеть, какие слова взаимосвязаны. Например, это позволяет нам видеть, какие слова упоминаются через копреференции, такие как *это*, и также учитывать контекст: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ru.png) +![](../../../../../translated_images/ru/CoreferenceResolution.861924d6d384a7d6.webp) > Изображение из [Блога Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ **BERT** (Bidirectional Encoder Representations from Transformers) — это очень большая многослойная трансформерная сеть с 12 слоями для *BERT-base* и 24 слоями для *BERT-large*. Модель сначала обучается на большом корпусе текстовых данных (WikiPedia + книги) с использованием неконтролируемого обучения (предсказание замаскированных слов в предложении). Во время предварительного обучения модель поглощает значительные уровни языкового понимания, которые затем могут быть использованы с другими наборами данных с помощью тонкой настройки. Этот процесс называется **переносным обучением**. -![изображение с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png) +![изображение с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) > Изображение [источник](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ru/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ru/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 9080cf8f..d5c8aeca 100644 --- a/translations/ru/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ru/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмы внимания** предоставляют способ взвешивания контекстного влияния каждого входного вектора на каждое предсказание RNN. Это реализуется путем создания \"ярлыков\" между промежуточными состояниями входной RNN и выходной RNN. Таким образом, при генерации выходного символа $y_t$ мы будем учитывать все скрытые состояния входа $h_i$ с различными весовыми коэффициентами $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показывающее модель энкодер/декодер с аддитивным слоем внимания](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ru.png) \n", + "![Изображение, показывающее модель энкодер/декодер с аддитивным слоем внимания](../../../../../translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp) \n", "*Модель энкодер-декодер с механизмом аддитивного внимания из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитируется из [этого блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица внимания $\\{\\alpha_{i,j}\\}$ представляет степень, с которой определенные входные слова влияют на генерацию конкретного слова в выходной последовательности. Ниже приведен пример такой матрицы:\n", "\n", - "![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png) \n", + "![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp) \n", "\n", "*Рисунок взят из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — это очень большая многослойная трансформерная сеть с 12 слоями для *BERT-base* и 24 для *BERT-large*. Модель сначала предварительно обучается на большом корпусе текстовых данных (WikiPedia + книги) с использованием обучения без учителя (предсказание замаскированных слов в предложении). Во время предварительного обучения модель усваивает значительный уровень понимания языка, который затем можно использовать с другими наборами данных с помощью тонкой настройки. Этот процесс называется **трансферным обучением**.\n", "\n", - "![Изображение с сайта http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png)\n", + "![Изображение с сайта http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Существует множество вариаций архитектур трансформеров, включая BERT, DistilBERT, BigBird, OpenGPT3 и другие, которые можно тонко настраивать. Пакет [HuggingFace](https://github.com/huggingface/) предоставляет репозиторий для обучения многих из этих архитектур с использованием PyTorch.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ru/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index d525d5ad..2d6c339c 100644 --- a/translations/ru/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ru/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизмы внимания** предоставляют способ взвешивания контекстного влияния каждого входного вектора на каждое предсказание выхода RNN. Это реализуется путем создания \"ярлыков\" между промежуточными состояниями входной RNN и выходной RNN. Таким образом, при генерации выходного символа $y_t$ мы учитываем все скрытые состояния входа $h_i$ с различными весовыми коэффициентами $\\alpha_{t,i}$.\n", "\n", - "![Изображение, показывающее модель encoder/decoder с аддитивным слоем внимания](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ru.png)\n", + "![Изображение, показывающее модель encoder/decoder с аддитивным слоем внимания](../../../../../translated_images/ru/encoder-decoder-attention.7a726296894fb567.webp)\n", "*Модель encoder-decoder с механизмом аддитивного внимания из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитируется из [этого блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица внимания $\\{\\alpha_{i,j}\\}$ представляет степень, в которой определенные слова входной последовательности участвуют в генерации конкретного слова выходной последовательности. Ниже приведен пример такой матрицы:\n", "\n", - "![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ru.png)\n", + "![Изображение, показывающее пример выравнивания, найденного RNNsearch-50, взято из Bahdanau - arviz.org](../../../../../translated_images/ru/bahdanau-fig3.09ba2d37f202a6af.webp)\n", "\n", "*Рисунок взят из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — это очень большая многослойная сеть трансформеров с 12 слоями для *BERT-base* и 24 для *BERT-large*. Модель сначала проходит предварительное обучение на большом корпусе текстовых данных (WikiPedia + книги) с использованием обучения без учителя (предсказание замаскированных слов в предложении). Во время предварительного обучения модель усваивает значительный уровень понимания языка, который затем можно использовать с другими наборами данных с помощью тонкой настройки. Этот процесс называется **обучением переноса**.\n", "\n", - "![изображение с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ru.png)\n", + "![изображение с http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ru/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp)\n", "\n", "Существует множество вариаций архитектур трансформеров, включая BERT, DistilBERT, BigBird, OpenGPT3 и другие, которые можно тонко настраивать.\n", "\n", diff --git a/translations/ru/lessons/5-NLP/19-NER/README.md b/translations/ru/lessons/5-NLP/19-NER/README.md index e1e3e30c..0f61d1ff 100644 --- a/translations/ru/lessons/5-NLP/19-NER/README.md +++ b/translations/ru/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Поскольку нам нужно установить однозначное соответствие между токенами и классами, мы можем обучить правую **многие-ко-многим** архитектуру нейронной сети из следующего изображения: -![Изображение, показывающее общие шаблоны рекуррентных нейронных сетей.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ru.jpg) +![Изображение, показывающее общие шаблоны рекуррентных нейронных сетей.](../../../../../translated_images/ru/unreasonable-effectiveness-of-rnn.541ead816778f42d.webp) > *Изображение из [этой статьи](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) Андрея Карпати ([Andrej Karpathy](http://karpathy.github.io/)). Модели классификации токенов для NER соответствуют правой архитектуре на этом изображении.* diff --git a/translations/ru/lessons/5-NLP/README.md b/translations/ru/lessons/5-NLP/README.md index b12fda9f..16dac834 100644 --- a/translations/ru/lessons/5-NLP/README.md +++ b/translations/ru/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обработка естественного языка -![Сводка задач NLP в виде рисунка](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ru.png) +![Сводка задач NLP в виде рисунка](../../../../translated_images/ru/ai-nlp.b22dcb8ca4707cea.webp) В этом разделе мы сосредоточимся на использовании нейронных сетей для решения задач, связанных с **обработкой естественного языка (NLP)**. Существует множество задач NLP, которые мы хотим, чтобы компьютеры могли решать: diff --git a/translations/ru/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ru/lessons/6-Other/23-MultiagentSystems/README.md index cc5ae9e2..5dacadf9 100644 --- a/translations/ru/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ru/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ После открытия модели вы попадете на главный экран NetLogo. Вот пример модели, описывающей популяцию волков и овец при ограниченных ресурсах (трава). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ru.png) +![NetLogo Main Screen](../../../../../translated_images/ru/NetLogo-Main.32653711ec1a01b3.webp) > Скриншот от Дмитрия Сошникова diff --git a/translations/ru/lessons/README.md b/translations/ru/lessons/README.md index 6d74ab08..7389eab4 100644 --- a/translations/ru/lessons/README.md +++ b/translations/ru/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обзор -![Обзор в виде рисунка](../../../translated_images/ai-overview.0857791951d19500.ru.png) +![Обзор в виде рисунка](../../../translated_images/ru/ai-overview.0857791951d19500.webp) > Рисунок от [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ru/lessons/X-Extras/X1-MultiModal/README.md b/translations/ru/lessons/X-Extras/X1-MultiModal/README.md index b0b40ce1..fe7079c8 100644 --- a/translations/ru/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ru/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Основная идея CLIP заключается в способности сравнивать текстовые запросы с изображением и определять, насколько хорошо изображение соответствует запросу. -![Архитектура CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ru.png) +![Архитектура CLIP](../../../../../translated_images/ru/clip-arch.b3dbf20b4e8ed8be.webp) > *Изображение из [этой статьи](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Предположим, нам нужно классифицировать изображения, например, на кошек, собак и людей. В этом случае мы можем подать модели изображение и серию текстовых запросов: "*изображение кошки*", "*изображение собаки*", "*изображение человека*". В полученном векторе из 3 вероятностей нам нужно просто выбрать индекс с наибольшим значением. -![CLIP для классификации изображений](../../../../../translated_images/clip-class.3af42ef0b2b19369.ru.png) +![CLIP для классификации изображений](../../../../../translated_images/ru/clip-class.3af42ef0b2b19369.webp) > *Изображение из [этой статьи](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP также можно использовать для **генерации Одно из важных отличий VQGAN от традиционного GAN заключается в том, что последний может создавать приличное изображение из любого входного вектора, тогда как VQGAN, скорее всего, создаст несогласованное изображение. Поэтому процесс создания изображения необходимо дополнительно направлять, и это можно сделать с помощью CLIP. -![Архитектура VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.ru.png) +![Архитектура VQGAN+CLIP](../../../../../translated_images/ru/vqgan.5027fe05051dfa31.webp) Чтобы сгенерировать изображение, соответствующее текстовому запросу, мы начинаем с некоторого случайного вектора кодирования, который передается через VQGAN для создания изображения. Затем CLIP используется для создания функции потерь, которая показывает, насколько хорошо изображение соответствует текстовому запросу. Цель состоит в минимизации этой функции потерь с использованием обратного распространения для корректировки параметров входного вектора. Отличная библиотека, реализующая VQGAN+CLIP, — это [Pixray](http://github.com/pixray/pixray). -![Изображение, созданное Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ru.png) | ![Изображение, созданное Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ru.png) | ![Изображение, созданное Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ru.png) +![Изображение, созданное Pixray](../../../../../translated_images/ru/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.webp) | ![Изображение, созданное Pixray](../../../../../translated_images/ru/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.webp) | ![Изображение, созданное Pixray](../../../../../translated_images/ru/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.webp) ----|----|---- Изображение, созданное по запросу *акварельный портрет молодого учителя литературы с книгой крупным планом* | Изображение, созданное по запросу *масляный портрет молодой учительницы информатики с компьютером крупным планом* | Изображение, созданное по запросу *масляный портрет пожилого учителя математики на фоне доски крупным планом* @@ -75,7 +75,7 @@ DALL-E — это версия GPT-3, обученная для генераци Основное отличие DALL-E 1 от DALL-E 2 заключается в том, что последняя генерирует более реалистичные изображения и произведения искусства. Примеры генерации изображений с помощью DALL-E: -![Изображение, созданное DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ru.png) | ![Изображение, созданное DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ru.png) | ![Изображение, созданное DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ru.png) +![Изображение, созданное DALL-E](../../../../../translated_images/ru/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Изображение, созданное DALL-E](../../../../../translated_images/ru/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Изображение, созданное DALL-E](../../../../../translated_images/ru/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Изображение, созданное по запросу *акварельный портрет молодого учителя литературы с книгой крупным планом* | Изображение, созданное по запросу *масляный портрет молодой учительницы информатики с компьютером крупным планом* | Изображение, созданное по запросу *масляный портрет пожилого учителя математики на фоне доски крупным планом* diff --git a/translations/sk/README.md b/translations/sk/README.md index 4919fa04..ae9b19b2 100644 --- a/translations/sk/README.md +++ b/translations/sk/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Učenie pre začiatočníkov v oblasti umelej inteligencie - Kurz -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.sk.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sk/ai-overview.0857791951d19500.png)| |:---:| | AI pre začiatočníkov - _Sketchnote od [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sk/lessons/1-Intro/README.md b/translations/sk/lessons/1-Intro/README.md index b9401204..2adcbf53 100644 --- a/translations/sk/lessons/1-Intro/README.md +++ b/translations/sk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do umelej inteligencie -![Zhrnutie obsahu Úvodu do umelej inteligencie v kresbe](../../../../translated_images/ai-intro.bf28d1ac4235881c.sk.png) +![Zhrnutie obsahu Úvodu do umelej inteligencie v kresbe](../../../../translated_images/sk/ai-intro.bf28d1ac4235881c.png) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Pôvodne boli počítače vynájdené [Charlesom Babbageom](https://en.wikipedia.org/wiki/Charles_Babbage) na prácu s číslami podľa presne definovaného postupu – algoritmu. Moderné počítače, hoci sú oveľa pokročilejšie ako pôvodný model navrhnutý v 19. storočí, stále fungujú na rovnakom princípe riadených výpočtov. Preto je možné naprogramovať počítač na vykonanie úlohy, ak poznáme presnú postupnosť krokov potrebných na dosiahnutie cieľa. -![Fotografia osoby](../../../../translated_images/dsh_age.d212a30d4e54fb5f.sk.png) +![Fotografia osoby](../../../../translated_images/sk/dsh_age.d212a30d4e54fb5f.png) > Fotografia od [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Viac informácií nájdete v článku **[Artificial General Intelligence](https: Jedným z problémov pri zaoberaní sa pojmom **[inteligencia](https://en.wikipedia.org/wiki/Intelligence)** je, že neexistuje jasná definícia tohto pojmu. Dá sa argumentovať, že inteligencia súvisí s **abstraktným myslením** alebo so **sebauvedomením**, ale nemôžeme ju presne definovať. -![Fotografia mačky](../../../../translated_images/photo-cat.8c8e8fb760ffe457.sk.jpg) +![Fotografia mačky](../../../../translated_images/sk/photo-cat.8c8e8fb760ffe457.jpg) > [Fotografia](https://unsplash.com/photos/75715CVEJhI) od [Amber Kipp](https://unsplash.com/@sadmax) z Unsplash @@ -98,13 +98,13 @@ Alternatívne môžeme skúsiť modelovať najjednoduchšie prvky v našom mozgu > | Čo tak ML? | | > |--------------|-----------| -> | Časť umelej inteligencie, ktorá je založená na tom, že počítač sa učí riešiť problém na základe niektorých dát, sa nazýva **strojové učenie**. V tomto kurze sa nebudeme zaoberať klasickým strojovým učením – odkazujeme vás na samostatný [kurz Strojové učenie pre začiatočníkov](http://aka.ms/ml-beginners). | ![ML pre začiatočníkov](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.sk.png) | +> | Časť umelej inteligencie, ktorá je založená na tom, že počítač sa učí riešiť problém na základe niektorých dát, sa nazýva **strojové učenie**. V tomto kurze sa nebudeme zaoberať klasickým strojovým učením – odkazujeme vás na samostatný [kurz Strojové učenie pre začiatočníkov](http://aka.ms/ml-beginners). | ![ML pre začiatočníkov](../../../../translated_images/sk/ml-for-beginners.9e4fed176fd5817d.png) | ## Stručná história AI Umelá inteligencia vznikla ako oblasť v polovici 20. storočia. Spočiatku bol symbolický prístup dominantný a viedol k niekoľkým dôležitým úspechom, ako napríklad expertné systémy – počítačové programy, ktoré dokázali pôsobiť ako odborníci v niektorých obmedzených problémových oblastiach. Avšak čoskoro sa ukázalo, že tento prístup nie je dobre škálovateľný. Extrahovanie vedomostí od experta, ich reprezentácia v počítači a udržiavanie tejto databázy vedomostí presnej sa ukázalo byť veľmi zložitou úlohou a príliš nákladnou na to, aby bola praktická v mnohých prípadoch. To viedlo k takzvanej [AI zime](https://en.wikipedia.org/wiki/AI_winter) v 70. rokoch. -Stručná história AI +Stručná história AI > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobne môžeme vidieť, ako sa prístup k vytváraniu „hovoriacich programov * Moderní asistenti, ako Cortana, Siri alebo Google Assistant, sú všetci hybridné systémy, ktoré používajú neurónové siete na prevod reči na text a rozpoznanie nášho zámeru, a potom využívajú nejaké uvažovanie alebo explicitné algoritmy na vykonanie požadovaných akcií. * V budúcnosti môžeme očakávať kompletný model založený na neurónových sieťach, ktorý bude sám zvládať dialóg. Nedávne GPT a [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) rodiny neurónových sietí ukazujú veľký úspech v tomto smere. -evolúcia Turingovho testu # Reprezentácia znalostí a expertné systémy -![Zhrnutie obsahu Symbolickej AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.sk.png) +![Zhrnutie obsahu Symbolickej AI](../../../../translated_images/sk/ai-symbolic.715a30cb610411a6.png) > Sketchnote od [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najčastejšie znalosti striktne nedefinujeme, ale zosúladíme ich s inými pr Problém **reprezentácie znalostí** teda spočíva v nájdení efektívneho spôsobu, ako reprezentovať znalosti v počítači vo forme údajov, aby boli automaticky použiteľné. To možno vnímať ako spektrum: -![Spektrum reprezentácie znalostí](../../../../translated_images/knowledge-spectrum.b60df631852c0217.sk.png) +![Spektrum reprezentácie znalostí](../../../../translated_images/sk/knowledge-spectrum.b60df631852c0217.png) > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Bloková syntax | Odsadenie | | | Jedným z prvých úspechov symbolickej AI boli tzv. **expertné systémy** - počítačové systémy navrhnuté tak, aby fungovali ako odborník v obmedzenej oblasti problémov. Boli založené na **báze znalostí** extrahovanej od jedného alebo viacerých ľudských odborníkov a obsahovali **odvodzovací mechanizmus**, ktorý vykonával odvodzovanie na jej základe. -![Štruktúra človeka](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.sk.png) | ![Systém založený na znalostiach](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.sk.png) +![Štruktúra človeka](../../../../translated_images/sk/arch-human.5d4d35f1bba3ab1c.png) | ![Systém založený na znalostiach](../../../../translated_images/sk/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Zjednodušená štruktúra ľudského nervového systému | Architektúra systému založeného na znalostiach @@ -106,7 +106,7 @@ Expertné systémy sú postavené podobne ako systém ľudského odvodzovania, k Ako príklad si vezmime nasledujúci expertný systém na určenie zvieraťa na základe jeho fyzických charakteristík: -![AND-OR strom](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sk.png) +![AND-OR strom](../../../../translated_images/sk/AND-OR-Tree.5592d2c70187f283.png) > Obrázok od [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index dbfbfd5b..56669638 100644 --- a/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "V prípade, že máme viac ako 2 triedy, softmax normalizuje pravdepodobnosti medzi všetkými z nich. Tu je diagram architektúry siete, ktorá vykonáva klasifikáciu číslic MNIST:\n", "\n", - "![MNIST Klasifikátor](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.sk.png)\n" + "![MNIST Klasifikátor](../../../../../translated_images/sk/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Nízka tréningová chyba - model dokáže dobre aproximovať tréningové dáta, pretože má dostatočnú vyjadrovaciu schopnosť.\n", "* Chyba na validačných dátach môže byť oveľa vyššia ako tréningová chyba a môže počas tréningu začať rásť - je to preto, že model si \"pamätá\" tréningové body a stráca \"celkový obraz\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.sk.png)\n", + "![Overfitting](../../../../../translated_images/sk/overfit.a0bd57f717c15769.png)\n", "\n", "> Na tomto obrázku `x` predstavuje tréningové dáta, `o` - validačné dáta. Vľavo - lineárny model (jednovrstvový), ktorý pomerne dobre aproximuje povahu dát. Vpravo - model s overfittingom, ktorý dokonale aproximuje tréningové dáta, ale prestáva dávať zmysel pri akýchkoľvek iných dátach (validačná chyba je veľmi vysoká).\n" ] diff --git a/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md index bfd786a2..9e051b98 100644 --- a/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Pretrénovanie je mimoriadne dôležitý koncept v strojovom učení, a je veľm Zvážte nasledujúci problém aproximácie 5 bodov (reprezentovaných `x` na grafoch nižšie): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.sk.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.sk.jpg) +![linear](../../../../../translated_images/sk/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sk/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Lineárny model, 2 parametre** | **Nelineárny model, 7 parametrov** Chyba trénovania = 5.3 | Chyba trénovania = 0 @@ -79,7 +79,7 @@ Je veľmi dôležité nájsť správnu rovnováhu medzi bohatstvom modelu (poče Ako môžete vidieť na grafe vyššie, pretrénovanie možno detekovať veľmi nízkou chybou trénovania a vysokou chybou validácie. Normálne počas trénovania vidíme, že chyby trénovania aj validácie začínajú klesať, a potom v určitom bode chyba validácie môže prestať klesať a začať stúpať. Toto bude znak pretrénovania a indikátor, že by sme mali pravdepodobne zastaviť trénovanie (alebo aspoň urobiť snímku modelu). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.sk.png) +![overfitting](../../../../../translated_images/sk/Overfitting.408ad91cd90b4371.png) ## Ako predísť pretrénovaniu diff --git a/translations/sk/lessons/3-NeuralNetworks/README.md b/translations/sk/lessons/3-NeuralNetworks/README.md index 936333b0..8a64bb62 100644 --- a/translations/sk/lessons/3-NeuralNetworks/README.md +++ b/translations/sk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do neurónových sietí -![Zhrnutie obsahu Úvodu do neurónových sietí v kresbe](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.sk.png) +![Zhrnutie obsahu Úvodu do neurónových sietí v kresbe](../../../../translated_images/sk/ai-neuralnetworks.1c687ae40bc86e83.png) Ako sme si povedali v úvode, jedným zo spôsobov, ako dosiahnuť inteligenciu, je trénovať **počítačový model** alebo **umelý mozog**. Od polovice 20. storočia vedci skúšali rôzne matematické modely, až kým sa v posledných rokoch tento smer neukázal ako mimoriadne úspešný. Takéto matematické modely mozgu sa nazývajú **neurónové siete**. @@ -36,13 +36,13 @@ V tomto kurze sa zameriame iba na modely neurónových sietí. Z biológie vieme, že náš mozog pozostáva z neurónových buniek (neurónov), pričom každý z nich má viacero "vstupov" (dendritov) a jeden "výstup" (axon). Dendrity aj axóny môžu viesť elektrické signály a spojenia medzi nimi — známe ako synapsie — môžu vykazovať rôzne stupne vodivosti, ktoré sú regulované neurotransmitermi. -![Model neurónu](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.sk.jpg) | ![Model neurónu](../../../../translated_images/artneuron.1a5daa88d20ebe6f.sk.png) +![Model neurónu](../../../../translated_images/sk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model neurónu](../../../../translated_images/sk/artneuron.1a5daa88d20ebe6f.png) ----|---- Skutočný neurón *([Obrázok](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) z Wikipédie)* | Umelý neurón *(Obrázok od autora)* Najjednoduchší matematický model neurónu teda obsahuje niekoľko vstupov X1, ..., XN a jeden výstup Y, a sériu váh W1, ..., WN. Výstup sa vypočíta ako: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kde f je nejaká nelineárna **aktivačná funkcia**. diff --git a/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md index 7b9435d7..3ed2164d 100644 --- a/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našom [OpenCV Notebook](OpenCV.ipynb) uvádzame niekoľko príkladov, kedy sa * **Predspracovanie fotografie Braillovej knihy**. Zameriavame sa na to, ako môžeme použiť prahovanie, detekciu vlastností, perspektívnu transformáciu a manipuláciu s NumPy na oddelenie jednotlivých Braillových symbolov na ďalšiu klasifikáciu neurónovou sieťou. -![Braillov obrázok](../../../../../translated_images/braille.341962ff76b1bd70.sk.jpeg) | ![Predspracovaný Braillov obrázok](../../../../../translated_images/braille-result.46530fea020b03c7.sk.png) | ![Braillove symboly](../../../../../translated_images/braille-symbols.0159185ab69d5339.sk.png) +![Braillov obrázok](../../../../../translated_images/sk/braille.341962ff76b1bd70.jpeg) | ![Predspracovaný Braillov obrázok](../../../../../translated_images/sk/braille-result.46530fea020b03c7.png) | ![Braillove symboly](../../../../../translated_images/sk/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) * **Detekcia pohybu vo videu pomocou rozdielu snímok**. Ak je kamera pevná, snímky z kamery by mali byť veľmi podobné. Keďže snímky sú reprezentované ako polia, jednoduchým odčítaním týchto polí pre dve po sebe idúce snímky získame rozdiel pixelov, ktorý by mal byť nízky pre statické snímky a vyšší, keď je v obrázku výrazný pohyb. -![Obrázok video snímok a rozdielov snímok](../../../../../translated_images/frame-difference.706f805491a0883c.sk.png) +![Obrázok video snímok a rozdielov snímok](../../../../../translated_images/sk/frame-difference.706f805491a0883c.png) > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našom [OpenCV Notebook](OpenCV.ipynb) uvádzame niekoľko príkladov, kedy sa - **Hustý optický tok** vypočíta vektorové pole, ktoré ukazuje, kam sa každý pixel pohybuje. - **Riedky optický tok** je založený na výbere niektorých výrazných vlastností na obrázku (napr. hrany) a budovaní ich trajektórie zo snímky na snímku. -![Obrázok optického toku](../../../../../translated_images/optical.1f4a94464579a83a.sk.png) +![Obrázok optického toku](../../../../../translated_images/sk/optical.1f4a94464579a83a.png) > Obrázok z [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index eb1674f0..9094ef24 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je sieť, ktorá dosiahla 92,7% presnosť v klasifikácii ImageNet top-5 v roku 2014. Má nasledujúcu štruktúru vrstiev: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sk.jpg) +![ImageNet Layers](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.jpg) Ako môžete vidieť, VGG nasleduje tradičnú pyramídovú architektúru, ktorá je sekvenciou vrstiev konvolúcie a pooling. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sk.jpg) +![ImageNet Pyramid](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.jpg) > Obrázok z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8163a3c4..19a8019b 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Takže v typickej CNN by bolo niekoľko konvolučných vrstiev, medzi ktorými by boli pooling vrstvy na zmenšenie rozmerov obrázka. Zároveň by sme zvýšili počet filtrov, pretože ako sa vzory stávajú pokročilejšími, existuje viac možných zaujímavých kombinácií, ktoré musíme hľadať.\n", "\n", - "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sk.png)\n", + "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kvôli zmenšovaniu priestorových rozmerov a zvyšovaniu rozmerov vlastností/filtrov sa táto architektúra nazýva aj **pyramídová architektúra**.\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 55726c7d..45084caa 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Takže v typickej CNN by bolo niekoľko konvolučných vrstiev, pričom medzi nimi by boli pooling vrstvy na zmenšenie rozmerov obrázka. Zároveň by sme zvýšili počet filtrov, pretože ako sa vzory stávajú pokročilejšími, existuje viac možných zaujímavých kombinácií, ktoré musíme hľadať.\n", "\n", - "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sk.png)\n", + "![Obrázok zobrazujúci niekoľko konvolučných vrstiev s pooling vrstvami.](../../../../../translated_images/sk/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kvôli zmenšovaniu priestorových rozmerov a zvyšovaniu rozmerov vlastností/filtrov sa táto architektúra nazýva aj **pyramídová architektúra**.\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md index 7cba585c..a24d3b9a 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V reálnom živote chceme byť schopní rozpoznať objekty na obrázku bez ohľa Na extrakciu vzorov použijeme koncept **konvolučných filtrov**. Ako viete, obrázok je reprezentovaný ako 2D-matica alebo 3D-tenzor s farebnou hĺbkou. Aplikácia filtra znamená, že vezmeme relatívne malú **jadrovú maticu filtra** a pre každý pixel v pôvodnom obrázku vypočítame vážený priemer so susednými bodmi. Môžeme si to predstaviť ako malé okno, ktoré sa posúva po celom obrázku a spriemeruje všetky pixely podľa váh v jadrovej matici filtra. -![Vertikálny filter hrán](../../../../../translated_images/filter-vert.b7148390ca0bc356.sk.png) | ![Horizontálny filter hrán](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.sk.png) +![Vertikálny filter hrán](../../../../../translated_images/sk/filter-vert.b7148390ca0bc356.png) | ![Horizontálny filter hrán](../../../../../translated_images/sk/filter-horiz.59b80ed4feb946ef.png) ----|---- > Obrázok od Dmitry Soshnikov @@ -38,7 +38,7 @@ Princíp fungovania CNN je založený na nasledujúcich dôležitých myšlienka * Môžeme navrhnúť sieť tak, aby sa filtre učili automaticky * Rovnaký prístup môžeme použiť na hľadanie vzorov vo vysokoúrovňových vlastnostiach, nielen v pôvodnom obrázku. Extrakcia vlastností pomocou CNN teda funguje na hierarchii vlastností, od nízkoúrovňových kombinácií pixelov až po vysokoúrovňové kombinácie častí obrázku. -![Hierarchická extrakcia vlastností](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.sk.png) +![Hierarchická extrakcia vlastností](../../../../../translated_images/sk/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Obrázok z [práce Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), založený na [ich výskume](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Väčšina CNN používaných na spracovanie obrázkov nasleduje tzv. pyramídov Ako príklad sa pozrime na architektúru VGG-16, siete, ktorá dosiahla 92,7% presnosť v top-5 klasifikácii ImageNet v roku 2014: -![Vrstvy ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sk.jpg) +![Vrstvy ImageNet](../../../../../translated_images/sk/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Pyramída ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sk.jpg) +![Pyramída ImageNet](../../../../../translated_images/sk/vgg-16-arch.64ff2137f50dd49f.jpg) > Obrázok z [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index cb247f5b..8e6893d2 100644 --- a/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vašou úlohou je vytrénovať konvolučnú neurónovú sieť na klasifikáciu r Použijeme [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ktorý obsahuje obrázky 37 rôznych plemien psov a mačiek. -![Dataset, s ktorým budeme pracovať](../../../../../../translated_images/data.50b2a9d5484bdbf0.sk.png) +![Dataset, s ktorým budeme pracovať](../../../../../../translated_images/sk/data.50b2a9d5484bdbf0.png) Na stiahnutie datasetu použite tento kód: diff --git a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 38453414..16c94a96 100644 --- a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Aby sme si predstavili ideálnu mačku, začneme s náhodným šumovým obrázkom a pokúsime sa použiť optimalizačnú techniku gradientného zostupu na úpravu obrázka tak, aby sieť rozpoznala mačku.\n", "\n", - "![Optimalizačná slučka](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sk.png)\n", + "![Optimalizačná slučka](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Tu je náš počiatočný obrázok:\n" ] diff --git a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md index c21c4925..b0108696 100644 --- a/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras aj PyTorch obsahujú funkcie na jednoduché načítanie predtrénovaných Tu sú ukážkové črty extrahované z obrázku mačky pomocou siete VGG-16: -![Črty extrahované VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.sk.png) +![Črty extrahované VGG-16](../../../../../translated_images/sk/features.6291f9c7ba3a0b95.png) ## Dataset Mačky vs. Psy @@ -48,19 +48,19 @@ Predtrénovaná neurónová sieť obsahuje rôzne vzory vo svojej *pamäti*, vr Jeden prístup, ktorý môžeme použiť, je začať s náhodným obrázkom a potom sa pokúsiť použiť techniku **optimalizácie pomocou gradientného zostupu**, aby sme upravili tento obrázok tak, že sieť začne myslieť, že je to mačka. -![Optimalizačný cyklus obrázku](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sk.png) +![Optimalizačný cyklus obrázku](../../../../../translated_images/sk/ideal-cat-loop.999fbb8ff306e044.png) Ak to však urobíme, dostaneme niečo veľmi podobné náhodnému šumu. Je to preto, že *existuje mnoho spôsobov, ako presvedčiť sieť, že vstupný obrázok je mačka*, vrátane niektorých, ktoré vizuálne nedávajú zmysel. Hoci tieto obrázky obsahujú veľa vzorov typických pre mačku, nič ich neobmedzuje, aby boli vizuálne zreteľné. Na zlepšenie výsledku môžeme do funkcie straty pridať ďalší člen, ktorý sa nazýva **variácia straty**. Je to metrika, ktorá ukazuje, ako podobné sú susedné pixely obrázku. Minimalizácia variácie straty robí obrázok hladším a zbavuje sa šumu - čím odhaľuje vizuálne príťažlivejšie vzory. Tu je príklad takýchto "ideálnych" obrázkov, ktoré sú klasifikované ako mačka a zebra s vysokou pravdepodobnosťou: -![Ideálna mačka](../../../../../translated_images/ideal-cat.203dd4597643d6b0.sk.png) | ![Ideálna zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.sk.png) +![Ideálna mačka](../../../../../translated_images/sk/ideal-cat.203dd4597643d6b0.png) | ![Ideálna zebra](../../../../../translated_images/sk/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideálna mačka* | *Ideálna zebra* Podobný prístup môže byť použitý na vykonanie tzv. **adversariálnych útokov** na neurónovú sieť. Predpokladajme, že chceme oklamať neurónovú sieť a urobiť z psa mačku. Ak vezmeme obrázok psa, ktorý je sieťou rozpoznaný ako pes, môžeme ho trochu upraviť pomocou optimalizácie gradientného zostupu, až kým sieť nezačne klasifikovať obrázok ako mačku: -![Obrázok psa](../../../../../translated_images/original-dog.8f68a67d2fe0911f.sk.png) | ![Obrázok psa klasifikovaný ako mačka](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.sk.png) +![Obrázok psa](../../../../../translated_images/sk/original-dog.8f68a67d2fe0911f.png) | ![Obrázok psa klasifikovaný ako mačka](../../../../../translated_images/sk/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Pôvodný obrázok psa* | *Obrázok psa klasifikovaný ako mačka* diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3d501856..d8ef7fa0 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Keďže trénujeme autoenkóder, aby zachytil čo najviac informácií z pôvodného obrázku pre presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zabudovanie** vstupných obrázkov, aby zachytila ich význam.\n", "\n", - "![Schéma Autoenkódera](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sk.jpg)\n", + "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Obrázok z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 0d4a0cfd..bfe97bc3 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Keďže trénujeme autoenkóder, aby zachytil čo najviac informácií z pôvodného obrázku pre presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zabudovanie** vstupných obrázkov, aby zachytila ich význam.\n", "\n", - "![Schéma Autoenkódera](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sk.jpg)\n", + "![Schéma Autoenkódera](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Obrázok z [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md index 9f3c661f..f983d469 100644 --- a/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Avšak, môžeme chcieť použiť surové (neoznačené) dáta na trénovanie CN Keďže trénujeme autoenkodér, aby zachytil čo najviac informácií z pôvodného obrázku na presnú rekonštrukciu, sieť sa snaží nájsť najlepšie **zobrazenie** vstupných obrázkov, aby zachytila ich význam. -![Schéma Autoenkodéra](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sk.jpg) +![Schéma Autoenkodéra](../../../../../translated_images/sk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Obrázok z [blogu Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 129becfd..0fb69ae9 100644 --- a/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modely klasifikácie obrázkov, s ktorými sme sa doteraz zaoberali, brali obrá ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Detekcia objektov](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.sk.png) +![Detekcia objektov](../../../../../translated_images/sk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Obrázok zo stránky [YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Predpokladajme, že chceme nájsť mačku na obrázku. Veľmi naivný prístup k 2. Spustiť klasifikáciu obrázkov na každej dlaždici. 3. Dlaždice, ktoré majú dostatočne vysokú aktiváciu, môžeme považovať za obsahujúce hľadaný objekt. -![Naivná detekcia objektov](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.sk.png) +![Naivná detekcia objektov](../../../../../translated_images/sk/naive-detection.e7f1ba220ccd08c6.png) > *Obrázok z [cvičebného notebooku](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Pri tejto úlohe sa môžete stretnúť s nasledujúcimi datasetmi: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 tried * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 tried, ohraničujúce rámy a segmentačné masky -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.sk.jpg) +![COCO](../../../../../translated_images/sk/coco-examples.71bc60380fa6cceb.jpg) ## Metriky detekcie objektov @@ -50,7 +50,7 @@ Pri tejto úlohe sa môžete stretnúť s nasledujúcimi datasetmi: Zatiaľ čo pri klasifikácii obrázkov je jednoduché merať, ako dobre algoritmus funguje, pri detekcii objektov musíme merať správnosť triedy, ako aj presnosť polohy predpokladaného ohraničujúceho rámu. Na tento účel používame tzv. **Prienik cez zjednotenie** (IoU), ktorý meria, ako dobre sa dva rámy (alebo dve ľubovoľné oblasti) prekrývajú. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.sk.png) +![IoU](../../../../../translated_images/sk/iou_equation.9a4751d40fff4e11.png) > *Obrázok 2 z [tohto výborného blogového príspevku o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Existujú dve hlavné triedy algoritmov detekcie objektov: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) používa [Selektívne vyhľadávanie](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) na generovanie hierarchickej štruktúry regiónov ROI, ktoré sú potom prechádzané extraktormi funkcií CNN a SVM klasifikátormi na určenie triedy objektu, a lineárnou regresiou na určenie súradníc *ohraničujúceho rámu*. [Oficiálny článok](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.sk.png) +![RCNN](../../../../../translated_images/sk/rcnn1.cae407020dfb1d1f.png) > *Obrázok od van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.sk.png) +![RCNN-1](../../../../../translated_images/sk/rcnn2.2d9530bb83516484.png) > *Obrázky z [tohto blogu](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Existujú dve hlavné triedy algoritmov detekcie objektov: Tento prístup je podobný R-CNN, ale regióny sú definované po aplikovaní konvolučných vrstiev. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.sk.png) +![FRCNN](../../../../../translated_images/sk/f-rcnn.3cda6d9bb4188875.png) > Obrázok z [oficiálneho článku](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Tento prístup je podobný R-CNN, ale regióny sú definované po aplikovaní ko Hlavnou myšlienkou tohto prístupu je použitie neurónovej siete na predpovedanie ROI - tzv. *Sieť na návrh regiónov*. [Článok](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.sk.png) +![FasterRCNN](../../../../../translated_images/sk/faster-rcnn.8d46c099b87ef30a.png) > Obrázok z [oficiálneho článku](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Tento algoritmus je ešte rýchlejší ako Faster R-CNN. Hlavná myšlienka je n 2. Funkcie sú spracované pomocou **Position-Sensitive Score Map**. Každý objekt z $C$ tried je rozdelený na $k\times k$ regióny, a trénujeme na predpovedanie častí objektov. 3. Pre každú časť z $k\times k$ regiónov všetky siete hlasujú za triedy objektov, a trieda objektu s maximálnym počtom hlasov je vybraná. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.sk.png) +![r-fcn image](../../../../../translated_images/sk/r-fcn.13eb88158b99a3da.png) > Obrázok z [oficiálneho článku](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritmus na detekciu objektov v reálnom čase s jedným prechodom. Hl * Obrázok je rozdelený na $S\times S$ regióny. * Pre každý región **CNN** predpovedá $n$ možných objektov, *súradnice ohraničujúceho rámu* a *dôveru*=*pravdepodobnosť* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.sk.png) + ![YOLO](../../../../../translated_images/sk/yolo.a2648ec82ee8bb4e.png) > Obrázok z [oficiálneho článku](https://arxiv.org/abs/1506.02640) diff --git a/translations/sk/lessons/4-ComputerVision/README.md b/translations/sk/lessons/4-ComputerVision/README.md index 4f924906..00d2625a 100644 --- a/translations/sk/lessons/4-ComputerVision/README.md +++ b/translations/sk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Počítačové videnie -![Zhrnutie obsahu o počítačovom videní v kresbe](../../../../translated_images/ai-computervision.6506ebebac3fbf76.sk.png) +![Zhrnutie obsahu o počítačovom videní v kresbe](../../../../translated_images/sk/ai-computervision.6506ebebac3fbf76.png) V tejto sekcii sa naučíme: diff --git a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index dfb393d3..320f7d9b 100644 --- a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je najčastejšie používaná tradičná vektorová reprezentácia. Každé slovo je priradené k indexu vektora a prvok vektora obsahuje počet výskytov daného slova v konkrétnom dokumente.\n", "\n", - "![Obrázok znázorňujúci, ako je reprezentácia Bag of Words uložená v pamäti.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sk.png) \n", + "![Obrázok znázorňujúci, ako je reprezentácia Bag of Words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Na BoW sa môžete pozerať aj ako na súčet všetkých one-hot-enkódovaných vektorov pre jednotlivé slová v texte.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 493c3e27..e195b43d 100644 --- a/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) je najjednoduchšia tradičná metóda reprezentácie textu pomocou vektorov. Každé slovo je priradené k indexu vektora a prvok vektora obsahuje počet výskytov daného slova v konkrétnom dokumente.\n", "\n", - "![Obrázok znázorňujúci, ako je reprezentácia Bag-of-words uložená v pamäti.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sk.png) \n", + "![Obrázok znázorňujúci, ako je reprezentácia Bag-of-words uložená v pamäti.](../../../../../translated_images/sk/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Na metódu BoW sa môžete pozerať aj ako na súčet všetkých vektorov s jedným aktívnym prvkom (one-hot-encoded) pre jednotlivé slová v texte.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index aec18390..a849f9b3 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Použitím embedding vrstvy ako prvej vrstvy v našej sieti môžeme prejsť od modelu bag-of-words k modelu **embedding bag**, kde najskôr každé slovo v našom texte prevedieme na zodpovedajúci embedding a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito embeddingami, ako napríklad `sum`, `average` alebo `max`.\n", "\n", - "![Obrázok zobrazujúci embedding klasifikátor pre päť slov v sekvencii.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sk.png)\n", + "![Obrázok zobrazujúci embedding klasifikátor pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša klasifikačná neurónová sieť začne embedding vrstvou, potom agregačnou vrstvou a na vrchu bude lineárny klasifikátor:\n" ] @@ -176,7 +176,7 @@ "\n", "V predchádzajúcej architektúre sme museli všetky sekvencie doplniť na rovnakú dĺžku, aby sa zmestili do minibatchu. Toto nie je najefektívnejší spôsob reprezentácie sekvencií s premenlivou dĺžkou - iný prístup by bol použiť **offset** vektor, ktorý by obsahoval posuny všetkých sekvencií uložených v jednom veľkom vektore.\n", "\n", - "![Obrázok zobrazujúci reprezentáciu sekvencií pomocou offset vektora](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.sk.png)\n", + "![Obrázok zobrazujúci reprezentáciu sekvencií pomocou offset vektora](../../../../../translated_images/sk/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Na obrázku vyššie je zobrazená sekvencia znakov, ale v našom príklade pracujeme so sekvenciami slov. Avšak, základný princíp reprezentácie sekvencií pomocou offset vektora zostáva rovnaký.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová.\n", "\n", - "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sk.png)\n", + "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Na experimentovanie s Word2Vec vektormi predtrénovanými na Google News dataset môžeme použiť knižnicu **gensim**. Nižšie nájdeme slová najviac podobné slovu 'neural'.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index ecffb39b..3e330b30 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Použitím embedding vrstvy ako prvej vrstvy v našej sieti môžeme prejsť z modelu bag-of-words na model **embedding bag**, kde najskôr každé slovo v našom texte prevedieme na zodpovedajúci embedding a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito embeddingmi, ako napríklad `sum`, `average` alebo `max`.\n", "\n", - "![Obrázok zobrazujúci embedding klasifikátor pre päť sekvenčných slov.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sk.png)\n", + "![Obrázok zobrazujúci embedding klasifikátor pre päť sekvenčných slov.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša klasifikačná neurónová sieť pozostáva z nasledujúcich vrstiev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová.\n", "\n", - "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sk.png)\n", + "![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Na experimentovanie s embeddingom Word2Vec predtrénovaným na Google News dataset môžeme použiť knižnicu **gensim**. Nižšie nájdeme slová najviac podobné slovu 'neural'.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/14-Embeddings/README.md b/translations/sk/lessons/5-NLP/14-Embeddings/README.md index 6250a217..4f26fe4e 100644 --- a/translations/sk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vstavaná vrstva (embedding layer) teda prijíma slovo ako vstup a produkuje vý Použitím vstavanej vrstvy ako prvej vrstvy v našej klasifikačnej sieti môžeme prejsť z modelu bag-of-words na model **embedding bag**, kde najprv každé slovo v texte prevedieme na zodpovedajúcu vstavanú reprezentáciu a potom vypočítame nejakú agregačnú funkciu nad všetkými týmito reprezentáciami, ako napríklad `sum`, `average` alebo `max`. -![Obrázok zobrazujúci klasifikátor s použitím vstavaných reprezentácií pre päť slov v sekvencii.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sk.png) +![Obrázok zobrazujúci klasifikátor s použitím vstavaných reprezentácií pre päť slov v sekvencii.](../../../../../translated_images/sk/embedding-classifier-example.b77f021a7ee67eee.png) > Obrázok od autora @@ -40,7 +40,7 @@ Na to potrebujeme predtrénovať náš model vstavaných reprezentácií na veľ CBoW je rýchlejší, zatiaľ čo skip-gram je pomalší, ale lepšie reprezentuje zriedkavé slová. -![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sk.png) +![Obrázok zobrazujúci algoritmy CBoW a Skip-Gram na konverziu slov na vektory.](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obrázok z [tohto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md index 6cda6a9b..e6b92a87 100644 --- a/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V našich predchádzajúcich príkladoch sme používali predtrénované sémant * **Continuous Bag-of-Words** (CBoW), kde predpovedáme stredný token $W_0$ v sekvencii tokenov $W_{-N}$, ..., $W_N$. * **Skip-gram**, kde predpovedáme množinu susedných tokenov {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} zo stredného tokenu $W_0$. -![obrázok z článku o konverzii slov na vektory](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sk.png) +![obrázok z článku o konverzii slov na vektory](../../../../../translated_images/sk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Obrázok z [tohto článku](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sk/lessons/5-NLP/16-RNN/README.md b/translations/sk/lessons/5-NLP/16-RNN/README.md index f279502d..f9817d2e 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/README.md +++ b/translations/sk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V predchádzajúcich sekciách sme používali bohaté sémantické reprezentác Na zachytenie významu textovej sekvencie potrebujeme použiť inú architektúru neurónovej siete, ktorá sa nazýva **rekurentná neurónová sieť** alebo RNN. V RNN prechádzame vetou cez sieť jeden symbol po druhom a sieť produkuje určitý **stav**, ktorý následne posunieme do siete spolu s ďalším symbolom. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.sk.png) +![RNN](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.png) > Obrázok od autora @@ -61,7 +61,7 @@ Diskutovali sme o rekurentných sieťach, ktoré fungujú jedným smerom, od za Rekurentná sieť, či už jednosmerná alebo obojstranná, zachytáva určité vzory v rámci sekvencie a môže ich uložiť do stavového vektora alebo preniesť do výstupu. Rovnako ako pri konvolučných sieťach, môžeme na prvú vrstvu postaviť ďalšiu rekurentnú vrstvu, aby sme zachytili vzory vyššej úrovne a stavali na vzoroch nižšej úrovne extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy je posunutý do ďalšej vrstvy ako vstup. -![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sk.jpg) +![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Obrázok z [tohto skvelého príspevku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza* diff --git a/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 329f29b1..aa386a76 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurentná sieť, či už jednosmerná alebo obojsmerná, zachytáva určité vzory v rámci sekvencie a môže ich uložiť do vektora stavu alebo odovzdať do výstupu. Rovnako ako pri konvolučných sieťach, môžeme na prvú vrstvu postaviť ďalšiu rekurentnú vrstvu, aby sme zachytili vzory na vyššej úrovni, ktoré sú vytvorené z nízkoúrovňových vzorov extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy je odovzdaný ako vstup do nasledujúcej vrstvy.\n", "\n", - "![Obrázok zobrazujúci viacvrstvovú LSTM-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sk.jpg)\n", + "![Obrázok zobrazujúci viacvrstvovú LSTM-RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Obrázok z [tohto skvelého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza*\n", "\n", diff --git a/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb index c6a49457..02eb1076 100644 --- a/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sk/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Na zachytenie významu textovej sekvencie použijeme architektúru neurónovej siete nazývanú **rekurentná neurónová sieť** (RNN). Pri použití RNN prechádzame vetou cez sieť po jednom tokene, pričom sieť produkuje určitý **stav**, ktorý následne odovzdáme s ďalším tokenom späť do siete.\n", "\n", - "![Obrázok znázorňujúci generovanie rekurentnej neurónovej siete.](../../../../../translated_images/rnn.27f5c29c53d727b5.sk.png)\n", + "![Obrázok znázorňujúci generovanie rekurentnej neurónovej siete.](../../../../../translated_images/sk/rnn.27f5c29c53d727b5.png)\n", "\n", "Pri danej vstupnej sekvencii tokenov $X_0,\\dots,X_n$ RNN vytvára sekvenciu blokov neurónovej siete a trénuje túto sekvenciu end-to-end pomocou spätného šírenia. Každý blok siete prijíma ako vstup dvojicu $(X_i,S_i)$ a produkuje výsledok $S_{i+1}$. Konečný stav $S_n$ alebo výstup $Y_n$ sa odovzdáva do lineárneho klasifikátora na produkciu výsledku. Všetky bloky siete zdieľajú rovnaké váhy a sú trénované end-to-end jedným priechodom spätného šírenia.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentné siete, či už jednosmerné alebo obojsmerné, zachytávajú vzory v rámci sekvencie a ukladajú ich do stavových vektorov alebo ich vracajú ako výstup. Podobne ako pri konvolučných sieťach, môžeme za prvú rekurentnú vrstvu pridať ďalšiu, aby sme zachytili vzory vyššej úrovne, ktoré sú vytvorené z nižších úrovní vzorov extrahovaných prvou vrstvou. To nás privádza k pojmu **viacvrstvová RNN**, ktorá pozostáva z dvoch alebo viacerých rekurentných sietí, kde výstup predchádzajúcej vrstvy slúži ako vstup pre nasledujúcu vrstvu.\n", "\n", - "![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sk.jpg)\n", + "![Obrázok zobrazujúci viacvrstvovú LSTM RNN](../../../../../translated_images/sk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Obrázok pochádza z [tohto skvelého článku](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) od Fernanda Lópeza.*\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 8598a678..b486f7e8 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Spôsob, akým budeme trénovať RNN na generovanie textu, je nasledovný. V každom kroku vezmeme sekvenciu znakov s dĺžkou `nchars` a požiadame sieť, aby pre každý vstupný znak vygenerovala nasledujúci výstupný znak:\n", "\n", - "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sk.png)\n", + "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png)\n", "\n", "V závislosti od konkrétneho scenára môžeme chcieť zahrnúť aj špeciálne znaky, ako napríklad *koniec sekvencie* ``. V našom prípade chceme sieť trénovať na nekonečné generovanie textu, preto nastavíme veľkosť každej sekvencie na `nchars` tokenov. Každý tréningový príklad tak bude pozostávať z `nchars` vstupov a `nchars` výstupov (čo je vstupná sekvencia posunutá o jeden symbol doľava). Minibatch bude obsahovať niekoľko takýchto sekvencií.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index d99f7833..bf819db5 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Spôsob, akým budeme trénovať RNN na generovanie nadpisov správ, je nasledovný. V každom kroku vezmeme jeden nadpis, ktorý bude poskytnutý RNN, a pre každý vstupný znak požiadame sieť, aby vygenerovala nasledujúci výstupný znak:\n", "\n", - "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sk.png)\n", + "![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Pre posledný znak našej sekvencie požiadame sieť, aby vygenerovala token ``.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md index e5c77274..04126ac6 100644 --- a/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V architektúre RNN, ktorú sme preberali v predchádzajúcej jednotke, každá To umožňuje rôzne neurónové architektúry, ktoré sú znázornené na obrázku nižšie: -![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sk.jpg) +![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Obrázok z blogového príspevku [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreja Karpatyho](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V tejto jednotke sa zameriame na jednoduché generatívne modely, ktoré nám po Budeme trénovať túto RNN na generovanie textu krok za krokom. Na každom kroku vezmeme sekvenciu znakov dĺžky `nchars` a požiadame sieť, aby pre každý vstupný znak vygenerovala ďalší výstupný znak: -![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sk.png) +![Obrázok zobrazujúci príklad generovania slova 'HELLO' pomocou RNN.](../../../../../translated_images/sk/rnn-generate.56c54afb52f9781d.png) Pri generovaní textu (počas inferencie) začíname s nejakým **podnetom**, ktorý prechádza cez RNN bunky na generovanie jeho medzistavu, a potom z tohto stavu začína generovanie. Generujeme jeden znak naraz a stav spolu s vygenerovaným znakom posielame ďalšej RNN bunke na generovanie ďalšieho znaku, až kým nevygenerujeme dostatok znakov. diff --git a/translations/sk/lessons/5-NLP/18-Transformers/README.md b/translations/sk/lessons/5-NLP/18-Transformers/README.md index ea421fa2..77a9e886 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Pri RNN sa sekvencia na sekvenciu implementuje pomocou dvoch rekurentných siet **Mechanizmy pozornosti** poskytujú spôsob váženia kontextového vplyvu každého vstupného vektora na každú výstupnú predikciu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupného RNN a výstupného RNN. Týmto spôsobom, pri generovaní výstupného symbolu yt, zohľadníme všetky skryté stavy vstupu hi, s rôznymi váhovými koeficientmi αt,i. -![Obrázok zobrazujúci model enkodér/dekodér s aditívnou vrstvou pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sk.png) +![Obrázok zobrazujúci model enkodér/dekodér s aditívnou vrstvou pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png) > Model enkodér-dekodér s aditívnym mechanizmom pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Maticu pozornosti {αi,j} by sme mohli interpretovať ako mieru, do akej určité vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice: -![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sk.png) +![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png) > Obrázok z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3) @@ -66,7 +66,7 @@ Výsledok, ktorý získame s pozičným embeddingom, zahŕňa pôvodný token aj Ďalej potrebujeme zachytiť nejaké vzory v rámci našej sekvencie. Na tento účel transformery používajú mechanizmus **vlastnej pozornosti**, ktorý je v podstate pozornosť aplikovaná na tú istú sekvenciu ako vstup a výstup. Aplikovanie vlastnej pozornosti nám umožňuje zohľadniť **kontext** v rámci vety a vidieť, ktoré slová sú navzájom prepojené. Napríklad nám umožňuje vidieť, na ktoré slová odkazujú koreferencie, ako *to*, a tiež zohľadniť kontext: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sk.png) +![](../../../../../translated_images/sk/CoreferenceResolution.861924d6d384a7d6.png) > Obrázok z [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Keďže každá vstupná pozícia je nezávisle mapovaná na každú výstupnú **BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová sieť transformera s 12 vrstvami pre *BERT-base* a 24 pre *BERT-large*. Model je najprv predtrénovaný na veľkom korpuse textových dát (WikiPedia + knihy) pomocou nesupervidovaného tréningu (predikcia maskovaných slov vo vete). Počas predtrénovania model absorbuje významné úrovne porozumenia jazyka, ktoré môžu byť následne využité s inými datasetmi pomocou jemného doladenia. Tento proces sa nazýva **transfer learning**. -![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sk.png) +![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Obrázok [zdroj](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 7f4798fe..38268ce3 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanizmy pozornosti** poskytujú spôsob, ako vážiť kontextuálny vplyv každého vstupného vektora na každú výstupnú predikciu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupnej RNN a výstupnej RNN. Týmto spôsobom, pri generovaní výstupného symbolu $y_t$, zohľadníme všetky skryté stavy vstupu $h_i$, s rôznymi váhovými koeficientmi $\\alpha_{t,i}$.\n", "\n", - "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sk.png)\n", + "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder s mechanizmom aditívnej pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Maticu pozornosti $\\{\\alpha_{i,j}\\}$ môžeme interpretovať ako mieru, do akej konkrétne vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice:\n", "\n", - "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sk.png)\n", + "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Obrázok prevzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová sieť Transformer s 12 vrstvami pre *BERT-base* a 24 pre *BERT-large*. Model je najprv predtrénovaný na veľkom korpuse textových dát (Wikipedia + knihy) pomocou nesupervidovaného tréningu (predikcia maskovaných slov vo vete). Počas predtrénovania model absorbuje významnú úroveň porozumenia jazyka, ktorú je možné následne využiť s inými dátovými súbormi pomocou jemného doladenia. Tento proces sa nazýva **transfer learning**.\n", "\n", - "![Obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sk.png)\n", + "![Obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existuje mnoho variácií architektúr Transformer, vrátane BERT, DistilBERT, BigBird, OpenGPT3 a ďalších, ktoré je možné jemne doladiť. Balík [HuggingFace](https://github.com/huggingface/) poskytuje úložisko na tréning mnohých z týchto architektúr pomocou PyTorch.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 812ceb2b..f3dda8db 100644 --- a/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mechanizmy pozornosti** poskytujú spôsob, ako vážiť kontextuálny vplyv každého vstupného vektora na každú predikciu výstupu RNN. Implementuje sa to vytvorením skratiek medzi medzistavmi vstupnej RNN a výstupnej RNN. Týmto spôsobom, pri generovaní výstupného symbolu $y_t$, zohľadníme všetky skryté stavy vstupu $h_i$, s rôznymi váhovými koeficientmi $\\alpha_{t,i}$.\n", "\n", - "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sk.png)\n", + "![Obrázok zobrazujúci model encoder/decoder s vrstvou aditívnej pozornosti](../../../../../translated_images/sk/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model encoder-decoder s mechanizmom aditívnej pozornosti podľa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citované z [tohto blogového príspevku](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matica pozornosti $\\{\\alpha_{i,j}\\}$ predstavuje mieru, do akej určité vstupné slová ovplyvňujú generovanie daného slova vo výstupnej sekvencii. Nižšie je príklad takejto matice:\n", "\n", - "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sk.png)\n", + "![Obrázok zobrazujúci vzorové zarovnanie nájdené RNNsearch-50, prevzaté z Bahdanau - arviz.org](../../../../../translated_images/sk/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Obrázok prevzatý z [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Obr.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je veľmi veľká viacvrstvová transformerová sieť s 12 vrstvami pre *BERT-base* a 24 vrstvami pre *BERT-large*. Model je najskôr predtrénovaný na veľkom korpuse textových dát (WikiPedia + knihy) pomocou nesupervidovaného učenia (predpovedanie maskovaných slov vo vete). Počas predtrénovania model absorbuje významnú úroveň porozumenia jazyka, ktorú je možné následne využiť s inými datasetmi pomocou jemného doladenia. Tento proces sa nazýva **transferové učenie**.\n", "\n", - "![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sk.png)\n", + "![obrázok z http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Existuje mnoho variácií transformerových architektúr vrátane BERT, DistilBERT, BigBird, OpenGPT3 a ďalších, ktoré je možné jemne doladiť.\n", "\n", diff --git a/translations/sk/lessons/5-NLP/19-NER/README.md b/translations/sk/lessons/5-NLP/19-NER/README.md index 542aad1a..4330909c 100644 --- a/translations/sk/lessons/5-NLP/19-NER/README.md +++ b/translations/sk/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorodenca | O Keďže potrebujeme vytvoriť jednoznačnú korešpondenciu medzi tokenmi a triedami, môžeme trénovať pravú **mnoho-na-mnoho** neurónovú sieť z tohto obrázku: -![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sk.jpg) +![Obrázok zobrazujúci bežné vzory rekurentných neurónových sietí.](../../../../../translated_images/sk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Obrázok z [tohto blogového príspevku](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) od [Andreja Karpathyho](http://karpathy.github.io/). Modely klasifikácie tokenov NER zodpovedajú najpravšej architektúre siete na tomto obrázku.* diff --git a/translations/sk/lessons/5-NLP/README.md b/translations/sk/lessons/5-NLP/README.md index 78203f5d..aca50226 100644 --- a/translations/sk/lessons/5-NLP/README.md +++ b/translations/sk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Spracovanie prirodzeného jazyka -![Zhrnutie úloh NLP v kresbe](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.sk.png) +![Zhrnutie úloh NLP v kresbe](../../../../translated_images/sk/ai-nlp.b22dcb8ca4707cea.png) V tejto sekcii sa zameriame na používanie neurónových sietí na riešenie úloh súvisiacich so **spracovaním prirodzeného jazyka (NLP)**. Existuje mnoho problémov v oblasti NLP, ktoré by sme chceli, aby počítače dokázali vyriešiť: diff --git a/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md index 5e4d6414..909cdea1 100644 --- a/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Môžete otvoriť jeden z modelov, napríklad **Biology → Flocking**. Po otvorení modelu sa dostanete na hlavnú obrazovku NetLogo. Tu je ukážkový model, ktorý popisuje populáciu vlkov a oviec, pričom zdroje (tráva) sú obmedzené. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.sk.png) +![NetLogo Main Screen](../../../../../translated_images/sk/NetLogo-Main.32653711ec1a01b3.png) > Snímka obrazovky od Dmitry Soshnikov diff --git a/translations/sk/lessons/README.md b/translations/sk/lessons/README.md index 0f3b5691..add695e4 100644 --- a/translations/sk/lessons/README.md +++ b/translations/sk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Prehľad -![Prehľad v kresbe](../../../translated_images/ai-overview.0857791951d19500.sk.png) +![Prehľad v kresbe](../../../translated_images/sk/ai-overview.0857791951d19500.png) > Kresba od [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sk/lessons/X-Extras/X1-MultiModal/README.md b/translations/sk/lessons/X-Extras/X1-MultiModal/README.md index 5be5b3a2..e08c10a8 100644 --- a/translations/sk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po úspechu transformerových modelov pri riešení úloh spracovania prirodzen Hlavnou myšlienkou CLIP je schopnosť porovnávať textové podnety s obrázkom a určiť, ako dobre obrázok zodpovedá danému podnetu. -![Architektúra CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.sk.png) +![Architektúra CLIP](../../../../../translated_images/sk/clip-arch.b3dbf20b4e8ed8be.png) > *Obrázok z [tohto blogového príspevku](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Po predtrénovaní modelu mu môžeme poskytnúť dávku obrázkov a dávku text Predpokladajme, že potrebujeme klasifikovať obrázky, napríklad medzi mačkami, psami a ľuďmi. V tomto prípade môžeme modelu poskytnúť obrázok a sériu textových podnetov: "*obrázok mačky*", "*obrázok psa*", "*obrázok človeka*". Vo výslednom vektore s 3 pravdepodobnosťami stačí vybrať index s najvyššou hodnotou. -![CLIP pre klasifikáciu obrázkov](../../../../../translated_images/clip-class.3af42ef0b2b19369.sk.png) +![CLIP pre klasifikáciu obrázkov](../../../../../translated_images/sk/clip-class.3af42ef0b2b19369.png) > *Obrázok z [tohto blogového príspevku](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Viac o VQGAN sa dozviete na webovej stránke [Taming Transformers](https://compv Jedným z dôležitých rozdielov medzi VQGAN a tradičným GAN je, že tradičný GAN dokáže vytvoriť slušný obrázok z akéhokoľvek vstupného vektora, zatiaľ čo VQGAN pravdepodobne vytvorí obrázok, ktorý nebude koherentný. Preto je potrebné ďalej usmerňovať proces tvorby obrázka, čo sa dá dosiahnuť pomocou CLIP. -![Architektúra VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.sk.png) +![Architektúra VQGAN+CLIP](../../../../../translated_images/sk/vqgan.5027fe05051dfa31.png) Na generovanie obrázka zodpovedajúceho textovému podnetu začíname s náhodným kódovacím vektorom, ktorý prechádza cez VQGAN a vytvára obrázok. Potom sa použije CLIP na vytvorenie stratovej funkcie, ktorá ukazuje, ako dobre obrázok zodpovedá textovému podnetu. Cieľom je minimalizovať túto stratu pomocou spätného šírenia na úpravu parametrov vstupného vektora. Skvelá knižnica, ktorá implementuje VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Obrázok vytvorený Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.sk.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.sk.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.sk.png) +![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Obrázok vytvorený z podnetu *detailný akvarelový portrét mladého učiteľa literatúry s knihou* | Obrázok vytvorený z podnetu *detailný olejový portrét mladej učiteľky informatiky s počítačom* | Obrázok vytvorený z podnetu *detailný olejový portrét starého učiteľa matematiky pred tabuľou* @@ -75,7 +75,7 @@ Na rozdiel od CLIP prijíma DALL-E text aj obrázok ako jeden tok tokenov pre ob Hlavný rozdiel medzi DALL-E 1 a 2 je v tom, že DALL-E 2 generuje realistickejšie obrázky a umenie. Príklady generovania obrázkov s DALL-E: -![Obrázok vytvorený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.sk.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.sk.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.sk.png) +![Obrázok vytvorený Pixray](../../../../../translated_images/sk/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Obrázok vytvorený Pixray](../../../../../translated_images/sk/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Obrázok vytvorený z podnetu *detailný akvarelový portrét mladého učiteľa literatúry s knihou* | Obrázok vytvorený z podnetu *detailný olejový portrét mladej učiteľky informatiky s počítačom* | Obrázok vytvorený z podnetu *detailný olejový portrét starého učiteľa matematiky pred tabuľou* diff --git a/translations/sl/README.md b/translations/sl/README.md index 2351cf61..8ac42b8d 100644 --- a/translations/sl/README.md +++ b/translations/sl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Umetna inteligenca za začetnike - učni načrt -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.sl.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sl/ai-overview.0857791951d19500.png)| |:---:| | AI za začetnike - _Sketchnote avtorja [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sl/lessons/1-Intro/README.md b/translations/sl/lessons/1-Intro/README.md index c7d21fbf..6b8a6ce9 100644 --- a/translations/sl/lessons/1-Intro/README.md +++ b/translations/sl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod v umetno inteligenco -![Povzetek vsebine uvoda v umetno inteligenco v skici](../../../../translated_images/ai-intro.bf28d1ac4235881c.sl.png) +![Povzetek vsebine uvoda v umetno inteligenco v skici](../../../../translated_images/sl/ai-intro.bf28d1ac4235881c.png) > Skica avtorja [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Računalnike je prvotno izumil [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) za obdelavo števil po natančno določenem postopku – algoritmu. Sodobni računalniki, čeprav bistveno naprednejši od prvotnega modela iz 19. stoletja, še vedno sledijo isti ideji nadzorovanih izračunov. Tako je mogoče programirati računalnik za izvedbo naloge, če poznamo natančen zaporedje korakov, potrebnih za dosego cilja. -![Fotografija osebe](../../../../translated_images/dsh_age.d212a30d4e54fb5f.sl.png) +![Fotografija osebe](../../../../translated_images/sl/dsh_age.d212a30d4e54fb5f.png) > Fotografija avtorja [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Za več informacij glejte **[Splošna umetna inteligenca](https://en.wikipedia.o Ena od težav pri obravnavi izraza **[inteligenca](https://en.wikipedia.org/wiki/Intelligence)** je, da ni jasne definicije tega pojma. Nekateri trdijo, da je inteligenca povezana z **abstraktnim razmišljanjem** ali **samozavedanjem**, vendar je ne moremo ustrezno opredeliti. -![Fotografija mačke](../../../../translated_images/photo-cat.8c8e8fb760ffe457.sl.jpg) +![Fotografija mačke](../../../../translated_images/sl/photo-cat.8c8e8fb760ffe457.jpg) > [Fotografija](https://unsplash.com/photos/75715CVEJhI) avtorja [Amber Kipp](https://unsplash.com/@sadmax) iz Unsplash @@ -98,13 +98,13 @@ Alternativno lahko poskusimo modelirati najpreprostejše elemente v naših možg > | Kaj pa ML? | | > |--------------|-----------| -> | Del umetne inteligence, ki temelji na tem, da se računalnik nauči reševati problem na podlagi nekaterih podatkov, se imenuje **strojno učenje**. Klasičnega strojnega učenja v tem tečaju ne bomo obravnavali – napotujemo vas na ločen učni načrt [Strojno učenje za začetnike](http://aka.ms/ml-beginners). | ![ML za začetnike](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.sl.png) | +> | Del umetne inteligence, ki temelji na tem, da se računalnik nauči reševati problem na podlagi nekaterih podatkov, se imenuje **strojno učenje**. Klasičnega strojnega učenja v tem tečaju ne bomo obravnavali – napotujemo vas na ločen učni načrt [Strojno učenje za začetnike](http://aka.ms/ml-beginners). | ![ML za začetnike](../../../../translated_images/sl/ml-for-beginners.9e4fed176fd5817d.png) | ## Kratka zgodovina umetne inteligence Umetna inteligenca se je kot področje začela sredi dvajsetega stoletja. Sprva je bil simbolični pristop prevladujoč in je prinesel številne pomembne uspehe, kot so ekspertni sistemi – računalniški programi, ki so lahko delovali kot strokovnjaki na nekaterih omejenih problematičnih področjih. Vendar se je kmalu izkazalo, da tak pristop ni dobro skalabilen. Pridobivanje znanja od strokovnjaka, njegovo predstavljanje v računalniku in ohranjanje točnosti baze znanja se je izkazalo za zelo zapleteno nalogo in predrago za praktično uporabo v mnogih primerih. To je privedlo do tako imenovane [zime umetne inteligence](https://en.wikipedia.org/wiki/AI_winter) v 70. letih. -Kratka zgodovina umetne inteligence +Kratka zgodovina umetne inteligence > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Podobno lahko vidimo, kako se je spreminjal pristop k ustvarjanju "govorečih pr * Sodobni asistenti, kot so Cortana, Siri ali Google Assistant, so vsi hibridni sistemi, ki uporabljajo nevronske mreže za pretvorbo govora v besedilo in prepoznavanje našega namena, nato pa uporabijo nekaj razmišljanja ali eksplicitnih algoritmov za izvedbo zahtevanih dejanj. * V prihodnosti lahko pričakujemo popoln model, ki temelji na nevronskih mrežah, za samostojno obravnavo dialoga. Nedavne družine nevronskih mrež GPT in [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) kažejo velik uspeh pri tem. -evolucija Turingovega testa +evolucija Turingovega testa > Slika Dmitry Soshnikov, [fotografija](https://unsplash.com/photos/r8LmVbUKgns) avtorja [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nedavne raziskave na področju umetne inteligence diff --git a/translations/sl/lessons/2-Symbolic/Animals.ipynb b/translations/sl/lessons/2-Symbolic/Animals.ipynb index 1fa4a0e7..153cd484 100644 --- a/translations/sl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "V tem primeru bomo implementirali preprost sistem, ki temelji na znanju, za določanje živali na podlagi nekaterih fizičnih značilnosti. Sistem lahko predstavimo z naslednjim AND-OR drevesom (to je del celotnega drevesa, zlahka lahko dodamo še več pravil):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sl.png)\n" + "![](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/sl/lessons/2-Symbolic/README.md b/translations/sl/lessons/2-Symbolic/README.md index 211e1ea2..ca132102 100644 --- a/translations/sl/lessons/2-Symbolic/README.md +++ b/translations/sl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Predstavitev znanja in ekspertni sistemi -![Povzetek vsebine simbolne umetne inteligence](../../../../translated_images/ai-symbolic.715a30cb610411a6.sl.png) +![Povzetek vsebine simbolne umetne inteligence](../../../../translated_images/sl/ai-symbolic.715a30cb610411a6.png) > Sketchnote avtorja [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Najpogosteje znanja ne definiramo strogo, ampak ga uskladimo z drugimi povezanim Tako je problem **predstavitve znanja** najti učinkovit način za predstavitev znanja znotraj računalnika v obliki podatkov, da bi bilo samodejno uporabno. To lahko vidimo kot spekter: -![Spekter predstavitve znanja](../../../../translated_images/knowledge-spectrum.b60df631852c0217.sl.png) +![Spekter predstavitve znanja](../../../../translated_images/sl/knowledge-spectrum.b60df631852c0217.png) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok-sintaksa | Zamik | | | Eden zgodnjih uspehov simbolne umetne inteligence so bili tako imenovani **ekspertni sistemi** - računalniški sistemi, zasnovani za delovanje kot strokovnjak na omejenem področju problemov. Temeljili so na **bazi znanja**, pridobljeni od enega ali več človeških strokovnjakov, in vsebovali **inferenčni mehanizem**, ki je izvajal razmišljanje na podlagi te baze. -![Človeška arhitektura](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.sl.png) | ![Arhitektura sistema, ki temelji na znanju](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.sl.png) +![Človeška arhitektura](../../../../translated_images/sl/arch-human.5d4d35f1bba3ab1c.png) | ![Arhitektura sistema, ki temelji na znanju](../../../../translated_images/sl/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Poenostavljena struktura človeškega nevronskega sistema | Arhitektura sistema, ki temelji na znanju @@ -106,7 +106,7 @@ Ekspertni sistemi so zgrajeni podobno kot človeški sistem razmišljanja, ki vs Kot primer si poglejmo naslednji ekspertni sistem za določanje živali na podlagi njihovih fizičnih značilnosti: -![AND-OR drevo](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sl.png) +![AND-OR drevo](../../../../translated_images/sl/AND-OR-Tree.5592d2c70187f283.png) > Slika avtorja [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 3639ce5f..dbdbb8d5 100644 --- a/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Če imamo več kot 2 razreda, bo softmax normaliziral verjetnosti za vse razrede. Tukaj je diagram arhitekture mreže, ki izvaja klasifikacijo MNIST številk:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.sl.png)\n" + "![MNIST Classifier](../../../../../translated_images/sl/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Nizka izguba na učnih podatkih - model lahko dobro približa učne podatke, ker ima dovolj izražalne moči.\n", "* Izguba na validacijskih podatkih je lahko veliko višja kot izguba na učnih podatkih in se lahko med učenjem začne povečevati - to je zato, ker model \"zapomni\" učne točke in izgubi \"celotno sliko\".\n", "\n", - "![Prilagajanje na učne podatke](../../../../../translated_images/overfit.a0bd57f717c15769.sl.png)\n", + "![Prilagajanje na učne podatke](../../../../../translated_images/sl/overfit.a0bd57f717c15769.png)\n", "\n", "> Na tej sliki `x` predstavlja učne podatke, `o` - validacijske podatke. Levo - linearen model (enoplastni), ki precej dobro približa naravo podatkov. Desno - model, ki je preveč prilagojen učnim podatkom, model popolnoma dobro približa učne podatke, vendar izgubi smisel pri drugih podatkih (napaka na validacijskih podatkih je zelo visoka).\n" ] diff --git a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 62f00be5..b2f83a11 100644 --- a/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Prenaučenje je izjemno pomemben koncept v strojnem učenju, zato je zelo pomemb Razmislimo o naslednjem problemu približevanja 5 točk (predstavljenih z `x` na spodnjih grafih): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.sl.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.sl.jpg) +![linear](../../../../../translated_images/sl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sl/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linearen model, 2 parametra** | **Nelinearen model, 7 parametrov** Napaka pri učenju = 5.3 | Napaka pri učenju = 0 @@ -79,7 +79,7 @@ Zelo pomembno je najti pravo ravnovesje med kompleksnostjo modela (številom par Kot lahko vidite na zgornjem grafu, lahko prenaučenje zaznamo z zelo nizko napako pri učenju in visoko napako pri validaciji. Običajno med učenjem vidimo, da se napake pri učenju in validaciji zmanjšujejo, nato pa se v nekem trenutku napaka pri validaciji preneha zmanjševati in začne naraščati. To bo znak prenaučenja in indikator, da bi morali verjetno ustaviti učenje (ali vsaj narediti posnetek modela). -![prenaučenje](../../../../../translated_images/Overfitting.408ad91cd90b4371.sl.png) +![prenaučenje](../../../../../translated_images/sl/Overfitting.408ad91cd90b4371.png) ## Kako preprečiti prenaučenje diff --git a/translations/sl/lessons/3-NeuralNetworks/README.md b/translations/sl/lessons/3-NeuralNetworks/README.md index 570ecfa5..f66953d5 100644 --- a/translations/sl/lessons/3-NeuralNetworks/README.md +++ b/translations/sl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod v nevronske mreže -![Povzetek vsebine uvoda v nevronske mreže v skici](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.sl.png) +![Povzetek vsebine uvoda v nevronske mreže v skici](../../../../translated_images/sl/ai-neuralnetworks.1c687ae40bc86e83.png) Kot smo razpravljali v uvodu, je eden od načinov za dosego inteligence treniranje **računalniškega modela** ali **umetnih možganov**. Od sredine 20. stoletja so raziskovalci preizkušali različne matematične modele, dokler se v zadnjih letih ta smer ni izkazala za izjemno uspešno. Takšni matematični modeli možganov se imenujejo **nevronske mreže**. @@ -36,13 +36,13 @@ V tem učnem načrtu se bomo osredotočili le na modele nevronskih mrež. Iz biologije vemo, da naši možgani sestojijo iz nevralnih celic (nevronov), od katerih ima vsaka več "vhodov" (dendritov) in en "izhod" (akson). Tako dendriti kot aksoni lahko prenašajo električne signale, povezave med njimi — znane kot sinapse — pa lahko kažejo različne stopnje prevodnosti, ki jih uravnavajo nevrotransmiterji. -![Model nevrona](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.sl.jpg) | ![Model nevrona](../../../../translated_images/artneuron.1a5daa88d20ebe6f.sl.png) +![Model nevrona](../../../../translated_images/sl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Model nevrona](../../../../translated_images/sl/artneuron.1a5daa88d20ebe6f.png) ----|---- Pravi nevron *([Slika](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) iz Wikipedije)* | Umetni nevron *(Slika avtorja)* Tako najpreprostejši matematični model nevrona vsebuje več vhodov X1, ..., XN in en izhod Y ter vrsto uteži W1, ..., WN. Izhod se izračuna kot: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kjer je f neka nelinearna **aktivacijska funkcija**. diff --git a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md index 3aa31787..f90a3e97 100644 --- a/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ V našem [OpenCV Notebook](OpenCV.ipynb) podajamo nekaj primerov, kdaj se račun * **Predobdelava fotografije Braillove knjige**. Osredotočamo se na to, kako lahko uporabimo pragovno obdelavo, zaznavanje značilnosti, perspektivno transformacijo in manipulacije z NumPy za ločevanje posameznih Braillovih simbolov za nadaljnjo klasifikacijo z nevronsko mrežo. -![Slika Braillove knjige](../../../../../translated_images/braille.341962ff76b1bd70.sl.jpeg) | ![Predobdelana slika Braillove knjige](../../../../../translated_images/braille-result.46530fea020b03c7.sl.png) | ![Braillovi simboli](../../../../../translated_images/braille-symbols.0159185ab69d5339.sl.png) +![Slika Braillove knjige](../../../../../translated_images/sl/braille.341962ff76b1bd70.jpeg) | ![Predobdelana slika Braillove knjige](../../../../../translated_images/sl/braille-result.46530fea020b03c7.png) | ![Braillovi simboli](../../../../../translated_images/sl/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Slika iz [OpenCV.ipynb](OpenCV.ipynb) * **Zaznavanje gibanja v videu z razliko med okvirji**. Če je kamera fiksna, bi morali biti okvirji iz kamere med seboj precej podobni. Ker so okvirji predstavljeni kot polja, bomo z odštevanjem teh polj za dva zaporedna okvirja dobili razliko med piksli, ki bi morala biti nizka za statične okvirje in postati višja, ko je v sliki zaznano večje gibanje. -![Slika video okvirjev in razlik med okvirji](../../../../../translated_images/frame-difference.706f805491a0883c.sl.png) +![Slika video okvirjev in razlik med okvirji](../../../../../translated_images/sl/frame-difference.706f805491a0883c.png) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ V našem [OpenCV Notebook](OpenCV.ipynb) podajamo nekaj primerov, kdaj se račun - **Gost optični tok** izračuna vektorsko polje, ki kaže, kam se premika vsak piksel. - **Redek optični tok** temelji na zaznavanju nekaterih značilnih značilnosti slike (npr. robov) in gradnji njihove trajektorije od okvirja do okvirja. -![Slika optičnega toka](../../../../../translated_images/optical.1f4a94464579a83a.sl.png) +![Slika optičnega toka](../../../../../translated_images/sl/optical.1f4a94464579a83a.png) > Slika iz [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index d43748c6..855d4cb1 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 je mreža, ki je leta 2014 dosegla 92,7 % natančnost pri razvrščanju ImageNet top-5. Ima naslednjo strukturo slojev: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sl.jpg) +![ImageNet Layers](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.jpg) Kot lahko vidite, VGG sledi tradicionalni piramidni arhitekturi, ki je zaporedje slojev konvolucije in združevanja. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sl.jpg) +![ImageNet Pyramid](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.jpg) > Slika iz [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 26893cf3..f8b22410 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Tako bi v tipičnem CNN-ju imeli več konvolucijskih plasti, med katerimi bi bili sloji za združevanje, da zmanjšamo dimenzije slike. Prav tako bi povečali število filtrov, saj postajajo vzorci bolj kompleksni – obstaja več možnih zanimivih kombinacij, ki jih moramo iskati.\n", "\n", - "![Slika, ki prikazuje več konvolucijskih plasti s sloji za združevanje.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sl.png)\n", + "![Slika, ki prikazuje več konvolucijskih plasti s sloji za združevanje.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Zaradi zmanjševanja prostorskih dimenzij in povečevanja dimenzij značilnosti/filtrov se ta arhitektura imenuje tudi **piramidna arhitektura**.\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 4e83d692..04d9cb65 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Tako bi v tipičnem CNN-ju imeli več konvolucijskih plasti, z vmesnimi pooling sloji za zmanjšanje dimenzij slike. Prav tako bi povečali število filtrov, saj z naprednejšimi vzorci obstaja več možnih zanimivih kombinacij, ki jih moramo iskati.\n", "\n", - "![Slika, ki prikazuje več konvolucijskih plasti s pooling sloji.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sl.png)\n", + "![Slika, ki prikazuje več konvolucijskih plasti s pooling sloji.](../../../../../translated_images/sl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Zaradi zmanjševanja prostorskih dimenzij in povečevanja dimenzij značilnosti/filtrov se ta arhitektura imenuje tudi **piramidna arhitektura**.\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md index c54cf25c..556fb8d1 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ V resničnem življenju želimo prepoznati predmete na sliki ne glede na njihov Za ekstrakcijo vzorcev bomo uporabili koncept **konvolucijskih filtrov**. Kot veste, je slika predstavljena z 2D-matriko ali 3D-tenzorjem z barvno globino. Uporaba filtra pomeni, da vzamemo relativno majhno matriko **jedra filtra** in za vsak piksel v izvirni sliki izračunamo uteženo povprečje z okoliškimi točkami. To si lahko predstavljamo kot majhno okno, ki drsi čez celotno sliko in povpreči vse piksle glede na uteži v matriki jedra filtra. -![Filter za navpične robove](../../../../../translated_images/filter-vert.b7148390ca0bc356.sl.png) | ![Filter za vodoravne robove](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.sl.png) +![Filter za navpične robove](../../../../../translated_images/sl/filter-vert.b7148390ca0bc356.png) | ![Filter za vodoravne robove](../../../../../translated_images/sl/filter-horiz.59b80ed4feb946ef.png) ----|---- > Slika: Dmitry Soshnikov @@ -38,7 +38,7 @@ Delovanje CNN temelji na naslednjih pomembnih idejah: * Mrežo lahko zasnujemo tako, da se filtri učijo samodejno * Enak pristop lahko uporabimo za iskanje vzorcev v visokih značilnostih, ne le v izvirni sliki. Tako ekstrakcija značilnosti v CNN deluje na hierarhiji značilnosti, začenši z nizkoročnimi kombinacijami pikslov do višjih kombinacij delov slike. -![Hierarhična ekstrakcija značilnosti](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.sl.png) +![Hierarhična ekstrakcija značilnosti](../../../../../translated_images/sl/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Slika iz [članka Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), na podlagi [njihove raziskave](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Večina CNN-jev, ki se uporabljajo za obdelavo slik, sledi tako imenovani pirami Na primer, poglejmo arhitekturo VGG-16, mreže, ki je leta 2014 dosegla 92,7 % natančnost v top-5 klasifikaciji na ImageNet: -![Sloji ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sl.jpg) +![Sloji ImageNet](../../../../../translated_images/sl/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramida ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sl.jpg) +![Piramida ImageNet](../../../../../translated_images/sl/vgg-16-arch.64ff2137f50dd49f.jpg) > Slika iz [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index e2beb770..05ec7086 100644 --- a/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Vaša naloga je, da usposobite konvolucijsko nevronsko mrežo za razvrščanje r Uporabili bomo [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ki vsebuje slike 37 različnih pasem psov in mačk. -![Podatkovna zbirka, s katero bomo delali](../../../../../../translated_images/data.50b2a9d5484bdbf0.sl.png) +![Podatkovna zbirka, s katero bomo delali](../../../../../../translated_images/sl/data.50b2a9d5484bdbf0.png) Za prenos podatkovne zbirke uporabite naslednji del kode: diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index e9b8dc97..e3567873 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Za vizualizacijo idealne mačke bomo začeli s sliko naključnega šuma in poskusili uporabiti tehniko optimizacije z gradientnim spustom, da prilagodimo sliko tako, da bo mreža prepoznala mačko.\n", "\n", - "![Optimizacijska zanka](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sl.png)\n", + "![Optimizacijska zanka](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Tukaj je naša začetna slika:\n" ] diff --git a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md index 22eabfff..a0b8b8c8 100644 --- a/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Tako Keras kot PyTorch vsebujeta funkcije za enostavno nalaganje vnaprej naučen Tukaj so primeri značilnosti, izvlečenih iz slike mačke z mrežo VGG-16: -![Značilnosti, izvlečene z VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.sl.png) +![Značilnosti, izvlečene z VGG-16](../../../../../translated_images/sl/features.6291f9c7ba3a0b95.png) ## Nabor podatkov Mačke proti psom @@ -48,19 +48,19 @@ Vnaprej naučena nevronska mreža vsebuje različne vzorce v svojem *možganu*, Eden od pristopov, ki ga lahko uporabimo, je začeti z naključno sliko in nato poskusiti uporabiti tehniko **optimizacije z gradientnim spustom**, da prilagodimo to sliko tako, da mreža začne misliti, da je to mačka. -![Zanka optimizacije slike](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sl.png) +![Zanka optimizacije slike](../../../../../translated_images/sl/ideal-cat-loop.999fbb8ff306e044.png) Če to storimo, bomo dobili nekaj, kar je zelo podobno naključnemu šumu. To je zato, ker *obstaja veliko načinov, kako mreži narediti vtis, da je vhodna slika mačka*, vključno z nekaterimi, ki vizualno nimajo smisla. Čeprav te slike vsebujejo veliko vzorcev, značilnih za mačko, ni ničesar, kar bi jih omejevalo, da bi bile vizualno prepoznavne. Za izboljšanje rezultata lahko v funkcijo izgube dodamo še en člen, imenovan **izguba variacije**. To je metrika, ki kaže, kako podobni so sosednji piksli slike. Zmanjšanje izgube variacije naredi sliko bolj gladko in odstrani šum – s tem razkrije bolj vizualno privlačne vzorce. Tukaj je primer takšnih "idealnih" slik, ki so z visoko verjetnostjo razvrščene kot mačka in zebra: -![Idealna mačka](../../../../../translated_images/ideal-cat.203dd4597643d6b0.sl.png) | ![Idealna zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.sl.png) +![Idealna mačka](../../../../../translated_images/sl/ideal-cat.203dd4597643d6b0.png) | ![Idealna zebra](../../../../../translated_images/sl/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Idealna mačka* | *Idealna zebra* Podoben pristop lahko uporabimo za izvajanje tako imenovanih **adversarnih napadov** na nevronsko mrežo. Recimo, da želimo zavajati nevronsko mrežo in narediti, da pes izgleda kot mačka. Če vzamemo sliko psa, ki jo mreža prepozna kot psa, jo lahko nato nekoliko prilagodimo z optimizacijo gradientnega spusta, dokler mreža ne začne razvrščati slike kot mačko: -![Slika psa](../../../../../translated_images/original-dog.8f68a67d2fe0911f.sl.png) | ![Slika psa, razvrščena kot mačka](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.sl.png) +![Slika psa](../../../../../translated_images/sl/original-dog.8f68a67d2fe0911f.png) | ![Slika psa, razvrščena kot mačka](../../../../../translated_images/sl/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Izvirna slika psa* | *Slika psa, razvrščena kot mačka* diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index d3884efd..d27d1898 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, mreža poskuša najti najboljšo **vgraditev** vhodnih slik, da zajame njihov pomen.\n", "\n", - "![Diagram avtoenkoderja](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sl.jpg)\n", + "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index e635cfc2..c39ece07 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, omrežje poskuša najti najboljšo **vgraditev** vhodnih slik, da zajame njihov pomen.\n", "\n", - "![Diagram avtoenkoderja](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sl.jpg)\n", + "![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md index c77f8b74..33de3626 100644 --- a/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Vendar pa bi morda želeli uporabiti surove (neoznačene) podatke za treniranje Ker treniramo avtoenkoder, da zajame čim več informacij iz izvirne slike za natančno rekonstrukcijo, mreža poskuša najti najboljšo **vgradnjo** vhodnih slik, da zajame njihov pomen. -![Diagram avtoenkoderja](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sl.jpg) +![Diagram avtoenkoderja](../../../../../translated_images/sl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Slika iz [Keras bloga](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md index d27af041..bc4415dc 100644 --- a/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Modeli za klasifikacijo slik, s katerimi smo se doslej ukvarjali, so vzeli sliko ## [Predhodni kviz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Zaznavanje objektov](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.sl.png) +![Zaznavanje objektov](../../../../../translated_images/sl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Slika s [spletne strani YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Modeli za klasifikacijo slik, s katerimi smo se doslej ukvarjali, so vzeli sliko 2. Na vsaki ploščici izvedemo klasifikacijo slik. 3. Tiste ploščice, ki imajo dovolj visoko aktivacijo, lahko štejemo, da vsebujejo iskani objekt. -![Naivno zaznavanje objektov](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.sl.png) +![Naivno zaznavanje objektov](../../../../../translated_images/sl/naive-detection.e7f1ba220ccd08c6.png) > *Slika iz [zvezka z vajami](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Pri tej nalogi lahko naletite na naslednje podatkovne nabore: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) – 20 razredov * [COCO](http://cocodataset.org/#home) – Pogosti objekti v kontekstu. 80 razredov, okvirji in maske za segmentacijo -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.sl.jpg) +![COCO](../../../../../translated_images/sl/coco-examples.71bc60380fa6cceb.jpg) ## Merila za zaznavanje objektov @@ -50,7 +50,7 @@ Pri tej nalogi lahko naletite na naslednje podatkovne nabore: Medtem ko je za klasifikacijo slik enostavno meriti, kako dobro deluje algoritem, moramo pri zaznavanju objektov meriti tako pravilnost razreda kot tudi natančnost določene lokacije okvirja. Za slednje uporabljamo tako imenovani **Presek nad unijo** (IoU), ki meri, kako dobro se dve škatli (ali dve poljubni območji) prekrivata. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.sl.png) +![IoU](../../../../../translated_images/sl/iou_equation.9a4751d40fff4e11.png) > *Slika 2 iz [te odlične blog objave o IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Obstajata dve glavni skupini algoritmov za zaznavanje objektov: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) uporablja [Selektivno iskanje](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) za generiranje hierarhične strukture regij ROI, ki se nato prenesejo skozi CNN ekstraktorje značilnosti in SVM-klasifikatorje za določanje razreda objekta ter linearno regresijo za določanje koordinat *okvirja*. [Uradni članek](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.sl.png) +![RCNN](../../../../../translated_images/sl/rcnn1.cae407020dfb1d1f.png) > *Slika iz van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.sl.png) +![RCNN-1](../../../../../translated_images/sl/rcnn2.2d9530bb83516484.png) > *Slike iz [tega bloga](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Obstajata dve glavni skupini algoritmov za zaznavanje objektov: Ta pristop je podoben R-CNN, vendar se regije določijo po tem, ko so bile uporabljene konvolucijske plasti. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.sl.png) +![FRCNN](../../../../../translated_images/sl/f-rcnn.3cda6d9bb4188875.png) > Slika iz [uradnega članka](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ta pristop je podoben R-CNN, vendar se regije določijo po tem, ko so bile upora Glavna ideja tega pristopa je uporaba nevronske mreže za napovedovanje ROI – tako imenovane *Mreže za predlaganje regij*. [Članek](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.sl.png) +![FasterRCNN](../../../../../translated_images/sl/faster-rcnn.8d46c099b87ef30a.png) > Slika iz [uradnega članka](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ta algoritem je še hitrejši od Faster R-CNN. Glavna ideja je naslednja: 1. Značilnosti obdelamo z **Zemljevidom občutljivih na položaj**. Vsak objekt iz $C$ razredov je razdeljen na $k\times k$ regij, in treniramo za napovedovanje delov objektov. 1. Za vsak del iz $k\times k$ regij vse mreže glasujejo za razrede objektov, in izbran je razred objekta z največ glasovi. -![r-fcn slika](../../../../../translated_images/r-fcn.13eb88158b99a3da.sl.png) +![r-fcn slika](../../../../../translated_images/sl/r-fcn.13eb88158b99a3da.png) > Slika iz [uradnega članka](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO je algoritem za zaznavanje v realnem času z enim prehodom. Glavna ideja je * Slika je razdeljena na $S\times S$ regij. * Za vsako regijo **CNN** napove $n$ možnih objektov, koordinate *okvirja* in *zaupanje*=*verjetnost* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.sl.png) + ![YOLO](../../../../../translated_images/sl/yolo.a2648ec82ee8bb4e.png) > Slika iz [uradnega članka](https://arxiv.org/abs/1506.02640) diff --git a/translations/sl/lessons/4-ComputerVision/README.md b/translations/sl/lessons/4-ComputerVision/README.md index 22336b45..d8569a77 100644 --- a/translations/sl/lessons/4-ComputerVision/README.md +++ b/translations/sl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Računalniški vid -![Povzetek vsebine o računalniškem vidu v skici](../../../../translated_images/ai-computervision.6506ebebac3fbf76.sl.png) +![Povzetek vsebine o računalniškem vidu v skici](../../../../translated_images/sl/ai-computervision.6506ebebac3fbf76.png) V tem poglavju bomo spoznali: diff --git a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 9b68bef2..1673dd9a 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) je najpogosteje uporabljena tradicionalna predstavitev vektorskih podatkov. Vsaka beseda je povezana z indeksom vektorja, element vektorja pa vsebuje število pojavitev besede v določenem dokumentu.\n", "\n", - "![Slika prikazuje, kako je predstavitev vektorja Bag of Words shranjena v pomnilniku.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sl.png) \n", + "![Slika prikazuje, kako je predstavitev vektorja Bag of Words shranjena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Na Bag of Words lahko gledate tudi kot na vsoto vseh vektorjev, kodiranih z metodo one-hot, za posamezne besede v besedilu.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 192bef89..5d54cd4d 100644 --- a/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Predstavitev z metodo vreče besed** (BoW) je najpreprostejša za razumevanje med tradicionalnimi vektorskimi predstavitvami. Vsaka beseda je povezana z indeksom vektorja, element vektorja pa vsebuje število pojavitev posamezne besede v določenem dokumentu.\n", "\n", - "![Slika, ki prikazuje, kako je predstavitev z metodo vreče besed predstavljena v pomnilniku.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sl.png) \n", + "![Slika, ki prikazuje, kako je predstavitev z metodo vreče besed predstavljena v pomnilniku.](../../../../../translated_images/sl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Opomba**: Na metodo BoW lahko gledate tudi kot na vsoto vseh eno-vroče kodiranih vektorjev za posamezne besede v besedilu.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index e3b65e2e..bf762706 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Z uporabo vgrajevalne plasti kot prve plasti v naši mreži lahko preklopimo iz modela vreče besed na model **vreče vgrajevanja**, kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vgrajevanje, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vgrajevanji, kot so `sum`, `average` ali `max`.\n", "\n", - "![Slika, ki prikazuje klasifikator vgrajevanja za pet zaporednih besed.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sl.png)\n", + "![Slika, ki prikazuje klasifikator vgrajevanja za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša nevronska mreža klasifikatorja se bo začela z vgrajevalno plastjo, nato agregatno plastjo in linearnim klasifikatorjem na vrhu:\n" ] @@ -176,7 +176,7 @@ "\n", "V prejšnji arhitekturi smo morali vsa zaporedja zapolniti do enake dolžine, da so ustrezala mini seriji. To ni najbolj učinkovit način za predstavitev zaporedij spremenljive dolžine – drugačen pristop bi bil uporaba **offset** vektorja, ki bi vseboval zamike vseh zaporedij, shranjenih v enem velikem vektorju.\n", "\n", - "![Slika, ki prikazuje predstavitev zaporedij z zamiki](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.sl.png)\n", + "![Slika, ki prikazuje predstavitev zaporedij z zamiki](../../../../../translated_images/sl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Opomba**: Na zgornji sliki prikazujemo zaporedje znakov, vendar v našem primeru delamo z zaporedji besed. Kljub temu splošno načelo predstavitve zaporedij z offset vektorjem ostaja enako.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW je hitrejši, medtem ko je skip-gram počasnejši, vendar bolje predstavlja redke besede.\n", "\n", - "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sl.png)\n", + "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Za eksperimentiranje z vektorsko predstavitvijo Word2Vec, predhodno naučeno na zbirki podatkov Google News, lahko uporabimo knjižnico **gensim**. Spodaj poiščemo besede, ki so najbolj podobne 'neural'.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index d248ad71..42903066 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Z uporabo vdelavnega sloja kot prvega sloja v naši mreži lahko preklopimo iz modela vreče besed (bag-of-words) na model **vreče vdelav** (embedding bag), kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vdelavo, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vdelavami, na primer `sum`, `average` ali `max`.\n", "\n", - "![Slika, ki prikazuje klasifikator z vdelavami za pet zaporednih besed.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sl.png)\n", + "![Slika, ki prikazuje klasifikator z vdelavami za pet zaporednih besed.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Naša nevronska mreža za klasifikacijo je sestavljena iz naslednjih slojev:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW je hitrejši, medtem ko je skip-gram počasnejši, vendar bolje predstavlja redke besede.\n", "\n", - "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sl.png)\n", + "![Slika, ki prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Za eksperimentiranje z Word2Vec vektorskimi predstavitvami, predhodno naučenimi na zbirki podatkov Google News, lahko uporabimo knjižnico **gensim**. Spodaj poiščemo besede, ki so najbolj podobne 'neural'.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/14-Embeddings/README.md b/translations/sl/lessons/5-NLP/14-Embeddings/README.md index 9b86e71f..5fae5918 100644 --- a/translations/sl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Tako bi plast vdelave sprejela besedo kot vhod in ustvarila izhodni vektor dolo Z uporabo plasti vdelave kot prve plasti v našem klasifikacijskem omrežju lahko preklopimo iz modela vreče besed na model **vreče vdelav**, kjer najprej vsako besedo v našem besedilu pretvorimo v ustrezno vdelavo, nato pa izračunamo neko agregatno funkcijo nad vsemi temi vdelavami, kot so `sum`, `average` ali `max`. -![Slika prikazuje klasifikator vdelav za pet besed v zaporedju.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sl.png) +![Slika prikazuje klasifikator vdelav za pet besed v zaporedju.](../../../../../translated_images/sl/embedding-classifier-example.b77f021a7ee67eee.png) > Slika avtorja @@ -40,7 +40,7 @@ Da bi to dosegli, moramo naš model vdelave predhodno trenirati na veliki zbirki CBoW je hitrejši, medtem ko je preskok-gram počasnejši, vendar bolje predstavlja redke besede. -![Slika prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sl.png) +![Slika prikazuje algoritma CBoW in Skip-Gram za pretvorbo besed v vektorje.](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Slika iz [tega članka](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md index 50f1e09c..3e670294 100644 --- a/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ V prejšnjih primerih smo uporabljali že vnaprej naučene semantične vektorske * **Neprekinjena vreča besed** (CBoW), kjer napovedujemo srednji token $W_0$ v zaporedju tokenov $W_{-N}$, ..., $W_N$. * **Skip-gram**, kjer napovedujemo niz sosednjih tokenov {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} iz srednjega tokena $W_0$. -![slika iz članka o pretvorbi besed v vektorje](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sl.png) +![slika iz članka o pretvorbi besed v vektorje](../../../../../translated_images/sl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Slika iz [tega članka](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sl/lessons/5-NLP/16-RNN/README.md b/translations/sl/lessons/5-NLP/16-RNN/README.md index 2e3b576f..5edbfe06 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/README.md +++ b/translations/sl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ V prejšnjih poglavjih smo uporabljali bogate semantične reprezentacije besedil Da bi zajeli pomen zaporedja besedila, moramo uporabiti drugo arhitekturo nevronske mreže, imenovano **rekurentna nevronska mreža** ali RNN. Pri RNN stavke pošiljamo skozi mrežo en simbol naenkrat, mreža pa ustvari neko **stanje**, ki ga nato ponovno pošljemo v mrežo skupaj z naslednjim simbolom. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.sl.png) +![RNN](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.png) > Slika avtorja @@ -61,7 +61,7 @@ Razpravljali smo o rekurentnih mrežah, ki delujejo v eno smer, od začetka zapo Rekurentna mreža, bodisi enosmerna ali dvosmerna, zajame določene vzorce znotraj zaporedja in jih lahko shrani v vektorsko stanje ali prenese v izhod. Tako kot pri konvolucijskih mrežah lahko na prvo plast zgradimo drugo rekurentno plast, da zajamemo vzorce višje ravni in gradimo na nizkoročnih vzorcih, ki jih je zajela prva plast. To nas pripelje do pojma **večslojne RNN**, ki je sestavljena iz dveh ali več rekurentnih mrež, kjer se izhod prejšnje plasti prenese v naslednjo plast kot vhod. -![Slika, ki prikazuje večslojno LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sl.jpg) +![Slika, ki prikazuje večslojno LSTM RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Slika iz [tega čudovitega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) avtorja Fernanda Lópeza* diff --git a/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b1e1c4f7..5b0442d3 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Rekurzivna mreža, enosmerna ali dvosmerna, zajame določene vzorce znotraj zaporedja in jih lahko shrani v vektor stanja ali prenese v izhod. Tako kot pri konvolucijskih mrežah lahko na prvo plast zgradimo drugo rekurzivno plast, da zajamemo vzorce višje ravni, ki so zgrajeni iz vzorcev nižje ravni, ki jih je izločila prva plast. To nas pripelje do pojma **večplastne RNN**, ki je sestavljena iz dveh ali več rekurzivnih mrež, kjer se izhod prejšnje plasti prenese v naslednjo plast kot vhod.