From a15b0aace0624c994c23f5e863b9a3b48b101a96 Mon Sep 17 00:00:00 2001 From: "localizeflow[bot]" Date: Thu, 1 Jan 2026 14:20:55 +0000 Subject: [PATCH] chore(i18n): sync translations with latest source changes (chunk 71/148, 100 files) --- .../13-TextRep/TextRepresentationTF.ipynb | 2 +- .../14-Embeddings/EmbeddingsPyTorch.ipynb | 6 +- .../5-NLP/14-Embeddings/EmbeddingsTF.ipynb | 4 +- .../id/lessons/5-NLP/14-Embeddings/README.md | 4 +- .../5-NLP/15-LanguageModeling/README.md | 2 +- .../id/lessons/5-NLP/16-RNN/README.md | 4 +- .../id/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb | 2 +- .../id/lessons/5-NLP/16-RNN/RNNTF.ipynb | 4 +- .../GenerativePyTorch.ipynb | 2 +- .../17-GenerativeNetworks/GenerativeTF.ipynb | 2 +- .../5-NLP/17-GenerativeNetworks/README.md | 4 +- .../lessons/5-NLP/18-Transformers/README.md | 8 +- .../18-Transformers/TransformersPyTorch.ipynb | 6 +- .../18-Transformers/TransformersTF.ipynb | 6 +- .../id/lessons/5-NLP/19-NER/README.md | 2 +- 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.../09-Autoencoders/README.md | 2 +- .../11-ObjectDetection/README.md | 20 +-- .../it/lessons/4-ComputerVision/README.md | 2 +- .../TextRepresentationPyTorch.ipynb | 2 +- .../13-TextRep/TextRepresentationTF.ipynb | 2 +- .../14-Embeddings/EmbeddingsPyTorch.ipynb | 6 +- .../5-NLP/14-Embeddings/EmbeddingsTF.ipynb | 4 +- .../it/lessons/5-NLP/14-Embeddings/README.md | 4 +- .../5-NLP/15-LanguageModeling/README.md | 2 +- .../it/lessons/5-NLP/16-RNN/README.md | 4 +- .../it/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb | 2 +- .../it/lessons/5-NLP/16-RNN/RNNTF.ipynb | 4 +- .../GenerativePyTorch.ipynb | 2 +- .../17-GenerativeNetworks/GenerativeTF.ipynb | 2 +- .../5-NLP/17-GenerativeNetworks/README.md | 4 +- .../lessons/5-NLP/18-Transformers/README.md | 8 +- .../18-Transformers/READMEtransformers.md | 8 +- .../18-Transformers/TransformersPyTorch.ipynb | 6 +- .../18-Transformers/TransformersTF.ipynb | 6 +- .../it/lessons/5-NLP/19-NER/README.md | 2 +- translations/it/lessons/5-NLP/README.md | 2 +- .../6-Other/23-MultiagentSystems/README.md | 2 +- translations/it/lessons/README.md | 2 +- .../lessons/X-Extras/X1-MultiModal/README.md | 8 +- translations/ja/README.md | 120 +++++++------- translations/ja/lessons/1-Intro/README.md | 10 +- .../ja/lessons/2-Symbolic/Animals.ipynb | 2 +- translations/ja/lessons/2-Symbolic/README.md | 8 +- .../04-OwnFramework/OwnFramework.ipynb | 2 +- .../3-NeuralNetworks/05-Frameworks/README.md | 4 +- .../ja/lessons/3-NeuralNetworks/README.md | 6 +- .../4-ComputerVision/06-IntroCV/README.md | 6 +- .../07-ConvNets/CNN_Architectures.md | 4 +- .../07-ConvNets/ConvNetsPyTorch.ipynb | 2 +- .../07-ConvNets/ConvNetsTF.ipynb | 2 +- .../4-ComputerVision/07-ConvNets/README.md | 8 +- .../07-ConvNets/lab/README.md | 2 +- .../AdversarialCat_TF.ipynb | 2 +- .../08-TransferLearning/README.md | 8 +- .../09-Autoencoders/AutoEncodersPyTorch.ipynb | 2 +- .../09-Autoencoders/AutoencodersTF.ipynb | 2 +- .../09-Autoencoders/README.md | 2 +- .../11-ObjectDetection/README.md | 20 +-- .../ja/lessons/4-ComputerVision/README.md | 2 +- .../TextRepresentationPyTorch.ipynb | 2 +- .../13-TextRep/TextRepresentationTF.ipynb | 2 +- .../14-Embeddings/EmbeddingsPyTorch.ipynb | 6 +- .../5-NLP/14-Embeddings/EmbeddingsTF.ipynb | 4 +- .../ja/lessons/5-NLP/14-Embeddings/README.md | 4 +- .../5-NLP/15-LanguageModeling/README.md | 2 +- .../ja/lessons/5-NLP/16-RNN/README.md | 4 +- .../ja/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb | 2 +- .../ja/lessons/5-NLP/16-RNN/RNNTF.ipynb | 4 +- .../GenerativePyTorch.ipynb | 2 +- .../17-GenerativeNetworks/GenerativeTF.ipynb | 2 +- .../5-NLP/17-GenerativeNetworks/README.md | 4 +- .../lessons/5-NLP/18-Transformers/README.md | 8 +- .../18-Transformers/READMEtransformers.md | 8 +- .../18-Transformers/TransformersPyTorch.ipynb | 6 +- .../18-Transformers/TransformersTF.ipynb | 6 +- .../ja/lessons/5-NLP/19-NER/README.md | 2 +- translations/ja/lessons/5-NLP/README.md | 2 +- .../6-Other/23-MultiagentSystems/README.md | 2 +- translations/ja/lessons/README.md | 2 +- 100 files changed, 344 insertions(+), 344 deletions(-) diff --git a/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb b/translations/id/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb index 15ed4a4a..dcf75b96 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.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.id.png) \n", + "![Gambar yang menunjukkan bagaimana representasi vektor bag-of-words disimpan dalam memori.](../../../../../translated_images/bag-of-words-example.606fc1738f1d7ba9.id.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 67dba07b..e497319c 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.id.png)\n", + "![Gambar yang menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.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.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.id.png)\n", + "![Gambar yang menunjukkan representasi urutan dengan offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.id.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.id.png)\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", "\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 5a9eb783..e6e779d5 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.id.png)\n", + "![Gambar menunjukkan pengklasifikasi embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.id.png)\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", "\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 59b48e2c..07b296e8 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.id.png) +![Gambar menunjukkan classifier embedding untuk lima kata dalam urutan.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.id.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.id.png) +![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 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 b39c2dbf..58c75e4a 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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.id.png) +![gambar dari makalah tentang mengonversi kata menjadi vektor](../../../../../translated_images/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.id.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 09d4b97a..bba487a8 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.id.png) +![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.id.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.id.jpg) +![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.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 2a501752..b04bedfa 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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.id.jpg)\n", + "![Gambar yang menunjukkan Multilayer long-short-term-memory- RNN](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.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 a48d2414..901bad23 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.id.png)\n", + "![Gambar menunjukkan contoh pembuatan jaringan saraf berulang.](../../../../../translated_images/rnn.27f5c29c53d727b5.id.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.id.jpg)\n", + "![Gambar menunjukkan RNN LSTM multilayer](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.id.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 080e8470..b3e04820 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.id.png)\n", + "![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.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 10d8a2c8..e66aec73 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.id.png)\n", + "![Gambar yang menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.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 5d51a652..d0bc5b0c 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.id.jpg) +![Gambar menunjukkan pola umum jaringan neural rekuren.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.id.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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.id.png) +![Gambar menunjukkan contoh RNN menghasilkan kata 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.id.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 dbbd7c12..c9696d3e 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.id.png) +![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.id.png) +![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.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.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.id.png) +![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.id.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.id.png) +![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.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 39e93547..0a83f6bd 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.id.png)\n", + "![Gambar yang menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.id.png)\n", + "![Gambar yang menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.id.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.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 53e0fbe5..49067e77 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.id.png)\n", + "![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.id.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.id.png)\n", + "![Gambar menunjukkan contoh alignment yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.id.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.id.png)\n", + "![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.id.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 7b467b9c..9d2b6ecc 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.id.jpg) +![Gambar menunjukkan pola umum jaringan neural berulang.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.id.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 4db06cd4..4ec79243 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.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.id.png) +![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.id.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 517388e1..d421702a 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.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.id.png) +![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.id.png) > Tangkapan layar oleh Dmitry Soshnikov diff --git a/translations/id/lessons/README.md b/translations/id/lessons/README.md index 9f008186..2f6d1258 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.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.id.png) +![Gambaran Umum dalam sebuah sketsa](../../../translated_images/ai-overview.0857791951d19500.id.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 0338d950..0fa6b0f9 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.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.id.png) +![Arsitektur CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.id.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.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.id.png) +![CLIP untuk Klasifikasi Gambar](../../../../../translated_images/clip-class.3af42ef0b2b19369.id.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.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.id.png) +![Arsitektur VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.id.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.