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b/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.tl.png deleted file mode 100644 index 15b1f807..00000000 Binary files a/translated_images/yolo.a2648ec82ee8bb4ea27537677adb482fd4b733ca1705c561b6a24a85102dced5.tl.png and /dev/null differ diff --git a/translations/ms/README.md b/translations/ms/README.md index 4d966bee..d9068ed2 100644 --- a/translations/ms/README.md +++ b/translations/ms/README.md @@ -1,8 +1,8 @@ -**Jika anda ingin mempunyai sokongan bahasa terjemahan tambahan disenaraikan [di sini](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Jika anda ingin menyokong bahasa terjemahan tambahan, senarai bahasa disokong terdapat [di sini](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Sertai Komuniti [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Apa yang akan anda pelajari +## Apa yang anda akan pelajari -**[Peta Minda Kursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Peta Fikiran Kursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Dalam kurikulum ini, anda akan mempelajari: +Dalam kurikulum ini, anda akan belajar: * Pendekatan berbeza kepada Kecerdasan Buatan, termasuk pendekatan simbolik "lama" dengan **Perwakilan Pengetahuan** dan penaakulan ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Rangkaian Neural** dan **Pembelajaran Mendalam**, yang menjadi teras AI moden. Kami akan menggambarkan konsep di sebalik topik penting ini menggunakan kod dalam dua rangka kerja paling popular - [TensorFlow](http://Tensorflow.org) dan [PyTorch](http://pytorch.org). -* **Seni Bina Neural** untuk bekerja dengan imej dan teks. Kami akan merangkumi model terkini tetapi mungkin sedikit kurang dalam keadaan seni tertinggi. +* **Rangkaian Neural** dan **Pembelajaran Mendalam**, yang merupakan teras AI moden. Kami akan menggambarkan konsep di sebalik topik penting ini menggunakan kod dalam dua rangka kerja paling popular - [TensorFlow](http://Tensorflow.org) dan [PyTorch](http://pytorch.org). +* **Seni Bina Neural** untuk bekerja dengan imej dan teks. Kami akan merangkumi model terkini tetapi mungkin agak kurang dalam yang termaju. * Pendekatan AI yang kurang popular, seperti **Algoritma Genetik** dan **Sistem Multi-Ejen**. Apa yang tidak akan kami liputi dalam kurikulum ini: -> [Cari semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Temui semua sumber tambahan untuk kursus ini dalam koleksi Microsoft Learn kami](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Kajian kes perniagaan menggunakan **AI dalam Perniagaan**. Pertimbangkan untuk mengambil laluan pembelajaran [Pengenalan AI untuk pengguna perniagaan](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) di Microsoft Learn, atau [Sekolah Perniagaan AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum) yang dibangunkan bersama [INSEAD](https://www.insead.edu/). +* Kes perniagaan menggunakan **AI dalam Perniagaan**. Pertimbangkan mengambil laluan pembelajaran [Pengenalan kepada AI untuk pengguna perniagaan](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) di Microsoft Learn, atau [Sekolah Perniagaan AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), dibangunkan dengan kerjasama [INSEAD](https://www.insead.edu/). * **Pembelajaran Mesin Klasik**, yang diterangkan dengan baik dalam [Kurikulum Pembelajaran Mesin untuk Pemula](http://github.com/Microsoft/ML-for-Beginners). -* Aplikasi AI praktikal yang dibina menggunakan **[Perkhidmatan Kognitif](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Untuk ini, kami mencadangkan anda mulakan dengan modul Microsoft Learn untuk [penglihatan](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [pemprosesan bahasa semula jadi](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI Generatif dengan Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** dan lain-lain. -* **Rangka Kerja Awan ML** khusus, seperti [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), atau [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Pertimbangkan menggunakan laluan pembelajaran [Membina dan mengoperasikan penyelesaian pembelajaran mesin dengan Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) dan [Membina dan Mengendalikan Penyelesaian Pembelajaran Mesin dengan Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **AI Perbualan** dan **Chat Bots**. Terdapat laluan pembelajaran berasingan [Mencipta penyelesaian AI perbualan](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), dan anda juga boleh rujuk [pos blog ini](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) untuk maklumat lebih terperinci. -* **Matematik Mendalam** di sebalik pembelajaran mendalam. Untuk ini, kami mengesyorkan [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) oleh Ian Goodfellow, Yoshua Bengio dan Aaron Courville, yang juga boleh didapati dalam talian di [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Aplikasi AI praktikal yang dibina menggunakan **[Perkhidmatan Kognitif](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Untuk ini, kami mengesyorkan anda bermula dengan modul Microsoft