\n", "\n", - "![Slika, ki prikazuje večplastno dolgotrajno-kratkoročno pomnilniško RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sl.jpg)\n", + "![Slika, ki prikazuje večplastno dolgotrajno-kratkoročno pomnilniško RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Slika iz [tega čudovitega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) avtorja Fernanda Lópeza*\n", "\n", diff --git a/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2395b017..e70f6fb0 100644 --- a/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Za zajemanje pomena zaporedja besedila bomo uporabili arhitekturo nevronske mreže, imenovano **rekurentna nevronska mreža** ali RNN. Pri uporabi RNN stavke pošiljamo skozi mrežo en token naenkrat, mreža pa ustvari neko **stanje**, ki ga nato skupaj z naslednjim tokenom ponovno pošljemo v mrežo.\n", "\n", - "![Slika, ki prikazuje primer generiranja z rekurentno nevronsko mrežo.](../../../../../translated_images/rnn.27f5c29c53d727b5.sl.png)\n", + "![Slika, ki prikazuje primer generiranja z rekurentno nevronsko mrežo.](../../../../../translated_images/sl/rnn.27f5c29c53d727b5.png)\n", "\n", "Glede na vhodno zaporedje tokenov $X_0,\\dots,X_n$ RNN ustvari zaporedje blokov nevronske mreže in to zaporedje trenira od začetka do konca z uporabo povratnega razširjanja napake (backpropagation). Vsak blok mreže kot vhod prejme par $(X_i,S_i)$ in kot rezultat ustvari $S_{i+1}$. Končno stanje $S_n$ ali izhod $Y_n$ gre v linearni klasifikator, da ustvari rezultat. Vsi bloki mreže si delijo iste uteži in so trenirani od začetka do konca z enim prehodom povratnega razširjanja napake.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurentne mreže, enosmerne ali dvosmerne, zajamejo vzorce znotraj zaporedja in jih shranijo v vektorske stanja ali jih vrnejo kot izhod. Tako kot pri konvolucijskih mrežah lahko zgradimo še en rekurentni sloj, ki sledi prvemu, da zajame vzorce višje ravni, zgrajene iz vzorcev nižje ravni, ki jih je izvlekel prvi sloj. To nas pripelje do pojma **večplastnega RNN-ja**, ki je sestavljen iz dveh ali več rekurentnih mrež, kjer se izhod prejšnjega sloja posreduje naslednjemu sloju kot vhod.\n", "\n", - "![Slika, ki prikazuje večplastni dolgoročno-kratkoročni pomnilniški RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sl.jpg)\n", + "![Slika, ki prikazuje večplastni dolgoročno-kratkoročni pomnilniški RNN](../../../../../translated_images/sl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Slika iz [tega odličnega prispevka](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernanda Lópeza.*\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index f1b155dd..d0ecc1ba 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Način, kako bomo učili RNN za generiranje besedila, je naslednji. Na vsakem koraku bomo vzeli zaporedje znakov dolžine `nchars` in mreži naročili, naj za vsak vhodni znak ustvari naslednji izhodni znak:\n", "\n", - "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sl.png)\n", + "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Odvisno od dejanskega scenarija bomo morda želeli vključiti tudi nekatere posebne znake, kot je *konec zaporedja* ``. V našem primeru želimo mrežo naučiti generiranja neskončnega besedila, zato bomo določili, da je velikost vsakega zaporedja enaka `nchars` znakom. Posledično bo vsak učni primer sestavljen iz `nchars` vhodov in `nchars` izhodov (kar je vhodno zaporedje, premaknjeno za en simbol v levo). Miniserija bo sestavljena iz več takšnih zaporedij.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 603b7234..5cc38e8b 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Način, kako bomo učili RNN za generiranje novičarskih naslovov, je naslednji. Na vsakem koraku bomo vzeli en naslov, ki bo podan v RNN, in za vsak vhodni znak bomo od mreže zahtevali, da generira naslednji izhodni znak:\n", "\n", - "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sl.png)\n", + "![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Za zadnji znak našega zaporedja bomo od mreže zahtevali, da generira `` token.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md index 7cba5b7b..2e0fdebb 100644 --- a/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ V arhitekturi RNN, ki smo jo obravnavali v prejšnji enoti, je vsaka enota RNN p To omogoča različne nevronske arhitekture, prikazane na spodnji sliki: -![Slika, ki prikazuje pogoste vzorce rekurentnih nevronskih mrež.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sl.jpg) +![Slika, ki prikazuje pogoste vzorce rekurentnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Slika iz blog objave [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) avtorja [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ V tej enoti se bomo osredotočili na preproste generativne modele, ki nam pomaga To RNN bomo trenirali za generiranje besedila korak za korakom. Na vsakem koraku bomo vzeli zaporedje znakov dolžine `nchars` in mreži naročili, naj za vsak vhodni znak ustvari naslednji izhodni znak: -![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sl.png) +![Slika, ki prikazuje primer generiranja besede 'HELLO' z RNN.](../../../../../translated_images/sl/rnn-generate.56c54afb52f9781d.png) Pri generiranju besedila (med inferenco) začnemo z nekim **pozivom**, ki ga prenesemo skozi RNN celice za generiranje vmesnega stanja, nato pa se začne generiranje. Generiramo en znak naenkrat, stanje in generirani znak pa prenesemo v drugo RNN celico za generiranje naslednjega, dokler ne generiramo dovolj znakov. diff --git a/translations/sl/lessons/5-NLP/18-Transformers/README.md b/translations/sl/lessons/5-NLP/18-Transformers/README.md index 6a3c595b..1d0d200a 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Pri RNN-jih je zaporedje-v-zaporedje implementirano z dvema rekurzivnima mrežam **Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako napoved izhoda RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodnega RNN in izhodnega RNN. Na ta način bomo pri generiranju izhodnega simbola yt upoštevali vsa vhodna skrita stanja hi, z različnimi utežnimi koeficienti αt,i. -![Slika, ki prikazuje model enkoder/dekoder z aditivno plastjo pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sl.png) +![Slika, ki prikazuje model enkoder/dekoder z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png) > Model enkoder-dekoder z aditivnim mehanizmom pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), povzeto iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matrika pozornosti {αi,j} predstavlja stopnjo, do katere določene vhodne besede vplivajo na generiranje določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike: -![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sl.png) +![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png) > Slika iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3) @@ -66,7 +66,7 @@ Rezultat, ki ga dobimo s kodiranjem položaja, ugnezdi tako izvirni token kot nj Nato moramo zajeti nekatere vzorce znotraj našega zaporedja. Da bi to dosegli, transformatorji uporabljajo mehanizem **samopozornosti**, ki je v bistvu pozornost, uporabljena na istem zaporedju kot vhod in izhod. Uporaba samopozornosti nam omogoča, da upoštevamo **kontekst** znotraj stavka in vidimo, katere besede so medsebojno povezane. Na primer, omogoča nam, da vidimo, na katere besede se nanašajo koreference, kot je *to*, in tudi upoštevamo kontekst: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sl.png) +![](../../../../../translated_images/sl/CoreferenceResolution.861924d6d384a7d6.png) > Slika iz [Googlovega bloga](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Ker je vsak vhodni položaj neodvisno preslikan na vsak izhodni položaj, lahko **BERT** (Bidirectional Encoder Representations from Transformers) je zelo velika večplastna mreža transformatorjev z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno naučen na velikem korpusu besedilnih podatkov (WikiPedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zamaskiranih besed v stavku). Med predhodnim učenjem model absorbira pomembne ravni razumevanja jezika, ki jih je nato mogoče uporabiti z drugimi nabori podatkov z uporabo finega uglaševanja. Ta proces se imenuje **prenosno učenje**. -![slika iz http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sl.png) +![slika iz http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Slika [vir](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 1022e38b..1500d435 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako izhodno napoved RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodne RNN in izhodne RNN. Na ta način pri generiranju izhodnega simbola $y_t$ upoštevamo vsa vhodna skrita stanja $h_i$ z različnimi utežnimi koeficienti $\\alpha_{t,i}$.\n", "\n", - "![Slika, ki prikazuje model kodirnik/dekodirnik z aditivno plastjo pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sl.png)\n", + "![Slika, ki prikazuje model kodirnik/dekodirnik z aditivno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model kodirnik-dekodirnik z mehanizmom aditivne pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), povzeto iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrika pozornosti $\\{\\alpha_{i,j}\\}$ predstavlja stopnjo, do katere določene vhodne besede vplivajo na generacijo določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike:\n", "\n", - "![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sl.png)\n", + "![Slika, ki prikazuje vzorčno poravnavo, najdeno z RNNsearch-50, povzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Slika povzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Slika 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je zelo velik večplastni transformator z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno usposobljen na velikem korpusu besedilnih podatkov (Wikipedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zamaskiranih besed v stavku). Med predhodnim učenjem model pridobi pomembno raven razumevanja jezika, ki jo je nato mogoče uporabiti z drugimi nabori podatkov z uporabo prilagoditve. Ta proces se imenuje **prenosno učenje**.\n", "\n", - "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sl.png)\n", + "![Slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Obstaja veliko različic arhitektur transformatorjev, vključno z BERT, DistilBERT, BigBird, OpenGPT3 in drugimi, ki jih je mogoče prilagoditi. Paket [HuggingFace](https://github.com/huggingface/) ponuja repozitorij za učenje mnogih teh arhitektur s PyTorch.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index e940af0c..f175ec1f 100644 --- a/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mehanizmi pozornosti** omogočajo tehtanje kontekstualnega vpliva vsakega vhodnega vektorja na vsako izhodno napoved RNN. To se implementira z ustvarjanjem bližnjic med vmesnimi stanji vhodnega RNN in izhodnega RNN. Na ta način, ko generiramo izhodni simbol $y_t$, upoštevamo vsa skrita stanja vhodov $h_i$ z različnimi utežnimi koeficienti $\\alpha_{t,i}$.\n", "\n", - "![Slika prikazuje model kodirnik/dekodirnik z dodatno plastjo pozornosti](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sl.png)\n", + "![Slika prikazuje model kodirnik/dekodirnik z dodatno plastjo pozornosti](../../../../../translated_images/sl/encoder-decoder-attention.7a726296894fb567.png)\n", "*Model kodirnik-dekodirnik z mehanizmom dodatne pozornosti v [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citirano iz [tega bloga](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matrika pozornosti $\\{\\alpha_{i,j}\\}$ predstavlja stopnjo, do katere določene vhodne besede vplivajo na generacijo določene besede v izhodnem zaporedju. Spodaj je primer takšne matrike:\n", "\n", - "![Slika prikazuje vzorčno poravnavo, ki jo je našel RNNsearch-50, vzeto iz Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sl.png)\n", + "![Slika prikazuje vzorčno poravnavo, ki jo je našel RNNsearch-50, vzeto iz Bahdanau - arviz.org](../../../../../translated_images/sl/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Slika vzeta iz [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) je zelo velik večplastni transformatorni model z 12 plastmi za *BERT-base* in 24 za *BERT-large*. Model je najprej predhodno usposobljen na velikem korpusu besedilnih podatkov (WikiPedia + knjige) z uporabo nenadzorovanega učenja (napovedovanje zakritih besed v stavku). Med predhodnim usposabljanjem model pridobi pomembno raven razumevanja jezika, ki jo je nato mogoče uporabiti z drugimi nabori podatkov prek finega prilagajanja. Ta proces se imenuje **prenosno učenje**.\n", "\n", - "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sl.png)\n", + "![slika s http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Obstaja veliko različic transformatornih arhitektur, vključno z BERT, DistilBERT, BigBird, OpenGPT3 in drugimi, ki jih je mogoče fino prilagoditi.\n", "\n", diff --git a/translations/sl/lessons/5-NLP/19-NER/README.md b/translations/sl/lessons/5-NLP/19-NER/README.md index cf173e2e..dc54191a 100644 --- a/translations/sl/lessons/5-NLP/19-NER/README.md +++ b/translations/sl/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ novorojenčku | O Ker moramo vzpostaviti enako razmerje med tokeni in razredi, lahko iz te slike treniramo desno **mnogokratno-mnogokratno** nevronsko mrežo: -![Slika prikazuje običajne vzorce rekurzivnih nevronskih mrež.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sl.jpg) +![Slika prikazuje običajne vzorce rekurzivnih nevronskih mrež.](../../../../../translated_images/sl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Slika iz [tega bloga](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) avtorja [Andreja Karpathyja](http://karpathy.github.io/). Modeli klasifikacije tokenov NER ustrezajo desni arhitekturi mreže na tej sliki.* diff --git a/translations/sl/lessons/5-NLP/README.md b/translations/sl/lessons/5-NLP/README.md index 28834812..6dfe2e02 100644 --- a/translations/sl/lessons/5-NLP/README.md +++ b/translations/sl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Obdelava naravnega jezika -![Povzetek nalog NLP v skici](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.sl.png) +![Povzetek nalog NLP v skici](../../../../translated_images/sl/ai-nlp.b22dcb8ca4707cea.png) V tem poglavju se bomo osredotočili na uporabo nevronskih mrež za reševanje nalog, povezanih z **obdelavo naravnega jezika (NLP)**. Obstaja veliko NLP problemov, ki jih želimo, da jih računalniki rešujejo: diff --git a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md index 97e294e6..60145928 100644 --- a/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Odprite enega od modelov, na primer **Biology → Flocking**. Ko odprete model, pridete na glavni zaslon NetLogo. Tukaj je primer modela, ki opisuje populacijo volkov in ovc ob omejenih virih (trava). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.sl.png) +![NetLogo Main Screen](../../../../../translated_images/sl/NetLogo-Main.32653711ec1a01b3.png) > Posnetek zaslona avtorja Dmitry Soshnikov diff --git a/translations/sl/lessons/README.md b/translations/sl/lessons/README.md index b4b5210b..c715cc7f 100644 --- a/translations/sl/lessons/README.md +++ b/translations/sl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pregled -![Pregled v skici](../../../translated_images/ai-overview.0857791951d19500.sl.png) +![Pregled v skici](../../../translated_images/sl/ai-overview.0857791951d19500.png) > Skica avtorja [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md index 5bb4e151..d3ed61d9 100644 --- a/translations/sl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Po uspehu modelov transformatorjev pri reševanju nalog NLP so bile iste ali pod Glavna ideja CLIP je primerjati besedilne pozive s sliko in ugotoviti, kako dobro slika ustreza pozivu. -![CLIP Arhitektura](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.sl.png) +![CLIP Arhitektura](../../../../../translated_images/sl/clip-arch.b3dbf20b4e8ed8be.png) > *Slika iz [tega blog prispevka](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Ko je model predhodno treniran, mu lahko podamo paket slik in paket besedilnih p Recimo, da moramo razvrstiti slike med, na primer, mačke, pse in ljudi. V tem primeru lahko modelu podamo sliko in serijo besedilnih pozivov: "*slika mačke*", "*slika psa*", "*slika človeka*". V nastalem vektorju s tremi verjetnostmi moramo le izbrati indeks z najvišjo vrednostjo. -![CLIP za razvrščanje slik](../../../../../translated_images/clip-class.3af42ef0b2b19369.sl.png) +![CLIP za razvrščanje slik](../../../../../translated_images/sl/clip-class.3af42ef0b2b19369.png) > *Slika iz [tega blog prispevka](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Več o VQGAN si lahko preberete na spletni strani [Taming Transformers](https:// Ena pomembna razlika med VQGAN in tradicionalnim GAN je, da slednji lahko ustvari spodobno sliko iz katerega koli vhodnega vektorja, medtem ko VQGAN verjetno ustvari sliko, ki ni koherentna. Zato moramo dodatno usmerjati proces ustvarjanja slike, kar lahko storimo z uporabo CLIP. -![VQGAN+CLIP Arhitektura](../../../../../translated_images/vqgan.5027fe05051dfa31.sl.png) +![VQGAN+CLIP Arhitektura](../../../../../translated_images/sl/vqgan.5027fe05051dfa31.png) Za generiranje slike, ki ustreza besedilnemu pozivu, začnemo z naključnim kodirnim vektorjem, ki ga posredujemo VQGAN za ustvarjanje slike. Nato uporabimo CLIP za ustvarjanje funkcije izgube, ki kaže, kako dobro slika ustreza besedilnemu pozivu. Cilj je nato minimizirati to izgubo z uporabo povratnega razširjanja za prilagoditev parametrov vhodnega vektorja. Odlična knjižnica, ki implementira VQGAN+CLIP, je [Pixray](http://github.com/pixray/pixray). -![Slika, ustvarjena s Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.sl.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.sl.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.sl.png) +![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Slika, ustvarjena iz poziva *bližnji akvarelni portret mladega učitelja književnosti z knjigo* | Slika, ustvarjena iz poziva *bližnji oljni portret mlade učiteljice računalništva z računalnikom* | Slika, ustvarjena iz poziva *bližnji oljni portret starega učitelja matematike pred tablo* @@ -75,7 +75,7 @@ Za razliko od CLIP DALL-E prejme tako besedilo kot sliko kot enoten tok tokenov Glavna razlika med DALL.E 1 in 2 je, da generira bolj realistične slike in umetnost. Primeri generiranja slik z DALL-E: -![Slika, ustvarjena s Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.sl.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.sl.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.sl.png) +![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Slika, ustvarjena s Pixray](../../../../../translated_images/sl/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Slika, ustvarjena iz poziva *bližnji akvarelni portret mladega učitelja književnosti z knjigo* | Slika, ustvarjena iz poziva *bližnji oljni portret mlade učiteljice računalništva z računalnikom* | Slika, ustvarjena iz poziva *bližnji oljni portret starega učitelja matematike pred tablo* diff --git a/translations/sr/README.md b/translations/sr/README.md index cce87c11..19b61d12 100644 --- a/translations/sr/README.md +++ b/translations/sr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Вештачка интелигенција за почетнике - Наставни програм -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.sr.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sr/ai-overview.0857791951d19500.png)| |:---:| | Вештачка интелигенција за почетнике - _Скетчнот од [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sr/lessons/1-Intro/README.md b/translations/sr/lessons/1-Intro/README.md index 21a6aa19..4f53573e 100644 --- a/translations/sr/lessons/1-Intro/README.md +++ b/translations/sr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Увод у вештачку интелигенцију -![Резиме садржаја увода у вештачку интелигенцију у виду цртежа](../../../../translated_images/ai-intro.bf28d1ac4235881c.sr.png) +![Резиме садржаја увода у вештачку интелигенцију у виду цртежа](../../../../translated_images/sr/ai-intro.bf28d1ac4235881c.png) > Цртеж направио [Томоми Имура](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Првобитно, рачунаре је изумео [Чарлс Бебиџ](https://en.wikipedia.org/wiki/Charles_Babbage) да би обрађивали бројеве пратећи добро дефинисану процедуру - алгоритам. Савремени рачунари, иако значајно напреднији од оригиналног модела предложеног у 19. веку, и даље следе исту идеју контролисаних рачунања. Због тога је могуће програмирати рачунар да ради нешто ако знамо тачан низ корака који треба да се изврше да би се постигао циљ. -![Фотографија особе](../../../../translated_images/dsh_age.d212a30d4e54fb5f.sr.png) +![Фотографија особе](../../../../translated_images/sr/dsh_age.d212a30d4e54fb5f.png) > Фотографија од [Вики Сошникова](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Један од проблема када се бавимо термином **[интелигенција](https://en.wikipedia.org/wiki/Intelligence)** је то што не постоји јасна дефиниција овог термина. Може се тврдити да је интелигенција повезана са **апстрактним размишљањем**, или са **самосвешћу**, али не можемо је правилно дефинисати. -![Фотографија мачке](../../../../translated_images/photo-cat.8c8e8fb760ffe457.sr.jpg) +![Фотографија мачке](../../../../translated_images/sr/photo-cat.8c8e8fb760ffe457.jpg) > [Фотографија](https://unsplash.com/photos/75715CVEJhI) од [Амбер Кип](https://unsplash.com/@sadmax) са Unsplash-а @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | Шта је са МЛ? | | > |--------------|-----------| -> | Део Вештачке интелигенције који се заснива на томе да рачунар учи да реши проблем на основу неких података назива се **Машинско учење**. Нећемо разматрати класично машинско учење у овом курсу - упућујемо вас на посебан [Курс машинског учења за почетнике](http://aka.ms/ml-beginners). | ![МЛ за почетнике](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.sr.png) | +> | Део Вештачке интелигенције који се заснива на томе да рачунар учи да реши проблем на основу неких података назива се **Машинско учење**. Нећемо разматрати класично машинско учење у овом курсу - упућујемо вас на посебан [Курс машинског учења за почетнике](http://aka.ms/ml-beginners). | ![МЛ за почетнике](../../../../translated_images/sr/ml-for-beginners.9e4fed176fd5817d.png) | ## Кратка историја ВИ Вештачка интелигенција је започета као област средином двадесетог века. У почетку је симболичко резоновање било доминантан приступ, и довело је до бројних важних успеха, као што су експертски системи – компјутерски програми који су могли да делују као експерт у неким ограниченим проблемским доменима. Међутим, убрзо је постало јасно да се такав приступ не скалира добро. Извлачење знања од експерта, представљање у рачунару и одржавање те базе знања тачном испоставило се као веома сложен задатак, и превише скуп да би био практичан у многим случајевима. То је довело до такозване [зиме ВИ](https://en.wikipedia.org/wiki/AI_winter) 1970-их. -Кратка историја ВИ +Кратка историја ВИ > Слика од [Дмитрија Сошникова](http://soshnikov.com) diff --git a/translations/sr/lessons/2-Symbolic/Animals.ipynb b/translations/sr/lessons/2-Symbolic/Animals.ipynb index 541a0735..936a7bfc 100644 --- a/translations/sr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "У овом примеру, имплементираћемо једноставан систем заснован на знању који одређује животињу на основу неких физичких карактеристика. Систем се може представити следећим AND-OR стаблом (ово је део целог стабла, лако можемо додати још правила):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sr.png)\n" + "![](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/sr/lessons/2-Symbolic/README.md b/translations/sr/lessons/2-Symbolic/README.md index a6a7e60f..3cc1f63c 100644 --- a/translations/sr/lessons/2-Symbolic/README.md +++ b/translations/sr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представљање знања и експертски системи -![Резиме садржаја о симболичкој вештачкој интелигенцији](../../../../translated_images/ai-symbolic.715a30cb610411a6.sr.png) +![Резиме садржаја о симболичкој вештачкој интелигенцији](../../../../translated_images/sr/ai-symbolic.715a30cb610411a6.png) > Скетч од [Томоми Имура](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Дакле, проблем **представљања знања** је у проналажењу ефикасног начина за представљање знања унутар рачунара у облику података, како би оно било аутоматски употребљиво. Ово се може посматрати као спектар: -![Спектар представљања знања](../../../../translated_images/knowledge-spectrum.b60df631852c0217.sr.png) +![Спектар представљања знања](../../../../translated_images/sr/knowledge-spectrum.b60df631852c0217.png) > Слика од [Дмитрија Сошњикова](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | блок-синтакса | увлачење Један од раних успеха симболичке вештачке интелигенције били су такозвани **експертски системи** - рачунарски системи дизајнирани да делују као стручњаци у некој ограниченој области проблема. Они су се заснивали на **бази знања** извученој од једног или више људских стручњака и садржали су **инференцијски механизам** који је вршио закључивање на основу те базе. -![Људска архитектура](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.sr.png) | ![Архитектура система заснованог на знању](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.sr.png) +![Људска архитектура](../../../../translated_images/sr/arch-human.5d4d35f1bba3ab1c.png) | ![Архитектура система заснованог на знању](../../../../translated_images/sr/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Поједностављена структура људског нервног система | Архитектура система заснованог на знању @@ -106,7 +106,7 @@ Python | блок-синтакса | увлачење Као пример, размотримо следећи експертски систем за одређивање животиње на основу њених физичких карактеристика: -![AND-OR стабло](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sr.png) +![AND-OR стабло](../../../../translated_images/sr/AND-OR-Tree.5592d2c70187f283.png) > Слика од [Дмитрија Сошњикова](http://soshnikov.com) diff --git a/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index fc105571..115652e5 100644 --- a/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "У случају да имамо више од 2 класе, softmax ће нормализовати вероватноће за све њих. Овде је дијаграм архитектуре мреже која врши класификацију MNIST цифара:\n", "\n", - "![MNIST Класификатор](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.sr.png)\n" + "![MNIST Класификатор](../../../../../translated_images/sr/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Низак губитак на подацима за обуку - модел може добро да приближи податке за обуку, јер има довољно изражајне снаге.\n", "* Губитак на валидационим подацима може бити много већи од губитка на подацима за обуку и може почети да расте током обуке - то је зато што модел \"памти\" тачке из обуке и губи \"општу слику\".\n", "\n", - "![Претерано прилагођавање](../../../../../translated_images/overfit.a0bd57f717c15769.sr.png)\n", + "![Претерано прилагођавање](../../../../../translated_images/sr/overfit.a0bd57f717c15769.png)\n", "\n", "> На овој слици, `x` представља податке за обуку, `o` - валидационе податке. Лево - линеарни модел (један слој), прилично добро приближава природу података. Десно - модел са претераним прилагођавањем, савршено добро приближава податке за обуку, али губи смисао са било којим другим подацима (валидациони губитак је веома висок).\n" ] diff --git a/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md index d2bb0fd8..7c16917d 100644 --- a/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: Размотрите следећи проблем апроксимације 5 тачака (представљених са `x` на графицима испод): -![линеарно](../../../../../translated_images/overfit1.f24b71c6f652e59e.sr.jpg) | ![претренираност](../../../../../translated_images/overfit2.131f5800ae10ca5e.sr.jpg) +![линеарно](../../../../../translated_images/sr/overfit1.f24b71c6f652e59e.jpg) | ![претренираност](../../../../../translated_images/sr/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Линеарни модел, 2 параметра** | **Нелинеарни модел, 7 параметара** Грешка на тренингу = 5.3 | Грешка на тренингу = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: Као што можете видети на графику изнад, претренираност се може открити веома ниском грешком на тренингу и високом грешком на валидацији. Обично током тренинга видимо да и грешка на тренингу и грешка на валидацији почињу да опадају, а затим у једном тренутку грешка на валидацији може престати да опада и почети да расте. То ће бити знак претренираности и показатељ да би требало да зауставимо тренинг у том тренутку (или барем направимо снимак модела). -![претренираност](../../../../../translated_images/Overfitting.408ad91cd90b4371.sr.png) +![претренираност](../../../../../translated_images/sr/Overfitting.408ad91cd90b4371.png) ## Како спречити претренираност diff --git a/translations/sr/lessons/3-NeuralNetworks/README.md b/translations/sr/lessons/3-NeuralNetworks/README.md index a388ea5a..13220bf2 100644 --- a/translations/sr/lessons/3-NeuralNetworks/README.md +++ b/translations/sr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Увод у неуронске мреже -![Резиме садржаја увода у неуронске мреже у виду цртежа](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.sr.png) +![Резиме садржаја увода у неуронске мреже у виду цртежа](../../../../translated_images/sr/ai-neuralnetworks.1c687ae40bc86e83.png) Као што смо разговарали у уводу, један од начина да се постигне интелигенција је да се обучи **рачунарски модел** или **вештачки мозак**. Од средине 20. века, истраживачи су испробавали различите математичке моделе, све док се у последњим годинама овај правац није показао као изузетно успешан. Такви математички модели мозга називају се **неуронске мреже**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: Из биологије знамо да наш мозак састоји се од неуронских ћелија (неурона), од којих свака има више "улаза" (дендрита) и један "излаз" (аксон). И дендрити и аксони могу проводити електричне сигнале, а везе између њих — познате као синапсе — могу показивати различите степене проводљивости, које регулишу неуротрансмитери. -![Модел неурона](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.sr.jpg) | ![Модел неурона](../../../../translated_images/artneuron.1a5daa88d20ebe6f.sr.png) +![Модел неурона](../../../../translated_images/sr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модел неурона](../../../../translated_images/sr/artneuron.1a5daa88d20ebe6f.png) ----|---- Прави неурон *([Слика](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) са Википедије)* | Вештачки неурон *(Слика аутора)* Дакле, најједноставнији математички модел неурона садржи неколико улаза X1, ..., XN и један излаз Y, као и низ тежина W1, ..., WN. Излаз се рачуна као: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) где је f нека нелинеарна **активациона функција**. diff --git a/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md index 11fcc565..3a5283b8 100644 --- a/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV можете користити и за учитавање видео з * **Предобрада фотографије Брајеве књиге**. Фокусирамо се на то како можемо користити праговање, детекцију карактеристика, перспективну трансформацију и манипулације NumPy низовима да бисмо раздвојили појединачне Брајеве симболе за даљу класификацију помоћу неуронске мреже. -![Слика Брајеве књиге](../../../../../translated_images/braille.341962ff76b1bd70.sr.jpeg) | ![Предобрађена слика Брајеве књиге](../../../../../translated_images/braille-result.46530fea020b03c7.sr.png) | ![Брајеви симболи](../../../../../translated_images/braille-symbols.0159185ab69d5339.sr.png) +![Слика Брајеве књиге](../../../../../translated_images/sr/braille.341962ff76b1bd70.jpeg) | ![Предобрађена слика Брајеве књиге](../../../../../translated_images/sr/braille-result.46530fea020b03c7.png) | ![Брајеви симболи](../../../../../translated_images/sr/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Слика из [OpenCV.ipynb](OpenCV.ipynb) * **Детекција кретања у видеу коришћењем разлике кадрова**. Ако је камера фиксирана, онда би кадрови из видео записа требало да буду прилично слични један другом. Пошто су кадрови представљени као низови, само одузимањем тих низова за два узастопна кадра добићемо разлику пиксела, која би требало да буде мала за статичне кадрове, а да постане већа када постоји значајно кретање на слици. -![Слика видео кадрова и разлике кадрова](../../../../../translated_images/frame-difference.706f805491a0883c.sr.png) +![Слика видео кадрова и разлике кадрова](../../../../../translated_images/sr/frame-difference.706f805491a0883c.png) > Слика из [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ OpenCV можете користити и за учитавање видео з - **Густи оптички ток** израчунава векторско поље које показује за сваки пиксел где се креће - **Ретки оптички ток** заснован је на узимању неких карактеристичних елемената на слици (нпр. ивица) и изградњи њихове трајекторије од кадра до кадра. -![Слика оптичког тока](../../../../../translated_images/optical.1f4a94464579a83a.sr.png) +![Слика оптичког тока](../../../../../translated_images/sr/optical.1f4a94464579a83a.png) > Слика из [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 09d41db0..f6046111 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 је мрежа која је постигла тачност од 92.7% у ImageNet top-5 класификацији 2014. године. Има следећу структуру слојева: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sr.jpg) +![ImageNet Layers](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.jpg) Као што можете видети, VGG прати традиционалну пирамидалну архитектуру, која је низ слојева за конволуцију и пуловање. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sr.jpg) +![ImageNet Pyramid](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.jpg) > Слика са [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 72967992..a9ed8845 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Тако, у типичном CNN-у постоји неколико конволуционих слојева, са слојевима за пуловање између њих како би се смањиле димензије слике. Такође бисмо повећали број филтера, јер како обрасци постају сложенији, постоји више могућих занимљивих комбинација које треба тражити.\n", "\n", - "![Слика која приказује неколико конволуционих слојева са слојевима за пуловање.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sr.png)\n", + "![Слика која приказује неколико конволуционих слојева са слојевима за пуловање.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Због смањивања просторних димензија и повећавања димензија карактеристика/филтера, ова архитектура се такође назива **пирамидална архитектура**.\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 2f5cb842..fe03394c 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -359,7 +359,7 @@ "\n", "Тако би у типичном CNN-у постојало неколико конволуционих слојева, са pooling слојевима између њих како би се смањиле димензије слике. Такође бисмо повећали број филтера, јер како обрасци постају напреднији, постоји више могућих занимљивих комбинација које треба тражити.\n", "\n", - "![Слика која приказује неколико конволуционих слојева са pooling слојевима.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sr.png)\n", + "![Слика која приказује неколико конволуционих слојева са pooling слојевима.](../../../../../translated_images/sr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Због смањења просторних димензија и повећања димензија карактеристика/филтера, ова архитектура се такође назива **пирамидална архитектура**.\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md index 18e5fc98..ee6db5fd 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Да бисмо извукли шаблоне, користићемо концепт **конволуционих филтера**. Као што знате, слика је представљена као 2D-матрица или 3D-тензор са дубином боје. Примена филтера значи да узимамо релативно малу матрицу **језгра филтера**, и за сваки пиксел у оригиналној слици израчунавамо пондерисани просек са суседним тачкама. Ово можемо замислити као мали прозор који клизи преко целе слике и израчунава просек свих пиксела према тежинама у матрици језгра филтера. -![Филтер за вертикалне ивице](../../../../../translated_images/filter-vert.b7148390ca0bc356.sr.png) | ![Филтер за хоризонталне ивице](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.sr.png) +![Филтер за вертикалне ивице](../../../../../translated_images/sr/filter-vert.b7148390ca0bc356.png) | ![Филтер за хоризонталне ивице](../../../../../translated_images/sr/filter-horiz.59b80ed4feb946ef.png) ----|---- > Слика: Дмитриј Сошњиков @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Можемо дизајнирати мрежу тако да се филтери аутоматски тренирају * Можемо користити исти приступ за проналажење шаблона у карактеристикама високог нивоа, а не само у оригиналној слици. Тако екстракција карактеристика у CNN-у функционише на хијерархији карактеристика, почевши од комбинација пиксела ниског нивоа, па све до комбинација делова слике високог нивоа. -![Хијерархијска екстракција карактеристика](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.sr.png) +![Хијерархијска екстракција карактеристика](../../../../../translated_images/sr/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Слика из [рада Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), засновано на [њиховом истраживању](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Као пример, погледајмо архитектуру VGG-16, мреже која је постигла 92.7% тачности у ImageNet топ-5 класификацији 2014. године: -![Слојеви ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sr.jpg) +![Слојеви ImageNet](../../../../../translated_images/sr/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Пирамида ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sr.jpg) +![Пирамида ImageNet](../../../../../translated_images/sr/vgg-16-arch.64ff2137f50dd49f.jpg) > Слика са [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 72a295dd..e3d2e61d 100644 --- a/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Користићемо [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), који садржи слике 37 различитих раса паса и мачака. -![Скуп података са којим ћемо радити](../../../../../../translated_images/data.50b2a9d5484bdbf0.sr.png) +![Скуп података са којим ћемо радити](../../../../../../translated_images/sr/data.50b2a9d5484bdbf0.png) Да бисте преузели скуп података, користите овај код: diff --git a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 462be0d1..c99083f0 100644 --- a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Да бисмо визуализовали идеалну мачку, почећемо са сликом насумичног шума и покушаћемо да користимо технику оптимизације градијентног спуштања како бисмо прилагодили слику тако да мрежа препозна мачку.\n", "\n", - "![Оптимизациони циклус](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sr.png)\n", + "![Оптимизациони циклус](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Ово је наша почетна слика:\n" ] diff --git a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md index d8369be6..4669986f 100644 --- a/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ево примера карактеристика издвојених из слике мачке помоћу VGG-16 мреже: -![Карактеристике издвојене помоћу VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.sr.png) +![Карактеристике издвојене помоћу VGG-16](../../../../../translated_images/sr/features.6291f9c7ba3a0b95.png) ## Скуп података: Мачке и пси @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Један приступ који можемо применити је да почнемо са насумичном сликом и затим покушамо да користимо технику **оптимизације градијентног спуштања** како бисмо прилагодили ту слику на такав начин да мрежа почне да мисли да је то мачка. -![Петља оптимизације слике](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sr.png) +![Петља оптимизације слике](../../../../../translated_images/sr/ideal-cat-loop.999fbb8ff306e044.png) Међутим, ако то урадимо, добићемо нешто веома слично насумичном шуму. То је зато што *постоји много начина да мрежа помисли да је улазна слика мачка*, укључујући неке који визуелно немају смисла. Иако те слике садрже много образаца типичних за мачку, ништа их не ограничава да буду визуелно препознатљиве. Да бисмо побољшали резултат, можемо додати још један члан у функцију губитка, који се назива **губитак варијације**. То је метрика која показује колико су слични суседни пиксели слике. Минимизирање губитка варијације чини слику глаткијом и уклања шум - чиме открива визуелно привлачније обрасце. Ево примера таквих "идеалних" слика, које се класификују као мачка и као зебра са високом вероватноћом: -![Идеална мачка](../../../../../translated_images/ideal-cat.203dd4597643d6b0.sr.png) | ![Идеална зебра](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.sr.png) +![Идеална мачка](../../../../../translated_images/sr/ideal-cat.203dd4597643d6b0.png) | ![Идеална зебра](../../../../../translated_images/sr/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Идеална мачка* | *Идеална зебра* Сличан приступ може се користити за извођење такозваних **адверзаријалних напада** на неуронску мрежу. Претпоставимо да желимо да преваримо неуронску мрежу и учинимо да пас изгледа као мачка. Ако узмемо слику пса, коју мрежа препознаје као пса, можемо је мало изменити користећи оптимизацију градијентног спуштања, док мрежа не почне да је класификује као мачку: -![Слика пса](../../../../../translated_images/original-dog.8f68a67d2fe0911f.sr.png) | ![Слика пса класификована као мачка](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.sr.png) +![Слика пса](../../../../../translated_images/sr/original-dog.8f68a67d2fe0911f.png) | ![Слика пса класификована као мачка](../../../../../translated_images/sr/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Оригинална слика пса* | *Слика пса класификована као мачка* diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index dbb25a05..8d34eb8b 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Пошто тренирамо аутокодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбољу **репрезентацију** улазних слика како би ухватила њихово значење.\n", "\n", - "![Дијаграм аутокодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sr.jpg)\n", + "![Дијаграм аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 252ec17d..ab8ace41 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Пошто тренирамо аутокодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбоље **уграђивање** улазних слика како би ухватила њихово значење.\n", "\n", - "![Шема аутокодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sr.jpg)\n", + "![Шема аутокодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md index 4f392349..9552ace9 100644 --- a/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Пошто тренирамо аутоенкодер да ухвати што више информација из оригиналне слике ради тачне реконструкције, мрежа покушава да пронађе најбоље **уграђивање** улазних слика како би ухватила њихово значење. -![Дијаграм аутоенкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sr.jpg) +![Дијаграм аутоенкодера](../../../../../translated_images/sr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Слика са [Keras блога](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md index ba30d966..c09f7076 100644 --- a/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Квиз пре предавања](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Детекција објеката](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.sr.png) +![Детекција објеката](../../../../../translated_images/sr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Слика са [YOLO v2 веб сајта](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Покрените класификацију слике на свакој плочици. 3. Плочице које резултирају довољно високом активацијом могу се сматрати да садрже тражени објекат. -![Наивна детекција објеката](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.sr.png) +![Наивна детекција објеката](../../../../../translated_images/sr/naive-detection.e7f1ba220ccd08c6.png) > *Слика из [радне свеске за вежбе](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 класа * [COCO](http://cocodataset.org/#home) - Уобичајени објекти у контексту. 80 класа, оквири и маске за сегментацију -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.sr.jpg) +![COCO](../../../../../translated_images/sr/coco-examples.71bc60380fa6cceb.jpg) ## Метрике за детекцију објеката @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Док је за класификацију слика лако измерити колико добро алгоритам ради, за детекцију објеката морамо измерити и исправност класе, као и прецизност локације предвиђеног оквира. За ово друго користимо такозвани **Пресек преко уније** (IoU), који мери колико добро се два оквира (или две произвољне области) преклапају. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.sr.png) +![IoU](../../../../../translated_images/sr/iou_equation.9a4751d40fff4e11.png) > *Фигура 2 из [овог одличног блог поста о IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) користи [Селективно претраживање](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) за генерисање хијерархијске структуре ROI региона, који се затим прослеђују кроз CNN екстракторе карактеристика и SVM класификаторе за одређивање класе објекта, и линеарну регресију за одређивање координата *оквира*. [Званични рад](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.sr.png) +![RCNN](../../../../../translated_images/sr/rcnn1.cae407020dfb1d1f.png) > *Слика из ван де Санде и др. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.sr.png) +![RCNN-1](../../../../../translated_images/sr/rcnn2.2d9530bb83516484.png) > *Слике из [овог блога](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Овај приступ је сличан R-CNN-у, али региони се дефинишу након што су примењени слојеви конволуције. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.sr.png) +![FRCNN](../../../../../translated_images/sr/f-rcnn.3cda6d9bb4188875.png) > Слика из [званичног рада](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Главна идеја овог приступа је коришћење неуронске мреже за предвиђање ROI - такозване *мреже за предлог региона*. [Рад](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.sr.png) +![FasterRCNN](../../../../../translated_images/sr/faster-rcnn.8d46c099b87ef30a.png) > Слика из [званичног рада](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Карактеристике се обрађују помоћу **мапе оцена осетљивих на позицију**. Сваки објекат из $C$ класа се дели на $k\times k$ регије, и тренирамо мрежу да предвиђа делове објеката. 3. За сваки део из $k\times k$ регија све мреже гласају за класе објеката, и класа објекта са максималним бројем гласова се бира. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.sr.png) +![r-fcn image](../../../../../translated_images/sr/r-fcn.13eb88158b99a3da.png) > Слика из [званичног рада](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO је алгоритам за реално време са једним пр * Слика се дели на $S\times S$ регије. * За сваку регију, **CNN** предвиђа $n$ могућих објеката, координате *оквира* и *поузданост*=*вероватноћа* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.sr.png) + ![YOLO](../../../../../translated_images/sr/yolo.a2648ec82ee8bb4e.png) > Слика из [званичног рада](https://arxiv.org/abs/1506.02640) diff --git a/translations/sr/lessons/4-ComputerVision/README.md b/translations/sr/lessons/4-ComputerVision/README.md index 9fb6e9c6..1b972c04 100644 --- a/translations/sr/lessons/4-ComputerVision/README.md +++ b/translations/sr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Рачунарски вид -![Резиме садржаја о рачунарском виду у виду цртежа](../../../../translated_images/ai-computervision.6506ebebac3fbf76.sr.png) +![Резиме садржаја о рачунарском виду у виду цртежа](../../../../translated_images/sr/ai-computervision.6506ebebac3fbf76.png) У овом делу ћемо научити о: diff --git a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index b31789d3..88d58598 100644 --- a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Врећа речи** (BoW) представљање у виду вектора је најчешће коришћено традиционално представљање вектора. Свака реч је повезана са индексом вектора, а елемент вектора садржи број појављивања те речи у датом документу.\n", "\n", - "![Слика која приказује како је представљање вектора вреће речи приказано у меморији.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sr.png) \n", + "![Слика која приказује како је представљање вектора вреће речи приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Напомена**: Можете такође размишљати о BoW као о збиру свих вектора кодираних једним битом за појединачне речи у тексту.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 81b3a594..6f8b41b4 100644 --- a/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Торба речи** (BoW) представљање вектора је најједноставније за разумевање међу традиционалним представљањима вектора. Свака реч је повезана са индексом вектора, а елемент вектора садржи број појављивања сваке речи у датом документу.\n", "\n", - "![Слика која приказује како је представљање вектора методом \"торба речи\" приказано у меморији.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sr.png) \n", + "![Слика која приказује како је представљање вектора методом \"торба речи\" приказано у меморији.](../../../../../translated_images/sr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Можете такође размишљати о BoW као о збиру свих једноврсно-кодираних вектора за појединачне речи у тексту.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 96b32853..61ab039d 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Користећи слој за угњеждавање као први слој у нашој мрежи, можемо прећи са модела торбе речи на модел **торбе угњеждавања**, где прво конвертујемо сваку реч у нашем тексту у одговарајуће угњеждавање, а затим израчунавамо неку агрегатну функцију над свим тим угњеждавањима, као што су `sum`, `average` или `max`.\n", "\n", - "![Слика која приказује класификатор угњеждавања за пет речи у низу.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sr.png)\n", + "![Слика која приказује класификатор угњеждавања за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Наша неуронска мрежа класификатора ће почети са слојем за угњеждавање, затим слојем за агрегирање, и линеарним класификатором на врху:\n" ] @@ -176,7 +176,7 @@ "\n", "У претходној архитектури, морали смо да попунимо све секвенце до исте дужине како би се уклопиле у мини серију. Ово није најефикаснији начин за представљање секвенци променљиве дужине - други приступ би био коришћење **вектора офсета**, који би садржао офсете свих секвенци смештених у један велики вектор.\n", "\n", - "![Слика која приказује репрезентацију секвенци са офсетом](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.sr.png)\n", + "![Слика која приказује репрезентацију секвенци са офсетом](../../../../../translated_images/sr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: На слици изнад приказујемо секвенцу карактера, али у нашем примеру радимо са секвенцама речи. Међутим, општи принцип представљања секвенци помоћу вектора офсета остаје исти.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW је бржи, док је skip-gram спорији, али боље представља речи које се ређе јављају.\n", "\n", - "![Слика која приказује и CBoW и Skip-Gram алгоритме за претварање речи у векторе.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sr.png)\n", + "![Слика која приказује и CBoW и Skip-Gram алгоритме за претварање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Да бисмо експериментисали са Word2Vec угнежђењем претходно обученим на Google News скупу података, можемо користити библиотеку **gensim**. Испод налазимо речи које су најсличније речи 'neural'.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 66bfd4ef..2535bf3b 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Коришћењем слоја за уграђивање као првог слоја у нашој мрежи, можемо прећи са модела „вреће речи“ на модел **вреће уграђивања**, где прво претварамо сваку реч у нашем тексту у одговарајуће уграђивање, а затим израчунавамо неку агрегатну функцију над свим тим уграђивањима, као што су `sum`, `average` или `max`.\n", "\n", - "![Слика која приказује класификатор са уграђивањем за пет речи у низу.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sr.png)\n", + "![Слика која приказује класификатор са уграђивањем за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Наша неуронска мрежа класификатора састоји се од следећих слојева:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW је бржи, док је скип-грам спорији, али боље представља ретке речи.\n", "\n", - "![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sr.png)\n", + "![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Да бисмо експериментисали са Word2Vec уградњом претходно обученом на Google News скупу података, можемо користити библиотеку **gensim**. Испод налазимо речи које су најсличније 'neural'.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/14-Embeddings/README.md b/translations/sr/lessons/5-NLP/14-Embeddings/README.md index 70ff7647..7edfec36 100644 --- a/translations/sr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Коришћењем слоја угњеждења као првог слоја у нашој мрежи класификатора, можемо прећи са модела торбе речи на модел **торбе угњеждења**, где прво конвертујемо сваку реч у нашем тексту у одговарајуће угњеждење, а затим израчунавамо неку агрегатну функцију над свим тим угњеждењима, као што су `sum`, `average` или `max`. -![Слика која приказује класификатор заснован на угњеждењима за пет речи у низу.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sr.png) +![Слика која приказује класификатор заснован на угњеждењима за пет речи у низу.](../../../../../translated_images/sr/embedding-classifier-example.b77f021a7ee67eee.png) > Слика аутора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW је бржи, док је скип-грам спорији, али боље представља ретке речи. -![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sr.png) +![Слика која приказује алгоритме CBoW и Skip-Gram за конвертовање речи у векторе.](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Слика из [овог рада](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md index cfb5b521..4d2d6a7d 100644 --- a/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Континуирана врећа речи** (CBoW), где предвиђамо средишњи токен $W_0$ у низу токена $W_{-N}$, ..., $W_N$. * **Скип-грам**, где предвиђамо скуп суседних токена {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} на основу средишњег токена $W_0$. -![слика из рада о претварању речи у векторе](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sr.png) +![слика из рада о претварању речи у векторе](../../../../../translated_images/sr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Слика из [овог рада](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sr/lessons/5-NLP/16-RNN/README.md b/translations/sr/lessons/5-NLP/16-RNN/README.md index 5671aa0c..e3762a15 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/README.md +++ b/translations/sr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Да бисмо ухватили значење секвенце текста, потребно је да користимо другу архитектуру неуронске мреже, која се назива **рекурентна неуронска мрежа**, или RNN. У RNN-у, реченицу пропуштамо кроз мрежу један симбол по један, а мрежа производи неко **стање**, које затим поново прослеђујемо мрежи са следећим симболом. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.sr.png) +![RNN](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.png) > Слика аутора @@ -61,7 +61,7 @@ LSTM мрежа је организована на начин сличан RNN- Рекурентна мрежа, било једносмерна или двосмерна, хвата одређене обрасце унутар секвенце и може их складиштити у вектор стања или проследити у излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој на врху првог да ухватимо обрасце вишег нивоа и изградимо од образаца нижег нивоа које је извукао први слој. Ово нас доводи до концепта **вишеслојног RNN-а**, који се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз. -![Слика која приказује вишеслојни LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sr.jpg) +![Слика која приказује вишеслојни LSTM RNN](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Слика из [овог дивног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза* diff --git a/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 6fff7444..385ac8ee 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентна мрежа, једносмерна или двосмерна, хвата одређене обрасце унутар секвенце, и може их складиштити у вектор стања или проследити у излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој на врху првог како бисмо ухватили обрасце вишег нивоа, изграђене од образаца нижег нивоа које је извукао први слој. Ово нас доводи до концепта **вишеслојне РНН**, која се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз.\n", "\n", - "![Слика која приказује вишеслојну дугорочно-краткорочну меморијску РНН](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sr.jpg)\n", + "![Слика која приказује вишеслојну дугорочно-краткорочну меморијску РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Слика из [овог дивног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза*\n", "\n", diff --git a/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb index 804f4f44..5a6008e3 100644 --- a/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Да бисмо ухватили значење секвенце текста, користићемо архитектуру неуронске мреже која се зове **рекурентна неуронска мрежа**, или RNN. Када користимо RNN, пролазимо кроз реченицу кроз мрежу један токен по један, а мрежа производи неко **стање**, које затим поново прослеђујемо мрежи са следећим токеном.\n", "\n", - "![Слика која приказује пример генерисања рекурентне неуронске мреже.](../../../../../translated_images/rnn.27f5c29c53d727b5.sr.png)\n", + "![Слика која приказује пример генерисања рекурентне неуронске мреже.](../../../../../translated_images/sr/rnn.27f5c29c53d727b5.png)\n", "\n", "С обзиром на улазну секвенцу токена $X_0,\\dots,X_n$, RNN креира секвенцу блокова неуронске мреже и тренира ову секвенцу од почетка до краја користећи бацкпропагацију. Сваки блок мреже узима пар $(X_i,S_i)$ као улаз, и производи $S_{i+1}$ као резултат. Коначно стање $S_n$ или излаз $Y_n$ иде у линеарни класификатор да би произвео резултат. Сви блокови мреже деле исте тежине и тренирају се од почетка до краја користећи један пролаз бацкпропагације.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентне мреже, било једносмерне или двосмерне, хватају обрасце унутар секвенце и чувају их у векторима стања или их враћају као излаз. Као и код конволуционих мрежа, можемо изградити још један рекурентни слој након првог како бисмо ухватили обрасце вишег нивоа, изграђене од образаца нижег нивоа које је извукао први слој. Ово нас доводи до појма **вишеслојног РНН-а**, који се састоји од два или више рекурентних мрежа, где се излаз претходног слоја прослеђује следећем слоју као улаз.\n", "\n", - "![Слика која приказује вишеслојни дугорочно-краткорочно-меморијски РНН](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sr.jpg)\n", + "![Слика која приказује вишеслојни дугорочно-краткорочно-меморијски РНН](../../../../../translated_images/sr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Слика из [овог сјајног чланка](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) аутора Фернанда Лопеза.*\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index ac06299a..42ae3e80 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Начин на који ћемо тренирати RNN за генерисање текста је следећи. На сваком кораку, узимамо секвенцу карактера дужине `nchars` и тражимо од мреже да генерише следећи излазни карактер за сваки улазни карактер:\n", "\n", - "![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sr.png)\n", + "![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "У зависности од конкретног сценарија, можда ћемо желети да укључимо неке посебне карактере, као што је *крај секвенце* ``. У нашем случају, желимо само да обучимо мрежу за бесконачно генерисање текста, па ћемо фиксирати величину сваке секвенце да буде једнака `nchars` токенима. Сходно томе, сваки пример за тренирање ће се састојати од `nchars` улаза и `nchars` излаза (што је улазна секвенца померена за један симбол улево). Минибатч ће се састојати од неколико таквих секвенци.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 4b645248..49cfa4f4 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Начин на који ћемо тренирати RNN да генерише наслове вести је следећи. У сваком кораку, узимамо један наслов, који ће бити унет у RNN, и за сваки улазни карактер тражимо од мреже да генерише следећи излазни карактер:\n", "\n", - "![Слика која приказује пример генерације речи 'HELLO' помоћу RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sr.png)\n", + "![Слика која приказује пример генерације речи 'HELLO' помоћу RNN.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "За последњи карактер у нашој секвенци, тражићемо од мреже да генерише `` токен.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md index 0e43ca38..b8e032c4 100644 --- a/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ово омогућава различите неуронске архитектуре које су приказане на слици испод: -![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sr.jpg) +![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Слика из блога [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) аутора [Андреја Карпатија](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Обучаваћемо ову RNN да генерише текст корак по корак. На сваком кораку, узимамо секвенцу карактера дужине `nchars` и тражимо од мреже да генерише следећи излазни карактер за сваки улазни карактер: -![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sr.png) +![Слика која приказује пример RNN генерисања речи 'HELLO'.](../../../../../translated_images/sr/rnn-generate.56c54afb52f9781d.png) Када генеришемо текст (током инференције), почињемо са неким **подстицајем** (prompt), који се прослеђује кроз RNN ћелије да би се генерисало његово интермедијарно стање, а затим из тог стања почиње генерисање. Генеришемо један карактер у исто време и прослеђујемо стање и генерисани карактер следећој RNN ћелији да генерише следећи, све док не генеришемо довољно карактера. diff --git a/translations/sr/lessons/5-NLP/18-Transformers/README.md b/translations/sr/lessons/5-NLP/18-Transformers/README.md index 1ca0d7c9..244831a3 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механизми пажње** пружају начин за одређивање тежине контекстуалног утицаја сваког улазног вектора на сваку предикцију излазног РНМ-а. Ово се имплементира стварањем пречица између међустојања улазног РНМ-а и излазног РНМ-а. На овај начин, када генеришемо излазни симбол yt, узимамо у обзир сва улазна скривена стања hi, са различитим тежинским коефицијентима αt,i. -![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sr.png) +![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png) > Енкодер-декодер модел са адитивним механизмом пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матрица пажње {αi,j} представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице: -![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sr.png) +![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png) > Фигура из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Следеће, потребно је ухватити неке обрасце унутар наше секвенце. Да бисмо то урадили, трансформери користе механизам **само-пажње**, који је у суштини пажња примењена на исту секвенцу као улаз и излаз. Примена само-пажње омогућава нам да узмемо у обзир **контекст** унутар реченице и видимо које речи су међусобно повезане. На пример, омогућава нам да видимо на које речи се односе кореференце, као што је *оно*, и такође узмемо у обзир контекст: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sr.png) +![](../../../../../translated_images/sr/CoreferenceResolution.861924d6d384a7d6.png) > Слика из [Google блога](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) је веома велика трансформер мрежа са више слојева, са 12 слојева за *BERT-base* и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Wikipedia + књиге) користећи ненадгледано учење (предвиђање маскираних речи у реченици). Током претходног тренинга, модел апсорбује значајне нивое разумевања језика, који се затим могу искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансфер учење**. -![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sr.png) +![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Слика [извор](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 019ec7ee..2f796c51 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизми пажње** пружају начин да се тежински одреди контекстуални утицај сваког улазног вектора на сваку излазну предикцију RNN-а. Ово се имплементира стварањем пречица између интермедијарних стања улазног RNN-а и излазног RNN-а. На овај начин, када генеришемо излазни симбол $y_t$, узимамо у обзир сва улазна скривена стања $h_i$, са различитим тежинским коефицијентима $\\alpha_{t,i}$.\n", "\n", - "![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sr.png) \n", + "![Слика која приказује енкодер/декодер модел са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png) \n", "*Енкодер-декодер модел са адитивним механизмом пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица пажње $\\{\\alpha_{i,j}\\}$ представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице:\n", "\n", - "![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sr.png) \n", + "![Слика која приказује пример поравнања пронађеног помоћу RNNsearch-50, преузета из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png) \n", "\n", "*Слика преузета из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) је веома велика трансформер мрежа са више слојева, са 12 слојева за *BERT-base*, и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Википедија + књиге) користећи несупервизирано учење (предвиђање маскираних речи у реченици). Током претходног тренинга, модел апсорбује значајан ниво разумевања језика, који се затим може искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансфер учење**.\n", "\n", - "![Слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sr.png)\n", + "![Слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Постоји много варијација трансформер архитектура, укључујући BERT, DistilBERT, BigBird, OpenGPT3 и друге, које се могу фино подесити. [HuggingFace пакет](https://github.com/huggingface/) пружа репозиторијум за тренирање многих од ових архитектура са PyTorch-ом.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 5de1af2c..3c1bf77d 100644 --- a/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механизми пажње** пружају начин да се тежински одреди контекстуални утицај сваког улазног вектора на сваку излазну предикцију РНН-а. Ово се имплементира стварањем пречица између међустојања улазног РНН-а и излазног РНН-а. На овај начин, приликом генерисања излазног симбола $y_t$, узимамо у обзир сва улазна скривена стања $h_i$, са различитим тежинским коефицијентима $\\alpha_{t,i}$. \n", "\n", - "![Слика која приказује модел енкодера/декодера са адитивним слојем пажње](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sr.png)\n", + "![Слика која приказује модел енкодера/декодера са адитивним слојем пажње](../../../../../translated_images/sr/encoder-decoder-attention.7a726296894fb567.png)\n", "*Модел енкодера-декодера са механизмом адитивне пажње у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитиран из [овог блога](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матрица пажње $\\{\\alpha_{i,j}\\}$ представља степен у којем одређене улазне речи утичу на генерисање одређене речи у излазној секвенци. Испод је пример такве матрице:\n", "\n", - "![Слика која приказује пример поравнања које је пронашао RNNsearch-50, преузето из Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sr.png)\n", + "![Слика која приказује пример поравнања које је пронашао RNNsearch-50, преузето из Bahdanau - arviz.org](../../../../../translated_images/sr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Слика преузета из [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Сл.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) је веома велики трансформер мрежни модел са више слојева, који има 12 слојева за *BERT-base* и 24 за *BERT-large*. Модел се прво претходно тренира на великом корпусу текстуалних података (Википедија + књиге) користећи несупервизирано учење (предвиђање маскираних речи у реченици). Током претходног тренирања, модел усваја значајан ниво разумевања језика, који се затим може искористити са другим скуповима података кроз фино подешавање. Овај процес се назива **трансферно учење**.\n", "\n", - "![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sr.png)\n", + "![слика са http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Постоји много варијација трансформер архитектура, укључујући BERT, DistilBERT, BigBird, OpenGPT3 и друге, које се могу фино подесити.\n", "\n", diff --git a/translations/sr/lessons/5-NLP/19-NER/README.md b/translations/sr/lessons/5-NLP/19-NER/README.md index 99b475dd..4724e7b1 100644 --- a/translations/sr/lessons/5-NLP/19-NER/README.md +++ b/translations/sr/lessons/5-NLP/19-NER/README.md @@ -56,7 +56,7 @@ NER модели су у суштини **модели за класификац Пошто треба да изградимо један-на-један кореспонденцију између токена и класа, можемо обучити десни **многи-на-многе** модел неуронске мреже из ове слике: -![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sr.jpg) +![Слика која приказује уобичајене обрасце рекурентних неуронских мрежа.](../../../../../translated_images/sr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Слика из [овог блога](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) аутора [Андреја Карпатија](http://karpathy.github.io/). Модели за класификацију токена у NER-у одговарају десној архитектури мреже на овој слици.* diff --git a/translations/sr/lessons/5-NLP/README.md b/translations/sr/lessons/5-NLP/README.md index bfc6a4b1..4a106331 100644 --- a/translations/sr/lessons/5-NLP/README.md +++ b/translations/sr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обрада природног језика -![Резиме NLP задатака у скици](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.sr.png) +![Резиме NLP задатака у скици](../../../../translated_images/sr/ai-nlp.b22dcb8ca4707cea.png) У овом делу ћемо се фокусирати на коришћење неуронских мрежа за решавање задатака везаних за **обраду природног језика (NLP)**. Постоји много NLP проблема које желимо да рачунари могу да реше: diff --git a/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md index d4c71312..ed5d175c 100644 --- a/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Након отварања модела, бићете пребачени на главни екран NetLogo-а. Ево примера модела који описује популацију вукова и оваца, уз ограничене ресурсе (трава). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.sr.png) +![NetLogo Main Screen](../../../../../translated_images/sr/NetLogo-Main.32653711ec1a01b3.png) > Снимак екрана, Дмитриј Сошњиков diff --git a/translations/sr/lessons/README.md b/translations/sr/lessons/README.md index 903d97d9..10c0979c 100644 --- a/translations/sr/lessons/README.md +++ b/translations/sr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Преглед -![Преглед у виду цртежа](../../../translated_images/ai-overview.0857791951d19500.sr.png) +![Преглед у виду цртежа](../../../translated_images/sr/ai-overview.0857791951d19500.png) > Цртеж белешке од [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sr/lessons/X-Extras/X1-MultiModal/README.md b/translations/sr/lessons/X-Extras/X1-MultiModal/README.md index af9f842f..b69104e1 100644 --- a/translations/sr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Главна идеја CLIP-а је могућност поређења текстуалних упита са сликом и одређивање колико добро слика одговара упиту. -![CLIP Архитектура](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.sr.png) +![CLIP Архитектура](../../../../../translated_images/sr/clip-arch.b3dbf20b4e8ed8be.png) > *Слика из [овог блога](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP модел/библиотека доступан је на [OpenAI GitHub]( Претпоставимо да треба да класификујемо слике, рецимо, између мачака, паса и људи. У том случају, можемо моделу дати слику и низ текстуалних упита: "*слика мачке*", "*слика пса*", "*слика човека*". У резултујућем вектору од 3 вероватноће само треба изабрати индекс са највишом вредношћу. -![CLIP за класификацију слика](../../../../../translated_images/clip-class.3af42ef0b2b19369.sr.png) +![CLIP за класификацију слика](../../../../../translated_images/sr/clip-class.3af42ef0b2b19369.png) > *Слика из [овог блога](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP се такође може користити за **генерисање Једна од важних разлика између VQGAN-а и традиционалног GAN-а је та што други може произвести пристојну слику из било ког улазног вектора, док VQGAN вероватно производи слику која није кохерентна. Због тога је потребно додатно усмеравати процес креирања слике, а то се може урадити помоћу CLIP-а. -![VQGAN+CLIP Архитектура](../../../../../translated_images/vqgan.5027fe05051dfa31.sr.png) +![VQGAN+CLIP Архитектура](../../../../../translated_images/sr/vqgan.5027fe05051dfa31.png) Да бисмо генерисали слику која одговара текстуалном упиту, почињемо са неким насумичним вектором кодирања који се прослеђује кроз VQGAN да би се произвела слика. Затим се CLIP користи за креирање функције губитка која показује колико добро слика одговара текстуалном упиту. Циљ је затим минимизовати овај губитак, користећи уназадно ширење да би се прилагодили параметри улазног вектора. Одлична библиотека која имплементира VQGAN+CLIP је [Pixray](http://github.com/pixray/pixray). -![Слика коју је произвео Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.sr.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.sr.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.sr.png) +![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Слика генерисана на основу упита *акварелски портрет младог мушког професора књижевности са књигом* | Слика генерисана на основу упита *уљани портрет младе женске професорке рачунарских наука са рачунаром* | Слика генерисана на основу упита *уљани портрет старог мушког професора математике испред табле* @@ -75,7 +75,7 @@ DALL-E је верзија GPT-3 обучена за генерисање сли Главна разлика између DALL-E 1 и 2 је у томе што други генерише реалистичније слике и уметничка дела. Примери генерисања слика са DALL-E: -![Слика коју је произвео Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.sr.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.sr.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.sr.png) +![Слика коју је произвео Pixray](../../../../../translated_images/sr/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Слика коју је произвео Pixray](../../../../../translated_images/sr/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Слика генерисана на основу упита *акварелски портрет младог мушког професора књижевности са књигом* | Слика генерисана на основу упита *уљани портрет младе женске професорке рачунарских наука са рачунаром* | Слика генерисана на основу упита *уљани портрет старог мушког професора математике испред табле* diff --git a/translations/sv/README.md b/translations/sv/README.md index 70ec31aa..6875af55 100644 --- a/translations/sv/README.md +++ b/translations/sv/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificiell intelligens för nybörjare - Ett läroprogram -|![Sketchnote av @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.sv.png)| +|![Sketchnote av @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sv/ai-overview.0857791951d19500.png)| |:---:| | AI För Nybörjare - _Sketchnote av [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sv/lessons/1-Intro/README.md b/translations/sv/lessons/1-Intro/README.md index 291bfbaa..b8b3b5f4 100644 --- a/translations/sv/lessons/1-Intro/README.md +++ b/translations/sv/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion till AI -![Sammanfattning av innehållet i Introduktion till AI i en skiss](../../../../translated_images/ai-intro.bf28d1ac4235881c.sv.png) +![Sammanfattning av innehållet i Introduktion till AI i en skiss](../../../../translated_images/sv/ai-intro.bf28d1ac4235881c.png) > Skiss av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ursprungligen uppfanns datorer av [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) för att arbeta med siffror enligt en väl definierad procedur - en algoritm. Moderna datorer, även om de är betydligt mer avancerade än den ursprungliga modellen som föreslogs på 1800-talet, följer fortfarande samma idé om kontrollerade beräkningar. Därför är det möjligt att programmera en dator att göra något om vi vet den exakta sekvensen av steg som behövs för att uppnå målet. -![Foto av en person](../../../../translated_images/dsh_age.d212a30d4e54fb5f.sv.png) +![Foto av en person](../../../../translated_images/sv/dsh_age.d212a30d4e54fb5f.png) > Foto av [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ För mer information, se **[Artificiell General Intelligens](https://en.wikipedi Ett av problemen med att hantera termen **[intelligens](https://en.wikipedia.org/wiki/Intelligence)** är att det inte finns någon tydlig definition av termen. Man kan argumentera för att intelligens är kopplad till **abstrakt tänkande** eller **självmedvetenhet**, men vi kan inte definiera det ordentligt. -![Foto av en katt](../../../../translated_images/photo-cat.8c8e8fb760ffe457.sv.jpg) +![Foto av en katt](../../../../translated_images/sv/photo-cat.8c8e8fb760ffe457.jpg) > [Foto](https://unsplash.com/photos/75715CVEJhI) av [Amber Kipp](https://unsplash.com/@sadmax) från Unsplash @@ -98,13 +98,13 @@ Alternativt kan vi försöka modellera de enklaste elementen i vår hjärna – > | Vad sägs om ML? | | > |--------------|-----------| -> | En del av artificiell intelligens som bygger på att datorn lär sig att lösa ett problem baserat på viss data kallas **Machine Learning**. Vi kommer inte att behandla klassisk maskininlärning i denna kurs - vi hänvisar dig till en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) läroplan. | ![ML för nybörjare](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.sv.png) | +> | En del av artificiell intelligens som bygger på att datorn lär sig att lösa ett problem baserat på viss data kallas **Machine Learning**. Vi kommer inte att behandla klassisk maskininlärning i denna kurs - vi hänvisar dig till en separat [Machine Learning for Beginners](http://aka.ms/ml-beginners) läroplan. | ![ML för nybörjare](../../../../translated_images/sv/ml-for-beginners.9e4fed176fd5817d.png) | ## En kort historia om AI Artificiell intelligens startade som ett område i mitten av 1900-talet. Ursprungligen var symboliskt resonemang en dominerande ansats, och det ledde till ett antal viktiga framgångar, såsom expertsystem – datorprogram som kunde agera som en expert inom vissa begränsade problemområden. Men det blev snart klart att en sådan ansats inte skalar bra. Att extrahera kunskap från en expert, representera den i en dator och hålla den kunskapsbasen korrekt visade sig vara en mycket komplex uppgift och för dyr för att vara praktisk i många fall. Detta ledde till den så kallade [AI-vintern](https://en.wikipedia.org/wiki/AI_winter) på 1970-talet. -Kort historia om AI +Kort historia om AI > Bild av [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ På samma sätt kan vi se hur ansatsen för att skapa "talande program" (som kan * Moderna assistenter, såsom Cortana, Siri eller Google Assistant, är alla hybridsystem som använder neurala nätverk för att konvertera tal till text och känna igen vår avsikt, och sedan använder vissa resonemang eller explicita algoritmer för att utföra nödvändiga åtgärder. * I framtiden kan vi förvänta oss en komplett neuralbaserad modell som hanterar dialoger själv. Den senaste GPT- och [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)-familjen av neurala nätverk visar stor framgång i detta. -Turing-testets utveckling +Turing-testets utveckling > Bild av Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) av [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Senaste AI-forskningen diff --git a/translations/sv/lessons/2-Symbolic/Animals.ipynb b/translations/sv/lessons/2-Symbolic/Animals.ipynb index 0e945972..7bd622de 100644 --- a/translations/sv/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sv/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "I detta exempel kommer vi att implementera ett enkelt kunskapsbaserat system för att identifiera ett djur baserat på vissa fysiska egenskaper. Systemet kan representeras av följande AND-OR-träd (detta är en del av hela trädet, vi kan enkelt lägga till fler regler):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sv.png)\n" + "![](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/sv/lessons/2-Symbolic/README.md b/translations/sv/lessons/2-Symbolic/README.md index 7ac007b4..d3189272 100644 --- a/translations/sv/lessons/2-Symbolic/README.md +++ b/translations/sv/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kunskapsrepresentation och Expertsystem -![Sammanfattning av Symbolisk AI-innehåll](../../../../translated_images/ai-symbolic.715a30cb610411a6.sv.png) +![Sammanfattning av Symbolisk AI-innehåll](../../../../translated_images/sv/ai-symbolic.715a30cb610411a6.png) > Sketchnote av [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Oftast definierar vi inte strikt vad kunskap är, utan vi relaterar den till and Således är problemet med **kunskapsrepresentation** att hitta ett effektivt sätt att representera kunskap i en dator i form av data, för att göra den automatiskt användbar. Detta kan ses som ett spektrum: -![Spektrum för kunskapsrepresentation](../../../../translated_images/knowledge-spectrum.b60df631852c0217.sv.png) +![Spektrum för kunskapsrepresentation](../../../../translated_images/sv/knowledge-spectrum.b60df631852c0217.png) > Bild av [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blocksyntax | Indragning | | | En av de tidiga framgångarna med symbolisk AI var de så kallade **expertsystemen** - datorsystem som designades för att agera som experter inom ett begränsat problemområde. De baserades på en **kunskapsbas** som extraherades från en eller flera mänskliga experter och innehöll en **slutsatsmotor** som utförde resonerande ovanpå den. -![Mänsklig arkitektur](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.sv.png) | ![Kunskapsbaserat system](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.sv.png) +![Mänsklig arkitektur](../../../../translated_images/sv/arch-human.5d4d35f1bba3ab1c.png) | ![Kunskapsbaserat system](../../../../translated_images/sv/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Förenklad struktur av ett mänskligt nervsystem | Arkitektur av ett kunskapsbaserat system @@ -106,7 +106,7 @@ Expertsystem är byggda som det mänskliga resoneringssystemet, som innehåller Som ett exempel, låt oss överväga följande expertsystem för att bestämma ett djur baserat på dess fysiska egenskaper: -![AND-OR-träd](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sv.png) +![AND-OR-träd](../../../../translated_images/sv/AND-OR-Tree.5592d2c70187f283.png) > Bild av [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 8c163c93..0daadaef 100644 --- a/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sv/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Om vi har fler än 2 klasser kommer softmax att normalisera sannolikheterna över alla. Här är ett diagram över nätverksarkitekturen som utför MNIST-sifferklassificering:\n", "\n", - "![MNIST-klassificerare](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.sv.png)\n" + "![MNIST-klassificerare](../../../../../translated_images/sv/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Låg träningsförlust - modellen kan approximera träningsdata väl eftersom den har tillräcklig uttryckskraft.\n", "* Valideringsförlust kan vara mycket högre än träningsförlust och kan börja öka under träningen - detta beror på att modellen \"memorerar\" träningspunkterna och förlorar \"helhetsbilden\".\n", "\n", - "![Överanpassning](../../../../../translated_images/overfit.a0bd57f717c15769.sv.png)\n", + "![Överanpassning](../../../../../translated_images/sv/overfit.a0bd57f717c15769.png)\n", "\n", "> På denna bild står `x` för träningsdata, `o` - valideringsdata. Vänster - linjär modell (enlager), den approximera datans natur ganska väl. Höger - överanpassad modell, modellen approximera träningsdata perfekt, men slutar vara meningsfull för annan data (valideringsfelet är mycket högt).\n" ] diff --git a/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md index 66d52cf3..d32d8f44 100644 --- a/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sv/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Efter att ha bemästrat ramverken, låt oss repetera begreppet överanpassning. Tänk på följande problem med att approximera 5 punkter (representerade av `x` på graferna nedan): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.sv.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.sv.jpg) +![linear](../../../../../translated_images/sv/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sv/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linjär modell, 2 parametrar** | **Icke-linjär modell, 7 parametrar** Träningsfel = 5.3 | Träningsfel = 0 @@ -79,7 +79,7 @@ Det är mycket viktigt att hitta en korrekt balans mellan modellens komplexitet Som du kan se från grafen ovan kan överanpassning upptäckas genom ett mycket lågt träningsfel och ett högt valideringsfel. Normalt under träning ser vi både tränings- och valideringsfel minska, och sedan vid någon punkt kan valideringsfelet sluta minska och börja öka. Detta är ett tecken på överanpassning och en indikation på att vi förmodligen bör sluta träna vid denna punkt (eller åtminstone spara en ögonblicksbild av modellen). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.sv.png) +![overfitting](../../../../../translated_images/sv/Overfitting.408ad91cd90b4371.png) ## Hur man förhindrar överanpassning diff --git a/translations/sv/lessons/3-NeuralNetworks/README.md b/translations/sv/lessons/3-NeuralNetworks/README.md index 29475c9c..254efd7b 100644 --- a/translations/sv/lessons/3-NeuralNetworks/README.md +++ b/translations/sv/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion till neurala nätverk -![Sammanfattning av innehållet i Introduktion till neurala nätverk i en skiss](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.sv.png) +![Sammanfattning av innehållet i Introduktion till neurala nätverk i en skiss](../../../../translated_images/sv/ai-neuralnetworks.1c687ae40bc86e83.png) Som vi diskuterade i introduktionen är ett av sätten att uppnå intelligens att träna en **datormodell** eller en **artificiell hjärna**. Sedan mitten av 1900-talet har forskare testat olika matematiska modeller, och på senare år har denna riktning visat sig vara mycket framgångsrik. Sådana matematiska modeller av hjärnan kallas **neurala nätverk**. @@ -36,13 +36,13 @@ I denna kursplan kommer vi endast att fokusera på modeller för neurala nätver Från biologin vet vi att vår hjärna består av nervceller (neuroner), var och en med flera "ingångar" (dendriter) och en enda "utgång" (axon). Både dendriter och axoner kan leda elektriska signaler, och kopplingarna mellan dem — kända som synapser — kan uppvisa varierande grad av ledningsförmåga, som regleras av neurotransmittorer. -![Modell av en neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.sv.jpg) | ![Modell av en neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.sv.png) +![Modell av en neuron](../../../../translated_images/sv/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modell av en neuron](../../../../translated_images/sv/artneuron.1a5daa88d20ebe6f.png) ----|---- Verklig neuron *([Bild](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) från Wikipedia)* | Artificiell neuron *(Bild av författaren)* Således innehåller den enklaste matematiska modellen av en neuron flera ingångar X1, ..., XN och en utgång Y, samt en serie vikter W1, ..., WN. En utgång beräknas som: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) där f är någon icke-linjär **aktiveringsfunktion**. diff --git a/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md index cc3d3bad..45ea061f 100644 --- a/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sv/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ I vår [OpenCV Notebook](OpenCV.ipynb) ger vi några exempel på när datorseend * **Förbehandling av ett fotografi av en Braille-bok**. Vi fokuserar på hur vi kan använda tröskling, funktionsdetektion, perspektivtransformation och NumPy-manipulationer för att separera individuella Braille-symboler för vidare klassificering av ett neuralt nätverk. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.sv.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.sv.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.sv.png) +![Braille Image](../../../../../translated_images/sv/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/sv/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/sv/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Bild från [OpenCV.ipynb](OpenCV.ipynb) * **Detektera rörelse i video med hjälp av bildruteskillnad**. Om kameran är fast, bör bildrutor från kameraflödet vara ganska lika varandra. Eftersom bildrutor representeras som arrayer, kan vi genom att subtrahera dessa arrayer för två efterföljande bildrutor få pixeldifferensen, som bör vara låg för statiska bildrutor och bli högre när det finns betydande rörelse i bilden. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.sv.png) +![Image of video frames and frame differences](../../../../../translated_images/sv/frame-difference.706f805491a0883c.png) > Bild från [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ I vår [OpenCV Notebook](OpenCV.ipynb) ger vi några exempel på när datorseend - **Tätt optiskt flöde** beräknar vektorfältet som visar för varje pixel var den rör sig. - **Gles optiskt flöde** baseras på att ta några distinkta funktioner i bilden (t.ex. kanter) och bygga deras bana från bildruta till bildruta. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.sv.png) +![Image of Optical Flow](../../../../../translated_images/sv/optical.1f4a94464579a83a.png) > Bild från [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 97131683..8ec57129 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 är ett nätverk som uppnådde 92,7% noggrannhet i ImageNet top-5 klassificering år 2014. Det har följande lagerstruktur: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sv.jpg) +![ImageNet Layers](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.jpg) Som du kan se följer VGG en traditionell pyramidarkitektur, vilket är en sekvens av konvolutions- och poolinglager. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sv.jpg) +![ImageNet Pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.jpg) > Bild från [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 8e225f5f..fe4a422b 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Således skulle en typisk CNN innehålla flera konvolutionella lager, med pooling-lager mellan dem för att minska bildens dimensioner. Vi skulle också öka antalet filter, eftersom mönstren blir mer avancerade – det finns fler möjliga intressanta kombinationer som vi behöver leta efter.\n", "\n", - "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sv.png)\n", + "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grund av de minskande rumsliga dimensionerna och de ökande funktions-/filterdimensionerna kallas denna arkitektur också för **pyramidarkitektur**.\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index c0d9d364..f55f02be 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Således skulle en typisk CNN ha flera konvolutionella lager, med pooling-lager mellan dem för att minska bildens dimensioner. Vi skulle också öka antalet filter, eftersom mönstren blir mer avancerade – det finns fler möjliga intressanta kombinationer som vi behöver leta efter.\n", "\n", - "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sv.png)\n", + "![En bild som visar flera konvolutionella lager med pooling-lager.](../../../../../translated_images/sv/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "På grund av minskande rumsliga dimensioner och ökande funktions-/filterdimensioner kallas denna arkitektur också för **pyramidarkitektur**.\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md index 7c768db2..bc7dd8ce 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ I verkligheten vill vi kunna känna igen objekt på en bild oavsett deras exakta För att extrahera mönster kommer vi att använda begreppet **konvolutionella filter**. Som du vet representeras en bild av en 2D-matris eller en 3D-tensor med färgdjup. Att applicera ett filter innebär att vi tar en relativt liten **filterkärna**-matris, och för varje pixel i den ursprungliga bilden beräknar vi det viktade medelvärdet med angränsande punkter. Vi kan se detta som ett litet fönster som glider över hela bilden och jämnar ut alla pixlar enligt vikterna i filterkärnan. -![Vertikalt kantfilter](../../../../../translated_images/filter-vert.b7148390ca0bc356.sv.png) | ![Horisontellt kantfilter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.sv.png) +![Vertikalt kantfilter](../../../../../translated_images/sv/filter-vert.b7148390ca0bc356.png) | ![Horisontellt kantfilter](../../../../../translated_images/sv/filter-horiz.59b80ed4feb946ef.png) ----|---- > Bild av Dmitry Soshnikov @@ -38,7 +38,7 @@ Så här fungerar CNN baserat på följande viktiga idéer: * Vi kan designa nätverket så att filtren tränas automatiskt * Vi kan använda samma metod för att hitta mönster i hög-nivå egenskaper, inte bara i den ursprungliga bilden. Således arbetar CNN med en hierarki av egenskaper, från låg-nivå pixelkombinationer till högre nivå kombinationer av bilddelar. -![Hierarkisk egenskapsutvinning](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.sv.png) +![Hierarkisk egenskapsutvinning](../../../../../translated_images/sv/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Bild från [en artikel av Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baserad på [deras forskning](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ De flesta CNN som används för bildbehandling följer en så kallad pyramidarki Som exempel, låt oss titta på arkitekturen för VGG-16, ett nätverk som uppnådde 92,7% noggrannhet i ImageNet's topp-5 klassificering år 2014: -![ImageNet-lager](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sv.jpg) +![ImageNet-lager](../../../../../translated_images/sv/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet-pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sv.jpg) +![ImageNet-pyramid](../../../../../translated_images/sv/vgg-16-arch.64ff2137f50dd49f.jpg) > Bild från [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 839059b2..1403f161 100644 --- a/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sv/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Du behöver träna ett konvolutionellt neuralt nätverk för att klassificera ol Vi kommer att använda [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), som innehåller bilder av 37 olika raser av hundar och katter. -![Datasetet vi kommer att arbeta med](../../../../../../translated_images/data.50b2a9d5484bdbf0.sv.png) +![Datasetet vi kommer att arbeta med](../../../../../../translated_images/sv/data.50b2a9d5484bdbf0.png) För att ladda ner datasetet, använd följande kodsnutt: diff --git a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 181bc346..55c7975b 100644 --- a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "För att visualisera den ideala katten börjar vi med en slumpmässig brusbild och försöker använda gradientnedstigningsoptimeringstekniken för att justera bilden så att nätverket känner igen en katt.\n", "\n", - "![Optimeringsloop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sv.png)\n", + "![Optimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Här är vår startbild:\n" ] diff --git a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md index 7ceda4e3..8bb8ccf3 100644 --- a/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sv/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Både Keras och PyTorch innehåller funktioner för att enkelt ladda förtränad Här är exempel på funktioner som extraherats från en bild av en katt med VGG-16-nätverket: -![Funktioner extraherade av VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.sv.png) +![Funktioner extraherade av VGG-16](../../../../../translated_images/sv/features.6291f9c7ba3a0b95.png) ## Dataset för katter och hundar @@ -48,19 +48,19 @@ Ett förtränat neuralt nätverk innehåller olika mönster i sitt *"hjärna"*, En metod vi kan använda är att börja med en slumpmässig bild och sedan försöka använda **gradient descent-optimering** för att justera bilden så att nätverket börjar tro att det är en katt. -![Bildoptimeringsloop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sv.png) +![Bildoptimeringsloop](../../../../../translated_images/sv/ideal-cat-loop.999fbb8ff306e044.png) Om vi gör detta kommer vi dock att få något som liknar slumpmässigt brus. Detta beror på att *det finns många sätt att få nätverket att tro att inmatningsbilden är en katt*, inklusive sådana som inte är visuellt meningsfulla. Även om dessa bilder innehåller många mönster som är typiska för en katt, finns det inget som begränsar dem till att vara visuellt distinkta. För att förbättra resultatet kan vi lägga till en annan term i förlustfunktionen, kallad **variation loss**. Det är en metrik som visar hur lika angränsande pixlar i bilden är. Genom att minimera variation loss blir bilden mjukare och bruset försvinner - vilket avslöjar mer visuellt tilltalande mönster. Här är exempel på sådana "ideala" bilder som klassificeras som katt och zebra med hög sannolikhet: -![Ideal katt](../../../../../translated_images/ideal-cat.203dd4597643d6b0.sv.png) | ![Ideal zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.sv.png) +![Ideal katt](../../../../../translated_images/sv/ideal-cat.203dd4597643d6b0.png) | ![Ideal zebra](../../../../../translated_images/sv/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideal katt* | *Ideal zebra* En liknande metod kan användas för att utföra så kallade **adversarial attacks** på ett neuralt nätverk. Anta att vi vill lura ett neuralt nätverk och få en hund att se ut som en katt. Om vi tar en bild av en hund som nätverket känner igen som en hund, kan vi sedan justera den lite med gradient descent-optimering tills nätverket börjar klassificera den som en katt: -![Bild av en hund](../../../../../translated_images/original-dog.8f68a67d2fe0911f.sv.png) | ![Bild av en hund klassificerad som en katt](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.sv.png) +![Bild av en hund](../../../../../translated_images/sv/original-dog.8f68a67d2fe0911f.png) | ![Bild av en hund klassificerad som en katt](../../../../../translated_images/sv/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Originalbild av en hund* | *Bild av en hund klassificerad som en katt* diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 0a70d8a5..c3c54230 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Eftersom vi tränar autoencodern för att fånga så mycket information som möjligt från den ursprungliga bilden för en korrekt rekonstruktion, försöker nätverket hitta den bästa **inbäddningen** av inmatningsbilder för att fånga meningen.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sv.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Bild från [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index ab02f75b..ed86aa0b 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Eftersom vi tränar autoencodern att fånga så mycket information som möjligt från den ursprungliga bilden för att kunna återskapa den korrekt, försöker nätverket hitta den bästa **inbäddningen** av inmatningsbilder för att fånga dess innebörd.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sv.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Bild från [Keras blogg](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md index b73f3925..68c030a3 100644 --- a/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sv/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Men vi kanske vill använda rå (omärkt) data för att träna CNN-funktionsextr Eftersom vi tränar en autoencoder för att fånga så mycket information som möjligt från den ursprungliga bilden för en korrekt rekonstruktion, försöker nätverket hitta den bästa **embedding** av inmatningsbilder för att fånga dess betydelse. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sv.jpg) +![AutoEncoder Diagram](../../../../../translated_images/sv/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Bild från [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md index 0815b837..b064aced 100644 --- a/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sv/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ De bildklassificeringsmodeller vi har arbetat med hittills tar en bild och produ ## [Quiz före föreläsningen](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Objektigenkänning](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.sv.png) +![Objektigenkänning](../../../../../translated_images/sv/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Bild från [YOLO v2 webbplats](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Om vi ville hitta en katt på en bild, skulle en mycket naiv metod för objektig 2. Kör bildklassificering på varje ruta. 3. De rutor som resulterar i tillräckligt hög aktivering kan anses innehålla det aktuella objektet. -![Naiv objektigenkänning](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.sv.png) +![Naiv objektigenkänning](../../../../../translated_images/sv/naive-detection.e7f1ba220ccd08c6.png) > *Bild från [Övningsanteckningsbok](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Du kan stöta på följande dataset för denna uppgift: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klasser * [COCO](http://cocodataset.org/#home) - Vanliga objekt i kontext. 80 klasser, begränsningsrutor och segmenteringsmasker -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.sv.jpg) +![COCO](../../../../../translated_images/sv/coco-examples.71bc60380fa6cceb.jpg) ## Mätvärden för objektigenkänning @@ -50,7 +50,7 @@ Du kan stöta på följande dataset för denna uppgift: Medan det är enkelt att mäta hur väl algoritmen presterar för bildklassificering, behöver vi för objektigenkänning mäta både korrektheten av klassen och precisionen av den förutsagda begränsningsrutans position. För det senare använder vi den så kallade **Intersection over Union** (IoU), som mäter hur väl två rutor (eller två godtyckliga områden) överlappar. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.sv.png) +![IoU](../../../../../translated_images/sv/iou_equation.9a4751d40fff4e11.png) > *Figur 2 från [denna utmärkta bloggpost om IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Det finns två breda klasser av algoritmer för objektigenkänning: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) använder [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) för att generera en hierarkisk struktur av ROI-regioner, som sedan passerar genom CNN-funktionsextraktorer och SVM-klassificerare för att bestämma objektklassen, och linjär regression för att bestämma *begränsningsrutans* koordinater. [Officiell artikel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.sv.png) +![RCNN](../../../../../translated_images/sv/rcnn1.cae407020dfb1d1f.png) > *Bild från van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.sv.png) +![RCNN-1](../../../../../translated_images/sv/rcnn2.2d9530bb83516484.png) > *Bilder från [denna blogg](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Det finns två breda klasser av algoritmer för objektigenkänning: Denna metod liknar R-CNN, men regioner definieras efter att konvolutionslager har applicerats. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.sv.png) +![FRCNN](../../../../../translated_images/sv/f-rcnn.3cda6d9bb4188875.png) > Bild från [den officiella artikeln](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Denna metod liknar R-CNN, men regioner definieras efter att konvolutionslager ha Huvudidén med denna metod är att använda ett neuralt nätverk för att förutsäga ROI - så kallat *Region Proposal Network*. [Artikel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.sv.png) +![FasterRCNN](../../../../../translated_images/sv/faster-rcnn.8d46c099b87ef30a.png) > Bild från [den officiella artikeln](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Denna algoritm är ännu snabbare än Faster R-CNN. Huvudidén är följande: 2. Funktionerna bearbetas av **Position-Sensitive Score Map**. Varje objekt från $C$ klasser delas upp i $k\times k$ regioner, och vi tränar för att förutsäga delar av objekt. 3. För varje del från $k\times k$ regioner röstar alla nätverk för objektklasser, och den objektklass med flest röster väljs. -![r-fcn bild](../../../../../translated_images/r-fcn.13eb88158b99a3da.sv.png) +![r-fcn bild](../../../../../translated_images/sv/r-fcn.13eb88158b99a3da.png) > Bild från [officiell artikel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO är en realtidsalgoritm med ett enda pass. Huvudidén är följande: * Bilden delas upp i $S\times S$ regioner. * För varje region förutsäger **CNN** $n$ möjliga objekt, *begränsningsrutans* koordinater och *confidence*=*sannolikhet* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.sv.png) + ![YOLO](../../../../../translated_images/sv/yolo.a2648ec82ee8bb4e.png) > Bild från [officiell artikel](https://arxiv.org/abs/1506.02640) diff --git a/translations/sv/lessons/4-ComputerVision/README.md b/translations/sv/lessons/4-ComputerVision/README.md index e6205334..a82d9515 100644 --- a/translations/sv/lessons/4-ComputerVision/README.md +++ b/translations/sv/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Datorseende -![Sammanfattning av innehållet om datorseende i en skiss](../../../../translated_images/ai-computervision.6506ebebac3fbf76.sv.png) +![Sammanfattning av innehållet om datorseende i en skiss](../../../../translated_images/sv/ai-computervision.6506ebebac3fbf76.png) I den här delen kommer vi att lära oss om: diff --git a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index f098fc83..ea1ed663 100644 --- a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Påsmodell** (BoW) vektorrepresentation är den mest använda traditionella vektorrepresentationen. Varje ord är kopplat till ett vektorindex, och varje element i vektorn innehåller antalet förekomster av ett ord i ett givet dokument.\n", "\n", - "![Bild som visar hur en påsmodell-vektorrepresentation lagras i minnet.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sv.png) \n", + "![Bild som visar hur en påsmodell-vektorrepresentation lagras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Du kan också tänka på BoW som en summa av alla enskilda one-hot-kodade vektorer för individuella ord i texten.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 4dd81a5f..0e9f92da 100644 --- a/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sv/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektorrepresentation är den mest lättförståeliga traditionella vektorrepresentationen. Varje ord är kopplat till ett vektorindex, och ett element i vektorn innehåller antalet förekomster av varje ord i ett givet dokument.\n", "\n", - "![Bild som visar hur en bag-of-words vektorrepresentation representeras i minnet.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sv.png) \n", + "![Bild som visar hur en bag-of-words vektorrepresentation representeras i minnet.](../../../../../translated_images/sv/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Du kan också tänka på BoW som en summa av alla enskilt one-hot-kodade vektorer för individuella ord i texten.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 8a771849..3f11b772 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Genom att använda inbäddningslagret som det första lagret i vårt nätverk kan vi byta från bag-of-words till **embedding bag**-modellen, där vi först konverterar varje ord i vår text till motsvarande inbäddning och sedan beräknar någon aggregeringsfunktion över alla dessa inbäddningar, såsom `sum`, `average` eller `max`.\n", "\n", - "![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sv.png)\n", + "![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vårt klassificerande neurala nätverk kommer att börja med ett inbäddningslager, sedan ett aggregeringslager och en linjär klassificerare ovanpå det:\n" ] @@ -176,7 +176,7 @@ "\n", "I den tidigare arkitekturen behövde vi fylla ut alla sekvenser till samma längd för att passa in dem i en minibatch. Detta är inte det mest effektiva sättet att representera sekvenser med variabel längd - ett annat tillvägagångssätt skulle vara att använda en **offset**-vektor, som innehåller offset för alla sekvenser lagrade i en stor vektor.\n", "\n", - "![Bild som visar en offset-sekvensrepresentation](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.sv.png)\n", + "![Bild som visar en offset-sekvensrepresentation](../../../../../translated_images/sv/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: På bilden ovan visar vi en sekvens av tecken, men i vårt exempel arbetar vi med sekvenser av ord. Principen för att representera sekvenser med en offset-vektor förblir dock densamma.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord.\n", "\n", - "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sv.png)\n", + "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "För att experimentera med Word2Vec-inbäddningar förtränade på Google News-datasetet kan vi använda **gensim**-biblioteket. Nedan hittar vi de ord som är mest lik 'neural'.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 05cf39bd..a81129d4 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sv/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Genom att använda ett embedding-lager som det första lagret i vårt nätverk kan vi byta från bag-of-words till en **embedding bag**-modell, där vi först konverterar varje ord i vår text till motsvarande embedding och sedan beräknar någon aggregeringsfunktion över alla dessa embeddings, såsom `sum`, `average` eller `max`.\n", "\n", - "![Bild som visar en embedding-klassificerare för fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sv.png)\n", + "![Bild som visar en embedding-klassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Vårt klassificeringsnätverk består av följande lager:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord.\n", "\n", - "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sv.png)\n", + "![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "För att experimentera med Word2Vec-inbäddningen förtränad på Google News-datasetet kan vi använda **gensim**-biblioteket. Nedan hittar vi de ord som är mest liknande 'neural'.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/14-Embeddings/README.md b/translations/sv/lessons/5-NLP/14-Embeddings/README.md index 5143fbed..a74a66d4 100644 --- a/translations/sv/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sv/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Så, inbäddningslagret skulle ta ett ord som indata och producera en utdata-vek Genom att använda ett inbäddningslager som det första lagret i vårt klassificeringsnätverk kan vi byta från en bag-of-words till en **embedding bag**-modell, där vi först konverterar varje ord i vår text till motsvarande inbäddning och sedan beräknar någon aggregeringsfunktion över alla dessa inbäddningar, såsom `sum`, `average` eller `max`. -![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sv.png) +![Bild som visar en inbäddningsklassificerare för fem sekvensord.](../../../../../translated_images/sv/embedding-classifier-example.b77f021a7ee67eee.png) > Bild av författaren @@ -40,7 +40,7 @@ För att göra detta behöver vi förträna vår inbäddningsmodell på en stor CBoW är snabbare, medan skip-gram är långsammare men gör ett bättre jobb med att representera sällsynta ord. -![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sv.png) +![Bild som visar både CBoW- och Skip-Gram-algoritmer för att konvertera ord till vektorer.](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Bild från [denna artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md index 7bfbf69a..98c48798 100644 --- a/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sv/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ I våra tidigare exempel använde vi förtränade semantiska inbäddningar, men * **Continuous Bag-of-Words** (CBoW), där vi förutspår den mittersta token $W_0$ i en sekvens av tokens $W_{-N}$, ..., $W_N$. * **Skip-gram**, där vi förutspår en uppsättning närliggande tokens {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} från den mittersta token $W_0$. -![bild från artikel om att konvertera ord till vektorer](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sv.png) +![bild från artikel om att konvertera ord till vektorer](../../../../../translated_images/sv/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Bild från [denna artikel](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sv/lessons/5-NLP/16-RNN/README.md b/translations/sv/lessons/5-NLP/16-RNN/README.md index 68c09127..b4ba33ed 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/README.md +++ b/translations/sv/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ I tidigare avsnitt har vi använt rika semantiska representationer av text och e För att fånga betydelsen av en textsekvens behöver vi använda en annan neural nätverksarkitektur, som kallas för ett **rekurrent neuralt nätverk**, eller RNN. I RNN skickar vi vår mening genom nätverket en symbol i taget, och nätverket producerar ett **tillstånd**, som vi sedan skickar tillbaka till nätverket tillsammans med nästa symbol. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.sv.png) +![RNN](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.png) > Bild av författaren @@ -61,7 +61,7 @@ Vi har diskuterat rekurrenta nätverk som arbetar i en riktning, från början a Ett rekurrent nätverk, antingen enkelriktat eller bidirektionellt, fångar vissa mönster inom en sekvens och kan lagra dem i en tillståndsvektor eller skicka dem till utgången. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager ovanpå det första för att fånga högre nivåmönster och bygga från lågnivåmönster som extraherats av det första lagret. Detta leder oss till begreppet **flerskiktat RNN**, som består av två eller fler rekurrenta nätverk, där utgången från det föregående lagret skickas till nästa lager som inmatning. -![Bild som visar ett flerskiktat Long Short Term Memory-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sv.jpg) +![Bild som visar ett flerskiktat Long Short Term Memory-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López* diff --git a/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b1a45d75..d01ecce4 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Ett rekurrent nätverk, oavsett om det är enkelriktat eller bidirektionellt, fångar vissa mönster inom en sekvens och kan lagra dem i en tillståndsvektor eller skicka dem vidare till utdata. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager ovanpå det första för att fånga mönster på högre nivå, baserat på låg-nivå mönster som det första lagret har extraherat. Detta leder oss till begreppet **flerskiktad RNN**, som består av två eller fler rekurrenta nätverk, där utdata från det föregående lagret skickas som indata till nästa lager.\n", "\n", - "![Bild som visar en flerskiktad lång-korttidsminne-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sv.jpg)\n", + "![Bild som visar en flerskiktad lång-korttidsminne-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López*\n", "\n", diff --git a/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb index 540d369d..ee56c208 100644 --- a/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sv/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "För att fånga betydelsen av en textsekvens kommer vi att använda en neural nätverksarkitektur som kallas **rekurrenta neurala nätverk**, eller RNN. När vi använder en RNN skickar vi vår mening genom nätverket en token i taget, och nätverket producerar ett **tillstånd**, som vi sedan skickar vidare till nätverket tillsammans med nästa token.\n", "\n", - "![Bild som visar ett exempel på generering med rekurrenta neurala nätverk.](../../../../../translated_images/rnn.27f5c29c53d727b5.sv.png)\n", + "![Bild som visar ett exempel på generering med rekurrenta neurala nätverk.](../../../../../translated_images/sv/rnn.27f5c29c53d727b5.png)\n", "\n", "Givet en inmatningssekvens av token $X_0,\\dots,X_n$, skapar RNN en sekvens av neurala nätverksblock och tränar denna sekvens från början till slut med hjälp av backpropagation. Varje nätverksblock tar ett par $(X_i,S_i)$ som indata och producerar $S_{i+1}$ som resultat. Det slutliga tillståndet $S_n$ eller utdata $Y_n$ skickas till en linjär klassificerare för att producera resultatet. Alla nätverksblock delar samma vikter och tränas från början till slut med en enda backpropagation-pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Rekurrenta nätverk, oavsett om de är enkelriktade eller bidirektionella, fångar mönster inom en sekvens och lagrar dem i tillståndsvektorer eller returnerar dem som output. Precis som med konvolutionella nätverk kan vi bygga ett annat rekurrent lager efter det första för att fånga högre nivåmönster, byggda från lägre nivåmönster som extraherats av det första lagret. Detta leder oss till begreppet **flerskiktad RNN**, som består av två eller fler rekurrenta nätverk, där output från det föregående lagret skickas till nästa lager som input.\n", "\n", - "![Bild som visar ett flerskiktat lång-korttidsminnes-RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sv.jpg)\n", + "![Bild som visar ett flerskiktat lång-korttidsminnes-RNN](../../../../../translated_images/sv/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Bild från [detta fantastiska inlägg](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) av Fernando López.*\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 02a7087f..c3aca307 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Så här kommer vi att träna ett RNN för att generera text. Vid varje steg tar vi en sekvens av tecken med längden `nchars` och ber nätverket att generera nästa utmatningstecken för varje inmatningstecken:\n", "\n", - "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sv.png)\n", + "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Beroende på det faktiska scenariot kan vi också vilja inkludera några specialtecken, såsom *slut-på-sekvens* ``. I vårt fall vill vi bara träna nätverket för oändlig textgenerering, därför kommer vi att fixa storleken på varje sekvens till att vara lika med `nchars` tokens. Följaktligen kommer varje tränings-exempel att bestå av `nchars` inmatningar och `nchars` utmatningar (vilka är inmatningssekvensen förskjuten ett tecken åt vänster). Minibatchen kommer att bestå av flera sådana sekvenser.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 637ffc08..f0eb8a31 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "Så här kommer vi att träna ett RNN för att generera nyhetstitlar. Vid varje steg tar vi en titel, som matas in i ett RNN, och för varje inmatad tecken ber vi nätverket att generera nästa utmatade tecken:\n", "\n", - "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sv.png)\n", + "![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png)\n", "\n", "För det sista tecknet i vår sekvens kommer vi att be nätverket att generera ``-token.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md index b2f92c5b..ec9c3a0e 100644 --- a/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sv/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ I RNN-arkitekturen som vi diskuterade i föregående enhet, producerade varje RN Detta möjliggör olika neurala arkitekturer som visas i bilden nedan: -![Bild som visar vanliga mönster för återkommande neurala nätverk.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sv.jpg) +![Bild som visar vanliga mönster för återkommande neurala nätverk.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Bild från blogginlägget [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ I denna enhet kommer vi att fokusera på enkla generativa modeller som hjälper Vi kommer att träna denna RNN att generera text steg för steg. Vid varje steg tar vi en sekvens av tecken med längden `nchars` och ber nätverket att generera nästa outputtecken för varje inputtecken: -![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sv.png) +![Bild som visar ett exempel på RNN-generering av ordet 'HELLO'.](../../../../../translated_images/sv/rnn-generate.56c54afb52f9781d.png) När vi genererar text (under inferens), börjar vi med en **prompt**, som passeras genom RNN-celler för att generera dess mellanliggande tillstånd, och sedan börjar genereringen från detta tillstånd. Vi genererar ett tecken i taget och skickar tillståndet och det genererade tecknet till en annan RNN-cell för att generera nästa, tills vi har genererat tillräckligt många tecken. diff --git a/translations/sv/lessons/5-NLP/18-Transformers/README.md b/translations/sv/lessons/5-NLP/18-Transformers/README.md index 046a94a0..4eaa1749 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sv/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Med RNNs implementeras sekvens-till-sekvens med två rekurrenta nätverk, där e **Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje inmatningsvektor på varje utgångsprediktion av RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i inmatnings-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen yt, tar vi hänsyn till alla dolda inmatningstillstånd hi, med olika viktkoefficienter αt,i. -![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sv.png) +![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png) > Kodare-avkodare-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Uppmärksamhetsmatrisen {αi,j} representerar graden av påverkan som vissa inmatningsord har på genereringen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris: -![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sv.png) +![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png) > Figur från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Resultatet vi får med positionsinbäddning inbäddar både den ursprungliga tok Nästa steg är att fånga vissa mönster inom vår sekvens. För att göra detta använder transformatorer en **självuppmärksamhetsmekanism**, som i grunden är uppmärksamhet applicerad på samma sekvens som inmatning och utgång. Att applicera självuppmärksamhet gör att vi kan ta hänsyn till **kontext** inom meningen och se vilka ord som är relaterade till varandra. Till exempel gör det att vi kan se vilka ord som refereras av korreferenser, såsom *det*, och också ta kontexten i beaktande: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sv.png) +![](../../../../../translated_images/sv/CoreferenceResolution.861924d6d384a7d6.png) > Bild från [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Eftersom varje inmatningsposition mappas oberoende till varje utgångsposition k **BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskikts transformatornätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor textkorpus (Wikipedia + böcker) med hjälp av oövervakad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen betydande nivåer av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**. -![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sv.png) +![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Bild [källa](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 7dc9c4d7..bc6005a5 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sv/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje ingångsvektor på varje utgångsprediktion i RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i ingångs-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen $y_t$, tar vi hänsyn till alla dolda ingångstillstånd $h_i$, med olika viktkoefficienter $\\alpha_{t,i}$.\n", "\n", - "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sv.png)\n", + "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Uppmärksamhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerar graden till vilken vissa ingångsord bidrar till genereringen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris:\n", "\n", - "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sv.png)\n", + "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur hämtad från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskikts-transformernätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor textkorpus (Wikipedia + böcker) med hjälp av oövervakad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen en betydande nivå av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**.\n", "\n", - "![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sv.png)\n", + "![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Det finns många variationer av Transformer-arkitekturer, inklusive BERT, DistilBERT, BigBird, OpenGPT3 och fler, som kan finjusteras. [HuggingFace-paketet](https://github.com/huggingface/) tillhandahåller ett bibliotek för att träna många av dessa arkitekturer med PyTorch.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 4349fab9..029e6c8f 100644 --- a/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sv/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje inmatningsvektor på varje utgångsprediktion av RNN. Detta implementeras genom att skapa genvägar mellan mellanliggande tillstånd i inmatnings-RNN och utgångs-RNN. På detta sätt, när vi genererar utgångssymbolen $y_t$, tar vi hänsyn till alla dolda tillstånd $h_i$ från inmatningen, med olika viktkoefficienter $\\alpha_{t,i}$. \n", "\n", - "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sv.png)\n", + "![Bild som visar en encoder/decoder-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sv/encoder-decoder-attention.7a726296894fb567.png)\n", "*Encoder-decoder-modellen med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Uppmärksamhetsmatrisen $\\{\\alpha_{i,j}\\}$ representerar graden av påverkan som vissa inmatningsord har vid genereringen av ett visst ord i utgångssekvensen. Nedan är ett exempel på en sådan matris:\n", "\n", - "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sv.png)\n", + "![Bild som visar ett exempel på justering funnen av RNNsearch-50, hämtad från Bahdanau - arviz.org](../../../../../translated_images/sv/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Figur hämtad från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerskiktat transformatornätverk med 12 lager för *BERT-base* och 24 för *BERT-large*. Modellen förtränas först på en stor mängd textdata (WikiPedia + böcker) med hjälp av osuperviserad träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen en betydande nivå av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **transfer learning**.\n", "\n", - "![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sv.png)\n", + "![bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sv/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Det finns många variationer av Transformer-arkitekturer, inklusive BERT, DistilBERT, BigBird, OpenGPT3 och fler som kan finjusteras.\n", "\n", diff --git a/translations/sv/lessons/5-NLP/19-NER/README.md b/translations/sv/lessons/5-NLP/19-NER/README.md index 2db26591..9369b6e6 100644 --- a/translations/sv/lessons/5-NLP/19-NER/README.md +++ b/translations/sv/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ barn | O Eftersom vi behöver bygga en en-till-en-korrespondens mellan tokens och klasser, kan vi träna en högerställd **många-till-många** neural nätverksmodell från denna bild: -![Bild som visar vanliga återkommande neurala nätverksmönster.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sv.jpg) +![Bild som visar vanliga återkommande neurala nätverksmönster.](../../../../../translated_images/sv/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Bild från [denna bloggpost](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) av [Andrej Karpathy](http://karpathy.github.io/). NER tokenklassificeringsmodeller motsvarar den högra nätverksarkitekturen på denna bild.* diff --git a/translations/sv/lessons/5-NLP/README.md b/translations/sv/lessons/5-NLP/README.md index 6def43c1..6dc167f7 100644 --- a/translations/sv/lessons/5-NLP/README.md +++ b/translations/sv/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Naturlig Språkbearbetning -![Sammanfattning av NLP-uppgifter i en skiss](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.sv.png) +![Sammanfattning av NLP-uppgifter i en skiss](../../../../translated_images/sv/ai-nlp.b22dcb8ca4707cea.png) I den här delen kommer vi att fokusera på att använda neurala nätverk för att hantera uppgifter relaterade till **naturlig språkbearbetning (NLP)**. Det finns många NLP-problem som vi vill att datorer ska kunna lösa: diff --git a/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md index 3acddf4b..c63ba6e3 100644 --- a/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sv/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Du kan öppna en av modellerna, till exempel **Biology → Flocking**. Efter att ha öppnat modellen tas du till huvudskärmen i NetLogo. Här är en exempelmodell som beskriver populationen av vargar och får, givet begränsade resurser (gräs). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.sv.png) +![NetLogo Main Screen](../../../../../translated_images/sv/NetLogo-Main.32653711ec1a01b3.png) > Skärmdump av Dmitry Soshnikov diff --git a/translations/sv/lessons/README.md b/translations/sv/lessons/README.md index 0f87f49c..1168f258 100644 --- a/translations/sv/lessons/README.md +++ b/translations/sv/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Översikt -![Översikt i en skiss](../../../translated_images/ai-overview.0857791951d19500.sv.png) +![Översikt i en skiss](../../../translated_images/sv/ai-overview.0857791951d19500.png) > Skissanteckning av [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sv/lessons/X-Extras/X1-MultiModal/README.md b/translations/sv/lessons/X-Extras/X1-MultiModal/README.md index 3b69fc6b..1fa6693d 100644 --- a/translations/sv/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sv/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Efter framgången med transformer-modeller för att lösa NLP-uppgifter har samm Huvudidén med CLIP är att kunna jämföra textprompter med en bild och avgöra hur väl bilden motsvarar prompten. -![CLIP-arkitektur](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.sv.png) +![CLIP-arkitektur](../../../../../translated_images/sv/clip-arch.b3dbf20b4e8ed8be.png) > *Bild från [detta blogginlägg](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ När denna modell är förtränad kan vi ge den en batch av bilder och en batch Anta att vi behöver klassificera bilder mellan exempelvis katter, hundar och människor. I detta fall kan vi ge modellen en bild och en serie textprompter: "*en bild av en katt*", "*en bild av en hund*", "*en bild av en människa*". I den resulterande vektorn med tre sannolikheter behöver vi bara välja indexet med högst värde. -![CLIP för bildklassificering](../../../../../translated_images/clip-class.3af42ef0b2b19369.sv.png) +![CLIP för bildklassificering](../../../../../translated_images/sv/clip-class.3af42ef0b2b19369.png) > *Bild från [detta blogginlägg](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Lär dig mer om VQGAN på [Taming Transformers](https://compvis.github.io/taming En viktig skillnad mellan VQGAN och traditionella GAN är att den senare kan producera en hyfsad bild från vilken inputvektor som helst, medan VQGAN sannolikt kommer att producera en bild som inte är sammanhängande. Därför behöver vi ytterligare vägleda bildskapandeprocessen, och det kan göras med hjälp av CLIP. -![VQGAN+CLIP-arkitektur](../../../../../translated_images/vqgan.5027fe05051dfa31.sv.png) +![VQGAN+CLIP-arkitektur](../../../../../translated_images/sv/vqgan.5027fe05051dfa31.png) För att generera en bild som motsvarar en textprompt börjar vi med någon slumpmässig kodningsvektor som skickas genom VQGAN för att producera en bild. Sedan används CLIP för att skapa en förlustfunktion som visar hur väl bilden motsvarar textprompten. Målet är sedan att minimera denna förlust genom att använda backpropagation för att justera parametrarna för inputvektorn. Ett utmärkt bibliotek som implementerar VQGAN+CLIP är [Pixray](http://github.com/pixray/pixray). -![Bild skapad av Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.sv.png) | ![Bild skapad av Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.sv.png) | ![Bild skapad av Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.sv.png) +![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Bild genererad från prompten *en närbild akvarellporträtt av en ung manlig litteraturlärare med en bok* | Bild genererad från prompten *en närbild oljeporträtt av en ung kvinnlig datavetenskapslärare med en dator* | Bild genererad från prompten *en närbild oljeporträtt av en äldre manlig matematiklärare framför en svart tavla* @@ -75,7 +75,7 @@ Till skillnad från CLIP tar DALL-E emot både text och bild som en enda ström Den huvudsakliga skillnaden mellan DALL.E 1 och 2 är att den senare genererar mer realistiska bilder och konst. Exempel på bildgenerering med DALL-E: -![Bild skapad av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.sv.png) | ![Bild skapad av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.sv.png) | ![Bild skapad av Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.sv.png) +![Bild skapad av Pixray](../../../../../translated_images/sv/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Bild skapad av Pixray](../../../../../translated_images/sv/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Bild genererad från prompten *en närbild akvarellporträtt av en ung manlig litteraturlärare med en bok* | Bild genererad från prompten *en närbild oljeporträtt av en ung kvinnlig datavetenskapslärare med en dator* | Bild genererad från prompten *en närbild oljeporträtt av en äldre manlig matematiklärare framför en svart tavla* diff --git a/translations/sw/README.md b/translations/sw/README.md index 345f9630..4da3e7c3 100644 --- a/translations/sw/README.md +++ b/translations/sw/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Akili Bandia kwa Komesheni - Mtaala -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.sw.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sw/ai-overview.0857791951d19500.png)| |:---:| | AI Kwa Komesheni - _Sketchnote na [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/sw/lessons/1-Intro/README.md b/translations/sw/lessons/1-Intro/README.md index a908bf22..4a79416f 100644 --- a/translations/sw/lessons/1-Intro/README.md +++ b/translations/sw/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Utangulizi wa AI -![Muhtasari wa maudhui ya Utangulizi wa AI katika mchoro](../../../../translated_images/ai-intro.bf28d1ac4235881c.sw.png) +![Muhtasari wa maudhui ya Utangulizi wa AI katika mchoro](../../../../translated_images/sw/ai-intro.bf28d1ac4235881c.png) > Mchoro na [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Awali, kompyuta ziligunduliwa na [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) ili kufanya kazi na namba kwa kufuata utaratibu ulioelezwa vizuri - algorithimu. Kompyuta za kisasa, ingawa zimeendelea sana kuliko mfano wa awali uliopendekezwa katika karne ya 19, bado zinafuata wazo lile lile la hesabu zinazoongozwa. Kwa hivyo inawezekana kuipanga kompyuta kufanya jambo fulani ikiwa tunajua mlolongo halisi wa hatua tunazohitaji kuchukua ili kufanikisha lengo. -![Picha ya mtu](../../../../translated_images/dsh_age.d212a30d4e54fb5f.sw.png) +![Picha ya mtu](../../../../translated_images/sw/dsh_age.d212a30d4e54fb5f.png) > Picha na [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -45,7 +45,7 @@ Kwa maelezo zaidi rejelea **[Akili Bandia ya Kijumla](https://en.wikipedia.org/w Moja ya matatizo tunaposhughulikia neno **[Akili](https://en.wikipedia.org/wiki/Intelligence)** ni kwamba hakuna ufafanuzi wazi wa neno hili. Mtu anaweza kusema akili inahusiana na **fikra za dhahania**, au na **kujifahamu**, lakini hatuwezi kuifafanua ipasavyo. -![Picha ya Paka](../../../../translated_images/photo-cat.8c8e8fb760ffe457.sw.jpg) +![Picha ya Paka](../../../../translated_images/sw/photo-cat.8c8e8fb760ffe457.jpg) > [Picha](https://unsplash.com/photos/75715CVEJhI) na [Amber Kipp](https://unsplash.com/@sadmax) kutoka Unsplash @@ -97,13 +97,13 @@ Vinginevyo, tunaweza kujaribu kuiga vipengele rahisi zaidi ndani ya ubongo wetu > | Je, kuhusu ML? | | > |--------------|-----------| -> | Sehemu ya Akili Bandia inayotegemea kompyuta kujifunza kutatua tatizo kwa msingi wa data fulani inaitwa **Machine Learning**. Hatutazingatia ujifunzaji wa mashine wa kawaida katika kozi hii - tunakuelekeza kwenye mtaala tofauti wa [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.sw.png) | +> | Sehemu ya Akili Bandia inayotegemea kompyuta kujifunza kutatua tatizo kwa msingi wa data fulani inaitwa **Machine Learning**. Hatutazingatia ujifunzaji wa mashine wa kawaida katika kozi hii - tunakuelekeza kwenye mtaala tofauti wa [Machine Learning for Beginners](http://aka.ms/ml-beginners). | ![ML for Beginners](../../../../translated_images/sw/ml-for-beginners.9e4fed176fd5817d.png) | ## Historia Fupi ya AI Akili Bandia ilianza kama uwanja katikati ya karne ya ishirini. Awali, ufikiri wa kimaumbo ulikuwa njia maarufu, na ulisababisha mafanikio kadhaa muhimu, kama vile mifumo ya wataalamu – programu za kompyuta ambazo ziliweza kutenda kama mtaalamu katika baadhi ya maeneo ya tatizo yaliyopunguzwa. Hata hivyo, hivi karibuni ilibainika kuwa njia hiyo haipimi vizuri. Kutoa maarifa kutoka kwa mtaalamu, kuyaweka katika kompyuta, na kuweka msingi wa maarifa sahihi inageuka kuwa kazi ngumu sana, na ghali sana kuwa ya vitendo katika hali nyingi. Hii ilisababisha kile kinachoitwa [Majira ya Baridi ya AI](https://en.wikipedia.org/wiki/AI_winter) katika miaka ya 1970. -Historia Fupi ya AI +Historia Fupi ya AI > Picha na [Dmitry Soshnikov](http://soshnikov.com) @@ -123,7 +123,7 @@ Vivyo hivyo, tunaweza kuona jinsi mbinu za kuunda “programu zinazozungumza” * Wasaidizi wa kisasa, kama Cortana, Siri au Google Assistant ni mifumo mseto inayotumia Mitandao ya Neva kubadilisha hotuba kuwa maandishi na kutambua nia yetu, na kisha kutumia baadhi ya ufikiri au algorithimu wazi kutekeleza vitendo vinavyohitajika. * Katika siku zijazo, tunaweza kutarajia mfano kamili unaotegemea neva kushughulikia mazungumzo yenyewe. Familia ya hivi karibuni ya mitandao ya neva ya GPT na [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) inaonyesha mafanikio makubwa katika hili. -mabadiliko ya mtihani wa Turing +mabadiliko ya mtihani wa Turing > Picha na Dmitry Soshnikov, [picha](https://unsplash.com/photos/r8LmVbUKgns) na [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Utafiti wa Hivi Karibuni wa AI diff --git a/translations/sw/lessons/2-Symbolic/Animals.ipynb b/translations/sw/lessons/2-Symbolic/Animals.ipynb index e040c233..9c580061 100644 --- a/translations/sw/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sw/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Katika mfano huu, tutatekeleza mfumo rahisi unaotegemea maarifa ili kubaini mnyama kulingana na baadhi ya sifa za kimwili. Mfumo huu unaweza kuwakilishwa na mti wa AND-OR ufuatao (hii ni sehemu ya mti mzima, tunaweza kuongeza sheria zaidi kwa urahisi):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sw.png)\n" + "![](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/sw/lessons/2-Symbolic/README.md b/translations/sw/lessons/2-Symbolic/README.md index 4e44c4f9..e7d27c85 100644 --- a/translations/sw/lessons/2-Symbolic/README.md +++ b/translations/sw/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uwakilishi wa Maarifa na Mifumo ya Wataalamu -![Muhtasari wa maudhui ya AI ya Kimaandishi](../../../../translated_images/ai-symbolic.715a30cb610411a6.sw.png) +![Muhtasari wa maudhui ya AI ya Kimaandishi](../../../../translated_images/sw/ai-symbolic.715a30cb610411a6.png) > Sketchnote na [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Mara nyingi, hatufafanui maarifa kwa ukali, lakini tunayalinganisha na dhana nyi Kwa hivyo, tatizo la **uwakilishi wa maarifa** ni kutafuta njia bora ya kuwakilisha maarifa ndani ya kompyuta kwa njia ya data, ili yaweze kutumika kiotomatiki. Hili linaweza kuonekana kama wigo: -![Wigo wa uwakilishi wa maarifa](../../../../translated_images/knowledge-spectrum.b60df631852c0217.sw.png) +![Wigo wa uwakilishi wa maarifa](../../../../translated_images/sw/knowledge-spectrum.b60df631852c0217.png) > Picha na [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Syntax ya Block | Indent | | | Moja ya mafanikio ya awali ya AI ya kimaandishi yalikuwa mifumo ya **wataalamu** - mifumo ya kompyuta iliyoundwa kufanya kazi kama mtaalamu katika eneo fulani la tatizo. Ilitegemea **hifadhidata ya maarifa** iliyotolewa kutoka kwa mtaalamu mmoja au zaidi wa binadamu, na ilikuwa na **injini ya utoaji wa sababu** iliyofanya utoaji wa sababu juu yake. -![Muundo wa Binadamu](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.sw.png) | ![Mfumo Unaotegemea Maarifa](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.sw.png) +![Muundo wa Binadamu](../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.png) | ![Mfumo Unaotegemea Maarifa](../../../../translated_images/sw/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Muundo rahisi wa mfumo wa neva wa binadamu | Muundo wa mfumo unaotegemea maarifa @@ -106,7 +106,7 @@ Mifumo ya wataalamu imejengwa kama mfumo wa utoaji wa sababu wa binadamu, ambao Kwa mfano, hebu tuchunguze mfumo wa wataalamu wa kuamua mnyama kulingana na sifa zake za kimwili: -![Mti wa AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.sw.png) +![Mti wa AND-OR](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.png) > Picha na [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 4399d421..ce4928e6 100644 --- a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Iwapo tuna zaidi ya madarasa 2, softmax itasawazisha uwezekano katika madarasa yote. Hapa kuna mchoro wa usanifu wa mtandao unaofanya uainishaji wa tarakimu za MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.sw.png)\n" + "![MNIST Classifier](../../../../../translated_images/sw/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1259,7 +1259,7 @@ "* Hasara ya mafunzo ni ndogo - mfano unaweza kufanana vizuri na data ya mafunzo kwa sababu una uwezo wa kujieleza wa kutosha.\n", "* Hasara ya uthibitishaji inaweza kuwa kubwa zaidi kuliko hasara ya mafunzo na inaweza kuanza kuongezeka wakati wa mafunzo - hii ni kwa sababu mfano \"unakumbuka\" pointi za mafunzo na kupoteza \"picha kubwa.\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.sw.png)\n", + "![Overfitting](../../../../../translated_images/sw/overfit.a0bd57f717c15769.png)\n", "\n", "> Katika picha hii, `x` inawakilisha data ya mafunzo, `o` - data ya uthibitishaji. Kushoto - mfano wa mstari (tabaka moja), unafanana vizuri na asili ya data. Kulia - mfano uliopitiliza, mfano unafanana kikamilifu na data ya mafunzo, lakini hauleti maana yoyote kwa data nyingine yoyote (makosa ya uthibitishaji ni makubwa sana).\n" ] diff --git a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md index dc53117e..4521e03b 100644 --- a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting ni dhana muhimu sana katika ujifunzaji wa mashine, na ni muhimu sana Fikiria tatizo lifuatalo la kukadiria alama 5 (zinazoonyeshwa na `x` kwenye grafu hapa chini): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.sw.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.sw.jpg) +![linear](../../../../../translated_images/sw/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/sw/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Modeli ya mstari, vigezo 2** | **Modeli isiyo ya mstari, vigezo 7** Makosa ya mafunzo = 5.3 | Makosa ya mafunzo = 0 @@ -79,7 +79,7 @@ Ni muhimu sana kupata usawa sahihi kati ya utajiri wa modeli (idadi ya vigezo) n Kama unavyoona kutoka kwenye grafu hapo juu, overfitting inaweza kugunduliwa kwa makosa ya mafunzo ya chini sana, na makosa ya uthibitishaji ya juu. Kawaida wakati wa mafunzo tutaona makosa ya mafunzo na uthibitishaji yakianza kupungua, na kisha wakati fulani makosa ya uthibitishaji yanaweza kuacha kupungua na kuanza kuongezeka. Hii itakuwa ishara ya overfitting, na kiashiria kwamba tunapaswa labda kuacha mafunzo wakati huo (au angalau kufanya nakala ya modeli). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.sw.png) +![overfitting](../../../../../translated_images/sw/Overfitting.408ad91cd90b4371.png) ## Jinsi ya kuzuia overfitting diff --git a/translations/sw/lessons/3-NeuralNetworks/README.md b/translations/sw/lessons/3-NeuralNetworks/README.md index bea7cf91..9709271e 100644 --- a/translations/sw/lessons/3-NeuralNetworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Utangulizi wa Mitandao ya Neva -![Muhtasari wa maudhui ya Utangulizi wa Mitandao ya Neva katika mchoro](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.sw.png) +![Muhtasari wa maudhui ya Utangulizi wa Mitandao ya Neva katika mchoro](../../../../translated_images/sw/ai-neuralnetworks.1c687ae40bc86e83.png) Kama tulivyojadili katika utangulizi, mojawapo ya njia za kufanikisha akili ni kufundisha **mfano wa kompyuta** au **ubongo bandia**. Tangu katikati ya karne ya 20, watafiti walijaribu mifano mbalimbali ya kihisabati, hadi miaka ya hivi karibuni ambapo mwelekeo huu ulionekana kufanikiwa sana. Mifano hii ya kihisabati ya ubongo inaitwa **mitandao ya neva**. @@ -36,13 +36,13 @@ Katika mtaala huu, tutazingatia tu mifano ya mitandao ya neva. Kutoka kwa biolojia, tunajua kuwa ubongo wetu unajumuisha seli za neva (neuroni), kila moja ikiwa na "viingizo" vingi (dendriti) na "matokeo" moja (aksoni). Dendriti na aksoni zote mbili zinaweza kusafirisha ishara za umeme, na miunganisho kati yao — inayojulikana kama sinapsi — inaweza kuonyesha viwango tofauti vya usafirishaji, ambavyo vinadhibitiwa na nyurotransmita. -![Mfano wa Neva](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.sw.jpg) | ![Mfano wa Neva](../../../../translated_images/artneuron.1a5daa88d20ebe6f.sw.png) +![Mfano wa Neva](../../../../translated_images/sw/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Mfano wa Neva](../../../../translated_images/sw/artneuron.1a5daa88d20ebe6f.png) ----|---- Neva Halisi *([Picha](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) kutoka Wikipedia)* | Neva Bandia *(Picha na Mwandishi)* Kwa hivyo, mfano rahisi wa kihisabati wa neva una viingizo kadhaa X1, ..., XN na matokeo Y, na mfululizo wa uzito W1, ..., WN. Matokeo huhesabiwa kama: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ambapo f ni **kazi ya uanzishaji** isiyo ya mstari. diff --git a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md index eeb2b6d2..f24695d2 100644 --- a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Katika [OpenCV Notebook](OpenCV.ipynb) yetu, tunatoa mifano ya wakati uelewa wa * **Usindikaji wa awali wa picha ya kitabu cha Braille**. Tunazingatia jinsi tunavyoweza kutumia thresholding, utambuzi wa vipengele, mabadiliko ya mtazamo na manipulations za NumPy kutenganisha alama za Braille kwa uainishaji zaidi na mtandao wa neva. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.sw.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.sw.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.sw.png) +![Braille Image](../../../../../translated_images/sw/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/sw/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/sw/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) * **Kutambua harakati kwenye video kwa kutumia tofauti ya fremu**. Ikiwa kamera imetulia, basi fremu kutoka mlisho wa kamera zinapaswa kufanana sana. Kwa kuwa fremu zinawakilishwa kama safu, kwa kutoa tofauti ya safu hizo kwa fremu mbili mfululizo tutapata tofauti ya pikseli, ambayo inapaswa kuwa ndogo kwa fremu tuli, na kuwa kubwa zaidi mara kuna harakati kubwa kwenye picha. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.sw.png) +![Image of video frames and frame differences](../../../../../translated_images/sw/frame-difference.706f805491a0883c.png) > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Katika [OpenCV Notebook](OpenCV.ipynb) yetu, tunatoa mifano ya wakati uelewa wa - **Dense Optical Flow** huhesabu uwanja wa vekta unaoonyesha kwa kila pikseli inahama wapi. - **Sparse Optical Flow** inategemea kuchukua vipengele vya kipekee kwenye picha (mfano, kingo), na kujenga mwelekeo wake kutoka fremu hadi fremu. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.sw.png) +![Image of Optical Flow](../../../../../translated_images/sw/optical.1f4a94464579a83a.png) > Picha kutoka [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 09fed9dc..e1e9fe35 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ni mtandao uliopata usahihi wa 92.7% katika uainishaji wa ImageNet top-5 mwaka 2014. Una muundo wa tabaka zifuatazo: -![Tabaka za ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sw.jpg) +![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.jpg) Kama unavyoona, VGG inafuata muundo wa jadi wa piramidi, ambao ni mfululizo wa tabaka za convolution-pooling. -![Piramidi ya ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sw.jpg) +![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.jpg) > Picha kutoka [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 75202c40..d12b64be 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Kwa hivyo, katika CNN ya kawaida kutakuwa na tabaka kadhaa za convolution, zikiwa na tabaka za pooling kati yao ili kupunguza vipimo vya picha. Pia tungeongeza idadi ya vichujio, kwa sababu kadri mifumo inavyokuwa ya hali ya juu - kuna mchanganyiko zaidi wa kuvutia ambao tunahitaji kutafuta.\n", "\n", - "![Picha inayoonyesha tabaka kadhaa za convolution zikiwa na tabaka za pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sw.png)\n", + "![Picha inayoonyesha tabaka kadhaa za convolution zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kwa sababu ya kupungua kwa vipimo vya anga na kuongezeka kwa vipimo vya sifa/vichujio, usanifu huu pia huitwa **usanifu wa piramidi**.\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index c2a28aaa..c964c7ca 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -360,7 +360,7 @@ "\n", "Kwa hivyo, katika CNN ya kawaida kutakuwa na tabaka kadhaa za convolutional, zikiwa na tabaka za pooling kati yao ili kupunguza vipimo vya picha. Pia tungeongeza idadi ya vichujio, kwa sababu mifumo inavyokuwa ya hali ya juu zaidi - kuna mchanganyiko mwingi wa kuvutia ambao tunahitaji kutafuta.\n", "\n", - "![Picha inayoonyesha tabaka kadhaa za convolutional zikiwa na tabaka za pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.sw.png)\n", + "![Picha inayoonyesha tabaka kadhaa za convolutional zikiwa na tabaka za pooling.](../../../../../translated_images/sw/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Kwa sababu ya kupungua kwa vipimo vya anga na kuongezeka kwa vipimo vya sifa/vichujio, usanifu huu pia huitwa **usanifu wa piramidi**.\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md index 0ee943b0..27503844 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Katika maisha halisi, tunataka kuwa na uwezo wa kutambua vitu kwenye picha bila Ili kutoa mifumo, tutatumia dhana ya **vichujio vya convolutional**. Kama unavyojua, picha inawakilishwa na matriki ya 2D, au tensor ya 3D yenye kina cha rangi. Kutumia kichujio kunamaanisha kwamba tunachukua matriki ndogo ya **kernel ya kichujio**, na kwa kila pikseli kwenye picha ya awali tunahesabu wastani wa uzito na pointi za jirani. Tunaweza kuona hili kama dirisha dogo linalosonga juu ya picha nzima, na kujumlisha pikseli zote kulingana na uzito katika matriki ya kernel ya kichujio. -![Kichujio cha Mstari Wima](../../../../../translated_images/filter-vert.b7148390ca0bc356.sw.png) | ![Kichujio cha Mstari Mlalo](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.sw.png) +![Kichujio cha Mstari Wima](../../../../../translated_images/sw/filter-vert.b7148390ca0bc356.png) | ![Kichujio cha Mstari Mlalo](../../../../../translated_images/sw/filter-horiz.59b80ed4feb946ef.png) ----|---- > Picha na Dmitry Soshnikov @@ -38,7 +38,7 @@ Njia CNN zinavyofanya kazi inategemea mawazo muhimu yafuatayo: * Tunaweza kubuni mtandao kwa njia ambayo vichujio vinajifunza kiotomatiki * Tunaweza kutumia mbinu hiyo hiyo kutafuta mifumo kwenye vipengele vya kiwango cha juu, si tu kwenye picha ya awali. Kwa hivyo uchimbaji wa vipengele vya CNN hufanya kazi kwenye uhierakia wa vipengele, kuanzia mchanganyiko wa pikseli za kiwango cha chini, hadi mchanganyiko wa kiwango cha juu wa sehemu za picha. -![Uchimbaji wa Vipengele vya Kihierakia](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.sw.png) +![Uchimbaji wa Vipengele vya Kihierakia](../../../../../translated_images/sw/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Picha kutoka [karatasi ya Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), kulingana na [utafiti wao](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN nyingi zinazotumika kwa usindikaji wa picha hufuata kile kinachoitwa muundo Kwa mfano, hebu tuangalie muundo wa VGG-16, mtandao uliopata usahihi wa 92.7% katika uainishaji wa juu-5 wa ImageNet mwaka 2014: -![Tabaka za ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.sw.jpg) +![Tabaka za ImageNet](../../../../../translated_images/sw/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Piramidi ya ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.sw.jpg) +![Piramidi ya ImageNet](../../../../../translated_images/sw/vgg-16-arch.64ff2137f50dd49f.jpg) > Picha kutoka [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 1aa10ae6..9b740d07 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Unahitaji kufundisha mtandao wa neva wa convolutional ili kuainisha aina tofauti Tutatumia [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), ambayo ina picha za aina 37 tofauti za mbwa na paka. -![Dataset tutakayoshughulikia](../../../../../../translated_images/data.50b2a9d5484bdbf0.sw.png) +![Dataset tutakayoshughulikia](../../../../../../translated_images/sw/data.50b2a9d5484bdbf0.png) Ili kupakua dataset, tumia kipande hiki cha msimbo: diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 61fc0a03..b19c8fd0 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Ili kuonyesha paka bora, tutaanza na picha ya kelele ya nasibu, na tutajaribu kutumia mbinu ya uboreshaji ya gradient descent kurekebisha picha ili mtandao utambue paka.\n", "\n", - "![Mzunguko wa Uboreshaji](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sw.png)\n", + "![Mzunguko wa Uboreshaji](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Hii hapa ni picha yetu ya kuanzia:\n" ] diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md index aa33a3d7..02525dba 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras na PyTorch zote zina kazi za kupakia kwa urahisi uzito wa mtandao wa neva Hapa kuna vipengele vya mfano vilivyotolewa kutoka kwa picha ya paka na mtandao wa VGG-16: -![Vipengele vilivyotolewa na VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.sw.png) +![Vipengele vilivyotolewa na VGG-16](../../../../../translated_images/sw/features.6291f9c7ba3a0b95.png) ## Seti ya Data ya Paka na Mbwa @@ -48,19 +48,19 @@ Mtandao wa neva uliyojifunza kabla una mifumo tofauti ndani ya "ubongo" wake, ik Njia moja tunayoweza kuchukua ni kuanza na picha ya nasibu, kisha kujaribu kutumia mbinu ya **ufanisi wa gradient descent** kurekebisha picha hiyo kwa namna ambayo mtandao unaanza kufikiria kuwa ni paka. -![Mzunguko wa Uboreshaji wa Picha](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.sw.png) +![Mzunguko wa Uboreshaji wa Picha](../../../../../translated_images/sw/ideal-cat-loop.999fbb8ff306e044.png) Hata hivyo, tukifanya hivyo, tutapata kitu kinachofanana sana na kelele ya nasibu. Hii ni kwa sababu *kuna njia nyingi za kufanya mtandao kufikiria picha ya ingizo ni paka*, ikiwa ni pamoja na baadhi ambazo hazina maana kwa macho. Ingawa picha hizo zina mifumo mingi inayotambulika kwa paka, hakuna kitu kinachozuia kuwa tofauti kwa macho. Ili kuboresha matokeo, tunaweza kuongeza kipengele kingine kwenye kazi ya hasara, kinachoitwa **variation loss**. Ni kipimo kinachoonyesha jinsi pikseli za jirani za picha zinavyofanana. Kupunguza variation loss hufanya picha kuwa laini, na kuondoa kelele - hivyo kufichua mifumo inayovutia zaidi kwa macho. Hapa kuna mfano wa picha "bora" zinazotambuliwa kama paka na kama punda milia kwa uwezekano mkubwa: -![Paka Bora](../../../../../translated_images/ideal-cat.203dd4597643d6b0.sw.png) | ![Punda Milia Bora](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.sw.png) +![Paka Bora](../../../../../translated_images/sw/ideal-cat.203dd4597643d6b0.png) | ![Punda Milia Bora](../../../../../translated_images/sw/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Paka Bora* | *Punda Milia Bora* Njia sawa inaweza kutumika kufanya kile kinachoitwa **mashambulizi ya kihasama** kwenye mtandao wa neva. Tuseme tunataka kudanganya mtandao wa neva na kufanya mbwa aonekane kama paka. Ikiwa tutachukua picha ya mbwa, ambayo inatambuliwa na mtandao kama mbwa, tunaweza kuirekebisha kidogo kwa kutumia ufanisi wa gradient descent, hadi mtandao uanze kuainisha kama paka: -![Picha ya Mbwa](../../../../../translated_images/original-dog.8f68a67d2fe0911f.sw.png) | ![Picha ya mbwa inayotambuliwa kama paka](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.sw.png) +![Picha ya Mbwa](../../../../../translated_images/sw/original-dog.8f68a67d2fe0911f.png) | ![Picha ya mbwa inayotambuliwa kama paka](../../../../../translated_images/sw/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Picha ya asili ya mbwa* | *Picha ya mbwa inayotambuliwa kama paka* diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b034439a..3048e674 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi iwezekanavyo kutoka kwenye picha ya awali kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana yake.\n", "\n", - "![Mchoro wa AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sw.jpg)\n", + "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 67521615..40c1d14c 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi iwezekanavyo kutoka kwenye picha ya asili kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana.\n", "\n", - "![Mchoro wa AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sw.jpg)\n", + "![Mchoro wa AutoEncoder](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md index f6bc2cd4..bcda26f5 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Hata hivyo, tunaweza kutaka kutumia data ghafi (isiyo na lebo) kwa kufundisha CN Kwa kuwa tunafundisha autoencoder ili kunasa taarifa nyingi kutoka kwenye picha ya asili iwezekanavyo kwa ajili ya ujenzi sahihi, mtandao unajaribu kupata **embedding** bora ya picha za pembejeo ili kunasa maana yake. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.sw.jpg) +![AutoEncoder Diagram](../../../../../translated_images/sw/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Picha kutoka [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md index 1ca0d0fe..5768246e 100644 --- a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Mifano ya uainishaji wa picha tuliyojifunza hadi sasa ilichukua picha na kutoa m ## [Jaribio la awali la somo](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Utambuzi wa Vitu](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.sw.png) +![Utambuzi wa Vitu](../../../../../translated_images/sw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Picha kutoka [tovuti ya YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Tukidhani tunataka kutambua paka kwenye picha, njia rahisi ya utambuzi wa vitu i 2. Fanya uainishaji wa picha kwenye kila kigae. 3. Vigae vile vinavyotoa matokeo ya juu vya kutosha vinaweza kuchukuliwa kuwa na kitu kinachotafutwa. -![Utambuzi Rahisi wa Vitu](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.sw.png) +![Utambuzi Rahisi wa Vitu](../../../../../translated_images/sw/naive-detection.e7f1ba220ccd08c6.png) > *Picha kutoka [Daftari la Mazoezi](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Unaweza kukutana na seti zifuatazo za data kwa kazi hii: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - madarasa 20 * [COCO](http://cocodataset.org/#home) - Vitu vya Kawaida katika Muktadha. Madarasa 80, maboksi ya mipaka na maski za kugawanya -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.sw.jpg) +![COCO](../../../../../translated_images/sw/coco-examples.71bc60380fa6cceb.jpg) ## Vipimo vya Utambuzi wa Vitu @@ -50,7 +50,7 @@ Unaweza kukutana na seti zifuatazo za data kwa kazi hii: Wakati kwa uainishaji wa picha ni rahisi kupima jinsi algorithimu inavyofanya kazi, kwa utambuzi wa vitu tunahitaji kupima usahihi wa darasa, pamoja na usahihi wa eneo la boksi lililotabiriwa. Kwa hili la mwisho, tunatumia kipimo kinachoitwa **Muingiliano juu ya Muungano** (IoU), ambacho hupima jinsi maboksi mawili (au maeneo mawili yoyote) yanavyofanana. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.sw.png) +![IoU](../../../../../translated_images/sw/iou_equation.9a4751d40fff4e11.png) > *Mchoro wa 2 kutoka [blogu hii bora kuhusu IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Kuna makundi mawili makuu ya algorithimu za utambuzi wa vitu: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) hutumia [Utafutaji wa Kuchagua](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) kuzalisha muundo wa kihierarkia wa maeneo ya ROI, ambayo kisha hupitishwa kupitia viondoa sifa vya CNN na vianuai vya SVM ili kubaini darasa la kitu, na usawazishaji wa mstari ili kubaini m coordinates ya *maboksi ya mipaka*. [Karatasi Rasmi](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.sw.png) +![RCNN](../../../../../translated_images/sw/rcnn1.cae407020dfb1d1f.png) > *Picha kutoka van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.sw.png) +![RCNN-1](../../../../../translated_images/sw/rcnn2.2d9530bb83516484.png) > *Picha kutoka [blogu hii](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Kuna makundi mawili makuu ya algorithimu za utambuzi wa vitu: Mbinu hii ni sawa na R-CNN, lakini maeneo hufafanuliwa baada ya tabaka za convolution kutumika. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.sw.png) +![FRCNN](../../../../../translated_images/sw/f-rcnn.3cda6d9bb4188875.png) > Picha kutoka [Karatasi Rasmi](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Mbinu hii ni sawa na R-CNN, lakini maeneo hufafanuliwa baada ya tabaka za convol Wazo kuu la mbinu hii ni kutumia mtandao wa neva kutabiri ROI - kinachoitwa *Mtandao wa Mapendekezo ya Maeneo*. [Karatasi](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.sw.png) +![FasterRCNN](../../../../../translated_images/sw/faster-rcnn.8d46c099b87ef30a.png) > Picha kutoka [karatasi rasmi](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Algorithimu hii ni ya haraka zaidi kuliko Faster R-CNN. Wazo kuu ni kama ifuatav 2. Sifa zinashughulikiwa na **Ramani ya Alama Inayozingatia Nafasi**. Kila kitu kutoka $C$ madarasa hugawanywa kwa maeneo $k\times k$, na tunafundisha kutabiri sehemu za vitu. 3. Kwa kila sehemu kutoka maeneo $k\times k$ mitandao yote hupiga kura kwa madarasa ya vitu, na darasa la kitu lenye kura nyingi zaidi huchaguliwa. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.sw.png) +![r-fcn image](../../../../../translated_images/sw/r-fcn.13eb88158b99a3da.png) > Picha kutoka [karatasi rasmi](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO ni algorithimu ya wakati halisi ya kupita mara moja. Wazo kuu ni kama ifuat * Picha inagawanywa katika maeneo $S\times S$. * Kwa kila eneo, **CNN** inatabiri vitu $n$ vinavyowezekana, m coordinates ya *maboksi ya mipaka* na *uhakika*=*uwezekano* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.sw.png) + ![YOLO](../../../../../translated_images/sw/yolo.a2648ec82ee8bb4e.png) > Picha kutoka [karatasi rasmi](https://arxiv.org/abs/1506.02640) diff --git a/translations/sw/lessons/4-ComputerVision/README.md b/translations/sw/lessons/4-ComputerVision/README.md index 2ea83fc6..3fe1f7ac 100644 --- a/translations/sw/lessons/4-ComputerVision/README.md +++ b/translations/sw/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Maono ya Kompyuta -![Muhtasari wa maudhui ya Maono ya Kompyuta katika mchoro](../../../../translated_images/ai-computervision.6506ebebac3fbf76.sw.png) +![Muhtasari wa maudhui ya Maono ya Kompyuta katika mchoro](../../../../translated_images/sw/ai-computervision.6506ebebac3fbf76.png) Katika sehemu hii tutajifunza kuhusu: diff --git a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index b593b458..4fe7a4e1 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) ni uwakilishi wa vector unaotumika sana katika mbinu za jadi. Kila neno linaunganishwa na faharasa ya vector, na kipengele cha vector kinaonyesha idadi ya mara neno fulani linavyotokea katika hati fulani.\n", "\n", - "![Picha inayoonyesha jinsi uwakilishi wa vector wa Bag of Words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sw.png) \n", + "![Picha inayoonyesha jinsi uwakilishi wa vector wa Bag of Words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Unaweza pia kufikiria BoW kama jumla ya vectors zote zilizowakilishwa kwa njia ya one-hot-encoding kwa maneno ya kibinafsi katika maandishi.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 0d5eaeb3..cc7deace 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/sw/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) ni uwakilishi wa vekta wa jadi ambao ni rahisi zaidi kueleweka. Kila neno linaunganishwa na faharasa ya vekta, na kipengele cha vekta kinaonyesha idadi ya mara neno fulani linavyotokea katika hati fulani.\n", "\n", - "![Picha inayoonyesha jinsi uwakilishi wa vekta wa bag-of-words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.sw.png) \n", + "![Picha inayoonyesha jinsi uwakilishi wa vekta wa bag-of-words unavyohifadhiwa kwenye kumbukumbu.](../../../../../translated_images/sw/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Unaweza pia kufikiria BoW kama jumla ya vekta zote za one-hot-encoded kwa maneno binafsi katika maandishi.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 455a1ffc..2ffcae34 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu, tunaweza kubadilisha kutoka mfuko wa maneno (bag-of-words) kwenda kwenye mfano wa **embedding bag**, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi fulani ya jumla juu ya embeddings zote hizo, kama vile `sum`, `average` au `max`.\n", "\n", - "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sw.png)\n", + "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Mtandao wetu wa neva wa kuainisha utaanza na safu ya embedding, kisha safu ya jumlisho, na classifier ya linear juu yake:\n" ] @@ -176,7 +176,7 @@ "\n", "Katika usanifu wa awali, tulihitaji kuongeza urefu wa mfuatano wote ili kufanana na urefu mmoja kwa ajili ya kuingiza kwenye kundi dogo (minibatch). Hii si njia bora zaidi ya kuwakilisha mfuatano wa urefu tofauti - njia nyingine inaweza kuwa kutumia **offset** vector, ambayo itahifadhi nafasi za mfuatano wote uliowekwa kwenye vector moja kubwa.\n", "\n", - "![Picha inayoonyesha uwakilishi wa mfuatano wa offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.sw.png)\n", + "![Picha inayoonyesha uwakilishi wa mfuatano wa offset](../../../../../translated_images/sw/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Katika picha hapo juu, tunaonyesha mfuatano wa herufi, lakini katika mfano wetu tunafanya kazi na mfuatano wa maneno. Hata hivyo, kanuni ya jumla ya kuwakilisha mfuatano kwa kutumia offset vector inabaki ile ile.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ni ya haraka, wakati skip-gram ni ya polepole, lakini inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida.\n", "\n", - "![Picha inayoonyesha algorithimu za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sw.png)\n", + "![Picha inayoonyesha algorithimu za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Ili kujaribu kuingiza kwa Word2Vec iliyofundishwa awali kwenye seti ya data ya Google News, tunaweza kutumia maktaba ya **gensim**. Hapa chini tunapata maneno yanayofanana zaidi na 'neural'\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 5cb3356b..ea34fdc1 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/sw/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu, tunaweza kubadilisha kutoka bag-of-words kwenda kwenye modeli ya **embedding bag**, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi fulani ya jumla juu ya embeddings hizo zote, kama `sum`, `average` au `max`.\n", "\n", - "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sw.png)\n", + "![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Mtandao wetu wa neural classifier unajumuisha safu zifuatazo:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW ni ya haraka, na ingawa skip-gram ni polepole, inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida.\n", "\n", - "![Picha inayoonyesha algoriti za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sw.png)\n", + "![Picha inayoonyesha algoriti za CBoW na Skip-Gram za kubadilisha maneno kuwa vekta.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Ili kujaribu embedding ya Word2Vec iliyofundishwa awali kwenye seti ya data ya Google News, tunaweza kutumia maktaba ya **gensim**. Hapa chini tunapata maneno yanayofanana zaidi na 'neural'.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/README.md b/translations/sw/lessons/5-NLP/14-Embeddings/README.md index 202c141b..0c77f9d8 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sw/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Kwa hivyo, safu ya embedding itachukua neno kama ingizo, na kutoa vector ya mato Kwa kutumia safu ya embedding kama safu ya kwanza katika mtandao wetu wa classifier, tunaweza kubadilisha kutoka bag-of-words kwenda kwenye **embedding bag** model, ambapo tunabadilisha kila neno katika maandishi yetu kuwa embedding inayolingana, kisha tunahesabu kazi ya jumla juu ya embeddings hizo zote, kama vile `sum`, `average` au `max`. -![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.sw.png) +![Picha inayoonyesha classifier ya embedding kwa maneno matano ya mfululizo.](../../../../../translated_images/sw/embedding-classifier-example.b77f021a7ee67eee.png) > Picha na mwandishi @@ -40,7 +40,7 @@ Ili kufanya hivyo, tunahitaji kufundisha awali (pre-train) mfano wetu wa embeddi CBoW ni ya haraka, wakati skip-gram ni ya polepole, lakini inafanya kazi bora ya kuwakilisha maneno yasiyo ya kawaida. -![Picha inayoonyesha CBoW na Skip-Gram algorithms za kubadilisha maneno kuwa vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sw.png) +![Picha inayoonyesha CBoW na Skip-Gram algorithms za kubadilisha maneno kuwa vectors.](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Picha kutoka [karatasi hii](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md index 8f143639..5e1492d3 100644 --- a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Katika mifano yetu ya awali, tulitumia uwakilishi wa semantiki uliokwisha fundis * **Mfuko Endelevu wa Maneno** (CBoW), ambapo tunatabiri tokeni ya katikati $W_0$ katika mlolongo wa tokeni $W_{-N}$, ..., $W_N$. * **Skip-gram**, ambapo tunatabiri seti ya tokeni za jirani {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} kutoka tokeni ya katikati $W_0$. -![picha kutoka karatasi kuhusu kubadilisha maneno kuwa vekta](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.sw.png) +![picha kutoka karatasi kuhusu kubadilisha maneno kuwa vekta](../../../../../translated_images/sw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Picha kutoka [karatasi hii](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/sw/lessons/5-NLP/16-RNN/README.md b/translations/sw/lessons/5-NLP/16-RNN/README.md index f60e1ee5..df42ad78 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/README.md +++ b/translations/sw/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Katika sehemu zilizopita, tumekuwa tukitumia uwakilishi wa kisemantiki wa maandi Ili kunasa maana ya mlolongo wa maandishi, tunahitaji kutumia usanifu mwingine wa mtandao wa neural, unaoitwa **mtandao wa neural unaojirudia**, au RNN. Katika RNN, tunapitisha sentensi yetu kupitia mtandao neno moja kwa wakati, na mtandao huzalisha hali fulani (**state**), ambayo tunapitisha tena kwenye mtandao pamoja na neno linalofuata. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.sw.png) +![RNN](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.png) > Picha na mwandishi @@ -61,7 +61,7 @@ Tumeelezea mitandao ya kujirudia inayofanya kazi kwa mwelekeo mmoja, kutoka mwan Mtandao wa kujirudia, iwe wa mwelekeo mmoja au wa mwelekeo mbili, hunasa mifumo fulani ndani ya mlolongo, na inaweza kuihifadhi kwenye vekta ya hali au kuipitisha kwenye matokeo. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga safu nyingine ya kujirudia juu ya ile ya kwanza ili kunasa mifumo ya kiwango cha juu na kujenga kutoka kwa mifumo ya kiwango cha chini iliyotolewa na safu ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi** ambayo inajumuisha mitandao miwili au zaidi ya kujirudia, ambapo matokeo ya safu ya awali hupitishwa kwa safu inayofuata kama pembejeo. -![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sw.jpg) +![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Picha kutoka [makala hii nzuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ya Fernando López* diff --git a/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 13038371..af085ef7 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Mtandao wa kurudiarudia, wa mwelekeo mmoja au wa mwelekeo mbili, unakamata mifumo fulani ndani ya mlolongo, na inaweza kuihifadhi kwenye vekta ya hali au kuipitisha kwenye pato. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga safu nyingine ya kurudiarudia juu ya safu ya kwanza ili kukamata mifumo ya kiwango cha juu, iliyojengwa kutoka kwa mifumo ya kiwango cha chini iliyotolewa na safu ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi**, ambayo inajumuisha mitandao miwili au zaidi ya kurudiarudia, ambapo pato la safu ya awali linapitishwa kwa safu inayofuata kama pembejeo.\n", "\n", - "![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sw.jpg)\n", + "![Picha inayoonyesha RNN ya tabaka nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Picha kutoka [makala hii nzuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ya Fernando López*\n", "\n", diff --git a/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb index 00f5e5e9..5c0c5a8c 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/sw/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Ili kukamata maana ya mlolongo wa maandishi, tutatumia usanifu wa mtandao wa neva unaoitwa **mtandao wa neva wa kurudia**, au RNN. Tunapotumia RNN, tunapitisha sentensi yetu kupitia mtandao tokeni moja kwa wakati, na mtandao huzalisha **hali fulani**, ambayo tunapitisha tena kwa mtandao pamoja na tokeni inayofuata.\n", "\n", - "![Picha inayoonyesha mfano wa uzalishaji wa mtandao wa neva wa kurudia.](../../../../../translated_images/rnn.27f5c29c53d727b5.sw.png)\n", + "![Picha inayoonyesha mfano wa uzalishaji wa mtandao wa neva wa kurudia.](../../../../../translated_images/sw/rnn.27f5c29c53d727b5.png)\n", "\n", "Kwa kuzingatia mlolongo wa tokeni za ingizo $X_0,\\dots,X_n$, RNN huunda mlolongo wa vizuizi vya mtandao wa neva, na hufundisha mlolongo huu kutoka mwanzo hadi mwisho kwa kutumia backpropagation. Kila kizuizi cha mtandao huchukua jozi $(X_i,S_i)$ kama ingizo, na huzalisha $S_{i+1}$ kama matokeo. Hali ya mwisho $S_n$ au matokeo $Y_n$ huingia kwenye classifier ya mstari ili kutoa matokeo. Vizuizi vyote vya mtandao vinashiriki uzito sawa, na hufundishwa kutoka mwanzo hadi mwisho kwa kutumia mchakato mmoja wa backpropagation.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Mitandao ya kurudia, iwe ya mwelekeo mmoja au mwelekeo mbili, huchukua mifumo ndani ya mlolongo, na kuihifadhi katika vekta za hali au kuzirudisha kama matokeo. Kama ilivyo kwa mitandao ya convolutional, tunaweza kujenga tabaka nyingine ya kurudia kufuatia ya kwanza ili kuchukua mifumo ya kiwango cha juu, iliyojengwa kutoka mifumo ya kiwango cha chini iliyotolewa na tabaka ya kwanza. Hii inatupeleka kwenye dhana ya **RNN ya tabaka nyingi**, ambayo inajumuisha mitandao miwili au zaidi ya kurudia, ambapo matokeo ya tabaka ya awali hupitishwa kwa tabaka inayofuata kama pembejeo.\n", "\n", - "![Picha inayoonyesha RNN ya Tabaka Nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.sw.jpg)\n", + "![Picha inayoonyesha RNN ya Tabaka Nyingi ya kumbukumbu ya muda mrefu na mfupi](../../../../../translated_images/sw/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Picha kutoka [chapisho hili zuri](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) na Fernando López.*\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 535f42f5..ffb11220 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Njia tutakayotumia kufundisha RNN ili kuzalisha maandishi ni kama ifuatavyo. Kwenye kila hatua, tutachukua mlolongo wa herufi zenye urefu wa `nchars`, na kuiomba mtandao uzalishe herufi inayofuata kwa kila herufi ya ingizo:\n", "\n", - "![Picha inayoonyesha mfano wa RNN ikizalisha neno 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sw.png)\n", + "![Picha inayoonyesha mfano wa RNN ikizalisha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Kulingana na hali halisi, tunaweza pia kutaka kujumuisha baadhi ya herufi maalum, kama vile *mwisho-wa-mlolongo* ``. Katika hali yetu, tunataka tu kufundisha mtandao kwa ajili ya uzalishaji wa maandishi usio na mwisho, kwa hivyo tutarekebisha ukubwa wa kila mlolongo kuwa sawa na tokeni `nchars`. Kwa hivyo, kila mfano wa mafunzo utajumuisha viingizo `nchars` na matokeo `nchars` (ambayo ni mlolongo wa ingizo uliosogezwa herufi moja kushoto). Minibatch itajumuisha mifululizo kadhaa kama hiyo.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 9fb1b80a..abad4b87 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Njia tutakayotumia kufundisha RNN ili kutengeneza vichwa vya habari ni kama ifuatavyo. Kwenye kila hatua, tutachukua kichwa kimoja, ambacho kitaingizwa kwenye RNN, na kwa kila herufi ya ingizo tutaiomba mtandao kutengeneza herufi inayofuata ya matokeo:\n", "\n", - "![Picha inayoonyesha mfano wa kizazi cha RNN cha neno 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sw.png)\n", + "![Picha inayoonyesha mfano wa kizazi cha RNN cha neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Kwa herufi ya mwisho ya mlolongo wetu, tutaiomba mtandao kutengeneza tokeni ``.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md index 1883bb24..52100fa6 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Katika usanifu wa RNN tuliojadili katika kitengo kilichopita, kila kitengo cha R Hii inaruhusu usanifu tofauti wa neural unaoonyeshwa kwenye picha hapa chini: -![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neural ya kurudia.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sw.jpg) +![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neural ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Picha kutoka kwa chapisho la blogu [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) na [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Katika kitengo hiki, tutazingatia mifano rahisi ya kizazi inayotusaidia kuzalish Tutafundisha RNN hii kuzalisha maandishi hatua kwa hatua. Katika kila hatua, tutachukua mfululizo wa herufi za urefu `nchars`, na kuomba mtandao kuzalisha herufi inayofuata kwa kila herufi ya pembejeo: -![Picha inayoonyesha mfano wa kizazi cha RNN wa neno 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.sw.png) +![Picha inayoonyesha mfano wa kizazi cha RNN wa neno 'HELLO'.](../../../../../translated_images/sw/rnn-generate.56c54afb52f9781d.png) Wakati wa kuzalisha maandishi (wakati wa utabiri), tunaanza na **msukumo fulani**, ambao unapitia seli za RNN ili kuzalisha hali yake ya kati, na kisha kutoka hali hii kizazi kinaanza. Tunazalisha herufi moja kwa wakati, na kupitisha hali na herufi iliyozalishwa kwa seli nyingine ya RNN ili kuzalisha inayofuata, hadi tutakapozalisha herufi za kutosha. diff --git a/translations/sw/lessons/5-NLP/18-Transformers/README.md b/translations/sw/lessons/5-NLP/18-Transformers/README.md index 542ff1b3..f5b2817e 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Kwa kutumia RNNs, mlolongo-kwa-mlolongo unatekelezwa na mitandao miwili ya kurud **Mbinu za Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa matokeo wa RNN. Njia ya kutekeleza hii ni kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo yt, tutazingatia hali zote za siri za pembejeo hi, kwa kutumia viwango tofauti vya uzito αt,i. -![Picha inayoonyesha modeli ya encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sw.png) +![Picha inayoonyesha modeli ya encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) > Modeli ya encoder-decoder na mbinu ya uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyotajwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Matriki ya uangalizi {αi,j} ingeonyesha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mlolongo wa matokeo. Hapa chini kuna mfano wa matriki kama hiyo: -![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, iliyochukuliwa kutoka Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sw.png) +![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, iliyochukuliwa kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) > Mchoro kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Matokeo tunayopata na embedding ya nafasi yanajumuisha tokeni asilia na nafasi y Kisha, tunahitaji kunasa mifumo fulani ndani ya mlolongo wetu. Ili kufanya hivyo, transformers hutumia mbinu ya **uangalizi wa kibinafsi**, ambayo kimsingi ni uangalizi unaotumika kwa mlolongo sawa kama pembejeo na matokeo. Kutumia uangalizi wa kibinafsi kunatuwezesha kuzingatia **muktadha** ndani ya sentensi, na kuona ni maneno gani yanayohusiana. Kwa mfano, inatuwezesha kuona ni maneno gani yanayorejelewa na marejeo, kama *it*, na pia kuzingatia muktadha: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sw.png) +![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.png) > Picha kutoka [Blogu ya Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Kwa kuwa kila nafasi ya pembejeo inalinganishwa kwa uhuru na kila nafasi ya mato **BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Modeli hii kwanza inafundishwa awali kwenye hifadhidata kubwa ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya kusimamiwa (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, modeli inachukua viwango vikubwa vya uelewa wa lugha ambavyo vinaweza kutumika na hifadhidata nyingine kwa kutumia kurekebisha. Mchakato huu unaitwa **transfer learning**. -![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sw.png) +![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Picha [chanzo](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md index 3b4b8828..52005c44 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ Med RNN:er implementeras sekvens-till-sekvens av två återkommande nätverk, d **Uppmärksamhetsmekanismer** ger ett sätt att vikta den kontextuella påverkan av varje ingångsvektor på varje utdataförutsägelse av RNN. Sättet det implementeras på är genom att skapa genvägar mellan mellanliggande tillstånd av ingångs-RNN och utgångs-RNN. På detta sätt, när vi genererar utdata symbol yt, kommer vi att ta hänsyn till alla ingångs dolda tillstånd hi, med olika viktkoefficienter αt,i. -![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sw.png) +![Bild som visar en kodare/avkodare-modell med ett additivt uppmärksamhetslager](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) > Kodare-avkodare-modell med additiv uppmärksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad från [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Uppmärksamhetsmatrisen {αi,j} skulle representera graden av att vissa ingångsord spelar en roll i generationen av ett givet ord i utgångssekvensen. Nedan är ett exempel på en sådan matris: -![Bild som visar en exempeljustering som hittats av RNNsearch-50, tagen från Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sw.png) +![Bild som visar en exempeljustering som hittats av RNNsearch-50, tagen från Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) > Figur från [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -57,7 +57,7 @@ Resultatet vi får med positionsinbäddning inbäddas både den ursprungliga tok Nästa steg är att fånga vissa mönster inom vår sekvens. För att göra detta använder transformatorer en **självuppmärksamhets**mekanism, som i grunden är uppmärksamhet tillämpad på samma sekvens som ingång och utgång. Tillämpning av självuppmärksamhet gör att vi kan ta hänsyn till **kontext** inom meningen och se vilka ord som är relaterade. Till exempel gör det att vi kan se vilka ord som hänvisas till av referenser, såsom *det*, och även ta kontexten i beaktande: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.sw.png) +![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.png) > Bild från [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ Eftersom varje ingångsposition mappas oberoende till varje utgångsposition kan **BERT** (Bidirectional Encoder Representations from Transformers) är ett mycket stort flerlagers transformatornätverk med 12 lager för *BERT-base*, och 24 för *BERT-large*. Modellen förtränas först på en stor korpus av textdata (WikiPedia + böcker) med hjälp av osupervised träning (förutsäga maskerade ord i en mening). Under förträningen absorberar modellen betydande nivåer av språkförståelse som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **överföringsinlärning**. -![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sw.png) +![Bild från http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Bild [källa](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index a5acd12d..826771d5 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/sw/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Utaratibu wa Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa RNN. Hii inatekelezwa kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo $y_t$, tutazingatia hali zote za siri za pembejeo $h_i$, kwa kutumia viwango tofauti vya uzito $\\alpha_{t,i}$.\n", "\n", - "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sw.png) \n", + "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png) \n", "*Mfano wa encoder-decoder na utaratibu wa uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyonukuliwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriki ya uangalizi $\\{\\alpha_{i,j}\\}$ inawakilisha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mlolongo wa matokeo. Hapo chini kuna mfano wa matriki kama hiyo:\n", "\n", - "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sw.png) \n", + "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png) \n", "\n", "*Picha imetolewa kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Mchoro wa 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Mfano huu kwanza hufunzwa awali kwenye hifadhidata kubwa ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya kusimamiwa (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, mfano huu hujifunza kiwango kikubwa cha uelewa wa lugha ambacho kinaweza kutumika na hifadhidata nyingine kwa kutumia marekebisho madogo. Mchakato huu unaitwa **ujifunzaji wa uhamisho**.\n", "\n", - "![Picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sw.png) \n", + "![Picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) \n", "\n", "Kuna tofauti nyingi za usanifu wa Transformer ikiwa ni pamoja na BERT, DistilBERT, BigBird, OpenGPT3 na nyinginezo ambazo zinaweza kufanyiwa marekebisho madogo. [Paket ya HuggingFace](https://github.com/huggingface/) inatoa hifadhidata ya mafunzo kwa usanifu mwingi wa aina hii kwa kutumia PyTorch.\n", "\n", diff --git a/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 478aded5..fcfe2dff 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/sw/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Mbinu za Uangalizi** hutoa njia ya kupima athari ya muktadha wa kila vector ya pembejeo kwenye kila utabiri wa matokeo wa RNN. Njia inavyotekelezwa ni kwa kuunda njia za mkato kati ya hali za kati za RNN ya pembejeo na RNN ya matokeo. Kwa njia hii, tunapozalisha alama ya matokeo $y_t$, tutazingatia hali zote zilizofichwa za pembejeo $h_i$, kwa kutumia viwango tofauti vya uzito $\\alpha_{t,i}$.\n", "\n", - "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.sw.png)\n", + "![Picha inayoonyesha mfano wa encoder/decoder na safu ya uangalizi wa kuongeza](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.png)\n", "*Mfano wa encoder-decoder na mbinu ya uangalizi wa kuongeza katika [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), iliyotajwa kutoka [blogu hii](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Matriki ya uangalizi $\\{\\alpha_{i,j}\\}$ itaonyesha kiwango ambacho maneno fulani ya pembejeo yanachangia katika uzalishaji wa neno fulani katika mfuatano wa matokeo. Hapo chini kuna mfano wa matriki kama hiyo:\n", "\n", - "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.sw.png)\n", + "![Picha inayoonyesha mpangilio wa mfano uliopatikana na RNNsearch-50, kutoka Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Picha iliyotolewa kutoka [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ni mtandao mkubwa sana wa tabaka nyingi wa transformer wenye tabaka 12 kwa *BERT-base*, na 24 kwa *BERT-large*. Modeli hii huanza kwa kufundishwa awali kwenye mkusanyiko mkubwa wa data ya maandishi (WikiPedia + vitabu) kwa kutumia mafunzo yasiyo ya usimamizi (kutabiri maneno yaliyofichwa katika sentensi). Wakati wa mafunzo ya awali, modeli hujifunza kiwango kikubwa cha uelewa wa lugha ambacho kinaweza kutumika na seti nyingine za data kwa kutumia kurekebisha mafunzo. Mchakato huu unaitwa **transfer learning**.\n", "\n", - "![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.sw.png)\n", + "![picha kutoka http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Kuna aina nyingi za usanifu wa Transformer ikiwa ni pamoja na BERT, DistilBERT, BigBird, OpenGPT3 na nyinginezo ambazo zinaweza kurekebishwa. \n", "\n", diff --git a/translations/sw/lessons/5-NLP/19-NER/README.md b/translations/sw/lessons/5-NLP/19-NER/README.md index dada1f81..eb66bb2d 100644 --- a/translations/sw/lessons/5-NLP/19-NER/README.md +++ b/translations/sw/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Kwa kuwa tunahitaji kujenga uhusiano wa moja kwa moja kati ya tokeni na madarasa, tunaweza kufundisha mfano wa mtandao wa neva wa **wengi-kwa-wengi** kutoka kwenye picha hii: -![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neva ya kurudia.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.sw.jpg) +![Picha inayoonyesha mifumo ya kawaida ya mitandao ya neva ya kurudia.](../../../../../translated_images/sw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Picha kutoka [blogu hii](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) na [Andrej Karpathy](http://karpathy.github.io/). Mifano ya uainishaji wa tokeni ya NER inahusiana na usanifu wa mtandao wa kulia kabisa kwenye picha hii.* diff --git a/translations/sw/lessons/5-NLP/README.md b/translations/sw/lessons/5-NLP/README.md index 76351fd1..15627514 100644 --- a/translations/sw/lessons/5-NLP/README.md +++ b/translations/sw/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Usindikaji wa Lugha Asilia -![Muhtasari wa kazi za NLP katika mchoro](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.sw.png) +![Muhtasari wa kazi za NLP katika mchoro](../../../../translated_images/sw/ai-nlp.b22dcb8ca4707cea.png) Katika sehemu hii, tutazingatia kutumia Mitandao ya Neural kushughulikia kazi zinazohusiana na **Usindikaji wa Lugha Asilia (NLP)**. Kuna matatizo mengi ya NLP ambayo tunataka kompyuta iweze kuyatatua: diff --git a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md index 905dbb48..2a90c775 100644 --- a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Unaweza kufungua mojawapo ya miundo, kwa mfano **Biology → Flocking** Baada ya kufungua mfano, unapelekwa kwenye skrini kuu ya NetLogo. Hapa kuna mfano wa mfano unaoelezea idadi ya mbwa mwitu na kondoo, kwa kuzingatia rasilimali finyu (nyasi). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.sw.png) +![NetLogo Main Screen](../../../../../translated_images/sw/NetLogo-Main.32653711ec1a01b3.png) > Picha ya skrini na Dmitry Soshnikov diff --git a/translations/sw/lessons/README.md b/translations/sw/lessons/README.md index 91cf19ea..80fd660d 100644 --- a/translations/sw/lessons/README.md +++ b/translations/sw/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Muhtasari -![Muhtasari katika mchoro](../../../translated_images/ai-overview.0857791951d19500.sw.png) +![Muhtasari katika mchoro](../../../translated_images/sw/ai-overview.0857791951d19500.png) > Mchoro wa maandishi na [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md index 11e6cbc2..3590d053 100644 --- a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Baada ya mafanikio ya mifano ya transformer katika kutatua kazi za NLP, usanifu Wazo kuu la CLIP ni kuwa na uwezo wa kulinganisha maelezo ya maandishi na picha na kuamua jinsi picha inavyolingana na maelezo hayo. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.sw.png) +![CLIP Architecture](../../../../../translated_images/sw/clip-arch.b3dbf20b4e8ed8be.png) > *Picha kutoka [blogu hii](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Mara tu mfano huu unapokuwa umefunzwa, tunaweza kuupa kundi la picha na kundi la Tuseme tunahitaji kuainisha picha kati ya, kwa mfano, paka, mbwa na binadamu. Katika hali hii, tunaweza kuupa mfano picha, na mfululizo wa maelezo ya maandishi: "*picha ya paka*", "*picha ya mbwa*", "*picha ya binadamu*". Katika vekta inayotokana ya uwezekano 3 tunahitaji tu kuchagua faharasa yenye thamani ya juu zaidi. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.sw.png) +![CLIP for Image Classification](../../../../../translated_images/sw/clip-class.3af42ef0b2b19369.png) > *Picha kutoka [blogu hii](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Jifunze zaidi kuhusu VQGAN kwenye tovuti ya [Taming Transformers](https://compvi Moja ya tofauti muhimu kati ya VQGAN na GAN ya jadi ni kwamba ya mwisho inaweza kuzalisha picha nzuri kutoka kwa vekta yoyote ya pembejeo, wakati VQGAN ina uwezekano wa kuzalisha picha ambayo haitakuwa thabiti. Kwa hivyo, tunahitaji kuongoza zaidi mchakato wa uundaji wa picha, na hilo linaweza kufanywa kwa kutumia CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.sw.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/sw/vqgan.5027fe05051dfa31.png) Ili kuzalisha picha inayolingana na maelezo ya maandishi, tunaanza na vekta ya usimbaji wa nasibu ambayo inapitia VQGAN ili kuzalisha picha. Kisha CLIP hutumika kuzalisha kazi ya hasara inayonyesha jinsi picha inavyolingana na maelezo ya maandishi. Lengo basi ni kupunguza hasara hii, kwa kutumia kurudi nyuma ili kurekebisha vigezo vya vekta ya pembejeo. Maktaba nzuri inayotekeleza VQGAN+CLIP ni [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.sw.png) | ![Picture produced by pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.sw.png) | ![Picture produced by Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.sw.png) +![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/sw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Picha iliyozalishwa kutoka maelezo *a closeup watercolor portrait of young male teacher of literature with a book* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of young female teacher of computer science with a computer* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of old male teacher of mathematics in front of blackboard* @@ -75,7 +75,7 @@ Tofauti na CLIP, DALL-E hupokea maandishi na picha kama mkondo mmoja wa tokeni k Tofauti kuu kati ya DALL.E 1 na 2, ni kwamba inazalisha picha na sanaa za kweli zaidi. Mifano ya uzalishaji wa picha na DALL-E: -![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.sw.png) | ![Picture produced by pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.sw.png) | ![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.sw.png) +![Picture produced by Pixray](../../../../../translated_images/sw/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Picture produced by pixray](../../../../../translated_images/sw/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Picture produced by Pixray](../../../../../translated_images/sw/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Picha iliyozalishwa kutoka maelezo *a closeup watercolor portrait of young male teacher of literature with a book* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of young female teacher of computer science with a computer* | Picha iliyozalishwa kutoka maelezo *a closeup oil portrait of old male teacher of mathematics in front of blackboard* diff --git a/translations/ta/README.md b/translations/ta/README.md index 77710988..2945a4fe 100644 --- a/translations/ta/README.md +++ b/translations/ta/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # கலை நுண்ணறிவு ஆரம்பக் கற்கைத் திட்டம் -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ta.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ta/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _சித்திரக்குறிப்பு [@girlie_mac](https://twitter.com/girlie_mac) மூலம்_ | diff --git a/translations/ta/lessons/1-Intro/README.md b/translations/ta/lessons/1-Intro/README.md index 0c2ccf58..16aa1fdf 100644 --- a/translations/ta/lessons/1-Intro/README.md +++ b/translations/ta/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI அறிமுகம் -![AI அறிமுகத்தின் சுருக்கம் ஒரு டூடிலில்](../../../../translated_images/ai-intro.bf28d1ac4235881c.ta.png) +![AI அறிமுகத்தின் சுருக்கம் ஒரு டூடிலில்](../../../../translated_images/ta/ai-intro.bf28d1ac4235881c.png) > [Tomomi Imura](https://twitter.com/girlie_mac) இன் ஸ்கெட்ச் நோட் @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: முதலில், கணினிகளை [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) எண் கணக்கீடுகளை ஒரு நன்கு வரையறுக்கப்பட்ட செயல்முறை - ஒரு ஆல்கொரிதம் - மூலம் செயல்படுத்த உருவாக்கினார். 19ஆம் நூற்றாண்டில் முன்மாதிரியாக உருவாக்கப்பட்ட கணினி மாடலின் அடிப்படையில், நவீன கணினிகள் மிகவும் மேம்பட்டவை என்றாலும், கட்டுப்படுத்தப்பட்ட கணக்கீடுகளின் அடிப்படையில் செயல்படுகின்றன. எனவே, ஒரு குறிப்பிட்ட இலக்கை அடைய தேவையான செயல்முறைகளை நன்கு அறிந்தால், கணினியை ஒரு செயல்பாட்டிற்கு நிரலிட முடியும். -![ஒரு நபரின் புகைப்படம்](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ta.png) +![ஒரு நபரின் புகைப்படம்](../../../../translated_images/ta/dsh_age.d212a30d4e54fb5f.png) > [Vickie Soshnikova](http://twitter.com/vickievalerie) இன் புகைப்படம் @@ -45,7 +45,7 @@ CO_OP_TRANSLATOR_METADATA: **[Intelligence](https://en.wikipedia.org/wiki/Intelligence)** என்ற சொல்லைச் சிக்கலாகக் கருதும்போது, இந்த சொல்லுக்கு தெளிவான வரையறை இல்லை என்பது ஒரு பிரச்சனையாகும். அறிவு **மூலதன சிந்தனை** அல்லது **சுய விழிப்புணர்வு** உடன் தொடர்புடையது என்று ஒருவர் வாதிடலாம், ஆனால் அதை சரியாக வரையறுக்க முடியாது. -![ஒரு பூனையின் புகைப்படம்](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ta.jpg) +![ஒரு பூனையின் புகைப்படம்](../../../../translated_images/ta/photo-cat.8c8e8fb760ffe457.jpg) > [Amber Kipp](https://unsplash.com/@sadmax) இன் Unsplash புகைப்படம் [Photo](https://unsplash.com/photos/75715CVEJhI) @@ -97,13 +97,13 @@ AGI பற்றி பேசும்போது, ​​நாம் உண் > | ML பற்றி என்ன? | | > |--------------|-----------| -> | சில data அடிப்படையில் ஒரு பிரச்சனையைத் தீர்க்க computer learning அடிப்படையில் Artificial Intelligence இன் ஒரு பகுதி **Machine Learning** என்று அழைக்கப்படுகிறது. இந்த course இல் classical machine learning ஐ நாம் பரிசீலிக்க மாட்டோம் - [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum ஐப் பார்க்கவும். | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ta.png) | +> | சில data அடிப்படையில் ஒரு பிரச்சனையைத் தீர்க்க computer learning அடிப்படையில் Artificial Intelligence இன் ஒரு பகுதி **Machine Learning** என்று அழைக்கப்படுகிறது. இந்த course இல் classical machine learning ஐ நாம் பரிசீலிக்க மாட்டோம் - [Machine Learning for Beginners](http://aka.ms/ml-beginners) curriculum ஐப் பார்க்கவும். | ![ML for Beginners](../../../../translated_images/ta/ml-for-beginners.9e4fed176fd5817d.png) | ## AI இன் சுருக்கமான வரலாறு Artificial Intelligence 20ஆம் நூற்றாண்டின் நடுப்பகுதியில் ஒரு துறையாக தொடங்கப்பட்டது. ஆரம்பத்தில், symbolic reasoning ஒரு முக்கியமான approach ஆக இருந்தது, மேலும் இது expert systems போன்ற சில முக்கியமான வெற்றிகளை உருவாக்கியது – குறிப்பிட்ட பிரச்சனை துறைகளில் ஒரு நிபுணராக செயல்படக்கூடிய computer programs. இருப்பினும், இந்த approach நன்றாக scale ஆகாது என்பது விரைவில் தெளிவாகியது. ஒரு நிபுணரிடமிருந்து அறிவை எடுப்பது, அதை கணினியில் பிரதிநிதித்துவப்படுத்துவது மற்றும் அந்த knowledgebase ஐ துல்லியமாக வைத்திருப்பது மிகவும் சிக்கலான task ஆகும், மேலும் பல சந்தர்ப்பங்களில் நடைமுறைக்கு மிகவும் செலவாகும். இது 1970களில் [AI Winter](https://en.wikipedia.org/wiki/AI_winter) என அழைக்கப்படும் நிலைக்கு வழிவகுத்தது. -AI வரலாற்றின் சுருக்கம் +AI வரலாற்றின் சுருக்கம் > [Dmitry Soshnikov](http://soshnikov.com) இன் படம் @@ -123,7 +123,7 @@ Artificial Intelligence 20ஆம் நூற்றாண்டின் நட * Cortana, Siri அல்லது Google Assistant போன்ற modern assistants அனைத்தும் hybrid systems ஆகும், அவை speech ஐ text ஆக மாற்ற neural networks ஐ பயன்படுத்துகின்றன மற்றும் நம் intent ஐ recognize செய்கின்றன, பின்னர் தேவையான actions ஐ செய்ய reasoning அல்லது explicit algorithms ஐ employ செய்கின்றன. * எதிர்காலத்தில், dialogue ஐ தானாகவே handle செய்ய ஒரு complete neural-based model ஐ எதிர்பார்க்கலாம். சமீபத்திய GPT மற்றும் [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) neural networks குடும்பம் இதில் சிறந்த வெற்றியை காட்டுகின்றன. -Turing Test இன் evolution +Turing Test இன் evolution > படம்: Dmitry Soshnikov, [புகைப்படம்](https://unsplash.com/photos/r8LmVbUKgns): [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## சமீபத்திய AI ஆராய்ச்சி diff --git a/translations/ta/lessons/2-Symbolic/Animals.ipynb b/translations/ta/lessons/2-Symbolic/Animals.ipynb index 91c0a6e9..a7138574 100644 --- a/translations/ta/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ta/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "இந்த எடுத்துக்காட்டில், சில உடல் பண்புகளின் அடிப்படையில் விலங்குகளை தீர்மானிக்க ஒரு எளிய அறிவு அடிப்படையிலான அமைப்பை செயல்படுத்துவோம். இந்த அமைப்பு கீழே உள்ள AND-OR மரத்தால் பிரதிநிதித்துவம் செய்யப்படுகிறது (இது முழு மரத்தின் ஒரு பகுதி மட்டுமே, மேலும் சில விதிகளை எளிதாக சேர்க்கலாம்):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ta.png)\n" + "![](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ta/lessons/2-Symbolic/README.md b/translations/ta/lessons/2-Symbolic/README.md index 3efb276b..e9f1d110 100644 --- a/translations/ta/lessons/2-Symbolic/README.md +++ b/translations/ta/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # அறிவு பிரதிநிதித்துவம் மற்றும் நிபுணர் அமைப்புகள் -![சின்னவியல் AI உள்ளடக்கத்தின் சுருக்கம்](../../../../translated_images/ai-symbolic.715a30cb610411a6.ta.png) +![சின்னவியல் AI உள்ளடக்கத்தின் சுருக்கம்](../../../../translated_images/ta/ai-symbolic.715a30cb610411a6.png) > [Tomomi Imura](https://twitter.com/girlie_mac) அவர்களின் சின்னவியல் குறிப்பு @@ -35,13 +35,13 @@ AI-யின் ஆரம்ப காலங்களில், புத்த * **அறிவு** என்பது தகவலை நமது உலக மாதிரியில் ஒருங்கிணைப்பதாகும். உதாரணமாக, ஒரு கணினி என்ன என்பதை நாங்கள் கற்றுக்கொண்ட பிறகு, அது எப்படி செயல்படுகிறது, அதன் விலை எவ்வளவு, மற்றும் அது எதற்காக பயன்படுத்தப்படலாம் என்பதற்கான சில கருத்துக்கள் நமக்கு உருவாகின்றன. இந்த தொடர்புடைய கருத்துக்களின் வலை நமது அறிவை உருவாக்குகிறது. * **ஞானம்** என்பது உலகத்தைப் பற்றிய நமது புரிதலின் மேலும் ஒரு நிலையாகும், மேலும் இது *மெட்டா-அறிவை* பிரதிநிதித்துவப்படுத்துகிறது, உதாரணமாக, அறிவு எப்போது மற்றும் எப்படி பயன்படுத்தப்பட வேண்டும் என்பதற்கான கருத்து. - + *படம் [விக்கிப்பீடியாவில் இருந்து](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - சொந்த வேலை, CC BY-SA 4.0* அதனால், **அறிவு பிரதிநிதித்துவம்** என்ற பிரச்சினை என்பது கணினியில் தரவின் வடிவத்தில் அறிவை பிரதிநிதித்துவப்படுத்துவதற்கான சில பயனுள்ள வழிகளை கண்டுபிடிப்பதாகும், இதை தானாகவே பயன்படுத்த முடியும். இது ஒரு வரம்பாகக் காணப்படுகிறது: -![அறிவு பிரதிநிதித்துவ வரம்பு](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ta.png) +![அறிவு பிரதிநிதித்துவ வரம்பு](../../../../translated_images/ta/knowledge-spectrum.b60df631852c0217.png) > [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் @@ -94,7 +94,7 @@ Block Syntax | Indent | | | சின்னவியல் AI-யின் ஆரம்ப வெற்றிகளில் ஒன்று **நிபுணர் அமைப்புகள்** - குறிப்பிட்ட பிரச்சினை துறையில் நிபுணராக செயல்பட வடிவமைக்கப்பட்ட கணினி அமைப்புகள். அவை **knowledge base** மற்றும் **inference engine** ஆகியவற்றின் அடிப்படையில் உருவாக்கப்பட்டன. -![மனித அமைப்பு](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ta.png) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ta.png) +![மனித அமைப்பு](../../../../translated_images/ta/arch-human.5d4d35f1bba3ab1c.png) | ![அறிவு அடிப்படையிலான அமைப்பு](../../../../translated_images/ta/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ மனித நரம்பு அமைப்பின் எளிமையான அமைப்பு | அறிவு அடிப்படையிலான அமைப்பின் கட்டமைப்பு @@ -106,7 +106,7 @@ Block Syntax | Indent | | | உதாரணமாக, ஒரு விலங்கின் உடல் பண்புகளை அடிப்படையாகக் கொண்டு அதைத் தீர்மானிக்கும் பின்வரும் நிபுணர் அமைப்பைப் பார்ப்போம்: -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ta.png) +![AND-OR Tree](../../../../translated_images/ta/AND-OR-Tree.5592d2c70187f283.png) > [Dmitry Soshnikov](http://soshnikov.com) அவர்களின் படங்கள் @@ -170,7 +170,7 @@ Semantic Web இல் முக்கியமான கருத்து **On Semantic Web இல், அனைத்து பிரதிநிதித்துவங்களும் triplets அடிப்படையில் அமைக்கப்பட்டுள்ளன. ஒவ்வொரு பொருளும் மற்றும் ஒவ்வொரு தொடர்பும் URI மூலம் தனித்துவமாக அடையாளம் காணப்படுகிறது. உதாரணமாக, இந்த AI Curriculum-ஐ Dmitry Soshnikov ஜனவரி 1, 2022 அன்று உருவாக்கியதாகக் கூற விரும்பினால், நாம் பயன்படுத்தக்கூடிய triplets இவை: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -181,7 +181,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre சிக்கலான சூழலில், உருவாக்குநர்களின் பட்டியலை வரையறுக்க விரும்பினால், RDF-ல் வரையறுக்கப்பட்ட சில தரவமைப்புகளை பயன்படுத்தலாம். - + > மேலே உள்ள வரைபடங்கள் [Dmitry Soshnikov](http://soshnikov.com) மூலம். @@ -205,7 +205,7 @@ GROUP BY ?eyeColorLabel > ✅ உங்கள் சொந்த Ontology-களை உருவாக்க அல்லது ஏற்கனவே உள்ளவற்றைத் திறக்க முயற்சிக்க விரும்பினால், [Protégé](https://protege.stanford.edu/) என்ற ஒரு சிறந்த காட்சி Ontology தொகுப்பியைப் பயன்படுத்தலாம். இதைப் பதிவிறக்கவும் அல்லது ஆன்லைனில் பயன்படுத்தவும். - + *Romanov குடும்ப Ontology-யுடன் திறந்த Web Protégé தொகுப்பி. Dmitry Soshnikov மூலம் எடுத்த படக்காட்சி* diff --git a/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md index 928d6e7e..b89f95fe 100644 --- a/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|ஃப்ராங்க் ரோசன்பிளாட் | மார்க் 1 பெர்செப்ட்ரான்| +|ஃப்ராங்க் ரோசன்பிளாட் | மார்க் 1 பெர்செப்ட்ரான்| > படங்கள் [விக்கிபீடியாவில் இருந்து](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) இங்கு f என்பது ஒரு படி செயல்பாட்டு செயல்பாடு - + ## பெர்செப்ட்ரானை பயிற்சி செய்வது diff --git a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 06f7d177..49d74130 100644 --- a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "நாம் இரும நிலை வகைப்பாட்டுக்கான தரவுத்தொகுப்பை உருவாக்கியுள்ளோம். ஆனால், தொடக்கத்திலிருந்தே இதை பல நிலை வகைப்பாடாகக் கருதுவோம், இதனால் நமது குறியீட்டை எளிதாக பல நிலை வகைப்பாட்டுக்கு மாற்ற முடியும். இந்தச் சூழலில், நமது ஒரே அடுக்கு பர்செப்ட்ரான் பின்வரும் கட்டமைப்பைக் கொண்டிருக்கும்:\n", "\n", - "\n", + "\n", "\n", "நெட்வொர்க்கின் இரண்டு வெளியீடுகள் இரண்டு வகைகளை குறிக்கின்றன, மேலும் இரண்டு வெளியீடுகளின் மத்தியில் அதிக மதிப்பைக் கொண்ட வகை சரியான தீர்வாக இருக்கும்.\n", "\n", @@ -489,7 +489,7 @@ "\n", "நாம் 2 வகைகளுக்கு மேல் கொண்டிருந்தால், softmax அனைத்து வகைகளிலும் சாத்தியங்களை சாதாரணமாக்கும். இங்கே MNIST இலக்க வகைப்பாட்டை செய்யும் நெட்வொர்க் கட்டமைப்பின் ஒரு வரைபடம் உள்ளது:\n", "\n", - "![MNIST வகைப்பாட்டாளர்](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ta.png)\n" + "![MNIST வகைப்பாட்டாளர்](../../../../../translated_images/ta/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -616,7 +616,7 @@ "\n", "## கணினி வரைபடம்\n", "\n", - "\n", + "\n", "\n", "இது வரை, நெட்வொர்க் பல்வேறு அடுக்குகளுக்கான வகுப்புகளை நாம் வரையறுத்துள்ளோம். அந்த அடுக்குகளின் அமைப்பை **கணினி வரைபடம்** எனக் குறிப்பிடலாம். இப்போது, கொடுக்கப்பட்ட பயிற்சி தரவுத்தொகுப்பிற்கான (அல்லது அதன் ஒரு பகுதிக்கான) loss ஐ கீழ்க்காணும் முறையில் கணக்கிடலாம்:\n" ] @@ -683,7 +683,7 @@ "source": [ "## பின்னோக்கி பரவல்\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -708,7 +708,7 @@ "* இது node $z$ இல் மாற்றங்களை $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$ மூலம் குறிக்கிறது\n", "* இந்த பிழையை குறைக்க, அளவுருக்களை சரிசெய்ய வேண்டும்: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (மேலும் $b$ க்கும் இதே)\n", "\n", - "\n", + "\n", "\n", "இந்த செயல்முறை நெட்வொர்க்கின் வெளியீட்டிலிருந்து அதன் அளவுருக்களுக்கு இழப்புப் பிழையை பகிரத் தொடங்குகிறது. எனவே இந்த செயல்முறை **பின்செலுத்தல்** என்று அழைக்கப்படுகிறது.\n", "\n", @@ -1261,7 +1261,7 @@ "* குறைந்த பயிற்சி இழப்பு - மாதிரி பயிற்சி தரவுகளை நன்றாக அணுக முடியும், ஏனெனில் இது போதுமான வெளிப்பாட்டு சக்தி கொண்டது. \n", "* சரிபார்ப்பு இழப்பு (validation loss) பயிற்சி இழப்பை விட அதிகமாக இருக்கலாம் மற்றும் பயிற்சியின் போது அதிகரிக்க தொடங்கலாம் - இதன் காரணம் மாதிரி பயிற்சி புள்ளிகளை \"நினைவில் வைத்துக்கொள்கிறது\" மற்றும் \"மொத்தப் படத்தை\" இழக்கிறது.\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.ta.png)\n", + "![Overfitting](../../../../../translated_images/ta/overfit.a0bd57f717c15769.png)\n", "\n", "> இந்த படத்தில், `x` என்பது பயிற்சி தரவுகளைக் குறிக்கிறது, `o` என்பது சரிபார்ப்பு தரவுகளைக் குறிக்கிறது. இடது பக்கம் - நேரியல் மாதிரி (ஒரு அடுக்கு), இது தரவுகளின் இயல்பை நன்றாக அணுகுகிறது. வலது பக்கம் - அதிகப் பயிற்சி பெற்ற மாதிரி, இது பயிற்சி தரவுகளை சரியாக அணுகுகிறது, ஆனால் பிற தரவுகளுடன் பொருந்துவதில் அர்த்தமற்றதாக மாறுகிறது (சரிபார்ப்பு பிழை மிகவும் அதிகமாக உள்ளது).\n" ] diff --git a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md index f02fcfce..d2cd77cd 100644 --- a/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: இந்த வெளிப்பாடுகளின் இடது-மிகவும் பகுதி அனைத்தும் ஒரே மாதிரியாக இருப்பதால், இழப்பு செயல்பாட்டிலிருந்து "பின்சென்று" கணக்கீட்டு வரைபடத்தின் மூலம் வேறுபாடுகளை பயனுள்ளதாகக் கணக்கிட முடியும். எனவே, பன்மடங்கு அடுக்கப்பட்ட பர்செப்ட்ரானை பயிற்சி செய்யும் முறை **பின்செலுத்தல்** அல்லது 'backprop' என அழைக்கப்படுகிறது. -கணக்கீட்டு வரைபடம் +கணக்கீட்டு வரைபடம் > TODO: படம் மேற்கோள் diff --git a/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md index cbe8cdc5..db80a4cb 100644 --- a/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting என்பது இயந்திர கற்றலில் கீழே உள்ள வரைபடங்களில் `x` மூலம் குறிக்கப்படும் 5 புள்ளிகளை அணுகும் சிக்கலான பிரச்சினையைப் பரிசீலிக்கவும்: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ta.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ta.jpg) +![linear](../../../../../translated_images/ta/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ta/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **நேர்கோட்ட மாதிரி, 2 அளவுருக்கள்** | **நேர்மற்ற மாதிரி, 7 அளவுருக்கள்** பயிற்சி பிழை = 5.3 | பயிற்சி பிழை = 0 @@ -79,7 +79,7 @@ Overfitting என்பது இயந்திர கற்றலில் மேலே உள்ள வரைபடத்தில் காண்பது போல, பயிற்சி பிழை மிகவும் குறைவாகவும், சரிபார்ப்பு பிழை மிகவும் அதிகமாகவும் இருந்தால், அது overfitting என்பதை காட்டுகிறது. பொதுவாக பயிற்சியின் போது, பயிற்சி மற்றும் சரிபார்ப்பு பிழைகள் இரண்டும் குறையத் தொடங்கும், பின்னர் ஒரு கட்டத்தில் சரிபார்ப்பு பிழை குறையாமல் அதிகரிக்கத் தொடங்கலாம். இது overfitting-ஐக் காட்டும் ஒரு அறிகுறியாக இருக்கும், மேலும் இந்த கட்டத்தில் பயிற்சியை நிறுத்த வேண்டும் (அல்லது குறைந்தது மாதிரியின் ஒரு நகலை சேமிக்க வேண்டும்). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ta.png) +![overfitting](../../../../../translated_images/ta/Overfitting.408ad91cd90b4371.png) ## Overfitting-ஐ எப்படி தடுக்கலாம் diff --git a/translations/ta/lessons/3-NeuralNetworks/README.md b/translations/ta/lessons/3-NeuralNetworks/README.md index c31bd263..5cb77d66 100644 --- a/translations/ta/lessons/3-NeuralNetworks/README.md +++ b/translations/ta/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # நரம்பு வலையமைப்புகளுக்கான அறிமுகம் -![நரம்பு வலையமைப்புகளுக்கான உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ta.png) +![நரம்பு வலையமைப்புகளுக்கான உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-neuralnetworks.1c687ae40bc86e83.png) அறிமுகத்தில் நாம் விவாதித்தபடி, நுண்ணறிவை அடைய ஒரு வழி **கணினி மாதிரி** அல்லது **கிரகித்தல் மூளை** உருவாக்குவது ஆகும். 20ஆம் நூற்றாண்டின் நடுப்பகுதியில் இருந்து, ஆராய்ச்சியாளர்கள் பல்வேறு கணித மாதிரிகளை முயற்சித்தனர், சமீபத்திய ஆண்டுகளில் இந்த திசை மிகுந்த வெற்றியை பெற்றது. மூளையின் இவ்வகை கணித மாதிரிகள் **நரம்பு வலையமைப்புகள்** என்று அழைக்கப்படுகின்றன. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: உயிரியல் அறிவியல் மூலம், நமது மூளை நரம்பு செல்களால் (நரம்புகள்) ஆனது என்பதை நாம் அறிகிறோம், அவற்றில் ஒவ்வொன்றும் பல "உள்ளீடுகள்" (டெண்ட்ரைட்கள்) மற்றும் ஒரு "வெளியீடு" (ஆக்சான்) கொண்டுள்ளது. டெண்ட்ரைட்கள் மற்றும் ஆக்சான்கள் மின்சார சிக்னல்களை நடத்த முடியும், மேலும் அவற்றுக்கிடையிலான இணைப்புகள் — சினாப்ஸ்கள் என்று அழைக்கப்படும் — மாறுபட்ட அளவிலான நடத்துதலைக் காட்ட முடியும், இது நரம்பு சுரப்பிகளால் கட்டுப்படுத்தப்படுகிறது. -![நரம்பு செலின் மாதிரி](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ta.jpg) | ![நரம்பு செலின் மாதிரி](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ta.png) +![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![நரம்பு செலின் மாதிரி](../../../../translated_images/ta/artneuron.1a5daa88d20ebe6f.png) ----|---- உயிரியல் நரம்பு செல் *([Wikipedia](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) இல் இருந்து படம்)* | கிரகித்தல் நரம்பு செல் *(ஆசிரியரின் படம்)* அதனால், ஒரு நரம்பு செலின் எளிய கணித மாதிரி பல உள்ளீடுகள் X1, ..., XN மற்றும் ஒரு வெளியீடு Y, மற்றும் ஒரு வரிசை எடைகள் W1, ..., WN கொண்டுள்ளது. வெளியீடு கீழ்க்கண்டவாறு கணக்கிடப்படுகிறது: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) இங்கு f என்பது சில கோட்பாட்டியல் **செயல்பாட்டு செயல்பாடு** ஆகும். diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md index eb8e09d4..f1feaba4 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ OpenCV-ஐ வீடியோ ஃப்ரேம்களை ஃப்ரேம * **ப்ரெயில் புத்தகத்தின் புகைப்படத்தை முன்னோட்ட செயலாக்கம்**. ப்ரெயில் சின்னங்களை தனித்தனியாக பிரித்து, நரம்பியல் வலையமைப்பால் மேலும் வகைப்படுத்துவதற்கான முன்னோட்ட செயலாக்கம், தெளிவாக்கம், அம்ச கண்டறிதல், பர்ஸ்பெக்டிவ் மாற்றம் மற்றும் NumPy மாற்றங்களை எப்படி பயன்படுத்தலாம் என்பதை நாங்கள் கவனம் செலுத்துகிறோம். -![ப்ரெயில் படம்](../../../../../translated_images/braille.341962ff76b1bd70.ta.jpeg) | ![முன்னோட்ட செயலாக்கம் செய்யப்பட்ட ப்ரெயில் படம்](../../../../../translated_images/braille-result.46530fea020b03c7.ta.png) | ![ப்ரெயில் சின்னங்கள்](../../../../../translated_images/braille-symbols.0159185ab69d5339.ta.png) +![ப்ரெயில் படம்](../../../../../translated_images/ta/braille.341962ff76b1bd70.jpeg) | ![முன்னோட்ட செயலாக்கம் செய்யப்பட்ட ப்ரெயில் படம்](../../../../../translated_images/ta/braille-result.46530fea020b03c7.png) | ![ப்ரெயில் சின்னங்கள்](../../../../../translated_images/ta/braille-symbols.0159185ab69d5339.png) ----|-----|----- > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து * **ஃப்ரேம் வேறுபாட்டைப் பயன்படுத்தி வீடியோவில் இயக்கத்தை கண்டறிதல்**. கேமரா நிலையானதாக இருந்தால், கேமரா ஃபீடில் இருந்து ஃப்ரேம்கள் ஒருவருக்கொருவர் மிகவும் ஒத்ததாக இருக்க வேண்டும். ஃப்ரேம்கள் வரிசைகளாக பிரதிநிதித்துவம் செய்யப்படுவதால், இரண்டு தொடர்ச்சியான ஃப்ரேம்களுக்கு வரிசைகளை கழிப்பதன் மூலம் பிக்சல் வேறுபாட்டைப் பெறலாம், இது நிலையான ஃப்ரேம்களுக்கு குறைவாக இருக்கும், மற்றும் படத்தில் முக்கியமான இயக்கம் ஏற்பட்டால் அதிகமாக மாறும். -![வீடியோ ஃப்ரேம்கள் மற்றும் ஃப்ரேம் வேறுபாடுகளின் படம்](../../../../../translated_images/frame-difference.706f805491a0883c.ta.png) +![வீடியோ ஃப்ரேம்கள் மற்றும் ஃப்ரேம் வேறுபாடுகளின் படம்](../../../../../translated_images/ta/frame-difference.706f805491a0883c.png) > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து @@ -89,7 +89,7 @@ OpenCV-ஐ வீடியோ ஃப்ரேம்களை ஃப்ரேம - **தனிமையான ஆப்டிகல் ஃப்ளோ** ஒவ்வொரு பிக்சலுக்கும் அது எங்கு நகர்கிறது என்பதை காட்டும் வெக்டர் புலத்தை கணக்கிடுகிறது. - **சிறிய ஆப்டிகல் ஃப்ளோ** படத்தில் சில தனித்துவமான அம்சங்களை (எ.கா. விளிமைகள்) அடிப்படையாகக் கொண்டு, ஃப்ரேம்-பை-ஃப்ரேம் அவற்றின் பாதையை உருவாக்குகிறது. -![ஆப்டிகல் ஃப்ளோ படம்](../../../../../translated_images/optical.1f4a94464579a83a.ta.png) +![ஆப்டிகல் ஃப்ளோ படம்](../../../../../translated_images/ta/optical.1f4a94464579a83a.png) > படம் [OpenCV.ipynb](OpenCV.ipynb) இல் இருந்து @@ -115,7 +115,7 @@ AI நிகழ்ச்சியில் இருந்து [இந்த இந்த ஆய்வகத்தில், நீங்கள் எளிய சைகைகளுடன் ஒரு வீடியோ எடுப்பீர்கள், மற்றும் உங்கள் இலக்கு ஆப்டிகல் ஃப்ளோவைப் பயன்படுத்தி மேலே/கீழே/இடது/வலது இயக்கங்களை எடுக்க வேண்டும். -கை இயக்க ஃப்ரேம் +கை இயக்க ஃப்ரேம் --- diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index a5c67a47..bad39a64 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "[இந்த வீடியோவை](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) கவனியுங்கள், இதில் ஒரு நபரின் கையடக்கம் நிலையான பின்னணியில் இடது/வலது/மேலே/கீழே நகர்கிறது.\n", "\n", - "\"கையடக்க\n", + "\"கையடக்க\n", "\n", "**உங்கள் இலக்கு** Optical Flow ஐ பயன்படுத்தி, வீடியோவில் எந்த பகுதிகள் மேலே/கீழே/இடது/வலது இயக்கங்களை கொண்டுள்ளன என்பதை கண்டறிவதாக இருக்கும்.\n", "\n", diff --git a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 9573cb37..f59794a2 100644 --- a/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/ta/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [இந்த வீடியோவை](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) கவனியுங்கள், இதில் ஒரு நபரின் கைப்பிடி நிலையான பின்னணியில் இடது/வலது/மேலே/கீழே நகர்கிறது. -கை நகர்வு ஃப்ரேம் +கை நகர்வு ஃப்ரேம் **உங்கள் இலக்கு** ஆப்டிக்கல் ஃப்ளோவை பயன்படுத்தி, வீடியோவின் எந்த பகுதிகள் மேலே/கீழே/இடது/வலது இயக்கங்களை கொண்டுள்ளன என்பதை கண்டறிய வேண்டும். diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index f3791da3..d8da40d6 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 என்பது 2014 ஆம் ஆண்டில் ImageNet top-5 வகைப்படுத்தலில் 92.7% துல்லியத்தை அடைந்த ஒரு நெட்வொர்க் ஆகும். இதன் அடுக்குகளின் அமைப்பு பின்வருமாறு உள்ளது: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ta.jpg) +![ImageNet Layers](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.jpg) நீங்கள் காணும் படி, VGG ஒரு பாரம்பரிய பyramிட் கட்டமைப்பை பின்பற்றுகிறது, இது கான்வல்யூஷன்-பூலிங் அடுக்குகளின் வரிசையாகும். -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ta.jpg) +![ImageNet Pyramid](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) இல் இருந்து படம் @@ -25,7 +25,7 @@ VGG-16 என்பது 2014 ஆம் ஆண்டில் ImageNet top-5 ResNet என்பது 2015 ஆம் ஆண்டில் Microsoft Research மூலம் முன்மொழியப்பட்ட மாடல்களின் குடும்பமாகும். ResNet இன் முக்கிய யோசனை **மீதமுள்ள பிளாக்குகளை** பயன்படுத்துவது: - + > [இந்தக் கட்டுரையில்](https://arxiv.org/pdf/1512.03385.pdf) இருந்து படம் @@ -37,7 +37,7 @@ Identity pass-through ஐ பயன்படுத்துவதற்கான Google Inception கட்டமைப்பு இந்த யோசனையை மேலும் ஒரு படி முன்னேற்றுகிறது, மேலும் ஒவ்வொரு நெட்வொர்க் அடுக்கையும் பல்வேறு பாதைகளின் கலவையாக உருவாக்குகிறது: - + > [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) இல் இருந்து படம் diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 3ca17bf2..8adbff2e 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "அதனால், ஒரு சாதாரண CNNல் பல கன்வல்யூஷன் அடுக்குகள் இருக்கும், அவற்றின் இடையே பூலிங் அடுக்குகள் இருக்கும், இது படத்தின் பரிமாணங்களை குறைக்க உதவுகிறது. மேலும், வடிகட்டிகளின் எண்ணிக்கையை அதிகரிக்க வேண்டும், ஏனெனில் வடிவங்கள் மேலும் மேம்பட்டவையாக மாறும்போது, நாம் தேட வேண்டிய சாத்தியமான சுவாரஸ்யமான இணைப்புகள் அதிகமாக இருக்கும்.\n", "\n", - "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் கொண்ட ஒரு படத்தை காட்டும் படம்.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ta.png)\n", + "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் கொண்ட ஒரு படத்தை காட்டும் படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "பரப்பளவின் பரிமாணங்கள் குறைவதாலும், அம்சங்கள்/வடிகட்டிகளின் பரிமாணங்கள் அதிகரிப்பதாலும், இந்த கட்டமைப்பை **பிரமிட் கட்டமைப்பு** என்று அழைக்கப்படுகிறது.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index dd511769..03579e24 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "சாதாரண கணினி பார்வையில், படத்தில் பல வடிகட்டிகள் பயன்படுத்தப்பட்டு அம்சங்கள் உருவாக்கப்பட்டன, பின்னர் அவை இயந்திரக் கற்றல் الگوریتمால் வகைப்பாட்டாளரை உருவாக்க பயன்படுத்தப்பட்டன. இந்த வடிகட்டிகள் உண்மையில் சில விலங்குகளின் பார்வை அமைப்பில் உள்ள நரம்பு அமைப்புகளுக்கு ஒத்ததாக உள்ளன.\n", "\n", - "\n", + "\n", "\n", "ஆனால், ஆழமான கற்றலில், வகைப்பாட்டு பிரச்சினையை தீர்க்க **சிறந்த குவிகலன வடிகட்டிகளை** கற்றுக்கொள்ளும் நெட்வொர்க்குகளை உருவாக்குகிறோம். இதை செய்ய, **குவிகலன அடுக்குகளை** அறிமுகப்படுத்துகிறோம்.\n" ] @@ -358,7 +358,7 @@ "\n", "அதனால், ஒரு சாதாரண CNNயில் பல கன்வல்யூஷன் அடுக்குகள் இருக்கும், அவற்றின் இடையே பூலிங் அடுக்குகள் இருக்கும், இது படத்தின் பரிமாணங்களை குறைக்க உதவுகிறது. மேலும், வடிகட்டிகளின் எண்ணிக்கையை அதிகரிப்போம், ஏனெனில் வடிவங்கள் மேலும் மேம்பட்டவையாக மாறும்போது - நாம் தேட வேண்டிய சாத்தியமான சுவாரஸ்யமான இணைப்புகள் அதிகமாக இருக்கும்.\n", "\n", - "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் காட்டும் ஒரு படம்.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ta.png)\n", + "![பல கன்வல்யூஷன் அடுக்குகள் மற்றும் பூலிங் அடுக்குகளைக் காட்டும் ஒரு படம்.](../../../../../translated_images/ta/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "பரப்பளவின் பரிமாணங்கள் குறைவதால் மற்றும் அம்சங்கள்/வடிகட்டிகளின் பரிமாணங்கள் அதிகரிப்பதால், இந்த கட்டமைப்பை **பிரமிட் கட்டமைப்பு** என்று அழைக்கப்படுகிறது.