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.id.png) | ![Gambar yang dihasilkan oleh Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.id.png) +![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 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 48e2d445..35c0dce4 100644 --- a/translations/it/README.md +++ b/translations/it/README.md @@ -1,8 +1,8 @@ -[Arabo](../ar/README.md) | [Bengalese](../bn/README.md) | [Bulgaro](../bg/README.md) | [Birmano (Myanmar)](../my/README.md) | [Cinese (semplificato)](../zh/README.md) | [Cinese (tradizionale, Hong Kong)](../hk/README.md) | [Cinese (tradizionale, Macao)](../mo/README.md) | [Cinese (tradizionale, Taiwan)](../tw/README.md) | [Croato](../hr/README.md) | [Ceco](../cs/README.md) | [Danese](../da/README.md) | [Olandese](../nl/README.md) | [Estone](../et/README.md) | [Finlandese](../fi/README.md) | [Francese](../fr/README.md) | [Tedesco](../de/README.md) | [Greco](../el/README.md) | [Ebraico](../he/README.md) | [Hindi](../hi/README.md) | [Ungherese](../hu/README.md) | [Indonesiano](../id/README.md) | [Italiano](./README.md) | [Giapponese](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malese](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalese](../ne/README.md) | [Pidgin nigeriano](../pcm/README.md) | [Norvegese](../no/README.md) | [Persiano (Farsi)](../fa/README.md) | [Polacco](../pl/README.md) | [Portoghese (Brasile)](../br/README.md) | [Portoghese (Portogallo)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Serbo (Cirillico)](../sr/README.md) | [Slovacco](../sk/README.md) | [Sloveno](../sl/README.md) | [Spagnolo](../es/README.md) | [Swahili](../sw/README.md) | [Svedese](../sv/README.md) | [Tagalog (Filippino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thailandese](../th/README.md) | [Turco](../tr/README.md) | [Ucraino](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) +[Arabo](../ar/README.md) | [Bengalese](../bn/README.md) | [Bulgaro](../bg/README.md) | [Birmano (Myanmar)](../my/README.md) | [Cinese (semplificato)](../zh/README.md) | [Cinese (tradizionale, Hong Kong)](../hk/README.md) | [Cinese (tradizionale, Macau)](../mo/README.md) | [Cinese (tradizionale, Taiwan)](../tw/README.md) | [Croato](../hr/README.md) | [Ceco](../cs/README.md) | [Danese](../da/README.md) | [Olandese](../nl/README.md) | [Estone](../et/README.md) | [Finlandese](../fi/README.md) | [Francese](../fr/README.md) | [Tedesco](../de/README.md) | [Greco](../el/README.md) | [Ebraico](../he/README.md) | [Hindi](../hi/README.md) | [Ungherese](../hu/README.md) | [Indonesiano](../id/README.md) | [Italiano](./README.md) | [Giapponese](../ja/README.md) | [Kannada](../kn/README.md) | [Coreano](../ko/README.md) | [Lituano](../lt/README.md) | [Malese](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepalese](../ne/README.md) | [Pidgin nigeriano](../pcm/README.md) | [Norvegese](../no/README.md) | [Persiano (Farsi)](../fa/README.md) | [Polacco](../pl/README.md) | [Portoghese (Brasile)](../br/README.md) | [Portoghese (Portogallo)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romeno](../ro/README.md) | [Russo](../ru/README.md) | [Serbo (Cirillico)](../sr/README.md) | [Slovacco](../sk/README.md) | [Sloveno](../sl/README.md) | [Spagnolo](../es/README.md) | [Swahili](../sw/README.md) | [Svedese](../sv/README.md) | [Tagalog (Filippino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thailandese](../th/README.md) | [Turco](../tr/README.md) | [Ucraino](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamita](../vi/README.md) -**Se desideri avere traduzioni aggiuntive le lingue supportate sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Se desideri avere ulteriori traduzioni le lingue supportate sono elencate [qui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Unisciti alla comunità -[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) +[![Discord di Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) ## Cosa imparerai @@ -48,89 +48,89 @@ Esplora il mondo della **Intelligenza Artificiale** (AI) con il nostro curriculu In questo curriculum imparerai: -* Diversi approcci all'Intelligenza Artificiale, incluso l'approccio simbolico "buono e vecchio" con **Rappresentazione della Conoscenza** e ragionamento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Reti Neurali** e **Deep Learning**, che sono al centro dell'IA moderna. Illustreremo i concetti dietro questi importanti argomenti usando codice in due dei framework più popolari - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). -* **Architetture neurali** per lavorare con immagini e testo. Copriremo modelli recenti ma potremmo essere un po' indietro rispetto allo stato dell'arte. -* Approcci meno popolari all'IA, come **Algoritmi genetici** e **Sistemi multi-agente**. +* Diversi approcci all'Intelligenza Artificiale, incluso il "buon vecchio" approccio simbolico con **Rappresentazione della conoscenza** e ragionamento ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Reti Neurali** e **Deep Learning**, che sono al cuore dell'IA moderna. Illustreremo i concetti dietro questi importanti argomenti usando codice in due dei framework più popolari - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). +* **Architetture neurali** per lavorare con immagini e testo. Copriremo modelli recenti ma potremmo essere un po' carenti rispetto allo stato dell'arte. +* Approcci meno diffusi dell'IA, come **Algoritmi genetici** e **Sistemi multi-agente**. Cosa non tratteremo in questo curriculum: > [Trova tutte le risorse aggiuntive per questo corso nella nostra raccolta Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Casi aziendali sull'uso dell'**IA nel business**. Prendi in considerazione il percorso di apprendimento [Introduzione all'IA per utenti aziendali](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) su Microsoft Learn, o [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), sviluppato in collaborazione con [INSEAD](https://www.insead.edu/). -* **Apprendimento automatico classico**, che è ben descritto nel nostro [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). -* Applicazioni pratiche di IA create usando **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Per questo, raccomandiamo di iniziare con i moduli Microsoft Learn per la [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), l'[elaborazione del linguaggio naturale](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e altri. +* Casi aziendali sull'utilizzo dell'**IA nel business**. Considera di seguire il percorso di apprendimento [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) su Microsoft Learn, o [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), sviluppato in collaborazione con [INSEAD](https://www.insead.edu/). +* **Machine Learning classico**, che è ben descritto nel nostro [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). +* Applicazioni pratiche di IA costruite utilizzando **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Per questo, ti consigliamo di iniziare con i moduli di Microsoft Learn per [visione](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [elaborazione del linguaggio naturale](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI Generativa con Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e altri. * Specifici **framework cloud** per ML, come [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 l'utilizzo dei percorsi di apprendimento [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **IA conversazionale** e **Chatbot**. Esiste un percorso di apprendimento separato [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e puoi anche fare riferimento a [questo post del blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) per maggiori dettagli. -* La **matematica approfondita** dietro il deep learning. Per questo, raccomandiamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) di Ian Goodfellow, Yoshua Bengio e Aaron Courville, che è anche disponibile online su [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* **IA conversazionale** e **chat bot**. Esiste un percorso di apprendimento separato [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e puoi anche fare riferimento a [questo post del blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) per maggiori dettagli. +* **Matematica approfondita** dietro il deep learning. Per questo, raccomandiamo [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) di Ian Goodfellow, Yoshua Bengio e Aaron Courville, che è anche disponibile online su [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Per una dolce introduzione agli argomenti _IA nel Cloud_ potresti prendere in considerazione il percorso di apprendimento [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Per un'introduzione graduale agli argomenti _IA nel cloud_ puoi considerare di seguire il percorso di apprendimento [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Contenuto -| | Link della lezione | PyTorch/Keras/TensorFlow | Lab | +| | Link della lezione | PyTorch/Keras/TensorFlow | Laboratorio | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [Configurazione del corso](./lessons/0-course-setup/setup.md) | [Configura il tuo ambiente di sviluppo](./lessons/0-course-setup/how-to-run.md) | | | I | [**Introduzione all'IA**](./lessons/1-Intro/README.md) | | | | 01 | [Introduzione e storia dell'IA](./lessons/1-Intro/README.md) | - | - | | II | **IA simbolica** | -| 02 | [Rappresentazione della conoscenza e sistemi esperti](./lessons/2-Symbolic/README.md) | [Sistemi esperti](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo concettuale](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| 02 | [Rappresentazione della conoscenza e Sistemi esperti](./lessons/2-Symbolic/README.md) | [Sistemi esperti](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo concettuale](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Introduzione alle reti neurali**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Perceptron multistrato e creazione del nostro framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Introduzione ai framework (PyTorch/TensorFlow) e 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) | [Laboratorio](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Visione artificiale**](./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)| [Esplora la Visione Artificiale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Introduzione alla Visione Artificiale. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorio](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 04 | [Perceptrone multistrato e creazione del nostro framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Laboratorio](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introduzione ai framework (PyTorch/TensorFlow) e sovradattamento](./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 | [**Visione Artificiale**](./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)| [Esplora la Visione Artificiale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Introduzione alla Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Laboratorio](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Reti Neurali Convoluzionali](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Architetture 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 | [Reti pre-addestrate e Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Trucchi di addestramento](./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) | +| 08 | [Reti pre-addestrate e apprendimento per trasferimento](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Trucchi di addestramento](./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 | [Autoencoder e 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 | [Generative Adversarial Networks & Artistic Style Transfer](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 10 | [Reti Generative Avversarie & Trasferimento di Stile Artistico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Rilevamento oggetti](./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 | [Segmentazione semantica. 