Learn untuk [penglihatan](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [pemprosesan bahasa semula jadi](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI Generatif dengan Perkhidmatan Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** dan lain-lain. +* Rangka kerja ML **Awan Khusus**, seperti [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), atau [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Pertimbangkan menggunakan laluan pembelajaran [Membangun dan mengendalikan penyelesaian pembelajaran mesin dengan Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) dan [Membangun dan Mengendalikan Penyelesaian Pembelajaran Mesin dengan Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **AI Percakapan** dan **Chat Bots**. Terdapat laluan pembelajaran terpisah [Cipta penyelesaian AI percakapan](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), dan anda juga boleh merujuk kepada [catatan blog ini](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) untuk lebih terperinci. +* **Matematik Mendalam** di sebalik pembelajaran mendalam. Untuk ini, kami mencadangkan [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) oleh Ian Goodfellow, Yoshua Bengio dan Aaron Courville, yang juga tersedia dalam talian di [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Untuk pengenalan lembut kepada topik _AI di Awan_, anda boleh pertimbangkan mengambil Laluan Pembelajaran [Mula dengan kecerdasan buatan di Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Untuk pengenalan yang lembut kepada topik _AI di Awan_ anda boleh mempertimbangkan mengambil Laluan Pembelajaran [Mula dengan kecerdasan buatan di Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Kandungan -| | Pautan Pelajaran | PyTorch/Keras/TensorFlow | Makmal | +| | Pautan Pelajaran | PyTorch/Keras/TensorFlow | Makmal | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Persediaan Kursus](./lessons/0-course-setup/setup.md) | [Persediaan Persekitaran Pembangunan Anda](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Persediaan Kursus](./lessons/0-course-setup/setup.md) | [Persiapkan Persekitaran Pembangunan Anda](./lessons/0-course-setup/how-to-run.md) | | | I | [**Pengenalan kepada AI**](./lessons/1-Intro/README.md) | | | | 01 | [Pengenalan dan Sejarah AI](./lessons/1-Intro/README.md) | - | - | | II | **AI Simbolik** | | 02 | [Perwakilan Pengetahuan dan Sistem Pakar](./lessons/2-Symbolic/README.md) | [Sistem Pakar](./lessons/2-Symbolic/Animals.ipynb) / [Ontologi](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Graf Konsep](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | | III | [**Pengenalan kepada Rangkaian Neural**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Makmal](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | -| 04 | [Multi-Layered Perceptron and Creating our own Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Makmal](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Intro to Frameworks (PyTorch/TensorFlow) and 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) | [Makmal](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| 04 | [Perceptron Berlapis Pelbagai dan Mewujudkan Kerangka Kerja Kami Sendiri](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Makmal](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Pengenalan kepada Kerangka Kerja (PyTorch/TensorFlow) dan 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) | [Makmal](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | | IV | [**Penglihatan Komputer**](./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)| [Terokai Penglihatan Komputer di Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Pengenalan kepada Penglihatan Komputer. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Makmal](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Rangkaian Neural Konvolusi](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Seni Bina 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) | [Makmal](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Rangkaian Pralatih dan Pembelajaran Pindahan](./lessons/4-ComputerVision/08-TransferLearning/README.md) dan [Trik Latihan](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Makmal](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoencoder dan VAE](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 07 | [Rangkaian Neural Konvolusional](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Seni Bina 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) | [Makmal](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Rangkaian Pra-Latihan dan Pemindahan Pembelajaran](./lessons/4-ComputerVision/08-TransferLearning/README.md) dan [Trik Latihan](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Makmal](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoencoders dan 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 | [Rangkaian Adversarial Generatif & Pemindahan Gaya Artistik](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | | 11 | [Pengesanan Objek](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Makmal](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Segmentasi Semantik. 