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md index 3da4784e..b647eccd 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: முறைகளை எடுக்க, **கன்வல்யூஷனல் ஃபில்டர்கள்** என்ற கருத்தை பயன்படுத்துவோம். நீங்கள் அறிந்தபடி, ஒரு படம் 2D-மாட்ரிக்ஸ் அல்லது நிற ஆழத்துடன் 3D-டென்சராக பிரதிநிதித்துவம் செய்யப்படுகிறது. ஒரு ஃபில்டரைப் பயன்படுத்துவது என்பது, சிறிய **ஃபில்டர் கர்னல்** மாட்ரிக்ஸை எடுத்து, மூலப்படத்தில் உள்ள ஒவ்வொரு பிக்சலுக்கும் அண்டை புள்ளிகளுடன் எடை செய்யப்பட்ட சராசரியை கணக்கிடுவதாகும். இதை ஒரு சிறிய சாளரம் முழு படத்திலும் நகர்ந்து, ஃபில்டர் கர்னல் மாட்ரிக்ஸில் உள்ள எடைகளுக்கு ஏற்ப அனைத்து பிக்சல்களையும் சராசரி செய்யும் முறையாகக் காணலாம். -![நெடுவரை விளிம்பு ஃபில்டர்](../../../../../translated_images/filter-vert.b7148390ca0bc356.ta.png) | ![கிடைமட்ட விளிம்பு ஃபில்டர்](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ta.png) +![நெடுவரை விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-vert.b7148390ca0bc356.png) | ![கிடைமட்ட விளிம்பு ஃபில்டர்](../../../../../translated_images/ta/filter-horiz.59b80ed4feb946ef.png) ----|---- > படம்: டிமிட்ரி சோஷ்னிகோவ் உதாரணமாக, MNIST எண்களுக்கு 3x3 நெடுவரை விளிம்பு மற்றும் கிடைமட்ட விளிம்பு ஃபில்டர்களை பயன்படுத்தினால், மூலப்படத்தில் உள்ள நெடுவரை மற்றும் கிடைமட்ட விளிம்புகள் உள்ள இடங்களில் முக்கியமான மதிப்புகளை (உதா. உயர் மதிப்புகள்) பெறலாம். எனவே, இந்த இரண்டு ஃபில்டர்களை விளிம்புகளை "தேட" பயன்படுத்தலாம். அதேபோல், பிற குறைந்த நிலை முறைகளைத் தேடுவதற்காக வெவ்வேறு ஃபில்டர்களை வடிவமைக்கலாம்: - + > [லியூங்-மாலிக் ஃபில்டர் வங்கி](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) படத்தை @@ -38,7 +38,7 @@ CNN-கள் செயல்படும் முறை பின்வரு * ஃபில்டர்கள் தானாக பயிற்சி பெறும் வகையில் நெட்வொர்க்கை வடிவமைக்க முடியும் * மூலப்படத்தில் மட்டுமல்லாமல், உயர் நிலை அம்சங்களில் முறைகளை கண்டுபிடிக்க இதே அணுகுமுறையைப் பயன்படுத்த முடியும். எனவே, CNN அம்ச எடுக்கும் செயல்பாடு குறைந்த நிலை பிக்சல் சேர்க்கைகளிலிருந்து தொடங்கி, படத்தின் பகுதிகளின் உயர் நிலை சேர்க்கை வரை அம்சங்களின் ஒரு அடுக்குக்கோபுரத்தில் செயல்படுகிறது. -![அடுக்குக்கோபுர அம்ச எடுக்கும்](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ta.png) +![அடுக்குக்கோபுர அம்ச எடுக்கும்](../../../../../translated_images/ta/FeatureExtractionCNN.d9b456cbdae7cb64.png) > [ஹிஸ்லாப்-லின்ச்](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) எழுதிய ஒரு ஆய்வுக் கட்டுரையிலிருந்து படம், [அவர்களின் ஆராய்ச்சி](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) அடிப்படையில் @@ -55,9 +55,9 @@ CNN-கள் செயல்படும் முறை பின்வரு உதாரணமாக, VGG-16 என்ற கட்டமைப்பின் கட்டமைப்பைப் பார்ப்போம், இது 2014 இல் ImageNet-இன் Top-5 வகைப்படுத்தலில் 92.7% துல்லியத்தை அடைந்தது: -![ImageNet லேயர்கள்](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ta.jpg) +![ImageNet லேயர்கள்](../../../../../translated_images/ta/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet பyramிட்](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ta.jpg) +![ImageNet பyramிட்](../../../../../translated_images/ta/vgg-16-arch.64ff2137f50dd49f.jpg) > [ரிசர்ச்கேட்](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) மூலம் படம் diff --git a/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9fa96bbd..37e38fa5 100644 --- a/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ta/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 37 வெவ்வேறு இனங்களுக்கான நாய்கள் மற்றும் பூனைகளின் படங்களை கொண்ட [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ஐ நாம் பயன்படுத்துவோம். -![நாம் கையாளும் தரவுத்தொகுப்பு](../../../../../../translated_images/data.50b2a9d5484bdbf0.ta.png) +![நாம் கையாளும் தரவுத்தொகுப்பு](../../../../../../translated_images/ta/data.50b2a9d5484bdbf0.png) தரவுத்தொகுப்பைப் பதிவிறக்க, இந்தக் குறியீட்டு துண்டைப் பயன்படுத்தவும்: diff --git a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 28393ee2..fd3fd22b 100644 --- a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "சிறந்த பூனை உருவத்தை காட்சிப்படுத்த, நாம் ஒரு சீரற்ற சத்தம் கொண்ட படத்துடன் தொடங்குவோம், பின்னர் ஒரு நெட்வொர்க் பூனையை அடையாளம் காணும் வகையில் படத்தை சரிசெய்ய_gradient descent_ மேம்பாட்டு முறையை பயன்படுத்துவோம்.\n", "\n", - "![மேம்பாட்டு சுழற்சி](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ta.png)\n", + "![மேம்பாட்டு சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "இது தான் நமது தொடக்க படம்:\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md index f3c8ef32..db61c848 100644 --- a/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ta/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras மற்றும் PyTorch இரண்டிலும் பொது இங்கே VGG-16 நெட்வொர்க்கால் ஒரு பூனையின் படத்திலிருந்து எடுக்கப்பட்ட மாதிரி அம்சங்கள் உள்ளன: -![VGG-16 மூலம் எடுக்கப்பட்ட அம்சங்கள்](../../../../../translated_images/features.6291f9c7ba3a0b95.ta.png) +![VGG-16 மூலம் எடுக்கப்பட்ட அம்சங்கள்](../../../../../translated_images/ta/features.6291f9c7ba3a0b95.png) ## பூனைகள் vs. நாய்கள் தரவுத்தொகுப்பு @@ -48,19 +48,19 @@ Keras மற்றும் PyTorch இரண்டிலும் பொது ஒரு அணுகுமுறை என்னவென்றால், ஒரு சீரற்ற படத்துடன் தொடங்குவது, பின்னர் **கிரேடியண்ட் டிசென்ட் ஆப்டிமைசேஷன்** தொழில்நுட்பத்தைப் பயன்படுத்தி அந்த படத்தை சரிசெய்வது, அதனால் நெட்வொர்க்கு அதை பூனையாக நினைக்கத் தொடங்குகிறது. -![படத்தை சரிசெய்யும் சுழற்சி](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ta.png) +![படத்தை சரிசெய்யும் சுழற்சி](../../../../../translated_images/ta/ideal-cat-loop.999fbb8ff306e044.png) ஆனால், இதைச் செய்தால், சீரற்ற சத்தத்துடன் மிகவும் ஒத்ததாக ஏதாவது கிடைக்கும். இது *நெட்வொர்க்கு உள்ளீடு ஒரு பூனை என்று நினைக்க பல வழிகள் உள்ளன*, அதில் சில காட்சியளிப்பதில் அர்த்தமற்றவை. அந்த படங்கள் பூனைக்கு பொதுவான பல வடிவங்களை கொண்டிருந்தாலும், அவற்றை காட்சிப்படுத்துவதற்குத் தனித்துவமாக இருக்க வேண்டும் என்று கட்டாயப்படுத்த எதுவும் இல்லை. முடிவை மேம்படுத்த, **variation loss** என்று அழைக்கப்படும் மற்றொரு சொற்றொடரை இழப்புச் செயல்பாட்டில் சேர்க்கலாம். இது படத்தின் அடுத்தடுத்த பிக்சல்கள் எவ்வளவு ஒத்திருக்கின்றன என்பதை காட்டும் அளவீடு. variation loss ஐ குறைப்பது படத்தை மென்மையாக ஆக்குகிறது, மேலும் சத்தத்தை அகற்றுகிறது - இதனால் காட்சிப்படுத்துவதற்கு அழகான வடிவங்களை வெளிப்படுத்துகிறது. இங்கே பூனை மற்றும் ஜெப்ரா என்று அதிக சாத்தியத்துடன் வகைப்படுத்தப்படும் "சிறந்த" படங்களின் உதாரணம் உள்ளது: -![சிறந்த பூனை](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ta.png) | ![சிறந்த ஜெப்ரா](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ta.png) +![சிறந்த பூனை](../../../../../translated_images/ta/ideal-cat.203dd4597643d6b0.png) | ![சிறந்த ஜெப்ரா](../../../../../translated_images/ta/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *சிறந்த பூனை* | *சிறந்த ஜெப்ரா* இதே அணுகுமுறையை **எதிர்மறை தாக்குதல்** என்று அழைக்கப்படும் செயல்பாட்டை செய்ய பயன்படுத்தலாம். ஒரு நரம்பியல் நெட்வொர்க்கை ஏமாற்றி, ஒரு நாயை பூனையாக மாற்ற விரும்புகிறோம் என்று நினைத்தால், நாயின் படத்தை எடுத்து, அது நெட்வொர்க்கால் நாய் என்று அடையாளம் காணப்படுகிறது, பின்னர் அதை சிறிது மாற்றி, நெட்வொர்க்கு அதை பூனையாக வகைப்படுத்தும் வரை கிரேடியண்ட் டிசென்ட் ஆப்டிமைசேஷனைப் பயன்படுத்தலாம்: -![நாயின் படம்](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ta.png) | ![பூனையாக வகைப்படுத்தப்படும் நாயின் படம்](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ta.png) +![நாயின் படம்](../../../../../translated_images/ta/original-dog.8f68a67d2fe0911f.png) | ![பூனையாக வகைப்படுத்தப்படும் நாயின் படம்](../../../../../translated_images/ta/adversarial-dog.d9fc7773b0142b89.png) -----|----- *மூல நாயின் படம்* | *பூனையாக வகைப்படுத்தப்படும் நாயின் படம்* diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 3475b205..b440c809 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "ஆட்டோஎன்கோடரை அசல் படத்திலிருந்து அதிகமான தகவல்களைப் பிடிக்கவும், சரியான மீளுருவாக்கத்திற்காக பயிற்சி செய்யும் போது, நெட்வொர்க் உள்ளீட்டு படங்களின் சிறந்த **எம்பெடிங்** (embedding) ஐ கண்டறிந்து அதன் அர்த்தத்தைப் பிடிக்க முயற்சிக்கிறது.\n", "\n", - "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ta.jpg)\n", + "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> படம் [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) இலிருந்து\n", "\n", @@ -941,7 +941,7 @@ " * $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ விநியோகத்திலிருந்து `sample(z_val in code)` எனும் ஒரு வெக்டரை எடுத்துக்கொள்கிறோம்\n", " * Decoder, `sample` ஐ உள்ளீட்டு வெக்டராகக் கொண்டு முதன்மை படத்தை மீண்டும் உருவாக்க முயலுகிறது\n", "\n", - " \n", + " \n", "\n", " > இந்த படத்தை [இந்த வலைப்பதிவில்](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) Isaak Dykeman எழுதியுள்ளார்.\n" ] @@ -1264,7 +1264,7 @@ "\n", "இந்த அணுகுமுறையில் **மூன்று loss functions** உள்ளன: GAN-இன் generator loss, discriminator loss மற்றும் VAE-இன் reconstruction loss.\n", "\n", - " \n", + " \n", "\n", " > [இந்த வலைப்பதிவில்](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) Felipe Ducau எழுதிய படத்தைப் பார்க்கவும்\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 08bd1774..4fbe737f 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "நாம் ஆட்டோஎன்கோடரை அசல் படத்திலிருந்து சரியான மீளுருவாக்கத்திற்காக அதிக தகவல்களை பிடிக்க பயிற்சி செய்யும் போது, நெட்வொர்க் உள்ளீட்டு படங்களின் அர்த்தத்தை பிடிக்க சிறந்த **எம்பெடிங்** ஐ கண்டுபிடிக்க முயல்கிறது.\n", "\n", - "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ta.jpg)\n", + "![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*படம் [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) இலிருந்து*\n", "\n", @@ -888,7 +888,7 @@ " * $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ விநியோகத்திலிருந்து ஒரு வெக்டரை `sample` என எடுக்கிறோம்\n", " * `sample` ஐ உள்ளீட்டு வெக்டராக பயன்படுத்தி, Decoder முதன்மை படத்தை மீண்டும் உருவாக்க முயற்சிக்கிறது\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md index e8542412..5435ede0 100644 --- a/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ta/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNகளை பயிற்சி செய்யும்போது, ஒர ஆட்டோஎன்கோடரை அசல் படத்தின் தகவல்களை சரியாக மீண்டும் உருவாக்குவதற்காக அதிகமாக பிடிக்க பயிற்சி செய்யும் போது, நெட்வொர்க் சிறந்த **embedding** ஐ கண்டறிந்து, உள்ளீட்டு படங்களின் அர்த்தத்தை பிடிக்க முயற்சிக்கிறது. -![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ta.jpg) +![ஆட்டோஎன்கோடர் வரைபடம்](../../../../../translated_images/ta/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > படம் [Keras வலைப்பதிவு](https://blog.keras.io/building-autoencoders-in-keras.html) மூலம் @@ -46,7 +46,7 @@ VAE என்பது latent அளவுருக்களின் *புள * N(zmean,exp(zlog\_sigma)) விநியோகத்திலிருந்து `sample` வெக்டரை எடுக்கிறோம் * டிகோடர் `sample` ஐ உள்ளீட்டு வெக்டராக பயன்படுத்தி அசல் படத்தை டிகோடு செய்ய முயற்சிக்கிறது - + > படம் [இந்த வலைப்பதிவு](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) மூலம் Isaak Dykeman @@ -57,13 +57,13 @@ VAE என்பது latent அளவுருக்களின் *புள VAEs இன் ஒரு முக்கியமான நன்மை என்னவென்றால், புதிய படங்களை எளிதாக உருவாக்க முடியும், ஏனெனில் latent வெக்டர்களை எடுக்க வேண்டிய விநியோகத்தை நாங்கள் அறிந்திருக்கிறோம். உதாரணமாக, 2D latent வெக்டருடன் MNIST-ல் VAE ஐ பயிற்சி செய்தால், latent வெக்டரின் கூறுகளை மாறி வெவ்வேறு எண்களை பெறலாம்: -vaemnist +vaemnist > படம் [Dmitry Soshnikov](http://soshnikov.com) மூலம் latent அளவுரு இடத்தின் வெவ்வேறு பகுதிகளில் இருந்து latent வெக்டர்களை எடுக்க தொடங்கும்போது, படங்கள் ஒருவருக்கொருவர் கலக்க ஆரம்பிக்கின்றன என்பதை கவனிக்கவும். இந்த இடத்தை 2D-ல் காட்சிப்படுத்தவும் முடியும்: -vaemnist cluster +vaemnist cluster > படம் [Dmitry Soshnikov](http://soshnikov.com) மூலம் diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 302981c6..06202c6d 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ " * **ஜெனரேட்டர்** ஒரு சீரற்ற வெக்டரை எடுத்து, அதிலிருந்து ஒரு படத்தை உருவாக்க வேண்டும்\n", " * **டிஸ்கிரிமினேட்டர்** என்பது ஒரு நெட்வொர்க், இது மூலப்படம் (பயிற்சி தரவுத்தொகுப்பில் இருந்து) மற்றும் ஜெனரேட்டர் உருவாக்கிய படத்தை வேறுபடுத்த வேண்டும்.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> படம் [இந்த பயிற்சியில்](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) இருந்து.\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index 27c36e3f..5585a352 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ " * **ஜெனரேட்டர்** ஒரு சீரற்ற வெக்டரை எடுத்து, அதிலிருந்து ஒரு படத்தை உருவாக்க வேண்டும் \n", " * **டிஸ்கிரிமினேட்டர்** என்பது ஒரு நெட்வொர்க், இது அசல் படம் (பயிற்சி தரவுத்தொகுப்பிலிருந்து) மற்றும் ஜெனரேட்டர் உருவாக்கிய படத்தை வேறுபடுத்த வேண்டும்.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/ta/lessons/4-ComputerVision/10-GANs/README.md b/translations/ta/lessons/4-ComputerVision/10-GANs/README.md index d7b0984b..ccbecbdb 100644 --- a/translations/ta/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/ta/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN-இன் முக்கிய யோசனை இரண்டு நரம்பியல் நெட்வொர்க்குகளை ஒன்றுக்கொன்று எதிராகப் பயிற்சி செய்யும் முறையாகும்: - + > படம்: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ GAN-இன் முக்கிய யோசனை இரண்டு நரம > ✅ கான்வல்யூஷன் அடுக்கு ஒரு நேரியல் வடிகட்டி படத்தைச் சுற்றி செயல்படுவதால், டிகான்வல்யூஷன் அடிப்படையில் கான்வல்யூஷனுக்கு ஒத்ததாகும், மற்றும் அதே அடுக்கு தர்க்கத்தைப் பயன்படுத்தி செயல்படுத்தலாம். - + > படம்: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md index 63e5ffb1..bc70e5c0 100644 --- a/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ta/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [முன்-வகுப்பு வினாடி வினா](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![பொருள் கண்டறிதல்](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ta.png) +![பொருள் கண்டறிதல்](../../../../../translated_images/ta/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > படம் [YOLO v2 வலைத்தளத்திலிருந்து](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ஒவ்வொரு பகுதியிலும் பட வகைப்படுத்தலை இயக்கவும். 3. போதுமான அளவு செயல்பாட்டை உருவாக்கும் பகுதிகள், குறிப்பிட்ட பொருளை கொண்டதாக கருதலாம். -![எளிய பொருள் கண்டறிதல்](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ta.png) +![எளிய பொருள் கண்டறிதல்](../../../../../translated_images/ta/naive-detection.e7f1ba220ccd08c6.png) > *படம் [பயிற்சி நோட்புக்](ObjectDetection-TF.ipynb) இலிருந்து* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 வகைகள் * [COCO](http://cocodataset.org/#home) - சூழலில் பொதுவான பொருட்கள். 80 வகைகள், அளவுரு பெட்டிகள் மற்றும் பிரிவுப் முகமூடிகள் -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ta.jpg) +![COCO](../../../../../translated_images/ta/coco-examples.71bc60380fa6cceb.jpg) ## பொருள் கண்டறிதல் அளவுகோல்கள் @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: பட வகைப்படுத்தலுக்கான செயல்திறனை அளவிடுவது எளிதானது, ஆனால் பொருள் கண்டறிதலுக்கான வகையின் சரியானதையும், அளவுரு பெட்டியின் துல்லியத்தையும் அளவிட வேண்டும். இதற்காக **இணைப்பு மற்றும் ஒன்றிணைவு** (IoU) என்ற அளவுகோலைப் பயன்படுத்துகிறோம், இது இரண்டு பெட்டிகள் (அல்லது இரண்டு பகுதி பகுதிகள்) எவ்வளவு நன்றாக ஒத்துப்போகின்றன என்பதை அளவிடுகிறது. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ta.png) +![IoU](../../../../../translated_images/ta/iou_equation.9a4751d40fff4e11.png) > *[இந்த சிறந்த வலைப்பதிவிலிருந்து](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) IoU பற்றிய படங்கள்* @@ -97,11 +97,11 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) பயன்படுத்தி ROI பகுதிகளின் அடுக்கமைப்பை உருவாக்குகிறது, பின்னர் CNN அம்சங்களைப் பயன்படுத்தி SVM வகைப்படுத்திகளை இயக்கி பொருள் வகையைத் தீர்மானிக்கிறது, மற்றும் *அளவுரு பெட்டியின்* கோர்டினேட்டுகளை தீர்மானிக்க நேரியல் மீள்பார்வையை (linear regression) பயன்படுத்துகிறது. [அதிகாரப்பூர்வ ஆவணம்](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ta.png) +![RCNN](../../../../../translated_images/ta/rcnn1.cae407020dfb1d1f.png) > *படம் van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ta.png) +![RCNN-1](../../../../../translated_images/ta/rcnn2.2d9530bb83516484.png) > *படங்கள் [இந்த வலைப்பதிவிலிருந்து](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -109,7 +109,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க இந்த அணுகுமுறை R-CNN போன்றது, ஆனால் பகுதிகள் குவியல் அடுக்குகள் (convolution layers) பயன்படுத்தப்பட்ட பிறகு வரையறுக்கப்படுகின்றன. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ta.png) +![FRCNN](../../../../../translated_images/ta/f-rcnn.3cda6d9bb4188875.png) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -117,7 +117,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க இந்த அணுகுமுறையின் முக்கியமான கருத்து, ROIs (Regions of Interests) கணிக்க நரம்பியல் நெட்வொர்க்கை (neural network) பயன்படுத்துவது - *Region Proposal Network* என அழைக்கப்படுகிறது. [ஆவணம்](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ta.png) +![FasterRCNN](../../../../../translated_images/ta/faster-rcnn.8d46c099b87ef30a.png) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/pdf/1506.01497.pdf) @@ -129,7 +129,7 @@ IoU ஒரு குறிப்பிட்ட மதிப்புக்க 2. **Position-Sensitive Score Map** மூலம் அம்சங்கள் செயல்படுத்தப்படுகின்றன. $C$ வகைகளிலிருந்து ஒவ்வொரு பொருளும் $k\times k$ பகுதிகளால் பிரிக்கப்படுகிறது, மற்றும் பொருளின் பகுதிகளை கணிக்க பயிற்சி அளிக்கிறோம். 3. $k\times k$ பகுதிகளிலிருந்து ஒவ்வொரு பகுதியும் பொருள் வகைகளுக்கு வாக்களிக்கிறது, மற்றும் அதிகபட்ச வாக்குகளைப் பெறும் பொருள் வகை தேர்ந்தெடுக்கப்படுகிறது. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ta.png) +![r-fcn image](../../../../../translated_images/ta/r-fcn.13eb88158b99a3da.png) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/abs/1605.06409) @@ -140,7 +140,7 @@ YOLO ஒரு நேரடி ஒரு முறை அல்காரித * படம் $S\times S$ பகுதிகளாகப் பிரிக்கப்படுகிறது. * ஒவ்வொரு பகுதிக்கும் **CNN** $n$ சாத்தியமான பொருட்கள், *அளவுரு பெட்டியின்* கோர்டினேட்டுகள் மற்றும் *நம்பகத்தன்மை*=*சாத்தியம்* * IoU கணிக்கிறது. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ta.png) + ![YOLO](../../../../../translated_images/ta/yolo.a2648ec82ee8bb4e.png) > படம் [அதிகாரப்பூர்வ ஆவணத்திலிருந்து](https://arxiv.org/abs/1506.02640) diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md index dabca145..12b37fd1 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: உதாரணமாக, instance segmentation-இல் இந்த ஆடுகள் வேறு பொருட்களாகக் கருதப்படும், ஆனால் semantic segmentation-இல் அனைத்து ஆடுகளும் ஒரே வகையாகக் கருதப்படும். - + > படம் [இந்த வலைப்பதிவில் இருந்து](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **Encoder** உள்ளீடு படத்திலிருந்து அம்சங்களை எடுக்கிறது * **Decoder** அந்த அம்சங்களை **mask image**-ஆக மாற்றுகிறது, இதன் அளவு மற்றும் சேனல்கள் வகைகளின் எண்ணிக்கைக்கு இணையாக இருக்கும். - + > படம் [இந்த வெளியீட்டில் இருந்து](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ இந்த தொழில்நுட்பம் இந்த வகை மருத்துவ படங்களுக்கு மிகவும் பொருத்தமானது, ஆனால் மற்ற எந்த உண்மையான பயன்பாடுகளை நீங்கள் கற்பனை செய்ய முடியும்? -navi +navi > படம் PH2 Database-இல் இருந்து diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index 4358fdb7..d977b85f 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "உதாரணமாக, instance segmentation-ல் 10 ஆடுகள் தனித்தனியான பொருட்களாகக் கருதப்படும், semantic segmentation-ல் அனைத்து ஆடுகளும் ஒரே வகையாகக் காணப்படும்.\n", "\n", - "\n", + "\n", "\n", "> [இந்த வலைப்பதிவில்](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) இருந்து எடுத்த படம்\n", "\n", @@ -26,7 +26,7 @@ "* **Encoder** உள்ளீட்டு படத்திலிருந்து அம்சங்களை (features) எடுக்கிறது.\n", "* **Decoder** அந்த அம்சங்களை **முகமூடி படமாக** (mask image) மாற்றுகிறது, இது ஒரே அளவிலும் வகைகளின் எண்ணிக்கைக்கு இணையான சேனல்களுடன் இருக்கும்.\n", "\n", - "\n", + "\n", "\n", "> [இந்த வெளியீட்டில்](https://arxiv.org/pdf/2001.05566.pdf) இருந்து எடுத்த படம்\n" ] @@ -252,7 +252,7 @@ "\n", "எளிய என்கோடர்-டிகோடர் கட்டமைப்பை **SegNet** என்று அழைக்கப்படுகிறது. இது என்கோடரில் கான்வல்யூஷன்கள் மற்றும் பூலிங்களுடன் உள்ள நிலையான CNN-ஐ பயன்படுத்துகிறது, மேலும் டிகோடரில் கான்வல்யூஷன்கள் மற்றும் அப்சாம்ப்ளிங்களுடன் உள்ள டிகான்வல்யூஷன் CNN-ஐ பயன்படுத்துகிறது. பல அடுக்குகளைக் கொண்ட நெட்வொர்க்கை வெற்றிகரமாக பயிற்றுவிக்க பேட்ச் நார்மலைசேஷனை நம்புகிறது.\n", "\n", - "\n", + "\n", "\n", "> இந்த ஆய்வுக்கட்டுரையிலிருந்து படம்: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "இங்கே மிகவும் எளிய CNN கட்டமைப்பைப் பயன்படுத்துவோம், ஆனால் U-Net அம்சங்களைப் பெறுவதற்கான ResNet-50 போன்ற சிக்கலான encoder ஐயும் பயன்படுத்த முடியும்.\n", "\n", - "\n", + "\n", "\n", "> படத்தின் மூலமாக: Ronneberger, Olaf, Philipp Fischer, மற்றும் Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index ec26d90f..ab054b77 100644 --- a/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/ta/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "உதாரணமாக, இன்ஸ்டன்ஸ் செக்மென்டேஷனில் பத்து கார்கள் **வேறு** பொருட்கள், செமாண்டிக் செக்மென்டேஷனில் **அனைத்து** கார்கள் ஒரு வகுப்பாக இருக்கும்.\n", "\n", - "\n", + "\n", "\n", "> படம் [இந்த வலைப்பதிவில்](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) இருந்து\n", "\n", "கிட்டத்தட்ட அனைத்து கட்டமைப்புகளும் ஒரே அமைப்பைக் கொண்டுள்ளன. முதல் பகுதி **என்கோடர்**, இது உள்ளீட்டு படத்திலிருந்து அம்சங்களை எடுக்கிறது, இரண்டாவது பகுதி **டிகோடர்**, இது இந்த அம்சங்களை ஒரே உயரம் மற்றும் அகலத்துடன், மற்றும் சில சேனல்கள் கொண்ட படமாக மாற்றுகிறது, இது வகுப்புகளின் எண்ணிக்கைக்கு சமமாக இருக்கலாம்.\n", "\n", - "\n", + "\n", "\n", "> படம் [இந்த வெளியீட்டில்](https://arxiv.org/pdf/2001.05566.pdf) இருந்து\n" ] @@ -210,7 +210,7 @@ "\n", "எளிய என்கோடர் - டிகோடர் கட்டமைப்பு, என்கோடரில் கன்வல்யூஷன்கள், பூலிங்கள் மற்றும் டிகோடரில் கன்வல்யூஷன்கள், அப்சாம்பிளிங்கள் கொண்டது.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net பொதுவாக அம்சங்களைப் பிரித்தெடுப்பதற்கான இயல்பான என்கோடரை கொண்டுள்ளது, உதாரணமாக resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, மற்றும் Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/ta/lessons/4-ComputerVision/README.md b/translations/ta/lessons/4-ComputerVision/README.md index 4bf00d39..449cf2d5 100644 --- a/translations/ta/lessons/4-ComputerVision/README.md +++ b/translations/ta/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # கணினி பார்வை -![கணினி பார்வை உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ta.png) +![கணினி பார்வை உள்ளடக்கத்தின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-computervision.6506ebebac3fbf76.png) இந்த பிரிவில் நாம் கற்றுக்கொள்ள போவது: diff --git a/translations/ta/lessons/5-NLP/13-TextRep/README.md b/translations/ta/lessons/5-NLP/13-TextRep/README.md index 98a87ef3..4c3c8026 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/README.md +++ b/translations/ta/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: நடுநிலை மொழி செயலாக்க (NLP) பணிகளை நரம்பியல் வலையமைப்புகளுடன் தீர்க்க விரும்பினால், உரையை டென்சராக பிரதிநிதித்துவம் செய்ய ஒரு வழி தேவை. கணினிகள் ஏற்கனவே ASCII அல்லது UTF-8 போன்ற குறியீடுகளைப் பயன்படுத்தி உங்கள் திரையில் எழுத்துருக்களுக்கு வரைபடம் செய்யும் எண்களாக உரை எழுத்துக்களை பிரதிநிதித்துவம் செய்கின்றன. -ஒரு எழுத்தை ASCII மற்றும் பைனரி பிரதிநிதித்துவத்திற்கு வரைபடம் செய்யும் வரைபடத்தை காட்டும் படம் +ஒரு எழுத்தை ASCII மற்றும் பைனரி பிரதிநிதித்துவத்திற்கு வரைபடம் செய்யும் வரைபடத்தை காட்டும் படம் > [படத்தின் மூலதரவு](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: உரை வகைப்படுத்தல் போன்ற பணிகளை தீர்க்கும்போது, ​​நாங்கள் ஒரு நிலையான அளவுள்ள வெக்டராக உரையை பிரதிநிதித்துவம் செய்ய வேண்டும், இது இறுதி அடர்த்தியான வகைப்படுத்தலுக்கான உள்ளீடாக பயன்படுத்தப்படும். அதைச் செய்யும் எளிய வழிகளில் ஒன்று, அனைத்து தனிப்பட்ட வார்த்தை பிரதிநிதித்துவங்களை இணைப்பது, உதாரணமாக, அவற்றைச் சேர்ப்பது. ஒவ்வொரு வார்த்தையின் ஒற்றை-சூடான குறியீட்டுகளைச் சேர்த்தால், உரையின் உள்ளே ஒவ்வொரு வார்த்தையும் எத்தனை முறை தோன்றுகிறது என்பதை காட்டும் அதிர்வெண் வெக்டராக முடிவடையும். உரையின் இப்படிப்பட்ட பிரதிநிதித்துவம் **bag of words** (BoW) என்று அழைக்கப்படுகிறது. - + > எழுத்தாளர் உருவாக்கிய படம் diff --git a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index a6a3d1cf..8297b105 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) வெக்டர் பிரதிநிதித்துவம் என்பது மிகவும் பரவலாக பயன்படுத்தப்படும் பாரம்பரிய வெக்டர் பிரதிநிதித்துவமாகும். ஒவ்வொரு சொல்லும் ஒரு வெக்டர் குறியீட்டுடன் இணைக்கப்பட்டிருக்கும், வெக்டர் கூறு குறிப்பிட்ட ஆவணத்தில் ஒரு சொல்லின் நிகழ்வுகளின் எண்ணிக்கையை கொண்டிருக்கும்.\n", "\n", - "![Bag of Words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ta.png)\n", + "![Bag of Words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: BoW ஐ உரையில் உள்ள தனிப்பட்ட சொற்களுக்கான அனைத்து ஒரே-ஹாட்-கோடிடப்பட்ட வெக்டர்களின் கூட்டமாகவும் நீங்கள் சிந்திக்கலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index e4cd6d77..4ef7ac85 100644 --- a/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ta/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) வெக்டர் பிரதிநிதித்துவம் பாரம்பரிய வெக்டர் பிரதிநிதித்துவங்களில் மிகவும் எளிதாக புரிந்துகொள்ளக்கூடியது. ஒவ்வொரு சொல்லும் ஒரு வெக்டர் குறியீட்டுடன் இணைக்கப்பட்டிருக்கும், மேலும் ஒரு வெக்டர் கூறு ஒரு குறிப்பிட்ட ஆவணத்தில் ஒவ்வொரு சொல்லின் நிகழ்வுகளின் எண்ணிக்கையை கொண்டிருக்கும்.\n", "\n", - "![Bag-of-words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ta.png) \n", + "![Bag-of-words வெக்டர் பிரதிநிதித்துவம் நினைவகத்தில் எப்படி பிரதிநிதித்துவப்படுத்தப்படுகிறது என்பதை காட்டும் படம்.](../../../../../translated_images/ta/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: BoW-ஐ உரையில் உள்ள தனிப்பட்ட சொற்களுக்கான அனைத்து ஒரே-ஹாட்-கோடிடப்பட்ட வெக்டர்களின் கூட்டமாகவும் நீங்கள் சிந்திக்கலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index f94e9ab4..3f77c6a5 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "எங்கள் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரை பயன்படுத்துவதன் மூலம், நாம் bag-of-words மாடலிலிருந்து **embedding bag** மாடலுக்கு மாற முடியும், இதில் முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு சொல்லையும் தொடர்புடைய எம்பெடிங்காக மாற்றி, பின்னர் அந்த எம்பெடிங்களிலிருந்து `sum`, `average` அல்லது `max` போன்ற ஒரு தொகுப்புக் செயல்பாட்டை கணக்கிடலாம்.\n", "\n", - "![ஐந்து வரிசை சொற்களுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ta.png)\n", + "![ஐந்து வரிசை சொற்களுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "எங்கள் வகைப்பாட்டாளர் நரம்பியல் நெட்வொர்க் எம்பெடிங் லேயருடன் தொடங்கும், பின்னர் தொகுப்பு லேயர், மற்றும் அதன் மேல் லினியர் வகைப்பாட்டாளர்:\n" ] @@ -176,7 +176,7 @@ "\n", "முந்தைய கட்டமைப்பில், மினிபேட்சில் பொருந்துவதற்காக அனைத்து வரிசைகளையும் ஒரே நீளத்திற்கு பதம் செய்ய வேண்டியிருந்தது. மாறுபட்ட நீள வரிசைகளை பிரதிநிதித்துவப்படுத்த இது மிகவும் திறமையான வழி அல்ல - மற்றொரு அணுகுமுறை **offset** வெக்டரைப் பயன்படுத்துவது, இது ஒரு பெரிய வெக்டரில் சேமிக்கப்பட்ட அனைத்து வரிசைகளின் இடைவெளிகளை வைத்திருக்கும்.\n", "\n", - "![Offset வரிசை பிரதிநிதித்துவத்தை காட்டும் படம்](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ta.png)\n", + "![Offset வரிசை பிரதிநிதித்துவத்தை காட்டும் படம்](../../../../../translated_images/ta/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: மேலே உள்ள படத்தில், நாம் எழுத்துக்களின் வரிசையை காட்டுகிறோம், ஆனால் எங்கள் எடுத்துக்காட்டில் நாம் சொற்களின் வரிசைகளுடன் வேலை செய்கிறோம். இருப்பினும், offset வெக்டருடன் வரிசைகளை பிரதிநிதித்துவப்படுத்தும் பொது கொள்கை மாறாமல் இருக்கும்.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW வேகமாக செயல்படுகிறது, ஆனால் ஸ்கிப்-கிராம் மெதுவாக செயல்படுகிறது, ஆனால் அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்த சிறப்பாக செயல்படுகிறது.\n", "\n", - "![வார்த்தைகளை வெக்டார்களாக மாற்ற CBoW மற்றும் ஸ்கிப்-கிராம் அல்காரிதங்களை காட்டும் படம்.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ta.png)\n", + "![வார்த்தைகளை வெக்டார்களாக மாற்ற CBoW மற்றும் ஸ்கிப்-கிராம் அல்காரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News தரவுத்தொகுப்பில் முன்கூட்டியே பயிற்சி செய்யப்பட்ட word2vec எம்பெடிங்குடன் பரிசோதிக்க, **gensim** நூலகத்தை பயன்படுத்தலாம். கீழே 'neural' என்ற வார்த்தைக்கு மிகவும் ஒத்த வார்த்தைகளை காணலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 9f9c1db3..6d22003e 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ta/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "எங்கள் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரை பயன்படுத்துவதன் மூலம், **bag-of-words** மாடலிலிருந்து **embedding bag** மாடலுக்கு மாற முடியும். இதில், முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு வார்த்தையையும் அதற்கான எம்பெடிங்கில் மாற்றி, பின்னர் அந்த எம்பெடிங்குகளின் மீது `sum`, `average` அல்லது `max` போன்ற ஒரு தொகுப்புக் செயல்பாட்டை கணக்கிடலாம்.\n", "\n", - "![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ta.png)\n", + "![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "எங்கள் வகைப்பாட்டாளர் நரம்பியல் நெட்வொர்க்கில் பின்வரும் லேயர்கள் உள்ளன:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW வேகமாக செயல்படுகிறது, ஆனால் skip-gram மெதுவாக இருந்தாலும், அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்துவதில் சிறப்பாக செயல்படுகிறது.\n", "\n", - "![CBoW மற்றும் Skip-Gram அல்காரிதங்களை வார்த்தைகளை வெக்டார்களாக மாற்றும் முறையை காட்டும் படம்.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ta.png)\n", + "![CBoW மற்றும் Skip-Gram அல்காரிதங்களை வார்த்தைகளை வெக்டார்களாக மாற்றும் முறையை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News dataset-ல் முன்கூட்டியே பயிற்சி செய்யப்பட்ட Word2Vec embedding-ஐ பரிசோதிக்க, **gensim** நூலகத்தைப் பயன்படுத்தலாம். கீழே 'neural' என்ற வார்த்தைக்கு மிகவும் ஒத்த வார்த்தைகளை காணலாம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/14-Embeddings/README.md b/translations/ta/lessons/5-NLP/14-Embeddings/README.md index 9f811d07..b41fc3da 100644 --- a/translations/ta/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ta/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW அல்லது TF/IDF அடிப்படையில் வகைப எங்கள் வகைப்பாட்டாளர் நெட்வொர்க்கில் முதல் லேயராக எம்பெடிங் லேயரைப் பயன்படுத்துவதன் மூலம், BoW மாடலிலிருந்து **embedding bag** மாடலுக்கு மாறலாம், இதில் முதலில் எங்கள் உரையில் உள்ள ஒவ்வொரு வார்த்தையையும் தொடர்புடைய எம்பெடிங்காக மாற்றி, பின்னர் அந்த எம்பெடிங்குகளின் மீது `sum`, `average` அல்லது `max` போன்ற சில தொகுப்பு செயல்பாடுகளை கணக்கிடலாம். -![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ta.png) +![ஐந்து வரிசை வார்த்தைகளுக்கான எம்பெடிங் வகைப்பாட்டாளரை காட்டும் படம்.](../../../../../translated_images/ta/embedding-classifier-example.b77f021a7ee67eee.png) > படத்தை உருவாக்கியவர் @@ -40,7 +40,7 @@ BoW அல்லது TF/IDF அடிப்படையில் வகைப CBoW வேகமாக செயல்படுகிறது, ஆனால் skip-gram மெதுவாக செயல்படுகிறது, ஆனால் அரிதான வார்த்தைகளை பிரதிநிதித்துவப்படுத்த சிறந்த வேலை செய்கிறது. -![வார்த்தைகளை வெக்டர்களாக மாற்ற CBoW மற்றும் Skip-Gram ஆல்கொரிதங்களை காட்டும் படம்.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ta.png) +![வார்த்தைகளை வெக்டர்களாக மாற்ற CBoW மற்றும் Skip-Gram ஆல்கொரிதங்களை காட்டும் படம்.](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > [இந்த ஆவணத்தில்](https://arxiv.org/pdf/1301.3781.pdf) இருந்து படம் diff --git a/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md index 7469c895..5b34b79b 100644 --- a/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ta/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Word2Vec மற்றும் GloVe போன்ற அர்த்த அட * **கண்டினியூயஸ் பேக்-ஆஃப்-வேர்ட்ஸ்** (CBoW), இதில் நாம் ஒரு டோக்கன் வரிசையில் நடுவிலுள்ள டோக்கன் $W_0$ ஐ கணிக்கிறோம் $W_{-N}$, ..., $W_N$. * **ஸ்கிப்-கிராம்**, இதில் நாம் நடுவிலுள்ள டோக்கன் $W_0$ ஐ வைத்து அண்டை டோக்கன்களின் தொகுப்பை {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} கணிக்கிறோம். -![வார்த்தைகளை வெக்டர்களாக மாற்றும் ஆல்காரிதம்களின் உதாரணம்](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ta.png) +![வார்த்தைகளை வெக்டர்களாக மாற்றும் ஆல்காரிதம்களின் உதாரணம்](../../../../../translated_images/ta/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > படம் [இந்த ஆராய்ச்சி ஆவணத்திலிருந்து](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/ta/lessons/5-NLP/16-RNN/README.md b/translations/ta/lessons/5-NLP/16-RNN/README.md index b921bd1e..09420c0a 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/README.md +++ b/translations/ta/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: உரையின் வரிசை அர்த்தத்தைப் பிடிக்க, **மீண்டும் நிகழும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் மற்றொரு நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்த வேண்டும். RNN இல், நாங்கள் எங்கள் வாக்கியத்தை ஒவ்வொரு சின்னத்தையும் ஒரு நேரத்தில் வலையமைப்பில் அனுப்புகிறோம், மற்றும் வலையமைப்பு சில **நிலை** உருவாக்குகிறது, அதை அடுத்த சின்னத்துடன் மீண்டும் வலையமைப்பில் அனுப்புகிறோம். -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ta.png) +![RNN](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.png) > படத்தை உருவாக்கியவர் @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: ஒரு எளிய RNN செல்லில் இரண்டு எடை மடிக்கோவைகள் உள்ளன: ஒன்று உள்ளீட்டு சின்னத்தை மாற்றுகிறது (W என்று அழைக்கலாம்), மற்றொன்று உள்ளீட்டு நிலையை மாற்றுகிறது (H). இந்த வழக்கில் வலையமைப்பின் வெளியீடு σ(W×Xi+H×Si-1+b) என கணக்கிடப்படுகிறது, இங்கு σ செயல்பாட்டைச் செயல்படுத்தும் செயல்பாடு மற்றும் b கூடுதல் பாகுபாடு. -RNN செல் அமைப்பு +RNN செல் அமைப்பு > படத்தை உருவாக்கியவர் @@ -61,7 +61,7 @@ LSTM வலையமைப்பு RNN போலவே அமைக்கப் ஒரு மீண்டும் நிகழும் வலையமைப்பு, ஒரு திசை அல்லது இருவழி, ஒரு வரிசையின் குறிப்பிட்ட முறைமைகளைப் பிடிக்கிறது, மற்றும் அவற்றை நிலை வெக்டரில் சேமிக்க அல்லது வெளியீட்டிற்கு அனுப்ப முடியும். குவியல்முறை வலையமைப்புகளின் வழக்கில், முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளிலிருந்து கட்டமைக்க, முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளைப் பிடிக்க மற்றொரு மீண்டும் நிகழும் அடுக்கை மேலே கட்டமைக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீண்டும் நிகழும் வலையமைப்புகளைக் கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் வெளியீடு அடுத்த அடுக்கிற்கு உள்ளீடாக அனுப்பப்படுகிறது. -![பல அடுக்கு LSTM RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ta.jpg) +![பல அடுக்கு LSTM RNN](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Fernando López எழுதிய [இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்* diff --git a/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index ca0bcbb6..d2b72678 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "உரையின் வரிசை அர்த்தத்தைப் பிடிக்க, **மீளும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் மற்றொரு நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்த வேண்டும். RNN இல், நாங்கள் எங்கள் வாக்கியத்தை ஒவ்வொரு சின்னத்தையும் ஒரே நேரத்தில் வலையமைப்பில் கடத்துகிறோம், மற்றும் வலையமைப்பு சில **நிலை** உருவாக்குகிறது, அதை அடுத்த சின்னத்துடன் மீண்டும் வலையமைப்பில் கடத்துகிறோம்.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "உள்ளீட்டு டோக்கன்களின் வரிசை $X_0,\\dots,X_n$ கொடுக்கப்பட்டால், RNN நரம்பியல் வலைகள் வரிசையை உருவாக்குகிறது, மற்றும் இந்த வரிசையை முடிவு-to-end முறையில் பின்செலுத்தல் மூலம் பயிற்சி செய்கிறது. ஒவ்வொரு வலைகள் தொகுதியும் $(X_i,S_i)$ என்ற ஜோடியை உள்ளீடாக எடுத்து, $S_{i+1}$ என்ற முடிவை உருவாக்குகிறது. இறுதி நிலை $S_n$ அல்லது வெளியீடு $X_n$ ஒரு நேரியல் வகைப்பாட்டாளருக்குள் செலுத்தப்பட்டு முடிவை உருவாக்குகிறது. அனைத்து வலைகள் தொகுதிகளும் ஒரே எடைகளைப் பகிர்ந்து கொள்கின்றன, மற்றும் ஒரு பின்செலுத்தல் முறையில் முடிவு-to-end பயிற்சி செய்யப்படுகின்றன.\n", "\n", @@ -428,7 +428,7 @@ "\n", "ஒரு திசை அல்லது இரு திசை மீளச்சுழற்சி நெட்வொர்க்கு, ஒரு வரிசையின் குறிப்பிட்ட முறைமைகளை பிடித்து, அவற்றை state வெக்டரில் சேமிக்க அல்லது output-க்கு அனுப்ப முடியும். குவால்வோல்யூஷனல் நெட்வொர்க்குகளின் (convolutional networks) போல, முதல் அடுக்கால் எடுக்கப்பட்ட குறைந்த நிலை முறைமைகளிலிருந்து உயர் நிலை முறைமைகளை பிடிக்க, முதல் அடுக்கின் மேல் மற்றொரு மீளச்சுழற்சி அடுக்கை கட்டமைக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீளச்சுழற்சி நெட்வொர்க்குகளை கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் output அடுத்த அடுக்கிற்கு input ஆக அனுப்பப்படுகிறது.\n", "\n", - "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ta.jpg)\n", + "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando López எழுதிய [இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்*\n", "\n", diff --git a/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb index a2f4324b..8eaa859d 100644 --- a/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ta/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ஒரு உரை வரிசையின் அர்த்தத்தைப் பிடிக்க, **மீண்டும் நிகழும் நரம்பியல் வலை**, அல்லது RNN எனப்படும் நரம்பியல் வலை கட்டமைப்பைப் பயன்படுத்துவோம். RNN பயன்படுத்தும்போது, நாங்கள் எங்கள் வாக்கியத்தை வலைகளின் வழியாக ஒரு டோக்கன் ஒன்றாக அனுப்புகிறோம், மற்றும் வலைகள் சில **நிலை** உருவாக்குகிறது, அதை அடுத்த டோக்கனுடன் மீண்டும் வலைகளுக்கு அனுப்புகிறோம்.\n", "\n", - "![மீண்டும் நிகழும் நரம்பியல் வலை உருவாக்கத்தின் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/rnn.27f5c29c53d727b5.ta.png)\n", + "![மீண்டும் நிகழும் நரம்பியல் வலை உருவாக்கத்தின் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn.27f5c29c53d727b5.png)\n", "\n", "$X_0,\\dots,X_n$ என்ற உள்ளீட்டு டோக்கன் வரிசையைத் தரும்போது, RNN நரம்பியல் வலைகள் வரிசையை உருவாக்குகிறது, மற்றும் இந்த வரிசையை முடிவுக்கு கொண்டு செல்ல ஒரு பின்செலுத்தல் செயல்பாட்டைப் பயன்படுத்தி பயிற்சி செய்கிறது. ஒவ்வொரு வலைகள் தொகுதியும் $(X_i,S_i)$ என்ற ஜோடியை உள்ளீடாக எடுத்து, $S_{i+1}$ என்ற முடிவை உருவாக்குகிறது. இறுதி நிலை $S_n$ அல்லது வெளியீடு $Y_n$ ஒரு நேரியல் வகைப்பாட்டாளருக்கு செல்கிறது முடிவை உருவாக்க. அனைத்து வலைகள் தொகுதிகளும் ஒரே எடைகளைப் பகிர்ந்து கொள்கின்றன, மற்றும் ஒரு பின்செலுத்தல் செயல்பாட்டைப் பயன்படுத்தி முடிவுக்கு பயிற்சி செய்யப்படுகின்றன.\n", "\n", @@ -371,7 +371,7 @@ "\n", "மீள்நோக்கு நெட்வொர்க்குகள், ஒருதிசை அல்லது இருதிசை, ஒரு வரிசையின் உள்ளமைப்புகளைப் பிடித்து, அவற்றை நிலை வெக்டர்களில் சேமிக்கின்றன அல்லது அவற்றை வெளியீடாக திருப்பி விடுகின்றன. குவால்வோல்யூஷன் நெட்வொர்க்குகளின் போல், முதல் அடுக்கால் எடுக்கப்பட்ட கீழ்நிலை அமைப்புகளிலிருந்து மேல்நிலை அமைப்புகளைப் பிடிக்க, முதல் அடுக்கைத் தொடர்ந்து மற்றொரு மீள்நோக்கு அடுக்கை உருவாக்கலாம். இது **பல அடுக்கு RNN** என்ற கருத்துக்கு வழிவகுக்கிறது, இது இரண்டு அல்லது அதற்கு மேற்பட்ட மீள்நோக்கு நெட்வொர்க்குகளை கொண்டுள்ளது, இதில் முந்தைய அடுக்கின் வெளியீடு அடுத்த அடுக்கிற்கு உள்ளீடாக அனுப்பப்படுகிறது.\n", "\n", - "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ta.jpg)\n", + "![பல அடுக்கு நீண்ட-குறுகிய-கால நினைவக RNN-ஐ காட்டும் படம்](../../../../../translated_images/ta/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*[Fernando López எழுதிய இந்த அற்புதமான பதிவிலிருந்து](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) படம்.*\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 3afc9069..80f6fcb2 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN-ஐ உரை உருவாக்க பயிற்சி செய்வது இதுவே. ஒவ்வொரு படியிலும், `nchars` நீளமான எழுத்துக்களின் வரிசையை எடுத்து, ஒவ்வொரு உள்ளீட்டு எழுத்துக்கான அடுத்த வெளியீட்டு எழுத்தை உருவாக்க வலியுறுத்துவோம்:\n", "\n", - "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ta.png)\n", + "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png)\n", "\n", "உண்மையான சூழ்நிலைக்கு ஏற்ப, *end-of-sequence* `` போன்ற சில சிறப்பு எழுத்துக்களை சேர்க்க விரும்பலாம். எங்கள் நிலைமையில், முடிவில்லாத உரை உருவாக்கத்திற்காக நெட்வொர்க்கை பயிற்சி செய்ய விரும்புகிறோம், எனவே ஒவ்வொரு வரிசையின் அளவையும் `nchars` டோக்கன்களாக நிர்ணயிக்கிறோம். இதனால், ஒவ்வொரு பயிற்சி எடுத்துக்காட்டும் `nchars` உள்ளீடுகள் மற்றும் `nchars` வெளியீடுகளை (உள்ளீட்டு வரிசை ஒரு சின்னத்தை இடதுபுறம் நகர்த்தியது) கொண்டிருக்கும். மினிபேட்ச் பல இத்தகைய வரிசைகளைக் கொண்டிருக்கும்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 0d084f18..1aedf5db 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "செய்தி தலைப்புகளை உருவாக்க RNN-ஐ பயிற்சி செய்யும் முறை இதுவாகும். ஒவ்வொரு படியிலும், ஒரு தலைப்பை எடுத்து, அதை RNN-க்கு வழங்கி, ஒவ்வொரு உள்ளீட்டு எழுத்துக்காக, நெட்வொர்க்கை அடுத்த வெளியீட்டு எழுத்தை உருவாக்குமாறு கேட்கப்படும்:\n", "\n", - "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ta.png)\n", + "![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணத்தை காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png)\n", "\n", "எங்கள் வரிசையின் கடைசி எழுத்துக்காக, நெட்வொர்க்கை `` டோக்கனை உருவாக்குமாறு கேட்போம்.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md index 163a43c4..25913217 100644 --- a/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ta/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: இதனால், கீழே உள்ள படத்தில் காட்டப்பட்டுள்ள பல்வேறு நரம்பியல் கட்டமைப்புகள் உருவாகின்றன: -![பொதுவான மீளும் நரம்பியல் நெட்வொர்க் வடிவமைப்புகளை காட்டும் படம்.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ta.jpg) +![பொதுவான மீளும் நரம்பியல் நெட்வொர்க் வடிவமைப்புகளை காட்டும் படம்.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > [Andrej Karpaty](http://karpathy.github.io/) எழுதிய [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) என்ற வலைப்பதிவிலிருந்து படம் @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: இந்த RNN-ஐ படி படியாக உரை உருவாக்க பயிற்சி செய்யலாம். ஒவ்வொரு படியிலும், `nchars` நீளத்திலான எழுத்துக்களின் வரிசையை எடுத்து, ஒவ்வொரு உள்ளீட்டு எழுத்துக்கான அடுத்த வெளியீட்டு எழுத்தை நெட்வொர்க்கிடம் கேட்போம்: -![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணம் காட்டும் படம்.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ta.png) +![RNN மூலம் 'HELLO' என்ற வார்த்தையை உருவாக்கும் உதாரணம் காட்டும் படம்.](../../../../../translated_images/ta/rnn-generate.56c54afb52f9781d.png) உரை உருவாக்கும் போது (தீர்மானத்தில்), சில **தூண்டுதல்** மூலம் தொடங்குவோம், இது RNN செல்களுக்குள் செலுத்தப்பட்டு அதன் இடைநிலை நிலையை உருவாக்கும், பின்னர் இந்த நிலையிலிருந்து உருவாக்கம் தொடங்கும். ஒவ்வொரு நேரத்திலும் ஒரு எழுத்தை உருவாக்கி, அந்த நிலை மற்றும் உருவாக்கப்பட்ட எழுத்தை மற்றொரு RNN செலுக்கு அனுப்பி அடுத்த எழுத்தை உருவாக்குவோம், தேவையான அளவு எழுத்துகளை உருவாக்கும் வரை. - + > எழுத்தாளர் உருவாக்கிய படம் diff --git a/translations/ta/lessons/5-NLP/18-Transformers/README.md b/translations/ta/lessons/5-NLP/18-Transformers/README.md index e15b384d..1f9aea2e 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ta/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண **கவன механизмங்கள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம், உள்ளீட்டு RNN மற்றும் வெளியீட்டு RNN இன் இடைநிலை நிலைகளுக்கு இடையே குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், yt வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​hi உள்ளீட்டு மறைமாநிலங்களை, வெவ்வேறு எடை குணகங்கள் αt,i உடன் கணக்கில் எடுத்துக்கொள்வோம். -![என்கோடர்/டிகோடர் மாடல் மற்றும் சேர்க்கை கவன அடுக்கு](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ta.png) +![என்கோடர்/டிகோடர் மாடல் மற்றும் சேர்க்கை கவன அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் சேர்க்கை கவன механизмம் கொண்ட என்கோடர்-டிகோடர் மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இல் மேற்கோள். கவன அணி {αi,j} என்பது ஒரு வெளியீட்டு வரிசையில் ஒரு குறிப்பிட்ட வார்த்தையை உருவாக்குவதில் சில உள்ளீட்டு வார்த்தைகள் விளையாடும் அளவை பிரதிநிதித்துவப்படுத்தும். கீழே ஒரு அத்தகைய அணி எடுத்துக்காட்டாக உள்ளது: -![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டறியப்பட்ட மாதிரி ஒத்திசைவு](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ta.png) +![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டறியப்பட்ட மாதிரி ஒத்திசைவு](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம் @@ -56,7 +56,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண * டோக்கன் எம்பெடிங் போன்ற பயிற்சி செய்யக்கூடிய எம்பெடிங். இது இங்கு நாம் கருதும் அணுகுமுறை. டோக்கன்களுக்கும் அவற்றின் நிலைகளுக்கும் மேல் எம்பெடிங் அடுக்குகளைப் பயன்படுத்துகிறோம், இதனால் ஒரே பரிமாணங்களின் எம்பெடிங் வெக்டர்கள் கிடைக்கின்றன, பின்னர் அவற்றை ஒன்றாகச் சேர்க்கிறோம். * அசையாத நிலை குறியீட்டு செயல்பாடு, அசல் ஆவணத்தில் முன்மொழியப்பட்டது. - + > எழுத்தாளரின் படம் @@ -66,7 +66,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண அடுத்ததாக, நமது வரிசையில் சில முறைபாடுகளைப் பிடிக்க வேண்டும். இதைச் செய்ய, டிரான்ஸ்ஃபார்மர்கள் **சுய-கவன механизмம்** பயன்படுத்துகின்றன, இது உள்ளீட்டு மற்றும் வெளியீட்டாக ஒரே வரிசையில் கவனம் செலுத்துவது ஆகும். சுய-கவனத்தைப் பயன்படுத்துவது வாக்கியத்தின் **சூழலத்தை** கணக்கில் எடுத்துக்கொள்ளவும், எந்த வார்த்தைகள் தொடர்புடையவை என்பதைப் பார்க்கவும் உதவுகிறது. உதாரணமாக, *it* போன்ற இணைப்புகள் எந்த வார்த்தைகளை குறிப்பிடுகின்றன என்பதைப் பார்க்கவும், சூழலத்தை கணக்கில் எடுத்துக்கொள்ளவும் உதவுகிறது: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ta.png) +![](../../../../../translated_images/ta/CoreferenceResolution.861924d6d384a7d6.png) > [Google வலைப்பதிவு](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) இல் இருந்து எடுத்த படம் @@ -91,7 +91,7 @@ RNNகளுடன், வரிசை-முதல்-வரிசை இரண **BERT** (Bidirectional Encoder Representations from Transformers) என்பது 12 அடுக்குகளைக் கொண்ட *BERT-base* மற்றும் 24 அடுக்குகளைக் கொண்ட *BERT-large* ஆகியவற்றுடன் மிகப்பெரிய பல அடுக்கு டிரான்ஸ்ஃபார்மர் நெட்வொர்க் ஆகும். மாடல் முதலில் ஒரு பெரிய உரை தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) மேற்பயிற்சி செய்யப்படுகிறது, இது கண்காணிக்கப்படாத பயிற்சியைப் (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட வார்த்தைகளை கணிக்க) பயன்படுத்துகிறது. மேற்பயிற்சியின் போது, ​​மாடல் முக்கியமான அளவிலான மொழி புரிதலை உறிஞ்சுகிறது, இது பிற தரவுத்தொகுப்புகளுடன் நன்றாக தகுந்து பயன்படுத்தப்படலாம். இந்த செயல்முறை **மாற்றம் கற்றல்** என்று அழைக்கப்படுகிறது. -![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ta.png) +![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > படம் [மூலம்](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 77d8618d..fdf9033c 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ta/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**கவனிப்பு முறைமைகள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம் என்னவென்றால், உள்ளீட்டு RNN இன் இடைநிலை நிலைகள் மற்றும் வெளியீட்டு RNN இடையே குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், $y_t$ என்ற வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​வித்தியாசமான எடை குணகங்கள் $\\alpha_{t,i}$ உடன் அனைத்து உள்ளீட்டு மறைமறைநிலை நிலைகள் $h_i$ ஐ கருத்தில் கொள்ளுவோம்.\n", "\n", - "![குறியாக்கி/குறியாக்கி மாடல் மற்றும் சேர்க்கை கவனிப்பு அடுக்கு](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ta.png)\n", + "![குறியாக்கி/குறியாக்கி மாடல் மற்றும் சேர்க்கை கவனிப்பு அடுக்கு](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் சேர்க்கை கவனிப்பு முறைமையுடன் குறியாக்கி-குறியாக்கி மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இடமிருந்து மேற்கோள்]*\n", "\n", "கவனிப்பு அணி $\\{\\alpha_{i,j}\\}$ என்பது ஒரு குறிப்பிட்ட உள்ளீட்டு சொற்கள் வெளியீட்டு வரிசையில் ஒரு குறிப்பிட்ட சொல்லை உருவாக்குவதில் விளையாடும் அளவை பிரதிநிதித்துவப்படுத்தும். கீழே அத்தகைய அணியின் உதாரணம் உள்ளது:\n", "\n", - "![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டுபிடிக்கப்பட்ட மாதAlignment](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ta.png)\n", + "![Bahdanau - arviz.org இல் இருந்து எடுத்த RNNsearch-50 மூலம் கண்டுபிடிக்கப்பட்ட மாதAlignment](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம்]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) என்பது *BERT-base* க்கு 12 அடுக்குகள் மற்றும் *BERT-large* க்கு 24 அடுக்குகளுடன் மிகப்பெரிய பன்மடங்கு டிரான்ஸ்ஃபார்மர் நெட்வொர்க் ஆகும். இந்த மாடல் முதலில் பெரிய உர தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) மேற்பயிற்சி செய்யப்படுகிறது, இது மேற்பார்வையற்ற பயிற்சியை (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட சொற்களை கணிக்க) பயன்படுத்துகிறது. மேற்பயிற்சியின் போது, ​​மாடல் முக்கியமான அளவிலான மொழி புரிதலை உறிஞ்சுகிறது, இது பிற தரவுத்தொகுப்புகளுடன் நன்றாக அமைத்துக்கொள்ள முடியும். இந்த செயல்முறை **மாற்றக் கற்றல்** என்று அழைக்கப்படுகிறது.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ta.png)\n", + "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 மற்றும் பலவற்றை fine-tune செய்யக்கூடிய டிரான்ஸ்ஃபார்மர் கட்டமைப்புகளின் பல மாறுபாடுகள் உள்ளன. [HuggingFace package](https://github.com/huggingface/) PyTorch உடன் இந்த கட்டமைப்புகளில் பலவற்றை பயிற்சி செய்ய ஒரு களஞ்சியத்தை வழங்குகிறது.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index a861eba2..08a517ba 100644 --- a/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ta/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**கவனிப்பு முறைமைகள்** RNN இன் ஒவ்வொரு வெளியீட்டு கணிப்பில் உள்ளீட்டு வெக்டரின் சூழலியல் தாக்கத்தை எடுக்கும் ஒரு வழியை வழங்குகின்றன. இது செயல்படுத்தப்படும் விதம் என்னவென்றால், உள்ளீட்டு RNN இன் இடைநிலை நிலைகளுக்கும் வெளியீட்டு RNN க்கும் இடையில் குறுக்குவழிகளை உருவாக்குவதன் மூலம். இந்த முறையில், $y_t$ என்ற வெளியீட்டு சின்னத்தை உருவாக்கும்போது, ​​வித்தியாசமான எடை குணகங்கள் $\\alpha_{t,i}$ உடன் அனைத்து உள்ளீட்டு மறைமாநிலங்களை $h_i$ கணக்கில் எடுத்துக்கொள்வோம்.\n", "\n", - "![கூட்டல் கவனிப்பு அடுக்கு கொண்ட குறியாக்கி/குறியாக்கி மாடலைக் காட்டும் படம்](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ta.png)\n", + "![கூட்டல் கவனிப்பு அடுக்கு கொண்ட குறியாக்கி/குறியாக்கி மாடலைக் காட்டும் படம்](../../../../../translated_images/ta/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) இல் உள்ள கூட்டல் கவனிப்பு முறைமையுடன் குறியாக்கி-குறியாக்கி மாடல், [இந்த வலைப்பதிவு](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) இடமிருந்து மேற்கோள்]*\n", "\n", "கவனிப்பு அட்டவணை $\\{\\alpha_{i,j}\\}$ ஒரு குறிப்பிட்ட உள்ளீட்டு சொற்கள் வெளியீட்டு வரிசையில் ஒரு கொடுக்கப்பட்ட சொல்லை உருவாக்குவதில் எந்த அளவுக்கு பங்கு வகிக்கின்றன என்பதை பிரதிநிதித்துவப்படுத்தும். கீழே அத்தகைய அட்டவணையின் உதாரணம் உள்ளது:\n", "\n", - "![Bahdanau - arviz.org இல் இருந்து எடுத்துக்கொள்ளப்பட்ட RNNsearch-50 கண்ட ஒரு மாதAlignment-ஐக் காட்டும் படம்](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ta.png)\n", + "![Bahdanau - arviz.org இல் இருந்து எடுத்துக்கொள்ளப்பட்ட RNNsearch-50 கண்ட ஒரு மாதAlignment-ஐக் காட்டும் படம்](../../../../../translated_images/ta/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) இல் இருந்து எடுத்த படம்]*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "இந்த அடுக்கு இரண்டு `Embedding` அடுக்குகளை கொண்டுள்ளது: டோக்கன்களை எம்பெடிங் செய்ய (முன்னதாக நாம் விவாதித்த முறையில்) மற்றும் டோக்கன் இடங்களை. டோக்கன் இடங்கள் `tf.range` பயன்படுத்தி 0 முதல் `maxlen` வரை இயற்கை எண்களின் வரிசையாக உருவாக்கப்படுகின்றன, பின்னர் எம்பெடிங் அடுக்கில் அனுப்பப்படுகின்றன. இரண்டு எம்பெடிங் வெக்டர்கள் சேர்க்கப்பட்டு, உள்ளீட்டின் இடமாற்ற எம்பெடிங் பிரதிநிதித்துவத்தை உருவாக்குகின்றன, இதன் வடிவம் `maxlen`$\\times$`embed_dim`.\n", "\n", - "\n", + "\n", "\n", "இப்போது, டிரான்ஸ்ஃபார்மர் பிளாக்கை செயல்படுத்துவோம். இது முன்பு வரையறுக்கப்பட்ட எம்பெடிங் அடுக்கின் வெளியீட்டை எடுக்கும்:\n" ] @@ -134,7 +134,7 @@ "\n", "இந்த அடுக்கின் வெளியீடு பின்னர் `Dense` நெட்வொர்க்கில் (எங்கள் வழக்கில் - இரண்டு அடுக்கு perceptron) அனுப்பப்படுகிறது, மற்றும் முடிவில் உள்ள வெளியீட்டுடன் சேர்க்கப்படுகிறது (மீண்டும் சீரமைக்கப்படுகிறது).\n", "\n", - "\n", + "\n", "\n", "இப்போது, முழுமையான transformer மாதிரியை வரையறுக்க தயாராக உள்ளோம்:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) என்பது மிகப்பெரிய, பல அடுக்கு மாற்றி நெட்வொர்க் ஆகும், இதில் *BERT-base* க்கு 12 அடுக்குகள் மற்றும் *BERT-large* க்கு 24 அடுக்குகள் உள்ளன. இந்த மாடல் முதலில் பெரிய உரை தரவுத்தொகுப்பில் (WikiPedia + புத்தகங்கள்) கண்காணிக்கப்படாத பயிற்சியை (ஒரு வாக்கியத்தில் மறைக்கப்பட்ட வார்த்தைகளை கணிக்க) பயன்படுத்தி முன்பயிற்சி செய்யப்படுகிறது. முன்பயிற்சியின் போது, மாடல் முக்கியமான மொழி புரிதலை உறிஞ்சுகிறது, இதை பிற தரவுத்தொகுப்புகளுடன் நன்றாகச் சீரமைத்து பயன்படுத்தலாம். இந்த செயல்முறை **மாற்றக் கற்றல்** என்று அழைக்கப்படுகிறது.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ta.png)\n", + "![http://jalammar.github.io/illustrated-bert/ இல் இருந்து படம்](../../../../../translated_images/ta/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 மற்றும் பலவற்றைச் சேர்த்து நன்றாகச் சீரமைக்கக்கூடிய பல மாற்றி கட்டமைப்புகளின் மாறுபாடுகள் உள்ளன.\n", "\n", diff --git a/translations/ta/lessons/5-NLP/19-NER/README.md b/translations/ta/lessons/5-NLP/19-NER/README.md index 0df76f4d..399d4599 100644 --- a/translations/ta/lessons/5-NLP/19-NER/README.md +++ b/translations/ta/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் இயற்கை மொழி உரையாடல் சாட் பாட்டை உருவாக்க விரும்புகிறீர்கள் என்று நினைக்கவும், இது Amazon Alexa அல்லது Google Assistant போன்றது. புத்திசாலி சாட் பாட்டுகள் செயல்படுவது, பயனர் என்ன விரும்புகிறார் என்பதை *புரிந்து* கொள்ளும் வகையில் உள்ளீட்டு வாக்கியத்தில் உரை வகைப்படுத்தல் செய்வதன் மூலம். இந்த வகைப்படுத்தலின் முடிவு **நோக்கம்** என அழைக்கப்படுகிறது, இது சாட் பாட்டின் செயல்பாட்டை நிர்ணயிக்கிறது. -Bot NER +Bot NER > படத்தை உருவாக்கியவர் @@ -58,7 +58,7 @@ infant | O டோக்கன்கள் மற்றும் வகைகளுக்கு ஒரு-மற்றொரு தொடர்பை உருவாக்க வேண்டும் என்பதால், இந்த படத்திலிருந்து ஒரு சரியான **பல-மற்றொரு** நரம்பியல் வலை மாதிரியை உருவாக்கலாம்: -![சாதாரண மீள்நடப்ப நரம்பியல் வலைகள் பற்றிய படத்தை காட்டுகிறது.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ta.jpg) +![சாதாரண மீள்நடப்ப நரம்பியல் வலைகள் பற்றிய படத்தை காட்டுகிறது.](../../../../../translated_images/ta/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *இந்த [வலைப்பதிவில்](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) இருந்து [Andrej Karpathy](http://karpathy.github.io/) எழுதிய படம். NER டோக்கன் வகைப்படுத்தல் மாதிரிகள் இந்த படத்தின் வலது பக்கம் உள்ள வலை معماريக்கு ஒத்ததாக உள்ளது.* diff --git a/translations/ta/lessons/5-NLP/README.md b/translations/ta/lessons/5-NLP/README.md index 307597d8..f276e41a 100644 --- a/translations/ta/lessons/5-NLP/README.md +++ b/translations/ta/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # இயற்கை மொழி செயலாக்கம் -![NLP பணிகளின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ta.png) +![NLP பணிகளின் சுருக்கம் ஒரு ஓவியத்தில்](../../../../translated_images/ta/ai-nlp.b22dcb8ca4707cea.png) இந்த பிரிவில், **இயற்கை மொழி செயலாக்கம் (NLP)** தொடர்பான பணிகளை கையாள நரம்பியல் வலையமைப்புகளைப் பயன்படுத்துவதில் கவனம் செலுத்துவோம். கணினிகள் தீர்க்க வேண்டிய பல NLP பிரச்சினைகள் உள்ளன: diff --git a/translations/ta/lessons/6-Other/22-DeepRL/README.md b/translations/ta/lessons/6-Other/22-DeepRL/README.md index e13ddd00..b6723cfa 100644 --- a/translations/ta/lessons/6-Other/22-DeepRL/README.md +++ b/translations/ta/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL க்கான ஒரு சிறந்த கருவி [OpenAI Gym](htt சமநிலையின் எளிமையான பதிப்பு **CartPole** பிரச்சினையாக அறியப்படுகிறது. CartPole உலகில், இடது அல்லது வலது நோக்கி நகரும் ஒரு கிடைமட்ட ஸ்லைடர் உள்ளது, மேலும் இலக்கு என்பது ஸ்லைடரின் மேல் ஒரு செங்குத்து தண்டை சமநிலையைப் பேணுவது. -ஒரு CartPole +ஒரு CartPole இந்த சூழலை உருவாக்கி பயன்படுத்த, Python கோடின் சில வரிகள் தேவை: diff --git a/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md b/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md index 9444953b..ceb3b79f 100644 --- a/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/ta/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் நோக்கம் OpenAI சூழலில் [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ஐ கட்டுப்படுத்த RL முகவரை பயிற்சி செய்வதாகும். -Mountain Car +Mountain Car ## சூழல் diff --git a/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md index fd16a98f..32d06ece 100644 --- a/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ta/lessons/6-Other/23-MultiagentSystems/README.md @@ -60,7 +60,7 @@ NetLogo ஐ [பதிவிறக்கம்](https://ccl.northwestern.edu/net NetLogo இன் சிறப்பம்சம், இது நீங்கள் முயற்சிக்கக்கூடிய செயல்படும் மாதிரிகளின் நூலகத்தை கொண்டுள்ளது. **File → Models Library** க்கு செல்லவும், மேலும் நீங்கள் தேர்ந்தெடுக்க பல வகைகளின் மாதிரிகள் உள்ளன. -NetLogo Models Library +NetLogo Models Library > NetLogo மாதிரிகள் நூலகத்தின் ஸ்கிரீன்ஷாட் - Dmitry Soshnikov @@ -70,7 +70,7 @@ NetLogo இன் சிறப்பம்சம், இது நீங்க மாதிரியைத் திறந்த பிறகு, நீங்கள் NetLogo இன் முக்கிய திரைக்கு கொண்டு செல்லப்படுகிறீர்கள். இங்கு முடிவான வளங்கள் (பசுமை) கொண்ட ஓநாய்கள் மற்றும் செம்மறியாடுகளின் மக்கள் தொகையை விவரிக்கும் மாதிரி உள்ளது. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ta.png) +![NetLogo Main Screen](../../../../../translated_images/ta/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov இன் ஸ்கிரீன்ஷாட் diff --git a/translations/ta/lessons/README.md b/translations/ta/lessons/README.md index 9cdf984d..da9ea1c2 100644 --- a/translations/ta/lessons/README.md +++ b/translations/ta/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # மேலோட்டம் -![ஒரு ஓவியத்தில் மேலோட்டம்](../../../translated_images/ai-overview.0857791951d19500.ta.png) +![ஒரு ஓவியத்தில் மேலோட்டம்](../../../translated_images/ta/ai-overview.0857791951d19500.png) > ஸ்கெட்ச் குறிப்பு: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ta/lessons/X-Extras/X1-MultiModal/README.md b/translations/ta/lessons/X-Extras/X1-MultiModal/README.md index a916437e..baa3f1e3 100644 --- a/translations/ta/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ta/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP பணிகளைத் தீர்க்க டிரான்ஸ்ஃ CLIP இன் முக்கிய நோக்கம், உரை முன்மொழிவுகளை ஒரு படத்துடன் ஒப்பிட்டு, அந்த படம் முன்மொழிவுடன் எவ்வளவு பொருந்துகிறது என்பதைத் தீர்மானிக்க வேண்டும். -![CLIP கட்டமைப்பு](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ta.png) +![CLIP கட்டமைப்பு](../../../../../translated_images/ta/clip-arch.b3dbf20b4e8ed8be.png) > *[இந்த வலைப்பதிவில்](https://openai.com/blog/clip/) இருந்து எடுத்த படம்* @@ -29,7 +29,7 @@ CLIP மாடல்/நூலகம் [OpenAI GitHub](https://github.com/opena நாம் படங்களை, உதாரணமாக, பூனைகள், நாய்கள் மற்றும் மனிதர்கள் ஆகியவற்றுக்கு இடையில் வகைப்படுத்த வேண்டும் எனக் கருதுக. இந்த நிலையில், மாடலுக்கு ஒரு படம் மற்றும் ஒரு தொடர் உரை முன்மொழிவுகளை கொடுக்கலாம்: "*ஒரு பூனையின் படம்*", "*ஒரு நாயின் படம்*", "*ஒரு மனிதனின் படம்*". 3 சாத்தியக்கூறுகளின் விளைவாக கிடைக்கும் வெக்டாரில், அதிக மதிப்புள்ள குறியீட்டை தேர்ந்தெடுக்க வேண்டும். -![பட வகைப்படுத்தலுக்கான CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.ta.png) +![பட வகைப்படுத்தலுக்கான CLIP](../../../../../translated_images/ta/clip-class.3af42ef0b2b19369.png) > *[இந்த வலைப்பதிவில்](https://openai.com/blog/clip/) இருந்து எடுத்த படம்* @@ -53,13 +53,13 @@ VQGAN பற்றி மேலும் அறிய [Taming Transformers](http VQGAN மற்றும் பாரம்பரிய GAN களுக்கிடையிலான முக்கியமான வேறுபாடுகளில் ஒன்று, GAN எந்த உள்ளீட்டு வெக்டாரிலிருந்தும் ஒரு நல்ல படத்தை உருவாக்க முடியும், ஆனால் VQGAN ஒருங்கிணைந்த படத்தை உருவாக்க முடியாமல் போகலாம். எனவே, பட உருவாக்க செயல்முறையை மேலும் வழிநடத்த வேண்டும், இது CLIP ஐப் பயன்படுத்தி செய்ய முடியும். -![VQGAN+CLIP கட்டமைப்பு](../../../../../translated_images/vqgan.5027fe05051dfa31.ta.png) +![VQGAN+CLIP கட்டமைப்பு](../../../../../translated_images/ta/vqgan.5027fe05051dfa31.png) ஒரு உரை முன்மொழிவுக்கு பொருந்தும் படத்தை உருவாக்க, சில சீரற்ற குறியீட்டு வெக்டாருடன் தொடங்குகிறோம், இது VQGAN வழியாக அனுப்பப்பட்டு ஒரு படத்தை உருவாக்குகிறது. பின்னர் CLIP ஒரு இழப்புக் கோட்பாட்டை உருவாக்க பயன்படுத்தப்படுகிறது, இது படம் உரை முன்மொழிவுக்கு எவ்வளவு பொருந்துகிறது என்பதை காட்டுகிறது. பின்னர் இந்த இழப்பை குறைப்பதே நோக்கம், பின்னடைவு மூலம் உள்ளீட்டு வெக்டார் அளவுருக்களை சரிசெய்தல். VQGAN+CLIP ஐ செயல்படுத்தும் ஒரு சிறந்த நூலகம் [Pixray](http://github.com/pixray/pixray) -![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ta.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ta.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ta.png) +![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- முன்மொழிவு *ஒரு புத்தகத்துடன் இளம் ஆண் இலக்கிய ஆசிரியரின் நெருக்கமான நீர்வண்ண உருவப்படம்* | முன்மொழிவு *ஒரு கணினியுடன் இளம் பெண் கணினி அறிவியல் ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* | முன்மொழிவு *கரும்பலகையின் முன் முதிய ஆண் கணித ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* @@ -75,7 +75,7 @@ CLIP ஐ விட DALL-E உரை மற்றும் படத்தை ஒ DALL-E 1 மற்றும் 2 இன் முக்கியமான வேறுபாடு, இது மேலும் யதார்த்தமான படங்கள் மற்றும் கலைகளை உருவாக்குகிறது. DALL-E மூலம் உருவாக்கப்பட்ட படங்களின் உதாரணங்கள்: -![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ta.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ta.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ta.png) +![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray மூலம் உருவாக்கப்பட்ட படம்](../../../../../translated_images/ta/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- முன்மொழிவு *ஒரு புத்தகத்துடன் இளம் ஆண் இலக்கிய ஆசிரியரின் நெருக்கமான நீர்வண்ண உருவப்படம்* | முன்மொழிவு *ஒரு கணினியுடன் இளம் பெண் கணினி அறிவியல் ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* | முன்மொழிவு *கரும்பலகையின் முன் முதிய ஆண் கணித ஆசிரியரின் நெருக்கமான எண்ணெய் உருவப்படம்* diff --git a/translations/te/README.md b/translations/te/README.md index 69534e9f..eb7561f1 100644 --- a/translations/te/README.md +++ b/translations/te/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ప్రారంభులకు ఆర్టిఫిషియల్ ఇంటెలిజెన్స్ - ఒక పాఠ్యాంశం -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.te.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/te/ai-overview.0857791951d19500.png)| |:---:| | ప్రారంభులకు AI - _[ @girlie_mac](https://twitter.com/girlie_mac) వారి స్కెచ్ నోటు_ | diff --git a/translations/te/lessons/1-Intro/README.md b/translations/te/lessons/1-Intro/README.md index db1ca4d7..8ee2d6dc 100644 --- a/translations/te/lessons/1-Intro/README.md +++ b/translations/te/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # AI పరిచయం -![AI పరిచయ విషయాల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/ai-intro.bf28d1ac4235881c.te.png) +![AI పరిచయ విషయాల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-intro.bf28d1ac4235881c.png) > స్కెచ్‌నోట్: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: మూలంగా, కంప్యూటర్లు [చార్లెస్ బాబేజ్](https://en.wikipedia.org/wiki/Charles_Babbage) చేత సంఖ్యలపై ఒక సుస్పష్టమైన ప్రక్రియను అనుసరించి పనిచేయడానికి ఆవిష్కరించబడ్డాయి - ఒక అల్గోరిథం. ఆధునిక కంప్యూటర్లు, 19వ శతాబ్దంలో ప్రతిపాదించిన మోడల్ కంటే చాలా అభివృద్ధి చెందినప్పటికీ, ఇంకా నియంత్రిత గణనలను అనుసరిస్తాయి. కాబట్టి, లక్ష్యాన్ని సాధించడానికి అవసరమైన ఖచ్చితమైన దశలను మనం తెలుసుకుంటే, కంప్యూటర్‌ను ప్రోగ్రామ్ చేయడం సాధ్యం. -![వ్యక్తి ఫోటో](../../../../translated_images/dsh_age.d212a30d4e54fb5f.te.png) +![వ్యక్తి ఫోటో](../../../../translated_images/te/dsh_age.d212a30d4e54fb5f.png) > ఫోటో: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[మేధస్సు](https://en.wikipedia.org/wiki/Intelligence)** అనే పదం స్పష్టమైన నిర్వచనం లేకపోవడం ఒక సమస్య. మేధస్సు అనేది **సారాంశ ఆలోచన** లేదా **స్వీయ అవగాహన**కి సంబంధించినదని వాదించవచ్చు, కానీ దీన్ని సరైన రీతిలో నిర్వచించలేము. -![పిల్లి ఫోటో](../../../../translated_images/photo-cat.8c8e8fb760ffe457.te.jpg) +![పిల్లి ఫోటో](../../../../translated_images/te/photo-cat.8c8e8fb760ffe457.jpg) > ఫోటో: [Amber Kipp](https://unsplash.com/@sadmax) నుండి Unsplash @@ -98,13 +98,13 @@ AGI గురించి మాట్లాడేటప్పుడు ని > | ML గురించి ఏమిటి? | | > |--------------|-----------| -> | డేటా ఆధారంగా సమస్య పరిష్కరించడానికి కంప్యూటర్ నేర్చుకునే కృత్రిమ మేధస్సు భాగం **మిషన్ లెర్నింగ్** అని పిలవబడుతుంది. ఈ కోర్సులో క్లాసికల్ మిషన్ లెర్నింగ్ చర్చించము - మీరు ప్రత్యేక [Machine Learning for Beginners](http://aka.ms/ml-beginners) పాఠ్యాంశాన్ని చూడండి. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.te.png) | +> | డేటా ఆధారంగా సమస్య పరిష్కరించడానికి కంప్యూటర్ నేర్చుకునే కృత్రిమ మేధస్సు భాగం **మిషన్ లెర్నింగ్** అని పిలవబడుతుంది. ఈ కోర్సులో క్లాసికల్ మిషన్ లెర్నింగ్ చర్చించము - మీరు ప్రత్యేక [Machine Learning for Beginners](http://aka.ms/ml-beginners) పాఠ్యాంశాన్ని చూడండి. | ![ML for Beginners](../../../../translated_images/te/ml-for-beginners.9e4fed176fd5817d.png) | ## AI చరిత్ర సంక్షిప్తంగా కృత్రిమ మేధస్సు 20వ శతాబ్ద మధ్యలో ఒక రంగంగా ప్రారంభమైంది. మొదట, సింబాలిక్ తర్కం ప్రాచుర్యం పొందింది, ఇది నిపుణుల వ్యవస్థలు వంటి విజయాలను తీసుకువచ్చింది – కొన్ని పరిమిత సమస్యల పరిధిలో నిపుణులుగా పనిచేసే కంప్యూటర్ ప్రోగ్రాములు. కానీ ఈ దృక్పథం విస్తరించలేదని స్పష్టమైంది. నిపుణుల నుండి జ్ఞానాన్ని సేకరించడం, కంప్యూటర్‌లో ప్రతినిధ్యం చేయడం, జ్ఞానాన్ని సరిగ్గా ఉంచడం చాలా క్లిష్టమైన పని మరియు చాలా ఖరీదైనది. ఇది 1970లలో [AI వింటర్](https://en.wikipedia.org/wiki/AI_winter)కి దారితీసింది. -AI చరిత్ర సంక్షిప్తం +AI చరిత్ర సంక్షిప్తం > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/te/lessons/2-Symbolic/Animals.ipynb b/translations/te/lessons/2-Symbolic/Animals.ipynb index 1e2e35ae..8ddc1ab5 100644 --- a/translations/te/lessons/2-Symbolic/Animals.ipynb +++ b/translations/te/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ఈ నమూనాలో, కొన్ని శారీరక లక్షణాల ఆధారంగా జంతువును గుర్తించడానికి ఒక సులభమైన జ్ఞానాధారిత వ్యవస్థను అమలు చేస్తాము. ఈ వ్యవస్థను క్రింది AND-OR చెట్టు ద్వారా ప్రదర్శించవచ్చు (ఇది మొత్తం చెట్టు యొక్క ఒక భాగం, మేము సులభంగా మరిన్ని నియమాలను జోడించవచ్చు):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.te.png)\n" + "![](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/te/lessons/2-Symbolic/README.md b/translations/te/lessons/2-Symbolic/README.md index 2c24376e..c125354d 100644 --- a/translations/te/lessons/2-Symbolic/README.md +++ b/translations/te/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # జ్ఞాన ప్రాతినిధ్యం మరియు నిపుణుల వ్యవస్థలు -![సాంబాలిక AI విషయాల సారాంశం](../../../../translated_images/ai-symbolic.715a30cb610411a6.te.png) +![సాంబాలిక AI విషయాల సారాంశం](../../../../translated_images/te/ai-symbolic.715a30cb610411a6.png) > స్కెచ్ నోట్ [Tomomi Imura](https://twitter.com/girlie_mac) ద్వారా @@ -35,13 +35,13 @@ AI ప్రారంభ దశల్లో, తెలివైన వ్యవ * **జ్ఞానం** అనేది సమాచారాన్ని మన ప్రపంచ మోడల్‌లోకి సమీకరించడం. ఉదాహరణకు, కంప్యూటర్ అంటే ఏమిటి తెలుసుకున్న తర్వాత, అది ఎలా పనిచేస్తుంది, ధర ఎంత, దానిని ఏం కోసం ఉపయోగించవచ్చు అనే ఆలోచనలు కలుగుతాయి. ఈ సంబంధిత భావనల నెట్‌వర్క్ మన జ్ఞానాన్ని ఏర్పరుస్తుంది. * **ప్రజ్ఞ** అనేది మన ప్రపంచం గురించి మరొక స్థాయి అవగాహన, ఇది *మెటా-జ్ఞానం* ను సూచిస్తుంది, అంటే జ్ఞానాన్ని ఎప్పుడు మరియు ఎలా ఉపయోగించాలో తెలియజేస్తుంది. - + *చిత్రం [వికీపీడియా నుండి](https://commons.wikimedia.org/w/index.php?curid=37705247), Longlivetheux - స్వంత పని, CC BY-SA 4.0* కాబట్టి, **జ్ఞాన ప్రాతినిధ్యం** సమస్య అనేది కంప్యూటర్‌లో జ్ఞానాన్ని డేటా రూపంలో సమర్థవంతంగా ప్రాతినిధ్యం చేయడం, దాన్ని ఆటోమేటిక్‌గా ఉపయోగించుకునేలా చేయడం. దీన్ని ఒక స్పెక్ట్రమ్‌గా చూడవచ్చు: -![జ్ఞాన ప్రాతినిధ్యం స్పెక్ట్రమ్](../../../../translated_images/knowledge-spectrum.b60df631852c0217.te.png) +![జ్ఞాన ప్రాతినిధ్యం స్పెక్ట్రమ్](../../../../translated_images/te/knowledge-spectrum.b60df631852c0217.png) > చిత్రం [Dmitry Soshnikov](http://soshnikov.com) ద్వారా @@ -94,7 +94,7 @@ Block Syntax | Indent | | | సాంబాలిక AI ప్రారంభ విజయాలలో ఒకటి **నిపుణుల వ్యవస్థలు** - కొన్ని పరిమిత సమస్యల పరిధిలో నిపుణులుగా వ్యవహరించే కంప్యూటర్ వ్యవస్థలు. ఇవి ఒక లేదా ఎక్కువ మానవ నిపుణుల నుండి తీసుకున్న **జ్ఞాన బేస్** ఆధారంగా ఉండి, దాని పై తర్కం చేసే **ఇన్ఫరెన్స్ ఇంజిన్** కలిగి ఉంటాయి. -![మానవ నిర్మాణం](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.te.png) | ![జ్ఞాన ఆధారిత వ్యవస్థ](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.te.png) +![మానవ నిర్మాణం](../../../../translated_images/te/arch-human.5d4d35f1bba3ab1c.png) | ![జ్ఞాన ఆధారిత వ్యవస్థ](../../../../translated_images/te/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ మానవ న్యూరల్ సిస్టమ్ సరళీకృత నిర్మాణం | జ్ఞాన ఆధారిత వ్యవస్థ నిర్మాణం @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ఉదాహరణకు, ఒక జంతువును దాని శారీరక లక్షణాల ఆధారంగా గుర్తించే నిపుణుల వ్యవస్థను పరిశీలిద్దాం: -![AND-OR చెట్టు](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.te.png) +![AND-OR చెట్టు](../../../../translated_images/te/AND-OR-Tree.5592d2c70187f283.png) > చిత్రం [Dmitry Soshnikov](http://soshnikov.com) ద్వారా @@ -168,7 +168,7 @@ THEN the animal is a carnivore సెమాంటిక్ వెబ్‌లో అన్ని ప్రాతినిధ్యాలు ట్రిప్లెట్లపై ఆధారపడి ఉంటాయి. ప్రతి వస్తువు మరియు ప్రతి సంబంధం ప్రత్యేకంగా URI ద్వారా గుర్తించబడతాయి. ఉదాహరణకు, ఈ AI పాఠ్యాంశం డిమిత్రి సోష్నికోవ్ 2022 జనవరి 1న అభివృద్ధి చేశారని చెప్పాలంటే, మనం ఉపయోగించగల ట్రిప్లెట్లు ఇవి: - + ``` http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” @@ -179,7 +179,7 @@ http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/cre మరింత క్లిష్టమైన సందర్భంలో, సృష్టికర్తల జాబితాను నిర్వచించాలంటే, RDFలో నిర్వచించిన కొన్ని డేటా నిర్మాణాలను ఉపయోగించవచ్చు. - + > పై చిత్రాలు [డిమిత్రి సోష్నికోవ్](http://soshnikov.com) చేత రూపొందించబడ్డవి @@ -203,7 +203,7 @@ GROUP BY ?eyeColorLabel > ✅ మీ స్వంత ఆంటాలజీలు నిర్మించడానికి లేదా ఉన్న వాటిని తెరవడానికి, [Protégé](https://protege.stanford.edu/) అనే అద్భుతమైన విజువల్ ఆంటాలజీ ఎడిటర్ ఉంది. దాన్ని డౌన్లోడ్ చేసుకోండి లేదా ఆన్‌లైన్‌లో ఉపయోగించండి. - + *Web Protégé ఎడిటర్ రోమానోవ్ కుటుంబ ఆంటాలజీతో తెరవబడింది. స్క్రీన్‌షాట్ డిమిత్రి సోష్నికోవ్ చేత* diff --git a/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md index 677ae0e9..af7ce9d9 100644 --- a/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/te/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > చిత్రాలు [వికీపీడియా నుండి](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +34,7 @@ y(x) = f(wTx) ఇక్కడ f అనేది స్టెప్ యాక్టివేషన్ ఫంక్షన్ - + ## పర్సెప్ట్రాన్ శిక్షణ diff --git a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 3ef30b8f..0a579027 100644 --- a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -373,7 +373,7 @@ "\n", "మేము బైనరీ వర్గీకరణ సమస్య కోసం ఒక డేటాసెట్ సృష్టించాము. అయితే, మొదటినుండి దీన్ని బహుళ వర్గీకరణంగా పరిగణించుకుందాం, తద్వారా మనం తర్వాత సులభంగా మన కోడ్‌ను బహుళ వర్గీకరణకు మార్చుకోవచ్చు. ఈ సందర్భంలో, మన ఒక-పరత పర్సెప్ట్రాన్ క్రింది ఆర్కిటెక్చర్ కలిగి ఉంటుంది:\n", "\n", - "\n", + "\n", "\n", "నెట్‌వర్క్ యొక్క రెండు అవుట్పుట్లు రెండు వర్గాలకు సంబంధించినవి, మరియు రెండు అవుట్పుట్లలో అత్యధిక విలువ ఉన్న వర్గం సరైన పరిష్కారంగా పరిగణించబడుతుంది.\n", "\n", @@ -489,7 +489,7 @@ "\n", "మనం 2 కంటే ఎక్కువ తరగతులు ఉన్నప్పుడు, softmax వాటి అంతటా సంభావ్యతలను సరిచేస్తుంది. ఇక్కడ MNIST అంకెల వర్గీకరణ చేసే నెట్‌వర్క్ నిర్మాణం యొక్క చిత్రణ ఉంది:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.dc7ba633d2467ef3.te.png)\n" + "![MNIST Classifier](../../../../../translated_images/te/Cross-Entropy-Loss.dc7ba633d2467ef3.png)\n" ] }, { @@ -620,7 +620,7 @@ "\n", "## గణనాత్మక గ్రాఫ్\n", "\n", - "\n", + "\n", "\n", "ఇప్పటి వరకు, మనం నెట్‌వర్క్ యొక్క వేర్వేరు లేయర్ల కోసం వేర్వేరు క్లాసులను నిర్వచించాము. ఆ లేయర్ల సమ్మేళనం **గణనాత్మక గ్రాఫ్**గా ప్రాతినిధ్యం వహించవచ్చు. ఇప్పుడు మనం ఇచ్చిన శిక్షణ డేటాసెట్ (లేదా దాని భాగం) కోసం లాస్‌ను క్రింది విధంగా లెక్కించవచ్చు:\n" ] @@ -687,7 +687,7 @@ "source": [ "## వెనుకకు ప్రేరణ\n", "\n", - "\n", + "\n", "\n", "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", "\\begin{align}\n", @@ -712,7 +712,7 @@ "* ఇది నోడ్ $z$ లో మార్పులకు సరిపోతుంది: $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", "* ఈ లోపాన్ని తగ్గించడానికి, మనం పారామితులను తగిన విధంగా సర్దుబాటు చేయాలి: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (మరియు అదే విధంగా $b$ కోసం)\n", "\n", - "\n", + "\n", "\n", "ఈ ప్రక్రియ నెట్‌వర్క్ అవుట్‌పుట్ నుండి దాని పారామితుల వరకు లాస్ లోపాన్ని వెనుకకు పంపడం ప్రారంభిస్తుంది. అందుకే ఈ ప్రక్రియను **బ్యాక్ ప్రొపగేషన్** అంటారు.\n", "\n", @@ -1265,7 +1265,7 @@ "* తక్కువ శిక్షణ నష్టం - మోడల్ శిక్షణ డేటాను బాగా అంచనా వేయగలదు, ఎందుకంటే దానికి సరిపడా వ్యక్తీకరణ శక్తి ఉంది.\n", "* ధృవీకరణ నష్టం శిక్షణ నష్టంకంటే చాలా ఎక్కువగా ఉండవచ్చు మరియు శిక్షణ సమయంలో పెరుగుతూనే ఉండవచ్చు - ఇది మోడల్ శిక్షణ పాయింట్లను \"మరచిపోకుండా\" ఉంచడం వల్ల, \"మొత్తం దృశ్యం\" కోల్పోతుంది.\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.te.png)\n", + "![Overfitting](../../../../../translated_images/te/overfit.a0bd57f717c15769.png)\n", "\n", "> ఈ చిత్రంలో, `x` శిక్షణ డేటాను సూచిస్తుంది, `o` - ధృవీకరణ డేటాను. ఎడమవైపు - రేఖీయ మోడల్ (ఒకే-పట్టా), ఇది డేటా స్వభావాన్ని బాగా అంచనా వేస్తుంది. కుడివైపు - ఓవర్‌ఫిట్టెడ్ మోడల్, మోడల్ శిక్షణ డేటాను పూర్తిగా అంచనా వేస్తుంది, కానీ ఇతర ఏ డేటాతోనూ అర్థం చేసుకోలేకపోతుంది (ధృవీకరణ లోపం చాలా ఎక్కువ).\n" ] diff --git a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 5a75cf51..028f49e0 100644 --- a/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/te/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -65,7 +65,7 @@ CO_OP_TRANSLATOR_METADATA: గమనించండి, ఈ అన్ని వ్యక్తీకరణల ఎడమవైపు భాగం ఒకటే ఉంటుంది, కాబట్టి మనం లాస్ ఫంక్షన్ నుండి "వెనుకకు" కంప్యూటేషనల్ గ్రాఫ్ ద్వారా డెరివేటివ్స్‌ను సమర్థవంతంగా లెక్కించవచ్చు. అందువల్ల, మల్టీ-లేయర్డ్ పర్సెప్ట్రాన్ శిక్షణ పద్ధతిని **బ్యాక్‌ప్రొపగేషన్** లేదా 'బ్యాక్‌ప్రాప్' అంటారు. -compute graph +compute graph > TODO: చిత్రం మూలం diff --git a/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md index 8a88d9d3..483f0555 100644 --- a/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/te/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: క్రింది సమస్యను పరిగణించండి, 5 డాట్లను (గ్రాఫ్‌లలో `x`గా సూచించబడినవి) సన్నిహితంగా అంచనా వేయడం: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.te.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.te.jpg) +![linear](../../../../../translated_images/te/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/te/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **లీనియర్ మోడల్, 2 పారామీటర్లు** | **నాన్-లీనియర్ మోడల్, 7 పారామీటర్లు** శిక్షణ లోపం = 5.3 | శిక్షణ లోపం = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: పై గ్రాఫ్ నుండి మీరు చూడగలిగినట్లుగా, ఓవర్‌ఫిట్టింగ్ చాలా తక్కువ శిక్షణ లోపం మరియు ఎక్కువ వాలిడేషన్ లోపం ద్వారా గుర్తించవచ్చు. సాధారణంగా శిక్షణ సమయంలో శిక్షణ మరియు వాలిడేషన్ లోపాలు రెండూ తగ్గడం ప్రారంభిస్తాయి, ఆపై ఏదో సమయంలో వాలిడేషన్ లోపం తగ్గడం ఆగి పెరుగుతుండవచ్చు. ఇది ఓవర్‌ఫిట్టింగ్ సంకేతం, మరియు ఆ సమయంలో శిక్షణను ఆపడం (లేదా కనీసం మోడల్ యొక్క స్నాప్‌షాట్ తీసుకోవడం) అవసరం. -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.te.png) +![overfitting](../../../../../translated_images/te/Overfitting.408ad91cd90b4371.png) ## ఓవర్‌ఫిట్టింగ్ ఎలా నివారించాలి diff --git a/translations/te/lessons/3-NeuralNetworks/README.md b/translations/te/lessons/3-NeuralNetworks/README.md index 0dd752e2..81a67032 100644 --- a/translations/te/lessons/3-NeuralNetworks/README.md +++ b/translations/te/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # న్యూరల్ నెట్‌వర్క్స్ పరిచయం -![Intro Neural Networks విషయాల సారాంశం డూడిల్‌లో](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.te.png) +![Intro Neural Networks విషయాల సారాంశం డూడిల్‌లో](../../../../translated_images/te/ai-neuralnetworks.1c687ae40bc86e83.png) మనం పరిచయంలో చర్చించినట్లుగా, మేధస్సును సాధించడానికి ఒక మార్గం **కంప్యూటర్ మోడల్** లేదా **కృత్రిమ మెదడు**ను శిక్షణ ఇవ్వడం. 20వ శతాబ్దం మధ్య నుండి పరిశోధకులు వివిధ గణిత మోడల్స్ ప్రయత్నించారు, ఇటీవల సంవత్సరాలలో ఈ దిశ చాలా విజయవంతమైంది. మెదడుకు ఇలాంటి గణిత మోడల్స్‌ను **న్యూరల్ నెట్‌వర్క్స్** అంటారు. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: జీవశాస్త్రం ప్రకారం, మన మెదడు న్యూరల్ సెల్స్ (న్యూరాన్లు)తో కూడి ఉంటుంది, ప్రతి న్యూరాన్‌కు అనేక "ఇన్‌పుట్లు" (డెండ్రైట్స్) మరియు ఒకే "అవుట్‌పుట్" (ఆక్సాన్) ఉంటుంది. డెండ్రైట్స్ మరియు ఆక్సాన్లు విద్యుత్ సంకేతాలను ప్రసారం చేయగలవు, మరియు వాటి మధ్య కనెక్షన్లు — సైనాప్సెస్ అని పిలవబడతాయి — వివిధ స్థాయిలలో విద్యుత్ ప్రసరణ సామర్థ్యాన్ని చూపుతాయి, ఇవి న్యూరోట్రాన్స్‌మిటర్ల ద్వారా నియంత్రించబడతాయి. -![న్యూరాన్ మోడల్](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.te.jpg) | ![న్యూరాన్ మోడల్](../../../../translated_images/artneuron.1a5daa88d20ebe6f.te.png) +![న్యూరాన్ మోడల్](../../../../translated_images/te/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![న్యూరాన్ మోడల్](../../../../translated_images/te/artneuron.1a5daa88d20ebe6f.png) ----|---- నిజమైన న్యూరాన్ *([చిత్రం](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) వికీపీడియా నుండి)* | కృత్రిమ న్యూరాన్ *(చిత్రం రచయిత ద్వారా)* కాబట్టి, న్యూరాన్ యొక్క సులభమైన గణిత మోడల్‌లో అనేక ఇన్‌పుట్లు X1, ..., XN, ఒక అవుట్‌పుట్ Y మరియు ఒక శ్రేణి వెయిట్లు W1, ..., WN ఉంటాయి. అవుట్‌పుట్ ఈ విధంగా లెక్కించబడుతుంది: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) ఇక్కడ f అనేది ఒక నాన్-లీనియర్ **యాక్టివేషన్ ఫంక్షన్**. diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md index 3835b77d..f93fe235 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/README.md @@ -75,14 +75,14 @@ OpenCV ను ఉపయోగించి వీడియోను ఫ్రే * **బ్రెయిల్ పుస్తకం ఫోటో ప్రీ-ప్రాసెసింగ్**. thresholding, ఫీచర్ డిటెక్షన్, perspective transformation మరియు NumPy మార్పులతో వ్యక్తిగత బ్రెయిల్ చిహ్నాలను వేరుచేసి, తరువాత న్యూరల్ నెట్‌వర్క్ ద్వారా వర్గీకరణ కోసం ఎలా సిద్ధం చేయాలో మనం దృష్టి సారిస్తాము. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.te.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.te.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.te.png) +![Braille Image](../../../../../translated_images/te/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/te/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/te/braille-symbols.0159185ab69d5339.png) ----|-----|----- > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి * **ఫ్రేమ్ తేడా ఉపయోగించి వీడియోలో కదలిక గుర్తించడం**. కెమెరా స్థిరంగా ఉంటే, కెమెరా ఫీడ్ నుండి ఫ్రేమ్‌లు ఒకదానితో ఒకటి చాలా సమానంగా ఉంటాయి. ఫ్రేమ్‌లు అర్రేలుగా ప్రాతినిధ్యం వహిస్తాయి, కాబట్టి రెండు వరుస ఫ్రేమ్‌ల అర్రేల మధ్య తేడాను తీసుకుంటే, స్థిరమైన ఫ్రేమ్‌లకు తేడా తక్కువగా ఉంటుంది, మరియు చిత్రంలో గణనీయమైన కదలిక ఉన్నప్పుడు ఎక్కువగా ఉంటుంది. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.te.png) +![Image of video frames and frame differences](../../../../../translated_images/te/frame-difference.706f805491a0883c.png) > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి @@ -91,7 +91,7 @@ OpenCV ను ఉపయోగించి వీడియోను ఫ్రే - **Dense Optical Flow** ప్రతి పిక్సెల్ ఎక్కడికి కదులుతుందో చూపించే వెక్టర్ ఫీల్డ్‌ను లెక్కిస్తుంది - **Sparse Optical Flow** చిత్రంలోని కొన్ని ప్రత్యేక లక్షణాలను (ఉదా: అంచులు) తీసుకుని, వాటి ట్రాజెక్టరీని ఫ్రేమ్ నుండి ఫ్రేమ్ వరకు నిర్మిస్తుంది. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.te.png) +![Image of Optical Flow](../../../../../translated_images/te/optical.1f4a94464579a83a.png) > చిత్రం [OpenCV.ipynb](OpenCV.ipynb) నుండి @@ -117,7 +117,7 @@ optical flow గురించి మరింత చదవండి [ఈ అ ఈ ల్యాబ్‌లో, మీరు సులభమైన జెస్తర్స్‌తో వీడియో తీసుకుంటారు, మరియు optical flow ఉపయోగించి పైకి/కిందకి/ఎడమ/కుడి కదలికలను వెలికి తీయడం మీ లక్ష్యం. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb index c9e71d87..3c08c5b0 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -10,7 +10,7 @@ "\n", "ఒక వ్యక్తి చేతి తాళం స్థిరమైన నేపథ్యం మీద ఎడమ/కుడి/పై/కింద కదలుతున్న [ఈ వీడియో](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4) ను పరిశీలించండి.\n", "\n", - "\"Palm\n", + "\"Palm\n", "\n", "**మీ లక్ష్యం** ఆప్టికల్ ఫ్లో ఉపయోగించి వీడియోలో ఎక్కడ ఎడమ/కుడి/పై/కింద కదలికలు ఉన్నాయో గుర్తించడం.\n", "\n", diff --git a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md index f2b10d2a..d3525aa7 100644 --- a/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/te/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: [ఈ వీడియో](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4)ను పరిశీలించండి, ఇందులో ఒక వ్యక్తి చేతి తలుపు స్థిరమైన నేపథ్యంపై ఎడమ/కుడి/పై/కింద కదులుతుంది. -Palm Movement Frame +Palm Movement Frame **మీ లక్ష్యం** ఆప్టికల్ ఫ్లో ఉపయోగించి వీడియోలో ఎక్కడ ఎడమ/కుడి/పై/కింద కదలికలు ఉన్నాయో గుర్తించడం. diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 2043eaec..286936c3 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 అనేది 2014లో ImageNet టాప్-5 వర్గీకరణలో 92.7% ఖచ్చితత్వాన్ని సాధించిన నెట్‌వర్క్. దీని లేయర్ నిర్మాణం ఈ విధంగా ఉంది: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.te.jpg) +![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.jpg) మీరు చూడగలిగినట్లుగా, VGG సంప్రదాయమైన పిరమిడ్ నిర్మాణాన్ని అనుసరిస్తుంది, ఇది కన్‌వల్యూషన్-పూలింగ్ లేయర్ల శ్రేణి. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.te.jpg) +![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.jpg) > చిత్రం [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) నుండి @@ -25,7 +25,7 @@ VGG-16 అనేది 2014లో ImageNet టాప్-5 వర్గీకర ResNet అనేది 2015లో Microsoft Research ప్రతిపాదించిన మోడల్స్ కుటుంబం. ResNet యొక్క ప్రధాన ఆలోచన **రెసిడ్యువల్ బ్లాక్స్** ఉపయోగించడం: - + > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1512.03385.pdf) నుండి @@ -37,7 +37,7 @@ ResNet అనేది 2015లో Microsoft Research ప్రతిపాది Google Inception నిర్మాణం ఈ ఆలోచనను మరింత ముందుకు తీసుకెళ్తుంది, ప్రతి నెట్‌వర్క్ లేయర్‌ను అనేక మార్గాల సమ్మేళనంగా నిర్మిస్తుంది: - + > చిత్రం [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) నుండి diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 5f522606..56e0ab90 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "కాబట్టి, సాధారణ CNNలో అనేక కన్వల్యూషనల్ లేయర్లు ఉంటాయి, వాటి మధ్యలో పూలింగ్ లేయర్లు ఉంటాయి, ఇవి చిత్ర పరిమాణాలను తగ్గిస్తాయి. అలాగే ఫిల్టర్ల సంఖ్యను పెంచుతాము, ఎందుకంటే నమూనాలు మరింత అభివృద్ధి చెందుతున్నప్పుడు - మనం వెతకవలసిన ఆసక్తికరమైన సంయోజనాలు ఎక్కువగా ఉంటాయి.\n", "\n", - "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.te.png)\n", + "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "స్థల పరిమాణాలు తగ్గడం మరియు ఫీచర్/ఫిల్టర్ల పరిమాణాలు పెరగడం వల్ల, ఈ నిర్మాణాన్ని **పిరమిడ్ నిర్మాణం** అని కూడా పిలుస్తారు.\n" ] diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 69ff119e..3d7c5489 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -112,7 +112,7 @@ "\n", "సాంప్రదాయ కంప్యూటర్ విజన్‌లో, చిత్రంపై అనేక ఫిల్టర్లు వర్తింపజేసి ఫీచర్లను ఉత్పత్తి చేస్తారు, ఆ ఫీచర్లు తరువాత మెషీన్ లెర్నింగ్ అల్గోరిథం ద్వారా క్లాసిఫయర్ నిర్మించడానికి ఉపయోగిస్తారు. ఆ ఫిల్టర్లు వాస్తవానికి కొన్ని జంతువుల విజన్ సిస్టమ్‌లో లభ్యమయ్యే న్యూరల్ నిర్మాణాలకు సమానంగా ఉంటాయి.\n", "\n", - "\n", + "\n", "\n", "కానీ, డీప్ లెర్నింగ్‌లో మేము క్లాసిఫికేషన్ సమస్యను పరిష్కరించడానికి ఉత్తమ కన్‌వల్యూషనల్ ఫిల్టర్లను **శిక్షణ పొందే** నెట్‌వర్క్‌లను నిర్మిస్తాము. దీని కోసం, మేము **కన్‌వల్యూషనల్ లేయర్లు**ను పరిచయం చేస్తాము.\n" ] @@ -358,7 +358,7 @@ "\n", "కాబట్టి, సాధారణ CNNలో అనేక కన్వల్యూషనల్ లేయర్లు ఉంటాయి, వాటి మధ్యలో పూలింగ్ లేయర్లు ఉంటాయి చిత్ర పరిమాణాలను తగ్గించడానికి. ఫిల్టర్ల సంఖ్యను కూడా పెంచుతాము, ఎందుకంటే నమూనాలు మరింత అభివృద్ధి చెందుతున్నప్పుడు - మనం వెతకవలసిన ఆసక్తికరమైన సంయోజనాలు ఎక్కువగా ఉంటాయి.\n", "\n", - "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.te.png)\n", + "![పూలింగ్ లేయర్లతో కూడిన అనేక కన్వల్యూషనల్ లేయర్లను చూపించే చిత్రం.](../../../../../translated_images/te/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "స్థల పరిమాణాలు తగ్గడం మరియు ఫీచర్/ఫిల్టర్ల పరిమాణాలు పెరగడం వలన, ఈ నిర్మాణాన్ని **పిరమిడ్ ఆర్కిటెక్చర్** అని కూడా పిలుస్తారు.\n" ] diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md index 73e9e53f..ad3c5444 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,14 +17,14 @@ CO_OP_TRANSLATOR_METADATA: నమూనాలను తీసుకోవడానికి, మనం **కన్వల్యూషనల్ ఫిల్టర్స్** అనే భావనను ఉపయోగిస్తాము. మీరు తెలుసుకున్నట్లుగా, చిత్రం 2D-మ్యాట్రిక్స్ లేదా రంగు లోతుతో 3D-టెన్సర్ ద్వారా ప్రాతినిధ్యం వహిస్తుంది. ఫిల్టర్ వర్తింపజేయడం అంటే, మనం తక్కువ పరిమాణం గల **ఫిల్టర్ కర్నెల్** మ్యాట్రిక్స్ తీసుకుని, అసలు చిత్రంలోని ప్రతి పిక్సెల్ కోసం పక్కనున్న పాయింట్లతో బరువు గల సగటును లెక్కించడం. దీన్ని ఒక చిన్న విండో మొత్తం చిత్రంపై స్లయిడ్ అవుతూ, ఫిల్టర్ కర్నెల్ మ్యాట్రిక్స్ లోని బరువుల ప్రకారం అన్ని పిక్సెల్స్ సగటు తీసుకోవడం లాగా చూడవచ్చు. -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.te.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.te.png) +![Vertical Edge Filter](../../../../../translated_images/te/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/te/filter-horiz.59b80ed4feb946ef.png) ----|---- > చిత్రాన్ని Dmitry Soshnikov అందించారు ఉదాహరణకు, మనం MNIST అంకెలపై 3x3 నిలువు అంచు మరియు ఆడవారపు అంచు ఫిల్టర్స్ వర్తింపజేస్తే, అసలు చిత్రంలో నిలువు మరియు ఆడవారపు అంచులు ఉన్న చోట హైలైట్స్ (ఉదా: ఎక్కువ విలువలు) పొందవచ్చు. కాబట్టి ఆ రెండు ఫిల్టర్స్ అంచులను "వెతకడానికి" ఉపయోగించవచ్చు. అలాగే, మనం ఇతర తక్కువ స్థాయి నమూనాలను వెతకడానికి వివిధ ఫిల్టర్స్ రూపొందించవచ్చు: - + > [Leung-Malik ఫిల్టర్ బ్యాంక్](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) చిత్రం @@ -38,7 +38,7 @@ CNNలు పనిచేసే విధానం ఈ క్రింది మ * ఫిల్టర్స్ స్వయంచాలకంగా శిక్షణ పొందేలా నెట్‌వర్క్‌ను రూపొందించవచ్చు * అసలు చిత్రంలో మాత్రమే కాకుండా, ఉన్నత స్థాయి లక్షణాలలో కూడా నమూనాలను కనుగొనడానికి అదే విధానాన్ని ఉపయోగించవచ్చు. కాబట్టి CNN లక్షణాల తీసుకోవడం తక్కువ స్థాయి పిక్సెల్ సంయోజనాల నుండి మొదలుకొని, చిత్ర భాగాల ఉన్నత స్థాయి సంయోజనాల వరకు లక్షణాల హైరార్కీపై పనిచేస్తుంది. -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.te.png) +![Hierarchical Feature Extraction](../../../../../translated_images/te/FeatureExtractionCNN.d9b456cbdae7cb64.png) > [Hislop-Lynch పేపర్](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) నుండి చిత్రం, వారి [గవేషణ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) ఆధారంగా @@ -55,9 +55,9 @@ CNNలు పనిచేసే విధానం ఈ క్రింది మ ఉదాహరణకు, 2014లో ImageNet టాప్-5 వర్గీకరణలో 92.7% ఖచ్చితత్వం సాధించిన VGG-16 ఆర్కిటెక్చర్‌ను చూద్దాం: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.te.jpg) +![ImageNet Layers](../../../../../translated_images/te/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.te.jpg) +![ImageNet Pyramid](../../../../../translated_images/te/vgg-16-arch.64ff2137f50dd49f.jpg) > [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) నుండి చిత్రం diff --git a/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 2c6f25cb..e8a6f70e 100644 --- a/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/te/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: మేము [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ఉపయోగించబోతున్నాము, ఇది 37 వేర్వేరు జాతుల కుక్కలు మరియు పిల్లుల చిత్రాలను కలిగి ఉంది. -![మేము ఉపయోగించబోయే డేటాసెట్](../../../../../../translated_images/data.50b2a9d5484bdbf0.te.png) +![మేము ఉపయోగించబోయే డేటాసెట్](../../../../../../translated_images/te/data.50b2a9d5484bdbf0.png) డేటాసెట్‌ను డౌన్లోడ్ చేసుకోవడానికి, ఈ కోడ్ స్నిపెట్ ఉపయోగించండి: diff --git a/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 22b0c3a1..162ebdcf 100644 --- a/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/te/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "సరైన పిల్లిని చూపించడానికి, మనం ఒక యాదృచ్ఛిక శబ్ద చిత్రం నుండి ప్రారంభించి, గ్రేడియంట్ డిసెంట్ ఆప్టిమైజేషన్ సాంకేతికతను ఉపయోగించి చిత్రాన్ని సర్దుబాటు చేస్తూ, నెట్‌వర్క్ ఒక పిల్లిని గుర్తించగలుగుతుంది.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.te.png)\n", + "![Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "ఇది మన ప్రారంభ చిత్రం:\n" ] diff --git a/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md index 2eb512f9..c7cb78ff 100644 --- a/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/te/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras మరియు PyTorch రెండింటిలోనూ సాధా ఇక్కడ VGG-16 నెట్‌వర్క్ ద్వారా పిల్లి చిత్రంలో నుండి తీసుకున్న కొన్ని ఫీచర్లు ఉన్నాయి: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.te.png) +![Features extracted by VGG-16](../../../../../translated_images/te/features.6291f9c7ba3a0b95.png) ## పిల్లులు vs. కుక్కల డేటాసెట్ @@ -48,19 +48,19 @@ Keras మరియు PyTorch రెండింటిలోనూ సాధా మనము తీసుకోగల ఒక విధానం, యాదృచ్ఛిక చిత్రంతో ప్రారంభించి, ఆ చిత్రాన్ని **గ్రాడియెంట్ డిసెంట్ ఆప్టిమైజేషన్** సాంకేతికత ఉపయోగించి సవరించడం, తద్వారా నెట్‌వర్క్ ఆ చిత్రాన్ని పిల్లిగా భావించడం ప్రారంభిస్తుంది. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.te.png) +![Image Optimization Loop](../../../../../translated_images/te/ideal-cat-loop.999fbb8ff306e044.png) కానీ, ఇలాచేస్తే, మనం యాదృచ్ఛిక శబ్దం లాంటి దృశ్యాన్ని పొందుతాము. ఇది ఎందుకంటే *నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాన్ని పిల్లిగా భావించడానికి అనేక మార్గాలు ఉన్నాయి*, వాటిలో కొన్ని దృశ్యంగా అర్థం కానివి కూడా ఉంటాయి. ఆ చిత్రాలు పిల్లికి సాధారణమైన చాలా నమూనాలను కలిగి ఉన్నప్పటికీ, అవి దృశ్యంగా ప్రత్యేకంగా ఉండాలని ఎటువంటి నియంత్రణ లేదు. ఫలితాన్ని మెరుగుపరచడానికి, మనం లాస్ ఫంక్షన్‌లో మరో పదాన్ని చేర్చవచ్చు, దీనిని **వేరియేషన్ లాస్** అంటారు. ఇది చిత్రంలోని పొరుగువారి పిక్సెల్స్ ఎంత సమానంగా ఉన్నాయో చూపే ప్రమాణం. వేరియేషన్ లాస్‌ను తగ్గించడం ద్వారా చిత్రం మృదువుగా మారుతుంది, శబ్దం తొలగిపోతుంది - తద్వారా మరింత ఆకర్షణీయమైన నమూనాలు బయటపడతాయి. ఇక్కడ పిల్లిగా మరియు జెబ్రాగా అధిక సంభావ్యతతో వర్గీకరించబడిన "ఆదర్శ" చిత్రాల ఉదాహరణ ఉంది: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.te.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.te.png) +![Ideal Cat](../../../../../translated_images/te/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/te/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *ఆదర్శ పిల్లి* | *ఆదర్శ జెబ్రా* ఇలాంటి విధానం న్యూరల్ నెట్‌వర్క్‌పై **ప్రత్యర్థి దాడులు** చేయడానికి కూడా ఉపయోగించవచ్చు. ఒక నెట్‌వర్క్‌ను మోసం చేసి కుక్కను పిల్లిలా చూపించాలనుకుంటే, కుక్క చిత్రాన్ని తీసుకుని, అది నెట్‌వర్క్ ద్వారా కుక్కగా గుర్తించబడినప్పుడు, గ్రాడియెంట్ డిసెంట్ ఆప్టిమైజేషన్ ఉపయోగించి కొంత సవరించి, నెట్‌వర్క్ దాన్ని పిల్లిగా వర్గీకరించడం ప్రారంభించే వరకు ప్రయత్నించవచ్చు: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.te.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.te.png) +![Picture of a Dog](../../../../../translated_images/te/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/te/adversarial-dog.d9fc7773b0142b89.png) -----|----- *కుక్క యొక్క అసలు చిత్రం* | *పిల్లిగా వర్గీకరించబడిన కుక్క చిత్రం* diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index debb088b..c2eadcf6 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "మనం ఆటోఎంకోడర్‌ను అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టుకోవడానికి శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టుకోవడానికి ఉత్తమమైన **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.te.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి\n", "\n", @@ -941,7 +941,7 @@ " * మేము $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ పంపిణీ నుండి `sample(z_val in code)` అనే వెక్టర్‌ను నమూనా తీసుకుంటాము\n", " * డీకోడర్ ఆ `sample` ను ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని పునర్నిర్మించడానికి ప్రయత్నిస్తుంది\n", "\n", - " \n", + " \n", "\n", " > ఇమేజ్ [ఈ బ్లాగ్ పోస్ట్](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) నుండి, ఇసాక్ డైకెమన్ రచన\n" ] @@ -1264,7 +1264,7 @@ "\n", "ఈ విధానంలో మనకు **మూడు నష్టాల ఫంక్షన్లు** ఉంటాయి: GANల నుండి జనరేటర్ నష్టం, discriminator నష్టం మరియు VAE నుండి పునర్నిర్మాణ నష్టం.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/) నుండి ఫెలిపే డుకౌ ద్వారా\n" ] diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 750d3769..127b4a1d 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "మనం ఆటోఎంకోడర్‌ను అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టుకోవడానికి శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టుకోవడానికి ఉత్తమమైన **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.te.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి*\n", "\n", @@ -888,7 +888,7 @@ " * మేము $N(\\mathrm{z\\_mean},e^{\\mathrm{z\\_log\\_sigma}})$ పంపిణీ నుండి `sample` అనే వెక్టర్‌ను నమూనా తీసుకుంటాము\n", " * డీకోడర్ `sample`ని ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని డీకోడ్ చేయడానికి ప్రయత్నిస్తుంది\n", "\n", - " \n" + " \n" ] }, { diff --git a/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md index 9d6c3277..7c7fc4a5 100644 --- a/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/te/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CNNలను శిక్షణ ఇచ్చేటప్పుడు, ఒక స మనం అసలు చిత్రంలోని సమాచారాన్ని ఎక్కువగా పట్టు కోవడానికి ఆటోఎంకోడర్‌ను శిక్షణ ఇస్తున్నందున, నెట్‌వర్క్ ఇన్‌పుట్ చిత్రాల అర్థాన్ని పట్టు కోవడానికి ఉత్తమ **ఎంబెడ్డింగ్**ను కనుగొనడానికి ప్రయత్నిస్తుంది. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.te.jpg) +![AutoEncoder Diagram](../../../../../translated_images/te/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > చిత్రం [Keras బ్లాగ్](https://blog.keras.io/building-autoencoders-in-keras.html) నుండి @@ -46,7 +46,7 @@ VAE అనేది లాటెంట్ పారామీటర్ల *సా * మనం N(zmean,exp(zlog_sigma)) పంపిణీ నుండి ఒక `sample` వెక్టర్‌ను సాంపిల్ చేస్తాము * డీకోడర్ `sample`ని ఇన్‌పుట్ వెక్టర్‌గా ఉపయోగించి అసలు చిత్రాన్ని పునఃసృష్టించడానికి ప్రయత్నిస్తుంది - + > చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) నుండి, ఇసాక్ డైకెమన్ @@ -57,13 +57,13 @@ VAE అనేది లాటెంట్ పారామీటర్ల *సా VAEల ముఖ్యమైన లాభం ఏమిటంటే, మనకు లాటెంట్ వెక్టర్లను ఎక్కడి నుండి సాంపిల్ చేయాలో తెలుసు కాబట్టి, కొత్త చిత్రాలను సులభంగా సృష్టించవచ్చు. ఉదాహరణకు, 2D లాటెంట్ వెక్టర్‌తో MNISTపై VAE శిక్షణ ఇస్తే, మనం లాటెంట్ వెక్టర్ భాగాలను మార్చి వేర్వేరు అంకెలను పొందవచ్చు: -vaemnist +vaemnist > చిత్రం [డ్మిత్రి సోష్నికోవ్](http://soshnikov.com) ద్వారా లాటెంట్ పారామీటర్ స్పేస్ యొక్క వేర్వేరు భాగాల నుండి లాటెంట్ వెక్టర్లను పొందడం ప్రారంభించినప్పుడు చిత్రాలు ఎలా కలిసిపోతున్నాయో గమనించండి. మనం ఈ స్పేస్‌ను 2Dలో కూడా దృశ్యీకరించవచ్చు: -vaemnist cluster +vaemnist cluster > చిత్రం [డ్మిత్రి సోష్నికోవ్](http://soshnikov.com) ద్వారా diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb b/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb index 0854d7b4..bebba2d6 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb @@ -15,7 +15,7 @@ "* **జనరేటర్** ఒక యాదృచ్ఛిక వెక్టర్ తీసుకుని, దానినుండి చిత్రం సృష్టించాలి\n", "* **డిస్క్రిమినేటర్** ఒక నెట్‌వర్క్, ఇది అసలు చిత్రం (శిక్షణ డేటాసెట్ నుండి) మరియు జనరేటర్ సృష్టించిన చిత్రాన్ని వేరుచేయగలగాలి.\n", "\n", - "\n" + "\n" ] }, { @@ -670,7 +670,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "> చిత్రం [ఈ ట్యుటోరియల్](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) నుండి తీసుకోబడింది\n" ] diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb b/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb index ed397992..d2f0248b 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/10-GANs/GANTF.ipynb @@ -15,7 +15,7 @@ "* **జనరేటర్** ఒక యాదృచ్ఛిక వెక్టర్ తీసుకుని, దానినుండి చిత్రం సృష్టించాలి\n", "* **డిస్క్రిమినేటర్** ఒక నెట్‌వర్క్, ఇది అసలు చిత్రం (శిక్షణ డేటాసెట్ నుండి) మరియు జనరేటర్ సృష్టించిన చిత్రాన్ని వేరుచేయాలి.\n", "\n", - "\n" + "\n" ] }, { diff --git a/translations/te/lessons/4-ComputerVision/10-GANs/README.md b/translations/te/lessons/4-ComputerVision/10-GANs/README.md index 4970aeb9..a8b5673d 100644 --- a/translations/te/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/te/lessons/4-ComputerVision/10-GANs/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: GAN యొక్క ప్రధాన ఆలోచన రెండు న్యూరల్ నెట్‌వర్క్స్‌ను ఒకదానితో ఒకటి పోటీగా శిక్షణ ఇవ్వడం: - + > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +41,7 @@ CNN డిస్క్రిమినేటర్ లో ఈ క్రింద > ✅ కన్వల్యూషన్ లేయర్ చిత్రం మీద లీనియర్ ఫిల్టర్ లాగా అమలు కావడంతో, డీకన్వల్యూషన్ కూడా కన్వల్యూషన్ లాగా ఉంటుంది మరియు అదే లేయర్ లాజిక్ ఉపయోగించి అమలు చేయవచ్చు. - + > చిత్రం: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md index 7682e73c..3754bea6 100644 --- a/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/te/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [ప్రీ-లెక్చర్ క్విజ్](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Object Detection](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.te.png) +![Object Detection](../../../../../translated_images/te/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > చిత్రం [YOLO v2 వెబ్ సైట్](https://pjreddie.com/darknet/yolov2/) నుండి @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ప్రతి టైల్స్‌పై ఇమేజ్ క్లాసిఫికేషన్ నడపండి. 3. తగినంతగా అధిక యాక్టివేషన్ ఉన్న టైల్స్ ఆ వస్తువు ఉన్నట్లు భావించవచ్చు. -![Naive Object Detection](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.te.png) +![Naive Object Detection](../../../../../translated_images/te/naive-detection.e7f1ba220ccd08c6.png) > *చిత్రం [Exercise Notebook](ObjectDetection-TF.ipynb) నుండి* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 తరగతులు * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 తరగతులు, బౌండింగ్ బాక్స్‌లు మరియు సెగ్మెంటేషన్ మాస్కులు -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.te.jpg) +![COCO](../../../../../translated_images/te/coco-examples.71bc60380fa6cceb.jpg) ## ఆబ్జెక్ట్ డిటెక్షన్ మెట్రిక్స్ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ఇమేజ్ క్లాసిఫికేషన్‌లో అల్గోరిథం ఎంత బాగా పనిచేస్తుందో కొలవడం సులభం, కానీ ఆబ్జెక్ట్ డిటెక్షన్‌లో క్లాస్ సరైనదా మరియు అంచనా వేయబడిన బౌండింగ్ బాక్స్ స్థానం ఎంత ఖచ్చితమో రెండింటినీ కొలవాలి. రెండవదానికి, మనం **ఇంటర్సెక్షన్ ఓవర్ యూనియన్** (IoU) అనే ప్రమాణాన్ని ఉపయోగిస్తాము, ఇది రెండు బాక్స్‌లు (లేదా ఏదైనా రెండు ప్రాంతాలు) ఎంత overlap అవుతాయో కొలుస్తుంది. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.te.png) +![IoU](../../../../../translated_images/te/iou_equation.9a4751d40fff4e11.png) > *చిత్రం [ఈ అద్భుతమైన IoU బ్లాగ్ పోస్ట్](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) నుండి* @@ -97,11 +97,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) ఉపయోగించి ROI ప్రాంతాల హైరార్కికల్ నిర్మాణాన్ని సృష్టిస్తుంది, వాటిని CNN ఫీచర్ ఎక్స్‌ట్రాక్టర్లు మరియు SVM-క్లాసిఫయర్లకు పంపించి వస్తువు తరగతిని నిర్ణయిస్తారు, మరియు లీనియర్ రెగ్రెషన్ ద్వారా *బౌండింగ్ బాక్స్* కోఆర్డినేట్లను అంచనా వేస్తారు. [అధికారిక పేపర్](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.te.png) +![RCNN](../../../../../translated_images/te/rcnn1.cae407020dfb1d1f.png) > *చిత్రం van de Sande et al. ICCV’11 నుండి* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.te.png) +![RCNN-1](../../../../../translated_images/te/rcnn2.2d9530bb83516484.png) > *చిత్రాలు [ఈ బ్లాగ్](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) నుండి* @@ -109,7 +109,7 @@ $$ ఈ విధానం R-CNNకి సమానంగా ఉంటుంది, కానీ ప్రాంతాలు కన్వల్యూషన్ లేయర్లు వరుసగా వర్తించిన తర్వాత నిర్వచించబడతాయి. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.te.png) +![FRCNN](../../../../../translated_images/te/f-rcnn.3cda6d9bb4188875.png) > చిత్రం [అధికారిక పేపర్](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 నుండి @@ -117,7 +117,7 @@ $$ ఈ విధానం ప్రధాన ఆలోచన ROIs అంచనా వేయడానికి న్యూరల్ నెట్‌వర్క్‌ను ఉపయోగించడం - దీనిని *Region Proposal Network* అంటారు. [పేపర్](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.te.png) +![FasterRCNN](../../../../../translated_images/te/faster-rcnn.8d46c099b87ef30a.png) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/pdf/1506.01497.pdf) నుండి @@ -129,7 +129,7 @@ $$ 2. ఫీచర్లు **Position-Sensitive Score Map** ద్వారా ప్రాసెస్ చేయబడతాయి. $C$ తరగతులలో ప్రతి వస్తువు $k\times k$ ప్రాంతాలుగా విభజించబడుతుంది, మరియు మనం వస్తువుల భాగాలను అంచనా వేయడానికి శిక్షణ పొందుతాము. 3. $k\times k$ ప్రాంతాల ప్రతి భాగం కోసం అన్ని నెట్‌వర్క్లు వస్తువు తరగతుల కోసం ఓటు వేస్తాయి, గరిష్ట ఓటు పొందిన వస్తువు తరగతి ఎంచుకోబడుతుంది. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.te.png) +![r-fcn image](../../../../../translated_images/te/r-fcn.13eb88158b99a3da.png) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/abs/1605.06409) నుండి @@ -140,7 +140,7 @@ YOLO ఒక రియల్‌టైమ్ ఒకసారి నడిపే * చిత్రాన్ని $S\times S$ ప్రాంతాలుగా విభజించడం * ప్రతి ప్రాంతం కోసం, **CNN** $n$ సాధ్యమైన వస్తువులు, *బౌండింగ్ బాక్స్* కోఆర్డినేట్లు మరియు *confidence* = *probability* * IoU అంచనా వేయడం. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.te.png) + ![YOLO](../../../../../translated_images/te/yolo.a2648ec82ee8bb4e.png) > చిత్రం [అధికారిక పేపర్](https://arxiv.org/abs/1506.02640) నుండి diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md index 9b6885ca..835bb616 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/README.md @@ -20,7 +20,7 @@ CO_OP_TRANSLATOR_METADATA: ఇన్స్టాన్స్ విభజనలో, ఈ గొర్రెలు వేర్వేరు ఆబ్జెక్టులు, కానీ సెమాంటిక్ విభజనలో అన్ని గొర్రెలు ఒకే వర్గంగా చూపబడతాయి. - + > చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: * **ఎంకోడర్** ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది * **డీకోడర్** ఆ ఫీచర్లను **మాస్క్ చిత్రం**గా మార్చుతుంది, అదే పరిమాణం మరియు వర్గాల సంఖ్యకు సరిపోయే ఛానెల్‌లతో. - + > చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి @@ -43,7 +43,7 @@ CO_OP_TRANSLATOR_METADATA: > ✅ ఈ సాంకేతికత ఈ రకమైన వైద్య చిత్రీకరణకు చాలా అనుకూలంగా ఉంటుంది, కానీ మీరు మరే ఇతర వాస్తవ ప్రపంచ అనువర్తనాలను ఊహించగలరా? -navi +navi > చిత్రం PH2 డేటాబేస్ నుండి diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb index d2c7ae02..57447387 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb @@ -17,7 +17,7 @@ "\n", "ఉదాహరణకు, ఇన్స్టాన్స్ విభజనలో 10 గొర్రెలు వేర్వేరు ఆబ్జెక్టులుగా ఉంటాయి, సెమాంటిక్ విభజనలో అన్ని గొర్రెలు ఒకే వర్గంగా సూచించబడతాయి.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి\n", "\n", @@ -26,7 +26,7 @@ "* **ఎంకోడర్** ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది\n", "* **డీకోడర్** ఆ ఫీచర్లను **మాస్క్ చిత్రం**గా మార్చుతుంది, ఇది వర్గాల సంఖ్యకు అనుగుణంగా అదే పరిమాణం మరియు ఛానెల్‌ల సంఖ్య కలిగి ఉంటుంది.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి\n" ] @@ -252,7 +252,7 @@ "\n", "సరళమైన ఎన్‌కోడర్-డీకోడర్ ఆర్కిటెక్చర్‌ను **SegNet** అంటారు. ఇది ఎన్‌కోడర్‌లో కన్వల్యూషన్లు మరియు పూలింగ్‌లతో సాధారణ CNN ఉపయోగిస్తుంది, మరియు డీకోడర్‌లో కన్వల్యూషన్లు మరియు అప్‌సాంప్లింగ్‌లతో కూడిన డీకన్వల్యూషన్ CNN ఉపయోగిస్తుంది. ఇది బహుళ-పట్టాల నెట్‌వర్క్‌ను విజయవంతంగా శిక్షణ ఇవ్వడానికి బ్యాచ్ నార్మలైజేషన్‌పై ఆధారపడి ఉంటుంది.\n", "\n", - "\n", + "\n", "\n", "> ఈ చిత్రం ఈ పేపర్ నుండి: Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -548,7 +548,7 @@ "\n", "ఇక్కడ మేము చాలా సాదారణ CNN ఆర్కిటెక్చర్‌ను ఉపయోగిస్తాము, కానీ U-Net ఫీచర్ ఎక్స్‌ట్రాక్షన్ కోసం ResNet-50 వంటి మరింత సంక్లిష్ట ఎన్‌కోడర్‌ను కూడా ఉపయోగించవచ్చు.\n", "\n", - "\n", + "\n", "\n", "> పేపర్ నుండి చిత్రం: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb index b9d5560f..fd48678a 100644 --- a/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb +++ b/translations/te/lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb @@ -12,13 +12,13 @@ "\n", "ఉదాహరణకు ఇన్స్టాన్స్ సెగ్మెంటేషన్‌లో పది కార్లు **వేరే** వస్తువులు, సేమాంటిక్ సెగ్మెంటేషన్‌లో **అన్ని** కార్లు ఒకే క్లాస్.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) నుండి\n", "\n", "దాదాపు అన్ని ఆర్కిటెక్చర్లు ఒకే నిర్మాణం కలిగి ఉంటాయి. మొదటి భాగం **ఎంకోడర్** ఇది ఇన్‌పుట్ చిత్రంలో నుండి ఫీచర్లను తీసుకుంటుంది, రెండవ భాగం **డీకోడర్** ఇది ఆ ఫీచర్లను అదే ఎత్తు, వెడల్పు మరియు కొన్ని ఛానెల్స్ సంఖ్యతో (క్లాసుల సంఖ్యకు సమానం కావచ్చు) ఉన్న చిత్రంగా మార్చుతుంది.\n", "\n", - "\n", + "\n", "\n", "> చిత్రం [ఈ ప్రచురణ](https://arxiv.org/pdf/2001.05566.pdf) నుండి\n" ] @@ -210,7 +210,7 @@ "\n", "సాదా ఎన్‌కోడర్ - డీకోడర్ నిర్మాణం, ఎన్‌కోడర్‌లో కన్వల్యూషన్లు, పూలింగ్‌లు మరియు డీకోడర్‌లో కన్వల్యూషన్లు, అప్‌సాంప్లింగ్‌లు ఉంటాయి.\n", "\n", - "\n", + "\n", "\n", "* Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). [SegNet: A deep convolutional\n", "encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561.pdf)\n" @@ -602,7 +602,7 @@ "\n", "U-Net సాధారణంగా ఫీచర్ ఎక్స్‌ట్రాక్షన్ కోసం డిఫాల్ట్ ఎంకోడర్ కలిగి ఉంటుంది, ఉదాహరణకు resnet50.\n", "\n", - "\n", + "\n", "\n", "* Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. [U-Net: Convolutional networks for biomedical image segmentation.](https://arxiv.org/pdf/1505.04597.pdf)\n" ] diff --git a/translations/te/lessons/4-ComputerVision/README.md b/translations/te/lessons/4-ComputerVision/README.md index 6a99c658..1b3751dd 100644 --- a/translations/te/lessons/4-ComputerVision/README.md +++ b/translations/te/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # కంప్యూటర్ విజన్ -![డూడిల్‌లో కంప్యూటర్ విజన్ విషయాల సారాంశం](../../../../translated_images/ai-computervision.6506ebebac3fbf76.te.png) +![డూడిల్‌లో కంప్యూటర్ విజన్ విషయాల సారాంశం](../../../../translated_images/te/ai-computervision.6506ebebac3fbf76.png) ఈ విభాగంలో మనం నేర్చుకోబోతున్నవి: diff --git a/translations/te/lessons/5-NLP/13-TextRep/README.md b/translations/te/lessons/5-NLP/13-TextRep/README.md index 22b35e23..5434d9ab 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/README.md +++ b/translations/te/lessons/5-NLP/13-TextRep/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: నేచురల్ లాంగ్వేజ్ ప్రాసెసింగ్ (NLP) పనులను న్యూరల్ నెట్‌వర్క్‌లతో పరిష్కరించాలంటే, టెక్స్ట్‌ను టెన్సార్లుగా ప్రాతినిధ్యం వహించే విధానం అవసరం. కంప్యూటర్లు ఇప్పటికే ASCII లేదా UTF-8 వంటి ఎంకోడింగ్లను ఉపయోగించి స్క్రీన్‌పై ఫాంట్లకు మ్యాప్ అయ్యే సంఖ్యలుగా టెక్స్ట్ అక్షరాలను ప్రాతినిధ్యం వహిస్తాయి. -Image showing diagram mapping a character to an ASCII and binary representation +Image showing diagram mapping a character to an ASCII and binary representation > [చిత్ర మూలం](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +48,7 @@ CO_OP_TRANSLATOR_METADATA: టెక్స్ట్ వర్గీకరణ వంటి పనులను పరిష్కరించేటప్పుడు, మనం ఒక స్థిర పరిమాణం వెక్టర్‌తో టెక్స్ట్‌ను ప్రాతినిధ్యం చేయగలగాలి, దీన్ని తుది డెన్స్ క్లాసిఫయర్‌కు ఇన్పుట్‌గా ఉపయోగిస్తాము. దీని కోసం ఒక సరళమైన మార్గం, ప్రతి పద ప్రాతినిధ్యాలను కలిపి, ఉదాహరణకు వాటిని జోడించడం. ప్రతి పదం వన్-హాట్ ఎంకోడింగ్‌లను జోడిస్తే, మనకు పదాల సంభావ్యతలను చూపించే ఫ్రీక్వెన్సీ వెక్టర్ వస్తుంది, అంటే ప్రతి పదం టెక్స్ట్‌లో ఎన్ని సార్లు వస్తుందో. ఈ విధంగా టెక్స్ట్ ప్రాతినిధ్యం **బ్యాగ్ ఆఫ్ వర్డ్స్** (BoW) అని పిలవబడుతుంది. - + > రచయితచే చిత్రీకరణ diff --git a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index b69ad0d4..42faf6e0 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**పదాల సంచయం** (BoW) వెక్టర్ ప్రాతినిధ్యం అనేది అత్యంత సాధారణంగా ఉపయోగించే సాంప్రదాయ వెక్టర్ ప్రాతినిధ్యం. ప్రతి పదం ఒక వెక్టర్ సూచికకు అనుసంధానించబడుతుంది, వెక్టర్ అంశం ఆ పదం ఒక నిర్దిష్ట డాక్యుమెంట్‌లో ఎన్ని సార్లు వచ్చిందో చూపిస్తుంది.\n", "\n", - "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.te.png) \n", + "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **గమనిక**: BoWని టెక్స్ట్‌లోని ఒక్కొక్క పదానికి సంబంధించిన ఒక-హాట్-ఎన్‌కోడ్ చేసిన వెక్టర్ల మొత్తం అని కూడా భావించవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 1fffed62..c66ab68c 100644 --- a/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/te/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**పదాల సంచయం** (BoW) వెక్టర్ ప్రాతినిధ్యం అనేది అతి సులభంగా అర్థం చేసుకునే సంప్రదాయ వెక్టర్ ప్రాతినిధ్యం. ప్రతి పదం ఒక వెక్టర్ సూచికతో అనుసంధానించబడుతుంది, మరియు ఒక వెక్టర్ అంశం ఒక నిర్దిష్ట డాక్యుమెంట్‌లో ప్రతి పదం ఎన్ని సార్లు వచ్చిందో చూపిస్తుంది.\n", "\n", - "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.te.png) \n", + "![పదాల సంచయం వెక్టర్ ప్రాతినిధ్యం మెమరీలో ఎలా ప్రాతినిధ్యం పొందుతుందో చూపించే చిత్రం.](../../../../../translated_images/te/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **గమనిక**: BoWని టెక్స్ట్‌లోని ఒక్కొక్క పదానికి సంబంధించిన ఒక-హాట్-ఎన్‌కోడ్ చేసిన వెక్టర్ల మొత్తం అని కూడా భావించవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 43e14370..765fbbe6 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "మన నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్‌ను ఉపయోగించడం ద్వారా, మనం బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇందులో మనం మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్ మొత్తం మీద `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము.\n", "\n", - "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్‌ను చూపించే చిత్రం.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.te.png)\n", + "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్‌ను చూపించే చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "మన క్లాసిఫయర్ న్యూరల్ నెట్‌వర్క్ ఎంబెడ్డింగ్ లేయర్‌తో ప్రారంభమవుతుంది, ఆపై సమాహార లేయర్, మరియు దాని పై భాగంలో లీనియర్ క్లాసిఫయర్ ఉంటుంది:\n" ] @@ -176,7 +176,7 @@ "\n", "మునుపటి ఆర్కిటెక్చర్‌లో, మినీబ్యాచ్‌లోకి సరిపడేలా అన్ని సీక్వెన్స్‌లను ఒకే పొడవు చేయడానికి ప్యాడ్ చేయాల్సి ఉండేది. ఇది వేరియబుల్ పొడవు సీక్వెన్స్‌లను ప్రాతినిధ్యం చేయడానికి అత్యంత సమర్థవంతమైన విధానం కాదు - మరో విధానం అంటే **ఆఫ్సెట్** వెక్టర్ ఉపయోగించడం, ఇది ఒక పెద్ద వెక్టర్‌లో నిల్వ ఉన్న అన్ని సీక్వెన్స్‌ల ఆఫ్సెట్లను కలిగి ఉంటుంది.\n", "\n", - "![Image showing an offset sequence representation](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.te.png)\n", + "![Image showing an offset sequence representation](../../../../../translated_images/te/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: పై చిత్రంలో, మనం అక్షరాల సీక్వెన్స్‌ను చూపిస్తున్నాము, కానీ మన ఉదాహరణలో మనం పదాల సీక్వెన్స్‌లతో పని చేస్తున్నాము. అయినప్పటికీ, ఆఫ్సెట్ వెక్టర్‌తో సీక్వెన్స్‌లను ప్రాతినిధ్యం చేయడం అనే సాధారణ సూత్రం అదే ఉంటుంది.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మెల్లగా ఉంటుంది, కానీ అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేస్తుంది.\n", "\n", - "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.te.png)\n", + "![Image showing both CBoW and Skip-Gram algorithms to convert words to vectors.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News డేటాసెట్‌పై ప్రీ-ట్రెయిన్ చేసిన word2vec ఎంబెడ్డింగ్‌తో ప్రయోగం చేయడానికి, మనం **gensim** లైబ్రరీని ఉపయోగించవచ్చు. క్రింద 'neural' కు అత్యంత సమానమైన పదాలను కనుగొంటాము\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 3daec1a5..af2dc824 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/te/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "మన నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్‌ను ఉపయోగించడం ద్వారా, మనం బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇక్కడ మనం మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్ మొత్తం మీద `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము.\n", "\n", - "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.te.png)\n", + "![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "మన క్లాసిఫయర్ న్యూరల్ నెట్‌వర్క్ క్రింది లేయర్లతో కూడి ఉంటుంది:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మందగించగా, అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేయగలదు.\n", "\n", - "![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ రెండింటినీ చూపించే చిత్రం.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.te.png)\n", + "![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ రెండింటినీ చూపించే చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News డేటాసెట్‌పై ప్రీట్రెయిన్ చేసిన Word2Vec ఎంబెడ్డింగ్‌తో ప్రయోగం చేయడానికి, **gensim** లైబ్రరీని ఉపయోగించవచ్చు. క్రింద 'neural' కు అత్యంత సమానమైన పదాలను కనుగొంటాం.\n", "\n", diff --git a/translations/te/lessons/5-NLP/14-Embeddings/README.md b/translations/te/lessons/5-NLP/14-Embeddings/README.md index 8b66ee58..af94cecc 100644 --- a/translations/te/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/te/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ BoW లేదా TF/IDF ఆధారంగా క్లాసిఫైయర్ మన క్లాసిఫైయర్ నెట్‌వర్క్‌లో మొదటి లేయర్‌గా ఎంబెడ్డింగ్ లేయర్ ఉపయోగించడం ద్వారా, మేము బాగ్-ఆఫ్-వర్డ్స్ నుండి **ఎంబెడ్డింగ్ బాగ్** మోడల్‌కు మారవచ్చు, ఇక్కడ మేము మొదట మన టెక్స్ట్‌లోని ప్రతి పదాన్ని సంబంధిత ఎంబెడ్డింగ్‌గా మార్చి, ఆ ఎంబెడ్డింగ్స్‌పై `sum`, `average` లేదా `max` వంటి ఏదైనా సమాహార ఫంక్షన్‌ను లెక్కిస్తాము. -![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫైయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.te.png) +![ఐదు వరుస పదాల కోసం ఎంబెడ్డింగ్ క్లాసిఫైయర్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/embedding-classifier-example.b77f021a7ee67eee.png) > చిత్రాన్ని రచయిత అందించారు @@ -40,7 +40,7 @@ BoW లేదా TF/IDF ఆధారంగా క్లాసిఫైయర్ CBoW వేగంగా ఉంటుంది, స్కిప్-గ్రామ్ మందగిస్తుంది, కానీ అరుదైన పదాలను బాగా ప్రాతినిధ్యం చేస్తుంది. -![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.te.png) +![పదాలను వెక్టర్లుగా మార్చడానికి CBoW మరియు స్కిప్-గ్రామ్ అల్గోరిథమ్స్ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1301.3781.pdf) నుండి diff --git a/translations/te/lessons/5-NLP/15-LanguageModeling/README.md b/translations/te/lessons/5-NLP/15-LanguageModeling/README.md index 0739ffe9..4f7a008b 100644 --- a/translations/te/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/te/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **కంటిన్యూయస్ బ్యాగ్-ఆఫ్-వర్డ్స్** (CBoW), ఇందులో టోకెన్ సీక్వెన్స్ $W_{-N}$, ..., $W_N$ లో మధ్య టోకెన్ $W_0$ ను అంచనా వేస్తాము. * **స్కిప్-గ్రామ్**, ఇందులో మధ్య టోకెన్ $W_0$ నుండి పొరుగువారైన టోకెన్ల సమూహం {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} ను అంచనా వేస్తాము. -![పదాలను వెక్టర్లుగా మార్చే అల్గోరిథమ్స్ పై పేపర్ నుండి చిత్రం](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.te.png) +![పదాలను వెక్టర్లుగా మార్చే అల్గోరిథమ్స్ పై పేపర్ నుండి చిత్రం](../../../../../translated_images/te/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > చిత్రం [ఈ పేపర్](https://arxiv.org/pdf/1301.3781.pdf) నుండి diff --git a/translations/te/lessons/5-NLP/16-RNN/README.md b/translations/te/lessons/5-NLP/16-RNN/README.md index 9606155d..79a122ad 100644 --- a/translations/te/lessons/5-NLP/16-RNN/README.md +++ b/translations/te/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మేము మరో న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్ ఉపయోగించాలి, దీనిని **రికరెంట్ న్యూరల్ నెట్‌వర్క్** లేదా RNN అంటారు. RNNలో, మేము వాక్యాన్ని ఒక్కో చిహ్నం ద్వారా నెట్‌వర్క్‌లో పంపుతాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాతి చిహ్నంతో మళ్లీ నెట్‌వర్క్‌కు ఇస్తాము. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.te.png) +![RNN](../../../../../translated_images/te/rnn.27f5c29c53d727b5.png) > చిత్రకారుడు @@ -31,7 +31,7 @@ CO_OP_TRANSLATOR_METADATA: సాదా RNN సెల్‌లో రెండు బరువు మ్యాట్రిక్స్‌లు ఉంటాయి: ఒకటి ఇన్‌పుట్ చిహ్నాన్ని మార్చుతుంది (దాన్ని W అంటాం), మరొకటి ఇన్‌పుట్ స్థితిని మార్చుతుంది (H). ఈ సందర్భంలో నెట్‌వర్క్ అవుట్‌పుట్ σ(W×Xi+H×Si-1+b) గా లెక్కించబడుతుంది, ఇక్కడ σ యాక్టివేషన్ ఫంక్షన్, b అదనపు బయాస్. -RNN Cell Anatomy +RNN Cell Anatomy > చిత్రకారుడు @@ -61,7 +61,7 @@ LSTM నెట్‌వర్క్ RNNకు సమానంగా ఏర్ప రికరెంట్ నెట్‌వర్క్, ఒక దిశలోనైనా లేదా బిడైరెక్షనల్‌గా ఉన్నా, సీక్వెన్స్‌లోని కొన్ని నమూనాలను పట్టుకుని, వాటిని స్థితి వెక్టర్‌లో నిల్వ చేయగలదు లేదా అవుట్‌పుట్‌గా పంపగలదు. కాంవల్యూషనల్ నెట్‌వర్క్స్ లాగా, మొదటి లేయర్ నుండి తీసుకున్న తక్కువ స్థాయి నమూనాలపై ఆధారపడి, మరొక రికరెంట్ లేయర్‌ను నిర్మించి, ఉన్నత స్థాయి నమూనాలను పట్టుకోవచ్చు. దీని ద్వారా **మల్టిలేయర్ RNN** అనే భావన వస్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్స్ కలిగి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్ తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపబడుతుంది. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.te.