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 | [**Elaborazione del Linguaggio Naturale**](./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) | [Esplora l'Elaborazione del Linguaggio Naturale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 12 | [Segmentazione Semantica. 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 | [**Elaborazione del Linguaggio Naturale**](./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) | [Esplora l'elaborazione del linguaggio naturale su Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Rappresentazione del testo. 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 | [Embedding semantici di parole. Word2Vec e 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) | | +| 14 | [Embedding semantici delle parole. Word2Vec e 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 | [Modellazione del linguaggio. Addestrare i propri embedding](./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 | [Reti Neurali Ricorrenti](./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 | [Reti ricorrenti generative](./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) | +| 17 | [Reti Ricorrenti Generative](./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 | [Riconoscimento di entità nominate](./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 | [Grandi modelli linguistici, programmazione di prompt e compiti 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 | [Riconoscimento delle entità nominate](./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 | [Grandi modelli linguistici, programmazione dei prompt e compiti 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 | **Altre tecniche di IA** || | | 21 | [Algoritmi genetici](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Apprendimento per rinforzo profondo](./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 | [Sistemi multi-agente](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etica dell'IA** | | | -| 24 | [Etica dell'IA e IA responsabile](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi di IA responsabile](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [Etica dell'IA e IA responsabile](./lessons/7-Ethics/README.md) | [Microsoft Learn: Principi per un'IA responsabile](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Extra** | | | | 25 | [Reti multimodali, CLIP e VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Ogni lezione contiene * Materiale di lettura preliminare -* Notebook Jupyter eseguibili, spesso specifici per il framework (**PyTorch** o **TensorFlow**). Il notebook eseguibile contiene anche molto materiale teorico, quindi per comprendere l'argomento è necessario seguire almeno una versione del notebook (o PyTorch o TensorFlow). -* **Laboratori** disponibili per alcuni argomenti, che ti danno l'opportunità di provare ad applicare il materiale appreso a un problema specifico. -* Alcune sezioni contengono link a [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moduli che trattano argomenti correlati. +* Notebook Jupyter eseguibili, spesso specifici per il framework (**PyTorch** o **TensorFlow**). Il notebook eseguibile contiene anche molto materiale teorico, quindi per comprendere l'argomento è necessario consultare almeno una versione del notebook (o PyTorch o TensorFlow). +* **Laboratori** disponibili per alcuni argomenti, che offrono l'opportunità di provare ad applicare il materiale appreso a un problema specifico. +* Alcune sezioni contengono link a [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) moduli che coprono argomenti correlati. ## Per iniziare -### 🎯 Nuovo all'IA? Inizia qui! +### 🎯 Nuovo nell'IA? Inizia qui! -Se sei completamente nuovo all'IA e vuoi esempi pratici e rapidi, dai un'occhiata ai nostri [**Esempi per principianti**](./examples/README.md)! Questi includono: +Se sei completamente nuovo all'IA e vuoi esempi rapidi e pratici, dai un'occhiata ai nostri [**Esempi per principianti**](./examples/README.md)! Questi includono: -- 🌟 **Hello AI World** - Il tuo primo programma di IA (riconoscimento dei pattern) -- 🧠 **Rete Neurale Semplice** - Costruisci una rete neurale da zero -- 🖼️ **Classificatore di immagini** - Classifica immagini con commenti dettagliati -- 💬 **Sentimento del testo** - Analizza testo positivo/negativo +- 🌟 **Hello AI World** - Il tuo primo programma di IA (riconoscimento di pattern) +- 🧠 **Simple Neural Network** - Costruisci una rete neurale da zero +- 🖼️ **Image Classifier** - Classifica immagini con commenti dettagliati +- 💬 **Sentiment del testo** - Analizza testo positivo/negativo -Questi esempi sono pensati per aiutarti a comprendere i concetti di IA prima di immergerti nel curriculum completo. +These examples are designed to help you understand AI concepts before diving into the full curriculum. ### 📚 Configurazione del curriculum completo -- Abbiamo creato una [lezione di configurazione](./lessons/0-course-setup/setup.md) per aiutarti a configurare il tuo ambiente di sviluppo. - Per gli educatori, abbiamo creato anche una [lezione di impostazione del curriculum](./lessons/0-course-setup/for-teachers.md) per voi! -- Come [eseguire il codice in VSCode o in Codepace](./lessons/0-course-setup/how-to-run.md) +- Abbiamo creato una [setup lesson](./lessons/0-course-setup/setup.md) per aiutarti con l'impostazione del tuo ambiente di sviluppo. - For Educators, we have created a [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) for you too! +- How to [Run the code in a VSCode or a Codepace](./lessons/0-course-setup/how-to-run.md) -Segui questi passaggi: +Follow these steps: Fork the Repository: Click on the "Fork" button at the top-right corner of this page. @@ -140,29 +140,29 @@ Don't forget to star (🌟) this repo to find it easier later. ## Incontra altri studenti -Unisciti al nostro [server Discord ufficiale sull'IA](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) per incontrare e fare networking con altri studenti che seguono questo corso e ottenere supporto. +Join our [official AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network with other learners taking this course and get support. -Se hai feedback sul prodotto o domande durante lo sviluppo visita il nostro [Forum sviluppatori Azure AI Foundry](https://aka.ms/foundry/forum) +If you have product feedback or questions whilst building visit our [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Quiz -> **Una nota sui quiz**: Tutti i quiz sono contenuti nella cartella Quiz-app in etc\quiz-app, o [Online Qui](https://ff-quizzes.netlify.app/) They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized. +> **Una nota sui quiz**: Tutti i quiz sono contenuti nella cartella Quiz-app in etc\quiz-app, o [Online qui](https://ff-quizzes.netlify.app/) They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized. -## Cerchiamo aiuto +## Aiuto richiesto -Hai suggerimenti o hai trovato errori di ortografia o di codice? Apri un issue o crea una pull request. +Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request. ## Ringraziamenti speciali * **✍️ Autore principale:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🔥 Editore:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Illustratore degli sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Creatore dei quiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 Contributori principali:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 Collaboratori principali:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Altri curricula -Il nostro team produce altri curricula! Dai un'occhiata: +Our team produces other curricula! Check out: ### LangChain @@ -171,52 +171,52 @@ Il nostro team produce altri curricula! Dai un'occhiata: --- -### Azure / Edge / MCP / Agenti +### Azure / Edge / MCP / Agents [![AZD per principianti](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 per principianti](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 per principianti](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) -[![Agenti AI per principianti](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 per principianti](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 AI generativa -[![AI generativa per principianti](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) -[![AI 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) -[![AI 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) -[![AI 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 Generative AI +[![Generative AI per principianti](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) --- -### Apprendimento principale +### Apprendimento di base [![ML per principianti](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 per principianti](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 per principianti](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) -[![Sicurezza informatica per principianti](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) -[![Sviluppo web per principianti](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) +[![Cybersecurity per principianti](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) +[![Web Dev per principianti](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 per principianti](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) -[![Sviluppo XR per principianti](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) +[![XR Development per principianti](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 per programmazione affiancata 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 per programmazione affiancata dall'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 per 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) ## Ottenere aiuto -Se rimani bloccato o hai domande sulla creazione di app AI, unisciti ad altri studenti e sviluppatori esperti nelle discussioni su MCP. È una comunità di supporto dove le domande sono benvenute e le conoscenze vengono condivise liberamente. +Se rimani bloccato o hai domande sulla creazione di app per l'IA. Unisciti ad altri studenti e sviluppatori esperti nelle discussioni su MCP. È una community di supporto dove le domande sono benvenute e le conoscenze vengono condivise liberamente. -[![Discord di 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) -Se hai feedback sul prodotto o riscontri errori durante lo sviluppo visita: +If you have product feedback or errors while building visit: -[![Forum sviluppatori 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) +[![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) --- -Dichiarazione di non responsabilità: -Questo documento è stato tradotto utilizzando il servizio di traduzione basato sull'intelligenza artificiale [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o inesattezze. Il documento originale nella sua lingua d'origine deve essere considerato la fonte autorevole. Per informazioni di natura critica, si consiglia una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali malintesi o interpretazioni errate derivanti dall'uso di questa traduzione. +**Dichiarazione di non responsabilità**: +Questo documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Pur impegnandoci per l'accuratezza, si prega di notare che le traduzioni automatiche potrebbero contenere errori o imprecisioni. Il documento originale nella sua lingua nativa deve essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione. \ No newline at end of file diff --git a/translations/it/lessons/1-Intro/README.md b/translations/it/lessons/1-Intro/README.md index d73e8c55..169709b2 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.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.it.png) +![Riepilogo del contenuto dell'introduzione all'IA in uno schizzo](../../../../translated_images/ai-intro.bf28d1ac4235881c.it.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.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.it.png) +![Foto di una persona](../../../../translated_images/dsh_age.d212a30d4e54fb5f.it.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.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.it.jpg) +![Foto di un gatto](../../../../translated_images/photo-cat.8c8e8fb760ffe457.it.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.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.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/ml-for-beginners.9e4fed176fd5817d.it.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 35078a3b..8f76c4d3 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.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.it.png)\n" + "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.it.png)\n" ] }, { diff --git a/translations/it/lessons/2-Symbolic/README.md b/translations/it/lessons/2-Symbolic/README.md index 6b7bd6d4..a2d49b3e 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.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.it.png) +![Riepilogo del contenuto sull'IA simbolica](../../../../translated_images/ai-symbolic.715a30cb610411a6.it.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.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.it.png) +![Spettro della rappresentazione della conoscenza](../../../../translated_images/knowledge-spectrum.b60df631852c0217.it.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.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.it.png) | ![Sistema basato sulla conoscenza](../../../../translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.it.png) +![Architettura umana](../../../../translated_images/arch-human.5d4d35f1bba3ab1c.it.png) | ![Sistema basato sulla conoscenza](../../../../translated_images/arch-kbs.3ec5c150b09fa8da.it.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.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.it.png) +![Albero AND-OR](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.it.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 81a69f62..16cd7449 100644 --- a/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/it/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -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.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.it.png)\n", + "![Overfitting](../../../../../translated_images/overfit.a0bd57f717c15769.it.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 5737a64a..a8a03c46 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.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.it.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.it.jpg) +![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.it.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.it.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.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.it.png) +![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.it.png) ## Come prevenire l'overfitting diff --git a/translations/it/lessons/3-NeuralNetworks/README.md b/translations/it/lessons/3-NeuralNetworks/README.md index ecbe2843..58be16ef 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.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.it.png) +![Riassunto del contenuto di Introduzione alle Reti Neurali in uno schizzo](../../../../translated_images/ai-neuralnetworks.1c687ae40bc86e83.it.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.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.it.jpg) | ![Modello di un Neurone](../../../../translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.it.png) +![Modello di un Neurone](../../../../translated_images/synapse-wikipedia.ed20a9e4726ea1c6.it.jpg) | ![Modello di un Neurone](../../../../translated_images/artneuron.1a5daa88d20ebe6f.it.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 0c937567..a67f9d59 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.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.it.jpeg) | ![Immagine Braille Pre-elaborata](../../../../../translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.it.png) | ![Simboli Braille](../../../../../translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.it.png) +![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 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.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.it.png) +![Immagine dei fotogrammi video e differenze tra fotogrammi](../../../../../translated_images/frame-difference.706f805491a0883c.it.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.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.it.png) +![Immagine di Optical Flow](../../../../../translated_images/optical.1f4a94464579a83a.it.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 8e965005..763d598e 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.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.it.jpg) +![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.it.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.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.it.jpg) +![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.it.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 ce9ca231..16fc6e83 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.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.it.png)\n", + "![Un'immagine che mostra diversi livelli di convoluzione con livelli di pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.it.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 08fa01fa..a74217f9 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.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.it.png)\n", + "![Un'immagine che mostra diversi strati convoluzionali con livelli di pooling.](../../../../../translated_images/cnn-pyramid.85915455759ef0ce.it.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 00219187..0eb0b78f 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.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.it.png) | ![Filtro per bordi orizzontali](../../../../../translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.it.png) +![Filtro per bordi verticali](../../../../../translated_images/filter-vert.b7148390ca0bc356.it.png) | ![Filtro per bordi orizzontali](../../../../../translated_images/filter-horiz.59b80ed4feb946ef.it.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.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.it.png) +![Estrazione gerarchica delle caratteristiche](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.it.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.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.it.jpg) +![Strati di ImageNet](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.it.jpg) -![Piramide di ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.it.jpg) +![Piramide di ImageNet](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.it.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 885bfa10..090efe1f 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.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.it.png) +![Dataset con cui lavoreremo](../../../../../../translated_images/data.50b2a9d5484bdbf0.it.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 32c3e993..a9594d06 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.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.it.png)\n", + "![Ciclo di Ottimizzazione](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.it.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 47f7aae0..dc2e1e81 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.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.it.png) +![Caratteristiche estratte da VGG-16](../../../../../translated_images/features.6291f9c7ba3a0b95.it.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.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.it.png) +![Ciclo di Ottimizzazione dell'Immagine](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.it.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.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.it.png) | ![Zebra Ideale](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.it.png) +![Gatto Ideale](../../../../../translated_images/ideal-cat.203dd4597643d6b0.it.png) | ![Zebra Ideale](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a.it.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.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.it.png) | ![Immagine di un cane classificata come gatto](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.it.png) +![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 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 e2ebb1a2..aa76c121 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.it.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.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 abfcf17a..00f8c0fb 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.it.jpg)\n", + "![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.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 b3835cda..18f3ff76 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.it.jpg) +![Diagramma AutoEncoder](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.it.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 464f8c69..cb1fc9ec 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.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.it.png) +![Rilevamento degli Oggetti](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.it.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.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.it.png) +![Rilevamento Naïf](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.it.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.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.it.jpg) +![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.it.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.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.it.png) +![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.it.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.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.it.png) +![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.it.png) > *Immagine da van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.it.png) +![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.it.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.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.it.png) +![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.it.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.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.it.png) +![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.it.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.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.it.png) +![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.it.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.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.it.png) + ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.it.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 be47cc8c..b95e14a1 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.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.it.png) +![Riassunto dei contenuti sulla Visione Artificiale in uno schizzo](../../../../translated_images/ai-computervision.6506ebebac3fbf76.it.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 30390104..ddb36143 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.