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg) *చిత్రం [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి, ఫెర్నాండో లోపెజ్ రచన* diff --git a/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index c3e6c174..8ab6d53a 100644 --- a/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -10,7 +10,7 @@ "\n", "టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మేము మరో న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్‌ను ఉపయోగించాలి, దీనిని **రికరెంట్ న్యూరల్ నెట్‌వర్క్** లేదా RNN అంటారు. RNNలో, మేము మా వాక్యాన్ని ఒక్కో చిహ్నం ద్వారా నెట్‌వర్క్‌లో పంపుతాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాతి చిహ్నంతో మళ్లీ నెట్‌వర్క్‌కు పంపుతాము.\n", "\n", - "\"RNN\"\n", + "\"RNN\"\n", "\n", "ఇన్‌పుట్ టోకెన్ల సీక్వెన్స్ $X_0,\\dots,X_n$ ఇచ్చినప్పుడు, RNN న్యూరల్ నెట్‌వర్క్ బ్లాక్స్ సీక్వెన్స్‌ను సృష్టించి, బ్యాక్ ప్రొపగేషన్ ఉపయోగించి ఈ సీక్వెన్స్‌ను ఎండ్-టు-ఎండ్ శిక్షణ ఇస్తుంది. ప్రతి నెట్‌వర్క్ బ్లాక్ ఒక జంట $(X_i,S_i)$ని ఇన్‌పుట్‌గా తీసుకుని, $S_{i+1}$ని ఫలితంగా ఉత్పత్తి చేస్తుంది. తుది స్థితి $S_n$ లేదా అవుట్‌పుట్ $X_n$ లీనియర్ క్లాసిఫయర్‌లోకి వెళ్లి ఫలితాన్ని ఉత్పత్తి చేస్తుంది. అన్ని నెట్‌వర్క్ బ్లాక్స్ ఒకే బరువులను పంచుకుంటాయి, మరియు ఒకే బ్యాక్ ప్రొపగేషన్ పాస్ ద్వారా ఎండ్-టు-ఎండ్ శిక్షణ పొందుతాయి.\n", "\n", @@ -428,7 +428,7 @@ "\n", "రికరెంట్ నెట్‌వర్క్, ఒక దిశలోనైనా లేదా ద్విముఖి అయినా, ఒక సీక్వెన్స్‌లోని నిర్దిష్ట నమూనాలను పట్టుకుంటుంది, వాటిని స్టేట్ వెక్టర్‌లో నిల్వ చేయగలదు లేదా అవుట్‌పుట్‌గా పంపగలదు. కన్వల్యూషనల్ నెట్‌వర్క్ల లాగా, మొదటి లేయర్ ద్వారా తీసుకున్న తక్కువ స్థాయి నమూనాలపై ఆధారపడి ఉన్నత స్థాయి నమూనాలను పట్టుకోవడానికి మళ్ళీ మరో రికరెంట్ లేయర్‌ను నిర్మించవచ్చు. ఇది మనల్ని **బహుళస్థాయి RNN** అనే భావనకు తీసుకువెళ్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్లతో కూడి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్‌ను తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపుతారు.\n", "\n", - "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.te.jpg)\n", + "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ఫెర్నాండో లోపెజ్ రాసిన [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి చిత్రం*\n", "\n", diff --git a/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb index 138c4ec7..1da5dbcd 100644 --- a/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/te/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "ఒక టెక్స్ట్ సీక్వెన్స్ అర్థాన్ని పట్టుకోవడానికి, మనం **పునరావృత న్యూరల్ నెట్‌వర్క్** లేదా RNN అనే న్యూరల్ నెట్‌వర్క్ ఆర్కిటెక్చర్‌ను ఉపయోగిస్తాము. RNN ఉపయోగించినప్పుడు, మన వాక్యాన్ని ఒక్కో టోకెన్‌గా నెట్‌వర్క్ ద్వారా పంపిస్తాము, మరియు నెట్‌వర్క్ కొన్ని **స్థితి**ని ఉత్పత్తి చేస్తుంది, ఆ స్థితిని తరువాత వచ్చే టోకెన్‌తో మళ్లీ నెట్‌వర్క్‌కు ఇస్తాము.\n", "\n", - "![పునరావృత న్యూరల్ నెట్‌వర్క్ జనరేషన్ ఉదాహరణ చూపిస్తున్న చిత్రం.](../../../../../translated_images/rnn.27f5c29c53d727b5.te.png)\n", + "![పునరావృత న్యూరల్ నెట్‌వర్క్ జనరేషన్ ఉదాహరణ చూపిస్తున్న చిత్రం.](../../../../../translated_images/te/rnn.27f5c29c53d727b5.png)\n", "\n", "ఇన్‌పుట్ సీక్వెన్స్ టోకెన్లు $X_0,\\dots,X_n$ ఇచ్చినప్పుడు, RNN న్యూరల్ నెట్‌వర్క్ బ్లాక్స్ సీక్వెన్స్‌ను సృష్టిస్తుంది, మరియు ఈ సీక్వెన్స్‌ను బ్యాక్‌ప్రొపగేషన్ ద్వారా ఎండ్-టు-ఎండ్ శిక్షణ ఇస్తుంది. ప్రతి నెట్‌వర్క్ బ్లాక్ జంట $(X_i,S_i)$ని ఇన్‌పుట్‌గా తీసుకుని, $S_{i+1}$ని ఫలితంగా ఉత్పత్తి చేస్తుంది. చివరి స్థితి $S_n$ లేదా అవుట్‌పుట్ $Y_n$ లీనియర్ క్లాసిఫైయర్‌లోకి వెళ్లి ఫలితాన్ని ఉత్పత్తి చేస్తుంది. అన్ని నెట్‌వర్క్ బ్లాక్స్ ఒకే బరువులను పంచుకుంటాయి, మరియు ఒకే బ్యాక్‌ప్రొపగేషన్ పాస్‌తో ఎండ్-టు-ఎండ్ శిక్షణ పొందుతాయి.\n", "\n", @@ -371,7 +371,7 @@ "\n", "రికరెంట్ నెట్‌వర్క్లు, ఏదైనా ఒక దిశలోనైనా లేదా ద్విముఖి అయినా, సీక్వెన్స్‌లోని నమూనాలను పట్టుకుని వాటిని స్టేట్ వెక్టర్లుగా నిల్వ చేస్తాయి లేదా అవి అవుట్‌పుట్‌గా ఇస్తాయి. కాంవల్యూషనల్ నెట్‌వర్క్ల లాగా, మొదటి లేయర్ ద్వారా తీసుకున్న తక్కువ స్థాయి నమూనాల నుండి నిర్మించిన ఉన్నత స్థాయి నమూనాలను పట్టుకోవడానికి మరొక రికరెంట్ లేయర్‌ను మొదటి లేయర్ తర్వాత నిర్మించవచ్చు. దీని ద్వారా మనకు **బహుళస్థాయి RNN** అనే భావన వస్తుంది, ఇది రెండు లేదా అంతకంటే ఎక్కువ రికరెంట్ నెట్‌వర్క్లతో కూడి ఉంటుంది, ఇక్కడ మునుపటి లేయర్ అవుట్‌పుట్‌ను తదుపరి లేయర్ ఇన్‌పుట్‌గా పంపిస్తారు.\n", "\n", - "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.te.jpg)\n", + "![బహుళస్థాయి లాంగ్-షార్ట్-టర్మ్-మెమరీ RNNని చూపించే చిత్రం](../../../../../translated_images/te/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ఫెర్నాండో లోపెజ్ రాసిన [ఈ అద్భుతమైన పోస్ట్](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) నుండి చిత్రం.*\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index ea359e59..f299894d 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN ను టెక్స్ట్ ఉత్పత్తి చేయడానికి శిక్షణ ఇవ్వడానికి మనం అనుసరించే విధానం ఈ విధంగా ఉంటుంది. ప్రతి దశలో, మనం `nchars` పొడవైన అక్షరాల సీక్వెన్స్ తీసుకుని, ప్రతి ఇన్‌పుట్ అక్షరానికి తదుపరి అవుట్‌పుట్ అక్షరాన్ని నెట్‌వర్క్ ఉత్పత్తి చేయమని అడుగుతాము:\n", "\n", - "!['HELLO' అనే పదం RNN ద్వారా ఉత్పత్తి చేయబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.te.png)\n", + "!['HELLO' అనే పదం RNN ద్వారా ఉత్పత్తి చేయబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png)\n", "\n", "నిజమైన పరిస్థితులపై ఆధారపడి, మనం కొన్ని ప్రత్యేక అక్షరాలను కూడా చేర్చాలనుకోవచ్చు, ఉదాహరణకు *end-of-sequence* ``. మన సందర్భంలో, మనం నిరంతర టెక్స్ట్ ఉత్పత్తి కోసం నెట్‌వర్క్‌ను శిక్షణ ఇవ్వాలనుకుంటున్నాము, కాబట్టి ప్రతి సీక్వెన్స్ పరిమాణాన్ని `nchars` టోకెన్లకు సమానంగా స్థిరపరుస్తాము. ఫలితంగా, ప్రతి శిక్షణ ఉదాహరణలో `nchars` ఇన్‌పుట్లు మరియు `nchars` అవుట్‌పుట్లు ఉంటాయి (ఇవి ఇన్‌పుట్ సీక్వెన్స్‌ను ఒక అక్షరం ఎడమకు షిఫ్ట్ చేసినవి). మినీబ్యాచ్‌లో ఇలాంటి అనేక సీక్వెన్స్‌లు ఉంటాయి.\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 59db386f..a99eb1d3 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "మేము వార్తా శీర్షికలను సృష్టించడానికి RNN ను శిక్షణ ఇవ్వడానికి విధానం ఈ విధంగా ఉంటుంది. ప్రతి దశలో, ఒక శీర్షిక తీసుకుని దాన్ని RNN లో ఇన్పుట్ గా ఇస్తాము, మరియు ప్రతి ఇన్పుట్ అక్షరానికి తర్వాతి అవుట్పుట్ అక్షరాన్ని నెట్‌వర్క్ సృష్టించాలని అడుగుతాము:\n", "\n", - "!['HELLO' అనే పదం RNN ద్వారా సృష్టించబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.te.png)\n", + "!['HELLO' అనే పదం RNN ద్వారా సృష్టించబడుతున్న ఉదాహరణను చూపించే చిత్రం.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png)\n", "\n", "మన సీక్వెన్స్ చివరి అక్షరానికి, నెట్‌వర్క్ `` టోకెన్ సృష్టించాలని అడుగుతాము.\n", "\n", diff --git a/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md index ce58e128..8613449a 100644 --- a/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/te/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ఇది క్రింది చిత్రంలో చూపిన వివిధ న్యూరల్ ఆర్కిటెక్చర్లకు అవకాశం ఇస్తుంది: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.te.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > చిత్రం బ్లాగ్ పోస్ట్ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) నుండి [Andrej Karpaty](http://karpathy.github.io/) రచయిత @@ -32,11 +32,11 @@ CO_OP_TRANSLATOR_METADATA: మనం ఈ RNNను దశలవారీగా టెక్స్ట్ ఉత్పత్తి చేయడానికి శిక్షణ ఇస్తాము. ప్రతి దశలో, `nchars` పొడవు కలిగిన క్యారెక్టర్ల సీక్వెన్స్ తీసుకుని, ప్రతి ఇన్‌పుట్ క్యారెక్టర్ కోసం తదుపరి అవుట్‌పుట్ క్యారెక్టర్‌ను నెట్‌వర్క్ ఉత్పత్తి చేయమని అడుగుతాము: -![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.te.png) +![Image showing an example RNN generation of the word 'HELLO'.](../../../../../translated_images/te/rnn-generate.56c54afb52f9781d.png) టెక్స్ట్ ఉత్పత్తి (ఇన్ఫరెన్స్ సమయంలో) చేస్తున్నప్పుడు, మనం కొన్ని **ప్రాంప్ట్**తో ప్రారంభిస్తాము, ఇది RNN సెల్స్ ద్వారా మధ్యవర్తి స్టేట్‌ను ఉత్పత్తి చేయడానికి పంపబడుతుంది, ఆ తర్వాత ఆ స్టేట్ నుండి ఉత్పత్తి ప్రారంభమవుతుంది. ఒక్కో క్యారెక్టర్‌ను ఒకేసారి ఉత్పత్తి చేసి, ఆ స్టేట్ మరియు ఉత్పత్తి చేసిన క్యారెక్టర్‌ను మరొక RNN సెల్‌కు పంపించి తదుపరి క్యారెక్టర్‌ను ఉత్పత్తి చేస్తాము, అవసరమైనంత క్యారెక్టర్లు ఉత్పత్తి అయ్యేవరకు. - + > చిత్రం రచయిత diff --git a/translations/te/lessons/5-NLP/18-Transformers/README.md b/translations/te/lessons/5-NLP/18-Transformers/README.md index afd4911d..ca37d31e 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/README.md +++ b/translations/te/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స **అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని తూగడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఇన్‌పుట్ RNN మరియు అవుట్‌పుట్ RNN మధ్య మధ్యవర్తి స్టేట్స్‌కు షార్ట్‌కట్స్ సృష్టించడం ద్వారా జరుగుతుంది. ఈ విధంగా, అవుట్‌పుట్ సింబల్ yt ఉత్పత్తి చేస్తున్నప్పుడు, మేము అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ hi ను వివిధ బరువు గుణకాలు αt,i తో పరిగణలోకి తీసుకుంటాము. -![ఎంకోడర్/డీకోడర్ మోడల్‌తో కూడిన యాడిటివ్ అటెన్షన్ లేయర్ చూపిస్తున్న చిత్రం](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.te.png) +![ఎంకోడర్/డీకోడర్ మోడల్‌తో కూడిన యాడిటివ్ అటెన్షన్ లేయర్ చూపిస్తున్న చిత్రం](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) లోని యాడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సైట్ చేయబడింది అటెన్షన్ మ్యాట్రిక్స్ {αi,j} ఒక నిర్దిష్ట అవుట్‌పుట్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత మేర పాత్ర పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది: -![RNNsearch-50 ద్వారా కనుగొనబడిన నమూనా అలైన్‌మెంట్ చూపిస్తున్న చిత్రం, Bahdanau నుండి తీసుకున్నది - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.te.png) +![RNNsearch-50 ద్వారా కనుగొనబడిన నమూనా అలైన్‌మెంట్ చూపిస్తున్న చిత్రం, Bahdanau నుండి తీసుకున్నది - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి ఫిగర్ (ఫిగర్ 3) @@ -56,7 +56,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స * ట్రైనబుల్ ఎంబెడ్డింగ్, టోకెన్ ఎంబెడ్డింగ్‌లకు సమానంగా. ఇక్కడ మేము ఈ విధానాన్ని పరిగణిస్తాము. టోకెన్లకు మరియు వాటి స్థానాలకు ఎంబెడ్డింగ్ లేయర్లు వర్తింపజేస్తాము, ఫలితంగా ఒకే పరిమాణాల ఎంబెడ్డింగ్ వెక్టర్లు వస్తాయి, వాటిని కలిపి ఉపయోగిస్తాము. * ఒరిజినల్ పేపర్‌లో ప్రతిపాదించిన స్థిరమైన పొజిషనల్ ఎంకోడింగ్ ఫంక్షన్. - + > రచయితచే రూపొందించిన చిత్రం @@ -66,7 +66,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స తర్వాత, సీక్వెన్స్‌లోని కొన్ని ప్యాటర్న్స్‌ను పట్టుకోవాలి. దీని కోసం, ట్రాన్స్‌ఫార్మర్స్ **సెల్ఫ్-అటెన్షన్** మెకానిజం ఉపయోగిస్తాయి, ఇది ఇన్‌పుట్ మరియు అవుట్‌పుట్ ఒకే సీక్వెన్స్‌పై అటెన్షన్ వర్తింపజేయడం. సెల్ఫ్-అటెన్షన్ వాక్యంలో **సందర్భం** పరిగణలోకి తీసుకోవడానికి, మరియు ఏ పదాలు పరస్పరం సంబంధం ఉన్నాయో చూడటానికి సహాయపడుతుంది. ఉదాహరణకు, ఇది *it* వంటి కోరెఫరెన్సులు సూచించే పదాలను గుర్తించడంలో సహాయపడుతుంది, అలాగే సందర్భాన్ని పరిగణలోకి తీసుకుంటుంది: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.te.png) +![](../../../../../translated_images/te/CoreferenceResolution.861924d6d384a7d6.png) > [Google బ్లాగ్](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) నుండి చిత్రం @@ -91,7 +91,7 @@ RNNలతో, సీక్వెన్స్-టు-సీక్వెన్స **BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ లేయర్ ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base*కి 12 లేయర్లు, *BERT-large*కి 24 లేయర్లు ఉన్నాయి. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా కార్పస్ (వికీపీడియా + పుస్తకాలు) పై అన్‌సూపర్వైజ్డ్ ట్రైనింగ్ (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది. ప్రీ-ట్రైనింగ్ సమయంలో మోడల్ భాషా అర్థం గణనీయంగా పెరుగుతుంది, దీన్ని తర్వాత ఇతర డేటాసెట్‌లతో ఫైన్-ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు. -![http://jalammar.github.io/illustrated-bert/ నుండి చిత్రం](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.te.png) +![http://jalammar.github.io/illustrated-bert/ నుండి చిత్రం](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > చిత్రం [మూలం](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index d79f0cef..48a269bc 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/te/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని బరువు వేయడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఏమిటంటే, ఇన్‌పుట్ RNN యొక్క మధ్యస్థితుల మరియు అవుట్‌పుట్ RNN మధ్య షార్ట్‌కట్స్ సృష్టించడం. ఈ విధంగా, అవుట్‌పుట్ సింబల్ $y_t$ ఉత్పత్తి చేస్తున్నప్పుడు, అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ $h_i$ ను వివిధ బరువు గుణకాలు $\\alpha_{t,i}$ తో పరిగణలోకి తీసుకుంటాము.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.te.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png)\n", "*అడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సేకరించబడింది*\n", "\n", "అటెన్షన్ మ్యాట్రిక్స్ $\\{\\alpha_{i,j}\\}$ ఒక నిర్దిష్ట అవుట్‌పుట్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత భాగం పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.te.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి తీసుకున్న చిత్రం (ఫిగర్ 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ-లేయర్ ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base* కోసం 12 లేయర్లు, *BERT-large* కోసం 24 లేయర్లు కలిగి ఉంటుంది. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా కార్పస్ (వికీపీడియా + పుస్తకాలు) పై అనుసూచిత శిక్షణ (unsupervised training) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం). ప్రీ-ట్రెయినింగ్ సమయంలో మోడల్ భాషా అవగాహనను గణనీయంగా గ్రహిస్తుంది, దీన్ని తరువాత ఇతర డేటాసెట్లతో ఫైన్-ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.te.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ట్రాన్స్‌ఫార్మర్ ఆర్కిటెక్చర్స్‌లో అనేక వేరియేషన్లు ఉన్నాయి, వాటిలో BERT, DistilBERT, BigBird, OpenGPT3 మరియు మరిన్ని ఉన్నాయి, వీటిని ఫైన్-ట్యూన్ చేయవచ్చు. [HuggingFace ప్యాకేజ్](https://github.com/huggingface/) PyTorchతో ఈ ఆర్కిటెక్చర్స్‌లో చాలా మోడల్స్ శిక్షణ కోసం రిపాజిటరీని అందిస్తుంది.\n", "\n", diff --git a/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 9a9ae8f0..ef24b6d9 100644 --- a/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/te/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**అటెన్షన్ మెకానిజమ్స్** RNN యొక్క ప్రతి అవుట్‌పుట్ అంచనాపై ప్రతి ఇన్‌పుట్ వెక్టర్ యొక్క సందర్భాత్మక ప్రభావాన్ని బరువు వేయడానికి ఒక మార్గాన్ని అందిస్తాయి. ఇది అమలు చేయబడే విధానం ఏమిటంటే, ఇన్‌పుట్ RNN యొక్క మధ్యస్థితుల మరియు అవుట్‌పుట్ RNN మధ్య షార్ట్‌కట్స్ సృష్టించడం. ఈ విధంగా, అవుట్‌పుట్ సింబల్ $y_t$ ను ఉత్పత్తి చేస్తున్నప్పుడు, మేము అన్ని ఇన్‌పుట్ హిడెన్ స్టేట్స్ $h_i$ ను వివిధ బరువు గుణకాలు $\\alpha_{t,i}$ తో పరిగణలోకి తీసుకుంటాము.\n", "\n", - "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.te.png)\n", + "![Image showing an encoder/decoder model with an additive attention layer](../../../../../translated_images/te/encoder-decoder-attention.7a726296894fb567.png)\n", "*అడిటివ్ అటెన్షన్ మెకానిజం ఉన్న ఎంకోడర్-డీకోడర్ మోడల్ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి, [ఈ బ్లాగ్ పోస్ట్](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) నుండి సైట్ చేయబడింది*\n", "\n", "అటెన్షన్ మ్యాట్రిక్స్ $\\{\\alpha_{i,j}\\}$ ఒక నిర్దిష్ట అవుట్‌పుట్ సీక్వెన్స్ పదం ఉత్పత్తిలో కొన్ని ఇన్‌పుట్ పదాలు ఎంత భాగం పోషిస్తున్నాయో సూచిస్తుంది. క్రింద అలాంటి మ్యాట్రిక్స్ ఉదాహరణ ఉంది:\n", "\n", - "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.te.png)\n", + "![Image showing a sample alignment found by RNNsearch-50, taken from Bahdanau - arviz.org](../../../../../translated_images/te/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) నుండి తీసుకున్న చిత్రం (ఫిగర్ 3)*\n", "\n", @@ -92,7 +92,7 @@ "source": [ "ఈ లేయర్ రెండు `Embedding` లేయర్లతో కూడి ఉంటుంది: టోకెన్లను ఎంబెడ్ చేయడానికి (ముందుగా చర్చించిన విధంగా) మరియు టోకెన్ స్థానాల కోసం. టోకెన్ స్థానాలు 0 నుండి `maxlen` వరకు సహజ సంఖ్యల శ్రేణిగా `tf.range` ఉపయోగించి సృష్టించబడతాయి, ఆపై ఎంబెడ్డింగ్ లేయర్ ద్వారా పంపబడతాయి. రెండు ఫలిత ఎంబెడ్డింగ్ వెక్టార్లు కలిపి, `maxlen`$\\times$`embed_dim` ఆకారంలో స్థానికంగా ఎంబెడ్ చేయబడిన ఇన్‌పుట్ ప్రాతినిధ్యాన్ని ఉత్పత్తి చేస్తాయి.\n", "\n", - "\n", + "\n", "\n", "ఇప్పుడు, ట్రాన్స్‌ఫార్మర్ బ్లాక్‌ను అమలు చేద్దాం. ఇది ముందుగా నిర్వచించిన ఎంబెడ్డింగ్ లేయర్ అవుట్‌పుట్‌ను తీసుకుంటుంది:\n" ] @@ -134,7 +134,7 @@ "\n", "ఈ లేయర్ అవుట్పుట్ తరువాత `Dense` నెట్‌వర్క్ (మన సందర్భంలో - రెండు లేయర్ పెర్సెప్ట్రాన్) ద్వారా పంపబడుతుంది, మరియు ఫలితం తుది అవుట్పుట్‌కు జోడించబడుతుంది (దీన్ని మళ్లీ సాధారణీకరణకు లోబెడతారు).\n", "\n", - "\n", + "\n", "\n", "ఇప్పుడు, పూర్తి ట్రాన్స్‌ఫార్మర్ మోడల్‌ను నిర్వచించడానికి మేము సిద్ధంగా ఉన్నాము:\n" ] @@ -235,7 +235,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) అనేది చాలా పెద్ద బహుళ పొరల ట్రాన్స్‌ఫార్మర్ నెట్‌వర్క్, *BERT-base* కోసం 12 పొరలు, *BERT-large* కోసం 24 పొరలు కలిగి ఉంటుంది. ఈ మోడల్ మొదట పెద్ద టెక్స్ట్ డేటా సేకరణ (వికీపీడియా + పుస్తకాలు) పై అనుసూచిత శిక్షణ (వాక్యంలో మాస్క్ చేసిన పదాలను అంచనా వేయడం) ద్వారా ప్రీ-ట్రెయిన్ చేయబడుతుంది. ప్రీ-ట్రెయినింగ్ సమయంలో మోడల్ భాషా అర్థం చేసుకోవడంలో గణనీయమైన స్థాయిని పొందుతుంది, దీన్ని తర్వాత ఇతర డేటాసెట్‌లతో ఫైన్ ట్యూనింగ్ ద్వారా ఉపయోగించవచ్చు. ఈ ప్రక్రియను **ట్రాన్స్‌ఫర్ లెర్నింగ్** అంటారు.\n", "\n", - "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.te.png)\n", + "![picture from http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/te/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 మరియు మరిన్ని వంటి అనేక ట్రాన్స్‌ఫార్మర్ వేరియేషన్లు ఉన్నాయి, వీటిని ఫైన్ ట్యూన్ చేయవచ్చు.\n", "\n", diff --git a/translations/te/lessons/5-NLP/19-NER/README.md b/translations/te/lessons/5-NLP/19-NER/README.md index 3c20e447..74a2be10 100644 --- a/translations/te/lessons/5-NLP/19-NER/README.md +++ b/translations/te/lessons/5-NLP/19-NER/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: మీరు అమెజాన్ అలెక్సా లేదా గూగుల్ అసిస్టెంట్ లాంటి సహజ భాషా చాట్ బాట్‌ను అభివృద్ధి చేయాలనుకుంటున్నారని ఊహించుకోండి. తెలివైన చాట్ బాట్లు పనిచేసే విధానం ఏమిటంటే, వినియోగదారు ఏమి కోరుకుంటున్నాడో అర్థం చేసుకోవడం కోసం ఇన్‌పుట్ వాక్యంపై టెక్స్ట్ వర్గీకరణ చేస్తాయి. ఈ వర్గీకరణ ఫలితం **ఇంటెంట్** అని పిలవబడుతుంది, ఇది చాట్ బాట్ ఏం చేయాలో నిర్ణయిస్తుంది. -Bot NER +Bot NER > చిత్రాన్ని రచయిత అందించారు @@ -58,7 +58,7 @@ infant | O టోకెన్లు మరియు తరగతుల మధ్య ఒకటి-కోటి అనుసంధానం అవసరం కాబట్టి, ఈ చిత్రంలో చూపినట్లుగా ఒక కుడి వైపు **బహుళ-కు-బహుళ** న్యూరల్ నెట్‌వర్క్ మోడల్‌ను శిక్షణ ఇవ్వవచ్చు: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.te.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/te/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) నుండి, రచయిత [అండ్రేజ్ కార్పతి](http://karpathy.github.io/). NER టోకెన్ వర్గీకరణ మోడల్స్ ఈ చిత్రంలో కుడి వైపు నెట్‌వర్క్ నిర్మాణానికి సరిపోతాయి.* diff --git a/translations/te/lessons/5-NLP/README.md b/translations/te/lessons/5-NLP/README.md index a3cc06eb..3b6bb721 100644 --- a/translations/te/lessons/5-NLP/README.md +++ b/translations/te/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # సహజ భాషా ప్రాసెసింగ్ -![NLP పనుల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.te.png) +![NLP పనుల సారాంశం ఒక డ్రాయింగ్‌లో](../../../../translated_images/te/ai-nlp.b22dcb8ca4707cea.png) ఈ విభాగంలో, మనం సహజ భాషా ప్రాసెసింగ్ (NLP) సంబంధిత పనులను నిర్వహించడానికి న్యూరల్ నెట్‌వర్క్‌లను ఉపయోగించడంపై దృష్టి పెట్టబోతున్నాము. కంప్యూటర్లు పరిష్కరించగలిగే అనేక NLP సమస్యలు ఉన్నాయి: diff --git a/translations/te/lessons/6-Other/22-DeepRL/README.md b/translations/te/lessons/6-Other/22-DeepRL/README.md index 9f5f12eb..819bcb75 100644 --- a/translations/te/lessons/6-Other/22-DeepRL/README.md +++ b/translations/te/lessons/6-Other/22-DeepRL/README.md @@ -34,7 +34,7 @@ RL కోసం అద్భుతమైన సాధనం [OpenAI Gym](https:/ సరళీకృత బలాన్సింగ్ వెర్షన్‌ను **కార్ట్‌పోల్** సమస్యగా పిలుస్తారు. కార్ట్‌పోల్ ప్రపంచంలో, మనకు ఎడమ లేదా కుడి వైపు కదలగల ఒక హారిజాంటల్ స్లైడర్ ఉంటుంది, మరియు స్లైడర్ పై ఒక నిలువు కంబళిని సమతుల్యం చేయడం లక్ష్యం. -a cartpole +a cartpole ఈ పరిసరాన్ని సృష్టించి ఉపయోగించడానికి, మనకు కొన్ని పాథాన్ కోడ్ లైన్లు అవసరం: diff --git a/translations/te/lessons/6-Other/22-DeepRL/lab/README.md b/translations/te/lessons/6-Other/22-DeepRL/lab/README.md index a24ac6d6..7f45e8b6 100644 --- a/translations/te/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/te/lessons/6-Other/22-DeepRL/lab/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: OpenAI పరిసరంలో [Mountain Car](https://www.gymlibrary.ml/environments/classic_control/mountain_car/) ను నియంత్రించడానికి RL ఏజెంట్‌ను శిక్షణ ఇవ్వడం మీ లక్ష్యం. -Mountain Car +Mountain Car ## పరిసరము diff --git a/translations/te/lessons/6-Other/23-MultiagentSystems/README.md b/translations/te/lessons/6-Other/23-MultiagentSystems/README.md index 0c15e86f..cc1b36b9 100644 --- a/translations/te/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/te/lessons/6-Other/23-MultiagentSystems/README.md @@ -61,7 +61,7 @@ ask turtles [ నెట్‌లాగోలో ఉన్న గొప్ప విషయం ఏమిటంటే, మీరు ప్రయత్నించగల పని చేసే మోడల్స్ లైబ్రరీ ఉంది. **File → Models Library**కి వెళ్లండి, మీరు ఎన్నుకోవడానికి అనేక మోడల్స్ వర్గాలు ఉంటాయి. -NetLogo Models Library +NetLogo Models Library > మోడల్స్ లైబ్రరీ స్క్రీన్‌షాట్ - Dmitry Soshnikov @@ -71,7 +71,7 @@ ask turtles [ మోడల్ తెరిచిన తర్వాత, మీరు ప్రధాన నెట్‌లాగో స్క్రీన్‌కు తీసుకువెళ్ళబడతారు. ఇక్కడ ఒక నమూనా మోడల్ ఉంది, ఇది పరిమిత వనరులు (గడ్డి) ఉన్నప్పుడు నక్కలు మరియు గొర్రెల జనాభాను వివరించేది. -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.te.png) +![NetLogo Main Screen](../../../../../translated_images/te/NetLogo-Main.32653711ec1a01b3.png) > స్క్రీన్‌షాట్ - Dmitry Soshnikov diff --git a/translations/te/lessons/README.md b/translations/te/lessons/README.md index 0226931a..9493ad30 100644 --- a/translations/te/lessons/README.md +++ b/translations/te/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # అవలోకనం -![డూడుల్‌లో అవలోకనం](../../../translated_images/ai-overview.0857791951d19500.te.png) +![డూడుల్‌లో అవలోకనం](../../../translated_images/te/ai-overview.0857791951d19500.png) > స్కెచ్‌నోట్: [టోమోమీ ఇమురా](https://twitter.com/girlie_mac) diff --git a/translations/te/lessons/X-Extras/X1-MultiModal/README.md b/translations/te/lessons/X-Extras/X1-MultiModal/README.md index 110b5a8b..f54476d5 100644 --- a/translations/te/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/te/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ NLP పనులను పరిష్కరించడంలో ట్రా CLIP యొక్క ప్రధాన ఆలోచన ఏమిటంటే, టెక్స్ట్ ప్రాంప్ట్‌లను ఒక చిత్రంతో పోల్చి, ఆ చిత్రం ప్రాంప్ట్‌కు ఎంతగా సరిపోతుందో నిర్ణయించగలగడం. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.te.png) +![CLIP Architecture](../../../../../translated_images/te/clip-arch.b3dbf20b4e8ed8be.png) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://openai.com/blog/clip/) నుండి* @@ -29,7 +29,7 @@ CLIP మోడల్/లైబ్రరీ [OpenAI GitHub](https://github.com/op ఉదాహరణకు, పిల్లులు, కుక్కలు మరియు మనుషుల మధ్య చిత్రాలను వర్గీకరించాలి అనుకుందాం. ఈ సందర్భంలో, మోడల్‌కు ఒక చిత్రం మరియు టెక్స్ట్ ప్రాంప్ట్‌ల సిరీస్ ఇవ్వవచ్చు: "*పిల్లి యొక్క చిత్రం*", "*కుక్క యొక్క చిత్రం*", "*మనిషి యొక్క చిత్రం*". 3 ప్రాబబిలిటీల వెక్టర్‌లో అత్యధిక విలువ ఉన్న సూచికను ఎంచుకోవడం సరిపోతుంది. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.te.png) +![CLIP for Image Classification](../../../../../translated_images/te/clip-class.3af42ef0b2b19369.png) > *చిత్రం [ఈ బ్లాగ్ పోస్ట్](https://openai.com/blog/clip/) నుండి* @@ -53,13 +53,13 @@ VQGAN గురించి మరింత తెలుసుకోడాని VQGAN మరియు సాంప్రదాయ GAN మధ్య ముఖ్య తేడా ఏమిటంటే, సాంప్రదాయ GAN ఏ ఇన్‌పుట్ వెక్టర్ నుండి సరైన చిత్రం ఉత్పత్తి చేయగలదు, కానీ VQGAN కొన్నిసార్లు సారూప్యమైన చిత్రం కాకపోవచ్చు. అందుకే, చిత్ర సృష్టి ప్రక్రియను మరింత మార్గనిర్దేశం చేయాలి, దీని కోసం CLIP ఉపయోగించవచ్చు. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.te.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/te/vqgan.5027fe05051dfa31.png) టెక్స్ట్ ప్రాంప్ట్‌కు అనుగుణంగా చిత్రం సృష్టించడానికి, మొదట రాండమ్ ఎంకోడింగ్ వెక్టర్ తీసుకుని దాన్ని VQGAN ద్వారా చిత్రంగా మార్చుతారు. ఆ తర్వాత CLIP ఉపయోగించి ఆ చిత్రం టెక్స్ట్ ప్రాంప్ట్‌కు ఎంతగా సరిపోతుందో చూపించే లాస్ ఫంక్షన్ తయారుచేస్తారు. ఆ లాస్‌ను తగ్గించడం లక్ష్యం, బ్యాక్ ప్రొపగేషన్ ద్వారా ఇన్‌పుట్ వెక్టర్ పరామితులను సర్దుబాటు చేస్తారు. VQGAN+CLIP ను అమలు చేసే గొప్ప లైబ్రరీ [Pixray](http://github.com/pixray/pixray) -![Picture produced by Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.te.png) | ![Picture produced by pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.te.png) | ![Picture produced by Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.te.png) +![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Picture produced by pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Picture produced by Pixray](../../../../../translated_images/te/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- *పుస్తకం తో యువ సాహిత్య ఉపాధ్యాయుడి watercolor సమీప చిత్రము* | *కంప్యూటర్ తో యువ కంప్యూటర్ సైన్స్ ఉపాధ్యాయురాలి oil సమీప చిత్రము* | *బ్లాక్‌బోర్డ్ ముందు వృద్ధ గణితం ఉపాధ్యాయుడి oil సమీప చిత్రము* @@ -75,7 +75,7 @@ CLIP తో భిన్నంగా, DALL-E టెక్స్ట్ మరి DALL.E 1 మరియు 2 మధ్య ప్రధాన తేడా ఏమిటంటే, DALL.E 2 మరింత వాస్తవికమైన చిత్రాలు మరియు కళను సృష్టిస్తుంది. DALL-E తో చిత్ర సృష్టి ఉదాహరణలు: -![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.te.png) | ![Picture produced by pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.te.png) | ![Picture produced by Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.te.png) +![Picture produced by Pixray](../../../../../translated_images/te/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Picture produced by pixray](../../../../../translated_images/te/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Picture produced by Pixray](../../../../../translated_images/te/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- *పుస్తకం తో యువ సాహిత్య ఉపాధ్యాయుడి watercolor సమీప చిత్రము* | *కంప్యూటర్ తో యువ కంప్యూటర్ సైన్స్ ఉపాధ్యాయురాలి oil సమీప చిత్రము* | *బ్లాక్‌బోర్డ్ ముందు వృద్ధ గణితం ఉపాధ్యాయుడి oil సమీప చిత్రము* diff --git a/translations/th/README.md b/translations/th/README.md index 65fac840..0310f178 100644 --- a/translations/th/README.md +++ b/translations/th/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # ปัญญาประดิษฐ์สำหรับผู้เริ่มต้น - หลักสูตร -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.th.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/th/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _สเก็ตช์โน้ตโดย [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/th/lessons/1-Intro/README.md b/translations/th/lessons/1-Intro/README.md index 16574cb8..5446df63 100644 --- a/translations/th/lessons/1-Intro/README.md +++ b/translations/th/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การแนะนำเกี่ยวกับ AI -![สรุปเนื้อหาเกี่ยวกับการแนะนำ AI ในรูปแบบภาพวาด](../../../../translated_images/ai-intro.bf28d1ac4235881c.th.png) +![สรุปเนื้อหาเกี่ยวกับการแนะนำ AI ในรูปแบบภาพวาด](../../../../translated_images/th/ai-intro.bf28d1ac4235881c.png) > ภาพวาดโดย [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: เดิมทีคอมพิวเตอร์ถูกคิดค้นโดย [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) เพื่อทำงานกับตัวเลขตามกระบวนการที่กำหนดไว้อย่างชัดเจน - หรือที่เรียกว่าอัลกอริทึม คอมพิวเตอร์สมัยใหม่ แม้ว่าจะมีความก้าวหน้ามากกว่ารุ่นต้นแบบในศตวรรษที่ 19 แต่ก็ยังคงยึดแนวคิดเดิมเกี่ยวกับการคำนวณที่ควบคุมได้ ดังนั้นจึงเป็นไปได้ที่จะเขียนโปรแกรมให้คอมพิวเตอร์ทำบางสิ่งได้ หากเรารู้ลำดับขั้นตอนที่แน่นอนที่เราต้องทำเพื่อให้บรรลุเป้าหมาย -![ภาพถ่ายของบุคคล](../../../../translated_images/dsh_age.d212a30d4e54fb5f.th.png) +![ภาพถ่ายของบุคคล](../../../../translated_images/th/dsh_age.d212a30d4e54fb5f.png) > ภาพถ่ายโดย [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ AI แบบอ่อนมีความเชี่ยวชาญสูง หนึ่งในปัญหาเมื่อพูดถึงคำว่า **[ความฉลาด](https://en.wikipedia.org/wiki/Intelligence)** คือไม่มีคำจำกัดความที่ชัดเจนของคำนี้ บางคนอาจโต้แย้งว่าความฉลาดเชื่อมโยงกับ **การคิดเชิงนามธรรม** หรือ **การรับรู้ตัวเอง** แต่เราไม่สามารถกำหนดมันได้อย่างเหมาะสม -![ภาพถ่ายของแมว](../../../../translated_images/photo-cat.8c8e8fb760ffe457.th.jpg) +![ภาพถ่ายของแมว](../../../../translated_images/th/photo-cat.8c8e8fb760ffe457.jpg) > [ภาพถ่าย](https://unsplash.com/photos/75715CVEJhI) โดย [Amber Kipp](https://unsplash.com/@sadmax) จาก Unsplash @@ -98,13 +98,13 @@ AI แบบอ่อนมีความเชี่ยวชาญสูง > | แล้ว ML ล่ะ? | | > |--------------|-----------| -> | ส่วนหนึ่งของปัญญาประดิษฐ์ที่อิงกับการเรียนรู้ของคอมพิวเตอร์ในการแก้ปัญหาตามข้อมูลบางอย่างเรียกว่า **Machine Learning** เราจะไม่พิจารณาการเรียนรู้ของเครื่องแบบคลาสสิกในหลักสูตรนี้ - เราแนะนำให้คุณดูหลักสูตร [Machine Learning for Beginners](http://aka.ms/ml-beginners) แยกต่างหาก | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.th.png) | +> | ส่วนหนึ่งของปัญญาประดิษฐ์ที่อิงกับการเรียนรู้ของคอมพิวเตอร์ในการแก้ปัญหาตามข้อมูลบางอย่างเรียกว่า **Machine Learning** เราจะไม่พิจารณาการเรียนรู้ของเครื่องแบบคลาสสิกในหลักสูตรนี้ - เราแนะนำให้คุณดูหลักสูตร [Machine Learning for Beginners](http://aka.ms/ml-beginners) แยกต่างหาก | ![ML for Beginners](../../../../translated_images/th/ml-for-beginners.9e4fed176fd5817d.png) | ## ประวัติย่อของ AI ปัญญาประดิษฐ์เริ่มต้นเป็นสาขาในช่วงกลางศตวรรษที่ 20 ในช่วงแรก วิธีการให้เหตุผลเชิงสัญลักษณ์เป็นวิธีที่แพร่หลาย และนำไปสู่ความสำเร็จที่สำคัญหลายประการ เช่น ระบบผู้เชี่ยวชาญ – โปรแกรมคอมพิวเตอร์ที่สามารถทำหน้าที่เป็นผู้เชี่ยวชาญในบางโดเมนปัญหาที่จำกัด อย่างไรก็ตาม ไม่นานก็ชัดเจนว่าวิธีการดังกล่าวไม่สามารถขยายขอบเขตได้ดี การดึงความรู้จากผู้เชี่ยวชาญ การแสดงในคอมพิวเตอร์ และการรักษาฐานความรู้ให้ถูกต้องกลายเป็นงานที่ซับซ้อนมาก และมีค่าใช้จ่ายสูงเกินไปที่จะนำไปใช้ในหลายกรณี สิ่งนี้นำไปสู่สิ่งที่เรียกว่า [AI Winter](https://en.wikipedia.org/wiki/AI_winter) ในทศวรรษ 1970 -ประวัติย่อของ AI +ประวัติย่อของ AI > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/th/lessons/2-Symbolic/Animals.ipynb b/translations/th/lessons/2-Symbolic/Animals.ipynb index 1dde2d47..fe65f971 100644 --- a/translations/th/lessons/2-Symbolic/Animals.ipynb +++ b/translations/th/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "ในตัวอย่างนี้ เราจะสร้างระบบที่ใช้ความรู้เพื่อระบุชนิดของสัตว์โดยอิงจากลักษณะทางกายภาพบางประการ ระบบนี้สามารถแสดงผลได้ในรูปแบบต้นไม้ AND-OR (นี่เป็นเพียงส่วนหนึ่งของต้นไม้ทั้งหมด เราสามารถเพิ่มกฎเพิ่มเติมได้อย่างง่ายดาย):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.th.png)\n" + "![](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/th/lessons/2-Symbolic/README.md b/translations/th/lessons/2-Symbolic/README.md index 239b2a08..bc8e5ad9 100644 --- a/translations/th/lessons/2-Symbolic/README.md +++ b/translations/th/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การแทนความรู้และระบบผู้เชี่ยวชาญ -![สรุปเนื้อหา Symbolic AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.th.png) +![สรุปเนื้อหา Symbolic AI](../../../../translated_images/th/ai-symbolic.715a30cb610411a6.png) > ภาพสเก็ตโน้ตโดย [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: ดังนั้น ปัญหาของ **การแทนความรู้** คือการหาวิธีที่มีประสิทธิภาพในการแทนความรู้ภายในคอมพิวเตอร์ในรูปแบบของข้อมูล เพื่อให้สามารถใช้งานได้โดยอัตโนมัติ สิ่งนี้สามารถมองได้ว่าเป็นสเปกตรัม: -![สเปกตรัมการแทนความรู้](../../../../translated_images/knowledge-spectrum.b60df631852c0217.th.png) +![สเปกตรัมการแทนความรู้](../../../../translated_images/th/knowledge-spectrum.b60df631852c0217.png) > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | หนึ่งในความสำเร็จแรกๆ ของ Symbolic AI คือ **ระบบผู้เชี่ยวชาญ (Expert Systems)** - ระบบคอมพิวเตอร์ที่ถูกออกแบบมาให้ทำหน้าที่เป็นผู้เชี่ยวชาญในโดเมนปัญหาที่จำกัด ระบบเหล่านี้มีพื้นฐานมาจาก **ฐานความรู้ (Knowledge Base)** ที่ดึงมาจากผู้เชี่ยวชาญมนุษย์ และมี **เครื่องมืออนุมาน (Inference Engine)** ที่ทำการให้เหตุผลบนฐานความรู้นั้น -![โครงสร้างระบบประสาทมนุษย์](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.th.png) | ![โครงสร้างระบบที่ใช้ความรู้](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.th.png) +![โครงสร้างระบบประสาทมนุษย์](../../../../translated_images/th/arch-human.5d4d35f1bba3ab1c.png) | ![โครงสร้างระบบที่ใช้ความรู้](../../../../translated_images/th/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ โครงสร้างระบบประสาทมนุษย์แบบง่าย | โครงสร้างของระบบที่ใช้ความรู้ @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ตัวอย่างเช่น ลองพิจารณาระบบผู้เชี่ยวชาญที่ใช้ในการระบุสัตว์ตามลักษณะทางกายภาพ: -![ต้นไม้ AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.th.png) +![ต้นไม้ AND-OR](../../../../translated_images/th/AND-OR-Tree.5592d2c70187f283.png) > ภาพโดย [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 525dc41a..3944f989 100644 --- a/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/th/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -489,7 +489,7 @@ "\n", "ในกรณีที่เรามีมากกว่า 2 คลาส softmax จะทำการปรับค่าความน่าจะเป็นให้เป็นสัดส่วนระหว่างทุกคลาส นี่คือตัวอย่างแผนภาพของสถาปัตยกรรมเครือข่ายที่ใช้สำหรับการจำแนกตัวเลข MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.th.png)\n" + "![MNIST Classifier](../../../../../translated_images/th/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1261,7 +1261,7 @@ "* ค่าความสูญเสียในการฝึกต่ำ - โมเดลสามารถประมาณค่าข้อมูลการฝึกได้ดี เพราะมีพลังในการแสดงออกเพียงพอ\n", "* ค่าความสูญเสียในการตรวจสอบอาจสูงกว่าค่าความสูญเสียในการฝึกมาก และอาจเริ่มเพิ่มขึ้นระหว่างการฝึก - เนื่องจากโมเดล \"จดจำ\" จุดข้อมูลการฝึก และสูญเสีย \"ภาพรวม\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.th.png)\n", + "![Overfitting](../../../../../translated_images/th/overfit.a0bd57f717c15769.png)\n", "\n", "> ในภาพนี้ `x` แทนข้อมูลการฝึก, `o` แทนข้อมูลการตรวจสอบ ซ้าย - โมเดลเชิงเส้น (ชั้นเดียว) ซึ่งประมาณค่าลักษณะของข้อมูลได้ค่อนข้างดี ขวา - โมเดลที่เกิด overfitting ซึ่งสามารถประมาณค่าข้อมูลการฝึกได้อย่างสมบูรณ์แบบ แต่ไม่สามารถใช้งานได้กับข้อมูลอื่น (ค่าความผิดพลาดในการตรวจสอบสูงมาก)\n" ] diff --git a/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md index 4145f6d5..f67e2ed9 100644 --- a/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/th/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting เป็นแนวคิดที่สำคัญมากใ ลองพิจารณาปัญหาการประมาณค่าจุด 5 จุด (แสดงด้วย `x` ในกราฟด้านล่าง): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.th.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.th.jpg) +![linear](../../../../../translated_images/th/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/th/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **โมเดลเชิงเส้น, 2 พารามิเตอร์** | **โมเดลไม่เชิงเส้น, 7 พารามิเตอร์** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Validation error = 5.1 | Validation error = 20 จากกราฟด้านบน เราสามารถตรวจจับ overfitting ได้จาก training error ที่ต่ำมาก และ validation error ที่สูง โดยปกติระหว่างการฝึก เราจะเห็นทั้ง training และ validation error ลดลง แต่ในบางจุด validation error อาจหยุดลดลงและเริ่มเพิ่มขึ้น นี่เป็นสัญญาณของ overfitting และเป็นตัวบ่งชี้ว่าเราควรหยุดการฝึกในจุดนี้ (หรืออย่างน้อยควรบันทึกสถานะของโมเดล) -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.th.png) +![overfitting](../../../../../translated_images/th/Overfitting.408ad91cd90b4371.png) ## วิธีป้องกัน overfitting diff --git a/translations/th/lessons/3-NeuralNetworks/README.md b/translations/th/lessons/3-NeuralNetworks/README.md index 15e37478..4b663cd9 100644 --- a/translations/th/lessons/3-NeuralNetworks/README.md +++ b/translations/th/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # แนะนำเกี่ยวกับเครือข่ายประสาทเทียม -![สรุปเนื้อหาเกี่ยวกับเครือข่ายประสาทเทียมในรูปวาด](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.th.png) +![สรุปเนื้อหาเกี่ยวกับเครือข่ายประสาทเทียมในรูปวาด](../../../../translated_images/th/ai-neuralnetworks.1c687ae40bc86e83.png) ตามที่เราได้พูดถึงในบทนำ หนึ่งในวิธีที่จะสร้างความฉลาดคือการฝึก **โมเดลคอมพิวเตอร์** หรือ **สมองเทียม** ตั้งแต่กลางศตวรรษที่ 20 นักวิจัยได้ลองใช้โมเดลทางคณิตศาสตร์ต่าง ๆ จนกระทั่งในช่วงไม่กี่ปีที่ผ่านมาแนวทางนี้ได้พิสูจน์แล้วว่าประสบความสำเร็จอย่างมาก โมเดลทางคณิตศาสตร์ของสมองเหล่านี้เรียกว่า **เครือข่ายประสาทเทียม** @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: จากชีววิทยา เราทราบว่าสมองของเราประกอบด้วยเซลล์ประสาท (neurons) ซึ่งแต่ละเซลล์มี "อินพุต" หลายตัว (dendrites) และ "เอาต์พุต" หนึ่งตัว (axon) ทั้ง dendrites และ axons สามารถนำสัญญาณไฟฟ้าได้ และการเชื่อมต่อระหว่างพวกมัน — ที่เรียกว่า synapses — สามารถแสดงระดับการนำไฟฟ้าที่แตกต่างกัน ซึ่งถูกควบคุมโดยสารสื่อประสาท -![โมเดลของเซลล์ประสาท](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.th.jpg) | ![โมเดลของเซลล์ประสาท](../../../../translated_images/artneuron.1a5daa88d20ebe6f.th.png) +![โมเดลของเซลล์ประสาท](../../../../translated_images/th/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![โมเดลของเซลล์ประสาท](../../../../translated_images/th/artneuron.1a5daa88d20ebe6f.png) ----|---- เซลล์ประสาทจริง *([ภาพ](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) จาก Wikipedia)* | เซลล์ประสาทเทียม *(ภาพโดยผู้เขียน)* ดังนั้น โมเดลทางคณิตศาสตร์ที่ง่ายที่สุดของเซลล์ประสาทจะมีอินพุตหลายตัว X1, ..., XN และเอาต์พุต Y พร้อมกับชุดของน้ำหนัก W1, ..., WN เอาต์พุตจะถูกคำนวณเป็น: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) โดยที่ f คือ **ฟังก์ชันการกระตุ้น** ที่ไม่เป็นเชิงเส้น diff --git a/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md index 198cf26c..e84d4685 100644 --- a/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/th/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **การประมวลผลภาพถ่ายของหนังสือเบรลล์** เรามุ่งเน้นที่การใช้ thresholding, การตรวจจับคุณลักษณะ, การแปลงมุมมอง และการปรับอาร์เรย์ NumPy เพื่อแยกสัญลักษณ์เบรลล์แต่ละตัวสำหรับการจำแนกผลลัพธ์โดยเครือข่ายประสาทเทียม -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.th.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.th.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.th.png) +![Braille Image](../../../../../translated_images/th/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/th/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/th/braille-symbols.0159185ab69d5339.png) ----|-----|----- > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) * **การตรวจจับการเคลื่อนไหวในวิดีโอโดยใช้ความแตกต่างของเฟรม** หากกล้องอยู่ในตำแหน่งคงที่ เฟรมจากกล้องควรมีความคล้ายคลึงกันมาก โดยเฟรมที่แสดงผลเป็นอาร์เรย์ เพียงแค่ลบอาร์เรย์ของเฟรมสองเฟรมที่ต่อเนื่องกัน คุณจะได้ความแตกต่างของพิกเซล ซึ่งควรต่ำสำหรับเฟรมที่นิ่ง และสูงขึ้นเมื่อมีการเคลื่อนไหวในภาพ -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.th.png) +![Image of video frames and frame differences](../../../../../translated_images/th/frame-difference.706f805491a0883c.png) > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Dense Optical Flow** คำนวณสนามเวกเตอร์ที่แสดงว่าพิกเซลแต่ละตัวเคลื่อนที่ไปที่ใด - **Sparse Optical Flow** ใช้คุณลักษณะเด่นในภาพ (เช่น ขอบ) และสร้างเส้นทางการเคลื่อนที่ของมันจากเฟรมหนึ่งไปยังอีกเฟรมหนึ่ง -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.th.png) +![Image of Optical Flow](../../../../../translated_images/th/optical.1f4a94464579a83a.png) > ภาพจาก [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index f447370d..1a7a5dd7 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 เป็นเครือข่ายที่ทำคะแนนความแม่นยำได้ถึง 92.7% ในการจัดประเภท ImageNet top-5 ในปี 2014 โดยมีโครงสร้างเลเยอร์ดังนี้: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.th.jpg) +![ImageNet Layers](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.jpg) ดังที่คุณเห็น VGG ใช้สถาปัตยกรรมแบบพีระมิดดั้งเดิม ซึ่งเป็นลำดับของเลเยอร์คอนโวลูชันและพูลลิ่ง -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.th.jpg) +![ImageNet Pyramid](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.jpg) > ภาพจาก [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 6e4c362a..050cefae 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "ดังนั้น ใน CNN ทั่วไปจะมีเลเยอร์คอนโวลูชันหลายชั้น โดยมีเลเยอร์การลดขนาดอยู่ระหว่างเพื่อช่วยลดมิติของภาพ นอกจากนี้เรายังเพิ่มจำนวนตัวกรอง เพราะเมื่อรูปแบบมีความซับซ้อนมากขึ้น จะมีการผสมผสานที่น่าสนใจมากขึ้นที่เราต้องค้นหา\n", "\n", - "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การลดขนาด.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.th.png)\n", + "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การลดขนาด.](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "เนื่องจากการลดมิติของพื้นที่และการเพิ่มมิติของฟีเจอร์/ตัวกรอง สถาปัตยกรรมนี้จึงถูกเรียกว่า **pyramid architecture**\n" ] diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index b9a32643..ba302360 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "ดังนั้น ใน CNN ทั่วไปจะมีเลเยอร์คอนโวลูชันหลายชั้น โดยมีเลเยอร์การทำพูลลิ่งแทรกอยู่ระหว่างเพื่อช่วยลดมิติของภาพ นอกจากนี้ เรายังเพิ่มจำนวนฟิลเตอร์ เพราะเมื่อรูปแบบมีความซับซ้อนมากขึ้น ก็จะมีการผสมผสานที่น่าสนใจมากขึ้นที่เราต้องค้นหา\n", "\n", - "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การทำพูลลิ่ง](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.th.png)\n", + "![ภาพแสดงเลเยอร์คอนโวลูชันหลายชั้นพร้อมเลเยอร์การทำพูลลิ่ง](../../../../../translated_images/th/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "เนื่องจากการลดมิติของพื้นที่และการเพิ่มมิติของฟีเจอร์/ฟิลเตอร์ สถาปัตยกรรมนี้จึงถูกเรียกว่า **pyramid architecture**\n" ] diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md index 06688bb1..bafb519a 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: เพื่อดึงรูปแบบเหล่านี้ออกมา เราจะใช้แนวคิดของ **ฟิลเตอร์คอนโวลูชัน** อย่างที่คุณทราบ ภาพถูกแสดงในรูปแบบเมทริกซ์ 2 มิติ หรือเทนเซอร์ 3 มิติที่มีความลึกของสี การใช้ฟิลเตอร์หมายถึงการนำเมทริกซ์ **ฟิลเตอร์เคอร์เนล** ขนาดเล็กมาใช้ และสำหรับแต่ละพิกเซลในภาพต้นฉบับ เราจะคำนวณค่าเฉลี่ยถ่วงน้ำหนักกับจุดที่อยู่ใกล้เคียง เราสามารถมองสิ่งนี้เหมือนหน้าต่างเล็ก ๆ ที่เลื่อนผ่านภาพทั้งหมด และเฉลี่ยค่าพิกเซลทั้งหมดตามน้ำหนักในเมทริกซ์ฟิลเตอร์เคอร์เนล -![ฟิลเตอร์ขอบแนวตั้ง](../../../../../translated_images/filter-vert.b7148390ca0bc356.th.png) | ![ฟิลเตอร์ขอบแนวนอน](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.th.png) +![ฟิลเตอร์ขอบแนวตั้ง](../../../../../translated_images/th/filter-vert.b7148390ca0bc356.png) | ![ฟิลเตอร์ขอบแนวนอน](../../../../../translated_images/th/filter-horiz.59b80ed4feb946ef.png) ----|---- > ภาพโดย Dmitry Soshnikov @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * เราสามารถออกแบบเครือข่ายในลักษณะที่ฟิลเตอร์ถูกฝึกฝนโดยอัตโนมัติ * เราสามารถใช้วิธีเดียวกันนี้เพื่อค้นหารูปแบบในคุณลักษณะระดับสูง ไม่ใช่แค่ในภาพต้นฉบับ ดังนั้นการดึงคุณลักษณะของ CNN จึงทำงานในลำดับชั้นของคุณลักษณะ โดยเริ่มจากการผสมผสานพิกเซลระดับต่ำไปจนถึงการผสมผสานระดับสูงของส่วนต่าง ๆ ของภาพ -![การดึงคุณลักษณะแบบลำดับชั้น](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.th.png) +![การดึงคุณลักษณะแบบลำดับชั้น](../../../../../translated_images/th/FeatureExtractionCNN.d9b456cbdae7cb64.png) > ภาพจาก [งานวิจัยโดย Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) ซึ่งอ้างอิงจาก [งานวิจัยของพวกเขา](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN ส่วนใหญ่ที่ใช้สำหรับการปร ตัวอย่างเช่น ลองดูสถาปัตยกรรมของ VGG-16 ซึ่งเป็นเครือข่ายที่ทำได้ถึงความแม่นยำ 92.7% ในการจัดอันดับ 5 อันดับแรกของ ImageNet ในปี 2014: -![ชั้นของ ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.th.jpg) +![ชั้นของ ImageNet](../../../../../translated_images/th/vgg-16-arch1.d901a5583b3a51ba.jpg) -![พีระมิดของ ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.th.jpg) +![พีระมิดของ ImageNet](../../../../../translated_images/th/vgg-16-arch.64ff2137f50dd49f.jpg) > ภาพจาก [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 5686474e..f9b2eb2a 100644 --- a/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/th/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: เราจะใช้ [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) ซึ่งมีภาพของสุนัขและแมวจาก 37 สายพันธุ์ที่แตกต่างกัน -![ชุดข้อมูลที่เราจะใช้](../../../../../../translated_images/data.50b2a9d5484bdbf0.th.png) +![ชุดข้อมูลที่เราจะใช้](../../../../../../translated_images/th/data.50b2a9d5484bdbf0.png) เพื่อดาวน์โหลดชุดข้อมูล ให้ใช้โค้ดนี้: diff --git a/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 684a9231..e7634709 100644 --- a/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/th/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "เพื่อสร้างภาพแมวที่สมบูรณ์แบบ เราจะเริ่มต้นด้วยภาพที่เป็นสัญญาณรบกวนแบบสุ่ม และจะใช้เทคนิคการปรับให้เหมาะสมด้วยการไล่ระดับ (gradient descent) เพื่อปรับภาพให้เครือข่ายสามารถจดจำว่าเป็นแมวได้\n", "\n", - "![วงจรการปรับให้เหมาะสม](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.th.png)\n", + "![วงจรการปรับให้เหมาะสม](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "นี่คือภาพเริ่มต้นของเรา:\n" ] diff --git a/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md index d52a3d88..ed541f92 100644 --- a/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/th/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: ตัวอย่างคุณลักษณะที่ดึงออกมาจากภาพแมวโดยเครือข่าย VGG-16: -![คุณลักษณะที่ดึงออกมาโดย VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.th.png) +![คุณลักษณะที่ดึงออกมาโดย VGG-16](../../../../../translated_images/th/features.6291f9c7ba3a0b95.png) ## ชุดข้อมูลแมวและสุนัข @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: วิธีหนึ่งที่เราสามารถทำได้คือเริ่มต้นด้วยภาพสุ่ม และพยายามใช้เทคนิค **การปรับแต่งด้วยการลดเกรเดียนต์** เพื่อปรับภาพนั้นให้เครือข่ายเริ่มคิดว่ามันคือแมว -![วงจรการปรับแต่งภาพ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.th.png) +![วงจรการปรับแต่งภาพ](../../../../../translated_images/th/ideal-cat-loop.999fbb8ff306e044.png) อย่างไรก็ตาม หากเราทำเช่นนี้ เราจะได้สิ่งที่คล้ายกับสัญญาณรบกวนแบบสุ่ม นั่นเป็นเพราะ *มีหลายวิธีที่จะทำให้เครือข่ายคิดว่าภาพอินพุตคือแมว* รวมถึงบางวิธีที่ไม่มีความหมายในเชิงภาพ แม้ว่าภาพเหล่านั้นจะมีรูปแบบที่เป็นลักษณะเฉพาะของแมว แต่ไม่มีอะไรบังคับให้มันดูโดดเด่นในเชิงภาพ เพื่อปรับปรุงผลลัพธ์ เราสามารถเพิ่มคำศัพท์อีกคำหนึ่งในฟังก์ชันการสูญเสีย ซึ่งเรียกว่า **การสูญเสียความแปรปรวน** เป็นเมตริกที่แสดงให้เห็นว่าพิกเซลที่อยู่ใกล้เคียงในภาพมีความคล้ายคลึงกันเพียงใด การลดการสูญเสียความแปรปรวนทำให้ภาพเรียบขึ้นและกำจัดสัญญาณรบกวน - เผยให้เห็นรูปแบบที่น่าดึงดูดในเชิงภาพมากขึ้น นี่คือตัวอย่างของภาพ "ในอุดมคติ" ที่ถูกจัดประเภทว่าเป็นแมวและม้าลายด้วยความน่าจะเป็นสูง: -![แมวในอุดมคติ](../../../../../translated_images/ideal-cat.203dd4597643d6b0.th.png) | ![ม้าลายในอุดมคติ](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.th.png) +![แมวในอุดมคติ](../../../../../translated_images/th/ideal-cat.203dd4597643d6b0.png) | ![ม้าลายในอุดมคติ](../../../../../translated_images/th/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *แมวในอุดมคติ* | *ม้าลายในอุดมคติ* วิธีการที่คล้ายกันสามารถใช้เพื่อทำการโจมตีแบบ **adversarial** บนเครือข่ายประสาท สมมติว่าเราต้องการหลอกเครือข่ายประสาทและทำให้สุนัขดูเหมือนแมว หากเราใช้ภาพของสุนัขที่เครือข่ายจำแนกได้ว่าเป็นสุนัข เราสามารถปรับแต่งมันเล็กน้อยโดยใช้การลดเกรเดียนต์จนกว่าเครือข่ายจะเริ่มจำแนกมันว่าเป็นแมว: -![ภาพของสุนัข](../../../../../translated_images/original-dog.8f68a67d2fe0911f.th.png) | ![ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.th.png) +![ภาพของสุนัข](../../../../../translated_images/th/original-dog.8f68a67d2fe0911f.png) | ![ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว](../../../../../translated_images/th/adversarial-dog.d9fc7773b0142b89.png) -----|----- *ภาพต้นฉบับของสุนัข* | *ภาพของสุนัขที่ถูกจัดประเภทว่าเป็นแมว* diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index aa360da5..0e3dc24f 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "เนื่องจากเรากำลังฝึก autoencoder เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **embedding** ที่ดีที่สุดของภาพนำเข้าเพื่อจับความหมาย\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.th.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> ภาพจาก [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index f81f25af..ad6bab17 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "เนื่องจากเรากำลังฝึก autoencoder เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **embedding** ที่ดีที่สุดของภาพนำเข้าเพื่อจับความหมายของภาพ\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.th.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*ภาพจาก [บล็อก Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md index 17eb83ea..ceb22d0f 100644 --- a/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/th/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: เนื่องจากเรากำลังฝึกออโตเอนโคเดอร์เพื่อจับข้อมูลจากภาพต้นฉบับให้ได้มากที่สุดเพื่อการสร้างใหม่ที่แม่นยำ เครือข่ายจึงพยายามค้นหา **การฝังตัว** ที่ดีที่สุดของภาพอินพุตเพื่อจับความหมายของภาพ -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.th.jpg) +![AutoEncoder Diagram](../../../../../translated_images/th/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > ภาพจาก [บล็อก Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md index 47dacc75..1e378196 100644 --- a/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/th/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [แบบทดสอบก่อนเรียน](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![การตรวจจับวัตถุ](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.th.png) +![การตรวจจับวัตถุ](../../../../../translated_images/th/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > ภาพจาก [เว็บไซต์ YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ใช้การจำแนกภาพในแต่ละส่วน 3. ส่วนที่มีการกระตุ้นสูงพอสมควรสามารถพิจารณาได้ว่ามีวัตถุที่เราต้องการอยู่ -![การตรวจจับวัตถุแบบพื้นฐาน](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.th.png) +![การตรวจจับวัตถุแบบพื้นฐาน](../../../../../translated_images/th/naive-detection.e7f1ba220ccd08c6.png) > *ภาพจาก [สมุดบันทึกการฝึกฝน](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 คลาส * [COCO](http://cocodataset.org/#home) - Common Objects in Context. มี 80 คลาส, กรอบวัตถุ และหน้ากากการแบ่งส่วน -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.th.jpg) +![COCO](../../../../../translated_images/th/coco-examples.71bc60380fa6cceb.jpg) ## ตัวชี้วัดสำหรับการตรวจจับวัตถุ @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: ในขณะที่การจำแนกภาพสามารถวัดผลได้ง่ายว่าประสิทธิภาพของอัลกอริทึมเป็นอย่างไร สำหรับการตรวจจับวัตถุ เราต้องวัดทั้งความถูกต้องของคลาส และความแม่นยำของตำแหน่งกรอบวัตถุที่คาดการณ์ได้ สำหรับอย่างหลัง เราใช้ตัวชี้วัดที่เรียกว่า **Intersection over Union** (IoU) ซึ่งวัดว่าพื้นที่สองส่วน (หรือพื้นที่ใดๆ) ซ้อนทับกันได้ดีเพียงใด -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.th.png) +![IoU](../../../../../translated_images/th/iou_equation.9a4751d40fff4e11.png) > *รูปที่ 2 จาก [บทความที่ยอดเยี่ยมเกี่ยวกับ IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ใช้ [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) เพื่อสร้างโครงสร้างลำดับชั้นของ ROI ซึ่งจะถูกส่งผ่าน CNN เพื่อดึงคุณลักษณะ และใช้ SVM-classifiers เพื่อกำหนดคลาสของวัตถุ และการถดถอยเชิงเส้นเพื่อกำหนดพิกัดของ *กรอบวัตถุ* [เอกสารอย่างเป็นทางการ](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.th.png) +![RCNN](../../../../../translated_images/th/rcnn1.cae407020dfb1d1f.png) > *ภาพจาก van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.th.png) +![RCNN-1](../../../../../translated_images/th/rcnn2.2d9530bb83516484.png) > *ภาพจาก [บทความนี้](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ วิธีนี้คล้ายกับ R-CNN แต่ ROI ถูกกำหนดหลังจากที่เลเยอร์คอนโวลูชันถูกประยุกต์ใช้แล้ว -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.th.png) +![FRCNN](../../../../../translated_images/th/f-rcnn.3cda6d9bb4188875.png) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ แนวคิดหลักของวิธีนี้คือการใช้เครือข่ายประสาทเพื่อทำนาย ROI ที่เรียกว่า *Region Proposal Network* [เอกสาร](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.th.png) +![FasterRCNN](../../../../../translated_images/th/faster-rcnn.8d46c099b87ef30a.png) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. คุณลักษณะถูกประมวลผลโดย **Position-Sensitive Score Map** วัตถุแต่ละตัวจาก $C$ คลาสถูกแบ่งออกเป็น $k\times k$ พื้นที่ และเราฝึกให้ทำนายส่วนต่างๆ ของวัตถุ 3. สำหรับแต่ละส่วนจาก $k\times k$ พื้นที่ เครือข่ายทั้งหมดจะโหวตให้คลาสของวัตถุ และคลาสที่มีคะแนนโหวตสูงสุดจะถูกเลือก -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.th.png) +![r-fcn image](../../../../../translated_images/th/r-fcn.13eb88158b99a3da.png) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO เป็นอัลกอริทึมแบบเรียลไทม * ภาพถูกแบ่งออกเป็น $S\times S$ พื้นที่ * สำหรับแต่ละพื้นที่ **CNN** ทำนายวัตถุ $n$ ชนิด, พิกัด *กรอบวัตถุ* และ *ความมั่นใจ*=*ความน่าจะเป็น* * IoU - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.th.png) + ![YOLO](../../../../../translated_images/th/yolo.a2648ec82ee8bb4e.png) > ภาพจาก [เอกสารอย่างเป็นทางการ](https://arxiv.org/abs/1506.02640) diff --git a/translations/th/lessons/4-ComputerVision/README.md b/translations/th/lessons/4-ComputerVision/README.md index e2cc92b6..256c52f8 100644 --- a/translations/th/lessons/4-ComputerVision/README.md +++ b/translations/th/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การมองเห็นของคอมพิวเตอร์ -![สรุปเนื้อหาเกี่ยวกับการมองเห็นของคอมพิวเตอร์ในรูปวาด](../../../../translated_images/ai-computervision.6506ebebac3fbf76.th.png) +![สรุปเนื้อหาเกี่ยวกับการมองเห็นของคอมพิวเตอร์ในรูปวาด](../../../../translated_images/th/ai-computervision.6506ebebac3fbf76.png) ในส่วนนี้เราจะเรียนรู้เกี่ยวกับ: diff --git a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 2485bf9e..3ccee307 100644 --- a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) เป็นการแสดงข้อความในรูปแบบเวกเตอร์ที่ใช้กันอย่างแพร่หลายที่สุดในวิธีการแบบดั้งเดิม โดยแต่ละคำจะถูกเชื่อมโยงกับดัชนีในเวกเตอร์ และแต่ละองค์ประกอบในเวกเตอร์จะแสดงจำนวนครั้งที่คำปรากฏในเอกสารที่กำหนด\n", "\n", - "![ภาพแสดงการแสดงข้อความแบบ Bag of Words ในหน่วยความจำ](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.th.png) \n", + "![ภาพแสดงการแสดงข้อความแบบ Bag of Words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: คุณสามารถมองว่า BoW เป็นผลรวมของเวกเตอร์แบบ one-hot-encoded ของคำแต่ละคำในข้อความได้เช่นกัน\n", "\n", diff --git a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index a72ee411..66d8e789 100644 --- a/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/th/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) เป็นการแสดงข้อความในรูปแบบเวกเตอร์ที่เข้าใจได้ง่ายที่สุดในบรรดาการแสดงข้อความแบบเวกเตอร์แบบดั้งเดิม โดยแต่ละคำจะถูกเชื่อมโยงกับดัชนีในเวกเตอร์ และแต่ละองค์ประกอบในเวกเตอร์จะบอกจำนวนครั้งที่คำแต่ละคำปรากฏในเอกสารที่กำหนด\n", "\n", - "![ภาพแสดงการแสดงข้อความแบบ bag-of-words ในหน่วยความจำ](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.th.png)\n", + "![ภาพแสดงการแสดงข้อความแบบ bag-of-words ในหน่วยความจำ](../../../../../translated_images/th/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **Note**: คุณสามารถคิดถึง BoW ว่าเป็นผลรวมของเวกเตอร์แบบ one-hot-encoded ของคำแต่ละคำในข้อความก็ได้เช่นกัน\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 13aa2022..503753ac 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "โดยการใช้ชั้นการฝังข้อมูลเป็นชั้นแรกในเครือข่ายของเรา เราสามารถเปลี่ยนจากโมเดล bag-of-words ไปเป็นโมเดล **embedding bag** ซึ่งเราจะเปลี่ยนคำแต่ละคำในข้อความของเราให้เป็นการฝังข้อมูลที่สอดคล้องกัน และจากนั้นคำนวณฟังก์ชันรวมบางอย่างจากการฝังข้อมูลเหล่านั้น เช่น `sum`, `average` หรือ `max`\n", "\n", - "![ภาพแสดงตัวอย่างตัวจำแนกที่ใช้การฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.th.png)\n", + "![ภาพแสดงตัวอย่างตัวจำแนกที่ใช้การฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "เครือข่ายประสาทเทียมของเราจะเริ่มต้นด้วยชั้นการฝังข้อมูล ตามด้วยชั้นการรวม และตัวจำแนกเชิงเส้นที่อยู่ด้านบน\n" ] @@ -176,7 +176,7 @@ "\n", "ในสถาปัตยกรรมก่อนหน้านี้ เราจำเป็นต้องเติมข้อมูลในทุกลำดับให้มีความยาวเท่ากันเพื่อให้สามารถใส่ลงในชุดข้อมูลย่อยได้ วิธีนี้ไม่ใช่วิธีที่มีประสิทธิภาพที่สุดในการแสดงผลลำดับที่มีความยาวแปรผัน - อีกวิธีหนึ่งคือการใช้ **เวกเตอร์ออฟเซ็ต** ซึ่งจะเก็บค่าตำแหน่งเริ่มต้นของลำดับทั้งหมดที่ถูกจัดเก็บในเวกเตอร์ขนาดใหญ่หนึ่งตัว\n", "\n", - "![ภาพแสดงการแสดงผลลำดับแบบออฟเซ็ต](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.th.png)\n", + "![ภาพแสดงการแสดงผลลำดับแบบออฟเซ็ต](../../../../../translated_images/th/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: ในภาพด้านบน เราแสดงลำดับของตัวอักษร แต่ในตัวอย่างของเรา เรากำลังทำงานกับลำดับของคำ อย่างไรก็ตาม หลักการทั่วไปในการแสดงผลลำดับด้วยเวกเตอร์ออฟเซ็ตยังคงเหมือนเดิม\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram ช้ากว่า แต่สามารถแสดงคำที่พบได้น้อยได้ดีกว่า\n", "\n", - "![ภาพแสดงอัลกอริทึม CBoW และ Skip-Gram สำหรับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.th.png)\n", + "![ภาพแสดงอัลกอริทึม CBoW และ Skip-Gram สำหรับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "เพื่อทดลองใช้การฝัง Word2Vec ที่ได้รับการฝึกฝนล่วงหน้าบนชุดข้อมูล Google News เราสามารถใช้ไลบรารี **gensim** ด้านล่างนี้คือตัวอย่างการค้นหาคำที่คล้ายกับ 'neural' มากที่สุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index eeaad161..8a4f7b51 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/th/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "เมื่อใช้ embedding layer เป็นเลเยอร์แรกในเครือข่ายของเรา เราสามารถเปลี่ยนจาก bag-of-words ไปเป็นโมเดล **embedding bag** โดยที่เราจะแปลงแต่ละคำในข้อความของเราให้เป็น embedding ที่สอดคล้องกัน และคำนวณฟังก์ชันรวมบางอย่างจาก embeddings เหล่านั้น เช่น `sum`, `average` หรือ `max`\n", "\n", - "![ภาพแสดงตัวอย่าง embedding classifier สำหรับคำในลำดับห้าคำ](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.th.png)\n", + "![ภาพแสดงตัวอย่าง embedding classifier สำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "เครือข่ายประสาทสำหรับการจำแนกของเราประกอบด้วยเลเยอร์ดังต่อไปนี้:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram แม้จะช้ากว่า แต่สามารถแสดงคำที่พบได้น้อยได้ดีกว่า\n", "\n", - "![ภาพแสดงอัลกอริธึม CBoW และ Skip-Gram สำหรับแปลงคำเป็นเวกเตอร์](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.th.png)\n", + "![ภาพแสดงอัลกอริธึม CBoW และ Skip-Gram สำหรับแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "เพื่อทดลองใช้การฝัง Word2Vec ที่ฝึกไว้ล่วงหน้าด้วยชุดข้อมูล Google News เราสามารถใช้ไลบรารี **gensim** ด้านล่างนี้เป็นตัวอย่างการค้นหาคำที่คล้ายกับ 'neural' มากที่สุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/14-Embeddings/README.md b/translations/th/lessons/5-NLP/14-Embeddings/README.md index 409b6691..ab62698e 100644 --- a/translations/th/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/th/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: โดยการใช้เลเยอร์การฝังข้อมูลเป็นเลเยอร์แรกในเครือข่ายจำแนกประเภทของเรา เราสามารถเปลี่ยนจากโมเดลถุงคำไปเป็น **embedding bag** ซึ่งเราจะเปลี่ยนคำแต่ละคำในข้อความของเราให้เป็นการฝังข้อมูลที่สอดคล้องกัน และคำนวณฟังก์ชันรวมบางอย่างจากการฝังข้อมูลเหล่านั้น เช่น `sum`, `average` หรือ `max` -![ภาพแสดงตัวอย่างการจำแนกประเภทด้วยการฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.th.png) +![ภาพแสดงตัวอย่างการจำแนกประเภทด้วยการฝังข้อมูลสำหรับคำในลำดับห้าคำ](../../../../../translated_images/th/embedding-classifier-example.b77f021a7ee67eee.png) > ภาพโดยผู้เขียน @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW ทำงานได้เร็วกว่า ในขณะที่ skip-gram ทำงานช้ากว่า แต่สามารถแสดงคำที่ไม่ค่อยปรากฏได้ดีกว่า -![ภาพแสดงทั้งอัลกอริทึม CBoW และ Skip-Gram ในการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.th.png) +![ภาพแสดงทั้งอัลกอริทึม CBoW และ Skip-Gram ในการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ภาพจาก [เอกสารนี้](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/th/lessons/5-NLP/15-LanguageModeling/README.md b/translations/th/lessons/5-NLP/15-LanguageModeling/README.md index 09552d88..c136835c 100644 --- a/translations/th/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/th/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW) โดยการทำนายโทเค็นตรงกลาง $W_0$ ในลำดับโทเค็น $W_{-N}$, ..., $W_N$ * **Skip-gram** โดยการทำนายชุดโทเค็นที่อยู่ใกล้เคียง {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} จากโทเค็นตรงกลาง $W_0$ -![ภาพจากงานวิจัยเกี่ยวกับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.th.png) +![ภาพจากงานวิจัยเกี่ยวกับการแปลงคำเป็นเวกเตอร์](../../../../../translated_images/th/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > ภาพจาก [งานวิจัยนี้](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/th/lessons/5-NLP/16-RNN/README.md b/translations/th/lessons/5-NLP/16-RNN/README.md index 1ed0f217..9147b1ff 100644 --- a/translations/th/lessons/5-NLP/16-RNN/README.md +++ b/translations/th/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: เพื่อจับความหมายของลำดับข้อความ เราจำเป็นต้องใช้สถาปัตยกรรมเครือข่ายประสาทอีกแบบหนึ่ง ซึ่งเรียกว่า **เครือข่ายประสาทแบบวนซ้ำ** หรือ RNN ใน RNN เราจะส่งประโยคผ่านเครือข่ายทีละสัญลักษณ์ และเครือข่ายจะสร้าง **สถานะ** ซึ่งเราจะส่งกลับเข้าเครือข่ายพร้อมกับสัญลักษณ์ถัดไป -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.th.png) +![RNN](../../../../../translated_images/th/rnn.27f5c29c53d727b5.png) > ภาพโดยผู้เขียน @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: เครือข่ายแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบบางอย่างภายในลำดับ และสามารถเก็บรูปแบบเหล่านั้นไว้ในเวกเตอร์สถานะหรือส่งผ่านไปยังผลลัพธ์ เช่นเดียวกับเครือข่ายแบบ convolutional เราสามารถสร้างเลเยอร์แบบวนซ้ำอีกเลเยอร์หนึ่งบนเลเยอร์แรกเพื่อจับรูปแบบระดับสูงและสร้างจากรูปแบบระดับต่ำที่ถูกดึงออกโดยเลเยอร์แรก สิ่งนี้นำเราไปสู่แนวคิดของ **RNN หลายเลเยอร์** ซึ่งประกอบด้วยเครือข่ายแบบวนซ้ำสองหรือมากกว่า โดยที่ผลลัพธ์ของเลเยอร์ก่อนหน้าจะถูกส่งไปยังเลเยอร์ถัดไปเป็นอินพุต -![ภาพแสดง RNN แบบหลายเลเยอร์ LSTM](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.th.jpg) +![ภาพแสดง RNN แบบหลายเลเยอร์ LSTM](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg) *ภาพจาก [โพสต์ที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López* diff --git a/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 1475e2bc..b49cca48 100644 --- a/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -420,7 +420,7 @@ "\n", "เครือข่ายแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบบางอย่างภายในลำดับ และสามารถเก็บรูปแบบเหล่านั้นไว้ในเวกเตอร์สถานะหรือส่งต่อไปยังผลลัพธ์ เช่นเดียวกับเครือข่ายแบบคอนโวลูชัน (convolutional networks) เราสามารถสร้างเลเยอร์แบบวนซ้ำอีกชั้นหนึ่งบนเลเยอร์แรกเพื่อจับรูปแบบในระดับที่สูงขึ้น ซึ่งสร้างขึ้นจากรูปแบบระดับต่ำที่เลเยอร์แรกสกัดออกมา สิ่งนี้นำเราไปสู่แนวคิดของ **multi-layer RNN** ซึ่งประกอบด้วยเครือข่ายแบบวนซ้ำสองชั้นหรือมากกว่า โดยที่ผลลัพธ์ของเลเยอร์ก่อนหน้าจะถูกส่งไปยังเลเยอร์ถัดไปเป็นอินพุต\n", "\n", - "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.th.jpg)\n", + "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ภาพจาก [บทความที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López*\n", "\n", diff --git a/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2cd7c2fa..ebc63664 100644 --- a/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/th/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "เพื่อจับความหมายของลำดับข้อความ เราจะใช้สถาปัตยกรรมเครือข่ายประสาทเทียมที่เรียกว่า **เครือข่ายประสาทเทียมแบบวนซ้ำ** หรือ RNN เมื่อใช้ RNN เราจะส่งประโยคของเราผ่านเครือข่ายทีละโทเค็น และเครือข่ายจะสร้าง **สถานะ** ซึ่งเราจะส่งกลับเข้าไปในเครือข่ายอีกครั้งพร้อมกับโทเค็นถัดไป\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างเครือข่ายประสาทเทียมแบบวนซ้ำ](../../../../../translated_images/rnn.27f5c29c53d727b5.th.png)\n", + "![ภาพแสดงตัวอย่างการสร้างเครือข่ายประสาทเทียมแบบวนซ้ำ](../../../../../translated_images/th/rnn.27f5c29c53d727b5.png)\n", "\n", "เมื่อได้รับลำดับโทเค็นอินพุต $X_0,\\dots,X_n$ RNN จะสร้างลำดับของบล็อกเครือข่ายประสาทเทียม และฝึกฝนลำดับนี้แบบ end-to-end โดยใช้ backpropagation แต่ละบล็อกเครือข่ายจะรับคู่ $(X_i,S_i)$ เป็นอินพุต และสร้าง $S_{i+1}$ เป็นผลลัพธ์ สถานะสุดท้าย $S_n$ หรือผลลัพธ์ $Y_n$ จะถูกส่งไปยังตัวจำแนกเชิงเส้นเพื่อสร้างผลลัพธ์ บล็อกเครือข่ายทั้งหมดใช้เวทเดียวกัน และถูกฝึกแบบ end-to-end โดยใช้การ backpropagation เพียงครั้งเดียว\n", "\n", @@ -369,7 +369,7 @@ "\n", "เครือข่ายประสาทแบบวนซ้ำ ไม่ว่าจะเป็นแบบทิศทางเดียวหรือสองทิศทาง จะจับรูปแบบภายในลำดับและเก็บไว้ในเวกเตอร์สถานะ (state vectors) หรือส่งคืนเป็นผลลัพธ์ เช่นเดียวกับเครือข่ายแบบคอนโวลูชัน (convolutional networks) เราสามารถสร้างชั้น recurrent อีกชั้นหนึ่งตามหลังชั้นแรกเพื่อจับรูปแบบในระดับที่สูงขึ้น ซึ่งสร้างขึ้นจากรูปแบบระดับต่ำที่ชั้นแรกดึงออกมาได้ สิ่งนี้นำเราไปสู่แนวคิดของ **multi-layer RNN** ซึ่งประกอบด้วยเครือข่ายประสาทแบบวนซ้ำสองชั้นหรือมากกว่า โดยที่ผลลัพธ์ของชั้นก่อนหน้าจะถูกส่งไปยังชั้นถัดไปเป็นข้อมูลนำเข้า\n", "\n", - "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.th.jpg)\n", + "![ภาพแสดง Multilayer long-short-term-memory- RNN](../../../../../translated_images/th/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*ภาพจาก [บทความที่ยอดเยี่ยมนี้](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) โดย Fernando López*\n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 006575a3..81de74b7 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "วิธีที่เราจะฝึก RNN เพื่อสร้างข้อความมีดังนี้ ในแต่ละขั้นตอน เราจะนำลำดับของตัวอักษรที่มีความยาว `nchars` และให้เครือข่ายสร้างตัวอักษรถัดไปสำหรับแต่ละตัวอักษรในลำดับอินพุต:\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/rnn-generate.56c54afb52f9781d.th.png)\n", + "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ขึ้นอยู่กับสถานการณ์จริง เราอาจต้องการเพิ่มตัวอักษรพิเศษบางตัว เช่น *end-of-sequence* `` ในกรณีของเรา เราต้องการฝึกเครือข่ายเพื่อสร้างข้อความแบบไม่มีที่สิ้นสุด ดังนั้นเราจะกำหนดขนาดของแต่ละลำดับให้เท่ากับโทเค็น `nchars` ดังนั้น ตัวอย่างการฝึกแต่ละตัวจะประกอบด้วยอินพุต `nchars` และเอาต์พุต `nchars` (ซึ่งเป็นลำดับอินพุตที่เลื่อนหนึ่งสัญลักษณ์ไปทางซ้าย) Minibatch จะประกอบด้วยลำดับดังกล่าวหลายชุด\n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 0b92688c..d88968be 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "วิธีที่เราจะฝึก RNN เพื่อสร้างหัวข้อข่าวมีดังนี้ ในแต่ละขั้นตอน เราจะนำหัวข้อข่าวหนึ่งหัวข้อมาใส่ใน RNN และสำหรับแต่ละตัวอักษรที่ป้อนเข้าไป เราจะให้เครือข่ายสร้างตัวอักษรถัดไป:\n", "\n", - "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/rnn-generate.56c54afb52f9781d.th.png)\n", + "![ภาพแสดงตัวอย่างการสร้างคำว่า 'HELLO' ด้วย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png)\n", "\n", "สำหรับตัวอักษรสุดท้ายของลำดับ เราจะให้เครือข่ายสร้างโทเค็น `` \n", "\n", diff --git a/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md index e1ffbef4..6c0c8c7f 100644 --- a/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/th/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Recurrent Neural Networks (RNNs) และรูปแบบเซลล์ท สิ่งนี้นำไปสู่สถาปัตยกรรมเครือข่ายประสาทที่แตกต่างกัน ซึ่งแสดงในภาพด้านล่าง: -![ภาพแสดงรูปแบบเครือข่ายประสาทแบบวนซ้ำทั่วไป](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.th.jpg) +![ภาพแสดงรูปแบบเครือข่ายประสาทแบบวนซ้ำทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > ภาพจากบล็อกโพสต์ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) โดย [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Recurrent Neural Networks (RNNs) และรูปแบบเซลล์ท เราจะฝึก RNN นี้เพื่อสร้างข้อความทีละขั้นตอน ในแต่ละขั้นตอน เราจะใช้ลำดับตัวอักษรที่มีความยาว `nchars` และให้เครือข่ายสร้างตัวอักษรถัดไปสำหรับแต่ละตัวอักษรในข้อมูลเข้า: -![ภาพแสดงตัวอย่างการสร้างคำ 'HELLO' โดย RNN](../../../../../translated_images/rnn-generate.56c54afb52f9781d.th.png) +![ภาพแสดงตัวอย่างการสร้างคำ 'HELLO' โดย RNN](../../../../../translated_images/th/rnn-generate.56c54afb52f9781d.png) เมื่อสร้างข้อความ (ในระหว่างการอนุมาน) เราจะเริ่มต้นด้วย **คำเริ่มต้น** ซึ่งจะถูกส่งผ่านเซลล์ RNN เพื่อสร้างสถานะกลาง และจากสถานะนี้การสร้างข้อความจะเริ่มต้น เราจะสร้างตัวอักษรทีละตัว และส่งสถานะและตัวอักษรที่สร้างไปยังเซลล์ RNN ตัวถัดไปเพื่อสร้างตัวอักษรถัดไป จนกว่าจะสร้างข้อความครบตามที่ต้องการ diff --git a/translations/th/lessons/5-NLP/18-Transformers/README.md b/translations/th/lessons/5-NLP/18-Transformers/README.md index 6dd3ad6c..1110df67 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/README.md +++ b/translations/th/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **กลไก Attention** ให้วิธีการในการถ่วงน้ำหนักผลกระทบเชิงบริบทของแต่ละเวกเตอร์ข้อมูลเข้าในแต่ละการทำนายผลลัพธ์ของ RNN วิธีการนี้ถูกดำเนินการโดยการสร้างทางลัดระหว่างสถานะกลางของ RNN ข้อมูลเข้าและ RNN ข้อมูลออก ด้วยวิธีนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ yt เราจะพิจารณาสถานะ hidden ทั้งหมด hi ของข้อมูลเข้า โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน αt,i -![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.th.png) +![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png) > โมเดล encoder-decoder พร้อมกลไก attention แบบ additive ใน [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) เมทริกซ์ attention {αi,j} จะเป็นตัวแทนระดับที่คำบางคำในข้อมูลเข้ามีบทบาทในการสร้างคำที่กำหนดในลำดับผลลัพธ์ ด้านล่างเป็นตัวอย่างของเมทริกซ์ดังกล่าว: -![ภาพแสดงการจัดแนวตัวอย่างที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.th.png) +![ภาพแสดงการจัดแนวตัวอย่างที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png) > ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: ต่อไป เราจำเป็นต้องจับรูปแบบบางอย่างในลำดับของเรา เพื่อทำสิ่งนี้ Transformers ใช้กลไก **self-attention** ซึ่งเป็น Attention ที่นำไปใช้กับลำดับเดียวกันทั้งข้อมูลเข้าและข้อมูลออก การใช้ self-attention ช่วยให้เราพิจารณา **บริบท** ภายในประโยค และดูว่าคำใดมีความสัมพันธ์กัน ตัวอย่างเช่น มันช่วยให้เราเห็นว่าคำใดถูกอ้างถึงโดยคำสรรพนาม เช่น *it* และยังพิจารณาบริบทด้วย: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.th.png) +![](../../../../../translated_images/th/CoreferenceResolution.861924d6d384a7d6.png) > ภาพจาก [บล็อกของ Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Encoder-decoder attention มีความคล้ายคลึงกับ **BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่าย Transformer ขนาดใหญ่มากที่มีหลายชั้น โดยมี 12 ชั้นสำหรับ *BERT-base* และ 24 ชั้นสำหรับ *BERT-large* โมเดลนี้ถูกฝึกเบื้องต้นด้วยชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบไม่ต้องมีการกำกับดูแล (การทำนายคำที่ถูกปิดบังในประโยค) ในระหว่างการฝึกเบื้องต้น โมเดลจะดูดซับความเข้าใจภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่นๆ ผ่านการปรับแต่งเพิ่มเติม กระบวนการนี้เรียกว่า **transfer learning** -![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.th.png) +![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > ภาพ [แหล่งที่มา](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 04d5f279..efa5ad34 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/th/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**กลไก Attention** เป็นวิธีการที่ช่วยให้น้ำหนักของผลกระทบเชิงบริบทของแต่ละเวกเตอร์นำเข้าต่อการทำนายผลลัพธ์ของ RNN แตกต่างกัน วิธีการนี้ถูกนำมาใช้โดยการสร้างทางลัดระหว่างสถานะกลางของ RNN นำเข้าและ RNN ผลลัพธ์ ด้วยวิธีนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ $y_t$ เราจะพิจารณา hidden states นำเข้าทั้งหมด $h_i$ โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน $\\alpha_{t,i}$\n", "\n", - "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.th.png)\n", + "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png)\n", "*โมเดล encoder-decoder พร้อมกลไก attention แบบ additive จาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "เมทริกซ์ Attention $\\{\\alpha_{i,j}\\}$ แสดงระดับที่คำบางคำในข้อมูลนำเข้ามีบทบาทในการสร้างคำในลำดับผลลัพธ์ ตัวอย่างของเมทริกซ์ดังกล่าวแสดงอยู่ด้านล่าง:\n", "\n", - "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.th.png)\n", + "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่าย Transformer ขนาดใหญ่มากที่มีหลายชั้น โดย *BERT-base* มี 12 ชั้น และ *BERT-large* มี 24 ชั้น โมเดลนี้ถูก pre-trained บนชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบ unsupervised (การทำนายคำที่ถูก mask ในประโยค) ในระหว่างการ pre-training โมเดลจะเรียนรู้ความเข้าใจภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่น ๆ ผ่านการ fine tuning กระบวนการนี้เรียกว่า **transfer learning**\n", "\n", - "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.th.png)\n", + "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "มีสถาปัตยกรรม Transformer หลากหลายรูปแบบ เช่น BERT, DistilBERT, BigBird, OpenGPT3 และอื่น ๆ ที่สามารถนำไป fine tune ได้ [HuggingFace package](https://github.com/huggingface/) มี repository สำหรับการฝึกสถาปัตยกรรมเหล่านี้หลายตัวด้วย PyTorch\n", "\n", diff --git a/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 80fc6022..a60def39 100644 --- a/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/th/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**กลไก Attention** เป็นวิธีการที่ช่วยให้น้ำหนักความสำคัญของแต่ละเวกเตอร์ข้อมูลเข้ามีผลต่อการทำนายผลลัพธ์ของ RNN โดยวิธีการนี้จะสร้างทางลัดระหว่างสถานะกลางของ RNN ข้อมูลเข้าและ RNN ข้อมูลออก ในลักษณะนี้ เมื่อสร้างสัญลักษณ์ผลลัพธ์ $y_t$ เราจะพิจารณาสถานะซ่อนของข้อมูลเข้าทั้งหมด $h_i$ โดยมีค่าสัมประสิทธิ์น้ำหนักที่แตกต่างกัน $\\alpha_{t,i}$\n", "\n", - "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น Attention แบบ additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.th.png)\n", + "![ภาพแสดงโมเดล encoder/decoder พร้อมชั้น Attention แบบ additive](../../../../../translated_images/th/encoder-decoder-attention.7a726296894fb567.png)\n", "*โมเดล encoder-decoder พร้อมกลไก Attention แบบ additive จาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) อ้างอิงจาก [บล็อกโพสต์นี้](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "เมทริกซ์ Attention $\\{\\alpha_{i,j}\\}$ จะเป็นตัวแทนระดับที่คำบางคำในข้อมูลเข้ามีบทบาทในการสร้างคำในลำดับผลลัพธ์ ตัวอย่างของเมทริกซ์ดังกล่าวแสดงอยู่ด้านล่าง:\n", "\n", - "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.th.png)\n", + "![ภาพแสดงตัวอย่างการจัดแนวที่พบโดย RNNsearch-50 จาก Bahdanau - arviz.org](../../../../../translated_images/th/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*ภาพจาก [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) เป็นเครือข่ายทรานส์ฟอร์เมอร์ขนาดใหญ่มากที่มีหลายชั้น โดย *BERT-base* มี 12 ชั้น และ *BERT-large* มี 24 ชั้น โมเดลนี้ถูกฝึกเบื้องต้นด้วยชุดข้อมูลข้อความขนาดใหญ่ (WikiPedia + หนังสือ) โดยใช้การฝึกแบบไม่มีการกำกับดูแล (การทำนายคำที่ถูกปิดบังในประโยค) ในระหว่างการฝึกเบื้องต้น โมเดลจะเรียนรู้ความเข้าใจในภาษาระดับสูง ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่น ๆ ได้โดยการปรับแต่งเพิ่มเติม กระบวนการนี้เรียกว่า **การเรียนรู้แบบถ่ายโอน** \n", "\n", - "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.th.png)\n", + "![ภาพจาก http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/th/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "มีสถาปัตยกรรม Transformer หลากหลายรูปแบบ เช่น BERT, DistilBERT, BigBird, OpenGPT3 และอื่น ๆ ที่สามารถปรับแต่งเพิ่มเติมได้ \n", "\n", diff --git a/translations/th/lessons/5-NLP/19-NER/README.md b/translations/th/lessons/5-NLP/19-NER/README.md index 3f81da82..87c472ad 100644 --- a/translations/th/lessons/5-NLP/19-NER/README.md +++ b/translations/th/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O เนื่องจากเราต้องสร้างความสัมพันธ์แบบหนึ่งต่อหนึ่งระหว่างโทเค็นและคลาส เราสามารถฝึกโมเดลเครือข่ายประสาทเทียมแบบ **many-to-many** ที่เหมาะสมจากภาพนี้: -![ภาพแสดงรูปแบบเครือข่ายประสาทเทียมแบบ recurrent ทั่วไป](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.th.jpg) +![ภาพแสดงรูปแบบเครือข่ายประสาทเทียมแบบ recurrent ทั่วไป](../../../../../translated_images/th/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *ภาพจาก [บล็อกโพสต์นี้](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) โดย [Andrej Karpathy](http://karpathy.github.io/) โมเดลการจัดประเภทโทเค็น NER สอดคล้องกับสถาปัตยกรรมเครือข่ายที่อยู่ทางขวาสุดในภาพนี้* diff --git a/translations/th/lessons/5-NLP/README.md b/translations/th/lessons/5-NLP/README.md index 3502a94f..cb38d8f8 100644 --- a/translations/th/lessons/5-NLP/README.md +++ b/translations/th/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การประมวลผลภาษาธรรมชาติ -![ภาพสรุปงาน NLP ในรูปวาด](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.th.png) +![ภาพสรุปงาน NLP ในรูปวาด](../../../../translated_images/th/ai-nlp.b22dcb8ca4707cea.png) ในส่วนนี้ เราจะมุ่งเน้นการใช้เครือข่ายประสาทเทียม (Neural Networks) เพื่อจัดการกับงานที่เกี่ยวข้องกับ **การประมวลผลภาษาธรรมชาติ (Natural Language Processing - NLP)** มีปัญหาหลายประเภทใน NLP ที่เราต้องการให้คอมพิวเตอร์สามารถแก้ไขได้: diff --git a/translations/th/lessons/6-Other/23-MultiagentSystems/README.md b/translations/th/lessons/6-Other/23-MultiagentSystems/README.md index 9a38e599..0382fcdb 100644 --- a/translations/th/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/th/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ หลังจากเปิดโมเดล คุณจะเข้าสู่หน้าจอหลักของ NetLogo นี่คือตัวอย่างโมเดลที่อธิบายประชากรของหมาป่าและแกะ โดยมีทรัพยากรจำกัด (หญ้า) -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.th.png) +![NetLogo Main Screen](../../../../../translated_images/th/NetLogo-Main.32653711ec1a01b3.png) > ภาพหน้าจอโดย Dmitry Soshnikov diff --git a/translations/th/lessons/README.md b/translations/th/lessons/README.md index 013573ef..417d0e2a 100644 --- a/translations/th/lessons/README.md +++ b/translations/th/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ภาพรวม -![ภาพรวมในรูปวาด](../../../translated_images/ai-overview.0857791951d19500.th.png) +![ภาพรวมในรูปวาด](../../../translated_images/th/ai-overview.0857791951d19500.png) > ภาพวาดโดย [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/th/lessons/X-Extras/X1-MultiModal/README.md b/translations/th/lessons/X-Extras/X1-MultiModal/README.md index 389b96ea..4cd60dbe 100644 --- a/translations/th/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/th/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: แนวคิดหลักของ CLIP คือการเปรียบเทียบข้อความ (text prompts) กับภาพ และประเมินว่าภาพนั้นสอดคล้องกับข้อความมากน้อยเพียงใด -![สถาปัตยกรรม CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.th.png) +![สถาปัตยกรรม CLIP](../../../../../translated_images/th/clip-arch.b3dbf20b4e8ed8be.png) > *ภาพจาก [บทความนี้](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: สมมติว่าเราต้องการจำแนกภาพระหว่างแมว สุนัข และมนุษย์ ในกรณีนี้ เราสามารถให้โมเดลรับภาพและข้อความ เช่น "*ภาพของแมว*", "*ภาพของสุนัข*", "*ภาพของมนุษย์*" ในเวกเตอร์ผลลัพธ์ที่มีความน่าจะเป็น 3 ค่า เราเพียงแค่เลือกดัชนีที่มีค่ามากที่สุด -![CLIP สำหรับการจำแนกภาพ](../../../../../translated_images/clip-class.3af42ef0b2b19369.th.png) +![CLIP สำหรับการจำแนกภาพ](../../../../../translated_images/th/clip-class.3af42ef0b2b19369.png) > *ภาพจาก [บทความนี้](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP ยังสามารถใช้สำหรับ **การสร้ ความแตกต่างสำคัญระหว่าง VQGAN และ GAN ทั่วไปคือ GAN สามารถสร้างภาพที่ดีจากเวกเตอร์อินพุตใด ๆ ได้ แต่ VQGAN อาจสร้างภาพที่ไม่สอดคล้องกัน ดังนั้นเราจำเป็นต้องมีการชี้นำเพิ่มเติมในกระบวนการสร้างภาพ ซึ่งสามารถทำได้โดยใช้ CLIP -![สถาปัตยกรรม VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.th.png) +![สถาปัตยกรรม VQGAN+CLIP](../../../../../translated_images/th/vqgan.5027fe05051dfa31.png) ในการสร้างภาพที่สอดคล้องกับข้อความ เราเริ่มต้นด้วยเวกเตอร์การเข้ารหัสแบบสุ่มที่ถูกส่งผ่าน VQGAN เพื่อสร้างภาพ จากนั้นใช้ CLIP เพื่อสร้างฟังก์ชันการสูญเสียที่แสดงว่าภาพสอดคล้องกับข้อความมากน้อยเพียงใด เป้าหมายคือการลดค่าฟังก์ชันการสูญเสียนี้โดยใช้การถ่ายทอดย้อนกลับ (back propagation) เพื่อปรับพารามิเตอร์ของเวกเตอร์อินพุต ไลบรารีที่ยอดเยี่ยมที่นำ VQGAN+CLIP มาใช้งานคือ [Pixray](http://github.com/pixray/pixray) -![ภาพที่สร้างโดย Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.th.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.th.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.th.png) +![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![ภาพที่สร้างโดย Pixray](../../../../../translated_images/th/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำของครูหนุ่มสอนวรรณกรรมพร้อมหนังสือ* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูสาวสอนวิทยาการคอมพิวเตอร์พร้อมคอมพิวเตอร์* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูชายสูงวัยสอนคณิตศาสตร์หน้ากระดานดำ* @@ -75,7 +75,7 @@ DALL-E เป็นเวอร์ชันของ GPT-3 ที่ถูกฝ ความแตกต่างหลักระหว่าง DALL-E 1 และ 2 คือ DALL-E 2 สามารถสร้างภาพและงานศิลปะที่สมจริงมากขึ้น ตัวอย่างการสร้างภาพด้วย DALL-E: -![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.th.png) | ![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.th.png) | ![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.th.png) +![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/th/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/th/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![ภาพที่สร้างโดย DALL-E](../../../../../translated_images/th/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำของครูหนุ่มสอนวรรณกรรมพร้อมหนังสือ* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูสาวสอนวิทยาการคอมพิวเตอร์พร้อมคอมพิวเตอร์* | ภาพที่สร้างจากข้อความ *ภาพเหมือนสีน้ำมันของครูชายสูงวัยสอนคณิตศาสตร์หน้ากระดานดำ* diff --git a/translations/tl/README.md b/translations/tl/README.md index 0af24cfb..8b3a4dd5 100644 --- a/translations/tl/README.md +++ b/translations/tl/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Artificial Intelligence for Beginners - Isang Kurikulum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.tl.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tl/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Sketchnote ni [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/tl/lessons/1-Intro/README.md b/translations/tl/lessons/1-Intro/README.md index b89efc6f..b1f73ba4 100644 --- a/translations/tl/lessons/1-Intro/README.md +++ b/translations/tl/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panimula sa AI -![Buod ng nilalaman ng Panimula sa AI sa isang doodle](../../../../translated_images/ai-intro.bf28d1ac4235881c.tl.png) +![Buod ng nilalaman ng Panimula sa AI sa isang doodle](../../../../translated_images/tl/ai-intro.bf28d1ac4235881c.png) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Noong una, ang mga computer ay naimbento ni [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) upang mag-operate sa mga numero gamit ang isang malinaw na proseso - isang algorithm. Ang mga modernong computer, kahit na mas advanced kaysa sa orihinal na modelo noong ika-19 na siglo, ay sumusunod pa rin sa parehong ideya ng kontroladong pagkalkula. Kaya't posible na i-program ang isang computer upang gawin ang isang bagay kung alam natin ang eksaktong pagkakasunod-sunod ng mga hakbang na kailangan upang makamit ang layunin. -![Larawan ng isang tao](../../../../translated_images/dsh_age.d212a30d4e54fb5f.tl.png) +![Larawan ng isang tao](../../../../translated_images/tl/dsh_age.d212a30d4e54fb5f.png) > Larawan ni [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Para sa karagdagang impormasyon, tingnan ang **[Artificial General Intelligence] Isa sa mga problema sa pagharap sa terminong **[Katalinuhan](https://en.wikipedia.org/wiki/Intelligence)** ay walang malinaw na depinisyon ng terminong ito. Maaaring sabihin ng iba na ang katalinuhan ay konektado sa **abstraktong pag-iisip**, o sa **sariling kamalayan**, ngunit hindi natin ito maipaliwanag nang maayos. -![Larawan ng Pusa](../../../../translated_images/photo-cat.8c8e8fb760ffe457.tl.jpg) +![Larawan ng Pusa](../../../../translated_images/tl/photo-cat.8c8e8fb760ffe457.jpg) > [Larawan](https://unsplash.com/photos/75715CVEJhI) ni [Amber Kipp](https://unsplash.com/@sadmax) mula sa Unsplash @@ -98,13 +98,13 @@ Sa kabilang banda, maaari nating subukang i-modelo ang pinakasimpleng elemento s > | Paano naman ang ML? | | > |--------------|-----------| -> | Ang bahagi ng Artificial Intelligence na batay sa pag-aaral ng computer upang lutasin ang problema batay sa ilang data ay tinatawag na **Machine Learning**. Hindi natin tatalakayin ang klasikong machine learning sa kursong ito - tinutukoy namin kayo sa hiwalay na [Machine Learning for Beginners](http://aka.ms/ml-beginners) na kurikulum. | ![ML for Beginners](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.tl.png) | +> | Ang bahagi ng Artificial Intelligence na batay sa pag-aaral ng computer upang lutasin ang problema batay sa ilang data ay tinatawag na **Machine Learning**. Hindi natin tatalakayin ang klasikong machine learning sa kursong ito - tinutukoy namin kayo sa hiwalay na [Machine Learning for Beginners](http://aka.ms/ml-beginners) na kurikulum. | ![ML for Beginners](../../../../translated_images/tl/ml-for-beginners.9e4fed176fd5817d.png) | ## Maikling Kasaysayan ng AI Ang Artificial Intelligence ay nagsimula bilang isang larangan noong kalagitnaan ng ikadalawampung siglo. Sa simula, ang symbolic reasoning ay isang pangunahing diskarte, at nagresulta ito sa ilang mahahalagang tagumpay, tulad ng mga expert systems – mga programang computer na kayang kumilos bilang eksperto sa ilang limitadong larangan ng problema. Gayunpaman, kalaunan ay naging malinaw na ang ganitong diskarte ay hindi masyadong epektibo. Ang pagkuha ng kaalaman mula sa isang eksperto, pagrepresenta nito sa computer, at pagpapanatili ng kaalaman na tumpak ay naging napakakomplikado at masyadong mahal upang maging praktikal sa maraming kaso. Ito ang nagresulta sa tinatawag na [AI Winter](https://en.wikipedia.org/wiki/AI_winter) noong dekada 1970. -Maikling Kasaysayan ng AI +Maikling Kasaysayan ng AI > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Katulad nito, makikita natin kung paano nagbago ang diskarte sa paglikha ng “m * Ang mga modernong assistant, tulad ng Cortana, Siri o Google Assistant ay lahat ng hybrid systems na gumagamit ng Neural networks upang i-convert ang pagsasalita sa teksto at kilalanin ang ating intensyon, at pagkatapos ay gumamit ng ilang pangangatwiran o tahasang algorithm upang maisagawa ang mga kinakailangang aksyon. * Sa hinaharap, maaari nating asahan ang isang kumpletong neural-based na modelo upang pangasiwaan ang pag-uusap nang mag-isa. Ang kamakailang GPT at [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) na pamilya ng neural networks ay nagpapakita ng mahusay na tagumpay sa larangang ito. -ebolusyon ng Turing test +ebolusyon ng Turing test > Larawan ni Dmitry Soshnikov, [larawan](https://unsplash.com/photos/r8LmVbUKgns) ni [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Kamakailang Pananaliksik sa AI diff --git a/translations/tl/lessons/2-Symbolic/Animals.ipynb b/translations/tl/lessons/2-Symbolic/Animals.ipynb index c04275d0..fd4b688e 100644 --- a/translations/tl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tl/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Sa halimbawang ito, magpapatupad tayo ng isang simpleng sistema na batay sa kaalaman upang matukoy ang isang hayop batay sa ilang pisikal na katangian. Ang sistema ay maaaring i-representa gamit ang sumusunod na AND-OR tree (ito ay bahagi lamang ng buong puno, madali nating maidaragdag ang iba pang mga patakaran):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.tl.png)\n" + "![](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/tl/lessons/2-Symbolic/README.md b/translations/tl/lessons/2-Symbolic/README.md index 23894399..b5f6679a 100644 --- a/translations/tl/lessons/2-Symbolic/README.md +++ b/translations/tl/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Representasyon ng Kaalaman at Mga Ekspertong Sistema -![Buod ng Nilalaman ng Symbolic AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.tl.png) +![Buod ng Nilalaman ng Symbolic AI](../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.png) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Kadalasan, hindi natin mahigpit na tinutukoy ang kaalaman, ngunit iniuugnay nati Kaya, ang problema ng **representasyon ng kaalaman** ay ang paghahanap ng epektibong paraan upang kumatawan sa kaalaman sa loob ng isang kompyuter sa anyo ng data, upang magamit ito nang awtomatiko. Ito ay maaaring makita bilang isang spectrum: -![Spectrum ng Representasyon ng Kaalaman](../../../../translated_images/knowledge-spectrum.b60df631852c0217.tl.png) +![Spectrum ng Representasyon ng Kaalaman](../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.png) > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | Isa sa mga maagang tagumpay ng symbolic AI ay ang tinatawag na **mga ekspertong sistema** - mga sistema ng kompyuter na idinisenyo upang kumilos bilang isang eksperto sa ilang limitadong domain ng problema. Ang mga ito ay nakabatay sa isang **knowledge base** na kinuha mula sa isa o higit pang mga eksperto, at naglalaman ng isang **inference engine** na gumagawa ng pangangatwiran sa ibabaw nito. -![Arkitektura ng Tao](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.tl.png) | ![Sistema na Batay sa Kaalaman](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.tl.png) +![Arkitektura ng Tao](../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.png) | ![Sistema na Batay sa Kaalaman](../../../../translated_images/tl/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Pinadaling istruktura ng neural system ng tao | Arkitektura ng sistema na batay sa kaalaman @@ -106,7 +106,7 @@ Ang mga ekspertong sistema ay binuo tulad ng sistema ng pangangatwiran ng tao, n Bilang halimbawa, isaalang-alang natin ang sumusunod na ekspertong sistema ng pagtukoy ng isang hayop batay sa mga pisikal na katangian nito: -![AND-OR Tree](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.tl.png) +![AND-OR Tree](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.png) > Larawan ni [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 94134975..4c5ea0f7 100644 --- a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Kung sakaling mayroon tayong higit sa 2 klase, ang softmax ay magno-normalize ng mga probabilidad sa lahat ng mga ito. Narito ang isang diagram ng network architecture na gumagawa ng MNIST digit classification:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.tl.png)\n" + "![MNIST Classifier](../../../../../translated_images/tl/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1250,7 +1250,7 @@ "* Mababa ang training loss - magaling ang model sa pag-approximate ng training data dahil sapat ang expressive power nito.\n", "* Ang validation loss ay maaaring mas mataas kaysa sa training loss at maaaring magsimulang tumaas habang nagte-training - ito ay dahil \"inaalala\" ng model ang mga training points, at nawawala ang \"kabuuang larawan.\"\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.tl.png)\n", + "![Overfitting](../../../../../translated_images/tl/overfit.a0bd57f717c15769.png)\n", "\n", "> Sa larawang ito, ang `x` ay kumakatawan sa training data, at ang `o` ay validation data. Kaliwa - linear model (one-layer), maayos nitong na-aapproximate ang likas na katangian ng data. Kanan - overfitted model, perpektong na-aapproximate ng model ang training data, pero nawawala ang saysay nito sa ibang data (napakataas ng validation error).\n" ] diff --git a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md index 38001bb5..9a3a07f2 100644 --- a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Ang overfitting ay isang napakahalagang konsepto sa machine learning, at napakah Isaalang-alang ang sumusunod na problema ng pag-aapproximate sa 5 puntos (na kinakatawan ng `x` sa mga graph sa ibaba): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.tl.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.tl.jpg) +![linear](../../../../../translated_images/tl/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/tl/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Linear model, 2 parameters** | **Non-linear model, 7 parameters** Training error = 5.3 | Training error = 0 @@ -79,7 +79,7 @@ Napakahalaga na mahanap ang tamang balanse sa pagitan ng dami ng parameters ng m Tulad ng makikita mula sa graph sa itaas, ang overfitting ay maaaring matukoy sa pamamagitan ng napakababang training error, at mataas na validation error. Karaniwan sa panahon ng training, makikita natin ang parehong training at validation errors na nagsisimulang bumaba, at pagkatapos ay sa isang punto maaaring tumigil ang validation error sa pagbaba at magsimulang tumaas. Ito ang magiging senyales ng overfitting, at indikasyon na dapat nating itigil ang training sa puntong ito (o kahit papaano gumawa ng snapshot ng model). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.tl.png) +![overfitting](../../../../../translated_images/tl/Overfitting.408ad91cd90b4371.png) ## Paano maiwasan ang overfitting diff --git a/translations/tl/lessons/3-NeuralNetworks/README.md b/translations/tl/lessons/3-NeuralNetworks/README.md index c3585b84..8e786b96 100644 --- a/translations/tl/lessons/3-NeuralNetworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panimula sa Neural Networks -![Buod ng nilalaman ng Intro Neural Networks sa isang doodle](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.tl.png) +![Buod ng nilalaman ng Intro Neural Networks sa isang doodle](../../../../translated_images/tl/ai-neuralnetworks.1c687ae40bc86e83.png) Tulad ng tinalakay natin sa panimula, isa sa mga paraan upang makamit ang katalinuhan ay ang pagsasanay ng isang **modelo ng computer** o isang **artipisyal na utak**. Simula noong kalagitnaan ng ika-20 siglo, sinubukan ng mga mananaliksik ang iba't ibang mga matematikal na modelo, hanggang sa mga nakaraang taon kung saan ang direksyong ito ay napatunayang napaka-epektibo. Ang ganitong mga matematikal na modelo ng utak ay tinatawag na **neural networks**. @@ -36,13 +36,13 @@ Sa kurikulum na ito, magtutuon lamang tayo sa mga modelo ng neural network. Mula sa biology, alam natin na ang ating utak ay binubuo ng mga neural cells (neurons), bawat isa ay may maraming "inputs" (dendrites) at isang "output" (axon). Parehong dendrites at axons ay maaaring magdala ng mga signal na elektrikal, at ang mga koneksyon sa pagitan nila — na kilala bilang synapses — ay maaaring magpakita ng iba't ibang antas ng conductivity, na kinokontrol ng mga neurotransmitters. -![Modelo ng Isang Neuron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.tl.jpg) | ![Modelo ng Isang Neuron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.tl.png) +![Modelo ng Isang Neuron](../../../../translated_images/tl/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Modelo ng Isang Neuron](../../../../translated_images/tl/artneuron.1a5daa88d20ebe6f.png) ----|---- Tunay na Neuron *([Larawan](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) mula sa Wikipedia)* | Artipisyal na Neuron *(Larawan ng May-akda)* Kaya, ang pinakasimpleng matematikal na modelo ng isang neuron ay naglalaman ng ilang inputs X1, ..., XN at isang output Y, at isang serye ng mga weights W1, ..., WN. Ang output ay kinakalkula bilang: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) kung saan ang f ay isang non-linear na **activation function**. diff --git a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md index ceedec86..6b66a11a 100644 --- a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Sa aming [OpenCV Notebook](OpenCV.ipynb), nagbibigay kami ng ilang mga halimbawa * **Pre-processing ng larawan ng isang Braille book**. Nakatuon kami sa kung paano namin magagamit ang thresholding, feature detection, perspective transformation, at NumPy manipulations upang paghiwalayin ang mga indibidwal na Braille symbols para sa karagdagang classification ng isang neural network. -![Braille Image](../../../../../translated_images/braille.341962ff76b1bd70.tl.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/braille-result.46530fea020b03c7.tl.png) | ![Braille Symbols](../../../../../translated_images/braille-symbols.0159185ab69d5339.tl.png) +![Braille Image](../../../../../translated_images/tl/braille.341962ff76b1bd70.jpeg) | ![Braille Image Pre-processed](../../../../../translated_images/tl/braille-result.46530fea020b03c7.png) | ![Braille Symbols](../../../../../translated_images/tl/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) * **Pagtuklas ng galaw sa video gamit ang frame difference**. Kung ang camera ay nakapirmi, ang mga frame mula sa camera feed ay dapat medyo magkatulad sa isa't isa. Dahil ang mga frame ay kinakatawan bilang arrays, sa pamamagitan lamang ng pagbabawas ng mga arrays para sa dalawang magkasunod na frame ay makakakuha tayo ng pixel difference, na dapat mababa para sa static frames, at magiging mas mataas kapag may makabuluhang galaw sa imahe. -![Image of video frames and frame differences](../../../../../translated_images/frame-difference.706f805491a0883c.tl.png) +![Image of video frames and frame differences](../../../../../translated_images/tl/frame-difference.706f805491a0883c.png) > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Sa aming [OpenCV Notebook](OpenCV.ipynb), nagbibigay kami ng ilang mga halimbawa - **Dense Optical Flow** ay kinakalkula ang vector field na nagpapakita kung saan gumagalaw ang bawat pixel - **Sparse Optical Flow** ay batay sa pagkuha ng ilang natatanging features sa imahe (hal. edges), at pagbuo ng kanilang trajectory mula frame to frame. -![Image of Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.tl.png) +![Image of Optical Flow](../../../../../translated_images/tl/optical.1f4a94464579a83a.png) > Imahe mula sa [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 1fe0d312..f1093dd7 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: Ang VGG-16 ay isang network na nakamit ang 92.7% na katumpakan sa ImageNet top-5 classification noong 2014. Ito ay may ganitong istruktura ng mga layer: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tl.jpg) +![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.jpg) Tulad ng nakikita mo, sinusunod ng VGG ang tradisyunal na pyramid architecture, na isang sunod-sunod na convolution-pooling layers. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tl.jpg) +![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.jpg) > Larawan mula sa [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 0a059a92..cdbd50b2 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Kaya, sa isang tipikal na CNN, magkakaroon ng ilang convolutional layers, na may pooling layers sa pagitan ng mga ito upang bawasan ang sukat ng imahe. Dagdag pa rito, pinapataas natin ang bilang ng mga filter, dahil habang nagiging mas advanced ang mga pattern - mas maraming posibleng kombinasyon na kailangang hanapin.\n", "\n", - "![Isang larawan na nagpapakita ng ilang convolutional layers na may pooling layers.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.tl.png)\n", + "![Isang larawan na nagpapakita ng ilang convolutional layers na may pooling layers.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Dahil sa pagbawas ng spatial dimensions at pagtaas ng feature/filter dimensions, ang arkitekturang ito ay tinatawag ding **pyramid architecture**.\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index fc89febe..6d36dd45 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Kaya, sa isang tipikal na CNN, magkakaroon ng ilang convolutional layer, na may mga pooling layer sa pagitan ng mga ito upang mabawasan ang dimensyon ng imahe. Dagdag pa rito, pinapataas natin ang bilang ng mga filter, dahil habang nagiging mas kumplikado ang mga pattern, mas maraming posibleng interesanteng kombinasyon ang kailangang hanapin.\n", "\n", - "![Isang larawan na nagpapakita ng ilang convolutional layer na may pooling layer.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.tl.png)\n", + "![Isang larawan na nagpapakita ng ilang convolutional layer na may pooling layer.](../../../../../translated_images/tl/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Dahil sa pagbawas ng spatial na dimensyon at pagtaas ng feature/filter na dimensyon, ang arkitekturang ito ay tinatawag ding **pyramid architecture**.\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md index 309ff802..8b3c16f8 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Sa totoong buhay, gusto nating makilala ang mga bagay sa isang larawan kahit saa Upang makuha ang mga pattern, gagamit tayo ng konsepto ng **convolutional filters**. Tulad ng alam mo, ang isang imahe ay kinakatawan ng isang 2D-matrix, o isang 3D-tensor na may color depth. Ang pag-aapply ng filter ay nangangahulugan na kukuha tayo ng medyo maliit na **filter kernel** matrix, at para sa bawat pixel sa orihinal na imahe, kinakalkula natin ang weighted average kasama ang mga kalapit na puntos. Maaari nating tingnan ito bilang isang maliit na bintana na gumagalaw sa buong imahe, at ina-average ang lahat ng pixels ayon sa mga weights sa filter kernel matrix. -![Vertical Edge Filter](../../../../../translated_images/filter-vert.b7148390ca0bc356.tl.png) | ![Horizontal Edge Filter](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.tl.png) +![Vertical Edge Filter](../../../../../translated_images/tl/filter-vert.b7148390ca0bc356.png) | ![Horizontal Edge Filter](../../../../../translated_images/tl/filter-horiz.59b80ed4feb946ef.png) ----|---- > Larawan ni Dmitry Soshnikov @@ -38,7 +38,7 @@ Ang paraan ng paggana ng CNN ay batay sa mga sumusunod na mahalagang ideya: * Maaari nating i-disensyo ang network sa paraang ang filters ay matututo nang awtomatiko * Maaari nating gamitin ang parehong paraan upang hanapin ang mga pattern sa high-level features, hindi lamang sa orihinal na imahe. Kaya ang feature extraction ng CNN ay gumagana sa isang hierarchy ng features, simula sa low-level pixel combinations, hanggang sa mas mataas na level na kombinasyon ng mga bahagi ng larawan. -![Hierarchical Feature Extraction](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.tl.png) +![Hierarchical Feature Extraction](../../../../../translated_images/tl/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Larawan mula sa [isang papel ni Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), batay sa [kanilang pananaliksik](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Karamihan sa mga CNN na ginagamit para sa pagproseso ng imahe ay sumusunod sa ti Halimbawa, tingnan natin ang arkitektura ng VGG-16, isang network na nakamit ang 92.7% accuracy sa ImageNet's top-5 classification noong 2014: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tl.jpg) +![ImageNet Layers](../../../../../translated_images/tl/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tl.jpg) +![ImageNet Pyramid](../../../../../translated_images/tl/vgg-16-arch.64ff2137f50dd49f.jpg) > Larawan mula sa [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index cdd0a6e8..cb273ec3 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Kailangan mong sanayin ang isang convolutional neural network upang uriin ang ib Gagamitin natin ang [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), na naglalaman ng mga larawan ng 37 iba't ibang lahi ng aso at pusa. -![Dataset na gagamitin natin](../../../../../../translated_images/data.50b2a9d5484bdbf0.tl.png) +![Dataset na gagamitin natin](../../../../../../translated_images/tl/data.50b2a9d5484bdbf0.png) Upang i-download ang dataset, gamitin ang code snippet na ito: diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index fdd4f26c..ffddfbd8 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Upang maipakita ang ideal na pusa, magsisimula tayo sa isang random na noise image, at susubukan nating gamitin ang gradient descent optimization technique upang ayusin ang imahe para makilala ng network ang isang pusa.\n", "\n", - "![Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.tl.png)\n", + "![Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Narito ang ating panimulang imahe:\n" ] diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md index 0b709da4..249ab3a4 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Parehong Keras at PyTorch ay may mga function upang madaling ma-load ang pre-tra Narito ang mga sample features na na-extract mula sa larawan ng isang pusa gamit ang VGG-16 network: -![Features extracted by VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.tl.png) +![Features extracted by VGG-16](../../../../../translated_images/tl/features.6291f9c7ba3a0b95.png) ## Dataset ng Cats vs. Dogs @@ -48,19 +48,19 @@ Ang pre-trained neural network ay naglalaman ng iba't ibang patterns sa loob ng Isang approach na maaari nating gawin ay magsimula sa isang random na imahe, at pagkatapos ay subukang gamitin ang **gradient descent optimization** technique upang i-adjust ang imahe na iyon sa paraang magsisimula ang network na isipin na ito ay isang pusa. -![Image Optimization Loop](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.tl.png) +![Image Optimization Loop](../../../../../translated_images/tl/ideal-cat-loop.999fbb8ff306e044.png) Gayunpaman, kung gagawin natin ito, makakakuha tayo ng isang bagay na halos katulad ng random noise. Ito ay dahil *maraming paraan upang mag-isip ang network na ang input image ay isang pusa*, kabilang ang ilan na hindi makatuwiran sa visual. Bagama't ang mga imahe ay naglalaman ng maraming patterns na tipikal para sa isang pusa, walang anumang constraint upang maging visually distinctive ang mga ito. Upang mapabuti ang resulta, maaari tayong magdagdag ng isa pang term sa loss function, na tinatawag na **variation loss**. Ito ay isang metric na nagpapakita kung gaano kahawig ang mga magkatabing pixels ng imahe. Ang pag-minimize ng variation loss ay nagpapakinis sa imahe, at nag-aalis ng noise - kaya mas naipapakita ang mas visually appealing patterns. Narito ang halimbawa ng mga "ideal" na imahe, na na-classify bilang pusa at zebra na may mataas na probability: -![Ideal Cat](../../../../../translated_images/ideal-cat.203dd4597643d6b0.tl.png) | ![Ideal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.tl.png) +![Ideal Cat](../../../../../translated_images/tl/ideal-cat.203dd4597643d6b0.png) | ![Ideal Zebra](../../../../../translated_images/tl/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ideal Cat* | *Ideal Zebra* Ang parehong approach ay maaaring gamitin upang magsagawa ng tinatawag na **adversarial attacks** sa isang neural network. Halimbawa, gusto nating linlangin ang isang neural network at gawing mukhang pusa ang isang aso. Kung kukunin natin ang imahe ng aso, na kinikilala ng network bilang aso, maaari natin itong i-tweak nang kaunti gamit ang gradient descent optimization, hanggang sa magsimulang i-classify ito ng network bilang pusa: -![Picture of a Dog](../../../../../translated_images/original-dog.8f68a67d2fe0911f.tl.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.tl.png) +![Picture of a Dog](../../../../../translated_images/tl/original-dog.8f68a67d2fe0911f.png) | ![Picture of a dog classified as a cat](../../../../../translated_images/tl/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Orihinal na larawan ng aso* | *Larawan ng aso na na-classify bilang pusa* diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index edddb797..8fc240bc 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Dahil sinasanay natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na larawan para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tl.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Larawan mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index a48d9c5b..d5ee5530 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Dahil sinasanay natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na larawan para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito.\n", "\n", - "![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tl.jpg)\n", + "![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Larawan mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md index 788ed321..886a09ac 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Gayunpaman, maaaring gusto nating gamitin ang raw (unlabeled) na data para sa pa Dahil tina-train natin ang autoencoder upang makuha ang pinakamaraming impormasyon mula sa orihinal na imahe para sa tumpak na reconstruction, sinusubukan ng network na hanapin ang pinakamahusay na **embedding** ng input images upang makuha ang kahulugan nito. -![AutoEncoder Diagram](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tl.jpg) +![AutoEncoder Diagram](../../../../../translated_images/tl/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Imahe mula sa [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md index 8a971f88..c708e534 100644 --- a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Ang mga modelo ng image classification na ating tinalakay hanggang ngayon ay tum ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Pag-detect ng Objekto](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.tl.png) +![Pag-detect ng Objekto](../../../../../translated_images/tl/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Larawan mula sa [YOLO v2 web site](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Kung nais nating hanapin ang isang pusa sa isang larawan, ang isang napakasimple 2. Patakbuhin ang image classification sa bawat tile. 3. Ang mga tile na may sapat na mataas na activation ay maaaring ituring na naglalaman ng hinahanap na objekto. -![Simpleng Pag-detect ng Objekto](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.tl.png) +![Simpleng Pag-detect ng Objekto](../../../../../translated_images/tl/naive-detection.e7f1ba220ccd08c6.png) > *Larawan mula sa [Exercise Notebook](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Maaaring makatagpo ka ng mga sumusunod na dataset para sa gawaing ito: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 klase * [COCO](http://cocodataset.org/#home) - Common Objects in Context. 80 klase, bounding boxes, at segmentation masks -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.tl.jpg) +![COCO](../../../../../translated_images/tl/coco-examples.71bc60380fa6cceb.jpg) ## Mga Sukatan para sa Pag-detect ng Objekto @@ -50,7 +50,7 @@ Maaaring makatagpo ka ng mga sumusunod na dataset para sa gawaing ito: Habang madali ang pagsukat ng performance ng algorithm sa image classification, sa pag-detect ng objekto kailangan nating sukatin ang tamang klase pati na rin ang eksaktong lokasyon ng inferred bounding box. Para sa huli, ginagamit natin ang tinatawag na **Intersection over Union** (IoU), na sumusukat kung gaano kahusay ang overlap ng dalawang kahon (o dalawang arbitrary na lugar). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.tl.png) +![IoU](../../../../../translated_images/tl/iou_equation.9a4751d40fff4e11.png) > *Figure 2 mula sa [napakagandang blog post na ito tungkol sa IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ May dalawang malawak na klase ng mga algorithm sa pag-detect ng objekto: Ang [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ay gumagamit ng [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) upang makabuo ng hierarchical structure ng mga ROI region, na pagkatapos ay ipinapasa sa CNN feature extractors at SVM-classifiers upang matukoy ang klase ng objekto, at linear regression upang matukoy ang mga coordinate ng *bounding box*. [Opisyal na Papel](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.tl.png) +![RCNN](../../../../../translated_images/tl/rcnn1.cae407020dfb1d1f.png) > *Larawan mula kay van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.tl.png) +![RCNN-1](../../../../../translated_images/tl/rcnn2.2d9530bb83516484.png) > *Mga larawan mula sa [blog na ito](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) @@ -110,7 +110,7 @@ Ang [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) ay gu Ang pamamaraang ito ay katulad ng R-CNN, ngunit ang mga region ay tinutukoy pagkatapos ma-apply ang convolution layers. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.tl.png) +![FRCNN](../../../../../translated_images/tl/f-rcnn.3cda6d9bb4188875.png) > Larawan mula sa [Opisyal na Papel](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Ang pamamaraang ito ay katulad ng R-CNN, ngunit ang mga region ay tinutukoy pagk Ang pangunahing ideya ng pamamaraang ito ay ang paggamit ng neural network upang mahulaan ang mga ROI - tinatawag na *Region Proposal Network*. [Papel](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.tl.png) +![FasterRCNN](../../../../../translated_images/tl/faster-rcnn.8d46c099b87ef30a.png) > Larawan mula sa [opisyal na papel](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Ang algorithm na ito ay mas mabilis pa kaysa sa Faster R-CNN. Ang pangunahing id 1. Ang mga feature ay pinoproseso ng **Position-Sensitive Score Map**. Ang bawat objekto mula sa $C$ klase ay hinahati sa $k\times k$ na mga region, at sinasanay upang mahulaan ang mga bahagi ng mga objekto. 1. Para sa bawat bahagi mula sa $k\times k$ na mga region, lahat ng network ay bumoboto para sa klase ng objekto, at ang klase ng objekto na may pinakamataas na boto ang pinipili. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.tl.png) +![r-fcn image](../../../../../translated_images/tl/r-fcn.13eb88158b99a3da.png) > Larawan mula sa [opisyal na papel](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ Ang YOLO ay isang realtime one-pass algorithm. Ang pangunahing ideya ay ang sumu * Ang larawan ay hinahati sa $S\times S$ na mga region. * Para sa bawat region, **CNN** ay hinuhulaan ang $n$ posibleng mga objekto, *bounding box* coordinates, at *confidence*=*probability* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.tl.png) + ![YOLO](../../../../../translated_images/tl/yolo.a2648ec82ee8bb4e.png) > Larawan mula sa [opisyal na papel](https://arxiv.org/abs/1506.02640) diff --git a/translations/tl/lessons/4-ComputerVision/README.md b/translations/tl/lessons/4-ComputerVision/README.md index 70be2417..7f4dd5ed 100644 --- a/translations/tl/lessons/4-ComputerVision/README.md +++ b/translations/tl/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Computer Vision -![Buod ng nilalaman ng Computer Vision sa isang doodle](../../../../translated_images/ai-computervision.6506ebebac3fbf76.tl.png) +![Buod ng nilalaman ng Computer Vision sa isang doodle](../../../../translated_images/tl/ai-computervision.6506ebebac3fbf76.png) Sa seksyong ito, matututo tayo tungkol sa: diff --git a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 241e9811..173e0db5 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "Ang **Bag of Words** (BoW) na representasyon ng vector ay ang pinakakaraniwang ginagamit na tradisyunal na representasyon ng vector. Ang bawat salita ay naka-link sa isang index ng vector, at ang elemento ng vector ay naglalaman ng bilang ng paglitaw ng isang salita sa isang partikular na dokumento.