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.it.png) \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", "\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 a19f1b42..c06734f7 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.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.it.png) \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", "\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 c17ac40f..5e824257 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.it.png)\n", + "![Immagine che mostra un classificatore embedding per cinque parole in sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.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.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.it.png)\n", + "![Immagine che mostra una rappresentazione di sequenza con offset](../../../../../translated_images/offset-sequence-representation.eb73fcefb29b46ee.it.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.it.png)\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", "\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 314f2224..4ef895fb 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.it.png)\n", + "![Immagine che mostra un classificatore con embedding per cinque parole di una sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.it.png)\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", "\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 eb350063..2efc97a1 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.it.png) +![Immagine che mostra un classificatore embedding per cinque parole di una sequenza.](../../../../../translated_images/embedding-classifier-example.b77f021a7ee67eee.it.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.it.png) +![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 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 5c3abe93..8682a762 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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.it.png) +![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 [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 2186cffd..9379d36e 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.it.png) +![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.it.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.it.jpg) +![Immagine che mostra una RNN LSTM multistrato](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.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 a5ca7fe9..f84315c2 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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.it.jpg)\n", + "![Immagine che mostra una rete RNN LSTM multilivello](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.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 66f8c9f8..7ee62f17 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.it.png)\n", + "![Immagine che mostra un esempio di generazione con rete neurale ricorrente.](../../../../../translated_images/rnn.27f5c29c53d727b5.it.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.it.jpg)\n", + "![Immagine che mostra una RNN multilivello con memoria a lungo termine](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.it.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 62db3dc0..21f5f65f 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.it.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.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 11c54369..11fa2ee2 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.it.png)\n", + "![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.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 b71b7fff..e50dc60f 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.it.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.it.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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.it.png) +![Immagine che mostra un esempio di generazione RNN della parola 'HELLO'.](../../../../../translated_images/rnn-generate.56c54afb52f9781d.it.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 410a23fd..e231bd77 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.it.png) +![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.it.png) +![Immagine che mostra un allineamento di esempio trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.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.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.it.png) +![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.it.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.it.png) +![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.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 fb0c82de..3cff3f6f 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.it.png) +![Image montrant un modèle encodeur/décodeur avec une couche d'attention additive](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.it.png) +![Image montrant un alignement d'exemple trouvé par RNNsearch-50, tirée de Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.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.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.it.png) +![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.it.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.it.png) +![image de http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.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 b7ff1a14..3c81fdfd 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.it.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.it.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.it.png)\n", + "![Immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.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 5e30639c..6c60d451 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.it.png)\n", + "![Immagine che mostra un modello encoder/decoder con uno strato di attenzione additiva](../../../../../translated_images/encoder-decoder-attention.7a726296894fb567.it.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.it.png)\n", + "![Immagine che mostra un allineamento campione trovato da RNNsearch-50, tratto da Bahdanau - arviz.org](../../../../../translated_images/bahdanau-fig3.09ba2d37f202a6af.it.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.it.png)\n", + "![immagine da http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.it.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 369b6ee5..cb408130 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.it.jpg) +![Immagine che mostra i modelli comuni di reti neurali ricorrenti.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.it.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 46361893..d7b40cbd 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.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.it.png) +![Riepilogo dei compiti NLP in uno schizzo](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.it.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 20a5cf66..6c299bc7 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.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.it.png) +![Schermata Principale di NetLogo](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.it.png) > Screenshot di Dmitry Soshnikov diff --git a/translations/it/lessons/README.md b/translations/it/lessons/README.md index b57c91ba..f08c41d4 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.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.it.png) +![Panoramica in uno schizzo](../../../translated_images/ai-overview.0857791951d19500.it.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 6f6c6343..19928cd7 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.b3dbf20b4e8ed8be1c38e2bc6100fd3cc257c33cda4692b301be91f791b13ea7.it.png) +![Architettura CLIP](../../../../../translated_images/clip-arch.b3dbf20b4e8ed8be.it.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.3af42ef0b2b19369a633df5f20ddf4f5a01d6c8ffa181e9d3a0572c19f919f72.it.png) +![CLIP per la Classificazione delle Immagini](../../../../../translated_images/clip-class.3af42ef0b2b19369.it.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.5027fe05051dfa3101950cfa930303f66e6478b9bd273e83766731796e462d9b.it.png) +![Architettura VQGAN+CLIP](../../../../../translated_images/vqgan.5027fe05051dfa31.it.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.2384968e9db8a0d09dc96de938b9f95bde8a7e1c721f48f286a7795bf16d56c7.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_young_female_teacher_of_computer_science_with_a_computer.e0b6495f210a439077e1c32cc8afdf714e634fe24dc78dc5aa45fd2f560b0ed5.it.png) | ![Immagine prodotta da Pixray](../../../../../translated_images/a_closeup_oil_portrait_of_old_male_teacher_of_math.5362e67aa7fc2683b9d36a613b364deb7454760cd39205623fc1e3938fa133c0.it.png) +![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 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 676a07bf..1e3cf221 100644 --- a/translations/ja/README.md +++ b/translations/ja/README.md @@ -1,8 +1,8 @@ -[アラビア語](../ar/README.md) | [ベンガル語](../bn/README.md) | [ブルガリア語](../bg/README.md) | [ビルマ語（ミャンマー）](../my/README.md) | [中国語（簡体字）](../zh/README.md) | [中国語（繁体字、香港）](../hk/README.md) | [中国語（繁体字、マカオ）](../mo/README.md) | [中国語（繁体字、台湾）](../tw/README.md) | [クロアチア語](../hr/README.md) | [チェコ語](../cs/README.md) | [デンマーク語](../da/README.md) | [オランダ語](../nl/README.md) | [エストニア語](../et/README.md) | [フィンランド語](../fi/README.md) | [フランス語](../fr/README.md) | [ドイツ語](../de/README.md) | [ギリシャ語](../el/README.md) | [ヘブライ語](../he/README.md) | [ヒンディー語](../hi/README.md) | [ハンガリー語](../hu/README.md) | [インドネシア語](../id/README.md) | [イタリア語](../it/README.md) | [日本語](./README.md) | [カンナダ語](../kn/README.md) | [韓国語](../ko/README.md) | [リトアニア語](../lt/README.md) | [マレー語](../ms/README.md) | [マラヤーラム語](../ml/README.md) | [マラーティー語](../mr/README.md) | [ネパール語](../ne/README.md) | [ナイジェリア・ピジン語](../pcm/README.md) | [ノルウェー語](../no/README.md) | [ペルシア語（ファルシー）](../fa/README.md) | [ポーランド語](../pl/README.md) | [ポルトガル語（ブラジル）](../br/README.md) | [ポルトガル語（ポルトガル）](../pt/README.md) | [パンジャブ語（グルムキー）](../pa/README.md) | [ルーマニア語](../ro/README.md) | [ロシア語](../ru/README.md) | [セルビア語（キリル）](../sr/README.md) | [スロバキア語](../sk/README.md) | [スロベニア語](../sl/README.md) | [スペイン語](../es/README.md) | [スワヒリ語](../sw/README.md) | [スウェーデン語](../sv/README.md) | [タガログ語（フィリピン）](../tl/README.md) | [タミル語](../ta/README.md) | [テルグ語](../te/README.md) | [タイ語](../th/README.md) | [トルコ語](../tr/README.md) | [ウクライナ語](../uk/README.md) | [ウルドゥー語](../ur/README.md) | [ベトナム語](../vi/README.md) +[アラビア語](../ar/README.md) | [ベンガル語](../bn/README.md) | [ブルガリア語](../bg/README.md) | [ビルマ語（ミャンマー）](../my/README.md) | [中国語（簡体字）](../zh/README.md) | [中国語（繁体字、香港）](../hk/README.md) | [中国語（繁体字、マカオ）](../mo/README.md) | [中国語（繁体字、台湾）](../tw/README.md) | [クロアチア語](../hr/README.md) | [チェコ語](../cs/README.md) | [デンマーク語](../da/README.md) | [オランダ語](../nl/README.md) | [エストニア語](../et/README.md) | [フィンランド語](../fi/README.md) | [フランス語](../fr/README.md) | [ドイツ語](../de/README.md) | [ギリシャ語](../el/README.md) | [ヘブライ語](../he/README.md) | [ヒンディー語](../hi/README.md) | [ハンガリー語](../hu/README.md) | [インドネシア語](../id/README.md) | [イタリア語](../it/README.md) | [日本語](./README.md) | [カンナダ語](../kn/README.md) | [韓国語](../ko/README.md) | [リトアニア語](../lt/README.md) | [マレー語](../ms/README.md) | [マラヤーラム語](../ml/README.md) | [マラーティー語](../mr/README.md) | [ネパール語](../ne/README.md) | [ナイジェリア・ピジン語](../pcm/README.md) | [ノルウェー語](../no/README.md) | [ペルシア語（ファルシ）](../fa/README.md) | [ポーランド語](../pl/README.md) | [ポルトガル語（ブラジル）](../br/README.md) | [ポルトガル語（ポルトガル）](../pt/README.md) | [パンジャブ語（グルムキー）](../pa/README.md) | [ルーマニア語](../ro/README.md) | [ロシア語](../ru/README.md) | [セルビア語（キリル）](../sr/README.md) | [スロバキア語](../sk/README.md) | [スロベニア語](../sl/README.md) | [スペイン語](../es/README.md) | [スワヒリ語](../sw/README.md) | [スウェーデン語](../sv/README.md) | [タガログ語（フィリピン）](../tl/README.md) | [タミル語](../ta/README.md) | [テルグ語](../te/README.md) | [タイ語](../th/README.md) | [トルコ語](../tr/README.md) | [ウクライナ語](../uk/README.md) | [ウルドゥー語](../ur/README.md) | [ベトナム語](../vi/README.md) -**追加の翻訳を希望する場合、サポートされている言語は[こちら](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)に一覧があります** +**追加の翻訳を希望する場合、サポートされている言語の一覧は[こちら](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)にあります** ## コミュニティに参加する [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## 学習内容 +## 何を学ぶか **[コースのマインドマップ](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** このカリキュラムで学ぶこと: -* 人工知能へのさまざまなアプローチ。古典的な「良き昔の」記号的アプローチ（**知識表現**と推論、([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）を含む）。 -* **ニューラルネットワーク**と**深層学習**は現代のAIの中核です。これら重要なトピックの背後にある概念を、最も人気のある2つのフレームワーク—[TensorFlow](http://Tensorflow.org) と [PyTorch](http://pytorch.org)—でのコードを使って示します。 -* 画像やテキストを扱うための**ニューラルアーキテクチャ**。最近のモデルを扱いますが、最先端（state-of-the-art）を完全に網羅していない場合があります。 +* 異なる人工知能のアプローチ、例えば伝統的な記号的アプローチである**知識表現**と推論（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。 +* **ニューラルネットワーク**と**ディープラーニング**。これらは現代のAIの中心にあり、最も人気のあるフレームワークの2つ、[TensorFlow](http://Tensorflow.org) と [PyTorch](http://pytorch.org) のコードを使って概念を説明します。 +* 画像やテキストを扱うための**ニューラルアーキテクチャ**。最近のモデルを取り上げますが、最先端の内容はやや不十分な場合があります。 * **遺伝的アルゴリズム**や**マルチエージェントシステム**など、あまり一般的でないAIアプローチ。 -このカリキュラムで扱わないもの： +このカリキュラムで扱わないもの: -> [このコースの追加リソースはMicrosoft Learnコレクションで確認できます](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [このコースの追加リソースはすべてMicrosoft Learnのコレクションで見つかります](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* **ビジネスにおけるAI**のビジネスケース。Microsoft Learn の学習パス [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) や、[INSEAD](https://www.insead.edu/) と協力して開発された [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) を検討してください。 -* **古典的機械学習**は、当社の [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) で詳しく説明されています。 -* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** を使って構築された実用的なAIアプリケーション。これについては、Microsoft Learn の [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** などのモジュールから始めることをお勧めします。 -* [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)、[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum) のような特定のML **クラウドフレームワーク**。[Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) および [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) の学習パスを検討してください。 -* **Conversational AI** と **チャットボット**。別途 [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) の学習パスがあり、詳細は [このブログ記事](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) も参照できます。 -* 深層学習の背後にある**高度な数学**。これについては、Ian Goodfellow、Yoshua Bengio、Aaron Courville 著の [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)（オンライン版は [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)）をおすすめします。 +* **ビジネスにおけるAI**のビジネス事例。Microsoft Learn の [ビジネスユーザー向け AI 入門](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) 学習パス、または [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)（[INSEAD](https://www.insead.edu/) と協力して開発）を受講することを検討してください。 +* **古典的な機械学習**。これは当社の [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners) で詳しく説明されています。 +* **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)** を使用して構築された実用的なAIアプリケーション。これについては、Microsoft Learn の [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[Generative AI with Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** などのモジュールから始めることをお勧めします。 +* [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)、[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum) のような特定の ML **クラウドフレームワーク**。[Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) や [Build and Operate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum) 学習パスを利用することをご検討ください。 +* **会話型AI**や**チャットボット**。別途 [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) の学習パスがあり、詳細については [このブログ記事](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) も参照できます。 +* ディープラーニングの背後にある**高度な数学**。この分野については Ian Goodfellow, Yoshua Bengio, Aaron Courville 著の [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) をお勧めします。オンラインでも [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) で利用可能です。 -クラウド上の AI トピックへのやさしい導入には、[Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) ラーニングパスを検討してください。 +クラウド上の AI に関するやさしい導入を希望する場合は、[Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum) 学習パスの受講を検討してください。 # コンテンツ @@ -73,7 +73,7 @@ Explore the world of **Artificial Intelligence** (AI) with our 12-week, 24-lesso | 0 | [コースのセットアップ](./lessons/0-course-setup/setup.md) | [開発環境のセットアップ](./lessons/0-course-setup/how-to-run.md) | | | I | [**AI入門**](./lessons/1-Intro/README.md) | | | | 01 | [AIの紹介と歴史](./lessons/1-Intro/README.md) | - | - | -| II | **シンボリックAI** | +| II | **記号的AI** | | 02 | [知識表現とエキスパートシステム](./lessons/2-Symbolic/README.md) | [エキスパートシステム](./lessons/2-Symbolic/Animals.ipynb) / [オントロジー](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念グラフ](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**ニューラルネットワーク入門**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [パーセプトロン](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [ノートブック](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [ラボ](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | @@ -81,54 +81,54 @@ Explore the world of **Artificial Intelligence** (AI) with our 12-week, 24-lesso | 05 | [フレームワーク入門（PyTorch/TensorFlow）と過学習](./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) | [ラボ](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**コンピュータビジョン**](./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)| [Microsoft Azureでコンピュータビジョンを探る](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [コンピュータビジョン入門。OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [ノートブック](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [ラボ](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [畳み込みニューラルネットワーク](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNNのアーキテクチャ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ラボ](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 07 | [畳み込みニューラルネットワーク](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNNアーキテクチャ](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [ラボ](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | | 08 | [事前学習済みネットワークと転移学習](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [トレーニングのコツ](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [ラボ](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | | 09 | [オートエンコーダと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 | [敵対的生成ネットワーク（GAN）とアーティスティックスタイル転送](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 10 | [敵対的生成ネットワークと芸術的スタイル転送](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [物体検出](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [ラボ](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [セマンティックセグメンテーション。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 | [**自然言語処理**](./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) | [Microsoft Azureで自然言語処理を探る](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [テキスト表現。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 | [意味的単語埋め込み。Word2Vecと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 | [言語モデル。独自埋め込みの学習](./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) | [ラボ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [意味論的単語埋め込み。Word2Vecと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 | [言語モデリング。独自の埋め込みを学習する](./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) | [ラボ](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [リカレントニューラルネットワーク](./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 | [生成リカレントネットワーク](./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) | [ラボ](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [トランスフォーマー。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 | [固有表現抽出](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [ラボ](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [大規模言語モデル、プロンプトプログラミングと少数ショットタスク](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **その他のAI技術** || | +| 20 | [大規模言語モデル、プロンプトプログラミング、少数ショットタスク](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **その他のAI手法** || | | 21 | [遺伝的アルゴリズム](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [ノートブック](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [深層強化学習](./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) | [ラボ](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [マルチエージェントシステム](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **AI倫理** | | | -| 24 | [AI倫理と責任あるAI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 責任ある AI の原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| 24 | [AI倫理と責任あるAI](./lessons/7-Ethics/README.md) | [Microsoft Learn: 責任あるAIの原則](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **その他** | | | | 25 | [マルチモーダルネットワーク、CLIPとVQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [ノートブック](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## 各レッスンに含まれるもの * 事前学習資料 -* 実行可能なJupyterノートブック（多くの場合、フレームワーク（**PyTorch** または **TensorFlow**）に特化しています）。実行可能なノートブックには理論的な内容も多く含まれているため、トピックを理解するには少なくともノートブックのいずれかのバージョン（**PyTorch** か **TensorFlow**）を一通り確認する必要があります。 -* **ラボ** は一部のトピックで利用可能で、学んだ内容を特定の問題に適用して試す機会を提供します。 -* 一部のセクションには関連トピックを扱う [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) モジュールへのリンクが含まれています。 +* 実行可能なJupyterノートブック（多くはフレームワーク固有（**PyTorch** または **TensorFlow**））。実行可能なノートブックには理論的な内容も多く含まれているため、トピックを理解するには少なくともノートブックのいずれかのバージョン（PyTorch または TensorFlow）を読み進める必要があります。 +* 一部のトピックでは**ラボ**が利用可能で、学んだ内容を特定の問題に適用してみる機会を提供します。 +* 一部のセクションには、関連トピックを扱う[**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)モジュールへのリンクが含まれています。 ## はじめに ### 🎯 AIが初めてですか？ここから始めましょう！ -If you're completely new to AI and want quick, hands-on examples, check out our [**Beginner-Friendly Examples**](./examples/README.md)! These include: +AIがまったく初めてで、手早く実践的な例を見たい場合は、[**初心者向けの例**](./examples/README.md)をご覧ください！これらには以下が含まれます: -- 🌟 **Hello AI World** - あなたの最初の AI プログラム（パターン認識） -- 🧠 **Simple Neural Network** - 最初からニューラルネットワークを構築する -- 🖼️ **Image Classifier** - 詳細なコメント付きで画像を分類する -- 💬 **テキスト感情** - 肯定的/否定的なテキストを分析する +- 🌟 **Hello AI World** - あなたの最初のAIプログラム（パターン認識） +- 🧠 **シンプルニューラルネットワーク** - ニューラルネットワークをゼロから構築する +- 🖼️ **画像分類器** - 詳細なコメント付きで画像を分類する +- 💬 **テキスト感情分析** - ポジティブ/ネガティブなテキストを分析する These examples are designed to help you understand AI concepts before diving into the full curriculum. ### 📚 フルカリキュラムのセットアップ -- We have created a [セットアップレッスン](./lessons/0-course-setup/setup.md) to help you with setting up your development environment. - For Educators, we have created a [カリキュラムセットアップレッスン](./lessons/0-course-setup/for-teachers.md) for you too! -- VSCodeまたはCodepaceで[コードを実行する方法](./lessons/0-course-setup/how-to-run.md) +- We have created a [setup lesson](./lessons/0-course-setup/setup.md) to help you with setting up your development environment. - For Educators, we have created a [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) for you too! +- コードを [VSCode または Codepace で実行する方法](./lessons/0-course-setup/how-to-run.md) Follow these steps: @@ -138,27 +138,27 @@ Clone the Repository: `git clone https://github.com/microsoft/AI-For-Beginners.g Don't forget to star (🌟) this repo to find it easier later. -## 他の学習者と出会う +## 他の学習者と交流する -Join our [公式AI Discordサーバー](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network with other learners taking this course and get support. +Join our [official AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) to meet and network with other learners taking this course and get support. -If you have product feedback or questions whilst building visit our [Azure AI Foundry 開発者フォーラム](https://aka.ms/foundry/forum) +If you have product feedback or questions whilst building visit our [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## クイズ +## Quizzes -> **クイズに関する注意**: All quizzes are contained in the Quiz-app folder in etc\quiz-app, or [オンラインはこちら](https://ff-quizzes.netlify.app/) They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized. +> **クイズに関する注意**: All quizzes are contained in the Quiz-app folder in etc\quiz-app, or [ここでオンライン](https://ff-quizzes.netlify.app/) They are linked from within the lessons the quiz app can be run locally or deployed to Azure; follow the instruction in the `quiz-app` folder. They are gradually being localized. ## ヘルプ募集 Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request. -## 特別な謝辞 +## 特別な感謝 -* **✍️ 主要著者:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **✍️ 主執筆者:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 編集者:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 スケッチノートイラストレーター:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ クイズ作成者:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **🙏 コアコントリビューター:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **🙏 コア貢献者:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## その他のカリキュラム @@ -175,15 +175,15 @@ Our team produces other curricula! Check out: [![AZD 入門](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 入門](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 入門](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エージェント入門](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 エージェント 入門](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 AI Series +### 生成AIシリーズ [![生成AI 入門](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) -[![生成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) -[![生成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) -[![生成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) +[![生成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) +[![生成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) +[![生成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) --- @@ -192,15 +192,15 @@ Our team produces other curricula! Check out: [![データサイエンス 入門](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 入門](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) [![サイバーセキュリティ 入門](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) -[![Web開発 入門](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) +[![Web 開発 入門](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 入門](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開発 入門](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) +[![XR 開発 入門](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 シリーズ -[![Copilot for 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) -[![C#/.NET向け Copilot](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) +[![AI ペアプログラミング向け Copilot](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) +[![C#/.NET 向け Copilot](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 アドベンチャー](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) @@ -217,6 +217,6 @@ If you have product feedback or errors while building visit: --- -免責事項： -本書はAI翻訳サービス「Co‑op Translator」（https://github.com/Azure/co-op-translator）を使用して翻訳されました。正確性には努めておりますが、自動翻訳には誤りや不正確な箇所が含まれる可能性があります。原文（原言語の文書）を正式な情報源として扱ってください。重要な情報については専門の人による翻訳を推奨します。本翻訳の利用に起因する誤解や誤訳について、当社は一切の責任を負いません。 +免責事項: +この文書は AI 翻訳サービス「Co-op Translator」(https://github.com/Azure/co-op-translator) を使用して翻訳されました。正確性の確保に努めておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があることをご了承ください。原文（原言語の文書）が正式な出典とみなされます。重要な情報については、専門の人間による翻訳を推奨します。本翻訳の利用により生じた誤解や解釈の相違について、当方は一切責任を負いません。 \ No newline at end of file diff --git a/translations/ja/lessons/1-Intro/README.md b/translations/ja/lessons/1-Intro/README.md index a994b7c8..df1c591f 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.bf28d1ac4235881c096f0ffdb320ba4102940eafcca4e9d7a55a03914361f8f3.ja.png) +![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) > 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.d212a30d4e54fb5f68b94a624aad64bc086124bcbbec9561ae5bd5da661e22d8.ja.png) +![人物の写真](../../../../translated_images/dsh_age.d212a30d4e54fb5f.ja.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.8c8e8fb760ffe45725c5b9f6b0d954e9bf114475c01c55adf0303982851b7eae.ja.jpg) +![猫の写真](../../../../translated_images/photo-cat.8c8e8fb760ffe457.ja.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.9e4fed176fd5817d7d1f7d358302515186579cbf09b2a6c5bd8092b345da7f22.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/ml-for-beginners.9e4fed176fd5817d.ja.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 ebace292..3cb41293 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.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ja.png)\n" + "![](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ja.png)\n" ] }, { diff --git a/translations/ja/lessons/2-Symbolic/README.md b/translations/ja/lessons/2-Symbolic/README.md index ce5f69b5..7b3c6c85 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.715a30cb610411a6964d2e2f23f24364cb338a07cb4844c1f97084d366e586c3.ja.png) +![Symbolic AIの内容の概要](../../../../translated_images/ai-symbolic.715a30cb610411a6.ja.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.b60df631852c0217e941485b79c9eee40ebd574f15f18609cec5758fcb384bf3.ja.png) +![知識表現のスペクトラム](../../../../translated_images/knowledge-spectrum.b60df631852c0217.ja.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.5d4d35f1bba3ab1cdfda96af2f10b89574eb31e9796d0e3011cd9beda1c35112.ja.png) | ![知識ベースシステムの構造](../../../../translated_images/arch-kbs.3ec5c150b09fa8dadc2beb0931a4983c9e2b03913a89eebcc103b5bb841b0212.ja.png) +![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.5592d2c70187f283703c8e9c0d69d6a786eb370f4ace67f9a7aae5ada3d260b0.ja.png) +![AND-ORツリー](../../../../translated_images/AND-OR-Tree.5592d2c70187f283.ja.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 d8993f0a..d9201098 100644 --- a/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb +++ b/translations/ja/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -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.a0bd57f717c157696f30c9c73fa7c3345c49b4280e412ff30c4a1a16ba29ff49.ja.png)\n", + "![過学習](../../../../../translated_images/overfit.a0bd57f717c15769.ja.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 99fc342b..1c45dbfd 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.f24b71c6f652e59e6bed7245ffbeaecc3ba320e16e2221f6832b432052c4da43.ja.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e41d12a411f5f705d9ee38b1b10916f284b787028dd55cc1c.ja.jpg) +![linear](../../../../../translated_images/overfit1.f24b71c6f652e59e.ja.jpg) | ![overfit](../../../../../translated_images/overfit2.131f5800ae10ca5e.ja.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.408ad91cd90b4371d0a81f4287e1409c359751adeb1ae450332af50e84f08c3e.ja.png) +![overfitting](../../../../../translated_images/Overfitting.408ad91cd90b4371.ja.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 13598661..68ba7f5e 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.1c687ae40bc86e834f497844866a26d3e0886650a67a4bbe29442e2f157d3b18.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/ai-neuralnetworks.1c687ae40bc86e83.ja.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.ed20a9e4726ea1c6a3ce8fec51c0b9bec6181946dca0fe4e829bc12fa3bacf01.ja.jpg) | ![ニューロンのモデル](../../../../translated_images/artneuron.1a5daa88d20ebe6f5824ddb89fba0bdaaf49f67e8230c1afbec42909df1fc17e.ja.png) +![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) ----|---- 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 b5dd9075..fbcb30b4 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.341962ff76b1bd7044409371d3de09ced5028132aef97344ea4b7468c1208126.ja.jpeg) | ![前処理された点字画像](../../../../../translated_images/braille-result.46530fea020b03c76aac532d7d6eeef7f6fb35b55b1001cd21627907dabef3ed.ja.png) | ![点字記号](../../../../../translated_images/braille-symbols.0159185ab69d533909dc4d7d26a1971b51401c6a80eb3a5584f250ea880af88b.ja.png) +![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) ----|-----|----- > 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.706f805491a0883c938e16447bf5eb2f7d69e812c7f743cbe7d7c7645168f81f.ja.png) +![ビデオフレームとフレーム差分の画像](../../../../../translated_images/frame-difference.706f805491a0883c.ja.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.1f4a94464579a83a10784f3c07fe7228514714b96782edf50e70ccd59d2d8c4f.ja.png) +![オプティカルフローの画像](../../../../../translated_images/optical.1f4a94464579a83a.ja.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 24c6bc50..4b6c7287 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.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ja.jpg) +![ImageNet Layers](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ja.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.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ja.jpg) +![ImageNet Pyramid](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ja.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 9fcb2968..03725aaa 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.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.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/cnn-pyramid.85915455759ef0ce.ja.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 3bb336df..277a8c89 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.85915455759ef0ce6a8cc9da2170492c85bfd9e1b61832650e2228e037039ec4.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/cnn-pyramid.85915455759ef0ce.ja.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 08ebce22..2b44b7b1 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.b7148390ca0bc356ddc7e55555d2481819c1e86ddde9dce4db5e71a69d6f887f.ja.png) | ![水平エッジフィルター](../../../../../translated_images/filter-horiz.59b80ed4feb946efbe201a7fe3ca95abb3364e266e6fd90820cb893b4d3a6dda.ja.png) +![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) ----|---- > 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.d9b456cbdae7cb643fde3032b81b2940e3cf8be842e29afac3f482725ba7f95c.ja.png) +![階層的特徴抽出](../../../../../translated_images/FeatureExtractionCNN.d9b456cbdae7cb64.ja.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.d901a5583b3a51baeaab3e768567d921e5d54befa46e1e642616c5458c934028.ja.jpg) +![ImageNet層](../../../../../translated_images/vgg-16-arch1.d901a5583b3a51ba.ja.jpg) -![ImageNetピラミッド](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49fdaa786e3f3a975b3f22615efd13efb19c5d22f12e01451a1.ja.jpg) +![ImageNetピラミッド](../../../../../translated_images/vgg-16-arch.64ff2137f50dd49f.ja.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 89286f21..1214dba7 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.50b2a9d5484bdbf0f52f5765b381cec9efe2bd296a98f007f90bedb6ac67f2a8.ja.png) +![扱うデータセット](../../../../../../translated_images/data.50b2a9d5484bdbf0.ja.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 599e114a..c071a709 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.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ja.png)\n", + "![最適化ループ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ja.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 5ca4da76..8fd87840 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.6291f9c7ba3a0b951af88fc9864632b9115365410765680680d30c927dd67354.ja.png) +![VGG-16による特徴抽出](../../../../../translated_images/features.6291f9c7ba3a0b95.ja.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.999fbb8ff306e044f997032f4eef9152b453e6a990e449bbfb107de2493cc37e.ja.png) +![画像最適化ループ](../../../../../translated_images/ideal-cat-loop.999fbb8ff306e044.ja.