\n", "\n", - "![Larawan na nagpapakita kung paano kinakatawan ang bag of words na representasyon ng vector sa memorya.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.tl.png) \n", + "![Larawan na nagpapakita kung paano kinakatawan ang bag of words na representasyon ng vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Maaari mo ring isipin ang BoW bilang kabuuan ng lahat ng one-hot-encoded na mga vector para sa bawat indibidwal na salita sa teksto.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index d918838d..c60628e5 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/tl/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Ang **Bag-of-words** (BoW) na representasyon ng vector ay ang pinakasimple at madaling maunawaan na tradisyunal na representasyon ng vector. Ang bawat salita ay nauugnay sa isang index ng vector, at ang isang elemento ng vector ay naglalaman ng bilang ng paglitaw ng bawat salita sa isang partikular na dokumento.\n", "\n", - "![Larawan na nagpapakita kung paano kinakatawan ang bag-of-words vector sa memorya.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.tl.png) \n", + "![Larawan na nagpapakita kung paano kinakatawan ang bag-of-words vector sa memorya.](../../../../../translated_images/tl/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Maaari mo ring isipin ang BoW bilang kabuuan ng lahat ng one-hot-encoded vectors para sa bawat indibidwal na salita sa teksto.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 63df080f..7a93dd84 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Sa paggamit ng embedding layer bilang unang layer sa ating network, maaari tayong lumipat mula sa bag-of-words patungo sa **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang isang aggregate function sa lahat ng mga embeddings na iyon, tulad ng `sum`, `average`, o `max`.\n", "\n", - "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tl.png)\n", + "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Ang ating classifier neural network ay magsisimula sa embedding layer, pagkatapos ay aggregation layer, at linear classifier sa ibabaw nito:\n" ] @@ -176,7 +176,7 @@ "\n", "Sa nakaraang arkitektura, kinakailangan nating i-pad ang lahat ng sequence upang magkapareho ang haba para magkasya ang mga ito sa isang minibatch. Hindi ito ang pinakaepektibong paraan upang i-representa ang mga sequence na may iba't ibang haba - isang alternatibong paraan ay ang paggamit ng **offset** vector, na maglalaman ng mga offset ng lahat ng sequence na nakaimbak sa isang malaking vector.\n", "\n", - "![Larawan na nagpapakita ng offset sequence representation](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.tl.png)\n", + "![Larawan na nagpapakita ng offset sequence representation](../../../../../translated_images/tl/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Sa larawan sa itaas, ipinapakita namin ang isang sequence ng mga karakter, ngunit sa ating halimbawa, nagtatrabaho tayo sa mga sequence ng salita. Gayunpaman, ang pangkalahatang prinsipyo ng pagre-representa ng mga sequence gamit ang offset vector ay nananatiling pareho.\n", "\n", @@ -311,7 +311,7 @@ "\n", "Mas mabilis ang CBoW, habang ang skip-gram ay mas mabagal, ngunit mas mahusay sa pag-representa ng mga bihirang salita.\n", "\n", - "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa mga vector.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tl.png)\n", + "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa mga vector.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Upang mag-eksperimento gamit ang word2vec embedding na pre-trained sa Google News dataset, maaari nating gamitin ang **gensim** library. Sa ibaba, makikita natin ang mga salitang pinakamalapit sa 'neural'.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a06868b5..a0e080ca 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/tl/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Sa pamamagitan ng paggamit ng embedding layer bilang unang layer sa ating network, maaari tayong lumipat mula sa bag-of-words patungo sa isang **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang isang aggregate function sa lahat ng mga embeddings na iyon, tulad ng `sum`, `average`, o `max`.\n", "\n", - "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tl.png)\n", + "![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence na salita.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Ang ating classifier neural network ay binubuo ng mga sumusunod na layer:\n", "\n", @@ -283,7 +283,7 @@ "\n", "Mas mabilis ang CBoW, ngunit habang mas mabagal ang skip-gram, mas mahusay ito sa pagrepresenta ng mga bihirang salita.\n", "\n", - "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tl.png)\n", + "![Larawan na nagpapakita ng parehong CBoW at Skip-Gram na mga algorithm para i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Upang mag-eksperimento gamit ang Word2Vec embedding na pre-trained sa Google News dataset, maaari nating gamitin ang **gensim** library. Sa ibaba, makikita natin ang mga salitang pinakamalapit sa 'neural'.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/README.md b/translations/tl/lessons/5-NLP/14-Embeddings/README.md index a67236ac..a6c84c8a 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tl/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Ang embedding layer ay kukuha ng isang salita bilang input, at magbibigay ng out Sa paggamit ng embedding layer bilang unang layer sa ating classifier network, maaari tayong lumipat mula sa bag-of-words patungo sa **embedding bag** model, kung saan una nating kino-convert ang bawat salita sa ating teksto sa kaukulang embedding, at pagkatapos ay kinakalkula ang ilang aggregate function sa lahat ng mga embedding na iyon, tulad ng `sum`, `average`, o `max`. -![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence words.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tl.png) +![Larawan na nagpapakita ng isang embedding classifier para sa limang sequence words.](../../../../../translated_images/tl/embedding-classifier-example.b77f021a7ee67eee.png) > Larawan mula sa may-akda @@ -40,7 +40,7 @@ Upang magawa ito, kailangan nating i-pre-train ang ating embedding model sa isan Mas mabilis ang CBoW, habang ang skip-gram ay mas mabagal, ngunit mas mahusay sa pagrepresenta ng mga bihirang salita. -![Larawan na nagpapakita ng parehong CBoW at Skip-Gram algorithms upang i-convert ang mga salita sa vectors.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tl.png) +![Larawan na nagpapakita ng parehong CBoW at Skip-Gram algorithms upang i-convert ang mga salita sa vectors.](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Larawan mula sa [papel na ito](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md index 55e4cd59..da18401b 100644 --- a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Sa mga naunang halimbawa, gumamit tayo ng mga pre-trained na semantic embeddings * **Continuous Bag-of-Words** (CBoW), kung saan hinuhulaan natin ang gitnang token $W_0$ sa isang hanay ng mga token $W_{-N}$, ..., $W_N$. * **Skip-gram**, kung saan hinuhulaan natin ang isang hanay ng mga kalapit na token {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} mula sa gitnang token $W_0$. -![larawan mula sa papel tungkol sa pag-convert ng mga salita sa vectors](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tl.png) +![larawan mula sa papel tungkol sa pag-convert ng mga salita sa vectors](../../../../../translated_images/tl/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Larawan mula sa [papel na ito](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tl/lessons/5-NLP/16-RNN/README.md b/translations/tl/lessons/5-NLP/16-RNN/README.md index 4d08de6c..ba664820 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/README.md +++ b/translations/tl/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Sa mga nakaraang seksyon, gumamit tayo ng mas mayamang semantic na representasyo Upang makuha ang kahulugan ng pagkakasunod-sunod ng teksto, kailangan nating gumamit ng ibang arkitektura ng neural network, na tinatawag na **recurrent neural network**, o RNN. Sa RNN, ipinapasa natin ang ating pangungusap sa network nang paisa-isang simbolo, at ang network ay gumagawa ng isang **estado**, na pagkatapos ay ipinapasa natin muli sa network kasama ang susunod na simbolo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.tl.png) +![RNN](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.png) > Larawan mula sa may-akda @@ -61,7 +61,7 @@ Napag-usapan natin ang mga recurrent networks na gumagana sa isang direksyon, mu Ang isang Recurrent network, alinman sa one-directional o bidirectional, ay kumukuha ng ilang patterns sa loob ng isang sequence, at maaaring i-store ang mga ito sa isang state vector o ipasa sa output. Tulad ng convolutional networks, maaari tayong bumuo ng isa pang recurrent layer sa ibabaw ng una upang makuha ang mas mataas na level na patterns at bumuo mula sa low-level patterns na nakuha ng unang layer. Ito ay humahantong sa konsepto ng isang **multi-layer RNN** na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input. -![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tl.jpg) +![Image showing a Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López* diff --git a/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 4d9da729..ffed0adf 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Ang recurrent network, one-directional man o bidirectional, ay nakakakuha ng ilang mga pattern sa loob ng isang sequence, at maaaring itago ang mga ito sa state vector o ipasa sa output. Tulad ng convolutional networks, maaari tayong magtayo ng isa pang recurrent layer sa ibabaw ng una upang makuha ang mas mataas na antas ng mga pattern, na binuo mula sa mga low-level pattern na nakuha ng unang layer. Ito ang nagdadala sa atin sa konsepto ng **multi-layer RNN**, na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input.\n", "\n", - "![Larawan na nagpapakita ng Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tl.jpg)\n", + "![Larawan na nagpapakita ng Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López*\n", "\n", diff --git a/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb index 8497601e..74964141 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/tl/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Upang makuha ang kahulugan ng isang sunod-sunod na teksto, gagamit tayo ng arkitektura ng neural network na tinatawag na **recurrent neural network**, o RNN. Kapag gumagamit ng RNN, ipinapasa natin ang ating pangungusap sa network nang paisa-isang token, at ang network ay gumagawa ng isang **estado**, na pagkatapos ay ipinapasa muli sa network kasama ang susunod na token.\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng pagbuo ng recurrent neural network.](../../../../../translated_images/rnn.27f5c29c53d727b5.tl.png)\n", + "![Larawan na nagpapakita ng halimbawa ng pagbuo ng recurrent neural network.](../../../../../translated_images/tl/rnn.27f5c29c53d727b5.png)\n", "\n", "Sa ibinigay na input sequence ng mga token $X_0,\\dots,X_n$, ang RNN ay lumilikha ng isang sunod-sunod na mga neural network block, at sinasanay ang sequence na ito mula simula hanggang dulo gamit ang backpropagation. Ang bawat network block ay tumatanggap ng pares $(X_i,S_i)$ bilang input, at gumagawa ng $S_{i+1}$ bilang resulta. Ang huling estado $S_n$ o output $Y_n$ ay ipinapasa sa isang linear classifier upang makabuo ng resulta. Ang lahat ng network block ay may parehong weights, at sinasanay mula simula hanggang dulo gamit ang isang backpropagation pass.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Ang mga recurrent network, unidirectional man o bidirectional, ay kumukuha ng mga pattern sa loob ng isang sequence, at iniimbak ang mga ito sa state vectors o ibinabalik bilang output. Tulad ng convolutional networks, maaari tayong magtayo ng isa pang recurrent layer kasunod ng una upang makuha ang mas mataas na antas ng mga pattern, na binuo mula sa mas mababang antas ng mga pattern na nakuha ng unang layer. Ito ang nagdadala sa atin sa konsepto ng isang **multi-layer RNN**, na binubuo ng dalawa o higit pang recurrent networks, kung saan ang output ng nakaraang layer ay ipinapasa sa susunod na layer bilang input.\n", "\n", - "![Larawan na nagpapakita ng isang Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tl.jpg)\n", + "![Larawan na nagpapakita ng isang Multilayer long-short-term-memory- RNN](../../../../../translated_images/tl/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Larawan mula sa [napakagandang post na ito](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) ni Fernando López.*\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index a79c95bd..91653a59 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Ang paraan ng pagsasanay natin sa RNN upang makabuo ng teksto ay ang sumusunod. Sa bawat hakbang, kukuha tayo ng isang sequence ng mga karakter na may haba na `nchars`, at hihilingin sa network na bumuo ng susunod na output na karakter para sa bawat input na karakter:\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng RNN na bumubuo ng salitang 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tl.png)\n", + "![Larawan na nagpapakita ng halimbawa ng RNN na bumubuo ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Depende sa aktwal na sitwasyon, maaaring gusto rin nating isama ang ilang espesyal na karakter, tulad ng *end-of-sequence* ``. Sa ating kaso, nais lamang nating sanayin ang network para sa walang katapusang pagbuo ng teksto, kaya't itatakda natin ang laki ng bawat sequence na maging katumbas ng `nchars` na mga token. Dahil dito, ang bawat halimbawa ng pagsasanay ay binubuo ng `nchars` na mga input at `nchars` na mga output (na siyang input sequence na inilipat ng isang simbolo sa kaliwa). Ang minibatch ay binubuo ng ilang ganitong mga sequence.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 821dcd92..a4090f3e 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Ang paraan ng pagsasanay natin sa RNN upang lumikha ng mga pamagat ng balita ay ganito. Sa bawat hakbang, kukuha tayo ng isang pamagat, na ipapasok sa isang RNN, at para sa bawat input na karakter, hihilingin natin sa network na lumikha ng susunod na output na karakter:\n", "\n", - "![Larawan na nagpapakita ng halimbawa ng RNN na lumilikha ng salitang 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tl.png)\n", + "![Larawan na nagpapakita ng halimbawa ng RNN na lumilikha ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Para sa huling karakter ng ating sequence, hihilingin natin sa network na lumikha ng `` token.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md index 0c832c04..bfd2c686 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Sa RNN architecture na tinalakay natin sa nakaraang unit, bawat RNN unit ay guma Ito ay nagbibigay-daan para sa iba't ibang neural architectures na ipinapakita sa larawan sa ibaba: -![Larawan na nagpapakita ng mga karaniwang pattern ng recurrent neural network.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tl.jpg) +![Larawan na nagpapakita ng mga karaniwang pattern ng recurrent neural network.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Larawan mula sa blog post [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ni [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Sa unit na ito, magpo-focus tayo sa simpleng generative models na tumutulong sa Sasanayin natin ang RNN na ito upang mag-generate ng teksto hakbang-hakbang. Sa bawat hakbang, kukuha tayo ng isang sequence ng mga character na may haba na `nchars`, at hihilingin sa network na mag-generate ng susunod na output character para sa bawat input character: -![Larawan na nagpapakita ng halimbawa ng RNN generation ng salitang 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tl.png) +![Larawan na nagpapakita ng halimbawa ng RNN generation ng salitang 'HELLO'.](../../../../../translated_images/tl/rnn-generate.56c54afb52f9781d.png) Kapag nag-generate ng teksto (sa panahon ng inference), magsisimula tayo sa isang **prompt**, na ipapasa sa RNN cells upang mag-generate ng intermediate state nito, at pagkatapos mula sa state na ito magsisimula ang generation. Mag-generate tayo ng isang character sa bawat pagkakataon, at ipapasa ang state at ang generated character sa isa pang RNN cell upang mag-generate ng susunod, hanggang sa makabuo tayo ng sapat na mga character. diff --git a/translations/tl/lessons/5-NLP/18-Transformers/README.md b/translations/tl/lessons/5-NLP/18-Transformers/README.md index 099f991f..3c22b910 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tl/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Sa RNNs, ang sequence-to-sequence ay ipinatutupad gamit ang dalawang recurrent n Ang **Attention Mechanisms** ay nagbibigay ng paraan upang timbangin ang contextual na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate states ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol yt, isasaalang-alang natin ang lahat ng input hidden states hi, na may iba't ibang weight coefficients αt,i. -![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tl.png) +![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png) > Ang encoder-decoder model na may additive attention mechanism sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ang attention matrix {αi,j} ay kumakatawan sa antas kung paano ang ilang input words ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang isang halimbawa ng ganitong matrix: -![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, mula sa Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tl.png) +![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png) > Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) @@ -66,7 +66,7 @@ Ang resulta na nakukuha natin sa positional embedding ay nag-e-embed sa parehong Susunod, kailangan nating makuha ang ilang mga pattern sa loob ng ating sequence. Upang gawin ito, gumagamit ang transformers ng **self-attention** mechanism, na mahalagang atensyon na inilapat sa parehong sequence bilang input at output. Ang pag-aapply ng self-attention ay nagbibigay-daan sa atin na isaalang-alang ang **context** sa loob ng pangungusap, at makita kung aling mga salita ang magkakaugnay. Halimbawa, pinapayagan tayo nitong makita kung aling mga salita ang tinutukoy ng mga coreferences, tulad ng *it*, at isaalang-alang din ang konteksto: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.tl.png) +![](../../../../../translated_images/tl/CoreferenceResolution.861924d6d384a7d6.png) > Larawan mula sa [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Dahil ang bawat input position ay na-map nang independyente sa bawat output posi Ang **BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layers para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang pre-trained sa isang malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpredikta ng mga masked words sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay sumisipsip ng makabuluhang antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset gamit ang fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**. -![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tl.png) +![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Larawan [source](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index e6b686ea..c184c644 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/tl/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "Ang **Mekanismo ng Atensyon** ay nagbibigay ng paraan upang timbangin ang kontekstwal na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate state ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol $y_t$, isasaalang-alang natin ang lahat ng input hidden states $h_i$, na may iba't ibang timbang na coefficients $\\alpha_{t,i}$.\n", "\n", - "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tl.png) \n", + "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png) \n", "*Ang encoder-decoder model na may additive attention mechanism mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), na binanggit mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ang attention matrix $\\{\\alpha_{i,j}\\}$ ay kumakatawan sa antas kung saan ang ilang input na salita ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang halimbawa ng ganitong matrix:\n", "\n", - "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tl.png) \n", + "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png) \n", "\n", "*Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "Ang **BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layer para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang pre-trained sa malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpapredikta ng mga masked na salita sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay sumisipsip ng makabuluhang antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset gamit ang fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**.\n", "\n", - "![Larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tl.png) \n", + "![Larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) \n", "\n", "Maraming mga bersyon ng Transformer architectures kabilang ang BERT, DistilBERT, BigBird, OpenGPT3, at iba pa na maaaring i-fine tune. Ang [HuggingFace package](https://github.com/huggingface/) ay nagbibigay ng repository para sa pag-train ng marami sa mga arkitekturang ito gamit ang PyTorch.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index ef688677..75e61567 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/tl/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "Ang **Mekanismo ng Atensyon** ay nagbibigay ng paraan upang timbangin ang kontekstwal na epekto ng bawat input vector sa bawat output prediction ng RNN. Ang paraan ng pagpapatupad nito ay sa pamamagitan ng paglikha ng mga shortcut sa pagitan ng mga intermediate state ng input RNN at output RNN. Sa ganitong paraan, kapag gumagawa ng output symbol $y_t$, isasaalang-alang natin ang lahat ng input hidden states $h_i$, na may iba't ibang weight coefficients $\\alpha_{t,i}$. \n", "\n", - "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tl.png)\n", + "![Larawan na nagpapakita ng encoder/decoder model na may additive attention layer](../../../../../translated_images/tl/encoder-decoder-attention.7a726296894fb567.png)\n", "*Ang encoder-decoder model na may additive attention mechanism mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), na binanggit mula sa [blog post na ito](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ang attention matrix $\\{\\alpha_{i,j}\\}$ ay kumakatawan sa antas kung saan ang ilang input na salita ay may papel sa pagbuo ng isang partikular na salita sa output sequence. Narito ang halimbawa ng ganitong matrix:\n", "\n", - "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tl.png)\n", + "![Larawan na nagpapakita ng sample alignment na natagpuan ng RNNsearch-50, kinuha mula sa Bahdanau - arviz.org](../../../../../translated_images/tl/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Larawan mula sa [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3)*\n", "\n", @@ -223,7 +223,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ay isang napakalaking multi-layer transformer network na may 12 layers para sa *BERT-base*, at 24 para sa *BERT-large*. Ang modelo ay unang sinanay gamit ang malaking corpus ng text data (WikiPedia + mga libro) gamit ang unsupervised training (pagpapredikta ng mga nakatagong salita sa isang pangungusap). Sa panahon ng pre-training, ang modelo ay nakakapulot ng mataas na antas ng pag-unawa sa wika na maaaring magamit sa iba pang mga dataset sa pamamagitan ng fine tuning. Ang prosesong ito ay tinatawag na **transfer learning**.\n", "\n", - "![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tl.png)\n", + "![larawan mula sa http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tl/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Maraming mga bersyon ng Transformer architectures kabilang ang BERT, DistilBERT, BigBird, OpenGPT3, at iba pa na maaaring i-fine tune.\n", "\n", diff --git a/translations/tl/lessons/5-NLP/19-NER/README.md b/translations/tl/lessons/5-NLP/19-NER/README.md index 8c1b48b3..28485802 100644 --- a/translations/tl/lessons/5-NLP/19-NER/README.md +++ b/translations/tl/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Dahil kailangan nating bumuo ng one-to-one na kaugnayan sa pagitan ng mga token at klase, maaari tayong mag-train ng tamang **many-to-many** neural network model mula sa larawang ito: -![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tl.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/tl/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Larawan mula sa [blog post na ito](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) ni [Andrej Karpathy](http://karpathy.github.io/). Ang mga NER token classification models ay tumutugma sa pinakakanan na network architecture sa larawang ito.* diff --git a/translations/tl/lessons/5-NLP/README.md b/translations/tl/lessons/5-NLP/README.md index a217f144..891d4007 100644 --- a/translations/tl/lessons/5-NLP/README.md +++ b/translations/tl/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Natural Language Processing -![Buod ng mga gawain sa NLP sa isang doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.tl.png) +![Buod ng mga gawain sa NLP sa isang doodle](../../../../translated_images/tl/ai-nlp.b22dcb8ca4707cea.png) Sa seksyong ito, magtutuon tayo sa paggamit ng Neural Networks upang harapin ang mga gawain na may kaugnayan sa **Natural Language Processing (NLP)**. Maraming mga problema sa NLP na nais nating masolusyunan ng mga computer: diff --git a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md index 217862cc..fc98eeac 100644 --- a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Maaari mong buksan ang isa sa mga modelo, halimbawa **Biology → Flock Pagkatapos buksan ang modelo, dadalhin ka sa pangunahing screen ng NetLogo. Narito ang isang sample na modelo na naglalarawan sa populasyon ng mga lobo at tupa, na may limitadong resources (damo). -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.tl.png) +![NetLogo Main Screen](../../../../../translated_images/tl/NetLogo-Main.32653711ec1a01b3.png) > Screenshot ni Dmitry Soshnikov diff --git a/translations/tl/lessons/README.md b/translations/tl/lessons/README.md index b50fc2f4..d1d83c0d 100644 --- a/translations/tl/lessons/README.md +++ b/translations/tl/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pangkalahatang-ideya -![Pangkalahatang-ideya sa isang doodle](../../../translated_images/ai-overview.0857791951d19500.tl.png) +![Pangkalahatang-ideya sa isang doodle](../../../translated_images/tl/ai-overview.0857791951d19500.png) > Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md index af82ae7a..4529b6b5 100644 --- a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Matapos ang tagumpay ng mga transformer model sa paglutas ng mga gawain sa NLP, Ang pangunahing ideya ng CLIP ay ang kakayahang ihambing ang mga text prompt sa isang imahe at tukuyin kung gaano kahusay na tumutugma ang imahe sa prompt. -![CLIP Architecture](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.tl.png) +![CLIP Architecture](../../../../../translated_images/tl/clip-arch.b3dbf20b4e8ed8be.png) > *Larawan mula sa [blog post na ito](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Kapag ang modelong ito ay na-pretrain na, maaari nating bigyan ito ng batch ng m Halimbawa, kailangan nating i-classify ang mga imahe sa pagitan ng, sabihin nating, pusa, aso, at tao. Sa kasong ito, maaari nating bigyan ang modelo ng isang imahe, at isang serye ng mga text prompt: "*isang larawan ng pusa*", "*isang larawan ng aso*", "*isang larawan ng tao*". Sa resultang vector ng 3 probabilidad, pipiliin lang natin ang index na may pinakamataas na halaga. -![CLIP for Image Classification](../../../../../translated_images/clip-class.3af42ef0b2b19369.tl.png) +![CLIP for Image Classification](../../../../../translated_images/tl/clip-class.3af42ef0b2b19369.png) > *Larawan mula sa [blog post na ito](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Alamin ang higit pa tungkol sa VQGAN sa [Taming Transformers](https://compvis.gi Isa sa mga mahalagang pagkakaiba ng VQGAN sa tradisyunal na GAN ay ang huli ay kayang gumawa ng disenteng imahe mula sa anumang input vector, habang ang VQGAN ay malamang na makagawa ng imahe na hindi coherent. Kaya, kailangan nating gabayan pa ang proseso ng paggawa ng imahe, at magagawa ito gamit ang CLIP. -![VQGAN+CLIP Architecture](../../../../../translated_images/vqgan.5027fe05051dfa31.tl.png) +![VQGAN+CLIP Architecture](../../../../../translated_images/tl/vqgan.5027fe05051dfa31.png) Upang makabuo ng isang imahe na tumutugma sa isang text prompt, nagsisimula tayo sa isang random encoding vector na ipinapasa sa VQGAN upang makabuo ng isang imahe. Pagkatapos, ginagamit ang CLIP upang makabuo ng isang loss function na nagpapakita kung gaano kahusay na tumutugma ang imahe sa text prompt. Ang layunin ay i-minimize ang loss na ito, gamit ang back propagation upang ayusin ang mga parameter ng input vector. Isang mahusay na library na nagpapatupad ng VQGAN+CLIP ay ang [Pixray](http://github.com/pixray/pixray). -![Larawang ginawa ng Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.tl.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.tl.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.tl.png) +![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Larawang ginawa mula sa prompt *isang closeup watercolor portrait ng batang lalaking guro ng panitikan na may hawak na libro* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng batang babaeng guro ng computer science na may hawak na computer* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng matandang lalaking guro ng matematika sa harap ng blackboard* @@ -75,7 +75,7 @@ Hindi tulad ng CLIP, ang DALL-E ay tumatanggap ng parehong teksto at imahe bilan Ang pangunahing pagkakaiba ng DALL.E 1 at 2 ay ang kakayahan nitong makabuo ng mas makatotohanang mga imahe at sining. Mga halimbawa ng image generation gamit ang DALL-E: -![Larawang ginawa ng Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.tl.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.tl.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.tl.png) +![Larawang ginawa ng Pixray](../../../../../translated_images/tl/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Larawang ginawa ng Pixray](../../../../../translated_images/tl/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Larawang ginawa mula sa prompt *isang closeup watercolor portrait ng batang lalaking guro ng panitikan na may hawak na libro* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng batang babaeng guro ng computer science na may hawak na computer* | Larawang ginawa mula sa prompt *isang closeup oil portrait ng matandang lalaking guro ng matematika sa harap ng blackboard* diff --git a/translations/tr/README.md b/translations/tr/README.md index 18e779fa..77946de3 100644 --- a/translations/tr/README.md +++ b/translations/tr/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Yeni Başlayanlar İçin Yapay Zeka - Bir Müfredat -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.tr.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tr/ai-overview.0857791951d19500.png)| |:---:| | Yeni Başlayanlar İçin Yapay Zeka - _[@girlie_mac](https://twitter.com/girlie_mac) tarafından Sketchnote_ | diff --git a/translations/tr/lessons/1-Intro/README.md b/translations/tr/lessons/1-Intro/README.md index ccda0749..f1ae656f 100644 --- a/translations/tr/lessons/1-Intro/README.md +++ b/translations/tr/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Yapay Zekaya Giriş -![Yapay Zeka içeriğinin özetini gösteren bir çizim](../../../../translated_images/ai-intro.bf28d1ac4235881c.tr.png) +![Yapay Zeka içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-intro.bf28d1ac4235881c.png) > Çizim: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Başlangıçta, bilgisayarlar [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) tarafından iyi tanımlanmış bir prosedürü - bir algoritmayı - takip ederek sayılar üzerinde işlem yapmak için icat edilmiştir. Modern bilgisayarlar, 19. yüzyılda önerilen orijinal modelden çok daha gelişmiş olmasına rağmen, hala kontrollü hesaplama fikrini takip etmektedir. Bu nedenle, bir hedefe ulaşmak için gereken adımların tam sırasını bildiğimiz sürece bir bilgisayarı bir şey yapması için programlamak mümkündür. -![Bir kişinin fotoğrafı](../../../../translated_images/dsh_age.d212a30d4e54fb5f.tr.png) +![Bir kişinin fotoğrafı](../../../../translated_images/tr/dsh_age.d212a30d4e54fb5f.png) > Fotoğraf: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ Daha fazla bilgi için **[Yapay Genel Zeka](https://en.wikipedia.org/wiki/Artifi **[Zeka](https://en.wikipedia.org/wiki/Intelligence)** terimiyle uğraşırken karşılaşılan sorunlardan biri, bu terimin net bir tanımının olmamasıdır. Zekanın **soyut düşünce** veya **öz farkındalık** ile bağlantılı olduğunu iddia edebilirsiniz, ancak bunu doğru bir şekilde tanımlayamayız. -![Bir kedinin fotoğrafı](../../../../translated_images/photo-cat.8c8e8fb760ffe457.tr.jpg) +![Bir kedinin fotoğrafı](../../../../translated_images/tr/photo-cat.8c8e8fb760ffe457.jpg) > [Fotoğraf](https://unsplash.com/photos/75715CVEJhI): [Amber Kipp](https://unsplash.com/@sadmax) tarafından Unsplash'tan alınmıştır. @@ -98,13 +98,13 @@ Alternatif olarak, beynimizdeki en basit öğeleri – bir nöronu – modelleme > | Peki ya ML? | | > |--------------|-----------| -> | Bazı verilere dayanarak bir problemi çözmeyi öğrenen bilgisayar temelli yapay zeka kısmına **Makine Öğrenimi** denir. Bu kursta klasik makine öğrenimini ele almayacağız - sizi ayrı bir [Makine Öğrenimi için Başlangıç](http://aka.ms/ml-beginners) müfredatına yönlendiriyoruz. | ![Başlangıç için ML](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.tr.png) | +> | Bazı verilere dayanarak bir problemi çözmeyi öğrenen bilgisayar temelli yapay zeka kısmına **Makine Öğrenimi** denir. Bu kursta klasik makine öğrenimini ele almayacağız - sizi ayrı bir [Makine Öğrenimi için Başlangıç](http://aka.ms/ml-beginners) müfredatına yönlendiriyoruz. | ![Başlangıç için ML](../../../../translated_images/tr/ml-for-beginners.9e4fed176fd5817d.png) | ## Yapay Zekanın Kısa Tarihi Yapay Zeka, yirminci yüzyılın ortalarında bir alan olarak başladı. Başlangıçta, sembolik akıl yürütme yaygın bir yaklaşımdı ve uzman sistemler gibi bazı sınırlı problem alanlarında uzman gibi davranabilen bilgisayar programları gibi önemli başarılara yol açtı. Ancak, bu yaklaşımın iyi ölçeklenmediği kısa sürede anlaşıldı. Bir uzmandan bilgi çıkarmak, bunu bir bilgisayarda temsil etmek ve bilgi tabanını doğru tutmak çok karmaşık bir görev ve birçok durumda pratik olmaktan çok pahalı olduğu ortaya çıktı. Bu, 1970'lerde [AI Kışı](https://en.wikipedia.org/wiki/AI_winter) olarak adlandırılan döneme yol açtı. -Yapay Zekanın Kısa Tarihi +Yapay Zekanın Kısa Tarihi > Görsel: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Benzer şekilde, "konuşan programlar" (Turing testini geçebilecek türden) olu * Cortana, Siri veya Google Asistan gibi modern asistanlar, konuşmayı metne dönüştürmek ve niyetimizi tanımak için Sinir ağlarını kullanan ve ardından gerekli eylemleri gerçekleştirmek için bazı akıl yürütme veya açık algoritmalar kullanan hibrit sistemlerdir. * Gelecekte, diyaloğu tamamen kendi başına yönetebilecek tam bir sinir temelli model bekleyebiliriz. Son zamanlardaki GPT ve [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) sinir ağı ailesi bu konuda büyük başarılar göstermektedir. -Turing testinin evrimi +Turing testinin evrimi > Görsel Dmitry Soshnikov tarafından, [fotoğraf](https://unsplash.com/photos/r8LmVbUKgns) [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) tarafından, Unsplash ## Son Dönem AI Araştırmaları diff --git a/translations/tr/lessons/2-Symbolic/Animals.ipynb b/translations/tr/lessons/2-Symbolic/Animals.ipynb index d0b064f6..ae609641 100644 --- a/translations/tr/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tr/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Bu örnekte, bazı fiziksel özelliklere dayanarak bir hayvanı belirlemek için basit bir bilgi tabanlı sistem uygulayacağız. Sistem aşağıdaki AND-OR ağacı ile temsil edilebilir (bu, ağacın bir kısmıdır, kolayca daha fazla kural ekleyebiliriz):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.tr.png)\n" + "![](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/tr/lessons/2-Symbolic/README.md b/translations/tr/lessons/2-Symbolic/README.md index 18aa8036..8b345f40 100644 --- a/translations/tr/lessons/2-Symbolic/README.md +++ b/translations/tr/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bilgi Temsili ve Uzman Sistemler -![Sembolik AI içeriği özeti](../../../../translated_images/ai-symbolic.715a30cb610411a6.tr.png) +![Sembolik AI içeriği özeti](../../../../translated_images/tr/ai-symbolic.715a30cb610411a6.png) > Sketchnote: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Sembolik AI'deki önemli kavramlardan biri **bilgi**dir. Bilgiyi *bilgi* veya *v Bu nedenle, **bilgi temsili** problemi, bilgiyi bir bilgisayar içinde veri şeklinde etkili bir şekilde temsil etmenin bir yolunu bulmaktır, böylece otomatik olarak kullanılabilir hale gelir. Bu bir spektrum olarak görülebilir: -![Bilgi temsili spektrumu](../../../../translated_images/knowledge-spectrum.b60df631852c0217.tr.png) +![Bilgi temsili spektrumu](../../../../translated_images/tr/knowledge-spectrum.b60df631852c0217.png) > Resim: [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Blok Sözdizimi | Girinti | | | Sembolik AI'nın erken başarılarından biri, **uzman sistemler** olarak adlandırılan sistemlerdi - belirli bir problem alanında uzman gibi davranmak üzere tasarlanmış bilgisayar sistemleri. Bu sistemler, bir veya daha fazla insan uzmandan çıkarılan bir **bilgi tabanı**na dayanıyordu ve bunun üzerinde akıl yürütme yapan bir **çıkarım motoru** içeriyordu. -![İnsan Mimarisi](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.tr.png) | ![Bilgi Tabanlı Sistem](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.tr.png) +![İnsan Mimarisi](../../../../translated_images/tr/arch-human.5d4d35f1bba3ab1c.png) | ![Bilgi Tabanlı Sistem](../../../../translated_images/tr/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ İnsan sinir sisteminin basitleştirilmiş yapısı | Bilgi tabanlı sistemin mimarisi @@ -106,7 +106,7 @@ Uzman sistemler, insan akıl yürütme sistemine benzer şekilde inşa edilir, b Örneğin, fiziksel özelliklere dayanarak bir hayvanı belirleyen bir uzman sistemini ele alalım: -![AND-OR Ağacı](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.tr.png) +![AND-OR Ağacı](../../../../translated_images/tr/AND-OR-Tree.5592d2c70187f283.png) > Resim: [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 9e3eeea2..cc205647 100644 --- a/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/tr/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Eğer 2'den fazla sınıfımız varsa, softmax tüm sınıflar arasında olasılıkları normalize edecektir. İşte MNIST rakam sınıflandırması yapan bir ağ mimarisinin diyagramı:\n", "\n", - "![MNIST Sınıflandırıcı](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.tr.png)\n" + "![MNIST Sınıflandırıcı](../../../../../translated_images/tr/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Düşük eğitim kaybı - model, yeterli ifade gücüne sahip olduğu için eğitim verisini iyi bir şekilde tahmin edebilir.\n", "* Doğrulama kaybı, eğitim kaybından çok daha yüksek olabilir ve eğitim sırasında artmaya başlayabilir - bunun nedeni modelin eğitim noktalarını \"ezberlemesi\" ve \"genel resmi\" kaybetmesidir.\n", "\n", - "![Aşırı Öğrenme](../../../../../translated_images/overfit.a0bd57f717c15769.tr.png)\n", + "![Aşırı Öğrenme](../../../../../translated_images/tr/overfit.a0bd57f717c15769.png)\n", "\n", "> Bu resimde, `x` eğitim verisini, `o` ise doğrulama verisini temsil eder. Sol tarafta - doğrusal model (tek katmanlı), verinin doğasını oldukça iyi tahmin eder. Sağ tarafta - aşırı öğrenmiş model, eğitim verisini mükemmel bir şekilde tahmin eder, ancak diğer herhangi bir veriyle anlam ifade etmeyi bırakır (doğrulama hatası çok yüksektir).\n" ] diff --git a/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md index 1a4694c8..49919946 100644 --- a/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tr/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Aşırı öğrenme, makine öğreniminde son derece önemli bir kavramdır ve do Aşağıdaki 5 noktayı (grafiklerde `x` ile gösterilen) yaklaşık olarak tahmin etme problemini düşünün: -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.tr.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.tr.jpg) +![linear](../../../../../translated_images/tr/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/tr/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Doğrusal model, 2 parametre** | **Doğrusal olmayan model, 7 parametre** Eğitim hatası = 5.3 | Eğitim hatası = 0 @@ -79,7 +79,7 @@ Modelin zenginliği (parametre sayısı) ile eğitim örneklerinin sayısı aras Yukarıdaki grafikten görebileceğiniz gibi, aşırı öğrenme çok düşük bir eğitim hatası ve yüksek bir doğrulama hatası ile tespit edilebilir. Normalde eğitim sırasında hem eğitim hem de doğrulama hatalarının azalmaya başladığını görürüz, ancak bir noktada doğrulama hatası azalmayı durdurabilir ve artmaya başlayabilir. Bu, aşırı öğrenmenin bir işareti ve eğitimi muhtemelen bu noktada durdurmamız gerektiğinin (veya en azından modelin bir anlık görüntüsünü almamız gerektiğinin) göstergesidir. -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.tr.png) +![overfitting](../../../../../translated_images/tr/Overfitting.408ad91cd90b4371.png) ## Aşırı Öğrenme Nasıl Önlenir? diff --git a/translations/tr/lessons/3-NeuralNetworks/README.md b/translations/tr/lessons/3-NeuralNetworks/README.md index 04a3fd1c..ab29ae66 100644 --- a/translations/tr/lessons/3-NeuralNetworks/README.md +++ b/translations/tr/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Sinir Ağlarına Giriş -![Sinir Ağlarına Giriş içeriğinin özetini gösteren bir çizim](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.tr.png) +![Sinir Ağlarına Giriş içeriğinin özetini gösteren bir çizim](../../../../translated_images/tr/ai-neuralnetworks.1c687ae40bc86e83.png) Giriş bölümünde tartıştığımız gibi, zekaya ulaşmanın yollarından biri bir **bilgisayar modeli** veya bir **yapay beyin** eğitmekten geçer. 20. yüzyılın ortalarından itibaren araştırmacılar farklı matematiksel modeller denediler ve son yıllarda bu yaklaşım büyük ölçüde başarılı oldu. Beynin bu tür matematiksel modellerine **sinir ağları** denir. @@ -36,13 +36,13 @@ Bu müfredatta yalnızca sinir ağı modellerine odaklanacağız. Biyolojiden biliyoruz ki beynimiz, her biri birden fazla "girişe" (dendritler) ve tek bir "çıkışa" (akson) sahip olan sinir hücrelerinden (nöronlar) oluşur. Hem dendritler hem de aksonlar elektrik sinyalleri iletebilir ve aralarındaki bağlantılar — sinapslar olarak bilinir — iletkenlik derecelerini değiştirebilir. Bu iletkenlik, nörotransmitterler tarafından düzenlenir. -![Bir Nöron Modeli](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.tr.jpg) | ![Bir Nöron Modeli](../../../../translated_images/artneuron.1a5daa88d20ebe6f.tr.png) +![Bir Nöron Modeli](../../../../translated_images/tr/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Bir Nöron Modeli](../../../../translated_images/tr/artneuron.1a5daa88d20ebe6f.png) ----|---- Gerçek Nöron *([Resim](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipedia'dan)* | Yapay Nöron *(Yazarın Görseli)* Dolayısıyla, bir nöronun en basit matematiksel modeli birkaç giriş X1, ..., XN ve bir çıkış Y ile bir dizi ağırlık W1, ..., WN içerir. Çıkış şu şekilde hesaplanır: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) burada f, bazı doğrusal olmayan **aktivasyon fonksiyonudur**. diff --git a/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md index 63e1bdae..2c12f497 100644 --- a/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tr/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Bir görüntüyü sinir ağına beslemeden önce, birkaç ön işleme adımı uy * **Braille kitabının bir fotoğrafını ön işleme**. Eşikleme, özellik tespiti, perspektif dönüşümü ve NumPy manipülasyonlarını kullanarak bireysel Braille sembollerini bir sinir ağı tarafından daha fazla sınıflandırma için ayırmaya odaklanıyoruz. -![Braille Görüntüsü](../../../../../translated_images/braille.341962ff76b1bd70.tr.jpeg) | ![Braille Görüntüsü Ön İşlenmiş](../../../../../translated_images/braille-result.46530fea020b03c7.tr.png) | ![Braille Sembolleri](../../../../../translated_images/braille-symbols.0159185ab69d5339.tr.png) +![Braille Görüntüsü](../../../../../translated_images/tr/braille.341962ff76b1bd70.jpeg) | ![Braille Görüntüsü Ön İşlenmiş](../../../../../translated_images/tr/braille-result.46530fea020b03c7.png) | ![Braille Sembolleri](../../../../../translated_images/tr/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. * **Video içinde hareketi kare farkı kullanarak tespit etme**. Kamera sabit ise, kamera akışından gelen kareler birbirine oldukça benzer olmalıdır. Kareler diziler olarak temsil edildiğinden, iki ardışık kare için bu dizileri çıkararak piksel farkını elde edebiliriz; bu fark statik kareler için düşük olmalı ve görüntüde önemli bir hareket olduğunda artmalıdır. -![Video kareleri ve kare farkları görüntüsü](../../../../../translated_images/frame-difference.706f805491a0883c.tr.png) +![Video kareleri ve kare farkları görüntüsü](../../../../../translated_images/tr/frame-difference.706f805491a0883c.png) > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. @@ -89,7 +89,7 @@ Bir görüntüyü sinir ağına beslemeden önce, birkaç ön işleme adımı uy - **Yoğun Optik Akış**, her pikselin nereye hareket ettiğini gösteren vektör alanını hesaplar. - **Seyrek Optik Akış**, görüntüdeki bazı belirgin özellikleri (örneğin kenarları) alır ve bunların kareden kareye olan hareket yolunu oluşturur. -![Optik Akış Görüntüsü](../../../../../translated_images/optical.1f4a94464579a83a.tr.png) +![Optik Akış Görüntüsü](../../../../../translated_images/tr/optical.1f4a94464579a83a.png) > Görüntü [OpenCV.ipynb](OpenCV.ipynb) dosyasından alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index e646d04a..19ab2fe0 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16, 2014 yılında ImageNet top-5 sınıflandırmasında %92.7 doğruluk elde eden bir ağdır. Katman yapısı şu şekildedir: -![ImageNet Katmanları](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tr.jpg) +![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.jpg) Gördüğünüz gibi, VGG geleneksel bir piramit mimarisini takip eder; bu, bir dizi evrişim-havuzlama katmanıdır. -![ImageNet Piramidi](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tr.jpg) +![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.jpg) > Görsel [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) kaynağından alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 949ab5d4..2e32b4ac 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -262,7 +262,7 @@ "\n", "Bu nedenle, tipik bir CNN'de birkaç evrişim katmanı bulunur ve bunların arasında görüntünün boyutlarını azaltmak için havuzlama katmanları yer alır. Ayrıca filtre sayısını artırırız, çünkü desenler daha karmaşık hale geldikçe - dikkate alınması gereken daha fazla ilginç kombinasyon ortaya çıkar.\n", "\n", - "![Havuzlama katmanları ile birkaç evrişim katmanını gösteren bir görüntü.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.tr.png)\n", + "![Havuzlama katmanları ile birkaç evrişim katmanını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Mekansal boyutların azalması ve özellik/filtre boyutlarının artması nedeniyle, bu mimariye aynı zamanda **piramit mimarisi** denir.\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 7f84279f..5b7a6c7d 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "Bu nedenle, tipik bir CNN'de birkaç evrişim katmanı bulunur ve bunların arasında görüntünün boyutlarını küçültmek için havuzlama katmanları yer alır. Ayrıca, desenler daha karmaşık hale geldikçe, aramamız gereken daha fazla ilginç kombinasyon olduğu için filtre sayısını artırırız.\n", "\n", - "![Birden fazla evrişim katmanını ve havuzlama katmanlarını gösteren bir görüntü.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.tr.png)\n", + "![Birden fazla evrişim katmanını ve havuzlama katmanlarını gösteren bir görüntü.](../../../../../translated_images/tr/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Mekansal boyutların azalması ve özellik/filtre boyutlarının artması nedeniyle, bu mimariye **piramit mimarisi** de denir.\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md index 1325e43c..9ecd04e1 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Gerçek hayatta, bir görüntüdeki nesneleri tam olarak nerede olduklarına bak Desenleri çıkarmak için **evrişimsel filtreler** kavramını kullanacağız. Bildiğiniz gibi, bir görüntü 2D bir matris veya renk derinliği olan bir 3D tensör olarak temsil edilir. Bir filtre uygulamak, nispeten küçük bir **filtre çekirdeği** matrisini alıp, orijinal görüntüdeki her bir piksel için komşu noktalarla ağırlıklı ortalamayı hesaplamak anlamına gelir. Bunu, filtre çekirdeği matrisindeki ağırlıklara göre tüm pikselleri ortalayan küçük bir pencerenin tüm görüntü üzerinde kayması gibi düşünebiliriz. -![Dikey Kenar Filtresi](../../../../../translated_images/filter-vert.b7148390ca0bc356.tr.png) | ![Yatay Kenar Filtresi](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.tr.png) +![Dikey Kenar Filtresi](../../../../../translated_images/tr/filter-vert.b7148390ca0bc356.png) | ![Yatay Kenar Filtresi](../../../../../translated_images/tr/filter-horiz.59b80ed4feb946ef.png) ----|---- > Görsel: Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN'lerin çalışma şekli şu önemli fikirlere dayanır: * Ağı, filtrelerin otomatik olarak eğitileceği şekilde tasarlayabiliriz. * Aynı yaklaşımı yalnızca orijinal görüntüde değil, yüksek seviyeli özelliklerdeki desenleri bulmak için de kullanabiliriz. Böylece, CNN özellik çıkarımı, düşük seviyeli piksel kombinasyonlarından başlayarak, görüntü parçalarının daha yüksek seviyeli kombinasyonlarına kadar bir özellik hiyerarşisi üzerinde çalışır. -![Hiyerarşik Özellik Çıkarımı](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.tr.png) +![Hiyerarşik Özellik Çıkarımı](../../../../../translated_images/tr/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Görsel: [Hislop-Lynch'in bir makalesinden](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), [araştırmalarına dayanarak](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Görüntü işleme için kullanılan çoğu CNN, sözde piramit mimarisini takip Örneğin, 2014 yılında ImageNet'in ilk 5 sınıflandırmasında %92.7 doğruluk elde eden VGG-16 ağının mimarisine bakalım: -![ImageNet Katmanları](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tr.jpg) +![ImageNet Katmanları](../../../../../translated_images/tr/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet Piramidi](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tr.jpg) +![ImageNet Piramidi](../../../../../translated_images/tr/vgg-16-arch.64ff2137f50dd49f.jpg) > Görsel: [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 87af9456..35f8409e 100644 --- a/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tr/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Bir evcil hayvan yuvası için tüm evcil hayvanları kataloglamak amacıyla bir [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) veri setini kullanacağız. Bu veri seti, 37 farklı köpek ve kedi cinsine ait görüntüler içerir. -![Çalışacağımız veri seti](../../../../../../translated_images/data.50b2a9d5484bdbf0.tr.png) +![Çalışacağımız veri seti](../../../../../../translated_images/tr/data.50b2a9d5484bdbf0.png) Veri setini indirmek için şu kod parçacığını kullanın: diff --git a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 2f8eff00..ce0fdc0b 100644 --- a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "İdeal kediyi görselleştirmek için rastgele bir gürültü görüntüsüyle başlayacağız ve bir ağın kediyi tanımasını sağlamak için görüntüyü ayarlamak amacıyla gradyan iniş optimizasyon tekniğini kullanmaya çalışacağız.\n", "\n", - "![Optimizasyon Döngüsü](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.tr.png)\n", + "![Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "İşte başlangıç görüntümüz:\n" ] diff --git a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md index 80ede453..cb0a7176 100644 --- a/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tr/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Hem Keras hem de PyTorch, yaygın mimariler için önceden eğitilmiş sinir ağ İşte VGG-16 ağı tarafından bir kedi resminden çıkarılan örnek özellikler: -![VGG-16 tarafından çıkarılan özellikler](../../../../../translated_images/features.6291f9c7ba3a0b95.tr.png) +![VGG-16 tarafından çıkarılan özellikler](../../../../../translated_images/tr/features.6291f9c7ba3a0b95.png) ## Kediler ve Köpekler Veri Kümesi @@ -48,19 +48,19 @@ Transfer öğrenimini ilgili not defterlerinde nasıl çalıştığını göreli Alabileceğimiz bir yaklaşım, rastgele bir görüntüyle başlamak ve ardından **gradyan iniş optimizasyonu** tekniğini kullanarak bu görüntüyü ağın bir kedi olduğunu düşünmesini sağlayacak şekilde ayarlamaktır. -![Görüntü Optimizasyon Döngüsü](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.tr.png) +![Görüntü Optimizasyon Döngüsü](../../../../../translated_images/tr/ideal-cat-loop.999fbb8ff306e044.png) Ancak bunu yaparsak, rastgele bir gürültüye çok benzeyen bir şey elde ederiz. Bunun nedeni, *ağın giriş görüntüsünü bir kedi olarak düşünmesini sağlamanın birçok yolu olmasıdır*, bunların bazıları görsel olarak mantıklı değildir. Bu görüntüler kediye özgü birçok desen içerirken, görsel olarak ayırt edici olmalarını sağlayacak bir kısıtlama yoktur. Sonucu iyileştirmek için kayıp fonksiyonuna **varyasyon kaybı** adı verilen başka bir terim ekleyebiliriz. Bu, görüntünün komşu piksellerinin ne kadar benzer olduğunu gösteren bir metriktir. Varyasyon kaybını minimize etmek, görüntüyü daha düzgün hale getirir ve gürültüyü ortadan kaldırır - böylece daha görsel olarak çekici desenler ortaya çıkar. İşte yüksek olasılıkla kedi ve zebra olarak sınıflandırılan bu "ideal" görüntülere bir örnek: -![İdeal Kedi](../../../../../translated_images/ideal-cat.203dd4597643d6b0.tr.png) | ![İdeal Zebra](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.tr.png) +![İdeal Kedi](../../../../../translated_images/tr/ideal-cat.203dd4597643d6b0.png) | ![İdeal Zebra](../../../../../translated_images/tr/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *İdeal Kedi* | *İdeal Zebra* Benzer bir yaklaşım, sinir ağına karşı **adversaryal saldırılar** gerçekleştirmek için kullanılabilir. Diyelim ki bir sinir ağını kandırmak ve bir köpeği kedi gibi göstermek istiyoruz. Eğer ağ tarafından köpek olarak tanınan bir köpek görüntüsü alırsak, bunu biraz ayarlayarak gradyan iniş optimizasyonu kullanabiliriz, ta ki ağ bunu kedi olarak sınıflandırana kadar: -![Köpek Resmi](../../../../../translated_images/original-dog.8f68a67d2fe0911f.tr.png) | ![Kedi olarak sınıflandırılan köpek resmi](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.tr.png) +![Köpek Resmi](../../../../../translated_images/tr/original-dog.8f68a67d2fe0911f.png) | ![Kedi olarak sınıflandırılan köpek resmi](../../../../../translated_images/tr/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Orijinal köpek resmi* | *Kedi olarak sınıflandırılan köpek resmi* diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 76b7aac1..b64d6ad9 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Otoenkoderi, orijinal görüntüden mümkün olduğunca fazla bilgiyi doğru bir şekilde yeniden oluşturmak için yakalamaya çalışacak şekilde eğittiğimizden, ağ, giriş görüntülerinin anlamını yakalamak için en iyi **gömülü temsili (embedding)** bulmaya çalışır.\n", "\n", - "![AutoEncoder Şeması](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tr.jpg)\n", + "![AutoEncoder Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Görsel [Keras blogu](https://blog.keras.io/building-autoencoders-in-keras.html)'ndan alınmıştır.\n", "\n", diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 9e768b55..e4590d14 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Otoenkoderi, orijinal görüntüden mümkün olduğunca fazla bilgi yakalamak ve doğru bir şekilde yeniden oluşturmak için eğittiğimizden, ağ giriş görüntülerinin anlamını yakalamak için en iyi **gömülü temsili** bulmaya çalışır.\n", "\n", - "![Otoenkoder Diyagramı](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tr.jpg)\n", + "![Otoenkoder Diyagramı](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*[Keras blogundan](https://blog.keras.io/building-autoencoders-in-keras.html) alınmış görüntü*\n", "\n", diff --git a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md index d6c42e4a..c186c663 100644 --- a/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tr/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Ancak, CNN özellik çıkarıcılarını eğitmek için ham (etiketlenmemiş) ve Otomatik kodlayıcıyı, orijinal görüntüden mümkün olduğunca fazla bilgi yakalamak ve doğru bir şekilde yeniden oluşturmak için eğittiğimizden, ağ en iyi **gömülü temsili** bulmaya çalışır. -![Otomatik Kodlayıcı Şeması](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tr.jpg) +![Otomatik Kodlayıcı Şeması](../../../../../translated_images/tr/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Görsel [Keras blogundan](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md index d4bcf4d1..a3e937ee 100644 --- a/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tr/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Bugüne kadar ele aldığımız görüntü sınıflandırma modelleri, bir gör ## [Ders Öncesi Test](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Nesne Tespiti](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.tr.png) +![Nesne Tespiti](../../../../../translated_images/tr/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Görsel [YOLO v2 web sitesi](https://pjreddie.com/darknet/yolov2/) üzerinden alınmıştır. @@ -25,7 +25,7 @@ Bir resimde bir kediyi bulmak istediğimizi varsayalım, nesne tespiti için ço 2. Her bir karede görüntü sınıflandırma işlemi gerçekleştirin. 3. Yeterince yüksek aktivasyon veren kareler, ilgili nesneyi içeriyor olarak kabul edilebilir. -![Naif Nesne Tespiti](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.tr.png) +![Naif Nesne Tespiti](../../../../../translated_images/tr/naive-detection.e7f1ba220ccd08c6.png) > *Görsel [Egzersiz Defteri](ObjectDetection-TF.ipynb) üzerinden alınmıştır.* @@ -42,7 +42,7 @@ Bu görev için aşağıdaki veri setleriyle karşılaşabilirsiniz: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 sınıf * [COCO](http://cocodataset.org/#home) - Bağlamdaki Yaygın Nesneler. 80 sınıf, sınır kutuları ve segmentasyon maskeleri -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.tr.jpg) +![COCO](../../../../../translated_images/tr/coco-examples.71bc60380fa6cceb.jpg) ## Nesne Tespiti Metrikleri @@ -50,7 +50,7 @@ Bu görev için aşağıdaki veri setleriyle karşılaşabilirsiniz: Görüntü sınıflandırma için algoritmanın ne kadar iyi performans gösterdiğini ölçmek kolaydır, ancak nesne tespiti için hem sınıfın doğruluğunu hem de tahmin edilen sınır kutusu konumunun hassasiyetini ölçmemiz gerekir. İkincisi için, **Kesişim Bölü Birleşim** (IoU) adı verilen bir ölçüm kullanırız, bu iki kutunun (veya iki rastgele alanın) ne kadar iyi örtüştüğünü ölçer. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.tr.png) +![IoU](../../../../../translated_images/tr/iou_equation.9a4751d40fff4e11.png) > *[Bu harika IoU blog yazısından](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) alınan Şekil 2.* @@ -98,11 +98,11 @@ Nesne tespiti algoritmaları iki geniş sınıfa ayrılır: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf), ROI bölgelerinin hiyerarşik yapısını oluşturmak için [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) kullanır. Bu bölgeler daha sonra CNN özellik çıkarıcıları ve SVM sınıflandırıcıları aracılığıyla nesne sınıfını belirlemek ve *sınır kutusu* koordinatlarını belirlemek için doğrusal regresyon ile işlenir. [Resmi Makale](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.tr.png) +![RCNN](../../../../../translated_images/tr/rcnn1.cae407020dfb1d1f.png) > *Görsel van de Sande ve ark. ICCV’11'dan alınmıştır.* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.tr.png) +![RCNN-1](../../../../../translated_images/tr/rcnn2.2d9530bb83516484.png) > *Görseller [bu blogdan](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) alınmıştır.* @@ -110,7 +110,7 @@ Nesne tespiti algoritmaları iki geniş sınıfa ayrılır: Bu yaklaşım R-CNN'e benzer, ancak bölgeler konvolüsyon katmanları uygulandıktan sonra tanımlanır. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.tr.png) +![FRCNN](../../../../../translated_images/tr/f-rcnn.3cda6d9bb4188875.png) > Görsel [Resmi Makale](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 üzerinden alınmıştır. @@ -118,7 +118,7 @@ Bu yaklaşım R-CNN'e benzer, ancak bölgeler konvolüsyon katmanları uyguland Bu yaklaşımın ana fikri, ROI'leri tahmin etmek için sinir ağı kullanmaktır - *Bölge Öneri Ağı* olarak adlandırılır. [Makale](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.tr.png) +![FasterRCNN](../../../../../translated_images/tr/faster-rcnn.8d46c099b87ef30a.png) > Görsel [Resmi Makale](https://arxiv.org/pdf/1506.01497.pdf) üzerinden alınmıştır. @@ -130,7 +130,7 @@ Bu algoritma, Daha Hızlı R-CNN'den bile daha hızlıdır. Ana fikir şu şekil 2. Özellikler **Pozisyon-Duyarlı Skor Haritası** tarafından işlenir. $C$ sınıflarından her bir nesne $k\times k$ bölgelere ayrılır ve nesne parçalarını tahmin etmek için eğitim yapılır. 3. $k\times k$ bölgelerden her bir parça için tüm ağlar nesne sınıfları için oy kullanır ve maksimum oyu alan nesne sınıfı seçilir. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.tr.png) +![r-fcn image](../../../../../translated_images/tr/r-fcn.13eb88158b99a3da.png) > Görsel [Resmi Makale](https://arxiv.org/abs/1605.06409) üzerinden alınmıştır. @@ -141,7 +141,7 @@ YOLO, gerçek zamanlı tek geçişli bir algoritmadır. Ana fikir şu şekildedi * Görüntü $S\times S$ bölgelere ayrılır. * Her bölge için **CNN**, $n$ olası nesneleri, *sınır kutusu* koordinatlarını ve *güven* = *olasılık* * IoU tahmin eder. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.tr.png) + ![YOLO](../../../../../translated_images/tr/yolo.a2648ec82ee8bb4e.png) > Görsel [Resmi Makale](https://arxiv.org/abs/1506.02640) üzerinden alınmıştır. diff --git a/translations/tr/lessons/4-ComputerVision/README.md b/translations/tr/lessons/4-ComputerVision/README.md index 01ee1eae..03270ae7 100644 --- a/translations/tr/lessons/4-ComputerVision/README.md +++ b/translations/tr/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bilgisayarlı Görü -![Bilgisayarlı Görü içeriğinin bir çizim özeti](../../../../translated_images/ai-computervision.6506ebebac3fbf76.tr.png) +![Bilgisayarlı Görü içeriğinin bir çizim özeti](../../../../translated_images/tr/ai-computervision.6506ebebac3fbf76.png) Bu bölümde şunları öğreneceğiz: diff --git a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 9e7acdb4..4dd935a4 100644 --- a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Kelime Torbası** (BoW) vektör temsili, en yaygın kullanılan geleneksel vektör temsilidir. Her kelime bir vektör indeksine bağlanır ve vektör elemanı, bir belgedeki bir kelimenin kaç kez geçtiğini içerir.\n", "\n", - "![Kelime torbası vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.tr.png) \n", + "![Kelime torbası vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Not**: BoW'yu, metindeki bireysel kelimeler için tekil olarak bir-hot kodlanmış vektörlerin toplamı olarak da düşünebilirsiniz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 7a3be057..6a6f3dc4 100644 --- a/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/tr/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Bag-of-words** (BoW) vektör temsili, anlaması en basit geleneksel vektör temsilidir. Her kelime bir vektör indeksine bağlanır ve bir vektör elemanı, belirli bir belgede her kelimenin kaç kez geçtiğini içerir.\n", "\n", - "![Bag-of-words vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.tr.png) \n", + "![Bag-of-words vektör temsilinin bellekte nasıl temsil edildiğini gösteren bir görsel.](../../../../../translated_images/tr/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Not**: BoW'yu, metindeki bireysel kelimeler için tekil olarak birleştirilmiş tüm one-hot-encoded vektörlerin toplamı olarak da düşünebilirsiniz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 975d23f0..957edf30 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Ağımızda ilk katman olarak gömme katmanını kullanarak, kelime torbasından **gömme torbası** modeline geçebiliriz. Bu modelde, metnimizdeki her kelimeyi ilgili gömmeye dönüştürürüz ve ardından bu gömmeler üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız.\n", "\n", - "![Beş sıralı kelime için bir gömme sınıflandırıcıyı gösteren görsel.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tr.png)\n", + "![Beş sıralı kelime için bir gömme sınıflandırıcıyı gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Sınıflandırıcı sinir ağımız, gömme katmanı ile başlayacak, ardından toplama katmanı ve en üstte doğrusal bir sınıflandırıcı ile devam edecektir:\n" ] @@ -176,7 +176,7 @@ "\n", "Önceki mimaride, dizileri bir minibatch'e sığdırmak için hepsini aynı uzunlukta doldurmamız gerekiyordu. Bu, değişken uzunluklu dizileri temsil etmenin en verimli yolu değildir - başka bir yaklaşım, tüm dizilerin bir büyük vektördeki offsetlerini tutan bir **offset** vektörü kullanmak olabilir.\n", "\n", - "![Offset dizi temsilini gösteren bir görüntü](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.tr.png)\n", + "![Offset dizi temsilini gösteren bir görüntü](../../../../../translated_images/tr/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Not**: Yukarıdaki resimde karakter dizisi gösteriyoruz, ancak örneğimizde kelime dizileriyle çalışıyoruz. Ancak, dizileri offset vektörüyle temsil etme genel prensibi aynı kalır.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW daha hızlıdır, ancak atlama-gramı daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha iyi bir iş çıkarır.\n", "\n", - "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görüntü.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tr.png)\n", + "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görüntü.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News veri seti üzerinde önceden eğitilmiş word2vec gömüsünü denemek için **gensim** kütüphanesini kullanabiliriz. Aşağıda 'neural' kelimesine en benzer kelimeleri buluyoruz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 8f79ebdc..2b2b4559 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/tr/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Ağımızdaki ilk katman olarak bir gömme katmanı kullanarak, kelime torbasından **gömme torbası** modeline geçebiliriz. Bu modelde, önce metnimizdeki her kelimeyi karşılık gelen gömmeye dönüştürürüz ve ardından bu gömmelerin tümü üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız.\n", "\n", - "![Beş sıralı kelime için bir gömme sınıflandırıcısını gösteren görsel.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tr.png)\n", + "![Beş sıralı kelime için bir gömme sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Sınıflandırıcı sinir ağımız şu katmanlardan oluşur:\n", "\n", @@ -281,7 +281,7 @@ "\n", "CBoW daha hızlıdır, ancak skip-gram daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha başarılıdır.\n", "\n", - "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görsel.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tr.png)\n", + "![Kelimeyi vektörlere dönüştürmek için hem CBoW hem de Skip-Gram algoritmalarını gösteren bir görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Google News veri seti üzerinde önceden eğitilmiş Word2Vec gömüsünü denemek için **gensim** kütüphanesini kullanabiliriz. Aşağıda 'neural' kelimesine en benzer kelimeleri buluyoruz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/14-Embeddings/README.md b/translations/tr/lessons/5-NLP/14-Embeddings/README.md index 4bb74db4..5b309b39 100644 --- a/translations/tr/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tr/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Bu nedenle, gömülü temsil katmanı bir kelimeyi giriş olarak alır ve belirl Sınıflandırıcı ağımızda ilk katman olarak bir gömülü temsil katmanı kullanarak, kelime torbasından **gömülü torba** modeline geçebiliriz. Bu modelde, önce metnimizdeki her kelimeyi ilgili gömülü temsile dönüştürürüz ve ardından bu gömülü temsillerin tümü üzerinde `sum`, `average` veya `max` gibi bir toplama fonksiyonu hesaplarız. -![Beş kelimelik bir dizinin gömülü temsil sınıflandırıcısını gösteren görsel.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tr.png) +![Beş kelimelik bir dizinin gömülü temsil sınıflandırıcısını gösteren görsel.](../../../../../translated_images/tr/embedding-classifier-example.b77f021a7ee67eee.png) > Görsel yazar tarafından oluşturulmuştur @@ -40,7 +40,7 @@ Bunu yapmak için, gömülü temsil modelimizi büyük bir metin koleksiyonu üz CBoW daha hızlıdır, ancak atlama-gram daha yavaş olmasına rağmen nadir kelimeleri temsil etmede daha iyi bir iş çıkarır. -![Kelimeyi vektöre dönüştürmek için kullanılan CBoW ve Skip-Gram algoritmalarını gösteren görsel.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tr.png) +![Kelimeyi vektöre dönüştürmek için kullanılan CBoW ve Skip-Gram algoritmalarını gösteren görsel.](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Görsel [bu makaleden](https://arxiv.org/pdf/1301.3781.pdf) alınmıştır diff --git a/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md index 6a275e76..a2ed3db7 100644 --- a/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tr/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Dil modellemesinin temel fikri, modelleri etiketlenmemiş veri kümeleri üzerin * **Continuous Bag-of-Words** (CBoW), burada bir token dizisindeki $W_{-N}$, ..., $W_N$ arasında ortadaki token $W_0$'ı tahmin ederiz. * **Skip-gram**, burada ortadaki token $W_0$'dan komşu tokenlerin bir setini {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} tahmin ederiz. -![Kelimeyi vektöre dönüştürme algoritmalarına dair makaleden görsel](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tr.png) +![Kelimeyi vektöre dönüştürme algoritmalarına dair makaleden görsel](../../../../../translated_images/tr/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Görsel [bu makaleden](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tr/lessons/5-NLP/16-RNN/README.md b/translations/tr/lessons/5-NLP/16-RNN/README.md index 63c4c512..86093636 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/README.md +++ b/translations/tr/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Metin dizisinin anlamını yakalamak için, **tekrarlayan sinir ağı** veya RNN adı verilen başka bir sinir ağı mimarisi kullanmamız gerekir. RNN'de, cümlemizi ağdan bir sembol biriminde geçiririz ve ağ bir **durum** üretir, bu durumu bir sonraki sembolle birlikte tekrar ağa geçiririz. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.tr.png) +![RNN](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.png) > Görsel yazar tarafından oluşturulmuştur @@ -61,7 +61,7 @@ Durum C'nin bileşenleri, açılıp kapatılabilen bayraklar olarak düşünüle Tek yönlü veya çift yönlü bir tekrarlayan ağ, bir dizideki belirli kalıpları yakalar ve bunları bir durum vektörüne depolayabilir veya çıktıya aktarabilir. Konvolüsyonel ağlarda olduğu gibi, ilk katman tarafından çıkarılan düşük seviyeli kalıplardan daha yüksek seviyeli kalıpları yakalamak ve inşa etmek için ilk katmanın üzerine başka bir tekrarlayan katman inşa edebiliriz. Bu bizi, önceki katmanın çıktısının bir sonraki katmana giriş olarak geçtiği iki veya daha fazla tekrarlayan ağdan oluşan bir **çok katmanlı RNN** kavramına götürür. -![Çok katmanlı uzun kısa süreli bellek RNN'yi gösteren görsel](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tr.jpg) +![Çok katmanlı uzun kısa süreli bellek RNN'yi gösteren görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg) *[Bu harika yazıdan](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Fernando López tarafından alınmıştır.* diff --git a/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 2c7c0121..5e225acc 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Tekrarlayan ağlar, tek yönlü veya çift yönlü olsun, bir dizideki belirli desenleri yakalar ve bunları durum vektörüne kaydedebilir veya çıktıya aktarabilir. Konvolüsyonel ağlarda olduğu gibi, birinci katman tarafından çıkarılan düşük seviyeli desenlerden daha yüksek seviyeli desenleri yakalamak için birinci katmanın üzerine başka bir tekrarlayan katman inşa edebiliriz. Bu bizi, bir önceki katmanın çıktısının bir sonraki katmana giriş olarak geçtiği, iki veya daha fazla tekrarlayan ağdan oluşan **çok katmanlı RNN** kavramına götürür.\n", "\n", - "![Çok Katmanlı Uzun-Kısa Süreli Bellek RNN'yi gösteren bir görsel](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tr.jpg)\n", + "![Çok Katmanlı Uzun-Kısa Süreli Bellek RNN'yi gösteren bir görsel](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando López'in [bu harika yazısından](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) alınmış bir görsel*\n", "\n", diff --git a/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb index 5bd5a0f6..b2015fc4 100644 --- a/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/tr/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Bir metin dizisinin anlamını yakalamak için, **tekrarlayan sinir ağı** veya RNN adı verilen bir sinir ağı mimarisi kullanacağız. RNN kullanırken, cümlemizi ağı birer birer token olarak geçiririz ve ağ bir **durum** üretir, bu durumu bir sonraki token ile birlikte tekrar ağa iletiriz.\n", "\n", - "![Tekrarlayan sinir ağı üretimine dair bir örnek gösteren görsel.](../../../../../translated_images/rnn.27f5c29c53d727b5.tr.png)\n", + "![Tekrarlayan sinir ağı üretimine dair bir örnek gösteren görsel.](../../../../../translated_images/tr/rnn.27f5c29c53d727b5.png)\n", "\n", "Token giriş dizisi $X_0,\\dots,X_n$ verildiğinde, RNN bir sinir ağı blokları dizisi oluşturur ve bu diziyi baştan sona geri yayılım kullanarak eğitir. Her ağ bloğu $(X_i,S_i)$ çiftini giriş olarak alır ve sonuç olarak $S_{i+1}$ üretir. Son durum $S_n$ veya çıktı $Y_n$, sonucu üretmek için doğrusal bir sınıflandırıcıya gider. Tüm ağ blokları aynı ağırlıkları paylaşır ve tek bir geri yayılım geçişiyle baştan sona eğitilir.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Tek yönlü veya çift yönlü tekrarlayan ağlar, bir dizideki desenleri yakalar ve bunları durum vektörlerine kaydeder veya çıktı olarak döndürür. Konvolüsyonel ağlarda olduğu gibi, birinci katman tarafından çıkarılan alt düzey desenlerden daha yüksek düzey desenleri yakalamak için birinci katmanın ardından başka bir tekrarlayan katman ekleyebiliriz. Bu bizi **çok katmanlı RNN** kavramına götürür; bu, iki veya daha fazla tekrarlayan ağdan oluşur ve önceki katmanın çıktısı bir sonraki katmana giriş olarak aktarılır.\n", "\n", - "![Çok katmanlı uzun-kısa süreli bellek RNN'yi gösteren bir resim](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tr.jpg)\n", + "![Çok katmanlı uzun-kısa süreli bellek RNN'yi gösteren bir resim](../../../../../translated_images/tr/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Fernando López'in [bu harika yazısından](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) alınmış bir resim.*\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index b6493dc1..5f7f904b 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "RNN'yi metin üretmek üzere eğitme yöntemimiz şu şekilde olacak. Her adımda, `nchars` uzunluğunda bir karakter dizisi alacağız ve ağdan her bir giriş karakteri için bir sonraki çıkış karakterini üretmesini isteyeceğiz:\n", "\n", - "![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir görsel.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tr.png)\n", + "![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir görsel.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Gerçek senaryoya bağlı olarak, *dizinin sonu* `` gibi bazı özel karakterleri de dahil etmek isteyebiliriz. Ancak bizim durumumuzda, ağı sonsuz metin üretimi için eğitmek istiyoruz, bu nedenle her bir dizinin boyutunu `nchars` tokenine eşit olarak sabitleyeceğiz. Sonuç olarak, her bir eğitim örneği `nchars` giriş ve `nchars` çıkıştan (giriş dizisinin bir sembol sola kaydırılmış hali) oluşacak. Minibatch, bu tür birkaç diziden oluşacak.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 4bce2ad8..5b7c47d7 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "RNN'i haber başlıkları üretmek için şu şekilde eğiteceğiz. Her adımda bir başlık alacağız, bu başlık bir RNN'e verilecek ve her bir giriş karakteri için ağdan bir sonraki çıkış karakterini üretmesini isteyeceğiz:\n", "\n", - "!['HELLO' kelimesinin bir RNN tarafından nasıl üretildiğini gösteren bir örnek görüntü.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tr.png)\n", + "!['HELLO' kelimesinin bir RNN tarafından nasıl üretildiğini gösteren bir örnek görüntü.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Dizimizin son karakteri için ağdan `` tokenini üretmesini isteyeceğiz.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md index 1ba40921..0c5236ec 100644 --- a/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tr/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Tekrarlayan Sinir Ağları (RNN'ler) ve Uzun Kısa Süreli Bellek Hücreleri (LS Bu, aşağıdaki resimde gösterilen farklı sinir ağı mimarilerini mümkün kılar: -![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir resim.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tr.jpg) +![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir resim.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Resim, [Andrej Karpaty](http://karpathy.github.io/) tarafından yazılan [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) blog yazısından alınmıştır. @@ -32,7 +32,7 @@ Bu birimde, metin üretmemize yardımcı olan basit üretici modeller üzerine o Bu RNN'yi adım adım metin üretmek için eğiteceğiz. Her adımda, `nchars` uzunluğunda bir karakter dizisi alacağız ve ağdan her giriş karakteri için bir sonraki çıktı karakterini üretmesini isteyeceğiz: -![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir resim.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tr.png) +![RNN'nin 'HELLO' kelimesini üretme örneğini gösteren bir resim.](../../../../../translated_images/tr/rnn-generate.56c54afb52f9781d.png) Metin üretirken (çıkarsama sırasında), bazı **başlangıç** verileriyle başlarız. Bu veri RNN hücrelerinden geçirilerek ara durumu oluşturur ve ardından üretim başlar. Her seferinde bir karakter üretiriz ve durumu ve üretilen karakteri bir sonraki karakteri üretmek için başka bir RNN hücresine geçiririz. Bu işlem yeterli sayıda karakter üretilene kadar devam eder. diff --git a/translations/tr/lessons/5-NLP/18-Transformers/README.md b/translations/tr/lessons/5-NLP/18-Transformers/README.md index 9a4dfa96..e7ac6db7 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tr/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNN'lerle diziden-diziye yaklaşımı, iki tekrarlayan ağ tarafından uygulanı **Dikkat Mekanizmaları**, RNN'nin her bir çıktı tahmininde her bir giriş vektörünün bağlamsal etkisini ağırlıklandırmanın bir yolunu sağlar. Bu, giriş RNN'nin ara durumları ile çıkış RNN arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü yt'yi oluştururken, farklı ağırlık katsayıları αt,i ile tüm giriş gizli durumlarını hi dikkate alırız. -![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tr.png) +![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) tarafından önerilen eklemeli dikkat mekanizması ile encoder-decoder modeli, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanmıştır. Dikkat matrisi {αi,j} belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında oynadığı rolü temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir: -![Bahdanau - arviz.org'dan alınan örnek hizalamayı gösteren görsel](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tr.png) +![Bahdanau - arviz.org'dan alınan örnek hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) (Şekil 3) tarafından önerilen şekil. @@ -66,7 +66,7 @@ Pozisyonel gömme ile elde ettiğimiz sonuç, hem orijinal tokenı hem de dizide Sonraki adımda, dizimizdeki bazı desenleri yakalamamız gerekir. Bunu yapmak için transformerlar **kendine dikkat** mekanizmasını kullanır; bu, giriş ve çıkış olarak aynı diziye uygulanan dikkattir. Kendine dikkat uygulamak, cümle içindeki **bağlamı** dikkate almamızı ve hangi kelimelerin birbirleriyle ilişkili olduğunu görmemizi sağlar. Örneğin, *it* gibi zamirlerin hangi kelimelere atıfta bulunduğunu görmemizi ve bağlamı dikkate almamızı sağlar: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.tr.png) +![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.png) > [Google Blogundan](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) alınan görsel. @@ -91,7 +91,7 @@ Her giriş pozisyonu bağımsız olarak her çıkış pozisyonuna eşlendiğinde **BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önceden eğitilir. Ön eğitim sırasında model, dil anlayışının önemli seviyelerini emer ve bu daha sonra diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenme** denir. -![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tr.png) +![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Görsel [kaynağı](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md index 15f7be5a..39e4bb58 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/tr/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ RNN'lerle, dizi-dizi işlemi, bir giriş dizisini gizli bir duruma dönüştüre **Dikkat Mekanizmaları**, RNN'nin her bir çıktı tahmini üzerindeki her giriş vektörünün bağlamsal etkisini ağırlıklandırmanın bir yolunu sunar. Bu, giriş RNN'sinin ara durumları ile çıktı RNN'si arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü yt üretilirken, farklı ağırlık katsayıları αt,i ile tüm giriş gizli durumları hi dikkate alınacaktır. -![Eklemeli dikkat katmanına sahip bir kodlayıcı/çözücü modelini gösteren resim](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tr.png) +![Eklemeli dikkat katmanına sahip bir kodlayıcı/çözücü modelini gösteren resim](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png) > Eklemeli dikkat mekanizmasına sahip kodlayıcı-çözücü modeli [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) tarafından, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntıdır. Dikkat matris {αi,j} belirli giriş kelimelerinin bir çıktı dizisindeki belirli bir kelimenin üretilmesindeki rolünü temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir: -![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek hizalamayı gösteren resim](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tr.png) +![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek hizalamayı gösteren resim](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf) (Şekil 3) @@ -57,7 +57,7 @@ Konumsal gömme ile elde ettiğimiz sonuç, hem orijinal tokenı hem de dizideki Sonraki adım, dizimizdeki bazı desenleri yakalamaktır. Bunu yapmak için, transformerlar **kendine dikkat** mekanizmasını kullanır; bu, esasen aynı diziyi girdi ve çıktı olarak uygulanan dikkattir. Kendine dikkati uygulamak, cümle içindeki **bağlamı** dikkate almamızı sağlar ve hangi kelimelerin birbiriyle ilişkili olduğunu görmemize olanak tanır. Örneğin, hangi kelimelerin *o* gibi referanslarla ifade edildiğini görmemizi sağlar ve ayrıca bağlamı dikkate alır: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.tr.png) +![](../../../../../translated_images/tr/CoreferenceResolution.861924d6d384a7d6.png) > Resim [Google Blogu](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) kaynaklı @@ -82,7 +82,7 @@ Her bir giriş konumu bağımsız olarak her bir çıkış konumuna eşlendiğin **BERT** (Transformerlardan İkili Kodlayıcı Temsilleri), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, öncelikle geniş bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümlede maskelenmiş kelimeleri tahmin etme) kullanılarak önceden eğitilir. Ön eğitim sırasında model, daha sonra ince ayar ile diğer veri kümeleri ile kullanılabilecek önemli düzeyde dil anlayışı kazanır. Bu süreç **aktarım öğrenimi** olarak adlandırılır. -![http://jalammar.github.io/illustrated-bert/ adresinden bir resim](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tr.png) +![http://jalammar.github.io/illustrated-bert/ adresinden bir resim](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Resim [kaynak](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index f8edc981..328c2bcb 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/tr/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Dikkat Mekanizmaları**, her bir giriş vektörünün RNN'nin her bir çıktı tahmini üzerindeki bağlamsal etkisini ağırlıklandırmanın bir yolunu sağlar. Bu, giriş RNN'nin ara durumları ile çıkış RNN arasında kısayollar oluşturarak uygulanır. Bu şekilde, çıktı sembolü $y_t$ oluşturulurken, farklı ağırlık katsayıları $\\alpha_{t,i}$ ile tüm giriş gizli durumlarını $h_i$ dikkate alırız.\n", "\n", - "![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tr.png)\n", + "![Eklenecek bir dikkat katmanı ile encoder/decoder modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau ve ark., 2015](https://arxiv.org/pdf/1409.0473.pdf) çalışmasından alınan ve [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanan eklemeli dikkat mekanizmalı encoder-decoder modeli]*\n", "\n", "Dikkat matrisi $\\{\\alpha_{i,j}\\}$, belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında oynadığı rol derecesini temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir:\n", "\n", - "![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek bir hizalamayı gösteren görsel](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tr.png)\n", + "![Bahdanau - arviz.org'dan alınan RNNsearch-50 tarafından bulunan örnek bir hizalamayı gösteren görsel](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau ve ark., 2015](https://arxiv.org/pdf/1409.0473.pdf) çalışmasından alınan (Şekil 3)]*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (WikiPedia + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önce önceden eğitilir. Önceden eğitim sırasında model, önemli bir dil anlayışı seviyesini emer ve bu daha sonra diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenme** denir.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tr.png)\n", + "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Transformer mimarilerinin BERT, DistilBERT, BigBird, OpenGPT3 ve daha fazlası gibi birçok varyasyonu vardır ve bunlar ince ayar yapılabilir. [HuggingFace paketi](https://github.com/huggingface/) PyTorch ile bu mimarilerin birçoğunu eğitmek için bir depo sağlar.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 96d3482c..a8701641 100644 --- a/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/tr/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Dikkat Mekanizmaları**, her bir giriş vektörünün RNN'nin her bir çıktı tahmini üzerindeki bağlamsal etkisini ağırlıklandırmanın bir yolunu sunar. Bu, giriş RNN'sinin ara durumları ile çıkış RNN'si arasında kısayollar oluşturarak uygulanır. Bu şekilde, $y_t$ çıktı sembolünü üretirken, farklı ağırlık katsayıları $\\alpha_{t,i}$ ile tüm giriş gizli durumlarını $h_i$ dikkate alırız.\n", "\n", - "![Eklendiği bir dikkat katmanına sahip kodlayıcı/çözücü modelini gösteren görsel](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tr.png)\n", + "![Eklendiği bir dikkat katmanına sahip kodlayıcı/çözücü modelini gösteren görsel](../../../../../translated_images/tr/encoder-decoder-attention.7a726296894fb567.png)\n", "*[Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf)'teki toplamsal dikkat mekanizmasına sahip kodlayıcı-çözücü modeli, [bu blog yazısından](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) alıntılanmıştır.*\n", "\n", "Dikkat matrisi $\\{\\alpha_{i,j}\\}$, belirli giriş kelimelerinin çıktı dizisindeki bir kelimenin oluşturulmasında ne derece etkili olduğunu temsil eder. Aşağıda böyle bir matrisin örneği verilmiştir:\n", "\n", - "![RNNsearch-50 tarafından bulunan bir hizalamayı gösteren görsel, Bahdanau - arviz.org'dan alınmıştır](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tr.png)\n", + "![RNNsearch-50 tarafından bulunan bir hizalamayı gösteren görsel, Bahdanau - arviz.org'dan alınmıştır](../../../../../translated_images/tr/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau ve diğerleri, 2015](https://arxiv.org/pdf/1409.0473.pdf)'ten alınan şekil (Şekil 3)*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers), *BERT-base* için 12 katman ve *BERT-large* için 24 katman içeren çok büyük bir çok katmanlı transformer ağıdır. Model, büyük bir metin veri kümesi (Vikipedi + kitaplar) üzerinde denetimsiz eğitim (bir cümledeki maskelenmiş kelimeleri tahmin etme) kullanılarak önce önceden eğitilir. Ön eğitim sırasında model, önemli bir dil anlama seviyesini öğrenir ve bu bilgi, diğer veri kümeleriyle ince ayar yapılarak kullanılabilir. Bu sürece **transfer öğrenimi** denir.\n", "\n", - "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tr.png)\n", + "![http://jalammar.github.io/illustrated-bert/ adresinden alınan görsel](../../../../../translated_images/tr/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "BERT, DistilBERT, BigBird, OpenGPT3 ve daha fazlası gibi ince ayar yapılabilen birçok Transformer mimarisi varyasyonu bulunmaktadır.\n", "\n", diff --git a/translations/tr/lessons/5-NLP/19-NER/README.md b/translations/tr/lessons/5-NLP/19-NER/README.md index a08f71ae..d53618bc 100644 --- a/translations/tr/lessons/5-NLP/19-NER/README.md +++ b/translations/tr/lessons/5-NLP/19-NER/README.md @@ -57,7 +57,7 @@ bebekte | O Tokenler ve sınıflar arasında birebir bir ilişki kurmamız gerektiğinden, bu resimden **çoktan-çoka** bir sinir ağı modeli eğitebiliriz: -![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir görsel.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tr.jpg) +![Yaygın tekrarlayan sinir ağı desenlerini gösteren bir görsel.](../../../../../translated_images/tr/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Görsel, [Andrej Karpathy](http://karpathy.github.io/) tarafından yazılmış [bu blog yazısından](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) alınmıştır. NER token sınıflandırma modelleri, bu resimdeki en sağdaki ağ mimarisine karşılık gelir.* diff --git a/translations/tr/lessons/5-NLP/README.md b/translations/tr/lessons/5-NLP/README.md index 0a3460d0..34cbbf98 100644 --- a/translations/tr/lessons/5-NLP/README.md +++ b/translations/tr/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Doğal Dil İşleme -![NLP görevlerinin bir çizimi](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.tr.png) +![NLP görevlerinin bir çizimi](../../../../translated_images/tr/ai-nlp.b22dcb8ca4707cea.png) Bu bölümde, **Doğal Dil İşleme (NLP)** ile ilgili görevleri çözmek için Sinir Ağlarını kullanmaya odaklanacağız. Bilgisayarların çözmesini istediğimiz birçok NLP problemi bulunmaktadır: diff --git a/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md index b12b192d..4e48d2ae 100644 --- a/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tr/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Bir modeli açabilirsiniz, örneğin **Biology → Flocking**. Modeli açtıktan sonra, ana NetLogo ekranına yönlendirilirsiniz. İşte sınırlı kaynaklar (ot) göz önüne alındığında kurtlar ve koyunların popülasyonunu açıklayan örnek bir model. -![NetLogo Ana Ekran](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.tr.png) +![NetLogo Ana Ekran](../../../../../translated_images/tr/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov tarafından ekran görüntüsü diff --git a/translations/tr/lessons/README.md b/translations/tr/lessons/README.md index 1001426d..94709979 100644 --- a/translations/tr/lessons/README.md +++ b/translations/tr/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Genel Bakış -![Bir çizimde genel bakış](../../../translated_images/ai-overview.0857791951d19500.tr.png) +![Bir çizimde genel bakış](../../../translated_images/tr/ai-overview.0857791951d19500.png) > Çizim notu: [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/tr/lessons/X-Extras/X1-MultiModal/README.md b/translations/tr/lessons/X-Extras/X1-MultiModal/README.md index 5bf4d526..88023847 100644 --- a/translations/tr/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tr/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Transformer modellerinin NLP görevlerini çözmedeki başarısından sonra, ayn CLIP'in temel fikri, metin istemlerini bir görüntüyle karşılaştırabilmek ve görüntünün istemle ne kadar iyi eşleştiğini belirlemektir. -![CLIP Mimari](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.tr.png) +![CLIP Mimari](../../../../../translated_images/tr/clip-arch.b3dbf20b4e8ed8be.png) > *Resim [bu blog yazısından](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Bu model önceden eğitildikten sonra, bir grup görüntü ve bir grup metin ist Diyelim ki görüntüleri kediler, köpekler ve insanlar arasında sınıflandırmamız gerekiyor. Bu durumda, modele bir görüntü ve bir dizi metin istemi verebiliriz: "*bir kedi resmi*", "*bir köpek resmi*", "*bir insan resmi*". Sonuçta elde edilen 3 olasılık vektöründe en yüksek değere sahip indeksi seçmemiz yeterlidir. -![CLIP ile Görüntü Sınıflandırma](../../../../../translated_images/clip-class.3af42ef0b2b19369.tr.png) +![CLIP ile Görüntü Sınıflandırma](../../../../../translated_images/tr/clip-class.3af42ef0b2b19369.png) > *Resim [bu blog yazısından](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN hakkında daha fazla bilgi edinmek için [Taming Transformers](https://com VQGAN ile geleneksel GAN arasındaki önemli farklardan biri, geleneksel GAN'ın herhangi bir giriş vektöründen düzgün bir görüntü üretebilmesi, ancak VQGAN'ın tutarlı bir görüntü üretme olasılığının düşük olmasıdır. Bu nedenle, görüntü oluşturma sürecini daha fazla yönlendirmemiz gerekir ve bu CLIP kullanılarak yapılabilir. -![VQGAN+CLIP Mimari](../../../../../translated_images/vqgan.5027fe05051dfa31.tr.png) +![VQGAN+CLIP Mimari](../../../../../translated_images/tr/vqgan.5027fe05051dfa31.png) Bir metin istemine karşılık gelen bir görüntü oluşturmak için, rastgele bir kodlama vektörüyle başlarız ve bu vektör VQGAN'dan geçirilerek bir görüntü oluşturulur. Daha sonra CLIP, görüntünün metin istemine ne kadar iyi uyduğunu gösteren bir kayıp fonksiyonu üretmek için kullanılır. Amaç, bu kaybı minimize etmek ve geri yayılım kullanarak giriş vektör parametrelerini ayarlamaktır. VQGAN+CLIP'i uygulayan harika bir kütüphane [Pixray](http://github.com/pixray/pixray)'dir. -![Pixray tarafından üretilen resim](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.tr.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.tr.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.tr.png) +![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- *Edebiyat öğretmeni olan genç bir erkeğin kitapla yakın çekim suluboya portresi* isteminden üretilen resim | *Bilgisayar bilimi öğretmeni olan genç bir kadının bilgisayarla yakın çekim yağlı boya portresi* isteminden üretilen resim | *Matematik öğretmeni olan yaşlı bir erkeğin kara tahta önünde yakın çekim yağlı boya portresi* isteminden üretilen resim @@ -75,7 +75,7 @@ CLIP'ten farklı olarak, DALL-E hem metni hem de görüntüyü bir dizi token ol DALL-E 1 ve 2 arasındaki temel fark, DALL-E 2'nin daha gerçekçi görüntüler ve sanat eserleri üretmesidir. DALL-E ile görüntü oluşturma örnekleri: -![Pixray tarafından üretilen resim](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.tr.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.tr.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.tr.png) +![Pixray tarafından üretilen resim](../../../../../translated_images/tr/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray tarafından üretilen resim](../../../../../translated_images/tr/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- *Edebiyat öğretmeni olan genç bir erkeğin kitapla yakın çekim suluboya portresi* isteminden üretilen resim | *Bilgisayar bilimi öğretmeni olan genç bir kadının bilgisayarla yakın çekim yağlı boya portresi* isteminden üretilen resim | *Matematik öğretmeni olan yaşlı bir erkeğin kara tahta önünde yakın çekim yağlı boya portresi* isteminden üretilen resim diff --git a/translations/tw/README.md b/translations/tw/README.md index d32c6e5b..04553fc8 100644 --- a/translations/tw/README.md +++ b/translations/tw/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 初學者的人工智慧課程 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.tw.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tw/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _筆記速寫由 [@girlie_mac](https://twitter.com/girlie_mac) 提供_ | diff --git a/translations/tw/lessons/1-Intro/README.md b/translations/tw/lessons/1-Intro/README.md index 81c400a9..122303c8 100644 --- a/translations/tw/lessons/1-Intro/README.md +++ b/translations/tw/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智慧簡介 -![人工智慧內容摘要的手繪圖](../../../../translated_images/ai-intro.bf28d1ac4235881c.tw.png) +![人工智慧內容摘要的手繪圖](../../../../translated_images/tw/ai-intro.bf28d1ac4235881c.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，電腦由 [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) 發明，用來根據明確定義的程序（演算法）操作數字。現代電腦雖然比19世紀提出的原型複雜得多，但仍然遵循受控計算的理念。因此，只要我們知道達成目標所需的精確步驟，就可以編程讓電腦完成某些事情。 -![一個人的照片](../../../../translated_images/dsh_age.d212a30d4e54fb5f.tw.png) +![一個人的照片](../../../../translated_images/tw/dsh_age.d212a30d4e54fb5f.png) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 在討論**[智能](https://en.wikipedia.org/wiki/Intelligence)**這個術語時，其中一個問題是我們對此並沒有明確的定義。有人可能認為智能與**抽象思維**或**自我意識**相關，但我們無法準確定義它。 -![貓的照片](../../../../translated_images/photo-cat.8c8e8fb760ffe457.tw.jpg) +![貓的照片](../../../../translated_images/tw/photo-cat.8c8e8fb760ffe457.jpg) > [照片](https://unsplash.com/photos/75715CVEJhI)由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，來自 Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那機器學習呢？ | | > |--------------|-----------| -> | 基於電腦通過某些數據學習解決問題的人工智慧部分稱為**機器學習**。我們不會在本課程中考慮傳統機器學習——我們推薦您參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.tw.png) | +> | 基於電腦通過某些數據學習解決問題的人工智慧部分稱為**機器學習**。我們不會在本課程中考慮傳統機器學習——我們推薦您參考單獨的 [機器學習初學者課程](http://aka.ms/ml-beginners)。 | ![機器學習初學者課程](../../../../translated_images/tw/ml-for-beginners.9e4fed176fd5817d.png) | ## 人工智慧的簡史 人工智慧作為一個領域始於20世紀中期。最初，符號推理是主要方法，並取得了一些重要成功，例如專家系統——能夠在某些有限問題領域中充當專家的電腦程序。然而，很快就發現這種方法的可擴展性不佳。從專家中提取知識、在電腦中表示知識並保持知識庫的準確性，事實證明是一項非常複雜且在許多情況下成本過高的任務。這導致了1970年代的所謂[人工智慧寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智慧簡史 +人工智慧簡史 > 圖片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 現代助理，例如 Cortana、Siri 或 Google Assistant，都是混合系統，使用神經網路將語音轉換為文本並識別我們的意圖，然後採用一些推理或明確算法執行所需操作。 * 未來，我們可能期待一個完全基於神經網路的模型能夠自行處理對話。最近的 GPT 和 [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) 神經網路系列顯示了巨大的成功。 -圖靈測試的演變 +圖靈測試的演變 > 圖片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns) 由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，Unsplash ## 最近的人工智慧研究 diff --git a/translations/tw/lessons/2-Symbolic/README.md b/translations/tw/lessons/2-Symbolic/README.md index b0d9fb0a..eb8b5ef7 100644 --- a/translations/tw/lessons/2-Symbolic/README.md +++ b/translations/tw/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 知識表示與專家系統 -![符號 AI 內容摘要](../../../../translated_images/ai-symbolic.715a30cb610411a6.tw.png) +![符號 AI 內容摘要](../../../../translated_images/tw/ai-symbolic.715a30cb610411a6.png) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 因此，**知識表示**的問題是找到某種有效的方法，將知識以數據的形式表示在計算機中，使其能夠自動使用。這可以看作是一個光譜： -![知識表示光譜](../../../../translated_images/knowledge-spectrum.b60df631852c0217.tw.png) +![知識表示光譜](../../../../translated_images/tw/knowledge-spectrum.b60df631852c0217.png) > 圖片來源：[Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | 區塊語法 | 縮排 符號 AI 的早期成功之一是所謂的**專家系統**——設計用於在某些有限問題領域中充當專家的計算機系統。它們基於從一位或多位人類專家提取的**知識庫**，並包含一個在其上進行推理的**推理引擎**。 -![人類架構](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.tw.png) | ![基於知識的系統架構](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.tw.png) +![人類架構](../../../../translated_images/tw/arch-human.5d4d35f1bba3ab1c.png) | ![基於知識的系統架構](../../../../translated_images/tw/arch-kbs.3ec5c150b09fa8da.png) ----------------------------------|---------------------------------------- 人類神經系統的簡化結構 | 基於知識的系統架構 @@ -106,7 +106,7 @@ Python | 區塊語法 | 縮排 例如，讓我們考慮以下基於動物物理特徵的專家系統： -![AND-OR 樹](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.tw.png) +![AND-OR 樹](../../../../translated_images/tw/AND-OR-Tree.5592d2c70187f283.png) > 圖片來源：[Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md index 329c3351..cf8df536 100644 --- a/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tw/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考慮以下近似 5 個點的問題（圖中的 `x` 表示點）： -![線性模型](../../../../../translated_images/overfit1.f24b71c6f652e59e.tw.jpg) | ![過擬合模型](../../../../../translated_images/overfit2.131f5800ae10ca5e.tw.jpg) +![線性模型](../../../../../translated_images/tw/overfit1.f24b71c6f652e59e.jpg) | ![過擬合模型](../../../../../translated_images/tw/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **線性模型，2 個參數** | **非線性模型，7 個參數** 訓練誤差 = 5.3 | 訓練誤差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 從上圖可以看出，過擬合可以通過非常低的訓練誤差和非常高的驗證誤差來檢測。通常在訓練過程中，我們會看到訓練誤差和驗證誤差都開始下降，但在某個時候，驗證誤差可能停止下降並開始上升。這將是過擬合的跡象，表明我們應該停止訓練（或者至少保存模型的快照）。 -![過擬合圖示](../../../../../translated_images/Overfitting.408ad91cd90b4371.tw.png) +![過擬合圖示](../../../../../translated_images/tw/Overfitting.408ad91cd90b4371.png) ## 如何防止過擬合 diff --git a/translations/tw/lessons/3-NeuralNetworks/README.md b/translations/tw/lessons/3-NeuralNetworks/README.md index 3e94ca4f..e6ee11ca 100644 --- a/translations/tw/lessons/3-NeuralNetworks/README.md +++ b/translations/tw/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神經網路簡介 -![神經網路內容摘要的手繪圖](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.tw.png) +![神經網路內容摘要的手繪圖](../../../../translated_images/tw/ai-neuralnetworks.1c687ae40bc86e83.png) 如我們在介紹中所討論的，實現智能的一種方法是訓練一個**計算機模型**或**人工大腦**。自20世紀中期以來，研究人員嘗試了不同的數學模型，直到最近這一方向證明非常成功。這些模仿大腦的數學模型被稱為**神經網路**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 從生物學中，我們知道大腦由神經細胞（神經元）組成，每個神經元有多個“輸入”（樹突）和一個“輸出”（軸突）。樹突和軸突都能傳導電信號，而它們之間的連接——稱為突觸——可以表現出不同程度的導電性，這些導電性由神經遞質調節。 -![神經元模型](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.tw.jpg) | ![神經元模型](../../../../translated_images/artneuron.1a5daa88d20ebe6f.tw.png) +![神經元模型](../../../../translated_images/tw/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神經元模型](../../../../translated_images/tw/artneuron.1a5daa88d20ebe6f.png) ----|---- 真實神經元 *（[圖片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) 來自維基百科）* | 人工神經元 *（作者提供圖片）* 因此，神經元的最簡單數學模型包含幾個輸入 X1, ..., XN 和一個輸出 Y，以及一系列權重 W1, ..., WN。輸出計算公式為： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某種非線性的**激活函數**。 diff --git a/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md index 8420e95b..57638d82 100644 --- a/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tw/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **預處理盲文書的照片**。我們專注於如何使用閾值處理、特徵檢測、透視變換和 NumPy 操作來分離單個盲文符號，以便進一步由神經網路進行分類。 -![盲文影像](../../../../../translated_images/braille.341962ff76b1bd70.tw.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/braille-result.46530fea020b03c7.tw.png) | ![盲文符號](../../../../../translated_images/braille-symbols.0159185ab69d5339.tw.png) +![盲文影像](../../../../../translated_images/tw/braille.341962ff76b1bd70.jpeg) | ![盲文影像預處理結果](../../../../../translated_images/tw/braille-result.46530fea020b03c7.png) | ![盲文符號](../../../../../translated_images/tw/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) * **使用幀差檢測影片中的運動**。如果相機是固定的，那麼相機畫面中的幀應該彼此非常相似。由於幀被表示為陣列，只需對兩個連續幀的陣列進行相減，我們就能得到像素差異，靜態幀的差異應該很低，而當影像中有顯著運動時，差異會變高。 -![影片幀和幀差的影像](../../../../../translated_images/frame-difference.706f805491a0883c.tw.png) +![影片幀和幀差的影像](../../../../../translated_images/tw/frame-difference.706f805491a0883c.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流** 計算每個像素的移動向量場 - **稀疏光流** 基於影像中的一些顯著特徵（例如邊緣），並從幀到幀構建它們的軌跡。 -![光流影像](../../../../../translated_images/optical.1f4a94464579a83a.tw.png) +![光流影像](../../../../../translated_images/tw/optical.1f4a94464579a83a.png) > 圖片來自 [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 60b04d54..24eac3bc 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 是一個在 2014 年 ImageNet top-5 分類中達到 92.7% 準確率的網路。它的層結構如下： -![ImageNet 層結構](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tw.jpg) +![ImageNet 層結構](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.jpg) 如圖所示，VGG 採用了傳統的金字塔架構，也就是一系列的卷積-池化層。 -![ImageNet 金字塔](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tw.jpg) +![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md index be3c0ea4..3645ff90 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 為了提取模式，我們將使用**卷積濾波器**的概念。正如你所知，圖像是由一個二維矩陣或具有顏色深度的三維張量表示的。應用濾波器意味著我們取一個相對較小的**濾波器核**矩陣，並對原始圖像中的每個像素與其鄰近點進行加權平均。我們可以將其視為一個小窗口滑過整個圖像，並根據濾波器核矩陣中的權重對所有像素進行平均。 -![垂直邊緣濾波器](../../../../../translated_images/filter-vert.b7148390ca0bc356.tw.png) | ![水平邊緣濾波器](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.tw.png) +![垂直邊緣濾波器](../../../../../translated_images/tw/filter-vert.b7148390ca0bc356.png) | ![水平邊緣濾波器](../../../../../translated_images/tw/filter-horiz.59b80ed4feb946ef.png) ----|---- > 圖片來源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基於以下重要思想： * 我們可以設計網絡，使濾波器能夠自動訓練 * 我們可以使用相同的方法來在高層次特徵中找到模式，而不僅僅是在原始圖像中。因此，CNN 的特徵提取在特徵的層次結構中工作，從低層次的像素組合開始，到更高層次的圖像部分組合。 -![層次特徵提取](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.tw.png) +![層次特徵提取](../../../../../translated_images/tw/FeatureExtractionCNN.d9b456cbdae7cb64.png) > 圖片來源：[Hislop-Lynch 的論文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基於[他們的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基於以下重要思想： 例如，讓我們看看 VGG-16 的架構，這是一個在 2014 年 ImageNet 的 top-5 分類中達到 92.7% 準確率的網絡： -![ImageNet 層](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.tw.jpg) +![ImageNet 層](../../../../../translated_images/tw/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet 金字塔](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.tw.jpg) +![ImageNet 金字塔](../../../../../translated_images/tw/vgg-16-arch.64ff2137f50dd49f.jpg) > 圖片來源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9bb452c7..eac3d47b 100644 --- a/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tw/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，該數據集包含 37 種不同品種的狗和貓的圖片。 -![我們將處理的數據集](../../../../../../translated_images/data.50b2a9d5484bdbf0.tw.png) +![我們將處理的數據集](../../../../../../translated_images/tw/data.50b2a9d5484bdbf0.png) 要下載數據集，請使用以下程式碼片段： diff --git a/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md index a2439185..c683e853 100644 --- a/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tw/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 以下是 VGG-16 網路從一張貓的圖片中提取的特徵示例： -![VGG-16 提取的特徵](../../../../../translated_images/features.6291f9c7ba3a0b95.tw.png) +![VGG-16 提取的特徵](../../../../../translated_images/tw/features.6291f9c7ba3a0b95.png) ## 貓與狗數據集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含了方便的函數，可以輕鬆加載一些常見 我們可以採取的一種方法是從一張隨機圖像開始，然後嘗試使用**梯度下降優化**技術調整該圖像，使得網路認為它是一隻貓。 -![圖像優化循環](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.tw.png) +![圖像優化循環](../../../../../translated_images/tw/ideal-cat-loop.999fbb8ff306e044.png) 然而，如果我們這樣做，我們會得到一些非常接近隨機噪聲的東西。這是因為*有很多方法可以讓網路認為輸入圖像是一隻貓*，其中一些方法在視覺上並不合理。雖然這些圖像包含了許多典型於貓的模式，但並沒有任何約束使它們在視覺上具有辨識度。 為了改善結果，我們可以在損失函數中添加另一個項，稱為**變異損失**。這是一種衡量圖像中相鄰像素相似程度的指標。最小化變異損失可以使圖像更平滑，並消除噪聲——從而揭示出更具視覺吸引力的模式。以下是一些這樣的「理想」圖像的例子，它們被高概率地分類為貓和斑馬： -![理想貓](../../../../../translated_images/ideal-cat.203dd4597643d6b0.tw.png) | ![理想斑馬](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.tw.png) +![理想貓](../../../../../translated_images/tw/ideal-cat.203dd4597643d6b0.png) | ![理想斑馬](../../../../../translated_images/tw/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *理想貓* | *理想斑馬* 類似的方法可以用來對神經網路進行所謂的**對抗性攻擊**。假設我們想要欺騙神經網路，讓一隻狗看起來像一隻貓。如果我們拿一張狗的圖片，該圖片被網路識別為狗，然後稍微調整它，使用梯度下降優化，直到網路開始將其分類為貓： -![狗的圖片](../../../../../translated_images/original-dog.8f68a67d2fe0911f.tw.png) | ![被分類為貓的狗圖片](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.tw.png) +![狗的圖片](../../../../../translated_images/tw/original-dog.8f68a67d2fe0911f.png) | ![被分類為貓的狗圖片](../../../../../translated_images/tw/adversarial-dog.d9fc7773b0142b89.png) -----|----- *原始狗圖片* | *被分類為貓的狗圖片* diff --git a/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md index 134e7977..af35c403 100644 --- a/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tw/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由於我們訓練自動編碼器以捕捉原始圖像中的盡可能多的信息以進行準確重建，網絡會嘗試找到最佳的**嵌入**來捕捉輸入圖像的含義。 -![自動編碼器示意圖](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.tw.jpg) +![自動編碼器示意圖](../../../../../translated_images/tw/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 圖片來源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md index de1dc4d2..76ec084f 100644 --- a/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tw/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [課前測驗](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![物件偵測](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.tw.png) +![物件偵測](../../../../../translated_images/tw/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 圖片來源：[YOLO v2 網站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 對每個區塊進行影像分類。 3. 對於分類結果有足夠高信心的區塊，可以認為包含目標物件。 -![簡單物件偵測](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.tw.png) +![簡單物件偵測](../../../../../translated_images/tw/naive-detection.e7f1ba220ccd08c6.png) > *圖片來源：[練習筆記本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含 20 個類別 * [COCO](http://cocodataset.org/#home) - 常見物件的上下文。