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.203dd4597643d6b0bd73038b87f9c0464322725e3a06ab145d25d4a861c70592.ja.png) | ![理想的なシマウマ](../../../../../translated_images/ideal-zebra.7f70e8b54ee15a7a314000bb5df38a6cfe086ea04d60df4d3ef313d046b98a2b.ja.png) +![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* | *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.8f68a67d2fe0911f33041c0f7fce8aa4ea919f9d3917ec4b468298522aeb6356.ja.png) | ![猫として分類される犬の画像](../../../../../translated_images/adversarial-dog.d9fc7773b0142b89752539bfbf884118de845b3851c5162146ea0b8809fc820f.ja.png) +![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) -----|----- *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 52fa9b6e..a2882f5b 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ja.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.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 0218f016..9363906a 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ja.jpg)\n", + "![オートエンコーダの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.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 172c43df..bb269e4a 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.5e6fc9ad98a5eb6197f3513cf3baf4dfbe1389a6ae74daebda64de9f1c99f142.ja.jpg) +![オートエンコーダーの図](../../../../../translated_images/autoencoder_schema.5e6fc9ad98a5eb61.ja.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 8d87a1be..cd5e58b9 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.b4bb3769353287be1b905373ed9c858102c054b16e4595c76ec3f7bba0feb549.ja.png) +![オブジェクト検出](../../../../../translated_images/Screen_Shot_2016-11-17_at_11.14.54_AM.b4bb3769353287be.ja.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.e7f1ba220ccd08c68a2ea8e06a7ed75c3fcc738c2372f9e00b7f4299a8659c01.ja.png) +![単純なオブジェクト検出](../../../../../translated_images/naive-detection.e7f1ba220ccd08c6.ja.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.71bc60380fa6cceb7caad48bd09e35b6028caabd363aa04fee89c414e0870e86.ja.jpg) +![COCO](../../../../../translated_images/coco-examples.71bc60380fa6cceb.ja.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.9a4751d40fff4e119ecd0a7bcca4e71ab1dc83e0d4f2a0d66ff0859736f593cf.ja.png) +![IoU](../../../../../translated_images/iou_equation.9a4751d40fff4e11.ja.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.cae407020dfb1d1fb572656e44f75cd6c512cc220591c116c506652c10e47f26.ja.png) +![RCNN](../../../../../translated_images/rcnn1.cae407020dfb1d1f.ja.png) > *画像出典: van de Sande et al. ICCV’11* -![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484ec65b250c22dbf37d3d23244f32864ebcb91d98fe7c3112c.ja.png) +![RCNN-1](../../../../../translated_images/rcnn2.2d9530bb83516484.ja.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.3cda6d9bb41888754037d2d9763e2298a96de5d9bc2a21db3147357aa5da9b1a.ja.png) +![FRCNN](../../../../../translated_images/f-rcnn.3cda6d9bb4188875.ja.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.8d46c099b87ef30ab2ea26dbc4bdd85b974a57ba8eb526f65dc4cd0a4711de30.ja.png) +![FasterRCNN](../../../../../translated_images/faster-rcnn.8d46c099b87ef30a.ja.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.13eb88158b99a3da50fa2787a6be5cb310d47f0e9655cc93a1090dc7aab338d1.ja.png) +![r-fcn image](../../../../../translated_images/r-fcn.13eb88158b99a3da.ja.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.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.ja.png) + ![YOLO](../../../../../translated_images/yolo.a2648ec82ee8bb4e.ja.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 007984e0..6250f4e3 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.6506ebebac3fbf76cdb78989d7d3dfea87e88285c0feaade53aa7804a22b248f.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/ai-computervision.6506ebebac3fbf76.ja.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 7d177f20..5e0f4610 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.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.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/bag-of-words-example.606fc1738f1d7ba9.ja.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 a2026bc1..b6d3d399 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.606fc1738f1d7ba98a9d693e3bcd706c6e83fa7bf8221e6e90d1a206d82f2ea4.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/bag-of-words-example.606fc1738f1d7ba9.ja.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 a79b7ec1..b8ce58de 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.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/embedding-classifier-example.b77f021a7ee67eee.ja.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.eb73fcefb29b46eecfbe74466077cfeb7c0f93a4f254850538a2efbc63517479.ja.png)\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", "\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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.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/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.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 afe7a6f5..7c34a3fa 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.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/embedding-classifier-example.b77f021a7ee67eee.ja.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.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/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.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 c6dda3b1..01ab6f68 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.b77f021a7ee67eeec8e68bfe11636c5b97d6eaa067515a129bfb1d0034b1ac5b.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/embedding-classifier-example.b77f021a7ee67eee.ja.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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.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/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.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 86933ab4..287433dc 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.fbe9207a726922f6f0f5de66427e8a6eda63809356114e28fb1fa5f4a83ebda7.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/example-algorithms-for-converting-words-to-vectors.fbe9207a726922f6.ja.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 f055bed3..b31e96d7 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.ja.png) +![RNN](../../../../../translated_images/rnn.27f5c29c53d727b5.ja.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ja.jpg) +![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.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 12ccab9c..89a94997 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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ja.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.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 a0ca13fa..db4308f7 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.27f5c29c53d727b546ad3961637a267f0fe9ec5ab01f2a26a853c92fcefbb574.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/rnn.27f5c29c53d727b5.ja.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.dd975e29bb2a59fe58b429db833932d734c81f211cad2783797a9608984acb8c.ja.jpg)\n", + "![多層長短期記憶RNNを示す画像](../../../../../translated_images/multi-layer-lstm.dd975e29bb2a59fe.ja.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 36ba59c2..55e51cef 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ja.png)\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", "\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 67077762..e892008e 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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ja.png)\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", "\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 e21f8e56..21e0a428 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.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/unreasonable-effectiveness-of-rnn.541ead816778f42d.ja.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.56c54afb52f9781d63a7c16ea9c1b86cb70e6e1eae6a742b56b7b37468576b17.ja.png) +![単語 'HELLO' を生成するRNNの例を示す画像](../../../../../translated_images/rnn-generate.56c54afb52f9781d.ja.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 95261416..53181c64 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.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/encoder-decoder-attention.7a726296894fb567.ja.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.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/bahdanau-fig3.09ba2d37f202a6af.ja.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.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ja.png) +![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ja.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ja.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.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 a76dd5fc..f0b37ae8 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.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/encoder-decoder-attention.7a726296894fb567.ja.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.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/bahdanau-fig3.09ba2d37f202a6af.ja.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.861924d6d384a7d68d8d0039d06a71a151f18a796b8b1330239d3590bd4947eb.ja.png) +![](../../../../../translated_images/CoreferenceResolution.861924d6d384a7d6.ja.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ja.png) +![http://jalammar.github.io/illustrated-bert/からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.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 ad08ad2a..083bfede 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.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/encoder-decoder-attention.7a726296894fb567.ja.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.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/bahdanau-fig3.09ba2d37f202a6af.ja.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ja.png)\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", "\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 2e8901eb..9c815ace 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.7a726296894fb567aa2898c94b17b3289087f6705c11907df8301df9e5eeb3de.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/encoder-decoder-attention.7a726296894fb567.ja.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-fig3.09ba2d37f202a6af11de6c82d2d197830ba5f4528d9ea430eb65fd3a75065973.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/bahdanau-fig3.09ba2d37f202a6af.ja.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.34f113ea5fec4362e39ee4381aab7cad06b5465a0b5f053a0f2aa05fbe14e746.ja.png)\n", + "![http://jalammar.github.io/illustrated-bert/ からの画像](../../../../../translated_images/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.ja.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 d011a2b8..e4dc37ac 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.541ead816778f42dce6c42d8a56c184729aa2378d059b851be4ce12b993033df.ja.jpg) +![Image showing common recurrent neural network patterns.](../../../../../translated_images/unreasonable-effectiveness-of-rnn.541ead816778f42d.ja.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 beedcaed..9d74d65e 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.b22dcb8ca4707ceaee8576db1c5f4089c8cac2f454e9e03ea554f07fda4556b8.ja.png) +![NLPタスクの概要を示すイラスト](../../../../translated_images/ai-nlp.b22dcb8ca4707cea.ja.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 fb77e9a4..2ee54bc4 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.32653711ec1a01b3cab22ec0b148e64193d0b979b055285bef329d5e3d6958c5.ja.png) +![NetLogo Main Screen](../../../../../translated_images/NetLogo-Main.32653711ec1a01b3.ja.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 10e46b66..6d8d4a89 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.0857791951d19500d0ef8b803d77110c738dcafc52306e6d68724742cd4af167.ja.png) +![概要のイラスト](../../../translated_images/ai-overview.0857791951d19500.ja.png) > スケッチノート作成者：[Tomomi Imura](https://twitter.com/girlie_mac)