包含 80 個類別、邊界框和分割遮罩 -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.tw.jpg) +![COCO](../../../../../translated_images/tw/coco-examples.71bc60380fa6cceb.jpg) ## 物件偵測的評估指標 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 在影像分類中，衡量算法表現相對簡單；但在物件偵測中，我們需要同時衡量類別的正確性以及推測邊界框位置的精確性。對於後者，我們使用所謂的**交集比聯集** (IoU)，它衡量兩個框（或任意兩個區域）的重疊程度。 -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.tw.png) +![IoU](../../../../../translated_images/tw/iou_equation.9a4751d40fff4e11.png) > *圖片來源：[這篇優秀的 IoU 部落格文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) 使用[選擇性搜索](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)生成 ROI 區域的層次結構，然後通過 CNN 特徵提取器和 SVM 分類器來確定物件類別，並通過線性回歸確定*邊界框*座標。[官方論文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.tw.png) +![RCNN](../../../../../translated_images/tw/rcnn1.cae407020dfb1d1f.png) > *圖片來源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.tw.png) +![RCNN-1](../../../../../translated_images/tw/rcnn2.2d9530bb83516484.png) > *圖片來源：[這篇部落格](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 這種方法與 R-CNN 類似，但區域是在卷積層應用後定義的。 -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.tw.png) +![FRCNN](../../../../../translated_images/tw/f-rcnn.3cda6d9bb4188875.png) > 圖片來源：[官方論文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 這種方法的主要思想是使用神經網路來預測 ROI，即所謂的*區域提案網路* (Region Proposal Network)。[論文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.tw.png) +![FasterRCNN](../../../../../translated_images/tw/faster-rcnn.8d46c099b87ef30a.png) > 圖片來源：[官方論文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特徵通過**位置敏感分數圖** (Position-Sensitive Score Map) 處理。每個類別 $C$ 的物件被分成 $k\times k$ 區域，我們訓練模型來預測物件的部分。 3. 對於 $k\times k$ 區域中的每個部分，所有網路對物件類別進行投票，選擇投票最多的物件類別。 -![r-fcn 圖片](../../../../../translated_images/r-fcn.13eb88158b99a3da.tw.png) +![r-fcn 圖片](../../../../../translated_images/tw/r-fcn.13eb88158b99a3da.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO 是一種即時的一次通過算法。主要思想如下： * 將圖片分成 $S\times S$ 區域。 * 對每個區域，**CNN** 預測 $n$ 個可能的物件、*邊界框*座標以及*信心值*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.tw.png) + ![YOLO](../../../../../translated_images/tw/yolo.a2648ec82ee8bb4e.png) > 圖片來源：[官方論文](https://arxiv.org/abs/1506.02640) diff --git a/translations/tw/lessons/4-ComputerVision/README.md b/translations/tw/lessons/4-ComputerVision/README.md index 7fc962ad..800b4e3c 100644 --- a/translations/tw/lessons/4-ComputerVision/README.md +++ b/translations/tw/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 電腦視覺 -![電腦視覺內容摘要手繪圖](../../../../translated_images/ai-computervision.6506ebebac3fbf76.tw.png) +![電腦視覺內容摘要手繪圖](../../../../translated_images/tw/ai-computervision.6506ebebac3fbf76.png) 在本節中，我們將學習以下內容： diff --git a/translations/tw/lessons/5-NLP/14-Embeddings/README.md b/translations/tw/lessons/5-NLP/14-Embeddings/README.md index 04b310b2..9cd78c98 100644 --- a/translations/tw/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tw/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通過在分類器網絡中使用嵌入層作為第一層，我們可以從詞袋模型切換到 **嵌入袋** 模型。在嵌入袋模型中，我們首先將文本中的每個詞轉換為相應的嵌入，然後對所有嵌入計算某種聚合函數，例如 `sum`、`average` 或 `max`。 -![展示五個序列詞的嵌入分類器的圖片。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.tw.png) +![展示五個序列詞的嵌入分類器的圖片。](../../../../../translated_images/tw/embedding-classifier-example.b77f021a7ee67eee.png) > 圖片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 訓練速度更快，而 Skip-Gram 雖然較慢，但在表示不常見詞方面效果更好。 -![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tw.png) +![展示 CBoW 和 Skip-Gram 算法如何將詞轉換為向量的圖片。](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md index 612f2b12..30e5cbd8 100644 --- a/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tw/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **連續詞袋模型** (CBoW)，在詞元序列 $W_{-N}$, ..., $W_N$ 中預測中間的詞元 $W_0$。 * **Skip-gram**，通過中間詞元 $W_0$ 預測一組相鄰的詞元 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![來自論文的將詞轉換為向量的算法示例](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.tw.png) +![來自論文的將詞轉換為向量的算法示例](../../../../../translated_images/tw/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 圖片來源：[這篇論文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/tw/lessons/5-NLP/16-RNN/README.md b/translations/tw/lessons/5-NLP/16-RNN/README.md index 97385f96..9da74156 100644 --- a/translations/tw/lessons/5-NLP/16-RNN/README.md +++ b/translations/tw/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 為了捕捉文本序列的意義，我們需要使用另一種神經網絡架構，稱為**循環神經網絡**（Recurrent Neural Network，簡稱 RNN）。在 RNN 中，我們將句子逐個符號地傳遞給網絡，網絡會生成某種**狀態**，然後將該狀態與下一個符號一起再次傳遞給網絡。 -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.tw.png) +![RNN](../../../../../translated_images/tw/rnn.27f5c29c53d727b5.png) > 圖片由作者提供 @@ -61,7 +61,7 @@ LSTM 網絡的組織方式與 RNN 類似，但有兩個狀態會從層到層傳 循環網絡（無論是單向還是雙向）能夠捕捉序列中的某些模式，並將它們存儲到狀態向量中或傳遞到輸出中。與卷積網絡類似，我們可以在第一層之上構建另一個循環層，以捕捉更高層次的模式，並基於第一層提取的低層次模式進行構建。這引出了**多層 RNN** 的概念，它由兩個或更多循環網絡組成，其中前一層的輸出作為輸入傳遞到下一層。 -![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.tw.jpg) +![顯示多層長短期記憶 RNN 的圖片](../../../../../translated_images/tw/multi-layer-lstm.dd975e29bb2a59fe.jpg) *圖片來自 Fernando López 的[這篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md index 6b687587..78483775 100644 --- a/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tw/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 這使得不同的神經網絡架構成為可能，如下圖所示： -![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tw.jpg) +![展示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 圖片來源：[Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我們將訓練這個 RNN 逐步生成文本。在每一步中，我們將取一個長度為 `nchars` 的字符序列，並要求網絡為每個輸入字符生成下一個輸出字符： -![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.tw.png) +![展示 RNN 生成單詞 'HELLO' 的示例圖片。](../../../../../translated_images/tw/rnn-generate.56c54afb52f9781d.png) 在生成文本（推理過程中），我們從某個**提示**開始，將其通過 RNN 單元生成中間狀態，然後從該狀態開始生成。我們一次生成一個字符，並將狀態和生成的字符傳遞給另一個 RNN 單元以生成下一個字符，直到生成足夠的字符。 diff --git a/translations/tw/lessons/5-NLP/18-Transformers/README.md b/translations/tw/lessons/5-NLP/18-Transformers/README.md index e2c0764d..c84ccee7 100644 --- a/translations/tw/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tw/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意機制**提供了一種方法，能夠對每個輸入向量對RNN輸出預測的上下文影響進行加權。其實現方式是通過在輸入RNN的中間狀態和輸出RNN之間建立捷徑。在生成輸出符號yt時，我們會考慮所有輸入隱藏狀態hi，並賦予不同的權重係數αt,i。 -![顯示具有加性注意層的編碼器/解碼器模型的圖片](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.tw.png) +![顯示具有加性注意層的編碼器/解碼器模型的圖片](../../../../../translated_images/tw/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意機制編碼器-解碼器模型，引用自[這篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意矩陣{αi,j}表示某些輸入詞在生成輸出序列中的某個詞時所起的作用程度。以下是一個這樣的矩陣示例： -![顯示由RNNsearch-50找到的樣本對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.tw.png) +![顯示由RNNsearch-50找到的樣本對齊的圖片，取自Bahdanau - arviz.org](../../../../../translated_images/tw/bahdanau-fig3.09ba2d37f202a6af.png) > 圖片來自[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)（圖3） @@ -66,7 +66,7 @@ Transformer的主要理念之一是避免RNN的序列特性，並創建一個在 接下來，我們需要捕捉序列中的一些模式。為此，Transformer使用了**自注意機制**，這本質上是將注意機制應用於相同的輸入和輸出序列。應用自注意機制使我們能夠考慮句子中的**上下文**，並查看哪些詞是相互關聯的。例如，它使我們能夠看到哪些詞是由指代詞（如*它*）指代的，並且能夠考慮上下文： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.tw.png) +![](../../../../../translated_images/tw/CoreferenceResolution.861924d6d384a7d6.png) > 圖片來自[Google的博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的主要理念之一是避免RNN的序列特性，並創建一個在 **BERT**（Bidirectional Encoder Representations from Transformers）是一個非常大的多層Transformer網絡，*BERT-base*有12層，*BERT-large*有24層。該模型首先在大規模文本數據（維基百科+書籍）上進行無監督訓練（預測句子中的被遮蔽詞）。在預訓練過程中，模型吸收了大量的語言理解能力，這些能力可以通過微調其他數據集來利用。這個過程稱為**遷移學習**。 -![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.tw.png) +![圖片來自http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/tw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 圖片[來源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/tw/lessons/5-NLP/19-NER/README.md b/translations/tw/lessons/5-NLP/19-NER/README.md index 0af912ff..fc19be86 100644 --- a/translations/tw/lessons/5-NLP/19-NER/README.md +++ b/translations/tw/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ NER 模型本質上是 **標記分類模型**，因為對於每個輸入的標 由於我們需要在標記和類別之間建立一對一的對應關係，我們可以從這張圖中訓練一個右側的 **多對多** 神經網絡模型： -![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.tw.jpg) +![顯示常見循環神經網絡模式的圖片。](../../../../../translated_images/tw/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *圖片來自 [這篇部落格文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)，作者為 [Andrej Karpathy](http://karpathy.github.io/)。NER 標記分類模型對應於圖片中最右側的網絡架構。* diff --git a/translations/tw/lessons/5-NLP/README.md b/translations/tw/lessons/5-NLP/README.md index 0a7798fe..21a2e7f4 100644 --- a/translations/tw/lessons/5-NLP/README.md +++ b/translations/tw/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然語言處理 -![NLP任務摘要手繪圖](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.tw.png) +![NLP任務摘要手繪圖](../../../../translated_images/tw/ai-nlp.b22dcb8ca4707cea.png) 在本章節中，我們將專注於使用神經網絡來處理與**自然語言處理 (NLP)**相關的任務。我們希望電腦能夠解決許多NLP問題： diff --git a/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md index 8ae99424..4dfa6c48 100644 --- a/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tw/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ NetLogo的一大優點是它包含一個可供試用的工作模型庫。進入* 打開模型後，你會進入NetLogo的主界面。以下是一個描述狼和羊的種群模型，給定有限資源（草地）。 -![NetLogo主界面](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.tw.png) +![NetLogo主界面](../../../../../translated_images/tw/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov提供的截圖 diff --git a/translations/tw/lessons/README.md b/translations/tw/lessons/README.md index 77176bd4..0fba1541 100644 --- a/translations/tw/lessons/README.md +++ b/translations/tw/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概覽 -![概覽手繪圖](../../../translated_images/ai-overview.0857791951d19500.tw.png) +![概覽手繪圖](../../../translated_images/tw/ai-overview.0857791951d19500.png) > 手繪筆記由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/tw/lessons/X-Extras/X1-MultiModal/README.md b/translations/tw/lessons/X-Extras/X1-MultiModal/README.md index e10c5a05..6d6908c4 100644 --- a/translations/tw/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tw/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP 的主要理念是能夠比較文本提示與圖像，並確定圖像與提示的匹配程度。 -![CLIP 架構](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.tw.png) +![CLIP 架構](../../../../../translated_images/tw/clip-arch.b3dbf20b4e8ed8be.png) > *圖片來自[這篇博客文章](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP 模型/庫可以從 [OpenAI GitHub](https://github.com/openai/CLIP) 獲取 假設我們需要將圖像分類為貓、狗和人類。在這種情況下，我們可以給模型一張圖像，以及一系列文本提示：“*一張貓的圖片*”、“*一張狗的圖片*”、“*一張人類的圖片*”。在結果的 3 個概率向量中，我們只需選擇值最高的索引。 -![CLIP 用於圖像分類](../../../../../translated_images/clip-class.3af42ef0b2b19369.tw.png) +![CLIP 用於圖像分類](../../../../../translated_images/tw/clip-class.3af42ef0b2b19369.png) > *圖片來自[這篇博客文章](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN 與普通 [GAN](../../4-ComputerVision/10-GANs/README.md) 的主要區別 VQGAN 與傳統 GAN 的一個重要區別在於，後者可以從任何輸入向量生成一張像樣的圖像，而 VQGAN 可能生成一張不連貫的圖像。因此，我們需要進一步引導圖像創建過程，而這可以通過 CLIP 完成。 -![VQGAN+CLIP 架構](../../../../../translated_images/vqgan.5027fe05051dfa31.tw.png) +![VQGAN+CLIP 架構](../../../../../translated_images/tw/vqgan.5027fe05051dfa31.png) 為了生成與文本提示相對應的圖像，我們從一些隨機編碼向量開始，將其傳遞給 VQGAN 以生成圖像。然後使用 CLIP 生成一個損失函數，該函數顯示圖像與文本提示的匹配程度。目標是最小化這個損失，通過反向傳播調整輸入向量參數。 一個實現 VQGAN+CLIP 的優秀庫是 [Pixray](http://github.com/pixray/pixray) -![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.tw.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.tw.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.tw.png) +![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![由 Pixray 生成的圖片](../../../../../translated_images/tw/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- 由提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 由提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 由提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 @@ -75,7 +75,7 @@ DALL-E 是一個基於 GPT-3 的版本，專門用於從提示生成圖像。它 DALL-E 1 和 2 的主要區別在於，DALL-E 2 能夠生成更真實的圖像和藝術作品。 使用 DALL-E 生成圖像的範例： -![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.tw.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.tw.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.tw.png) +![由 DALL-E 生成的圖片](../../../../../translated_images/tw/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/tw/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![由 DALL-E 生成的圖片](../../../../../translated_images/tw/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- 由提示 *一幅年輕男性文學教師手持書本的水彩特寫肖像* 生成的圖片 | 由提示 *一幅年輕女性計算機科學教師手持電腦的油畫特寫肖像* 生成的圖片 | 由提示 *一幅年長男性數學教師站在黑板前的油畫特寫肖像* 生成的圖片 diff --git a/translations/uk/README.md b/translations/uk/README.md index a5b4a14c..89222a4a 100644 --- a/translations/uk/README.md +++ b/translations/uk/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Штучний Інтелект для Початківців - Учбова Програма -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.uk.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/uk/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _Скетчнот від [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/uk/lessons/1-Intro/README.md b/translations/uk/lessons/1-Intro/README.md index 72bfb03f..b64c9f64 100644 --- a/translations/uk/lessons/1-Intro/README.md +++ b/translations/uk/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Вступ до штучного інтелекту -![Резюме змісту вступу до ШІ у вигляді малюнка](../../../../translated_images/ai-intro.bf28d1ac4235881c.uk.png) +![Резюме змісту вступу до ШІ у вигляді малюнка](../../../../translated_images/uk/ai-intro.bf28d1ac4235881c.png) > Малюнок від [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Спочатку комп’ютери були винайдені [Чарльзом Беббіджем](https://en.wikipedia.org/wiki/Charles_Babbage) для роботи з числами за чітко визначеною процедурою — алгоритмом. Сучасні комп’ютери, хоча й значно більш розвинені, ніж оригінальна модель, запропонована у 19 столітті, все ще дотримуються тієї ж ідеї контрольованих обчислень. Таким чином, можна запрограмувати комп’ютер виконувати щось, якщо ми знаємо точну послідовність кроків, необхідних для досягнення мети. -![Фото людини](../../../../translated_images/dsh_age.d212a30d4e54fb5f.uk.png) +![Фото людини](../../../../translated_images/uk/dsh_age.d212a30d4e54fb5f.png) > Фото від [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: Однією з проблем при роботі з терміном **[Інтелект](https://en.wikipedia.org/wiki/Intelligence)** є те, що немає чіткого визначення цього терміну. Можна стверджувати, що інтелект пов’язаний з **абстрактним мисленням** або **самосвідомістю**, але ми не можемо належним чином його визначити. -![Фото кота](../../../../translated_images/photo-cat.8c8e8fb760ffe457.uk.jpg) +![Фото кота](../../../../translated_images/uk/photo-cat.8c8e8fb760ffe457.jpg) > [Фото](https://unsplash.com/photos/75715CVEJhI) від [Amber Kipp](https://unsplash.com/@sadmax) з Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | А як щодо ML? | | > |--------------|-----------| -> | Частина штучного інтелекту, яка базується на навчанні комп’ютера вирішувати проблему на основі деяких даних, називається **Машинним навчанням**. Ми не будемо розглядати класичне машинне навчання в цьому курсі — ми рекомендуємо вам окремий навчальний курс [Машинне навчання для початківців](http://aka.ms/ml-beginners). | ![ML для початківців](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.uk.png) | +> | Частина штучного інтелекту, яка базується на навчанні комп’ютера вирішувати проблему на основі деяких даних, називається **Машинним навчанням**. Ми не будемо розглядати класичне машинне навчання в цьому курсі — ми рекомендуємо вам окремий навчальний курс [Машинне навчання для початківців](http://aka.ms/ml-beginners). | ![ML для початківців](../../../../translated_images/uk/ml-for-beginners.9e4fed176fd5817d.png) | ## Коротка історія ШІ Штучний інтелект як галузь був започаткований у середині двадцятого століття. Спочатку символічне міркування було переважним підходом, і це призвело до низки важливих успіхів, таких як експертні системи — комп’ютерні програми, які могли діяти як експерт у деяких обмежених проблемних областях. Однак незабаром стало зрозуміло, що такий підхід погано масштабується. Вилучення знань від експерта, представлення їх у комп’ютері та підтримка цієї бази знань у актуальному стані виявляється дуже складним завданням і занадто дорогим для практичного використання в багатьох випадках. Це призвело до так званої [Зими ШІ](https://en.wikipedia.org/wiki/AI_winter) у 1970-х роках. -Коротка історія ШІ +Коротка історія ШІ > Зображення від [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/uk/lessons/2-Symbolic/Animals.ipynb b/translations/uk/lessons/2-Symbolic/Animals.ipynb index 08cb7ae7..f6693b82 100644 --- a/translations/uk/lessons/2-Symbolic/Animals.ipynb +++ b/translations/uk/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "У цьому прикладі ми реалізуємо просту систему на основі знань для визначення тварини за деякими фізичними характеристиками. Система може бути представлена наступним деревом AND-OR (це частина всього дерева, ми легко можемо додати ще кілька правил):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.uk.png)\n" + "![](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/uk/lessons/2-Symbolic/README.md b/translations/uk/lessons/2-Symbolic/README.md index d06893fa..9ba0bfd2 100644 --- a/translations/uk/lessons/2-Symbolic/README.md +++ b/translations/uk/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Представлення знань та експертні системи -![Резюме змісту символічного AI](../../../../translated_images/ai-symbolic.715a30cb610411a6.uk.png) +![Резюме змісту символічного AI](../../../../translated_images/uk/ai-symbolic.715a30cb610411a6.png) > Скетчноут від [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: Таким чином, проблема **представлення знань** полягає у пошуку ефективного способу представлення знань у комп'ютері у формі даних, щоб зробити їх автоматично придатними для використання. Це можна розглядати як спектр: -![Спектр представлення знань](../../../../translated_images/knowledge-spectrum.b60df631852c0217.uk.png) +![Спектр представлення знань](../../../../translated_images/uk/knowledge-spectrum.b60df631852c0217.png) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | синтаксис блоку | відступи Одним із ранніх успіхів символічного AI були так звані **експертні системи** — комп'ютерні системи, які були розроблені для того, щоб діяти як експерт у певній обмеженій області задач. Вони базувалися на **базі знань**, отриманій від одного або кількох людських експертів, і містили **мотор виведення**, який виконував певні міркування на її основі. -![Архітектура людини](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.uk.png) | ![Система на основі знань](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.uk.png) +![Архітектура людини](../../../../translated_images/uk/arch-human.5d4d35f1bba3ab1c.png) | ![Система на основі знань](../../../../translated_images/uk/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Спрощена структура людської нервової системи | Архітектура системи на основі знань @@ -106,7 +106,7 @@ Python | синтаксис блоку | відступи Наприклад, розглянемо наступну експертну систему для визначення тварини на основі її фізичних характеристик: -![AND-OR дерево](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.uk.png) +![AND-OR дерево](../../../../translated_images/uk/AND-OR-Tree.5592d2c70187f283.png) > Зображення від [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 52160819..99b0f7c0 100644 --- a/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/uk/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Якщо у нас більше ніж 2 класи, softmax нормалізує ймовірності для всіх класів. Ось діаграма архітектури мережі, яка виконує класифікацію цифр MNIST:\n", "\n", - "![MNIST Класифікатор](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.uk.png)\n" + "![MNIST Класифікатор](../../../../../translated_images/uk/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1252,7 +1252,7 @@ "* Низька втрата на навчанні — модель добре наближає навчальні дані, оскільки має достатню виразну потужність.\n", "* Втрата на валідації може бути значно вищою за втрату на навчанні і може почати зростати під час навчання — це відбувається тому, що модель \"запам’ятовує\" навчальні точки і втрачає \"загальну картину\".\n", "\n", - "![Перенавчання](../../../../../translated_images/overfit.a0bd57f717c15769.uk.png)\n", + "![Перенавчання](../../../../../translated_images/uk/overfit.a0bd57f717c15769.png)\n", "\n", "> На цьому зображенні `x` позначає навчальні дані, `o` — валідаційні дані. Ліворуч — лінійна модель (одношарова), вона досить добре наближає природу даних. Праворуч — перенавчена модель, яка ідеально наближає навчальні дані, але перестає бути корисною для будь-яких інших даних (помилка на валідації дуже висока).\n" ] diff --git a/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md index bc8f0c69..24722722 100644 --- a/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/uk/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: Розглянемо наступну задачу апроксимації 5 точок (представлених як `x` на графіках нижче): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.uk.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.uk.jpg) +![linear](../../../../../translated_images/uk/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/uk/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Лінійна модель, 2 параметри** | **Нелінійна модель, 7 параметрів** Помилка навчання = 5.3 | Помилка навчання = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: Як видно з графіка вище, перенавчання можна виявити за дуже низькою помилкою навчання і високою помилкою валідації. Зазвичай під час навчання ми бачимо, як помилки навчання і валідації починають зменшуватися, а потім у певний момент помилка валідації може перестати зменшуватися і почати зростати. Це буде ознакою перенавчання і сигналом, що, ймовірно, слід припинити навчання (або принаймні зробити знімок моделі). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.uk.png) +![overfitting](../../../../../translated_images/uk/Overfitting.408ad91cd90b4371.png) ## Як запобігти перенавчанню diff --git a/translations/uk/lessons/3-NeuralNetworks/README.md b/translations/uk/lessons/3-NeuralNetworks/README.md index 618c26b6..bdcbedac 100644 --- a/translations/uk/lessons/3-NeuralNetworks/README.md +++ b/translations/uk/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Вступ до нейронних мереж -![Резюме змісту "Вступ до нейронних мереж" у вигляді малюнка](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.uk.png) +![Резюме змісту "Вступ до нейронних мереж" у вигляді малюнка](../../../../translated_images/uk/ai-neuralnetworks.1c687ae40bc86e83.png) Як ми обговорювали у вступі, одним із способів досягнення інтелекту є навчання **комп'ютерної моделі** або **штучного мозку**. З середини 20-го століття дослідники пробували різні математичні моделі, і лише в останні роки цей напрямок став надзвичайно успішним. Такі математичні моделі мозку називаються **нейронними мережами**. @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: З біології ми знаємо, що наш мозок складається з нейронних клітин (нейронів), кожна з яких має кілька "входів" (дендрити) і один "вихід" (аксон). Як дендрити, так і аксони можуть проводити електричні сигнали, а зв’язки між ними — відомі як синапси — можуть мати різний ступінь провідності, який регулюється нейромедіаторами. -![Модель нейрона](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.uk.jpg) | ![Модель нейрона](../../../../translated_images/artneuron.1a5daa88d20ebe6f.uk.png) +![Модель нейрона](../../../../translated_images/uk/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Модель нейрона](../../../../translated_images/uk/artneuron.1a5daa88d20ebe6f.png) ----|---- Реальний нейрон *([Зображення](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) з Вікіпедії)* | Штучний нейрон *(Зображення автора)* Таким чином, найпростіша математична модель нейрона містить кілька входів X1, ..., XN і один вихід Y, а також набір ваг W1, ..., WN. Вихід обчислюється як: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) де f — це деяка нелінійна **функція активації**. diff --git a/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md index e9d4ffb5..27659b08 100644 --- a/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/uk/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **Попередня обробка фотографії книги шрифтом Брайля**. Ми зосереджуємося на тому, як можна використовувати порогову обробку, виявлення особливостей, перспективне перетворення та маніпуляції з NumPy для відокремлення окремих символів шрифту Брайля для подальшої класифікації нейронною мережею. -![Зображення шрифту Брайля](../../../../../translated_images/braille.341962ff76b1bd70.uk.jpeg) | ![Попередньо оброблене зображення шрифту Брайля](../../../../../translated_images/braille-result.46530fea020b03c7.uk.png) | ![Символи шрифту Брайля](../../../../../translated_images/braille-symbols.0159185ab69d5339.uk.png) +![Зображення шрифту Брайля](../../../../../translated_images/uk/braille.341962ff76b1bd70.jpeg) | ![Попередньо оброблене зображення шрифту Брайля](../../../../../translated_images/uk/braille-result.46530fea020b03c7.png) | ![Символи шрифту Брайля](../../../../../translated_images/uk/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Зображення з [OpenCV.ipynb](OpenCV.ipynb) * **Виявлення руху у відео за допомогою різниці кадрів**. Якщо камера нерухома, то кадри з її потоку мають бути досить схожими один на одного. Оскільки кадри представлені у вигляді масивів, просто віднімаючи ці масиви для двох послідовних кадрів, ми отримаємо різницю пікселів, яка має бути низькою для статичних кадрів і ставати вищою, коли в зображенні є значний рух. -![Зображення кадрів відео та різниці кадрів](../../../../../translated_images/frame-difference.706f805491a0883c.uk.png) +![Зображення кадрів відео та різниці кадрів](../../../../../translated_images/uk/frame-difference.706f805491a0883c.png) > Зображення з [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **Щільний оптичний потік** обчислює векторне поле, яке показує, куди рухається кожен піксель. - **Рідкісний оптичний потік** базується на виборі деяких характерних особливостей на зображенні (наприклад, країв) і побудові їх траєкторії від кадру до кадру. -![Зображення оптичного потоку](../../../../../translated_images/optical.1f4a94464579a83a.uk.png) +![Зображення оптичного потоку](../../../../../translated_images/uk/optical.1f4a94464579a83a.png) > Зображення з [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 77ba6412..1785ed59 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 — це мережа, яка досягла точності 92.7% у класифікації ImageNet top-5 у 2014 році. Вона має наступну структуру шарів: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.uk.jpg) +![ImageNet Layers](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.jpg) Як видно, VGG слідує традиційній пірамідальній архітектурі, яка є послідовністю шарів згортки та пулінгу. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.uk.jpg) +![ImageNet Pyramid](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.jpg) > Зображення з [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 13b61f90..b76c903b 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Таким чином, у типовій CNN буде кілька згорткових шарів, між якими розташовані шари пулінгу для зменшення розмірів зображення. Ми також збільшуватимемо кількість фільтрів, оскільки, коли шаблони стають складнішими, з'являється більше можливих цікавих комбінацій, які потрібно шукати.\n", "\n", - "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.uk.png)\n", + "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Через зменшення просторових розмірів і збільшення розмірів ознак/фільтрів цю архітектуру також називають **пірамідальною архітектурою**.\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 48a87082..fa4cd729 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Таким чином, у типовій CNN буде кілька згорткових шарів, між якими розташовані шари пулінгу для зменшення розмірів зображення. Ми також збільшимо кількість фільтрів, оскільки, коли шаблони стають більш складними, з'являється більше можливих цікавих комбінацій, які потрібно шукати.\n", "\n", - "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.uk.png)\n", + "![Зображення, що показує кілька згорткових шарів із шарами пулінгу.](../../../../../translated_images/uk/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Через зменшення просторових розмірів і збільшення розмірів ознак/фільтрів ця архітектура також називається **пірамідальною архітектурою**.\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md index 17a9d450..230ec0a1 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: Для вилучення шаблонів ми будемо використовувати поняття **конволюційних фільтрів**. Як вам відомо, зображення представляється у вигляді 2D-матриці або 3D-тензора з глибиною кольору. Застосування фільтра означає, що ми беремо відносно невелику матрицю **ядра фільтра**, і для кожного пікселя в оригінальному зображенні обчислюємо зважене середнє з сусідніми точками. Це можна уявити як невелике вікно, яке ковзає по всьому зображенню, усереднюючи всі пікселі відповідно до ваг у матриці ядра фільтра. -![Фільтр вертикальних країв](../../../../../translated_images/filter-vert.b7148390ca0bc356.uk.png) | ![Фільтр горизонтальних країв](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.uk.png) +![Фільтр вертикальних країв](../../../../../translated_images/uk/filter-vert.b7148390ca0bc356.png) | ![Фільтр горизонтальних країв](../../../../../translated_images/uk/filter-horiz.59b80ed4feb946ef.png) ----|---- > Зображення Дмитра Сошникова @@ -38,7 +38,7 @@ CO_OP_TRANSLATOR_METADATA: * Ми можемо спроєктувати мережу таким чином, щоб фільтри навчалися автоматично * Ми можемо використовувати той самий підхід для пошуку шаблонів у високорівневих ознаках, а не лише в оригінальному зображенні. Таким чином, вилучення ознак у CNN працює на ієрархії ознак, починаючи з низькорівневих комбінацій пікселів і до високорівневих комбінацій частин зображення. -![Ієрархічне вилучення ознак](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.uk.png) +![Ієрархічне вилучення ознак](../../../../../translated_images/uk/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Зображення з [статті Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), засноване на [їхньому дослідженні](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CO_OP_TRANSLATOR_METADATA: Наприклад, давайте розглянемо архітектуру VGG-16, мережі, яка досягла 92.7% точності в топ-5 класифікації ImageNet у 2014 році: -![Шари ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.uk.jpg) +![Шари ImageNet](../../../../../translated_images/uk/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Піраміда ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.uk.jpg) +![Піраміда ImageNet](../../../../../translated_images/uk/vgg-16-arch.64ff2137f50dd49f.jpg) > Зображення з [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md index 9547446a..16979aa6 100644 --- a/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/uk/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: Ми будемо використовувати [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/), який містить зображення 37 різних порід собак і котів. -![Набір даних, з яким ми будемо працювати](../../../../../../translated_images/data.50b2a9d5484bdbf0.uk.png) +![Набір даних, з яким ми будемо працювати](../../../../../../translated_images/uk/data.50b2a9d5484bdbf0.png) Щоб завантажити набір даних, скористайтеся цим фрагментом коду: diff --git a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 534daf79..6f5c3540 100644 --- a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Щоб уявити ідеального кота, ми почнемо з випадкового шумового зображення і спробуємо використати метод оптимізації градієнтного спуску, щоб змінити зображення так, щоб мережа розпізнала кота.\n", "\n", - "![Цикл оптимізації](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.uk.png)\n", + "![Цикл оптимізації](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Ось наше початкове зображення:\n" ] diff --git a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md index 2b710b64..d7f381ba 100644 --- a/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/uk/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Ось приклади ознак, вилучених із зображення кота мережею VGG-16: -![Ознаки, вилучені мережею VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.uk.png) +![Ознаки, вилучені мережею VGG-16](../../../../../translated_images/uk/features.6291f9c7ba3a0b95.png) ## Набір даних "Коти проти собак" @@ -48,19 +48,19 @@ CO_OP_TRANSLATOR_METADATA: Один із підходів, який ми можемо використати, — це почати з випадкового зображення, а потім спробувати використати техніку **оптимізації градієнтного спуску**, щоб змінити це зображення таким чином, щоб мережа почала думати, що це кіт. -![Цикл оптимізації зображення](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.uk.png) +![Цикл оптимізації зображення](../../../../../translated_images/uk/ideal-cat-loop.999fbb8ff306e044.png) Однак, якщо ми це зробимо, ми отримаємо щось дуже схоже на випадковий шум. Це тому, що *існує багато способів змусити мережу думати, що вхідне зображення — це кіт*, включаючи ті, які не мають сенсу візуально. Хоча ці зображення містять багато шаблонів, характерних для кота, немає нічого, що обмежувало б їх бути візуально виразними. Щоб покращити результат, ми можемо додати ще один термін до функції втрат, який називається **варіаційна втрата**. Це метрика, яка показує, наскільки схожі сусідні пікселі зображення. Мінімізуючи варіаційну втрату, ми робимо зображення більш гладким і позбавляємося шуму — таким чином розкриваючи більш привабливі візуальні шаблони. Ось приклад таких "ідеальних" зображень, які класифікуються як кіт і як зебра з високою ймовірністю: -![Ідеальний кіт](../../../../../translated_images/ideal-cat.203dd4597643d6b0.uk.png) | ![Ідеальна зебра](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.uk.png) +![Ідеальний кіт](../../../../../translated_images/uk/ideal-cat.203dd4597643d6b0.png) | ![Ідеальна зебра](../../../../../translated_images/uk/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Ідеальний кіт* | *Ідеальна зебра* Схожий підхід можна використати для виконання так званих **атак на нейронну мережу**. Припустимо, ми хочемо обдурити нейронну мережу і змусити собаку виглядати як кіт. Якщо ми візьмемо зображення собаки, яке мережа розпізнає як собаку, ми можемо трохи змінити його за допомогою оптимізації градієнтного спуску, поки мережа не почне класифікувати його як кота: -![Зображення собаки](../../../../../translated_images/original-dog.8f68a67d2fe0911f.uk.png) | ![Зображення собаки, класифіковане як кіт](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.uk.png) +![Зображення собаки](../../../../../translated_images/uk/original-dog.8f68a67d2fe0911f.png) | ![Зображення собаки, класифіковане як кіт](../../../../../translated_images/uk/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Оригінальне зображення собаки* | *Зображення собаки, класифіковане як кіт* diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index bab3b031..62aa241d 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Оскільки ми тренуємо автоенкодер, щоб захопити якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їхній зміст.\n", "\n", - "![Схема Автоенкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.uk.jpg)\n", + "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Зображення з [Keras blog](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index d8fa0228..048f97f4 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Оскільки ми тренуємо автоенкодер, щоб захопити якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їхній зміст.\n", "\n", - "![Схема Автоенкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.uk.jpg)\n", + "![Схема Автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Зображення з [блогу Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md index 43e6ca09..ded38e7c 100644 --- a/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/uk/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Оскільки ми навчаємо автоенкодер захоплювати якомога більше інформації з оригінального зображення для точного відновлення, мережа намагається знайти найкраще **вбудовування** вхідних зображень, щоб передати їх зміст. -![Схема автоенкодера](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.uk.jpg) +![Схема автоенкодера](../../../../../translated_images/uk/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Зображення з [блогу Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md index 28577b92..8f182185 100644 --- a/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/uk/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [Квіз перед лекцією](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Виявлення об'єктів](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.uk.png) +![Виявлення об'єктів](../../../../../translated_images/uk/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Зображення з [веб-сайту YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Запустити класифікацію зображень для кожної плитки. 3. Ті плитки, які дають достатньо високу активацію, можна вважати такими, що містять потрібний об'єкт. -![Наївне виявлення об'єктів](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.uk.png) +![Наївне виявлення об'єктів](../../../../../translated_images/uk/naive-detection.e7f1ba220ccd08c6.png) > *Зображення з [зошита вправ](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) — 20 класів * [COCO](http://cocodataset.org/#home) — Загальні об'єкти в контексті. 80 класів, межові рамки та маски сегментації -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.uk.jpg) +![COCO](../../../../../translated_images/uk/coco-examples.71bc60380fa6cceb.jpg) ## Метрики для виявлення об'єктів @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: Для класифікації зображень легко виміряти, наскільки добре працює алгоритм, але для виявлення об'єктів потрібно оцінити як правильність класу, так і точність визначення розташування межової рамки. Для останнього використовується так звана **Перетин над об'єднанням** (IoU), яка вимірює, наскільки добре дві рамки (або дві довільні області) перекриваються. -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.uk.png) +![IoU](../../../../../translated_images/uk/iou_equation.9a4751d40fff4e11.png) > *Рисунок 2 з [цієї чудової статті про IoU](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) використовує [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) для створення ієрархічної структури регіонів ROI, які потім проходять через CNN для вилучення ознак і SVM-класифікатори для визначення класу об'єкта, а також лінійну регресію для визначення координат *межової рамки*. [Офіційна стаття](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.uk.png) +![RCNN](../../../../../translated_images/uk/rcnn1.cae407020dfb1d1f.png) > *Зображення з van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.uk.png) +![RCNN-1](../../../../../translated_images/uk/rcnn2.2d9530bb83516484.png) > *Зображення з [цієї статті](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ Цей підхід схожий на R-CNN, але регіони визначаються після застосування шарів згортки. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.uk.png) +![FRCNN](../../../../../translated_images/uk/f-rcnn.3cda6d9bb4188875.png) > Зображення з [офіційної статті](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ $$ Основна ідея цього підходу полягає у використанні нейронної мережі для прогнозування ROI — так званої *мережі пропозицій регіонів*. [Стаття](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.uk.png) +![FasterRCNN](../../../../../translated_images/uk/faster-rcnn.8d46c099b87ef30a.png) > Зображення з [офіційної статті](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. Ознаки обробляються **картою оцінок, чутливою до позиції**. Кожен об'єкт із $C$ класів ділиться на $k\times k$ регіонів, і ми навчаємося прогнозувати частини об'єктів. 3. Для кожної частини з $k\times k$ регіонів усі мережі голосують за класи об'єктів, і вибирається клас об'єкта з максимальним голосом. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.uk.png) +![r-fcn image](../../../../../translated_images/uk/r-fcn.13eb88158b99a3da.png) > Зображення з [офіційної статті](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO — це алгоритм реального часу з одним про * Зображення ділиться на $S\times S$ регіони. * Для кожного регіону **CNN** прогнозує $n$ можливих об'єктів, координати *межової рамки* та *довіру*=*ймовірність* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.uk.png) + ![YOLO](../../../../../translated_images/uk/yolo.a2648ec82ee8bb4e.png) > Зображення з [офіційної статті](https://arxiv.org/abs/1506.02640) diff --git a/translations/uk/lessons/4-ComputerVision/README.md b/translations/uk/lessons/4-ComputerVision/README.md index 0c17183d..3369ace9 100644 --- a/translations/uk/lessons/4-ComputerVision/README.md +++ b/translations/uk/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Комп'ютерний зір -![Резюме матеріалів про комп'ютерний зір у вигляді малюнка](../../../../translated_images/ai-computervision.6506ebebac3fbf76.uk.png) +![Резюме матеріалів про комп'ютерний зір у вигляді малюнка](../../../../translated_images/uk/ai-computervision.6506ebebac3fbf76.png) У цьому розділі ми дізнаємося про: diff --git a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index 7b6bc284..fca08285 100644 --- a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Мішок слів** (BoW) — це найпоширеніше традиційне представлення векторів. Кожне слово пов’язане з індексом вектора, а елемент вектора містить кількість появ слова в даному документі.\n", "\n", - "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам’яті.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.uk.png) \n", + "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам’яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Ви також можете уявити BoW як суму всіх векторів з одним активним елементом для окремих слів у тексті.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 9ed5b899..8aad0ccf 100644 --- a/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/uk/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**Мішок слів** (BoW) — це найпростіше для розуміння традиційне представлення векторів. Кожне слово пов'язане з індексом вектора, а елемент вектора містить кількість появ кожного слова в даному документі.\n", "\n", - "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам'яті.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.uk.png) \n", + "![Зображення, яке показує, як представлення вектора \"Мішок слів\" зберігається в пам'яті.](../../../../../translated_images/uk/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Ви також можете думати про BoW як про суму всіх векторів з одним активним елементом (one-hot-encoded) для окремих слів у тексті.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 6ada8e1b..9e6bf57a 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Використовуючи шар вбудовування як перший шар у нашій мережі, ми можемо перейти від моделі \"мішок слів\" до моделі **мішок вбудовувань**, де спочатку кожне слово в тексті перетворюється на відповідне вбудовування, а потім обчислюється певна агрегатна функція для всіх цих вбудовувань, наприклад `sum`, `average` або `max`.\n", "\n", - "![Зображення, що показує класифікатор вбудовувань для п’яти слів у послідовності.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.uk.png)\n", + "![Зображення, що показує класифікатор вбудовувань для п’яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Наша нейронна мережа класифікатора починатиметься з шару вбудовування, потім шару агрегування, а зверху — лінійного класифікатора:\n" ] @@ -176,7 +176,7 @@ "\n", "У попередній архітектурі нам потрібно було доповнювати всі послідовності до однакової довжини, щоб вони відповідали розміру мініпакету. Це не найефективніший спосіб представлення послідовностей змінної довжини — інший підхід полягає у використанні **вектора зсувів**, який містить зсуви всіх послідовностей, збережених в одному великому векторі.\n", "\n", - "![Зображення, що показує представлення послідовності за допомогою зсувів](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.uk.png)\n", + "![Зображення, що показує представлення послідовності за допомогою зсувів](../../../../../translated_images/uk/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Примітка**: На зображенні вище показано послідовність символів, але в нашому прикладі ми працюємо з послідовностями слів. Однак загальний принцип представлення послідовностей за допомогою вектора зсувів залишається тим самим.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW працює швидше, тоді як skip-gram повільніший, але краще справляється з представленням рідковживаних слів.\n", "\n", - "![Зображення, що демонструє алгоритми CBoW та Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.uk.png)\n", + "![Зображення, що демонструє алгоритми CBoW та Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Щоб експериментувати з вбудовуванням word2vec, попередньо навченим на наборі даних Google News, ми можемо використовувати бібліотеку **gensim**. Нижче наведено приклад пошуку слів, найбільш схожих на 'neural'.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index a1d79045..101f4e6c 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/uk/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Використовуючи шар вбудовування як перший шар у нашій мережі, ми можемо перейти від моделі \"мішка слів\" до моделі **мішка вбудовувань**, де спочатку кожне слово в тексті перетворюється на відповідне вбудовування, а потім обчислюється певна агрегатна функція для всіх цих вбудовувань, наприклад, `sum`, `average` або `max`.\n", "\n", - "![Зображення, що показує класифікатор з вбудовуванням для п’яти послідовних слів.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.uk.png)\n", + "![Зображення, що показує класифікатор з вбудовуванням для п’яти послідовних слів.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Наша нейронна мережа класифікатора складається з таких шарів:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW працює швидше, тоді як скіп-грам повільніший, але краще представляє рідковживані слова.\n", "\n", - "![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.uk.png)\n", + "![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Щоб експериментувати з вбудовуванням Word2Vec, попередньо навченим на наборі даних Google News, ми можемо використовувати бібліотеку **gensim**. Нижче наведено приклад пошуку слів, найбільш схожих до 'neural'.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/14-Embeddings/README.md b/translations/uk/lessons/5-NLP/14-Embeddings/README.md index 2cbdfc85..dc1f4c4d 100644 --- a/translations/uk/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/uk/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Використовуючи шар вбудовування як перший шар у нашій мережі класифікатора, ми можемо перейти від моделі "мішка слів" до моделі **мішка вбудовувань**, де спочатку кожне слово в нашому тексті перетворюється у відповідне вбудовування, а потім обчислюється певна агрегатна функція над усіма цими вбудовуваннями, така як `sum`, `average` або `max`. -![Зображення, що показує класифікатор на основі вбудовувань для п'яти слів у послідовності.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.uk.png) +![Зображення, що показує класифікатор на основі вбудовувань для п'яти слів у послідовності.](../../../../../translated_images/uk/embedding-classifier-example.b77f021a7ee67eee.png) > Зображення автора @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW працює швидше, тоді як скіп-грам повільніший, але краще представляє рідковживані слова. -![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.uk.png) +![Зображення, що показує алгоритми CBoW і Skip-Gram для перетворення слів у вектори.](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Зображення з [цієї статті](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md b/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md index bb75c575..b1c8b563 100644 --- a/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/uk/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Безперервний мішок слів** (CBoW), коли ми передбачаємо середній токен $W_0$ у послідовності токенів $W_{-N}$, ..., $W_N$. * **Skip-gram**, де ми передбачаємо набір сусідніх токенів {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} на основі середнього токена $W_0$. -![зображення з наукової статті про перетворення слів у вектори](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.uk.png) +![зображення з наукової статті про перетворення слів у вектори](../../../../../translated_images/uk/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Зображення з [цієї статті](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/uk/lessons/5-NLP/16-RNN/README.md b/translations/uk/lessons/5-NLP/16-RNN/README.md index fda267a2..db9e2dd2 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/README.md +++ b/translations/uk/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Щоб захопити значення текстової послідовності, нам потрібно використовувати іншу архітектуру нейронної мережі, яка називається **рекурентною нейронною мережею** або RNN. У RNN ми пропускаємо речення через мережу по одному символу за раз, і мережа створює певний **стан**, який ми потім передаємо назад у мережу разом із наступним символом. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.uk.png) +![RNN](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.png) > Зображення автора @@ -61,7 +61,7 @@ CO_OP_TRANSLATOR_METADATA: Рекурентна мережа, одностороння чи двонаправлена, захоплює певні шаблони в послідовності та може зберігати їх у векторі стану або передавати у вихід. Як і у випадку з згортковими мережами, ми можемо побудувати ще один рекурентний шар поверх першого, щоб захопити шаблони вищого рівня та побудувати з шаблонів нижчого рівня, які витягнув перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається наступному шару як вхід. -![Зображення, що показує багатошарову довготривалу короткочасну пам'ять RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.uk.jpg) +![Зображення, що показує багатошарову довготривалу короткочасну пам'ять RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса* diff --git a/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index b16b7d1c..10ce7e78 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Рекурентна мережа, одностороння чи двонаправлена, захоплює певні шаблони в межах послідовності та може зберігати їх у векторі стану або передавати у вихідні дані. Як і у випадку з згортковими мережами, ми можемо побудувати ще один рекурентний шар поверх першого, щоб захоплювати шаблони вищого рівня, створені з шаблонів нижчого рівня, які витягнув перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається до наступного шару як вхідні дані.\n", "\n", - "![Зображення багатошарової довготривалої пам’яті RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.uk.jpg)\n", + "![Зображення багатошарової довготривалої пам’яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса*\n", "\n", diff --git a/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb index d7760c0c..5a53dd3b 100644 --- a/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/uk/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Щоб захопити значення послідовності тексту, ми використаємо архітектуру нейронної мережі, яка називається **рекурентна нейронна мережа** (RNN). Використовуючи RNN, ми пропускаємо наше речення через мережу по одному токену за раз, і мережа генерує певний **стан**, який ми потім передаємо в мережу разом із наступним токеном.\n", "\n", - "![Зображення, що показує приклад генерації рекурентної нейронної мережі.](../../../../../translated_images/rnn.27f5c29c53d727b5.uk.png)\n", + "![Зображення, що показує приклад генерації рекурентної нейронної мережі.](../../../../../translated_images/uk/rnn.27f5c29c53d727b5.png)\n", "\n", "З огляду на вхідну послідовність токенів $X_0,\\dots,X_n$, RNN створює послідовність блоків нейронної мережі та навчає цю послідовність від початку до кінця за допомогою зворотного поширення. Кожен блок мережі приймає пару $(X_i,S_i)$ як вхід і генерує $S_{i+1}$ як результат. Кінцевий стан $S_n$ або вихід $Y_n$ передається в лінійний класифікатор для отримання результату. Усі блоки мережі мають однакові ваги та навчаються від початку до кінця за допомогою одного проходу зворотного поширення.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Рекурентні мережі, однонаправлені чи двонаправлені, захоплюють шаблони в межах послідовності та зберігають їх у векторі станів або повертають як вихідні дані. Як і у випадку з згортковими мережами, ми можемо створити ще один рекурентний шар після першого, щоб захопити шаблони вищого рівня, побудовані з шаблонів нижчого рівня, які витягує перший шар. Це приводить нас до поняття **багатошарової RNN**, яка складається з двох або більше рекурентних мереж, де вихід попереднього шару передається наступному шару як вхідні дані.\n", "\n", - "![Зображення багатошарової довготривалої пам'яті RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.uk.jpg)\n", + "![Зображення багатошарової довготривалої пам'яті RNN](../../../../../translated_images/uk/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Зображення з [цієї чудової статті](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) Фернандо Лопеса.*\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 6259a6ad..3da3b34f 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Метод, за яким ми будемо навчати RNN для генерації тексту, виглядає наступним чином. На кожному кроці ми беремо послідовність символів довжиною `nchars` і просимо мережу згенерувати наступний вихідний символ для кожного вхідного символу:\n", "\n", - "![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.uk.png)\n", + "![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Залежно від конкретного сценарію, ми також можемо захотіти включити деякі спеціальні символи, такі як *кінець послідовності* ``. У нашому випадку ми просто хочемо навчити мережу генерувати нескінченний текст, тому ми зафіксуємо розмір кожної послідовності, щоб він дорівнював `nchars` токенам. Таким чином, кожен навчальний приклад складатиметься з `nchars` входів і `nchars` виходів (які є вхідною послідовністю, зміщеною на один символ вліво). Мініпакет складатиметься з кількох таких послідовностей.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index a909c191..6db2090f 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Ось як ми будемо навчати RNN створювати заголовки новин. На кожному кроці ми беремо один заголовок, який подається в RNN, і для кожного вхідного символу ми просимо мережу згенерувати наступний вихідний символ:\n", "\n", - "![Зображення, що демонструє приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.uk.png)\n", + "![Зображення, що демонструє приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Для останнього символу нашої послідовності ми попросимо мережу згенерувати токен ``.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md index c5fb7eab..ec808fb0 100644 --- a/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/uk/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Це дозволяє створювати різні нейронні архітектури, які показані на зображенні нижче: -![Зображення, що показує поширені шаблони рекурентних нейронних мереж.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.uk.jpg) +![Зображення, що показує поширені шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Зображення з блогу [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) авторства [Андрея Карпаті](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: Ми навчимо цю RNN генерувати текст крок за кроком. На кожному кроці ми братимемо послідовність символів довжиною `nchars` і проситимемо мережу створити наступний вихідний символ для кожного вхідного символу: -![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.uk.png) +![Зображення, що показує приклад генерації слова 'HELLO' за допомогою RNN.](../../../../../translated_images/uk/rnn-generate.56c54afb52f9781d.png) Під час генерації тексту (під час інференсу) ми починаємо з деякого **запиту**, який передається через блоки RNN для створення його проміжного стану, а потім з цього стану починається генерація. Ми генеруємо один символ за раз і передаємо стан та створений символ до іншого блоку RNN для генерації наступного, поки не буде створено достатню кількість символів. diff --git a/translations/uk/lessons/5-NLP/18-Transformers/README.md b/translations/uk/lessons/5-NLP/18-Transformers/README.md index 6d4caca9..ebfb7f6b 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/README.md +++ b/translations/uk/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення "коротких шляхів" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу yt, ми враховуємо всі приховані стани hi вхідної мережі з різними коефіцієнтами ваги αt,i. -![Зображення моделі кодер/декодер з додатковим шаром уваги](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.uk.png) +![Зображення моделі кодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png) > Модель кодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Матриця уваги {αi,j} представляє ступінь, до якого певні слова у вході впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці: -![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.uk.png) +![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png) > Рисунок з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3) @@ -66,7 +66,7 @@ CO_OP_TRANSLATOR_METADATA: Далі нам потрібно захопити певні шаблони в нашій послідовності. Для цього трансформери використовують механізм **самоуваги**, який, по суті, є увагою, застосованою до тієї ж послідовності як до входу, так і до виходу. Застосування самоуваги дозволяє враховувати **контекст** у реченні та бачити, які слова взаємопов'язані. Наприклад, це дозволяє бачити, до яких слів відносяться кореференції, такі як *це*, а також враховувати контекст: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.uk.png) +![](../../../../../translated_images/uk/CoreferenceResolution.861924d6d384a7d6.png) > Зображення з [блогу Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ CO_OP_TRANSLATOR_METADATA: **BERT** (Bidirectional Encoder Representations from Transformers) — це дуже велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою ненаглядуваного навчання (передбачення замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**. -![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.uk.png) +![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Зображення [джерело](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 2642bd37..258dce82 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/uk/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення \"коротких шляхів\" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу $y_t$, ми враховуємо всі приховані стани входу $h_i$ з різними ваговими коефіцієнтами $\\alpha_{t,i}$.\n", "\n", - "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.uk.png)\n", + "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png)\n", "*Модель енкодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матриця уваги $\\{\\alpha_{i,j}\\}$ представляє ступінь, до якого певні вхідні слова впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці:\n", "\n", - "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.uk.png)\n", + "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Зображення взято з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — це дуже велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою неконтрольованого навчання (передбачення замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**.\n", "\n", - "![Зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.uk.png)\n", + "![Зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Існує багато варіацій архітектур трансформерів, включаючи BERT, DistilBERT, BigBird, OpenGPT3 та інші, які можна налаштовувати. Пакет [HuggingFace](https://github.com/huggingface/) надає репозиторій для навчання багатьох із цих архітектур за допомогою PyTorch.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index e7290219..22cec0b9 100644 --- a/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/uk/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Механізми уваги** забезпечують спосіб зважування контекстуального впливу кожного вхідного вектора на кожне передбачення виходу RNN. Це реалізується шляхом створення \"ярликів\" між проміжними станами вхідної RNN та вихідної RNN. Таким чином, при генерації вихідного символу $y_t$ ми враховуємо всі приховані стани входу $h_i$ з різними ваговими коефіцієнтами $\\alpha_{t,i}$.\n", "\n", - "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.uk.png)\n", + "![Зображення моделі енкодер/декодер з додатковим шаром уваги](../../../../../translated_images/uk/encoder-decoder-attention.7a726296894fb567.png)\n", "*Модель енкодер-декодер з механізмом додаткової уваги у [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), цитовано з [цього блогу](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Матриця уваги $\\{\\alpha_{i,j}\\}$ представляє ступінь, до якого певні вхідні слова впливають на генерацію конкретного слова у вихідній послідовності. Нижче наведено приклад такої матриці:\n", "\n", - "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.uk.png)\n", + "![Зображення прикладу вирівнювання, знайденого RNNsearch-50, взято з Bahdanau - arviz.org](../../../../../translated_images/uk/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Зображення взято з [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Рис.3)*\n", "\n", @@ -229,7 +229,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) — це велика багатошарова трансформерна мережа з 12 шарами для *BERT-base* і 24 для *BERT-large*. Модель спочатку проходить попереднє навчання на великому корпусі текстових даних (WikiPedia + книги) за допомогою неконтрольованого навчання (прогнозування замаскованих слів у реченні). Під час попереднього навчання модель засвоює значний рівень розуміння мови, який потім можна використовувати з іншими наборами даних за допомогою тонкого налаштування. Цей процес називається **трансферним навчанням**.\n", "\n", - "![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.uk.png)\n", + "![зображення з http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/uk/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Існує багато варіацій архітектур трансформерів, включаючи BERT, DistilBERT, BigBird, OpenGPT3 та інші, які можна налаштовувати.\n", "\n", diff --git a/translations/uk/lessons/5-NLP/19-NER/README.md b/translations/uk/lessons/5-NLP/19-NER/README.md index e785478d..9ed44b27 100644 --- a/translations/uk/lessons/5-NLP/19-NER/README.md +++ b/translations/uk/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Оскільки нам потрібно створити однозначну відповідність між токенами та класами, ми можемо навчити правосторонню **багатозначну** модель нейронної мережі з цієї схеми: -![Зображення, що показує загальні шаблони рекурентних нейронних мереж.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.uk.jpg) +![Зображення, що показує загальні шаблони рекурентних нейронних мереж.](../../../../../translated_images/uk/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Зображення з [цього блогу](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) від [Андрія Карпатія](http://karpathy.github.io/). Моделі класифікації токенів для NER відповідають правосторонній архітектурі мережі на цьому зображенні.* diff --git a/translations/uk/lessons/5-NLP/README.md b/translations/uk/lessons/5-NLP/README.md index 42a4d543..32df8ec0 100644 --- a/translations/uk/lessons/5-NLP/README.md +++ b/translations/uk/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Обробка природної мови -![Резюме задач NLP у вигляді малюнка](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.uk.png) +![Резюме задач NLP у вигляді малюнка](../../../../translated_images/uk/ai-nlp.b22dcb8ca4707cea.png) У цьому розділі ми зосередимося на використанні нейронних мереж для вирішення задач, пов'язаних із **обробкою природної мови (NLP)**. Існує багато проблем NLP, які ми хочемо, щоб комп'ютери могли вирішувати: diff --git a/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md b/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md index b0105e51..2cc7fd61 100644 --- a/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/uk/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ Після відкриття моделі ви потрапляєте на головний екран NetLogo. Ось приклад моделі, яка описує популяцію вовків і овець за умови обмежених ресурсів (трави). -![Головний екран NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.uk.png) +![Головний екран NetLogo](../../../../../translated_images/uk/NetLogo-Main.32653711ec1a01b3.png) > Знімок екрана від Дмитра Сошникова diff --git a/translations/uk/lessons/README.md b/translations/uk/lessons/README.md index d854a4e7..16fbb7bd 100644 --- a/translations/uk/lessons/README.md +++ b/translations/uk/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Огляд -![Огляд у вигляді замальовки](../../../translated_images/ai-overview.0857791951d19500.uk.png) +![Огляд у вигляді замальовки](../../../translated_images/uk/ai-overview.0857791951d19500.png) > Замальовка від [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/uk/lessons/X-Extras/X1-MultiModal/README.md b/translations/uk/lessons/X-Extras/X1-MultiModal/README.md index f49a953d..6088b60e 100644 --- a/translations/uk/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/uk/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Основна ідея CLIP полягає в тому, щоб порівнювати текстові запити із зображеннями та визначати, наскільки добре зображення відповідає запиту. -![Архітектура CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.uk.png) +![Архітектура CLIP](../../../../../translated_images/uk/clip-arch.b3dbf20b4e8ed8be.png) > *Зображення з [цього блогу](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CO_OP_TRANSLATOR_METADATA: Припустимо, нам потрібно класифікувати зображення, наприклад, між котами, собаками та людьми. У цьому випадку ми можемо надати моделі зображення та серію текстових запитів: "*зображення кота*", "*зображення собаки*", "*зображення людини*". У результатуючому векторі з 3 ймовірностей нам потрібно вибрати індекс із найвищим значенням. -![CLIP для класифікації зображень](../../../../../translated_images/clip-class.3af42ef0b2b19369.uk.png) +![CLIP для класифікації зображень](../../../../../translated_images/uk/clip-class.3af42ef0b2b19369.png) > *Зображення з [цього блогу](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ CLIP також можна використовувати для **генера Однією з важливих відмінностей між VQGAN і традиційним GAN є те, що останній може створити пристойне зображення з будь-якого вхідного вектора, тоді як VQGAN, ймовірно, створить зображення, яке не буде узгодженим. Тому нам потрібно додатково керувати процесом створення зображення, і це можна зробити за допомогою CLIP. -![Архітектура VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.uk.png) +![Архітектура VQGAN+CLIP](../../../../../translated_images/uk/vqgan.5027fe05051dfa31.png) Щоб створити зображення, яке відповідає текстовому запиту, ми починаємо з випадкового вектора кодування, який передається через VQGAN для створення зображення. Потім CLIP використовується для створення функції втрат, яка показує, наскільки добре зображення відповідає текстовому запиту. Мета полягає в тому, щоб мінімізувати цю втрату, використовуючи зворотне поширення для коригування параметрів вхідного вектора. Чудова бібліотека, яка реалізує VQGAN+CLIP, — це [Pixray](http://github.com/pixray/pixray). -![Зображення, створене Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.uk.png) | ![Зображення, створене Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.uk.png) | ![Зображення, створене Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.uk.png) +![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Зображення, створене Pixray](../../../../../translated_images/uk/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Зображення, створене за запитом *акварельний портрет молодого чоловіка-вчителя літератури з книгою* | Зображення, створене за запитом *масляний портрет молодої жінки-вчителя інформатики з комп'ютером* | Зображення, створене за запитом *масляний портрет старого чоловіка-вчителя математики перед дошкою* @@ -75,7 +75,7 @@ DALL-E — це версія GPT-3, навчена створювати зобр Основна відмінність між DALL-E 1 і 2 полягає в тому, що друга версія генерує більш реалістичні зображення та мистецтво. Приклади генерації зображень за допомогою DALL-E: -![Зображення, створене DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.uk.png) | ![Зображення, створене DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.uk.png) | ![Зображення, створене DALL-E](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.uk.png) +![Зображення, створене DALL-E](../../../../../translated_images/uk/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Зображення, створене DALL-E](../../../../../translated_images/uk/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Зображення, створене DALL-E](../../../../../translated_images/uk/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Зображення, створене за запитом *акварельний портрет молодого чоловіка-вчителя літератури з книгою* | Зображення, створене за запитом *масляний портрет молодої жінки-вчителя інформатики з комп'ютером* | Зображення, створене за запитом *масляний портрет старого чоловіка-вчителя математики перед дошкою* diff --git a/translations/ur/README.md b/translations/ur/README.md index 745275ff..ea0e68af 100644 --- a/translations/ur/README.md +++ b/translations/ur/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # مصنوعی ذہانت برائے مبتدی - ایک نصاب -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.ur.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ur/ai-overview.0857791951d19500.png)| |:---:| | AI For Beginners - _سکیچ نوٹ بذریعہ [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/ur/lessons/1-Intro/README.md b/translations/ur/lessons/1-Intro/README.md index 84db1f2f..a612bbda 100644 --- a/translations/ur/lessons/1-Intro/README.md +++ b/translations/ur/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # مصنوعی ذہانت کا تعارف -![مصنوعی ذہانت کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ai-intro.bf28d1ac4235881c.ur.png) +![مصنوعی ذہانت کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-intro.bf28d1ac4235881c.png) > خاکہ: [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ابتدائی طور پر، کمپیوٹرز کو [چارلس بیبج](https://en.wikipedia.org/wiki/Charles_Babbage) نے اعداد پر ایک واضح طریقہ کار کے تحت کام کرنے کے لیے ایجاد کیا تھا - ایک الگورتھم۔ جدید کمپیوٹرز، اگرچہ انیسویں صدی میں پیش کیے گئے اصل ماڈل سے کہیں زیادہ ترقی یافتہ ہیں، پھر بھی کنٹرول شدہ حسابات کے اسی خیال پر عمل کرتے ہیں۔ اس لیے، اگر ہمیں وہ درست مراحل معلوم ہوں جو کسی مقصد کو حاصل کرنے کے لیے ضروری ہیں، تو کمپیوٹر کو پروگرام کرنا ممکن ہے۔ -![ایک شخص کی تصویر](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ur.png) +![ایک شخص کی تصویر](../../../../translated_images/ur/dsh_age.d212a30d4e54fb5f.png) > تصویر: [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: **[ذہانت](https://en.wikipedia.org/wiki/Intelligence)** کی اصطلاح سے نمٹنے میں ایک مسئلہ یہ ہے کہ اس کی کوئی واضح تعریف نہیں ہے۔ کوئی یہ دلیل دے سکتا ہے کہ ذہانت کا تعلق **تجریدی سوچ** یا **خود آگاہی** سے ہے، لیکن ہم اسے مناسب طریقے سے بیان نہیں کر سکتے۔ -![بلی کی تصویر](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ur.jpg) +![بلی کی تصویر](../../../../translated_images/ur/photo-cat.8c8e8fb760ffe457.jpg) > [تصویر](https://unsplash.com/photos/75715CVEJhI) از [Amber Kipp](https://unsplash.com/@sadmax) Unsplash سے @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | مشین لرننگ کے بارے میں کیا خیال ہے؟ | | > |--------------|-----------| -> | مصنوعی ذہانت کا وہ حصہ جو کمپیوٹر کو کچھ ڈیٹا کی بنیاد پر مسئلہ حل کرنے کے لیے سیکھنے پر مبنی ہے، اسے **مشین لرننگ** کہا جاتا ہے۔ ہم اس کورس میں کلاسیکل مشین لرننگ پر غور نہیں کریں گے - ہم آپ کو ایک علیحدہ [مشین لرننگ کے ابتدائی کورس](http://aka.ms/ml-beginners) کی طرف رجوع کرتے ہیں۔ | ![مشین لرننگ کے ابتدائی کورس](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.ur.png) | +> | مصنوعی ذہانت کا وہ حصہ جو کمپیوٹر کو کچھ ڈیٹا کی بنیاد پر مسئلہ حل کرنے کے لیے سیکھنے پر مبنی ہے، اسے **مشین لرننگ** کہا جاتا ہے۔ ہم اس کورس میں کلاسیکل مشین لرننگ پر غور نہیں کریں گے - ہم آپ کو ایک علیحدہ [مشین لرننگ کے ابتدائی کورس](http://aka.ms/ml-beginners) کی طرف رجوع کرتے ہیں۔ | ![مشین لرننگ کے ابتدائی کورس](../../../../translated_images/ur/ml-for-beginners.9e4fed176fd5817d.png) | ## مصنوعی ذہانت کی مختصر تاریخ مصنوعی ذہانت کو بیسویں صدی کے وسط میں ایک شعبے کے طور پر شروع کیا گیا۔ ابتدا میں، علامتی استدلال ایک غالب طریقہ تھا، اور اس نے کئی اہم کامیابیاں حاصل کیں، جیسے ماہر نظام – کمپیوٹر پروگرامز جو محدود مسئلہ کے شعبوں میں ماہر کے طور پر کام کر سکتے تھے۔ تاہم، جلد ہی یہ واضح ہو گیا کہ یہ طریقہ اچھی طرح سے توسیع پذیر نہیں ہے۔ ماہر سے علم نکالنا، اسے کمپیوٹر میں پیش کرنا، اور اس علم کے ذخیرے کو درست رکھنا ایک بہت پیچیدہ کام ثابت ہوا، اور بہت سے معاملات میں عملی طور پر بہت مہنگا۔ اس نے 1970 کی دہائی میں نام نہاد [AI سردی](https://en.wikipedia.org/wiki/AI_winter) کو جنم دیا۔ -مصنوعی ذہانت کی مختصر تاریخ +مصنوعی ذہانت کی مختصر تاریخ > تصویر: [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * جدید اسسٹنٹس، جیسے Cortana، Siri یا Google Assistant، سب ہائبرڈ سسٹمز ہیں جو نیورل نیٹ ورکس کا استعمال کرتے ہیں تاکہ تقریر کو متن میں تبدیل کریں اور ہمارے ارادے کو پہچانیں، اور پھر کچھ استدلال یا واضح الگورتھمز کو مطلوبہ اعمال انجام دینے کے لیے استعمال کریں۔ * مستقبل میں، ہم توقع کر سکتے ہیں کہ ایک مکمل نیورل پر مبنی ماڈل خود ہی مکالمے کو سنبھالے گا۔ حالیہ GPT اور [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) نیورل نیٹ ورکس کے خاندان اس میں بڑی کامیابی دکھا رہے ہیں۔ -ٹورنگ ٹیسٹ کا ارتقاء +ٹورنگ ٹیسٹ کا ارتقاء > تصویر از دمتری سوشنیکوف، [تصویر](https://unsplash.com/photos/r8LmVbUKgns) از [مارینا ابروسیمووا](https://unsplash.com/@abrosimova_marina_foto)، انسپلیش ## حالیہ AI تحقیق diff --git a/translations/ur/lessons/2-Symbolic/Animals.ipynb b/translations/ur/lessons/2-Symbolic/Animals.ipynb index 34112bd0..77b9a186 100644 --- a/translations/ur/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ur/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "اس نمونے میں، ہم ایک سادہ علم پر مبنی نظام نافذ کریں گے جو کچھ جسمانی خصوصیات کی بنیاد پر جانور کا تعین کرے گا۔ اس نظام کو درج ذیل AND-OR درخت کے ذریعے ظاہر کیا جا سکتا ہے (یہ پورے درخت کا ایک حصہ ہے، ہم آسانی سے مزید قواعد شامل کر سکتے ہیں):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ur.png)\n" + "![](../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/ur/lessons/2-Symbolic/README.md b/translations/ur/lessons/2-Symbolic/README.md index d93cfd98..5a80b0e0 100644 --- a/translations/ur/lessons/2-Symbolic/README.md +++ b/translations/ur/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # علم کی نمائندگی اور ماہر نظام -![سمبولک AI کا خلاصہ](../../../../translated_images/ai-symbolic.715a30cb610411a6.ur.png) +![سمبولک AI کا خلاصہ](../../../../translated_images/ur/ai-symbolic.715a30cb610411a6.png) > اسکیچ نوٹ از [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ AI کے ابتدائی دنوں میں، ذہین نظام بنانے کے لی لہٰذا، **علم کی نمائندگی** کا مسئلہ یہ ہے کہ کمپیوٹر کے اندر علم کو ڈیٹا کی شکل میں مؤثر طریقے سے کیسے پیش کیا جائے، تاکہ اسے خودکار طور پر استعمال کیا جا سکے۔ اسے ایک اسپیکٹرم کے طور پر دیکھا جا سکتا ہے: -![علم کی نمائندگی کا اسپیکٹرم](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ur.png) +![علم کی نمائندگی کا اسپیکٹرم](../../../../translated_images/ur/knowledge-spectrum.b60df631852c0217.png) > تصویر از [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Block Syntax | Indent | | | سمبولک AI کی ابتدائی کامیابیوں میں سے ایک **ماہر نظام** تھے - کمپیوٹر سسٹمز جو کسی محدود مسئلہ کے دائرہ کار میں ماہر کے طور پر کام کرنے کے لیے ڈیزائن کیے گئے تھے۔ یہ ایک **علمی بنیاد** پر مبنی تھے جو ایک یا زیادہ انسانی ماہرین سے نکالا گیا تھا، اور ان میں ایک **استدلالی انجن** شامل تھا جو اس پر کچھ استدلال کرتا تھا۔ -![انسانی نظام کی ساخت](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.ur.png) | ![علم پر مبنی نظام کی ساخت](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.ur.png) +![انسانی نظام کی ساخت](../../../../translated_images/ur/arch-human.5d4d35f1bba3ab1c.png) | ![علم پر مبنی نظام کی ساخت](../../../../translated_images/ur/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ انسانی اعصابی نظام کی سادہ ساخت | علم پر مبنی نظام کی ساخت @@ -106,7 +106,7 @@ Block Syntax | Indent | | | ایک مثال کے طور پر، آئیے ایک ماہر نظام پر غور کریں جو کسی جانور کا تعین اس کی جسمانی خصوصیات کی بنیاد پر کرتا ہے: -![AND-OR درخت](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ur.png) +![AND-OR درخت](../../../../translated_images/ur/AND-OR-Tree.5592d2c70187f283.png) > تصویر از [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index bdd80541..912c7daf 100644 --- a/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ur/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -490,7 +490,7 @@ "\n", "اگر ہمارے پاس 2 سے زیادہ کلاسز ہوں، تو سافٹ میکس تمام کلاسز کے امکانات کو نارملائز کر دے گا۔ یہاں نیٹ ورک کی آرکیٹیکچر کا ایک خاکہ دیا گیا ہے جو MNIST ہندسوں کی درجہ بندی کرتا ہے:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.ur.png)\n" + "![MNIST Classifier](../../../../../translated_images/ur/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1251,7 +1251,7 @@ "* کم تربیتی نقصان - ماڈل تربیتی ڈیٹا کو اچھی طرح سمجھ سکتا ہے، کیونکہ اس میں کافی اظہاری طاقت ہوتی ہے۔\n", "* ویلیڈیشن نقصان تربیتی نقصان سے کہیں زیادہ ہو سکتا ہے اور تربیت کے دوران بڑھنا شروع کر سکتا ہے - اس کی وجہ یہ ہے کہ ماڈل تربیتی پوائنٹس کو \"یاد\" کر لیتا ہے اور \"مجموعی تصویر\" کھو دیتا ہے۔\n", "\n", - "![اوورفٹنگ](../../../../../translated_images/overfit.a0bd57f717c15769.ur.png)\n", + "![اوورفٹنگ](../../../../../translated_images/ur/overfit.a0bd57f717c15769.png)\n", "\n", "> اس تصویر میں، `x` تربیتی ڈیٹا کو ظاہر کرتا ہے، `o` - ویلیڈیشن ڈیٹا۔ بائیں طرف - لکیری ماڈل (ایک پرت)، یہ ڈیٹا کی نوعیت کو کافی حد تک اچھی طرح سمجھتا ہے۔ دائیں طرف - اوورفٹنگ ماڈل، ماڈل تربیتی ڈیٹا کو بالکل درست سمجھتا ہے، لیکن کسی دوسرے ڈیٹا کے ساتھ معنی کھو دیتا ہے (ویلیڈیشن نقصان بہت زیادہ ہے)\n" ] diff --git a/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md index fc512d66..207b9df2 100644 --- a/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ur/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* ذیل میں 5 نقاط کو اپروکسیمیٹ کرنے کے مسئلے پر غور کریں (گراف میں `x` کے ذریعے ظاہر کیے گئے): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ur.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ur.jpg) +![linear](../../../../../translated_images/ur/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/ur/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **لینیئر ماڈل، 2 پیرامیٹرز** | **نان-لینیئر ماڈل، 7 پیرامیٹرز** ٹریننگ ایرر = 5.3 | ٹریننگ ایرر = 0 @@ -79,7 +79,7 @@ High-level API| [Keras](IntroKeras.ipynb) | *PyTorch Lightning* جیسا کہ آپ اوپر گراف سے دیکھ سکتے ہیں، اوورفٹنگ کا پتہ بہت کم ٹریننگ ایرر اور بہت زیادہ ویلیڈیشن ایرر سے لگایا جا سکتا ہے۔ عام طور پر تربیت کے دوران ہم دیکھیں گے کہ ٹریننگ اور ویلیڈیشن ایرر دونوں کم ہونا شروع ہو جاتے ہیں، اور پھر کسی وقت ویلیڈیشن ایرر کم ہونا بند کر سکتا ہے اور بڑھنا شروع کر سکتا ہے۔ یہ اوورفٹنگ کی علامت ہوگی، اور اس بات کا اشارہ کہ ہمیں شاید اس وقت تربیت روک دینی چاہیے (یا کم از کم ماڈل کا اسنیپ شاٹ لینا چاہیے)۔ -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ur.png) +![overfitting](../../../../../translated_images/ur/Overfitting.408ad91cd90b4371.png) ## اوورفٹنگ کو کیسے روکا جائے؟ diff --git a/translations/ur/lessons/3-NeuralNetworks/README.md b/translations/ur/lessons/3-NeuralNetworks/README.md index 5e6888d5..78660272 100644 --- a/translations/ur/lessons/3-NeuralNetworks/README.md +++ b/translations/ur/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # نیورل نیٹ ورکس کا تعارف -![نیورل نیٹ ورکس کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.ur.png) +![نیورل نیٹ ورکس کے تعارف کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-neuralnetworks.1c687ae40bc86e83.png) جیسا کہ ہم نے تعارف میں بات کی تھی، ذہانت حاصل کرنے کے طریقوں میں سے ایک یہ ہے کہ ایک **کمپیوٹر ماڈل** یا **مصنوعی دماغ** کو تربیت دی جائے۔ بیسویں صدی کے وسط سے، محققین نے مختلف ریاضیاتی ماڈلز آزمائے، اور حالیہ برسوں میں یہ سمت بہت کامیاب ثابت ہوئی۔ دماغ کے ان ریاضیاتی ماڈلز کو **نیورل نیٹ ورکس** کہا جاتا ہے۔ @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: حیاتیات سے، ہم جانتے ہیں کہ ہمارا دماغ نیورل سیلز (نیورونز) پر مشتمل ہوتا ہے، جن میں سے ہر ایک کے پاس متعدد "ان پٹس" (ڈینڈریٹس) اور ایک "آؤٹ پٹ" (ایکسون) ہوتا ہے۔ ڈینڈریٹس اور ایکسون دونوں برقی سگنلز منتقل کر سکتے ہیں، اور ان کے درمیان کنکشنز — جنہیں سیناپسز کہا جاتا ہے — مختلف درجات کی کنڈکٹویٹی ظاہر کر سکتے ہیں، جو نیوروٹرانسمیٹرز کے ذریعے منظم کی جاتی ہیں۔ -![نیورون کا ماڈل](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.ur.jpg) | ![نیورون کا ماڈل](../../../../translated_images/artneuron.1a5daa88d20ebe6f.ur.png) +![نیورون کا ماڈل](../../../../translated_images/ur/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![نیورون کا ماڈل](../../../../translated_images/ur/artneuron.1a5daa88d20ebe6f.png) ----|---- حقیقی نیورون *([تصویر](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) ویکیپیڈیا سے)* | مصنوعی نیورون *(تصویر مصنف کی طرف سے)* لہٰذا، نیورون کا سب سے سادہ ریاضیاتی ماڈل کئی ان پٹس X1, ..., XN اور ایک آؤٹ پٹ Y پر مشتمل ہوتا ہے، اور ایک سلسلہ وزن W1, ..., WN۔ آؤٹ پٹ کا حساب درج ذیل طریقے سے کیا جاتا ہے: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) جہاں f کوئی غیر خطی **ایکٹیویشن فنکشن** ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md index 34352dad..c845a761 100644 --- a/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ur/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **بریل کتاب کی تصویر کی پری پروسیسنگ**۔ ہم اس پر توجہ دیتے ہیں کہ کس طرح تھریشولڈنگ، فیچر ڈیٹیکشن، پرسپیکٹیو تبدیلی اور NumPy تبدیلیوں کا استعمال کرکے بریل کے انفرادی علامات کو الگ کیا جا سکتا ہے تاکہ نیورل نیٹ ورک کے ذریعے مزید درجہ بندی کی جا سکے۔ -![بریل تصویر](../../../../../translated_images/braille.341962ff76b1bd70.ur.jpeg) | ![بریل تصویر پری پروسیسڈ](../../../../../translated_images/braille-result.46530fea020b03c7.ur.png) | ![بریل علامات](../../../../../translated_images/braille-symbols.0159185ab69d5339.ur.png) +![بریل تصویر](../../../../../translated_images/ur/braille.341962ff76b1bd70.jpeg) | ![بریل تصویر پری پروسیسڈ](../../../../../translated_images/ur/braille-result.46530fea020b03c7.png) | ![بریل علامات](../../../../../translated_images/ur/braille-symbols.0159185ab69d5339.png) ----|-----|----- > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے * **ویڈیو میں حرکت کا پتہ لگانا فریم فرق کے ذریعے**۔ اگر کیمرہ فکسڈ ہے، تو کیمرہ فیڈ کے فریمز ایک دوسرے سے کافی حد تک مشابہت رکھتے ہیں۔ چونکہ فریمز arrays کے طور پر ظاہر کیے جاتے ہیں، صرف ان arrays کو دو مسلسل فریمز کے لیے گھٹانے سے ہمیں پکسل فرق ملے گا، جو جامد فریمز کے لیے کم ہونا چاہیے، اور تصویر میں نمایاں حرکت ہونے پر زیادہ ہو جائے گا۔ -![ویڈیو فریمز اور فریم فرق کی تصویر](../../../../../translated_images/frame-difference.706f805491a0883c.ur.png) +![ویڈیو فریمز اور فریم فرق کی تصویر](../../../../../translated_images/ur/frame-difference.706f805491a0883c.png) > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **ڈینس آپٹیکل فلو** وہ ویکٹر فیلڈ بناتا ہے جو ہر پکسل کے لیے دکھاتا ہے کہ وہ کہاں حرکت کر رہا ہے۔ - **اسپارس آپٹیکل فلو** تصویر میں کچھ نمایاں خصوصیات (جیسے کنارے) لیتا ہے اور ان کی فریم سے فریم تک حرکت کی راہ بناتا ہے۔ -![آپٹیکل فلو کی تصویر](../../../../../translated_images/optical.1f4a94464579a83a.ur.png) +![آپٹیکل فلو کی تصویر](../../../../../translated_images/ur/optical.1f4a94464579a83a.png) > تصویر [OpenCV.ipynb](OpenCV.ipynb) سے diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 57ab4379..013a43ff 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 ایک نیٹ ورک ہے جس نے 2014 میں ImageNet کے ٹاپ-5 کلاسیفیکیشن میں 92.7% درستگی حاصل کی۔ اس کی درج ذیل لیئر ساخت ہے: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ur.jpg) +![ImageNet Layers](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.jpg) جیسا کہ آپ دیکھ سکتے ہیں، VGG ایک روایتی پیرامڈ آرکیٹیکچر کی پیروی کرتا ہے، جو کہ کنوولوشن-پولنگ لیئرز کی ترتیب ہے۔ -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ur.jpg) +![ImageNet Pyramid](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.jpg) > تصویر [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index 10c92132..e0ff7e63 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "اس طرح، ایک عام CNN میں کئی کنولوشنل لیئرز ہوں گی، جن کے درمیان پولنگ لیئرز ہوں گی تاکہ تصویر کے ڈائمینشنز کو کم کیا جا سکے۔ ہم فلٹرز کی تعداد بھی بڑھائیں گے، کیونکہ جیسے جیسے نمونے زیادہ پیچیدہ ہوتے جاتے ہیں - ہمیں زیادہ ممکنہ دلچسپ امتزاجات کو تلاش کرنے کی ضرورت ہوتی ہے۔\n", "\n", - "![ایک تصویر جو کئی کنولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھا رہی ہے۔](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ur.png)\n", + "![ایک تصویر جو کئی کنولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھا رہی ہے۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "اسپیشل ڈائمینشنز کو کم کرنے اور فیچر/فلٹرز ڈائمینشنز کو بڑھانے کی وجہ سے، اس آرکیٹیکچر کو **پیرامیڈ آرکیٹیکچر** بھی کہا جاتا ہے۔\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 1c543d28..33956811 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -361,7 +361,7 @@ "\n", "اس طرح، ایک عام CNN میں کئی کنوولوشنل لیئرز ہوتی ہیں، جن کے درمیان پولنگ لیئرز ہوتی ہیں تاکہ تصویر کے ڈائمینشنز کو کم کیا جا سکے۔ ہم فلٹرز کی تعداد بھی بڑھاتے ہیں، کیونکہ جیسے جیسے پیٹرنز زیادہ پیچیدہ ہوتے جاتے ہیں - ہمیں زیادہ ممکنہ دلچسپ امتزاجات کو تلاش کرنے کی ضرورت ہوتی ہے۔\n", "\n", - "![کئی کنوولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھانے والی ایک تصویر۔](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.ur.png)\n", + "![کئی کنوولوشنل لیئرز کو پولنگ لیئرز کے ساتھ دکھانے والی ایک تصویر۔](../../../../../translated_images/ur/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "اسپیشل ڈائمینشنز کو کم کرنے اور فیچر/فلٹرز کے ڈائمینشنز کو بڑھانے کی وجہ سے، اس آرکیٹیکچر کو **پیرامیڈ آرکیٹیکچر** بھی کہا جاتا ہے۔\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md index a0df0e73..cbcf51f6 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: پیٹرنز نکالنے کے لیے، ہم **کنولوشنل فلٹرز** کا تصور استعمال کریں گے۔ جیسا کہ آپ جانتے ہیں، ایک تصویر کو 2D-میٹرکس یا رنگ کی گہرائی کے ساتھ 3D-ٹینسر کے طور پر ظاہر کیا جاتا ہے۔ فلٹر لگانے کا مطلب یہ ہے کہ ہم ایک نسبتاً چھوٹا **فلٹر کرنل** میٹرکس لیتے ہیں، اور اصل تصویر کے ہر پکسل کے لیے ہم پڑوسی پوائنٹس کے ساتھ وزنی اوسط کا حساب لگاتے ہیں۔ ہم اسے اس طرح دیکھ سکتے ہیں جیسے ایک چھوٹی ونڈو پوری تصویر پر سلائیڈ کر رہی ہو، اور فلٹر کرنل میٹرکس میں وزن کے مطابق تمام پکسلز کو اوسط کر رہی ہو۔ -![عمودی کنارے فلٹر](../../../../../translated_images/filter-vert.b7148390ca0bc356.ur.png) | ![افقی کنارے فلٹر](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.ur.png) +![عمودی کنارے فلٹر](../../../../../translated_images/ur/filter-vert.b7148390ca0bc356.png) | ![افقی کنارے فلٹر](../../../../../translated_images/ur/filter-horiz.59b80ed4feb946ef.png) ----|---- > تصویر: دمتری سوشنیکوف @@ -38,7 +38,7 @@ CNN کے کام کرنے کا طریقہ درج ذیل اہم خیالات پر * ہم نیٹ ورک کو اس طرح ڈیزائن کر سکتے ہیں کہ فلٹرز خود بخود تربیت حاصل کریں * ہم اسی طریقے کو اعلی سطحی خصوصیات میں پیٹرنز تلاش کرنے کے لیے استعمال کر سکتے ہیں، نہ صرف اصل تصویر میں۔ اس طرح CNN خصوصیات نکالنے کا کام خصوصیات کی ایک درجہ بندی پر کرتا ہے، جو کم سطحی پکسل امتزاج سے شروع ہو کر تصویر کے حصوں کے اعلی سطحی امتزاج تک جاتا ہے۔ -![درجہ بندی خصوصیات نکالنا](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ur.png) +![درجہ بندی خصوصیات نکالنا](../../../../../translated_images/ur/FeatureExtractionCNN.d9b456cbdae7cb64.png) > تصویر: [ہسلپ-لنچ کے مقالے](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d) سے، [ان کی تحقیق](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) پر مبنی @@ -55,9 +55,9 @@ CNN کے کام کرنے کا طریقہ درج ذیل اہم خیالات پر ایک مثال کے طور پر، آئیے VGG-16 کی آرکیٹیکچر کو دیکھیں، ایک نیٹ ورک جس نے 2014 میں ImageNet کے ٹاپ-5 درجہ بندی میں 92.7% درستگی حاصل کی: -![ImageNet لیئرز](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ur.jpg) +![ImageNet لیئرز](../../../../../translated_images/ur/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet پیرامڈ](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ur.jpg) +![ImageNet پیرامڈ](../../../../../translated_images/ur/vgg-16-arch.64ff2137f50dd49f.jpg) > تصویر: [ریسرچ گیٹ](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) سے diff --git a/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md index af153038..b706e287 100644 --- a/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ur/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ہم [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) استعمال کریں گے، جس میں کتوں اور بلیوں کی 37 مختلف نسلوں کی تصاویر شامل ہیں۔ -![ہم جس ڈیٹا سیٹ سے نمٹیں گے](../../../../../../translated_images/data.50b2a9d5484bdbf0.ur.png) +![ہم جس ڈیٹا سیٹ سے نمٹیں گے](../../../../../../translated_images/ur/data.50b2a9d5484bdbf0.png) ڈیٹا سیٹ ڈاؤن لوڈ کرنے کے لیے، یہ کوڈ استعمال کریں: diff --git a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 5061edb4..1590c359 100644 --- a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "مثالی بلی کو دیکھنے کے لیے، ہم ایک بے ترتیب شور والی تصویر سے آغاز کریں گے، اور پھر گریڈینٹ ڈیسینٹ آپٹیمائزیشن تکنیک کا استعمال کرتے ہوئے تصویر کو اس طرح ایڈجسٹ کریں گے کہ نیٹ ورک بلی کو پہچان سکے۔\n", "\n", - "![آپٹیمائزیشن لوپ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ur.png)\n", + "![آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "یہ ہے ہماری ابتدائی تصویر:\n" ] diff --git a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md index 6a2e6070..438da23a 100644 --- a/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ur/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras اور PyTorch دونوں میں کچھ عام آرکیٹیکچرز کے یہاں VGG-16 نیٹ ورک کے ذریعے بلی کی تصویر سے نکالے گئے نمونہ فیچرز ہیں: -![VGG-16 کے ذریعے نکالے گئے فیچرز](../../../../../translated_images/features.6291f9c7ba3a0b95.ur.png) +![VGG-16 کے ذریعے نکالے گئے فیچرز](../../../../../translated_images/ur/features.6291f9c7ba3a0b95.png) ## بلیوں اور کتوں کا ڈیٹا سیٹ @@ -48,19 +48,19 @@ Keras اور PyTorch دونوں میں کچھ عام آرکیٹیکچرز کے ایک طریقہ یہ ہے کہ ہم ایک بے ترتیب تصویر سے شروع کریں، اور پھر **گریڈینٹ ڈیسینٹ آپٹیمائزیشن** تکنیک کا استعمال کرتے ہوئے اس تصویر کو اس طرح ایڈجسٹ کریں کہ نیٹ ورک یہ سوچنا شروع کر دے کہ یہ بلی ہے۔ -![امیج آپٹیمائزیشن لوپ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ur.png) +![امیج آپٹیمائزیشن لوپ](../../../../../translated_images/ur/ideal-cat-loop.999fbb8ff306e044.png) تاہم، اگر ہم ایسا کریں، تو ہمیں کچھ ایسا ملے گا جو بے ترتیب شور کے بہت قریب ہوگا۔ اس کی وجہ یہ ہے کہ *نیٹ ورک کو یہ سوچنے کے لیے کہ ان پٹ تصویر بلی ہے، بہت سے طریقے ہیں*، جن میں کچھ بصری طور پر معنی خیز نہیں ہیں۔ اگرچہ ان تصاویر میں بلی کے لیے عام پیٹرنز کی بہتات ہوتی ہے، لیکن انہیں بصری طور پر ممتاز ہونے کے لیے کچھ بھی مجبور نہیں کرتا۔ نتیجہ بہتر بنانے کے لیے، ہم نقصان کے فنکشن میں ایک اور اصطلاح شامل کر سکتے ہیں، جسے **ویریئشن لاس** کہا جاتا ہے۔ یہ ایک میٹرک ہے جو دکھاتا ہے کہ تصویر کے پڑوسی پکسلز کتنے مماثل ہیں۔ ویریئشن لاس کو کم کرنے سے تصویر ہموار ہو جاتی ہے، اور شور ختم ہو جاتا ہے - اس طرح زیادہ بصری طور پر دلکش پیٹرنز ظاہر ہوتے ہیں۔ یہاں ایسے "مثالی" تصاویر کی مثالیں ہیں، جو بلی اور زیبرا کے طور پر اعلیٰ امکان کے ساتھ درجہ بندی کی گئی ہیں: -![مثالی بلی](../../../../../translated_images/ideal-cat.203dd4597643d6b0.ur.png) | ![مثالی زیبرا](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.ur.png) +![مثالی بلی](../../../../../translated_images/ur/ideal-cat.203dd4597643d6b0.png) | ![مثالی زیبرا](../../../../../translated_images/ur/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *مثالی بلی* | *مثالی زیبرا* اسی طرح کا طریقہ نیورل نیٹ ورک پر **ایڈورسریل حملے** کرنے کے لیے استعمال کیا جا سکتا ہے۔ فرض کریں کہ ہم نیورل نیٹ ورک کو دھوکہ دینا چاہتے ہیں اور کتے کو بلی کی طرح دکھانا چاہتے ہیں۔ اگر ہم کتے کی تصویر لیں، جسے نیٹ ورک کتے کے طور پر پہچانتا ہے، تو ہم اسے تھوڑا سا ایڈجسٹ کر سکتے ہیں گریڈینٹ ڈیسینٹ آپٹیمائزیشن کا استعمال کرتے ہوئے، جب تک کہ نیٹ ورک اسے بلی کے طور پر درجہ بندی کرنا شروع نہ کر دے: -![کتے کی تصویر](../../../../../translated_images/original-dog.8f68a67d2fe0911f.ur.png) | ![بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.ur.png) +![کتے کی تصویر](../../../../../translated_images/ur/original-dog.8f68a67d2fe0911f.png) | ![بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر](../../../../../translated_images/ur/adversarial-dog.d9fc7773b0142b89.png) -----|----- *کتے کی اصل تصویر* | *بلی کے طور پر درجہ بندی کی گئی کتے کی تصویر* diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index 83a25b2b..191df249 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "چونکہ ہم آٹو اینکوڈر کو اس طرح تربیت دے رہے ہیں کہ وہ اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کرے تاکہ درست دوبارہ تشکیل ممکن ہو، نیٹ ورک ان پٹ تصاویر کے بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ ان کا مطلب واضح ہو۔\n", "\n", - "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ur.jpg)\n", + "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے\n", "\n", diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 59b63453..8845345a 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -21,7 +21,7 @@ "\n", "چونکہ ہم آٹو اینکوڈر کو تربیت دے رہے ہیں تاکہ اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کی جا سکیں تاکہ درست دوبارہ تعمیر ممکن ہو، نیٹ ورک ان پٹ تصاویر کی بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ ان کا مطلب سمجھا جا سکے۔\n", "\n", - "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ur.jpg)\n", + "![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے*\n", "\n", diff --git a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md index 99dbd058..3c141e36 100644 --- a/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ur/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: چونکہ ہم آٹو اینکوڈر کو اصل تصویر سے زیادہ سے زیادہ معلومات حاصل کرنے کے لیے تربیت دے رہے ہیں تاکہ درست تعمیر نو ہو سکے، نیٹ ورک ان پٹ تصاویر کی بہترین **ایمبیڈنگ** تلاش کرنے کی کوشش کرتا ہے تاکہ معنی کو حاصل کیا جا سکے۔ -![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ur.jpg) +![آٹو اینکوڈر ڈایاگرام](../../../../../translated_images/ur/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > تصویر [Keras بلاگ](https://blog.keras.io/building-autoencoders-in-keras.html) سے لی گئی ہے diff --git a/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md index f07dad2c..dc85fb8e 100644 --- a/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ur/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [لیکچر سے پہلے کا کوئز](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![آبجیکٹ ڈیٹیکشن](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ur.png) +![آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > تصویر [YOLO v2 ویب سائٹ](https://pjreddie.com/darknet/yolov2/) سے لی گئی ہے۔ @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ہر حصے پر امیج کلاسیفیکیشن چلائیں۔ 3. وہ حصے جن میں کافی زیادہ ایکٹیویشن ہو، انہیں مطلوبہ آبجیکٹ کے حامل سمجھا جا سکتا ہے۔ -![سادہ آبجیکٹ ڈیٹیکشن](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ur.png) +![سادہ آبجیکٹ ڈیٹیکشن](../../../../../translated_images/ur/naive-detection.e7f1ba220ccd08c6.png) > *تصویر [ایکسسرسائز نوٹ بک](ObjectDetection-TF.ipynb) سے لی گئی ہے۔* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 کلاسز * [COCO](http://cocodataset.org/#home) - عام اشیاء کے سیاق و سباق میں۔ 80 کلاسز، باؤنڈنگ باکسز اور سیگمنٹیشن ماسکس -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ur.jpg) +![COCO](../../../../../translated_images/ur/coco-examples.71bc60380fa6cceb.jpg) ## آبجیکٹ ڈیٹیکشن میٹرکس @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: امیج کلاسیفیکیشن کے لیے یہ آسان ہے کہ ہم الگورتھم کی کارکردگی کو ماپ سکیں، لیکن آبجیکٹ ڈیٹیکشن کے لیے ہمیں کلاس کی درستگی کے ساتھ ساتھ باؤنڈنگ باکس کی پیش گوئی کی درستگی کو بھی ماپنا ہوتا ہے۔ اس کے لیے ہم **انٹرسیکشن اوور یونین** (IoU) استعمال کرتے ہیں، جو دو باکسز (یا دو کسی بھی علاقے) کے اوورلیپ کو ماپتا ہے۔ -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ur.png) +![IoU](../../../../../translated_images/ur/iou_equation.9a4751d40fff4e11.png) > *تصویر 2 [اس بہترین بلاگ پوسٹ](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/) سے لی گئی ہے۔* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) [سیلیکٹیو سرچ](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) استعمال کرتا ہے تاکہ ROI ریجنز کی ہائیرارکل ساخت بنائی جا سکے، جنہیں پھر CNN فیچر ایکسٹریکٹرز اور SVM کلاسیفائرز کے ذریعے آبجیکٹ کلاس کا تعین کرنے کے لیے پاس کیا جاتا ہے، اور *باؤنڈنگ باکس* کے کوآرڈینیٹس کا تعین کرنے کے لیے لینیئر ریگریشن استعمال کی جاتی ہے۔ [آفیشل پیپر](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ur.png) +![RCNN](../../../../../translated_images/ur/rcnn1.cae407020dfb1d1f.png) > *تصویر van de Sande et al. ICCV’11 سے لی گئی ہے۔* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ur.png) +![RCNN-1](../../../../../translated_images/ur/rcnn2.2d9530bb83516484.png) > *تصاویر [اس بلاگ](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e) سے لی گئی ہیں۔* @@ -110,7 +110,7 @@ $$ یہ طریقہ R-CNN سے ملتا جلتا ہے، لیکن ریجنز کنوولوشن لیئرز کے بعد ڈیفائن کیے جاتے ہیں۔ -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ur.png) +![FRCNN](../../../../../translated_images/ur/f-rcnn.3cda6d9bb4188875.png) > تصویر [آفیشل پیپر](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)، [arXiv](https://arxiv.org/pdf/1504.08083.pdf)، 2015 سے لی گئی ہے۔ @@ -118,7 +118,7 @@ $$ اس طریقے کا بنیادی خیال یہ ہے کہ ریجنز کی پیش گوئی کے لیے نیورل نیٹ ورک استعمال کیا جائے - جسے *ریجن پروپوزل نیٹ ورک* کہا جاتا ہے۔ [پیپر](https://arxiv.org/pdf/1506.01497.pdf)، 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ur.png) +![FasterRCNN](../../../../../translated_images/ur/faster-rcnn.8d46c099b87ef30a.png) > تصویر [آفیشل پیپر](https://arxiv.org/pdf/1506.01497.pdf) سے لی گئی ہے۔ @@ -130,7 +130,7 @@ $$ 1. فیچرز **پوزیشن سینسیٹو اسکور میپ** کے ذریعے پروسیس کیے جاتے ہیں۔ $C$ کلاسز کے ہر آبجیکٹ کو $k\times k$ ریجنز میں تقسیم کیا جاتا ہے، اور ہم آبجیکٹس کے حصے پیش گوئی کرنے کی تربیت کرتے ہیں۔ 1. $k\times k$ ریجنز کے ہر حصے کے لیے تمام نیٹ ورکس آبجیکٹ کلاسز کے لیے ووٹ دیتے ہیں، اور زیادہ سے زیادہ ووٹ کے ساتھ آبجیکٹ کلاس منتخب کی جاتی ہے۔ -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ur.png) +![r-fcn image](../../../../../translated_images/ur/r-fcn.13eb88158b99a3da.png) > تصویر [آفیشل پیپر](https://arxiv.org/abs/1605.06409) سے لی گئی ہے۔ @@ -141,7 +141,7 @@ YOLO ایک ریئل ٹائم ون پاس الگورتھم ہے۔ بنیادی * تصویر کو $S\times S$ ریجنز میں تقسیم کیا جاتا ہے۔ * ہر ریجن کے لیے، **CNN** $n$ ممکنہ آبجیکٹس، *باؤنڈنگ باکس* کے کوآرڈینیٹس اور *کانفیڈنس*=*پروببلیٹی* * IoU کی پیش گوئی کرتا ہے۔ - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ur.png) + ![YOLO](../../../../../translated_images/ur/yolo.a2648ec82ee8bb4e.png) > تصویر [آفیشل پیپر](https://arxiv.org/abs/1506.02640) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/4-ComputerVision/README.md b/translations/ur/lessons/4-ComputerVision/README.md index 6fcbd8fa..a31b9d0b 100644 --- a/translations/ur/lessons/4-ComputerVision/README.md +++ b/translations/ur/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # کمپیوٹر وژن -![کمپیوٹر وژن کے مواد کا خلاصہ ایک خاکے میں](../../../../translated_images/ai-computervision.6506ebebac3fbf76.ur.png) +![کمپیوٹر وژن کے مواد کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-computervision.6506ebebac3fbf76.png) اس سیکشن میں ہم سیکھیں گے: diff --git a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index a8a8a8e0..d1fc87b2 100644 --- a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**الفاظ کے تھیلے** (BoW) ویکٹر کی نمائندگی سب سے زیادہ استعمال ہونے والی روایتی ویکٹر نمائندگی ہے۔ ہر لفظ کو ایک ویکٹر انڈیکس سے جوڑا جاتا ہے، اور ویکٹر عنصر کسی دیے گئے دستاویز میں کسی لفظ کے وقوعات کی تعداد کو ظاہر کرتا ہے۔\n", "\n", - "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر کی جاتی ہے۔](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ur.png)\n", + "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر کی جاتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **نوٹ**: آپ BoW کو متن میں انفرادی الفاظ کے لیے تمام ایک-ہاٹ-انکوڈڈ ویکٹرز کے مجموعے کے طور پر بھی سوچ سکتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 5baa8a19..8e8c2783 100644 --- a/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/ur/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "**الفاظ کے تھیلے** (BoW) ویکٹر کی نمائندگی سب سے آسان اور روایتی ویکٹر کی نمائندگی ہے۔ ہر لفظ کو ایک ویکٹر انڈیکس سے جوڑا جاتا ہے، اور ویکٹر عنصر کسی دیے گئے دستاویز میں ہر لفظ کے وقوعات کی تعداد کو ظاہر کرتا ہے۔\n", "\n", - "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر ہوتی ہے۔](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.ur.png)\n", + "![تصویر جو دکھاتی ہے کہ الفاظ کے تھیلے کی ویکٹر نمائندگی میموری میں کیسے ظاہر ہوتی ہے۔](../../../../../translated_images/ur/bag-of-words-example.606fc1738f1d7ba9.png)\n", "\n", "> **نوٹ**: آپ BoW کو متن میں انفرادی الفاظ کے لیے تمام ایک-ہاٹ-انکوڈڈ ویکٹرز کے مجموعے کے طور پر بھی سوچ سکتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 763de68a..fb2f32ae 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "اپنے نیٹ ورک میں ایمبیڈنگ لیئر کو پہلی لیئر کے طور پر استعمال کرتے ہوئے، ہم بیگ-آف-ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں تبدیل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے `sum`, `average` یا `max` کا حساب لگاتے ہیں۔\n", "\n", - "![پانچ سلسلہ وار الفاظ کے لیے ایمبیڈنگ کلاسیفائر دکھانے والی تصویر۔](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ur.png)\n", + "![پانچ سلسلہ وار الفاظ کے لیے ایمبیڈنگ کلاسیفائر دکھانے والی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ہمارا کلاسیفائر نیورل نیٹ ورک ایمبیڈنگ لیئر سے شروع ہوگا، پھر ایگریگیشن لیئر، اور اس کے اوپر ایک لینیئر کلاسیفائر ہوگا:\n" ] @@ -176,7 +176,7 @@ "\n", "پچھلی آرکیٹیکچر میں، ہمیں تمام ترتیبوں کو ایک ہی لمبائی تک بڑھانا پڑتا تھا تاکہ انہیں ایک منی بیچ میں فٹ کیا جا سکے۔ یہ مختلف لمبائی کی ترتیبوں کو ظاہر کرنے کا سب سے مؤثر طریقہ نہیں ہے - ایک اور طریقہ یہ ہو سکتا ہے کہ **آفسیٹ** ویکٹر استعمال کیا جائے، جو ایک بڑے ویکٹر میں محفوظ تمام ترتیبوں کے آفسیٹس کو رکھے۔\n", "\n", - "![آفسیٹ ترتیب کی نمائندگی دکھانے والی تصویر](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.ur.png)\n", + "![آفسیٹ ترتیب کی نمائندگی دکھانے والی تصویر](../../../../../translated_images/ur/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **نوٹ**: اوپر دی گئی تصویر میں، ہم حروف کی ترتیب دکھا رہے ہیں، لیکن ہمارے مثال میں ہم الفاظ کی ترتیب کے ساتھ کام کر رہے ہیں۔ تاہم، آفسیٹ ویکٹر کے ساتھ ترتیبوں کی نمائندگی کا عمومی اصول وہی رہتا ہے۔\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔\n", "\n", - "![CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھانے والی تصویر۔](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ur.png)\n", + "![CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھانے والی تصویر۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "گوگل نیوز ڈیٹاسیٹ پر پہلے سے ٹرین کیے گئے ورڈ2ویک ایمبیڈنگ کے ساتھ تجربہ کرنے کے لیے، ہم **gensim** لائبریری استعمال کر سکتے ہیں۔ نیچے ہم 'neural' کے سب سے زیادہ مشابہ الفاظ تلاش کرتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index bb10d831..9f653491 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/ur/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "جب ہم اپنے نیٹ ورک میں پہلی لیئر کے طور پر ایمبیڈنگ لیئر کا استعمال کرتے ہیں، تو ہم بیگ-آف-ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں منتقل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو اس کے متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے کہ `sum`، `average` یا `max` کا حساب لگاتے ہیں۔\n", "\n", - "![پانچ سیکوئنس الفاظ کے لیے ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ur.png)\n", + "![پانچ سیکوئنس الفاظ کے لیے ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "ہمارے کلاسیفائر نیورل نیٹ ورک میں درج ذیل لیئرز شامل ہیں:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن یہ کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔\n", "\n", - "![تصویر جو CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھا رہی ہے۔](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ur.png)\n", + "![تصویر جو CBoW اور اسکیپ-گرام الگورتھمز کو الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے دکھا رہی ہے۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "گوگل نیوز ڈیٹاسیٹ پر پری ٹرینڈ Word2Vec ایمبیڈنگ کے ساتھ تجربہ کرنے کے لیے، ہم **gensim** لائبریری استعمال کر سکتے ہیں۔ نیچے ہم 'neural' کے سب سے مشابہ الفاظ تلاش کرتے ہیں۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/14-Embeddings/README.md b/translations/ur/lessons/5-NLP/14-Embeddings/README.md index 38d12e83..fb87cc23 100644 --- a/translations/ur/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ur/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: ایمبیڈنگ لیئر کو ہمارے کلاسیفائر نیٹ ورک کی پہلی لیئر کے طور پر استعمال کرکے، ہم بیگ آف ورڈز ماڈل سے **ایمبیڈنگ بیگ** ماڈل میں تبدیل ہو سکتے ہیں، جہاں ہم پہلے اپنے متن کے ہر لفظ کو متعلقہ ایمبیڈنگ میں تبدیل کرتے ہیں، اور پھر ان تمام ایمبیڈنگز پر کوئی مجموعی فنکشن جیسے `sum`، `average` یا `max` کا حساب لگاتے ہیں۔ -![پانچ سیکوئنس الفاظ کے لیے ایک ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.ur.png) +![پانچ سیکوئنس الفاظ کے لیے ایک ایمبیڈنگ کلاسیفائر کی تصویر۔](../../../../../translated_images/ur/embedding-classifier-example.b77f021a7ee67eee.png) > تصویر مصنف کی جانب سے @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW تیز ہے، جبکہ اسکیپ-گرام سست ہے، لیکن کم استعمال ہونے والے الفاظ کی بہتر نمائندگی کرتا ہے۔ -![CBoW اور اسکیپ-گرام الگورتھمز کی تصویر جو الفاظ کو ویکٹرز میں تبدیل کرتے ہیں۔](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ur.png) +![CBoW اور اسکیپ-گرام الگورتھمز کی تصویر جو الفاظ کو ویکٹرز میں تبدیل کرتے ہیں۔](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > تصویر [اس مقالے](https://arxiv.org/pdf/1301.3781.pdf) سے diff --git a/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md index 84fd6527..299909a8 100644 --- a/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ur/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **Continuous Bag-of-Words** (CBoW)، جہاں ہم ٹوکن سیکوئنس $W_{-N}$, ..., $W_N$ میں درمیانی ٹوکن $W_0$ کی پیش گوئی کرتے ہیں۔ * **Skip-gram**، جہاں ہم درمیانی ٹوکن $W_0$ سے پڑوسی ٹوکنز {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} کی پیش گوئی کرتے ہیں۔ -![الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے پیپر سے تصویر](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ur.png) +![الفاظ کو ویکٹرز میں تبدیل کرنے کے لیے پیپر سے تصویر](../../../../../translated_images/ur/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > تصویر [اس پیپر](https://arxiv.org/pdf/1301.3781.pdf) سے لی گئی ہے۔ diff --git a/translations/ur/lessons/5-NLP/16-RNN/README.md b/translations/ur/lessons/5-NLP/16-RNN/README.md index 3d36bc43..1732e87b 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/README.md +++ b/translations/ur/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: متن کی ترتیب کے معنی کو سمجھنے کے لیے، ہمیں ایک اور نیورل نیٹ ورک آرکیٹیکچر استعمال کرنے کی ضرورت ہے، جسے **ری کرنٹ نیورل نیٹ ورک** یا RNN کہا جاتا ہے۔ RNN میں، ہم اپنے جملے کو نیٹ ورک کے ذریعے ایک وقت میں ایک علامت کے ساتھ گزارتے ہیں، اور نیٹ ورک کچھ **حالت** پیدا کرتا ہے، جسے ہم اگلی علامت کے ساتھ دوبارہ نیٹ ورک میں پاس کرتے ہیں۔ -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ur.png) +![RNN](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.png) > تصویر مصنف کی جانب سے @@ -61,7 +61,7 @@ LSTM نیٹ ورک RNN کی طرح منظم ہے، لیکن یہاں دو حال ایک ری کرنٹ نیٹ ورک، چاہے وہ ایک سمت میں ہو یا بائی ڈائریکشنل، ترتیب کے اندر کچھ پیٹرنز کو پکڑتا ہے، اور انہیں حالت ویکٹر میں ذخیرہ کر سکتا ہے یا آؤٹ پٹ میں منتقل کر سکتا ہے۔ جیسا کہ کنوولوشنل نیٹ ورکس کے ساتھ، ہم پہلے والے پر ایک اور ری کرنٹ لیئر بنا سکتے ہیں تاکہ اعلیٰ سطح کے پیٹرنز کو پکڑ سکیں اور پہلی لیئر کے ذریعے نکالے گئے کم سطح کے پیٹرنز سے تعمیر کریں۔ یہ ہمیں **ملٹی لیئر RNN** کے تصور کی طرف لے جاتا ہے، جو دو یا زیادہ ری کرنٹ نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلی لیئر کا آؤٹ پٹ اگلی لیئر میں ان پٹ کے طور پر پاس کیا جاتا ہے۔ -![ملٹی لیئر LSTM RNN کی تصویر](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ur.jpg) +![ملٹی لیئر LSTM RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg) *تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) فرنینڈو لوپیز کی جانب سے* diff --git a/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index 53cd73f2..94f43476 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Recurrent نیٹ ورک، چاہے وہ ایک طرفہ ہو یا دو طرفہ، ترتیب کے اندر کچھ خاص patterns کو پکڑتا ہے اور انہیں state ویکٹر میں محفوظ کر سکتا ہے یا آؤٹ پٹ میں منتقل کر سکتا ہے۔ جیسے convolutional نیٹ ورکس کے ساتھ، ہم پہلے layer کے اوپر ایک اور recurrent layer بنا سکتے ہیں تاکہ اعلیٰ سطح کے patterns کو پکڑا جا سکے، جو پہلے layer کے ذریعے نکالے گئے نچلی سطح کے patterns سے بنے ہوں۔ یہ ہمیں **کثیر پرت RNN** کے تصور تک لے جاتا ہے، جو دو یا زیادہ recurrent نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلے layer کا آؤٹ پٹ اگلے layer کو ان پٹ کے طور پر دیا جاتا ہے۔\n", "\n", - "![کثیر پرت long-short-term-memory- RNN کی تصویر](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ur.jpg)\n", + "![کثیر پرت long-short-term-memory- RNN کی تصویر](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*یہ تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) سے لی گئی ہے، جو فرنینڈو لوپیز نے لکھی ہے۔*\n", "\n", diff --git a/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb index 2994799c..f60f4ece 100644 --- a/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/ur/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "متن کے سلسلے کے معنی کو سمجھنے کے لیے، ہم ایک اعصابی نیٹ ورک آرکیٹیکچر استعمال کریں گے جسے **ریکرنٹ نیورل نیٹ ورک** یا RNN کہا جاتا ہے۔ RNN استعمال کرتے وقت، ہم اپنے جملے کو نیٹ ورک کے ذریعے ایک وقت میں ایک ٹوکن پاس کرتے ہیں، اور نیٹ ورک کچھ **حالت** پیدا کرتا ہے، جسے ہم اگلے ٹوکن کے ساتھ دوبارہ نیٹ ورک میں پاس کرتے ہیں۔\n", "\n", - "![ریکرنٹ نیورل نیٹ ورک کی تخلیق کی ایک مثال دکھانے والی تصویر۔](../../../../../translated_images/rnn.27f5c29c53d727b5.ur.png)\n", + "![ریکرنٹ نیورل نیٹ ورک کی تخلیق کی ایک مثال دکھانے والی تصویر۔](../../../../../translated_images/ur/rnn.27f5c29c53d727b5.png)\n", "\n", "دیے گئے ان پٹ ٹوکنز کے سلسلے $X_0,\\dots,X_n$ کے لیے، RNN اعصابی نیٹ ورک کے بلاکس کا ایک سلسلہ تخلیق کرتا ہے، اور اس سلسلے کو بیک پروپیگیشن کے ذریعے اختتام سے اختتام تک تربیت دیتا ہے۔ ہر نیٹ ورک بلاک ایک جوڑی $(X_i,S_i)$ کو ان پٹ کے طور پر لیتا ہے، اور نتیجے میں $S_{i+1}$ پیدا کرتا ہے۔ آخری حالت $S_n$ یا آؤٹ پٹ $Y_n$ کو ایک لکیری کلاسیفائر میں بھیجا جاتا ہے تاکہ نتیجہ پیدا کیا جا سکے۔ تمام نیٹ ورک بلاکس ایک جیسے وزن کا اشتراک کرتے ہیں، اور ایک بیک پروپیگیشن پاس کے ذریعے اختتام سے اختتام تک تربیت دی جاتی ہے۔\n", "\n", @@ -371,7 +371,7 @@ "\n", "ری کرنٹ نیٹ ورکس، چاہے یک طرفہ ہوں یا دو طرفہ، ترتیب کے اندر موجود پیٹرنز کو پکڑتے ہیں، اور انہیں اسٹیٹ ویکٹرز میں محفوظ کرتے ہیں یا آؤٹ پٹ کے طور پر واپس کرتے ہیں۔ جیسے کنوولوشنل نیٹ ورکس میں، ہم پہلے لیئر کے بعد ایک اور ری کرنٹ لیئر بنا سکتے ہیں تاکہ اعلیٰ سطح کے پیٹرنز کو پکڑا جا سکے، جو پہلے لیئر کے ذریعے نکالے گئے نچلے سطح کے پیٹرنز سے بنے ہوں۔ یہ ہمیں **کثیر پرت RNN** کے تصور تک لے جاتا ہے، جو دو یا زیادہ ری کرنٹ نیٹ ورکس پر مشتمل ہوتا ہے، جہاں پچھلی لیئر کا آؤٹ پٹ اگلی لیئر کو ان پٹ کے طور پر دیا جاتا ہے۔\n", "\n", - "![تصویر جو ایک کثیر پرت لمبی-مختصر-مدتی-میموری RNN دکھا رہی ہے](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ur.jpg)\n", + "![تصویر جو ایک کثیر پرت لمبی-مختصر-مدتی-میموری RNN دکھا رہی ہے](../../../../../translated_images/ur/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*تصویر [اس شاندار پوسٹ](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) سے لی گئی ہے، جو فرنینڈو لوپیز نے لکھی ہے۔*\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index fca0fe5f..ffad6508 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "ہم RNN کو ٹیکسٹ جنریٹ کرنے کے لیے اس طرح تربیت دیں گے۔ ہر قدم پر، ہم `nchars` لمبائی کے کرداروں کی ایک ترتیب لیں گے، اور نیٹ ورک سے کہیں گے کہ ہر ان پٹ کردار کے لیے اگلا آؤٹ پٹ کردار پیدا کرے:\n", "\n", - "![تصویر میں RNN کے ذریعے 'HELLO' لفظ کی جنریشن کا ایک مثال دکھایا گیا ہے۔](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ur.png)\n", + "![تصویر میں RNN کے ذریعے 'HELLO' لفظ کی جنریشن کا ایک مثال دکھایا گیا ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png)\n", "\n", "حقیقی منظرنامے کے مطابق، ہم کچھ خاص کرداروں کو بھی شامل کرنا چاہ سکتے ہیں، جیسے *end-of-sequence* ``۔ ہمارے معاملے میں، ہم صرف نیٹ ورک کو لامتناہی ٹیکسٹ جنریشن کے لیے تربیت دینا چاہتے ہیں، اس لیے ہم ہر ترتیب کا سائز `nchars` ٹوکنز کے برابر مقرر کریں گے۔ نتیجتاً، ہر تربیتی مثال `nchars` ان پٹس اور `nchars` آؤٹ پٹس پر مشتمل ہوگی (جو ان پٹ ترتیب کو ایک علامت بائیں طرف منتقل کرنے سے حاصل ہوں گے)۔ منی بیچ کئی ایسی ترتیبوں پر مشتمل ہوگا۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 1ec52b03..32ee54c0 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -113,7 +113,7 @@ "\n", "ہم RNN کو خبروں کے عنوانات بنانے کے لیے اس طرح تربیت دیں گے۔ ہر مرحلے پر، ہم ایک عنوان لیں گے، جسے RNN میں فیڈ کیا جائے گا، اور ہر ان پٹ کردار کے لیے ہم نیٹ ورک سے اگلا آؤٹ پٹ کردار پیدا کرنے کو کہیں گے:\n", "\n", - "![تصویر جو 'HELLO' لفظ کے RNN جنریشن کی مثال دکھا رہی ہے۔](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ur.png)\n", + "![تصویر جو 'HELLO' لفظ کے RNN جنریشن کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png)\n", "\n", "ہمارے سلسلے کے آخری کردار کے لیے، ہم نیٹ ورک سے `` ٹوکن پیدا کرنے کو کہیں گے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md index a5528984..2244b20e 100644 --- a/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ur/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: یہ مختلف نیورل آرکیٹیکچرز کی اجازت دیتا ہے، جیسا کہ نیچے دی گئی تصویر میں دکھایا گیا ہے: -![تصویر جو عام ریکرنٹ نیورل نیٹ ورک کے پیٹرنز دکھا رہی ہے۔](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ur.jpg) +![تصویر جو عام ریکرنٹ نیورل نیٹ ورک کے پیٹرنز دکھا رہی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > تصویر بلاگ پوسٹ [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) سے لی گئی ہے، از [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: ہم اس RNN کو متن قدم بہ قدم تخلیق کرنے کے لیے تربیت دیں گے۔ ہر قدم پر، ہم `nchars` کی لمبائی کے کریکٹرز کا ایک سیکوئنس لیں گے، اور نیٹ ورک سے ہر ان پٹ کریکٹر کے لیے اگلا آؤٹ پٹ کریکٹر پیدا کرنے کو کہیں گے: -![تصویر جو 'HELLO' لفظ کے RNN تخلیق کی مثال دکھا رہی ہے۔](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ur.png) +![تصویر جو 'HELLO' لفظ کے RNN تخلیق کی مثال دکھا رہی ہے۔](../../../../../translated_images/ur/rnn-generate.56c54afb52f9781d.png) جب متن تخلیق کرتے ہیں (انفرنس کے دوران)، ہم کچھ **پرومپٹ** کے ساتھ شروع کرتے ہیں، جسے RNN سیلز کے ذریعے اس کی درمیانی حالت پیدا کرنے کے لیے پاس کیا جاتا ہے، اور پھر اس حالت سے تخلیق شروع ہوتی ہے۔ ہم ایک وقت میں ایک کریکٹر تخلیق کرتے ہیں، اور حالت اور تخلیق شدہ کریکٹر کو اگلے کریکٹر تخلیق کرنے کے لیے دوسرے RNN سیل کو پاس کرتے ہیں، جب تک کہ ہم کافی کریکٹرز تخلیق نہ کر لیں۔ diff --git a/translations/ur/lessons/5-NLP/18-Transformers/README.md b/translations/ur/lessons/5-NLP/18-Transformers/README.md index 50a627ae..578438f4 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ur/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ RNNs کے ساتھ، sequence-to-sequence دو recurrent نیٹ ورکس کے ذ **توجہ کے طریقہ کار** RNN کے ہر آؤٹ پٹ پیش گوئی پر ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت yt پیدا کی جا رہی ہو، ہم تمام ان پٹ hidden states hi کو مختلف وزن کے coefficients αt,i کے ساتھ مدنظر رکھیں گے۔ -![تصویر جو encoder/decoder ماڈل کو additive attention layer کے ساتھ دکھا رہی ہے](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ur.png) +![تصویر جو encoder/decoder ماڈل کو additive attention layer کے ساتھ دکھا رہی ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) میں additive attention mechanism کے ساتھ encoder-decoder ماڈل، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے لیا گیا۔ توجہ میٹرکس {αi,j} اس حد کو ظاہر کرے گی کہ ان پٹ سیکوئنس میں موجود مخصوص الفاظ آؤٹ پٹ سیکوئنس میں دیے گئے لفظ کی تخلیق میں کتنا کردار ادا کرتے ہیں۔ نیچے ایک مثال دی گئی ہے: -![تصویر جو RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ alignment کو دکھا رہی ہے، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ur.png) +![تصویر جو RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ alignment کو دکھا رہی ہے، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png) > [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) سے تصویر (Fig.3) @@ -66,7 +66,7 @@ positional encoding کا خیال درج ذیل ہے: اگلا، ہمیں اپنی سیکوئنس کے اندر کچھ patterns کو capture کرنے کی ضرورت ہے۔ ایسا کرنے کے لیے، ٹرانسفارمرز **self-attention** mechanism استعمال کرتے ہیں، جو بنیادی طور پر وہی توجہ ہے جو ان پٹ اور آؤٹ پٹ کے طور پر ایک ہی سیکوئنس پر لاگو ہوتی ہے۔ self-attention کو لاگو کرنے سے ہمیں جملے کے اندر **context** کو مدنظر رکھنے کی اجازت ملتی ہے، اور یہ دیکھنے کی اجازت ملتی ہے کہ کون سے الفاظ آپس میں جڑے ہوئے ہیں۔ مثال کے طور پر، یہ ہمیں یہ دیکھنے کی اجازت دیتا ہے کہ کون سے الفاظ coreferences جیسے *it* کے ذریعے حوالہ دیے گئے ہیں، اور سیاق و سباق کو بھی مدنظر رکھتا ہے: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ur.png) +![](../../../../../translated_images/ur/CoreferenceResolution.861924d6d384a7d6.png) > [گوگل بلاگ](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) سے تصویر @@ -91,7 +91,7 @@ Encoder-decoder attention RNNs میں استعمال ہونے والے توجہ **BERT** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا multi-layer transformer نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 layers ہیں، اور *BERT-large* کے لیے 24 layers ہیں۔ ماڈل کو پہلے ایک بڑے text data corpus (WikiPedia + books) پر unsupervised training (جملے میں masked words کی پیش گوئی) کا استعمال کرتے ہوئے pre-train کیا جاتا ہے۔ pre-training کے دوران ماڈل زبان کی سمجھ کے اہم سطحوں کو جذب کرتا ہے، جنہیں پھر دیگر datasets کے ساتھ fine tuning کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **transfer learning** کہا جاتا ہے۔ -![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ur.png) +![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > تصویر [ماخذ](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 02dfaaf3..a0761aca 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/ur/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**توجہ کے میکانزم** ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو ہر آؤٹ پٹ پیش گوئی پر وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت $y_t$ پیدا کی جا رہی ہو، تو ہم تمام ان پٹ چھپی ہوئی حالتوں $h_i$ کو مختلف وزن کے گتانک $\\alpha_{t,i}$ کے ساتھ مدنظر رکھیں گے۔\n", "\n", - "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ur.png)\n", + "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png)\n", "*انکوڈر-ڈیکوڈر ماڈل اضافی توجہ کے میکانزم کے ساتھ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) میں، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے لیا گیا*\n", "\n", "توجہ میٹرکس $\\{\\alpha_{i,j}\\}$ اس حد کو ظاہر کرے گی کہ کس حد تک مخصوص ان پٹ الفاظ آؤٹ پٹ ترتیب میں دیے گئے لفظ کی تخلیق میں کردار ادا کرتے ہیں۔ نیچے ایک ایسی میٹرکس کی مثال دی گئی ہے:\n", "\n", - "![RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ ترتیب کی تصویر، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ur.png)\n", + "![RNNsearch-50 کے ذریعے پائی گئی ایک نمونہ ترتیب کی تصویر، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (شکل 3) سے لی گئی تصویر*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا ملٹی لیئر ٹرانسفارمر نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 تہیں، اور *BERT-large* کے لیے 24 تہیں ہیں۔ ماڈل کو پہلے بڑے متن کے ڈیٹا کارپس (ویکیپیڈیا + کتابیں) پر غیر نگرانی شدہ تربیت (ایک جملے میں ماسک کیے گئے الفاظ کی پیش گوئی) کا استعمال کرتے ہوئے پری ٹرین کیا جاتا ہے۔ پری ٹریننگ کے دوران ماڈل زبان کی سمجھ کا ایک اہم سطح جذب کرتا ہے، جسے پھر دیگر ڈیٹاسیٹس کے ساتھ فائن ٹیوننگ کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **ٹرانسفر لرننگ** کہا جاتا ہے۔\n", "\n", - "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ur.png)\n", + "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ٹرانسفارمر آرکیٹیکچرز کی بہت سی مختلف حالتیں ہیں، جن میں BERT، DistilBERT، BigBird، OpenGPT3 اور مزید شامل ہیں، جنہیں فائن ٹیون کیا جا سکتا ہے۔ [HuggingFace پیکیج](https://github.com/huggingface/) ان آرکیٹیکچرز میں سے بہت سے کو PyTorch کے ساتھ تربیت دینے کے لیے ایک ریپوزٹری فراہم کرتا ہے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index cb1a8725..3d7caaa3 100644 --- a/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/ur/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**توجہ کے میکانزم** ہر ان پٹ ویکٹر کے سیاق و سباق کے اثر کو RNN کے ہر آؤٹ پٹ پیش گوئی پر وزن دینے کا ایک ذریعہ فراہم کرتے ہیں۔ اس کو نافذ کرنے کا طریقہ یہ ہے کہ ان پٹ RNN کی درمیانی حالتوں اور آؤٹ پٹ RNN کے درمیان شارٹ کٹس بنائے جائیں۔ اس طرح، جب آؤٹ پٹ علامت $y_t$ پیدا کی جا رہی ہو، تو ہم تمام ان پٹ چھپی ہوئی حالتوں $h_i$ کو مختلف وزن کے گتانک $\\alpha_{t,i}$ کے ساتھ مدنظر رکھیں گے۔\n", "\n", - "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.ur.png)\n", + "![ایک انکوڈر/ڈیکوڈر ماڈل کی تصویر جس میں ایک اضافی توجہ کی تہہ شامل ہے](../../../../../translated_images/ur/encoder-decoder-attention.7a726296894fb567.png)\n", "*انکوڈر-ڈیکوڈر ماڈل اضافی توجہ کے میکانزم کے ساتھ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) سے لیا گیا، [اس بلاگ پوسٹ](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) سے حوالہ شدہ*\n", "\n", "توجہ میٹرکس $\\{\\alpha_{i,j}\\}$ اس حد کو ظاہر کرے گی کہ ان پٹ ترتیب کے کون سے الفاظ آؤٹ پٹ ترتیب میں دیے گئے لفظ کی تخلیق میں کردار ادا کرتے ہیں۔ نیچے ایک ایسی میٹرکس کی مثال دی گئی ہے:\n", "\n", - "![ایک نمونہ ترتیب کی تصویر جو RNNsearch-50 کے ذریعے پائی گئی، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.ur.png)\n", + "![ایک نمونہ ترتیب کی تصویر جو RNNsearch-50 کے ذریعے پائی گئی، Bahdanau - arviz.org سے لی گئی](../../../../../translated_images/ur/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*[Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (شکل 3) سے لی گئی تصویر*\n", "\n", @@ -225,7 +225,7 @@ "\n", "**بی ای آر ٹی** (Bidirectional Encoder Representations from Transformers) ایک بہت بڑا ملٹی لیئر ٹرانسفارمر نیٹ ورک ہے جس میں *BERT-base* کے لیے 12 لیئرز اور *BERT-large* کے لیے 24 لیئرز ہیں۔ یہ ماڈل پہلے بڑی مقدار میں ٹیکسٹ ڈیٹا (ویکیپیڈیا + کتابیں) پر غیر نگرانی شدہ تربیت کے ذریعے (جملے میں ماسک کیے گئے الفاظ کی پیش گوئی کرتے ہوئے) تربیت یافتہ ہوتا ہے۔ تربیت کے دوران ماڈل زبان کو سمجھنے کی ایک اہم سطح حاصل کرتا ہے، جسے پھر دیگر ڈیٹاسیٹس کے ساتھ فائن ٹیوننگ کے ذریعے استعمال کیا جا سکتا ہے۔ اس عمل کو **ٹرانسفر لرننگ** کہا جاتا ہے۔\n", "\n", - "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ur.png)\n", + "![تصویر http://jalammar.github.io/illustrated-bert/ سے](../../../../../translated_images/ur/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "ٹرانسفارمر آرکیٹیکچرز کی کئی اقسام ہیں، جن میں بی ای آر ٹی، ڈسٹل بی ای آر ٹی، بگ برڈ، اوپن جی پی ٹی 3 اور مزید شامل ہیں، جنہیں فائن ٹیون کیا جا سکتا ہے۔\n", "\n", diff --git a/translations/ur/lessons/5-NLP/19-NER/README.md b/translations/ur/lessons/5-NLP/19-NER/README.md index f89048a8..b77ef2ce 100644 --- a/translations/ur/lessons/5-NLP/19-NER/README.md +++ b/translations/ur/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O چونکہ ہمیں ٹوکنز اور کلاسز کے درمیان ایک سے ایک مطابقت پیدا کرنی ہوتی ہے، ہم اس تصویر سے ایک دائیں طرف **کئی سے کئی** نیورل نیٹ ورک ماڈل بنا سکتے ہیں: -![تصویر جو عام recurrent neural network patterns کو دکھاتی ہے۔](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ur.jpg) +![تصویر جو عام recurrent neural network patterns کو دکھاتی ہے۔](../../../../../translated_images/ur/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *تصویر [اس بلاگ پوسٹ](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) سے لی گئی ہے، جو [Andrej Karpathy](http://karpathy.github.io/) کی جانب سے ہے۔ NER ٹوکن درجہ بندی ماڈلز اس تصویر میں دائیں طرف کے نیٹ ورک آرکیٹیکچر سے مطابقت رکھتے ہیں۔* diff --git a/translations/ur/lessons/5-NLP/README.md b/translations/ur/lessons/5-NLP/README.md index e3e86664..fe178056 100644 --- a/translations/ur/lessons/5-NLP/README.md +++ b/translations/ur/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # قدرتی زبان کی پروسیسنگ -![NLP کے کاموں کا خلاصہ ایک خاکے میں](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ur.png) +![NLP کے کاموں کا خلاصہ ایک خاکے میں](../../../../translated_images/ur/ai-nlp.b22dcb8ca4707cea.png) اس حصے میں، ہم نیورل نیٹ ورکس کا استعمال کرتے ہوئے **قدرتی زبان کی پروسیسنگ (NLP)** سے متعلق کاموں کو حل کرنے پر توجہ مرکوز کریں گے۔ NLP کے کئی مسائل ہیں جنہیں ہم چاہتے ہیں کہ کمپیوٹر حل کر سکیں: diff --git a/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md index 85f1a2d4..bf24347c 100644 --- a/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ur/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ ask turtles [ ماڈل کھولنے کے بعد، آپ کو نیٹ لوگو کے مرکزی اسکرین پر لے جایا جاتا ہے۔ یہاں ایک نمونہ ماڈل ہے جو بھیڑیوں اور بھیڑوں کی آبادی کو محدود وسائل (گھاس) کے ساتھ بیان کرتا ہے۔ -![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ur.png) +![NetLogo Main Screen](../../../../../translated_images/ur/NetLogo-Main.32653711ec1a01b3.png) > دمتری سوشنیکوف کے ذریعہ اسکرین شاٹ diff --git a/translations/ur/lessons/README.md b/translations/ur/lessons/README.md index 5cb6ddb5..cc20d6b9 100644 --- a/translations/ur/lessons/README.md +++ b/translations/ur/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # جائزہ -![ڈوڈل میں جائزہ](../../../translated_images/ai-overview.0857791951d19500.ur.png) +![ڈوڈل میں جائزہ](../../../translated_images/ur/ai-overview.0857791951d19500.png) > اسکیچ نوٹ از [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/ur/lessons/X-Extras/X1-MultiModal/README.md b/translations/ur/lessons/X-Extras/X1-MultiModal/README.md index 9b05d439..b347c90a 100644 --- a/translations/ur/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ur/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP کا بنیادی خیال یہ ہے کہ ٹیکسٹ پرامپٹس کو کسی تصویر کے ساتھ موازنہ کیا جا سکے اور یہ معلوم کیا جا سکے کہ تصویر پرامپٹ سے کتنی مطابقت رکھتی ہے۔ -![CLIP آرکیٹیکچر](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.ur.png) +![CLIP آرکیٹیکچر](../../../../../translated_images/ur/clip-arch.b3dbf20b4e8ed8be.png) > *تصویر [اس بلاگ پوسٹ](https://openai.com/blog/clip/) سے لی گئی ہے* @@ -31,7 +31,7 @@ CLIP ماڈل/لائبریری [OpenAI GitHub](https://github.com/openai/CLIP) فرض کریں ہمیں تصاویر کو بلیوں، کتوں اور انسانوں کے درمیان کلاسیفائی کرنا ہے۔ اس صورت میں، ہم ماڈل کو ایک تصویر اور ٹیکسٹ پرامپٹس کی ایک سیریز دیتے ہیں: "*بلی کی تصویر*", "*کتے کی تصویر*", "*انسان کی تصویر*۔" نتیجے میں ملنے والے 3 پروبیبلیٹیز کے ویکٹر میں ہمیں صرف سب سے زیادہ ویلیو والے انڈیکس کو منتخب کرنا ہوگا۔ -![امیج کلاسیفکیشن کے لیے CLIP](../../../../../translated_images/clip-class.3af42ef0b2b19369.ur.png) +![امیج کلاسیفکیشن کے لیے CLIP](../../../../../translated_images/ur/clip-class.3af42ef0b2b19369.png) > *تصویر [اس بلاگ پوسٹ](https://openai.com/blog/clip/) سے لی گئی ہے* @@ -55,13 +55,13 @@ VQGAN کے بارے میں مزید جاننے کے لیے [Taming Transformers] VQGAN اور روایتی GAN کے درمیان ایک اہم فرق یہ ہے کہ روایتی GAN کسی بھی ان پٹ ویکٹر سے ایک معقول تصویر بنا سکتا ہے، جبکہ VQGAN ممکنہ طور پر ایک غیر مربوط تصویر بنا سکتا ہے۔ لہذا، ہمیں امیج کریشن کے عمل کو مزید گائیڈ کرنے کی ضرورت ہوتی ہے، اور یہ CLIP کے ذریعے کیا جا سکتا ہے۔ -![VQGAN+CLIP آرکیٹیکچر](../../../../../translated_images/vqgan.5027fe05051dfa31.ur.png) +![VQGAN+CLIP آرکیٹیکچر](../../../../../translated_images/ur/vqgan.5027fe05051dfa31.png) کسی ٹیکسٹ پرامپٹ سے مطابقت رکھنے والی تصویر بنانے کے لیے، ہم کسی رینڈم انکوڈنگ ویکٹر سے شروع کرتے ہیں، جو VQGAN کے ذریعے ایک تصویر تیار کرتا ہے۔ پھر CLIP ایک لاس فنکشن تیار کرتا ہے جو یہ ظاہر کرتا ہے کہ تصویر ٹیکسٹ پرامپٹ سے کتنی مطابقت رکھتی ہے۔ اس کے بعد مقصد یہ ہوتا ہے کہ اس لاس کو کم سے کم کیا جائے، بیک پروپیگیشن کے ذریعے ان پٹ ویکٹر کے پیرامیٹرز کو ایڈجسٹ کرتے ہوئے۔ VQGAN+CLIP کو نافذ کرنے والی ایک بہترین لائبریری [Pixray](http://github.com/pixray/pixray) ہے۔ -![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.ur.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.ur.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.ur.png) +![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- پرامپٹ سے تیار کردہ تصویر *ایک نوجوان مرد ادب کے استاد کی کتاب کے ساتھ واٹر کلر پورٹریٹ* | پرامپٹ سے تیار کردہ تصویر *ایک نوجوان خاتون کمپیوٹر سائنس کی استاد کا آئل پورٹریٹ کمپیوٹر کے ساتھ* | پرامپٹ سے تیار کردہ تصویر *ایک بوڑھے مرد ریاضی کے استاد کا آئل پورٹریٹ بلیک بورڈ کے سامنے* @@ -77,7 +77,7 @@ CLIP کے برعکس، DALL-E ٹیکسٹ اور تصویر دونوں کو ای DALL-E 1 اور 2 کے درمیان بنیادی فرق یہ ہے کہ DALL-E 2 زیادہ حقیقت پسندانہ تصاویر اور آرٹ تیار کرتا ہے۔ DALL-E کے ساتھ تصاویر کی مثالیں: -![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.ur.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.ur.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.ur.png) +![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Pixray کے ذریعے تیار کردہ تصویر](../../../../../translated_images/ur/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- پرامپٹ سے تیار کردہ تصویر *ایک نوجوان مرد ادب کے استاد کی کتاب کے ساتھ واٹر کلر پورٹریٹ* | پرامپٹ سے تیار کردہ تصویر *ایک نوجوان خاتون کمپیوٹر سائنس کی استاد کا آئل پورٹریٹ کمپیوٹر کے ساتھ* | پرامپٹ سے تیار کردہ تصویر *ایک بوڑھے مرد ریاضی کے استاد کا آئل پورٹریٹ بلیک بورڈ کے سامنے* diff --git a/translations/vi/README.md b/translations/vi/README.md index 760ecb72..fbb9b087 100644 --- a/translations/vi/README.md +++ b/translations/vi/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # Trí Tuệ Nhân Tạo Cho Người Mới Bắt Đầu - Một Chương Trình Học -|![Sketchnote bởi @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.vi.png)| +|![Sketchnote bởi @girlie_mac https://twitter.com/girlie_mac](../../translated_images/vi/ai-overview.0857791951d19500.png)| |:---:| | Trí Tuệ Nhân Tạo Cho Người Mới Bắt Đầu - _Sketchnote bởi [@girlie_mac](https://twitter.com/girlie_mac)_ | diff --git a/translations/vi/lessons/1-Intro/README.md b/translations/vi/lessons/1-Intro/README.md index cc061da8..b498e9ea 100644 --- a/translations/vi/lessons/1-Intro/README.md +++ b/translations/vi/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Giới thiệu về AI -![Tóm tắt nội dung Giới thiệu về AI trong một hình vẽ](../../../../translated_images/ai-intro.bf28d1ac4235881c.vi.png) +![Tóm tắt nội dung Giới thiệu về AI trong một hình vẽ](../../../../translated_images/vi/ai-intro.bf28d1ac4235881c.png) > Hình vẽ bởi [Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: Ban đầu, máy tính được phát minh bởi [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) để xử lý các con số theo một quy trình được định nghĩa rõ ràng - một thuật toán. Máy tính hiện đại, mặc dù tiên tiến hơn rất nhiều so với mô hình ban đầu được đề xuất vào thế kỷ 19, vẫn tuân theo ý tưởng về các tính toán có kiểm soát. Do đó, có thể lập trình một máy tính để thực hiện một việc gì đó nếu chúng ta biết chính xác chuỗi các bước cần thực hiện để đạt được mục tiêu. -![Ảnh một người](../../../../translated_images/dsh_age.d212a30d4e54fb5f.vi.png) +![Ảnh một người](../../../../translated_images/vi/dsh_age.d212a30d4e54fb5f.png) > Ảnh bởi [Vickie Soshnikova](http://twitter.com/vickievalerie) @@ -46,7 +46,7 @@ AI yếu rất chuyên biệt và không sở hữu khả năng nhận thức gi Một trong những vấn đề khi xử lý thuật ngữ **[Trí thông minh](https://en.wikipedia.org/wiki/Intelligence)** là không có định nghĩa rõ ràng cho thuật ngữ này. Một số người có thể cho rằng trí thông minh liên quan đến **tư duy trừu tượng**, hoặc đến **tự nhận thức**, nhưng chúng ta không thể định nghĩa nó một cách chính xác. -![Ảnh một con mèo](../../../../translated_images/photo-cat.8c8e8fb760ffe457.vi.jpg) +![Ảnh một con mèo](../../../../translated_images/vi/photo-cat.8c8e8fb760ffe457.jpg) > [Ảnh](https://unsplash.com/photos/75715CVEJhI) bởi [Amber Kipp](https://unsplash.com/@sadmax) từ Unsplash @@ -98,13 +98,13 @@ Ngược lại, chúng ta có thể cố gắng mô phỏng các yếu tố đơ > | Còn về ML thì sao? | | > |--------------|-----------| -> | Một phần của Trí tuệ Nhân tạo dựa trên việc máy tính học cách giải quyết vấn đề dựa trên một số dữ liệu được gọi là **Học máy**. Chúng ta sẽ không xem xét học máy cổ điển trong khóa học này - chúng tôi giới thiệu bạn đến chương trình học riêng [Học máy cho người mới bắt đầu](http://aka.ms/ml-beginners). | ![Học máy cho người mới bắt đầu](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.vi.png) | +> | Một phần của Trí tuệ Nhân tạo dựa trên việc máy tính học cách giải quyết vấn đề dựa trên một số dữ liệu được gọi là **Học máy**. Chúng ta sẽ không xem xét học máy cổ điển trong khóa học này - chúng tôi giới thiệu bạn đến chương trình học riêng [Học máy cho người mới bắt đầu](http://aka.ms/ml-beginners). | ![Học máy cho người mới bắt đầu](../../../../translated_images/vi/ml-for-beginners.9e4fed176fd5817d.png) | ## Lịch sử ngắn gọn về AI Trí tuệ Nhân tạo được bắt đầu như một lĩnh vực vào giữa thế kỷ 20. Ban đầu, lý luận biểu tượng là cách tiếp cận phổ biến, và nó đã dẫn đến một số thành công quan trọng, chẳng hạn như các hệ thống chuyên gia – các chương trình máy tính có thể hoạt động như một chuyên gia trong một số lĩnh vực vấn đề hạn chế. Tuy nhiên, sớm nhận ra rằng cách tiếp cận này không mở rộng tốt. Việc trích xuất kiến thức từ một chuyên gia, biểu diễn nó trong máy tính, và giữ cho cơ sở kiến thức đó chính xác hóa ra là một nhiệm vụ rất phức tạp, và quá tốn kém để thực tế trong nhiều trường hợp. Điều này dẫn đến cái gọi là [Mùa đông AI](https://en.wikipedia.org/wiki/AI_winter) vào những năm 1970. -Lịch sử ngắn gọn về AI +Lịch sử ngắn gọn về AI > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) @@ -124,7 +124,7 @@ Tương tự, chúng ta có thể thấy cách tiếp cận đối với việc * Các trợ lý hiện đại, chẳng hạn như Cortana, Siri hoặc Google Assistant đều là các hệ thống lai sử dụng Mạng nơ-ron để chuyển đổi giọng nói thành văn bản và nhận ra ý định của chúng ta, và sau đó sử dụng một số lý luận hoặc thuật toán rõ ràng để thực hiện các hành động cần thiết. * Trong tương lai, chúng ta có thể mong đợi một mô hình hoàn toàn dựa trên mạng nơ-ron để xử lý đối thoại một cách độc lập. Các mạng nơ-ron GPT gần đây và [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) cho thấy thành công lớn trong lĩnh vực này. -sự tiến hóa của bài kiểm tra Turing +sự tiến hóa của bài kiểm tra Turing > Hình ảnh của Dmitry Soshnikov, [ảnh](https://unsplash.com/photos/r8LmVbUKgns) bởi [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash ## Nghiên cứu AI gần đây diff --git a/translations/vi/lessons/2-Symbolic/Animals.ipynb b/translations/vi/lessons/2-Symbolic/Animals.ipynb index cc3884d6..9a5f1342 100644 --- a/translations/vi/lessons/2-Symbolic/Animals.ipynb +++ b/translations/vi/lessons/2-Symbolic/Animals.ipynb @@ -12,7 +12,7 @@ "\n", "Trong ví dụ này, chúng ta sẽ triển khai một hệ thống dựa trên kiến thức đơn giản để xác định một loài động vật dựa trên một số đặc điểm vật lý. Hệ thống có thể được biểu diễn bằng cây AND-OR sau đây (đây chỉ là một phần của toàn bộ cây, chúng ta có thể dễ dàng thêm một số quy tắc khác):\n", "\n", - "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.vi.png)\n" + "![](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.png)\n" ] }, { diff --git a/translations/vi/lessons/2-Symbolic/README.md b/translations/vi/lessons/2-Symbolic/README.md index a7e4351d..6d10451f 100644 --- a/translations/vi/lessons/2-Symbolic/README.md +++ b/translations/vi/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Đại diện Tri thức và Hệ thống Chuyên gia -![Tóm tắt nội dung AI biểu tượng](../../../../translated_images/ai-symbolic.715a30cb610411a6.vi.png) +![Tóm tắt nội dung AI biểu tượng](../../../../translated_images/vi/ai-symbolic.715a30cb610411a6.png) > Sketchnote bởi [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +41,7 @@ Thông thường, chúng ta không định nghĩa tri thức một cách nghiêm Do đó, vấn đề của **đại diện tri thức** là tìm một cách hiệu quả để biểu diễn tri thức bên trong máy tính dưới dạng dữ liệu, để có thể sử dụng tự động. Điều này có thể được xem như một phổ: -![Phổ đại diện tri thức](../../../../translated_images/knowledge-spectrum.b60df631852c0217.vi.png) +![Phổ đại diện tri thức](../../../../translated_images/vi/knowledge-spectrum.b60df631852c0217.png) > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Cú pháp khối | Thụt lề | | | Một trong những thành công ban đầu của AI biểu tượng là các **hệ thống chuyên gia** - các hệ thống máy tính được thiết kế để hoạt động như một chuyên gia trong một lĩnh vực vấn đề hạn chế. Chúng dựa trên một **cơ sở tri thức** được trích xuất từ một hoặc nhiều chuyên gia con người, và chứa một **động cơ suy luận** thực hiện một số lý luận dựa trên cơ sở đó. -![Kiến trúc con người](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.vi.png) | ![Hệ thống dựa trên tri thức](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.vi.png) +![Kiến trúc con người](../../../../translated_images/vi/arch-human.5d4d35f1bba3ab1c.png) | ![Hệ thống dựa trên tri thức](../../../../translated_images/vi/arch-kbs.3ec5c150b09fa8da.png) ---------------------------------------------|------------------------------------------------ Cấu trúc đơn giản của hệ thần kinh con người | Kiến trúc của hệ thống dựa trên tri thức @@ -106,7 +106,7 @@ Hệ thống chuyên gia được xây dựng giống như hệ thống lý lu Ví dụ, hãy xem xét hệ thống chuyên gia sau đây để xác định một loài động vật dựa trên các đặc điểm vật lý của nó: -![Cây AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.vi.png) +![Cây AND-OR](../../../../translated_images/vi/AND-OR-Tree.5592d2c70187f283.png) > Hình ảnh bởi [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb index 39469f92..98bc029c 100644 --- a/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/vi/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -487,7 +487,7 @@ "\n", "Trong trường hợp chúng ta có hơn 2 lớp, softmax sẽ chuẩn hóa xác suất trên tất cả các lớp. Dưới đây là sơ đồ kiến trúc mạng thực hiện phân loại chữ số MNIST:\n", "\n", - "![MNIST Classifier](../../../../../translated_images/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.vi.png)\n" + "![MNIST Classifier](../../../../../translated_images/vi/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" ] }, { @@ -1255,7 +1255,7 @@ "* Lỗi huấn luyện thấp - mô hình có thể xấp xỉ dữ liệu huấn luyện tốt vì nó có đủ khả năng biểu đạt.\n", "* Lỗi xác thực có thể cao hơn nhiều so với lỗi huấn luyện và có thể bắt đầu tăng trong quá trình huấn luyện - điều này xảy ra vì mô hình \"ghi nhớ\" các điểm dữ liệu huấn luyện và mất đi \"bức tranh tổng thể\".\n", "\n", - "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.vi.png)\n", + "![Overfitting](../../../../../translated_images/vi/overfit.a0bd57f717c15769.png)\n", "\n", "> Trong hình này, `x` đại diện cho dữ liệu huấn luyện, `o` - dữ liệu xác thực. Bên trái - mô hình tuyến tính (một lớp), nó xấp xỉ bản chất của dữ liệu khá tốt. Bên phải - mô hình bị quá khớp, mô hình xấp xỉ dữ liệu huấn luyện hoàn hảo, nhưng không còn ý nghĩa với bất kỳ dữ liệu nào khác (lỗi xác thực rất cao).\n" ] diff --git a/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md index 8e3aa7ef..a9978948 100644 --- a/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/vi/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ Overfitting là một khái niệm cực kỳ quan trọng trong học máy, và Hãy xem xét vấn đề sau đây về việc xấp xỉ 5 điểm (được biểu diễn bằng `x` trên các đồ thị dưới đây): -![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.vi.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.vi.jpg) +![linear](../../../../../translated_images/vi/overfit1.f24b71c6f652e59e.jpg) | ![overfit](../../../../../translated_images/vi/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **Mô hình tuyến tính, 2 tham số** | **Mô hình phi tuyến, 7 tham số** Lỗi huấn luyện = 5.3 | Lỗi huấn luyện = 0 @@ -79,7 +79,7 @@ Lỗi kiểm định = 5.1 | Lỗi kiểm định = 20 Như bạn có thể thấy từ đồ thị trên, overfitting có thể được phát hiện bằng lỗi huấn luyện rất thấp và lỗi kiểm định rất cao. Thông thường trong quá trình huấn luyện, chúng ta sẽ thấy cả lỗi huấn luyện và lỗi kiểm định bắt đầu giảm, và sau đó tại một thời điểm nào đó lỗi kiểm định có thể ngừng giảm và bắt đầu tăng. Đây sẽ là dấu hiệu của overfitting, và là chỉ báo rằng chúng ta nên dừng huấn luyện tại thời điểm này (hoặc ít nhất là lưu lại trạng thái của mô hình). -![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.vi.png) +![overfitting](../../../../../translated_images/vi/Overfitting.408ad91cd90b4371.png) ## Cách ngăn chặn overfitting diff --git a/translations/vi/lessons/3-NeuralNetworks/README.md b/translations/vi/lessons/3-NeuralNetworks/README.md index 39c8dcf8..c7ed17d8 100644 --- a/translations/vi/lessons/3-NeuralNetworks/README.md +++ b/translations/vi/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Giới thiệu về Mạng Nơ-ron -![Tóm tắt nội dung Giới thiệu Mạng Nơ-ron trong một hình vẽ](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.vi.png) +![Tóm tắt nội dung Giới thiệu Mạng Nơ-ron trong một hình vẽ](../../../../translated_images/vi/ai-neuralnetworks.1c687ae40bc86e83.png) Như chúng ta đã thảo luận trong phần giới thiệu, một trong những cách để đạt được trí tuệ là huấn luyện một **mô hình máy tính** hoặc một **bộ não nhân tạo**. Từ giữa thế kỷ 20, các nhà nghiên cứu đã thử nghiệm nhiều mô hình toán học khác nhau, cho đến những năm gần đây, hướng đi này đã chứng minh được sự thành công vượt bậc. Những mô hình toán học của bộ não này được gọi là **mạng nơ-ron**. @@ -36,13 +36,13 @@ Trong chương trình này, chúng ta sẽ chỉ tập trung vào các mô hình Từ sinh học, chúng ta biết rằng bộ não của chúng ta bao gồm các tế bào thần kinh (nơ-ron), mỗi tế bào có nhiều "đầu vào" (dendrite) và một "đầu ra" (axon). Cả dendrite và axon đều có thể dẫn truyền tín hiệu điện, và các kết nối giữa chúng — được gọi là synapse — có thể thể hiện các mức độ dẫn truyền khác nhau, được điều chỉnh bởi các chất dẫn truyền thần kinh. -![Mô hình của một Nơ-ron](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.vi.jpg) | ![Mô hình của một Nơ-ron](../../../../translated_images/artneuron.1a5daa88d20ebe6f.vi.png) +![Mô hình của một Nơ-ron](../../../../translated_images/vi/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![Mô hình của một Nơ-ron](../../../../translated_images/vi/artneuron.1a5daa88d20ebe6f.png) ----|---- Nơ-ron Thực *([Hình ảnh](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) từ Wikipedia)* | Nơ-ron Nhân Tạo *(Hình ảnh của Tác giả)* Do đó, mô hình toán học đơn giản nhất của một nơ-ron bao gồm nhiều đầu vào X1, ..., XN và một đầu ra Y, cùng một loạt các trọng số W1, ..., WN. Đầu ra được tính toán như sau: -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) trong đó f là một **hàm kích hoạt** phi tuyến. diff --git a/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md index 706303fb..153ba00a 100644 --- a/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/vi/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ Trong [OpenCV Notebook](OpenCV.ipynb), chúng tôi đưa ra một số ví dụ * **Tiền xử lý một bức ảnh của sách chữ Braille**. Chúng tôi tập trung vào cách sử dụng ngưỡng, phát hiện đặc điểm, biến đổi phối cảnh và thao tác NumPy để tách các ký hiệu Braille riêng lẻ để phân loại thêm bằng mạng nơ-ron. -![Hình ảnh Braille](../../../../../translated_images/braille.341962ff76b1bd70.vi.jpeg) | ![Hình ảnh Braille đã tiền xử lý](../../../../../translated_images/braille-result.46530fea020b03c7.vi.png) | ![Ký hiệu Braille](../../../../../translated_images/braille-symbols.0159185ab69d5339.vi.png) +![Hình ảnh Braille](../../../../../translated_images/vi/braille.341962ff76b1bd70.jpeg) | ![Hình ảnh Braille đã tiền xử lý](../../../../../translated_images/vi/braille-result.46530fea020b03c7.png) | ![Ký hiệu Braille](../../../../../translated_images/vi/braille-symbols.0159185ab69d5339.png) ----|-----|----- > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) * **Phát hiện chuyển động trong video bằng sự khác biệt giữa các khung hình**. Nếu camera cố định, thì các khung hình từ luồng camera sẽ khá giống nhau. Vì các khung hình được biểu diễn dưới dạng mảng, chỉ cần trừ các mảng của hai khung hình liên tiếp, chúng ta sẽ nhận được sự khác biệt pixel, điều này sẽ thấp đối với các khung hình tĩnh và trở nên cao hơn khi có chuyển động đáng kể trong hình ảnh. -![Hình ảnh các khung hình video và sự khác biệt giữa các khung hình](../../../../../translated_images/frame-difference.706f805491a0883c.vi.png) +![Hình ảnh các khung hình video và sự khác biệt giữa các khung hình](../../../../../translated_images/vi/frame-difference.706f805491a0883c.png) > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ Trong [OpenCV Notebook](OpenCV.ipynb), chúng tôi đưa ra một số ví dụ - **Dòng Quang học Dày đặc** tính toán trường vector cho thấy mỗi pixel đang di chuyển đến đâu. - **Dòng Quang học Thưa** dựa trên việc lấy một số đặc điểm nổi bật trong hình ảnh (ví dụ: các cạnh) và xây dựng quỹ đạo của chúng từ khung hình này sang khung hình khác. -![Hình ảnh Dòng Quang học](../../../../../translated_images/optical.1f4a94464579a83a.vi.png) +![Hình ảnh Dòng Quang học](../../../../../translated_images/vi/optical.1f4a94464579a83a.png) > Hình ảnh từ [OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 0ca4f896..195abdde 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16 là một mạng đạt độ chính xác 92.7% trong phân loại top-5 của ImageNet vào năm 2014. Nó có cấu trúc các lớp như sau: -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.vi.jpg) +![ImageNet Layers](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.jpg) Như bạn có thể thấy, VGG tuân theo kiến trúc hình kim tự tháp truyền thống, bao gồm một chuỗi các lớp tích chập và lớp pooling. -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.vi.jpg) +![ImageNet Pyramid](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.jpg) > Hình ảnh từ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb index b6469c25..204f9036 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -260,7 +260,7 @@ "\n", "Do đó, trong một CNN điển hình sẽ có một số lớp tích chập, với các lớp pooling xen kẽ giữa chúng để giảm kích thước của hình ảnh. Chúng ta cũng sẽ tăng số lượng bộ lọc, bởi vì khi các mẫu trở nên phức tạp hơn - sẽ có nhiều tổ hợp thú vị hơn mà chúng ta cần tìm kiếm.\n", "\n", - "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.vi.png)\n", + "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Do kích thước không gian giảm dần và kích thước đặc trưng/bộ lọc tăng dần, kiến trúc này cũng được gọi là **kiến trúc hình chóp**.\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb index 2abe4fd6..7cfa43d0 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -358,7 +358,7 @@ "\n", "Do đó, trong một CNN điển hình sẽ có một số lớp tích chập, với các lớp pooling xen kẽ để giảm kích thước của hình ảnh. Chúng ta cũng sẽ tăng số lượng bộ lọc, bởi vì khi các mẫu trở nên phức tạp hơn - có nhiều tổ hợp thú vị hơn mà chúng ta cần tìm kiếm.\n", "\n", - "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.vi.png)\n", + "![Hình ảnh minh họa một số lớp tích chập với các lớp pooling.](../../../../../translated_images/vi/cnn-pyramid.85915455759ef0ce.png)\n", "\n", "Do kích thước không gian giảm và kích thước đặc trưng/bộ lọc tăng, kiến trúc này cũng được gọi là **kiến trúc hình kim tự tháp**.\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md index 834594ac..7c3b7016 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ Trong thực tế, chúng ta muốn có khả năng nhận diện các đối t Để trích xuất các mẫu, chúng ta sẽ sử dụng khái niệm **bộ lọc tích chập**. Như bạn đã biết, một hình ảnh được biểu diễn bằng một ma trận 2D, hoặc một tensor 3D với độ sâu màu. Việc áp dụng một bộ lọc có nghĩa là chúng ta lấy một ma trận **hạt nhân bộ lọc** tương đối nhỏ, và đối với mỗi điểm ảnh trong hình ảnh gốc, chúng ta tính trung bình có trọng số với các điểm lân cận. Chúng ta có thể hình dung điều này như một cửa sổ nhỏ trượt qua toàn bộ hình ảnh, và tính trung bình tất cả các điểm ảnh theo các trọng số trong ma trận hạt nhân bộ lọc. -![Bộ lọc cạnh dọc](../../../../../translated_images/filter-vert.b7148390ca0bc356.vi.png) | ![Bộ lọc cạnh ngang](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.vi.png) +![Bộ lọc cạnh dọc](../../../../../translated_images/vi/filter-vert.b7148390ca0bc356.png) | ![Bộ lọc cạnh ngang](../../../../../translated_images/vi/filter-horiz.59b80ed4feb946ef.png) ----|---- > Hình ảnh của Dmitry Soshnikov @@ -38,7 +38,7 @@ Cách hoạt động của CNN dựa trên các ý tưởng quan trọng sau: * Chúng ta có thể thiết kế mạng theo cách mà các bộ lọc được huấn luyện tự động * Chúng ta có thể sử dụng cùng một phương pháp để tìm các mẫu trong các đặc trưng cấp cao, không chỉ trong hình ảnh gốc. Do đó, việc trích xuất đặc trưng của CNN hoạt động trên một hệ thống phân cấp các đặc trưng, bắt đầu từ các tổ hợp điểm ảnh cấp thấp, cho đến các tổ hợp cấp cao hơn của các phần trong hình ảnh. -![Trích xuất đặc trưng phân cấp](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.vi.png) +![Trích xuất đặc trưng phân cấp](../../../../../translated_images/vi/FeatureExtractionCNN.d9b456cbdae7cb64.png) > Hình ảnh từ [một bài báo của Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), dựa trên [nghiên cứu của họ](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ Hầu hết các CNN được sử dụng để xử lý hình ảnh đều tuâ Ví dụ, hãy xem kiến trúc của VGG-16, một mạng đạt được độ chính xác 92.7% trong phân loại top-5 của ImageNet vào năm 2014: -![Các lớp của ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.vi.jpg) +![Các lớp của ImageNet](../../../../../translated_images/vi/vgg-16-arch1.d901a5583b3a51ba.jpg) -![Kim tự tháp của ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.vi.jpg) +![Kim tự tháp của ImageNet](../../../../../translated_images/vi/vgg-16-arch.64ff2137f50dd49f.jpg) > Hình ảnh từ [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ff1ea40a..56de1069 100644 --- a/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/vi/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ Bạn cần huấn luyện một mạng nơ-ron tích chập để phân loại Chúng ta sẽ sử dụng [Bộ Dữ Liệu Thú Cưng Oxford-IIIT](https://www.robots.ox.ac.uk/~vgg/data/pets/), bộ dữ liệu này chứa hình ảnh của 37 giống loài chó và mèo khác nhau. -![Bộ dữ liệu chúng ta sẽ làm việc](../../../../../../translated_images/data.50b2a9d5484bdbf0.vi.png) +![Bộ dữ liệu chúng ta sẽ làm việc](../../../../../../translated_images/vi/data.50b2a9d5484bdbf0.png) Để tải bộ dữ liệu, sử dụng đoạn mã sau: diff --git a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb index 97c23669..810b483a 100644 --- a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/AdversarialCat_TF.ipynb @@ -50,7 +50,7 @@ "\n", "Để hình dung con mèo lý tưởng, chúng ta sẽ bắt đầu với một hình ảnh nhiễu ngẫu nhiên và cố gắng sử dụng kỹ thuật tối ưu hóa gradient descent để điều chỉnh hình ảnh sao cho mạng nhận diện được một con mèo.\n", "\n", - "![Vòng lặp tối ưu hóa](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.vi.png)\n", + "![Vòng lặp tối ưu hóa](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.png)\n", "\n", "Đây là hình ảnh ban đầu của chúng ta:\n" ] diff --git a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md index ddf49b58..0be43513 100644 --- a/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/vi/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Cả Keras và PyTorch đều có các hàm để dễ dàng tải trọng số Dưới đây là các đặc điểm mẫu được trích xuất từ một bức ảnh của một con mèo bởi mạng VGG-16: -![Các đặc điểm được trích xuất bởi VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.vi.png) +![Các đặc điểm được trích xuất bởi VGG-16](../../../../../translated_images/vi/features.6291f9c7ba3a0b95.png) ## Tập Dữ Liệu Mèo và Chó @@ -48,19 +48,19 @@ Mạng nơ-ron đã được huấn luyện sẵn chứa các mẫu khác nhau b Một cách tiếp cận mà chúng ta có thể thực hiện là bắt đầu với một hình ảnh ngẫu nhiên, sau đó cố gắng sử dụng kỹ thuật **tối ưu hóa gradient descent** để điều chỉnh hình ảnh đó sao cho mạng bắt đầu nghĩ rằng đó là một con mèo. -![Vòng lặp tối ưu hóa hình ảnh](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.vi.png) +![Vòng lặp tối ưu hóa hình ảnh](../../../../../translated_images/vi/ideal-cat-loop.999fbb8ff306e044.png) Tuy nhiên, nếu chúng ta làm điều này, chúng ta sẽ nhận được một thứ rất giống với nhiễu ngẫu nhiên. Điều này là do *có nhiều cách để khiến mạng nghĩ rằng hình ảnh đầu vào là một con mèo*, bao gồm một số cách không có ý nghĩa về mặt thị giác. Mặc dù những hình ảnh này chứa nhiều mẫu đặc trưng cho một con mèo, nhưng không có gì ràng buộc chúng phải rõ ràng về mặt thị giác. Để cải thiện kết quả, chúng ta có thể thêm một thuật ngữ khác vào hàm mất mát, được gọi là **mất mát biến đổi**. Đây là một chỉ số cho thấy mức độ tương đồng giữa các pixel lân cận của hình ảnh. Việc giảm thiểu mất mát biến đổi làm cho hình ảnh mượt mà hơn và loại bỏ nhiễu - từ đó làm lộ ra các mẫu hấp dẫn hơn về mặt thị giác. Dưới đây là ví dụ về các hình ảnh "lý tưởng" như vậy, được phân loại là mèo và ngựa vằn với xác suất cao: -![Mèo Lý Tưởng](../../../../../translated_images/ideal-cat.203dd4597643d6b0.vi.png) | ![Ngựa Vằn Lý Tưởng](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.vi.png) +![Mèo Lý Tưởng](../../../../../translated_images/vi/ideal-cat.203dd4597643d6b0.png) | ![Ngựa Vằn Lý Tưởng](../../../../../translated_images/vi/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *Mèo Lý Tưởng* | *Ngựa Vằn Lý Tưởng* Cách tiếp cận tương tự có thể được sử dụng để thực hiện cái gọi là **tấn công đối kháng** trên mạng nơ-ron. Giả sử chúng ta muốn đánh lừa một mạng nơ-ron và làm cho một con chó trông giống như một con mèo. Nếu chúng ta lấy hình ảnh của một con chó, được mạng nhận diện là một con chó, chúng ta có thể điều chỉnh nó một chút bằng cách sử dụng tối ưu hóa gradient descent, cho đến khi mạng bắt đầu phân loại nó là một con mèo: -![Hình ảnh của một con chó](../../../../../translated_images/original-dog.8f68a67d2fe0911f.vi.png) | ![Hình ảnh của một con chó được phân loại là mèo](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.vi.png) +![Hình ảnh của một con chó](../../../../../translated_images/vi/original-dog.8f68a67d2fe0911f.png) | ![Hình ảnh của một con chó được phân loại là mèo](../../../../../translated_images/vi/adversarial-dog.d9fc7773b0142b89.png) -----|----- *Hình ảnh gốc của một con chó* | *Hình ảnh của một con chó được phân loại là mèo* diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb index b81f4e60..ff33c190 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb @@ -21,7 +21,7 @@ "\n", "Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng sẽ cố gắng tìm **biểu diễn** tốt nhất của hình ảnh đầu vào để nắm bắt ý nghĩa.\n", "\n", - "![Sơ đồ AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.vi.jpg)\n", + "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "> Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n", "\n", diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb index 99bd5aa3..da5d9cc3 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb @@ -19,7 +19,7 @@ "\n", "Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng sẽ cố gắng tìm **embedding** tốt nhất của các hình ảnh đầu vào để nắm bắt ý nghĩa.\n", "\n", - "![Sơ đồ AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.vi.jpg)\n", + "![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg)\n", "\n", "*Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html)*\n", "\n", diff --git a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md index aedf9b99..fe31eeb0 100644 --- a/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/vi/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ Tuy nhiên, chúng ta có thể muốn sử dụng dữ liệu thô (không đư Vì chúng ta đang huấn luyện autoencoder để nắm bắt càng nhiều thông tin từ hình ảnh gốc càng tốt nhằm tái tạo chính xác, mạng cố gắng tìm **embedding** tốt nhất của hình ảnh đầu vào để nắm bắt ý nghĩa. -![Sơ đồ AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.vi.jpg) +![Sơ đồ AutoEncoder](../../../../../translated_images/vi/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > Hình ảnh từ [blog Keras](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md index bc3d6b2f..d6804f28 100644 --- a/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/vi/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ Các mô hình phân loại hình ảnh mà chúng ta đã làm việc trước ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![Phát Hiện Đối Tượng](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.vi.png) +![Phát Hiện Đối Tượng](../../../../../translated_images/vi/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > Hình ảnh từ [trang web YOLO v2](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ Giả sử chúng ta muốn tìm một con mèo trong một bức ảnh, một c 2. Chạy phân loại hình ảnh trên từng ô. 3. Những ô có kết quả kích hoạt đủ cao có thể được coi là chứa đối tượng cần tìm. -![Phát Hiện Đối Tượng Đơn Giản](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.vi.png) +![Phát Hiện Đối Tượng Đơn Giản](../../../../../translated_images/vi/naive-detection.e7f1ba220ccd08c6.png) > *Hình ảnh từ [Notebook Bài Tập](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ Bạn có thể gặp các tập dữ liệu sau cho nhiệm vụ này: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 20 lớp * [COCO](http://cocodataset.org/#home) - Các Đối Tượng Thông Thường Trong Ngữ Cảnh. 80 lớp, hộp bao và mặt nạ phân đoạn -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.vi.jpg) +![COCO](../../../../../translated_images/vi/coco-examples.71bc60380fa6cceb.jpg) ## Các Chỉ Số Đánh Giá Phát Hiện Đối Tượng @@ -50,7 +50,7 @@ Bạn có thể gặp các tập dữ liệu sau cho nhiệm vụ này: Trong khi đối với phân loại hình ảnh, việc đo lường hiệu suất của thuật toán khá dễ dàng, thì đối với phát hiện đối tượng, chúng ta cần đo lường cả độ chính xác của lớp, cũng như độ chính xác của vị trí hộp bao được suy ra. Đối với yếu tố sau, chúng ta sử dụng chỉ số **Intersection over Union** (IoU), đo lường mức độ chồng lấp giữa hai hộp (hoặc hai khu vực bất kỳ). -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.vi.png) +![IoU](../../../../../translated_images/vi/iou_equation.9a4751d40fff4e11.png) > *Hình 2 từ [bài viết blog xuất sắc về IoU này](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ Có hai loại thuật toán phát hiện đối tượng chính: [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf) sử dụng [Selective Search](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf) để tạo ra cấu trúc phân cấp của các vùng ROI, sau đó được đưa qua các bộ trích xuất đặc trưng CNN và các bộ phân loại SVM để xác định lớp đối tượng, và hồi quy tuyến tính để xác định tọa độ *hộp bao*. [Bài báo chính thức](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.vi.png) +![RCNN](../../../../../translated_images/vi/rcnn1.cae407020dfb1d1f.png) > *Hình ảnh từ van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.vi.png) +![RCNN-1](../../../../../translated_images/vi/rcnn2.2d9530bb83516484.png) > *Hình ảnh từ [bài blog này](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ Có hai loại thuật toán phát hiện đối tượng chính: Phương pháp này tương tự như R-CNN, nhưng các vùng được xác định sau khi các lớp tích chập đã được áp dụng. -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.vi.png) +![FRCNN](../../../../../translated_images/vi/f-rcnn.3cda6d9bb4188875.png) > Hình ảnh từ [Bài báo chính thức](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf), [arXiv](https://arxiv.org/pdf/1504.08083.pdf), 2015 @@ -118,7 +118,7 @@ Phương pháp này tương tự như R-CNN, nhưng các vùng được xác đ Ý tưởng chính của phương pháp này là sử dụng mạng nơ-ron để dự đoán các ROI - được gọi là *Mạng Đề Xuất Vùng* (Region Proposal Network). [Bài báo](https://arxiv.org/pdf/1506.01497.pdf), 2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.vi.png) +![FasterRCNN](../../../../../translated_images/vi/faster-rcnn.8d46c099b87ef30a.png) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ Thuật toán này thậm chí còn nhanh hơn Faster R-CNN. Ý tưởng chính 2. Các đặc trưng được xử lý bởi **Bản Đồ Điểm Nhạy Cảm Vị Trí**. Mỗi đối tượng từ $C$ lớp được chia thành các vùng $k\times k$, và chúng ta huấn luyện để dự đoán các phần của đối tượng. 3. Đối với mỗi phần từ các vùng $k\times k$, tất cả các mạng bỏ phiếu cho các lớp đối tượng, và lớp đối tượng có số phiếu cao nhất được chọn. -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.vi.png) +![r-fcn image](../../../../../translated_images/vi/r-fcn.13eb88158b99a3da.png) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO là một thuật toán một lần duy nhất thời gian thực. Ý tư * Hình ảnh được chia thành các vùng $S\times S$. * Đối với mỗi vùng, **CNN** dự đoán $n$ đối tượng có thể, tọa độ *hộp bao* và *độ tin cậy* = *xác suất* * IoU. - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.vi.png) + ![YOLO](../../../../../translated_images/vi/yolo.a2648ec82ee8bb4e.png) > Hình ảnh từ [bài báo chính thức](https://arxiv.org/abs/1506.02640) diff --git a/translations/vi/lessons/4-ComputerVision/README.md b/translations/vi/lessons/4-ComputerVision/README.md index 6687e637..99e3cfdd 100644 --- a/translations/vi/lessons/4-ComputerVision/README.md +++ b/translations/vi/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Thị giác Máy tính -![Tóm tắt nội dung Thị giác Máy tính dưới dạng hình vẽ](../../../../translated_images/ai-computervision.6506ebebac3fbf76.vi.png) +![Tóm tắt nội dung Thị giác Máy tính dưới dạng hình vẽ](../../../../translated_images/vi/ai-computervision.6506ebebac3fbf76.png) Trong phần này, chúng ta sẽ tìm hiểu về: diff --git a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb index d33e891e..115f03c1 100644 --- a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb @@ -199,7 +199,7 @@ "\n", "**Bag of Words** (BoW) là cách biểu diễn vector truyền thống được sử dụng phổ biến nhất. Mỗi từ được liên kết với một chỉ số vector, phần tử trong vector chứa số lần xuất hiện của từ đó trong một tài liệu cụ thể.\n", "\n", - "![Hình ảnh minh họa cách biểu diễn vector Bag of Words trong bộ nhớ.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.vi.png) \n", + "![Hình ảnh minh họa cách biểu diễn vector Bag of Words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Bạn cũng có thể nghĩ về BoW như tổng của tất cả các vector mã hóa một-hot cho từng từ riêng lẻ trong văn bản.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index c9b23beb..9c280a7b 100644 --- a/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb +++ b/translations/vi/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb @@ -188,7 +188,7 @@ "\n", "Biểu diễn vector **Bag-of-words** (BoW) là cách biểu diễn vector truyền thống đơn giản nhất để hiểu. Mỗi từ được liên kết với một chỉ số vector, và một phần tử trong vector chứa số lần xuất hiện của mỗi từ trong một tài liệu cụ thể.\n", "\n", - "![Hình ảnh minh họa cách biểu diễn vector bag-of-words trong bộ nhớ.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.vi.png) \n", + "![Hình ảnh minh họa cách biểu diễn vector bag-of-words trong bộ nhớ.](../../../../../translated_images/vi/bag-of-words-example.606fc1738f1d7ba9.png) \n", "\n", "> **Note**: Bạn cũng có thể nghĩ về BoW như tổng của tất cả các vector mã hóa một-hot cho từng từ trong văn bản.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb index 570b1087..d22d5fb7 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb @@ -62,7 +62,7 @@ "\n", "Bằng cách sử dụng lớp nhúng làm lớp đầu tiên trong mạng của chúng ta, chúng ta có thể chuyển từ mô hình túi từ (**bag-of-words**) sang mô hình túi nhúng (**embedding bag**), nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản thành nhúng tương ứng, sau đó tính toán một hàm tổng hợp nào đó trên tất cả các nhúng đó, chẳng hạn như `sum`, `average` hoặc `max`.\n", "\n", - "![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.vi.png)\n", + "![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Mạng neural phân loại của chúng ta sẽ bắt đầu với lớp nhúng, sau đó là lớp tổng hợp, và cuối cùng là bộ phân loại tuyến tính ở trên cùng:\n" ] @@ -176,7 +176,7 @@ "\n", "Trong kiến trúc trước, chúng ta cần đệm tất cả các chuỗi để có cùng độ dài nhằm đưa chúng vào một minibatch. Đây không phải là cách hiệu quả nhất để biểu diễn các chuỗi có độ dài biến đổi - một cách tiếp cận khác là sử dụng **vector offset**, chứa các điểm bắt đầu của tất cả các chuỗi được lưu trữ trong một vector lớn.\n", "\n", - "![Hình ảnh minh họa biểu diễn chuỗi bằng offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.vi.png)\n", + "![Hình ảnh minh họa biểu diễn chuỗi bằng offset](../../../../../translated_images/vi/offset-sequence-representation.eb73fcefb29b46ee.png)\n", "\n", "> **Note**: Trong hình trên, chúng ta minh họa một chuỗi ký tự, nhưng trong ví dụ của chúng ta, chúng ta đang làm việc với các chuỗi từ. Tuy nhiên, nguyên tắc chung của việc biểu diễn chuỗi bằng vector offset vẫn giữ nguyên.\n", "\n", @@ -311,7 +311,7 @@ "\n", "CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng làm tốt hơn trong việc biểu diễn các từ ít xuất hiện.\n", "\n", - "![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.vi.png)\n", + "![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Để thử nghiệm với nhúng word2vec được tiền huấn luyện trên tập dữ liệu Google News, chúng ta có thể sử dụng thư viện **gensim**. Dưới đây là cách tìm các từ giống nhất với 'neural'\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index 4af18d74..d8b7a6c9 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/translations/vi/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -37,7 +37,7 @@ "\n", "Bằng cách sử dụng lớp embedding làm lớp đầu tiên trong mạng của chúng ta, chúng ta có thể chuyển từ mô hình bag-of-words sang mô hình **embedding bag**, nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản thành embedding tương ứng, sau đó tính toán một số hàm tổng hợp trên tất cả các embedding đó, chẳng hạn như `sum`, `average` hoặc `max`.\n", "\n", - "![Hình ảnh minh họa một bộ phân loại embedding cho năm từ trong chuỗi.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.vi.png)\n", + "![Hình ảnh minh họa một bộ phân loại embedding cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png)\n", "\n", "Mạng neural phân loại của chúng ta bao gồm các lớp sau:\n", "\n", @@ -283,7 +283,7 @@ "\n", "CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng lại làm tốt hơn trong việc biểu diễn các từ ít xuất hiện.\n", "\n", - "![Hình minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.vi.png)\n", + "![Hình minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png)\n", "\n", "Để thử nghiệm với nhúng Word2Vec được tiền huấn luyện trên tập dữ liệu Google News, chúng ta có thể sử dụng thư viện **gensim**. Dưới đây là cách tìm các từ giống nhất với 'neural'.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/14-Embeddings/README.md b/translations/vi/lessons/5-NLP/14-Embeddings/README.md index 2b497f4e..5e50f550 100644 --- a/translations/vi/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/vi/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ Vì vậy, lớp nhúng sẽ nhận một từ làm đầu vào và tạo ra m Bằng cách sử dụng lớp nhúng làm lớp đầu tiên trong mạng phân loại của chúng ta, chúng ta có thể chuyển từ mô hình túi từ sang mô hình **túi nhúng**, nơi chúng ta đầu tiên chuyển đổi mỗi từ trong văn bản của mình thành nhúng tương ứng, và sau đó tính toán một số hàm tổng hợp trên tất cả các nhúng đó, chẳng hạn như `sum`, `average` hoặc `max`. -![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.vi.png) +![Hình ảnh minh họa một bộ phân loại nhúng cho năm từ trong chuỗi.](../../../../../translated_images/vi/embedding-classifier-example.b77f021a7ee67eee.png) > Hình ảnh của tác giả @@ -40,7 +40,7 @@ Mặc dù lớp nhúng đã học cách ánh xạ các từ sang biểu diễn v CBoW nhanh hơn, trong khi skip-gram chậm hơn nhưng làm tốt hơn trong việc biểu diễn các từ ít xuất hiện. -![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.vi.png) +![Hình ảnh minh họa cả hai thuật toán CBoW và Skip-Gram để chuyển đổi từ thành vector.](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Hình ảnh từ [bài báo này](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md b/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md index 26169aa0..e2a3e6b4 100644 --- a/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/vi/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ Trong các ví dụ trước, chúng ta đã sử dụng các biểu diễn ng * **Continuous Bag-of-Words** (CBoW), khi chúng ta dự đoán token ở giữa $W_0$ trong một chuỗi token $W_{-N}$, ..., $W_N$. * **Skip-gram**, nơi chúng ta dự đoán một tập hợp các token lân cận {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$} từ token ở giữa $W_0$. -![hình ảnh từ bài báo về chuyển đổi từ thành vector](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.vi.png) +![hình ảnh từ bài báo về chuyển đổi từ thành vector](../../../../../translated_images/vi/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > Hình ảnh từ [bài báo này](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/vi/lessons/5-NLP/16-RNN/README.md b/translations/vi/lessons/5-NLP/16-RNN/README.md index 2f0145e8..2f18e530 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/README.md +++ b/translations/vi/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ Trong các phần trước, chúng ta đã sử dụng các biểu diễn ngữ Để nắm bắt ý nghĩa của chuỗi văn bản, chúng ta cần sử dụng một kiến trúc mạng nơ-ron khác, được gọi là **mạng nơ-ron tái phục hồi**, hay RNN. Trong RNN, chúng ta đưa câu qua mạng từng ký hiệu một, và mạng sẽ tạo ra một **trạng thái**, sau đó chúng ta đưa trạng thái này vào mạng cùng với ký hiệu tiếp theo. -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.vi.png) +![RNN](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.png) > Hình ảnh của tác giả @@ -61,7 +61,7 @@ Chúng ta đã thảo luận về các mạng tái phục hồi hoạt động t Một mạng tái phục hồi, dù là một chiều hay hai chiều, nắm bắt các mẫu nhất định trong một chuỗi và có thể lưu trữ chúng vào một vector trạng thái hoặc truyền vào đầu ra. Tương tự như các mạng tích chập, chúng ta có thể xây dựng một lớp tái phục hồi khác trên lớp đầu tiên để nắm bắt các mẫu cấp cao hơn và xây dựng từ các mẫu cấp thấp được trích xuất bởi lớp đầu tiên. Điều này dẫn đến khái niệm về một **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng tái phục hồi, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo làm đầu vào. -![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.vi.jpg) +![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg) *Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López* diff --git a/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb b/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb index cb36702b..bc149176 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb @@ -422,7 +422,7 @@ "\n", "Mạng hồi quy, dù một chiều hay hai chiều, đều nắm bắt các mẫu nhất định trong một chuỗi và có thể lưu trữ chúng vào vector trạng thái hoặc truyền vào đầu ra. Tương tự như mạng tích chập, chúng ta có thể xây dựng một lớp hồi quy khác trên lớp đầu tiên để nắm bắt các mẫu cấp cao hơn, được xây dựng từ các mẫu cấp thấp do lớp đầu tiên trích xuất. Điều này dẫn đến khái niệm **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng hồi quy, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo làm đầu vào.\n", "\n", - "![Hình ảnh minh họa một mạng RNN LSTM nhiều lớp](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.vi.jpg)\n", + "![Hình ảnh minh họa một mạng RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López*\n", "\n", diff --git a/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb b/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb index ce4791ef..a2d095da 100644 --- a/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb +++ b/translations/vi/lessons/5-NLP/16-RNN/RNNTF.ipynb @@ -10,7 +10,7 @@ "\n", "Để nắm bắt ý nghĩa của một chuỗi văn bản, chúng ta sẽ sử dụng một kiến trúc mạng nơ-ron gọi là **mạng nơ-ron hồi quy**, hay RNN. Khi sử dụng RNN, chúng ta truyền câu qua mạng từng token một, và mạng tạo ra một **trạng thái**, sau đó chúng ta truyền trạng thái này vào mạng cùng với token tiếp theo.\n", "\n", - "![Hình minh họa một ví dụ về quá trình tạo mạng nơ-ron hồi quy.](../../../../../translated_images/rnn.27f5c29c53d727b5.vi.png)\n", + "![Hình minh họa một ví dụ về quá trình tạo mạng nơ-ron hồi quy.](../../../../../translated_images/vi/rnn.27f5c29c53d727b5.png)\n", "\n", "Với chuỗi đầu vào các token $X_0,\\dots,X_n$, RNN tạo ra một chuỗi các khối mạng nơ-ron và huấn luyện chuỗi này từ đầu đến cuối bằng cách sử dụng lan truyền ngược. Mỗi khối mạng nhận một cặp $(X_i,S_i)$ làm đầu vào và tạo ra $S_{i+1}$ làm kết quả. Trạng thái cuối cùng $S_n$ hoặc đầu ra $Y_n$ được đưa vào một bộ phân loại tuyến tính để tạo ra kết quả. Tất cả các khối mạng đều chia sẻ cùng một trọng số và được huấn luyện từ đầu đến cuối bằng một lần lan truyền ngược.\n", "\n", @@ -369,7 +369,7 @@ "\n", "Mạng hồi quy, dù là một chiều hay hai chiều, đều nắm bắt các mẫu trong một chuỗi và lưu trữ chúng vào các vector trạng thái hoặc trả về chúng dưới dạng đầu ra. Tương tự như mạng tích chập, chúng ta có thể xây dựng một lớp hồi quy khác sau lớp đầu tiên để nắm bắt các mẫu cấp cao hơn, được xây dựng từ các mẫu cấp thấp hơn mà lớp đầu tiên đã trích xuất. Điều này dẫn đến khái niệm về **RNN nhiều lớp**, bao gồm hai hoặc nhiều mạng hồi quy, trong đó đầu ra của lớp trước được truyền vào lớp tiếp theo dưới dạng đầu vào.\n", "\n", - "![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.vi.jpg)\n", + "![Hình ảnh minh họa một RNN LSTM nhiều lớp](../../../../../translated_images/vi/multi-layer-lstm.dd975e29bb2a59fe.jpg)\n", "\n", "*Hình ảnh từ [bài viết tuyệt vời này](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3) của Fernando López.*\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb index 0eabfa22..26c23cfd 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb @@ -119,7 +119,7 @@ "\n", "Cách chúng ta sẽ huấn luyện RNN để tạo văn bản như sau. Ở mỗi bước, chúng ta sẽ lấy một chuỗi ký tự có độ dài `nchars`, và yêu cầu mạng tạo ra ký tự đầu ra tiếp theo cho mỗi ký tự đầu vào:\n", "\n", - "![Hình minh họa ví dụ RNN tạo ra từ 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.vi.png)\n", + "![Hình minh họa ví dụ RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Tùy thuộc vào tình huống thực tế, chúng ta cũng có thể muốn bao gồm một số ký tự đặc biệt, chẳng hạn như *kết thúc chuỗi* ``. Trong trường hợp của chúng ta, mục tiêu là huấn luyện mạng để tạo văn bản liên tục, vì vậy chúng ta sẽ cố định kích thước của mỗi chuỗi bằng số lượng token `nchars`. Do đó, mỗi ví dụ huấn luyện sẽ bao gồm `nchars` đầu vào và `nchars` đầu ra (là chuỗi đầu vào được dịch sang trái một ký tự). Minibatch sẽ bao gồm một số chuỗi như vậy.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb index 38270e2b..6737c8ee 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb @@ -111,7 +111,7 @@ "\n", "Cách chúng ta sẽ huấn luyện RNN để tạo tiêu đề tin tức như sau. Ở mỗi bước, chúng ta sẽ lấy một tiêu đề, tiêu đề này sẽ được đưa vào RNN, và với mỗi ký tự đầu vào, chúng ta sẽ yêu cầu mạng tạo ra ký tự đầu ra tiếp theo:\n", "\n", - "![Hình ảnh minh họa việc RNN tạo ra từ 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.vi.png)\n", + "![Hình ảnh minh họa việc RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png)\n", "\n", "Đối với ký tự cuối cùng của chuỗi, chúng ta sẽ yêu cầu mạng tạo ra token ``.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md index bbe5b138..3dd5e9ad 100644 --- a/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/vi/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ Trong kiến trúc RNN mà chúng ta đã thảo luận trong bài trước, m Điều này cho phép các kiến trúc nơ-ron khác nhau như được hiển thị trong hình dưới đây: -![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.vi.jpg) +![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > Hình ảnh từ bài viết blog [Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) của [Andrej Karpaty](http://karpathy.github.io/) @@ -32,7 +32,7 @@ Trong bài này, chúng ta sẽ tập trung vào các mô hình tạo sinh đơn Chúng ta sẽ huấn luyện RNN này để tạo văn bản từng bước. Ở mỗi bước, chúng ta sẽ lấy một chuỗi ký tự có độ dài `nchars`, và yêu cầu mạng tạo ra ký tự đầu ra tiếp theo cho mỗi ký tự đầu vào: -![Hình ảnh minh họa RNN tạo ra từ 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.vi.png) +![Hình ảnh minh họa RNN tạo ra từ 'HELLO'.](../../../../../translated_images/vi/rnn-generate.56c54afb52f9781d.png) Khi tạo văn bản (trong quá trình suy luận), chúng ta bắt đầu với một **gợi ý**, được truyền qua các tế bào RNN để tạo trạng thái trung gian của nó, và sau đó từ trạng thái này bắt đầu quá trình tạo. Chúng ta tạo từng ký tự một, và truyền trạng thái cùng ký tự vừa tạo vào một tế bào RNN khác để tạo ký tự tiếp theo, cho đến khi tạo đủ số ký tự. diff --git a/translations/vi/lessons/5-NLP/18-Transformers/README.md b/translations/vi/lessons/5-NLP/18-Transformers/README.md index 093f20c9..16e2a0b5 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/README.md +++ b/translations/vi/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ Với RNNs, sequence-to-sequence được thực hiện bởi hai mạng hồi q **Cơ chế Attention** cung cấp một cách để cân nhắc tác động ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách nó được thực hiện là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra yt, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào hi, với các hệ số trọng số khác nhau αt,i. -![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.vi.png) +![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png) > Mô hình encoder-decoder với cơ chế attention cộng trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) Ma trận attention {αi,j} sẽ biểu thị mức độ mà các từ đầu vào nhất định ảnh hưởng đến việc tạo ra một từ cụ thể trong chuỗi đầu ra. Dưới đây là một ví dụ về ma trận như vậy: -![Hình ảnh hiển thị một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.vi.png) +![Hình ảnh hiển thị một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png) > Hình từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3) @@ -66,7 +66,7 @@ Kết quả mà chúng ta nhận được với nhúng vị trí là nhúng cả Tiếp theo, chúng ta cần nắm bắt một số mẫu trong chuỗi của mình. Để làm điều này, transformers sử dụng cơ chế **self-attention**, về cơ bản là attention được áp dụng cho cùng một chuỗi làm đầu vào và đầu ra. Việc áp dụng self-attention cho phép chúng ta xem xét **ngữ cảnh** trong câu và xem các từ nào có liên quan đến nhau. Ví dụ, nó cho phép chúng ta thấy các từ nào được tham chiếu bởi các đại từ như *it*, và cũng xem xét ngữ cảnh: -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.vi.png) +![](../../../../../translated_images/vi/CoreferenceResolution.861924d6d384a7d6.png) > Hình ảnh từ [Blog của Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Vì mỗi vị trí đầu vào được ánh xạ độc lập đến mỗi v **BERT** (Bidirectional Encoder Representations from Transformers) là một mạng transformer nhiều lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách huấn luyện không giám sát (dự đoán các từ bị che trong câu). Trong quá trình huấn luyện trước, mô hình hấp thụ mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác bằng cách tinh chỉnh. Quá trình này được gọi là **học chuyển giao**. -![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.vi.png) +![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > Hình ảnh [nguồn](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb b/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb index 367f27a1..4ae6ada5 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb +++ b/translations/vi/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb @@ -12,12 +12,12 @@ "\n", "**Cơ chế Attention** cung cấp một cách để cân nhắc mức độ ảnh hưởng ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách triển khai là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra $y_t$, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào $h_i$, với các hệ số trọng số khác nhau $\\alpha_{t,i}$.\n", "\n", - "![Hình ảnh mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.vi.png)\n", + "![Hình ảnh mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png)\n", "*Mô hình encoder-decoder với cơ chế attention cộng tính trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), được trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ma trận Attention $\\{\\alpha_{i,j}\\}$ sẽ đại diện cho mức độ mà các từ đầu vào cụ thể ảnh hưởng đến việc tạo ra một từ nhất định trong chuỗi đầu ra. Dưới đây là ví dụ về một ma trận như vậy:\n", "\n", - "![Hình ảnh minh họa sự liên kết mẫu được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.vi.png)\n", + "![Hình ảnh minh họa sự liên kết mẫu được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Hình ảnh được lấy từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3)*\n", "\n", @@ -35,7 +35,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) là một mạng Transformer đa lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách huấn luyện không giám sát (dự đoán các từ bị che trong một câu). Trong quá trình huấn luyện trước, mô hình hấp thụ một mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác thông qua việc tinh chỉnh. Quá trình này được gọi là **học chuyển giao** (transfer learning).\n", "\n", - "![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.vi.png)\n", + "![Hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Có nhiều biến thể của kiến trúc Transformer bao gồm BERT, DistilBERT, BigBird, OpenGPT3 và nhiều hơn nữa có thể được tinh chỉnh. Gói [HuggingFace](https://github.com/huggingface/) cung cấp kho lưu trữ để huấn luyện nhiều kiến trúc này với PyTorch.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb b/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb index 0c579554..28528eca 100644 --- a/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb +++ b/translations/vi/lessons/5-NLP/18-Transformers/TransformersTF.ipynb @@ -12,12 +12,12 @@ "\n", "**Cơ chế Attention** cung cấp một phương pháp để gán trọng số cho tác động ngữ cảnh của từng vector đầu vào lên từng dự đoán đầu ra của RNN. Cách triển khai là tạo các đường tắt giữa các trạng thái trung gian của RNN đầu vào và RNN đầu ra. Theo cách này, khi tạo ra ký hiệu đầu ra $y_t$, chúng ta sẽ xem xét tất cả các trạng thái ẩn đầu vào $h_i$, với các hệ số trọng số khác nhau $\\alpha_{t,i}$.\n", "\n", - "![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.vi.png)\n", + "![Hình ảnh mô tả mô hình encoder/decoder với lớp attention cộng tính](../../../../../translated_images/vi/encoder-decoder-attention.7a726296894fb567.png)\n", "*Mô hình encoder-decoder với cơ chế attention cộng tính trong [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), được trích dẫn từ [bài viết blog này](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)*\n", "\n", "Ma trận Attention $\\{\\alpha_{i,j}\\}$ sẽ biểu thị mức độ mà các từ đầu vào cụ thể ảnh hưởng đến việc tạo ra một từ nhất định trong chuỗi đầu ra. Dưới đây là ví dụ về một ma trận như vậy:\n", "\n", - "![Hình ảnh minh họa một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.vi.png)\n", + "![Hình ảnh minh họa một mẫu căn chỉnh được tìm thấy bởi RNNsearch-50, lấy từ Bahdanau - arviz.org](../../../../../translated_images/vi/bahdanau-fig3.09ba2d37f202a6af.png)\n", "\n", "*Hình ảnh được lấy từ [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Hình 3)*\n", "\n", @@ -231,7 +231,7 @@ "\n", "**BERT** (Bidirectional Encoder Representations from Transformers) là một mạng transformer nhiều lớp rất lớn với 12 lớp cho *BERT-base*, và 24 lớp cho *BERT-large*. Mô hình này được huấn luyện trước trên một tập dữ liệu văn bản lớn (WikiPedia + sách) bằng cách sử dụng phương pháp huấn luyện không giám sát (dự đoán các từ bị che trong câu). Trong quá trình huấn luyện trước, mô hình hấp thụ một mức độ hiểu biết ngôn ngữ đáng kể, sau đó có thể được tận dụng với các tập dữ liệu khác thông qua việc tinh chỉnh. Quá trình này được gọi là **học chuyển giao**.\n", "\n", - "![hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.vi.png)\n", + "![hình ảnh từ http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/vi/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png)\n", "\n", "Có nhiều biến thể của kiến trúc Transformer bao gồm BERT, DistilBERT, BigBird, OpenGPT3 và nhiều hơn nữa có thể được tinh chỉnh.\n", "\n", diff --git a/translations/vi/lessons/5-NLP/19-NER/README.md b/translations/vi/lessons/5-NLP/19-NER/README.md index 8fb0f69a..fc653628 100644 --- a/translations/vi/lessons/5-NLP/19-NER/README.md +++ b/translations/vi/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ infant | O Vì chúng ta cần xây dựng một sự tương ứng một-một giữa các token và các lớp, chúng ta có thể huấn luyện một mô hình mạng nơ-ron **nhiều-đến-nhiều** từ hình ảnh này: -![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.vi.jpg) +![Hình ảnh hiển thị các mẫu mạng nơ-ron hồi quy phổ biến.](../../../../../translated_images/vi/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *Hình ảnh từ [bài viết blog này](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) của [Andrej Karpathy](http://karpathy.github.io/). Các mô hình phân loại token NER tương ứng với kiến trúc mạng ở phía bên phải của hình ảnh này.* diff --git a/translations/vi/lessons/5-NLP/README.md b/translations/vi/lessons/5-NLP/README.md index 01fe3222..848dec6b 100644 --- a/translations/vi/lessons/5-NLP/README.md +++ b/translations/vi/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Xử lý Ngôn ngữ Tự nhiên -![Tóm tắt các nhiệm vụ NLP trong một hình vẽ](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.vi.png) +![Tóm tắt các nhiệm vụ NLP trong một hình vẽ](../../../../translated_images/vi/ai-nlp.b22dcb8ca4707cea.png) Trong phần này, chúng ta sẽ tập trung vào việc sử dụng Mạng Nơ-ron để xử lý các nhiệm vụ liên quan đến **Xử lý Ngôn ngữ Tự nhiên (NLP)**. Có rất nhiều vấn đề NLP mà chúng ta muốn máy tính có thể giải quyết: diff --git a/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md b/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md index 898b0f9e..0c744bd3 100644 --- a/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/vi/lessons/6-Other/23-MultiagentSystems/README.md @@ -70,7 +70,7 @@ Bạn có thể mở một trong các mô hình, ví dụ **Biology → Sau khi mở mô hình, bạn sẽ được đưa đến màn hình chính của NetLogo. Đây là một mô hình mẫu mô tả dân số của sói và cừu, với các tài nguyên hữu hạn (cỏ). -![Màn hình chính NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.vi.png) +![Màn hình chính NetLogo](../../../../../translated_images/vi/NetLogo-Main.32653711ec1a01b3.png) > Ảnh chụp màn hình của Dmitry Soshnikov diff --git a/translations/vi/lessons/README.md b/translations/vi/lessons/README.md index f3c828b4..5f866928 100644 --- a/translations/vi/lessons/README.md +++ b/translations/vi/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Tổng quan -![Tổng quan trong một hình vẽ minh họa](../../../translated_images/ai-overview.0857791951d19500.vi.png) +![Tổng quan trong một hình vẽ minh họa](../../../translated_images/vi/ai-overview.0857791951d19500.png) > Hình minh họa bởi [Tomomi Imura](https://twitter.com/girlie_mac) diff --git a/translations/vi/lessons/X-Extras/X1-MultiModal/README.md b/translations/vi/lessons/X-Extras/X1-MultiModal/README.md index bcac5096..173b6e04 100644 --- a/translations/vi/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/vi/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ Sau thành công của các mô hình transformer trong việc giải quyết c Ý tưởng chính của CLIP là có thể so sánh các gợi ý văn bản với một hình ảnh và xác định mức độ phù hợp của hình ảnh với gợi ý đó. -![Kiến trúc CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.vi.png) +![Kiến trúc CLIP](../../../../../translated_images/vi/clip-arch.b3dbf20b4e8ed8be.png) > *Hình ảnh từ [bài viết blog này](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ Khi mô hình này đã được huấn luyện trước, chúng ta có thể cu Giả sử chúng ta cần phân loại hình ảnh giữa, ví dụ, mèo, chó và con người. Trong trường hợp này, chúng ta có thể cung cấp cho mô hình một hình ảnh và một loạt các gợi ý văn bản: "*một bức ảnh của một con mèo*", "*một bức ảnh của một con chó*", "*một bức ảnh của một con người*". Trong vector kết quả gồm 3 xác suất, chúng ta chỉ cần chọn chỉ số có giá trị cao nhất. -![CLIP cho Phân loại Hình ảnh](../../../../../translated_images/clip-class.3af42ef0b2b19369.vi.png) +![CLIP cho Phân loại Hình ảnh](../../../../../translated_images/vi/clip-class.3af42ef0b2b19369.png) > *Hình ảnh từ [bài viết blog này](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ Tìm hiểu thêm về VQGAN tại trang web [Taming Transformers](https://compv Một trong những khác biệt quan trọng giữa VQGAN và GAN truyền thống là GAN truyền thống có thể tạo ra một hình ảnh khá tốt từ bất kỳ vector đầu vào nào, trong khi VQGAN có thể tạo ra một hình ảnh không nhất quán. Do đó, chúng ta cần hướng dẫn thêm quá trình tạo hình ảnh, và điều này có thể được thực hiện bằng cách sử dụng CLIP. -![Kiến trúc VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.vi.png) +![Kiến trúc VQGAN+CLIP](../../../../../translated_images/vi/vqgan.5027fe05051dfa31.png) Để tạo ra một hình ảnh tương ứng với một gợi ý văn bản, chúng ta bắt đầu với một vector mã hóa ngẫu nhiên được đưa qua VQGAN để tạo ra một hình ảnh. Sau đó, CLIP được sử dụng để tạo ra một hàm mất mát cho biết mức độ phù hợp của hình ảnh với gợi ý văn bản. Mục tiêu sau đó là giảm thiểu hàm mất mát này, sử dụng lan truyền ngược để điều chỉnh các tham số vector đầu vào. Một thư viện tuyệt vời triển khai VQGAN+CLIP là [Pixray](http://github.com/pixray/pixray) -![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.vi.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.vi.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.vi.png) +![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng màu nước của một giáo viên văn học trẻ tuổi với một cuốn sách* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên khoa học máy tính trẻ tuổi với một chiếc máy tính* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên toán học lớn tuổi trước bảng đen* @@ -75,7 +75,7 @@ Không giống như CLIP, DALL-E nhận cả văn bản và hình ảnh dưới Sự khác biệt chính giữa DALL.E 1 và 2 là DALL.E 2 tạo ra các hình ảnh và tác phẩm nghệ thuật chân thực hơn. Ví dụ về hình ảnh được tạo bởi DALL-E: -![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.vi.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.vi.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.vi.png) +![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![Hình ảnh được tạo bởi Pixray](../../../../../translated_images/vi/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng màu nước của một giáo viên văn học trẻ tuổi với một cuốn sách* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên khoa học máy tính trẻ tuổi với một chiếc máy tính* | Hình ảnh được tạo từ gợi ý *một bức chân dung cận cảnh bằng sơn dầu của một giáo viên toán học lớn tuổi trước bảng đen* diff --git a/translations/zh/README.md b/translations/zh/README.md index 9ae2ca40..e4d7c8d5 100644 --- a/translations/zh/README.md +++ b/translations/zh/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: # 人工智能初学者课程 -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ai-overview.0857791951d19500.zh.png)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/zh/ai-overview.0857791951d19500.png)| |:---:| | 人工智能初学者 - _由 [@girlie_mac](https://twitter.com/girlie_mac) 绘制的草图笔记_ | diff --git a/translations/zh/lessons/1-Intro/README.md b/translations/zh/lessons/1-Intro/README.md index 2f31b68a..f9740728 100644 --- a/translations/zh/lessons/1-Intro/README.md +++ b/translations/zh/lessons/1-Intro/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 人工智能简介 -![人工智能内容简介的涂鸦](../../../../translated_images/ai-intro.bf28d1ac4235881c.zh.png) +![人工智能内容简介的涂鸦](../../../../translated_images/zh/ai-intro.bf28d1ac4235881c.png) > 涂鸦作者：[Tomomi Imura](https://twitter.com/girlie_mac) @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 最初，计算机是由[查尔斯·巴贝奇](https://en.wikipedia.org/wiki/Charles_Babbage)发明的，用于按照明确的程序（算法）对数字进行操作。尽管现代计算机比19世纪提出的原始模型先进得多，但它们仍然遵循受控计算的基本理念。因此，如果我们知道实现目标所需的确切步骤序列，就可以编程让计算机完成某项任务。 -![一个人的照片](../../../../translated_images/dsh_age.d212a30d4e54fb5f.zh.png) +![一个人的照片](../../../../translated_images/zh/dsh_age.d212a30d4e54fb5f.png) > 照片由 [Vickie Soshnikova](http://twitter.com/vickievalerie) 提供 @@ -46,7 +46,7 @@ CO_OP_TRANSLATOR_METADATA: 在讨论**[智能](https://en.wikipedia.org/wiki/Intelligence)**这个术语时，一个问题是我们并没有对其明确的定义。有人认为智能与**抽象思维**或**自我意识**相关，但我们无法准确定义它。 -![一只猫的照片](../../../../translated_images/photo-cat.8c8e8fb760ffe457.zh.jpg) +![一只猫的照片](../../../../translated_images/zh/photo-cat.8c8e8fb760ffe457.jpg) > [照片](https://unsplash.com/photos/75715CVEJhI)由 [Amber Kipp](https://unsplash.com/@sadmax) 提供，来自Unsplash @@ -98,13 +98,13 @@ CO_OP_TRANSLATOR_METADATA: > | 那机器学习呢？ | | > |--------------|-----------| -> | 人工智能的一部分是基于计算机通过一些数据学习解决问题的，这被称为**机器学习**。我们不会在本课程中讨论经典的机器学习——请参考单独的[机器学习初学者课程](http://aka.ms/ml-beginners)。 | ![机器学习初学者](../../../../translated_images/ml-for-beginners.9e4fed176fd5817d.zh.png) | +> | 人工智能的一部分是基于计算机通过一些数据学习解决问题的，这被称为**机器学习**。我们不会在本课程中讨论经典的机器学习——请参考单独的[机器学习初学者课程](http://aka.ms/ml-beginners)。 | ![机器学习初学者](../../../../translated_images/zh/ml-for-beginners.9e4fed176fd5817d.png) | ## 人工智能简史 人工智能作为一个领域始于20世纪中期。最初，符号推理是一种流行的方法，并取得了一些重要的成功，例如专家系统——能够在某些有限问题领域中充当专家的计算机程序。然而，很快就发现这种方法并不具有良好的扩展性。从专家那里提取知识、在计算机中表示这些知识并保持知识库的准确性，结果证明是一项非常复杂且在许多情况下成本过高的任务。这导致了20世纪70年代所谓的[人工智能寒冬](https://en.wikipedia.org/wiki/AI_winter)。 -人工智能简史 +人工智能简史 > 图片由 [Dmitry Soshnikov](http://soshnikov.com) 提供 @@ -124,7 +124,7 @@ CO_OP_TRANSLATOR_METADATA: * 现代助手，如Cortana、Siri或Google Assistant，都是混合系统，使用神经网络将语音转换为文本并识别我们的意图，然后采用一些推理或明确的算法来执行所需的操作。 * 未来，我们可能会期待一个完全基于神经网络的模型能够自行处理对话。最近的GPT和[Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft)系列神经网络在这方面表现出色。 -图灵测试的演变 +图灵测试的演变 > 图片由 Dmitry Soshnikov 提供，[照片](https://unsplash.com/photos/r8LmVbUKgns)由 [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto) 提供，来源于 Unsplash ## 最近的人工智能研究 diff --git a/translations/zh/lessons/2-Symbolic/README.md b/translations/zh/lessons/2-Symbolic/README.md index a2e96c2b..db01d372 100644 --- a/translations/zh/lessons/2-Symbolic/README.md +++ b/translations/zh/lessons/2-Symbolic/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 知识表示与专家系统 -![符号 AI 内容摘要](../../../../translated_images/ai-symbolic.715a30cb610411a6.zh.png) +![符号 AI 内容摘要](../../../../translated_images/zh/ai-symbolic.715a30cb610411a6.png) > [Tomomi Imura](https://twitter.com/girlie_mac) 的手绘笔记 @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: 因此，**知识表示**的问题是找到一种有效的方法，将知识以数据的形式表示在计算机中，使其能够自动使用。这可以看作是一个光谱： -![知识表示光谱](../../../../translated_images/knowledge-spectrum.b60df631852c0217.zh.png) +![知识表示光谱](../../../../translated_images/zh/knowledge-spectrum.b60df631852c0217.png) > 图片来源：[Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +94,7 @@ Python | 块语法 | 缩进 符号 AI 的早期成功之一是所谓的**专家系统**——设计为在某些有限问题领域中充当专家的计算机系统。它们基于从一个或多个领域专家提取的**知识库**，并包含一个在其之上执行推理的**推理引擎**。 -![人类架构](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.zh.png) | ![基于知识的系统架构](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.zh.png) +![人类架构](../../../../translated_images/zh/arch-human.5d4d35f1bba3ab1c.png) | ![基于知识的系统架构](../../../../translated_images/zh/arch-kbs.3ec5c150b09fa8da.png) ----------------------------------|---------------------------------------- 人类神经系统的简化结构 | 基于知识的系统的架构 @@ -106,7 +106,7 @@ Python | 块语法 | 缩进 例如，让我们考虑以下基于动物物理特征的专家系统： -![AND-OR 树](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.zh.png) +![AND-OR 树](../../../../translated_images/zh/AND-OR-Tree.5592d2c70187f283.png) > 图片来源：[Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md index cda9ffc1..87d9eefd 100644 --- a/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/zh/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -58,7 +58,7 @@ CO_OP_TRANSLATOR_METADATA: 考虑以下拟合 5 个点的问题（图中的 `x` 表示点）： -![线性模型](../../../../../translated_images/overfit1.f24b71c6f652e59e.zh.jpg) | ![过拟合模型](../../../../../translated_images/overfit2.131f5800ae10ca5e.zh.jpg) +![线性模型](../../../../../translated_images/zh/overfit1.f24b71c6f652e59e.jpg) | ![过拟合模型](../../../../../translated_images/zh/overfit2.131f5800ae10ca5e.jpg) -------------------------|-------------------------- **线性模型，2 个参数** | **非线性模型，7 个参数** 训练误差 = 5.3 | 训练误差 = 0 @@ -79,7 +79,7 @@ CO_OP_TRANSLATOR_METADATA: 从上图可以看出，过拟合可以通过非常低的训练误差和非常高的验证误差来检测。通常在训练过程中，我们会看到训练误差和验证误差都开始下降，但在某个点之后，验证误差可能停止下降并开始上升。这是过拟合的信号，表明我们可能应该停止训练（或者至少保存模型的快照）。 -![过拟合](../../../../../translated_images/Overfitting.408ad91cd90b4371.zh.png) +![过拟合](../../../../../translated_images/zh/Overfitting.408ad91cd90b4371.png) ## 如何防止过拟合 diff --git a/translations/zh/lessons/3-NeuralNetworks/README.md b/translations/zh/lessons/3-NeuralNetworks/README.md index 8e9dc573..401512d4 100644 --- a/translations/zh/lessons/3-NeuralNetworks/README.md +++ b/translations/zh/lessons/3-NeuralNetworks/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 神经网络简介 -![神经网络内容总结涂鸦](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.zh.png) +![神经网络内容总结涂鸦](../../../../translated_images/zh/ai-neuralnetworks.1c687ae40bc86e83.png) 正如我们在介绍中讨论的那样，实现智能的一种方法是训练一个**计算机模型**或一个**人工大脑**。自20世纪中期以来，研究人员尝试了各种数学模型，直到近年来，这一方向取得了巨大的成功。这些大脑的数学模型被称为**神经网络**。 @@ -36,13 +36,13 @@ CO_OP_TRANSLATOR_METADATA: 从生物学中我们知道，大脑由神经细胞（神经元）组成，每个神经元有多个“输入”（树突）和一个“输出”（轴突）。树突和轴突都可以传导电信号，它们之间的连接——称为突触——可以表现出不同程度的导电性，这种导电性由神经递质调节。 -![神经元模型](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.zh.jpg) | ![神经元模型](../../../../translated_images/artneuron.1a5daa88d20ebe6f.zh.png) +![神经元模型](../../../../translated_images/zh/synapse-wikipedia.ed20a9e4726ea1c6.jpg) | ![神经元模型](../../../../translated_images/zh/artneuron.1a5daa88d20ebe6f.png) ----|---- 真实神经元 *（[图片](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg)来自维基百科）* | 人工神经元 *（作者提供图片）* 因此，神经元的最简单数学模型包含若干输入 X1, ..., XN 和一个输出 Y，以及一系列权重 W1, ..., WN。输出的计算公式为： -Y = f\left(\sum_{i=1}^N X_iW_i\right) +Y = f\left(\sum_{i=1}^N X_iW_i\right) 其中 f 是某种非线性的**激活函数**。 diff --git a/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md index 44f5cb57..dd3d8b74 100644 --- a/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/zh/lessons/4-ComputerVision/06-IntroCV/README.md @@ -73,14 +73,14 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) * **对盲文书籍的照片进行预处理**。我们重点介绍如何使用阈值处理、特征检测、透视变换和NumPy操作来分离单个盲文符号，以便神经网络进一步分类。 -![盲文图像](../../../../../translated_images/braille.341962ff76b1bd70.zh.jpeg) | ![盲文图像预处理结果](../../../../../translated_images/braille-result.46530fea020b03c7.zh.png) | ![盲文符号](../../../../../translated_images/braille-symbols.0159185ab69d5339.zh.png) +![盲文图像](../../../../../translated_images/zh/braille.341962ff76b1bd70.jpeg) | ![盲文图像预处理结果](../../../../../translated_images/zh/braille-result.46530fea020b03c7.png) | ![盲文符号](../../../../../translated_images/zh/braille-symbols.0159185ab69d5339.png) ----|-----|----- > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) * **使用帧差检测视频中的运动**。如果摄像机是固定的，那么摄像机画面中的帧应该彼此非常相似。由于帧被表示为数组，只需对两个连续帧的数组进行减法运算，就可以得到像素差异，对于静态帧来说差异应该很小，而当图像中有显著运动时差异会变大。 -![视频帧和帧差异图像](../../../../../translated_images/frame-difference.706f805491a0883c.zh.png) +![视频帧和帧差异图像](../../../../../translated_images/zh/frame-difference.706f805491a0883c.png) > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) @@ -89,7 +89,7 @@ im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) - **密集光流**计算每个像素的运动向量场。 - **稀疏光流**基于图像中的一些显著特征（例如边缘），并从帧到帧构建它们的轨迹。 -![光流图像](../../../../../translated_images/optical.1f4a94464579a83a.zh.png) +![光流图像](../../../../../translated_images/zh/optical.1f4a94464579a83a.png) > 图片来源：[OpenCV.ipynb](OpenCV.ipynb) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 8b4a029f..8b7d7a1a 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -13,11 +13,11 @@ CO_OP_TRANSLATOR_METADATA: VGG-16是一种网络，在2014年的ImageNet top-5分类中达到了92.7%的准确率。它的层结构如下： -![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.zh.jpg) +![ImageNet Layers](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.jpg) 如图所示，VGG采用了传统的金字塔架构，即一系列卷积-池化层的组合。 -![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.zh.jpg) +![ImageNet Pyramid](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.jpg) > 图片来源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md index b87bdcd4..f7629b67 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/README.md @@ -17,7 +17,7 @@ CO_OP_TRANSLATOR_METADATA: 为了提取模式，我们将使用**卷积滤波器**的概念。正如你所知，图像可以用二维矩阵或带有颜色深度的三维张量来表示。应用滤波器意味着我们使用一个相对较小的**滤波核**矩阵，并对原始图像中的每个像素与其邻近点进行加权平均计算。我们可以将其视为一个小窗口在整个图像上滑动，并根据滤波核矩阵中的权重对所有像素进行平均。 -![垂直边缘滤波器](../../../../../translated_images/filter-vert.b7148390ca0bc356.zh.png) | ![水平边缘滤波器](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.zh.png) +![垂直边缘滤波器](../../../../../translated_images/zh/filter-vert.b7148390ca0bc356.png) | ![水平边缘滤波器](../../../../../translated_images/zh/filter-horiz.59b80ed4feb946ef.png) ----|---- > 图片来源：Dmitry Soshnikov @@ -38,7 +38,7 @@ CNN 的工作方式基于以下重要思想： * 我们可以设计网络，使滤波器能够自动训练 * 我们可以使用相同的方法来发现高级特征中的模式，而不仅仅是原始图像中的模式。因此，CNN 的特征提取在特征的层次结构中工作，从低级像素组合开始，到更高级的图像部分组合。 -![层次特征提取](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.zh.png) +![层次特征提取](../../../../../translated_images/zh/FeatureExtractionCNN.d9b456cbdae7cb64.png) > 图片来源：[Hislop-Lynch 的论文](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d)，基于[他们的研究](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) @@ -55,9 +55,9 @@ CNN 的工作方式基于以下重要思想： 例如，让我们看看 VGG-16 的架构，这是一种在 2014 年 ImageNet 的 top-5 分类中实现了 92.7% 准确率的网络： -![ImageNet 层次结构](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.zh.jpg) +![ImageNet 层次结构](../../../../../translated_images/zh/vgg-16-arch1.d901a5583b3a51ba.jpg) -![ImageNet 金字塔](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.zh.jpg) +![ImageNet 金字塔](../../../../../translated_images/zh/vgg-16-arch.64ff2137f50dd49f.jpg) > 图片来源：[Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) diff --git a/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md index f0c696b2..174d0287 100644 --- a/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/zh/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: 我们将使用 [Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/)，该数据集包含37种不同品种的猫和狗的图像。 -![我们将处理的数据集](../../../../../../translated_images/data.50b2a9d5484bdbf0.zh.png) +![我们将处理的数据集](../../../../../../translated_images/zh/data.50b2a9d5484bdbf0.png) 要下载数据集，请使用以下代码片段： diff --git a/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md index f283aa31..f31e6a14 100644 --- a/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/zh/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -29,7 +29,7 @@ Keras 和 PyTorch 都包含函数，可以轻松加载一些常见架构的预 以下是 VGG-16 网络从一张猫的图片中提取的示例特征： -![VGG-16 提取的特征](../../../../../translated_images/features.6291f9c7ba3a0b95.zh.png) +![VGG-16 提取的特征](../../../../../translated_images/zh/features.6291f9c7ba3a0b95.png) ## 猫与狗数据集 @@ -48,19 +48,19 @@ Keras 和 PyTorch 都包含函数，可以轻松加载一些常见架构的预 我们可以采取的一种方法是从一个随机图像开始，然后尝试使用**梯度下降优化**技术调整该图像，使网络认为它是一只猫。 -![图像优化循环](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.zh.png) +![图像优化循环](../../../../../translated_images/zh/ideal-cat-loop.999fbb8ff306e044.png) 然而，如果我们这样做，我们会得到一些非常类似于随机噪声的东西。这是因为*有很多方法可以让网络认为输入图像是一只猫*，包括一些在视觉上没有意义的方式。虽然这些图像包含了许多典型的猫的模式，但没有任何约束使它们在视觉上具有辨识度。 为了改善结果，我们可以在损失函数中添加另一个项，称为**变化损失**。它是一种度量，显示图像中相邻像素的相似程度。最小化变化损失可以使图像更平滑，并消除噪声，从而揭示更具视觉吸引力的模式。以下是一些这样的“理想”图像示例，它们被高概率分类为猫和斑马： -![理想猫](../../../../../translated_images/ideal-cat.203dd4597643d6b0.zh.png) | ![理想斑马](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.zh.png) +![理想猫](../../../../../translated_images/zh/ideal-cat.203dd4597643d6b0.png) | ![理想斑马](../../../../../translated_images/zh/ideal-zebra.7f70e8b54ee15a7a.png) -----|----- *理想猫* | *理想斑马* 类似的方法可以用来对神经网络进行所谓的**对抗性攻击**。假设我们想欺骗一个神经网络，让一只狗看起来像一只猫。如果我们拿一张被网络识别为狗的狗的图片，然后稍微调整它，直到网络开始将其分类为猫： -![狗的图片](../../../../../translated_images/original-dog.8f68a67d2fe0911f.zh.png) | ![被分类为猫的狗的图片](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89.zh.png) +![狗的图片](../../../../../translated_images/zh/original-dog.8f68a67d2fe0911f.png) | ![被分类为猫的狗的图片](../../../../../translated_images/zh/adversarial-dog.d9fc7773b0142b89.png) -----|----- *狗的原始图片* | *被分类为猫的狗的图片* diff --git a/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md index 91f9444e..42a90876 100644 --- a/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/zh/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 由于我们训练自动编码器的目标是尽可能捕捉原始图像的信息以实现准确的重建，网络会尝试找到输入图像的最佳**嵌入**以捕捉其意义。 -![自动编码器示意图](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.zh.jpg) +![自动编码器示意图](../../../../../translated_images/zh/autoencoder_schema.5e6fc9ad98a5eb61.jpg) > 图片来源：[Keras 博客](https://blog.keras.io/building-autoencoders-in-keras.html) diff --git a/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md index 8833d4c4..384941ec 100644 --- a/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/zh/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ## [课前测验](https://ff-quizzes.netlify.app/en/ai/quiz/21) -![目标检测](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.zh.png) +![目标检测](../../../../../translated_images/zh/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.png) > 图片来源：[YOLO v2 网站](https://pjreddie.com/darknet/yolov2/) @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 2. 对每个小块进行图像分类。 3. 对于分类结果激活值足够高的小块，可以认为其中包含目标物体。 -![简单目标检测](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.zh.png) +![简单目标检测](../../../../../translated_images/zh/naive-detection.e7f1ba220ccd08c6.png) > *图片来源：[练习笔记本](ObjectDetection-TF.ipynb)* @@ -42,7 +42,7 @@ CO_OP_TRANSLATOR_METADATA: * [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) - 包含20个类别 * [COCO](http://cocodataset.org/#home) - 常见物体上下文数据集。包含80个类别、边界框和分割掩码 -![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.zh.jpg) +![COCO](../../../../../translated_images/zh/coco-examples.71bc60380fa6cceb.jpg) ## 目标检测评估指标 @@ -50,7 +50,7 @@ CO_OP_TRANSLATOR_METADATA: 对于图像分类来说，评估算法性能相对简单；但对于目标检测，我们需要同时评估类别的正确性以及推断出的边界框位置的精确性。后者通常使用**交并比**（IoU）来衡量两个框（或任意两个区域）的重叠程度。 -![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.zh.png) +![IoU](../../../../../translated_images/zh/iou_equation.9a4751d40fff4e11.png) > *图片来源：[这篇关于IoU的优秀博客文章](https://pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/)* @@ -98,11 +98,11 @@ $$ [R-CNN](http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf)使用[选择性搜索](http://www.huppelen.nl/publications/selectiveSearchDraft.pdf)生成ROI区域的层次结构，然后通过CNN特征提取器和SVM分类器确定物体类别，并通过线性回归确定*边界框*坐标。[官方论文](https://arxiv.org/pdf/1506.01497v1.pdf) -![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.zh.png) +![RCNN](../../../../../translated_images/zh/rcnn1.cae407020dfb1d1f.png) > *图片来源：van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.zh.png) +![RCNN-1](../../../../../translated_images/zh/rcnn2.2d9530bb83516484.png) > *图片来源：[这篇博客](https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e)* @@ -110,7 +110,7 @@ $$ 这种方法与R-CNN类似，但区域是在应用卷积层之后定义的。 -![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.zh.png) +![FRCNN](../../../../../translated_images/zh/f-rcnn.3cda6d9bb4188875.png) > 图片来源：[官方论文](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)，[arXiv](https://arxiv.org/pdf/1504.08083.pdf)，2015 @@ -118,7 +118,7 @@ $$ 这种方法的核心思想是使用神经网络预测ROI——即所谓的*区域提议网络*。[论文](https://arxiv.org/pdf/1506.01497.pdf)，2016 -![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.zh.png) +![FasterRCNN](../../../../../translated_images/zh/faster-rcnn.8d46c099b87ef30a.png) > 图片来源：[官方论文](https://arxiv.org/pdf/1506.01497.pdf) @@ -130,7 +130,7 @@ $$ 2. 特征通过**位置敏感得分图**处理。每个类别$C$的物体被划分为$k\times k$区域，并训练预测物体的各部分。 3. 对于$k\times k$区域中的每个部分，所有网络对物体类别进行投票，选择投票最多的类别。 -![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.zh.png) +![r-fcn image](../../../../../translated_images/zh/r-fcn.13eb88158b99a3da.png) > 图片来源：[官方论文](https://arxiv.org/abs/1605.06409) @@ -141,7 +141,7 @@ YOLO是一种实时单次检测算法。其核心思想如下： * 将图像划分为$S\times S$区域。 * 对每个区域，**CNN**预测$n$个可能的物体、*边界框*坐标和*置信度*=*概率* * IoU。 - ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.zh.png) + ![YOLO](../../../../../translated_images/zh/yolo.a2648ec82ee8bb4e.png) > 图片来源：[官方论文](https://arxiv.org/abs/1506.02640) diff --git a/translations/zh/lessons/4-ComputerVision/README.md b/translations/zh/lessons/4-ComputerVision/README.md index 68faae8f..c5695ad0 100644 --- a/translations/zh/lessons/4-ComputerVision/README.md +++ b/translations/zh/lessons/4-ComputerVision/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 计算机视觉 -![计算机视觉内容总结涂鸦](../../../../translated_images/ai-computervision.6506ebebac3fbf76.zh.png) +![计算机视觉内容总结涂鸦](../../../../translated_images/zh/ai-computervision.6506ebebac3fbf76.png) 在本节中，我们将学习以下内容： diff --git a/translations/zh/lessons/5-NLP/14-Embeddings/README.md b/translations/zh/lessons/5-NLP/14-Embeddings/README.md index 24f25667..d56ecc00 100644 --- a/translations/zh/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/zh/lessons/5-NLP/14-Embeddings/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 通过在分类器网络中使用嵌入层作为第一层，我们可以从词袋模型切换到 **嵌入袋** 模型。在嵌入袋模型中，我们首先将文本中的每个单词转换为对应的嵌入，然后对所有这些嵌入计算某种聚合函数，例如 `sum`、`average` 或 `max`。 -![展示五个序列单词的嵌入分类器的图片。](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.zh.png) +![展示五个序列单词的嵌入分类器的图片。](../../../../../translated_images/zh/embedding-classifier-example.b77f021a7ee67eee.png) > 图片由作者提供 @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: CBoW 速度更快，而 Skip-Gram 虽然较慢，但在表示不常见单词方面表现更好。 -![展示 CBoW 和 Skip-Gram 算法将单词转换为向量的图片。](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.zh.png) +![展示 CBoW 和 Skip-Gram 算法将单词转换为向量的图片。](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 图片来源于 [这篇论文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md b/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md index 608c7134..7786ce46 100644 --- a/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/zh/lessons/5-NLP/15-LanguageModeling/README.md @@ -23,7 +23,7 @@ CO_OP_TRANSLATOR_METADATA: * **连续词袋模型** (CBoW)，通过预测标记序列 $W_{-N}$, ..., $W_N$ 中的中间标记 $W_0$。 * **Skip-gram**，通过中间标记 $W_0$ 来预测一组邻近标记 {$W_{-N},\dots, W_{-1}, W_1,\dots, W_N$}。 -![论文中关于将单词转换为向量的算法示例](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.zh.png) +![论文中关于将单词转换为向量的算法示例](../../../../../translated_images/zh/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.png) > 图片来源于[这篇论文](https://arxiv.org/pdf/1301.3781.pdf) diff --git a/translations/zh/lessons/5-NLP/16-RNN/README.md b/translations/zh/lessons/5-NLP/16-RNN/README.md index 1916b47e..d2e5e5ea 100644 --- a/translations/zh/lessons/5-NLP/16-RNN/README.md +++ b/translations/zh/lessons/5-NLP/16-RNN/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 为了捕捉文本序列的意义，我们需要使用另一种神经网络架构，称为**循环神经网络**（Recurrent Neural Network，RNN）。在 RNN 中，我们将句子逐个符号输入网络，网络会生成某种**状态**，然后将该状态与下一个符号一起再次输入网络。 -![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.zh.png) +![RNN](../../../../../translated_images/zh/rnn.27f5c29c53d727b5.png) > 图片由作者提供 @@ -61,7 +61,7 @@ LSTM 网络的组织方式与 RNN 类似，但有两个状态会从层到层传 循环网络，无论是单向还是双向，都能捕捉序列中的某些模式，并将其存储到状态向量中或传递到输出中。与卷积网络类似，我们可以在第一层之上构建另一层循环网络，以捕捉更高级的模式，并从第一层提取的低级模式中构建。这引出了**多层 RNN** 的概念，它由两个或更多循环网络组成，其中前一层的输出作为输入传递到下一层。 -![显示多层长短时记忆 RNN 的图片](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.zh.jpg) +![显示多层长短时记忆 RNN 的图片](../../../../../translated_images/zh/multi-layer-lstm.dd975e29bb2a59fe.jpg) *图片来自 Fernando López 的[这篇精彩文章](https://towardsdatascience.com/from-a-lstm-cell-to-a-multilayer-lstm-network-with-pytorch-2899eb5696f3)* diff --git a/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md index 611b0251..9ef1d2c3 100644 --- a/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/zh/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -19,7 +19,7 @@ CO_OP_TRANSLATOR_METADATA: 这使得可以构建不同的神经网络架构，如下图所示： -![展示常见循环神经网络模式的图片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.zh.jpg) +![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > 图片来源于 [Andrej Karpaty](http://karpathy.github.io/) 的博客文章 [循环神经网络的非凡有效性](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) @@ -32,7 +32,7 @@ CO_OP_TRANSLATOR_METADATA: 我们将训练这个 RNN 逐步生成文本。在每一步中，我们将取长度为 `nchars` 的字符序列，并要求网络为每个输入字符生成下一个输出字符： -![展示 RNN 生成单词 'HELLO' 的示例图片。](../../../../../translated_images/rnn-generate.56c54afb52f9781d.zh.png) +![展示 RNN 生成单词 'HELLO' 的示例图片。](../../../../../translated_images/zh/rnn-generate.56c54afb52f9781d.png) 在生成文本（推理阶段）时，我们从某个**提示**开始，将其传递给 RNN 单元以生成中间状态，然后从该状态开始生成。我们一次生成一个字符，并将状态和生成的字符传递给另一个 RNN 单元以生成下一个字符，直到生成足够的字符。 diff --git a/translations/zh/lessons/5-NLP/18-Transformers/README.md b/translations/zh/lessons/5-NLP/18-Transformers/README.md index 75aaf6e2..be8f3a7f 100644 --- a/translations/zh/lessons/5-NLP/18-Transformers/README.md +++ b/translations/zh/lessons/5-NLP/18-Transformers/README.md @@ -20,13 +20,13 @@ CO_OP_TRANSLATOR_METADATA: **注意机制**提供了一种方法，可以对每个输入向量对RNN输出预测的上下文影响进行加权。其实现方式是创建输入RNN的中间状态与输出RNN之间的快捷路径。这样，在生成输出符号yt时，我们会考虑所有输入隐藏状态hi，并赋予不同的权重系数αt,i。 -![显示带有加性注意层的编码器/解码器模型的图像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.zh.png) +![显示带有加性注意层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.png) > [Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)中的加性注意机制编码器-解码器模型，图片来源于[这篇博客](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意矩阵{αi,j}表示某些输入词在生成输出序列中某个词时的影响程度。下面是一个这样的矩阵示例： -![显示由RNNsearch-50找到的样本对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.zh.png) +![显示由RNNsearch-50找到的样本对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.png) > 图片来自[Bahdanau等人，2015](https://arxiv.org/pdf/1409.0473.pdf)（图3） @@ -66,7 +66,7 @@ Transformer的核心思想之一是避免RNN的顺序处理特性，并创建一 接下来，我们需要捕捉序列中的一些模式。为此，Transformer使用了**自注意机制**，即将注意机制同时应用于输入和输出的同一序列。应用自注意机制使我们能够考虑句子中的**上下文**，并查看哪些词是相互关联的。例如，它可以帮助我们识别代词*it*所指代的词，并考虑上下文： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.zh.png) +![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.png) > 图片来源于[谷歌博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -91,7 +91,7 @@ Transformer的核心思想之一是避免RNN的顺序处理特性，并创建一 **BERT**（Bidirectional Encoder Representations from Transformers，双向编码器表示）是一个非常大的多层Transformer网络，*BERT-base*有12层，*BERT-large*有24层。模型首先在大规模文本数据（维基百科+书籍）上进行无监督训练（预测句子中的被遮蔽词）。在预训练过程中，模型吸收了大量的语言理解能力，这些能力可以通过微调其他数据集来利用。这一过程称为**迁移学习**。 -![图片来源于http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.zh.png) +![图片来源于http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 图片[来源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md index 2e5060dd..9da91135 100644 --- a/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ b/translations/zh/lessons/5-NLP/18-Transformers/READMEtransformers.md @@ -11,13 +11,13 @@ **注意力机制**提供了一种对每个输入向量在RNN每个输出预测中的上下文影响进行加权的方法。它的实现方式是创建输入RNN和输出RNN之间的中间状态的快捷方式。通过这种方式，在生成输出符号 yt 时，我们将考虑所有输入隐藏状态 hi，并使用不同的权重系数 αt,i。 -![展示带有加性注意力层的编码器/解码器模型的图像](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.zh.png) +![展示带有加性注意力层的编码器/解码器模型的图像](../../../../../translated_images/zh/encoder-decoder-attention.7a726296894fb567.png) > 该编码器-解码器模型与加性注意力机制见于 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf)，引用自 [这篇博客文章](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) 注意力矩阵 {αi,j} 表示某些输入单词在生成输出序列中给定单词的程度。以下是这样一个矩阵的示例： -![展示由RNNsearch-50找到的示例对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.zh.png) +![展示由RNNsearch-50找到的示例对齐的图像，取自Bahdanau - arviz.org](../../../../../translated_images/zh/bahdanau-fig3.09ba2d37f202a6af.png) > 图自 [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (图3) @@ -57,7 +57,7 @@ 接下来，我们需要捕捉序列中的一些模式。为此，变换器使用**自注意力**机制，这本质上是对同一序列应用的注意力。应用自注意力使我们能够考虑句子中的**上下文**，并查看哪些单词是相互关联的。例如，它使我们能够看到哪些单词是由指代词（如 *它*）引用的，并且还考虑上下文： -![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.zh.png) +![](../../../../../translated_images/zh/CoreferenceResolution.861924d6d384a7d6.png) > 图自 [Google博客](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) @@ -82,7 +82,7 @@ **BERT**（来自变换器的双向编码器表示）是一个非常大的多层变换器网络，其中 *BERT-base* 有12层，*BERT-large* 有24层。该模型首先在一个大型文本数据集（维基百科 + 书籍）上进行预训练，采用无监督训练（预测句子中被屏蔽的单词）。在预训练期间，模型吸收了显著的语言理解能力，这可以通过微调与其他数据集结合使用。这个过程被称为**迁移学习**。 -![图片来自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.zh.png) +![图片来自 http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/zh/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.png) > 图片 [来源](http://jalammar.github.io/illustrated-bert/) diff --git a/translations/zh/lessons/5-NLP/19-NER/README.md b/translations/zh/lessons/5-NLP/19-NER/README.md index 73aad397..6e9f0379 100644 --- a/translations/zh/lessons/5-NLP/19-NER/README.md +++ b/translations/zh/lessons/5-NLP/19-NER/README.md @@ -58,7 +58,7 @@ NER模型本质上是**标记分类模型**，因为对于每个输入标记， 由于我们需要在标记和类别之间建立一一对应关系，我们可以从下图中训练一个最右侧的**多对多**神经网络模型： -![展示常见循环神经网络模式的图片。](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.zh.jpg) +![展示常见循环神经网络模式的图片。](../../../../../translated_images/zh/unreasonable-effectiveness-of-rnn.541ead816778f42d.jpg) > *图片来自[这篇博客文章](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)作者为[Andrej Karpathy](http://karpathy.github.io/)。NER标记分类模型对应于此图片中最右侧的网络架构。* diff --git a/translations/zh/lessons/5-NLP/README.md b/translations/zh/lessons/5-NLP/README.md index 9f45ae39..40be79d3 100644 --- a/translations/zh/lessons/5-NLP/README.md +++ b/translations/zh/lessons/5-NLP/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 自然语言处理 -![NLP任务总结图](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.zh.png) +![NLP任务总结图](../../../../translated_images/zh/ai-nlp.b22dcb8ca4707cea.png) 在本节中，我们将重点使用神经网络来处理与**自然语言处理 (NLP)**相关的任务。我们希望计算机能够解决许多NLP问题： diff --git a/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md b/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md index eb48258b..9284de18 100644 --- a/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/zh/lessons/6-Other/23-MultiagentSystems/README.md @@ -71,7 +71,7 @@ NetLogo的一个优点是它包含一个可供尝试的工作模型库。进入* 打开模型后，你会进入NetLogo的主界面。以下是一个描述狼和羊种群的示例模型，考虑到有限资源（草地）。 -![NetLogo主界面](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.zh.png) +![NetLogo主界面](../../../../../translated_images/zh/NetLogo-Main.32653711ec1a01b3.png) > Dmitry Soshnikov提供的截图 diff --git a/translations/zh/lessons/README.md b/translations/zh/lessons/README.md index 7575b4fc..e03fe13f 100644 --- a/translations/zh/lessons/README.md +++ b/translations/zh/lessons/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 概述 -![概述的手绘图](../../../translated_images/ai-overview.0857791951d19500.zh.png) +![概述的手绘图](../../../translated_images/zh/ai-overview.0857791951d19500.png) > 手绘笔记由 [Tomomi Imura](https://twitter.com/girlie_mac) 提供 diff --git a/translations/zh/lessons/X-Extras/X1-MultiModal/README.md b/translations/zh/lessons/X-Extras/X1-MultiModal/README.md index 1602731f..a4e5e4ea 100644 --- a/translations/zh/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/zh/lessons/X-Extras/X1-MultiModal/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: CLIP的核心思想是能够比较文本提示与图像，并确定图像与提示的匹配程度。 -![CLIP架构](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.zh.png) +![CLIP架构](../../../../../translated_images/zh/clip-arch.b3dbf20b4e8ed8be.png) > *图片来源于[这篇博客](https://openai.com/blog/clip/)* @@ -29,7 +29,7 @@ CLIP模型/库可以从[OpenAI GitHub](https://github.com/openai/CLIP)获取。 假设我们需要将图像分类为猫、狗和人类。在这种情况下，我们可以给模型一个图像，以及一系列文本提示：“*一张猫的图片*”、“*一张狗的图片*”、“*一张人类的图片*”。在结果的3个概率向量中，我们只需选择值最高的索引。 -![CLIP用于图像分类](../../../../../translated_images/clip-class.3af42ef0b2b19369.zh.png) +![CLIP用于图像分类](../../../../../translated_images/zh/clip-class.3af42ef0b2b19369.png) > *图片来源于[这篇博客](https://openai.com/blog/clip/)* @@ -53,13 +53,13 @@ VQGAN与普通[GAN](../../4-ComputerVision/10-GANs/README.md)的主要区别在 VQGAN与传统GAN的一个重要区别在于，后者可以从任何输入向量生成一个不错的图像，而VQGAN可能生成一个不连贯的图像。因此，我们需要进一步引导图像创建过程，这可以通过CLIP来实现。 -![VQGAN+CLIP架构](../../../../../translated_images/vqgan.5027fe05051dfa31.zh.png) +![VQGAN+CLIP架构](../../../../../translated_images/zh/vqgan.5027fe05051dfa31.png) 为了生成与文本提示相对应的图像，我们从一些随机编码向量开始，将其传递给VQGAN以生成图像。然后使用CLIP生成一个损失函数，显示图像与文本提示的匹配程度。目标是通过反向传播调整输入向量参数以最小化该损失。 一个实现VQGAN+CLIP的优秀库是[Pixray](http://github.com/pixray/pixray)。 -![Pixray生成的图片](../../../../../translated_images/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.zh.png) | ![Pixray生成的图片](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.zh.png) | ![Pixray生成的图片](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.zh.png) +![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_watercolor_portrait_of_young_male_teacher_of_literature_with_a_book.2384968e9db8a0d0.png) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a4390.png) | ![Pixray生成的图片](../../../../../translated_images/zh/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683.png) ----|----|---- 从提示*一张年轻男性文学教师拿着书的水彩特写肖像*生成的图片 | 从提示*一张年轻女性计算机科学教师拿着电脑的油画特写肖像*生成的图片 | 从提示*一张老年男性数学教师站在黑板前的油画特写肖像*生成的图片 @@ -75,7 +75,7 @@ DALL-E是一个基于GPT-3的版本，能够根据提示生成图像。它使用 DALL-E 1和2的主要区别在于，后者能够生成更真实的图像和艺术作品。 以下是使用DALL-E生成图像的示例： -![DALL-E生成的图片](../../../../../translated_images/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.zh.png) | ![DALL-E生成的图片](../../../../../translated_images/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.zh.png) | ![DALL-E生成的图片](../../../../../translated_images/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.zh.png) +![DALL-E生成的图片](../../../../../translated_images/zh/DALL·E%202023-06-20%2015.56.56%20-%20a%20closeup%20watercolor%20portrait%20of%20young%20male%20teacher%20of%20literature%20with%20a%20book.6c235e8271d9ed10ce985d86aeb241a58518958647973af136912116b9518fce.png) | ![DALL-E生成的图片](../../../../../translated_images/zh/DALL·E%202023-06-20%2015.57.43%20-%20a%20closeup%20oil%20portrait%20of%20young%20female%20teacher%20of%20computer%20science%20with%20a%20computer.f21dc4166340b6c8b4d1cb57efd1e22127407f9b28c9ac7afe11344065369e64.png) | ![DALL-E生成的图片](../../../../../translated_images/zh/DALL·E%202023-06-20%2015.58.42%20-%20%20a%20closeup%20oil%20portrait%20of%20old%20male%20teacher%20of%20mathematics%20in%20front%20of%20blackboard.d331c2dfbdc3f7c46aa65c0809066f5e7ed4b49609cd259852e760df21051e4a.png) ----|----|---- 从提示*一张年轻男性文学教师拿着书的水彩特写肖像*生成的图片 | 从提示*一张年轻女性计算机科学教师拿着电脑的油画特写肖像*生成的图片 | 从提示*一张老年男性数学教师站在黑板前的油画特写肖像*生成的图片