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 | [**Pemprosesan Bahasa Semula Jadi**](./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) | [Terokai Pemprosesan Bahasa Semula Jadi di Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Perwakilan Teks. 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 | [Penterjemahan perkataan semantik. Word2Vec dan 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 | [Pemodelan Bahasa. Melatih penterjemahan anda sendiri](./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) | [Makmal](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 14 | [Penanaman kata semantik. Word2Vec dan 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 | [Pemodelan Bahasa. Melatih penanaman anda sendiri](./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) | [Makmal](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | | 16 | [Rangkaian Neural Berulang](./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 | [Rangkaian Berulang Generatif](./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) | [Makmal](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 17 | [Rangkaian Generatif Berulang](./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) | [Makmal](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformer. 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 | [Pengenalan Entiti Bernama](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Makmal](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Model Bahasa Besar, Pengaturcaraan Prompt dan Tugasan 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 | [Pengecaman Entiti Bernama](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Makmal](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Model Bahasa Besar, Pengaturcaraan Prompt dan Tugas 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 | **Teknik AI Lain** || | | 21 | [Algoritma Genetik](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Pembelajaran Penguatan Mendalam](./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) | [Makmal](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Sistem Pelbagai Ejen](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 23 | [Sistem Agen Berbilang](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Etika AI** | | | | 24 | [Etika AI dan AI Bertanggungjawab](./lessons/7-Ethics/README.md) | [Microsoft Learn: Prinsip AI Bertanggungjawab](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Tambahan** | | | -| 25 | [Rangkaian Multi-Mod, CLIP dan VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Rangkaian Multi-Modal, CLIP dan VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Setiap pelajaran mengandungi * Bahan pra-bacaan -* Jupyter Notebook yang boleh dilaksanakan, yang selalunya khusus kepada rangka kerja (**PyTorch** atau **TensorFlow**). Notebook boleh dilaksanakan juga mengandungi banyak bahan teori, jadi untuk memahami topik anda perlu lalui sekurang-kurangnya satu versi notebook (sama ada PyTorch atau TensorFlow). -* **Makmal** yang disediakan untuk beberapa topik, yang memberi anda peluang untuk mencuba menggunakan bahan yang telah anda pelajari kepada satu masalah tertentu. -* Sesetengah bahagian mengandungi pautan ke modul [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) yang merangkumi topik berkaitan. +* Buku Jupyter yang boleh dijalankan, yang sering khusus untuk kerangka kerja (**PyTorch** atau **TensorFlow**). Buku nota yang boleh dijalankan juga mengandungi banyak bahan teori, jadi untuk memahami topik anda perlu melalui sekurang-kurangnya satu versi buku nota (sama ada PyTorch atau TensorFlow). +* **Makmal** tersedia untuk beberapa topik, yang memberi anda peluang untuk mencuba menerapkan bahan yang telah anda pelajari kepada masalah tertentu. +* Sesetengah bahagian mengandungi pautan ke modul [**Microsoft Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) yang merangkumi topik yang berkaitan. -## Mula Bermula +## Memulakan -### 🎯 Baru dalam AI? Mula Di Sini! +### 🎯 Baru dalam AI? Mula di Sini! -Jika anda benar-benar baru dalam AI dan mahukan contoh pantas yang boleh dicuba, cuba lihat [**Contoh Mesra Pemula**](./examples/README.md) kami! Ini termasuk: +Jika anda benar-benar baru dalam AI dan mahukan contoh pantas dan praktikal, lihat [**Contoh Mesra Pemula**](./examples/README.md) kami! Contoh ini termasuk: -- 🌟 **Hello AI World** - Program AI pertama anda (pengenalan corak) -- 🧠 **Rangkaian Neural Ringkas** - Bina rangkaian neural dari awal -- 🖼️ **Pengklasifikasi Imej** - Klasifikasikan imej dengan komen terperinci +- 🌟 **Hello AI World** - Program AI pertama anda (pengenalan pola) +- 🧠 **Rangkaian Neural Mudah** - Membina rangkaian neural dari awal +- 🖼️ **Pengelasan Imej** - Mengelas imej dengan komen terperinci - 💬 **Sentimen Teks** - Analisis teks positif/negatif Contoh-contoh ini direka untuk membantu anda memahami konsep AI sebelum meneroka keseluruhan kurikulum. ### 📚 Persediaan Kurikulum Penuh -- Kami telah mencipta [pelajaran persediaan](./lessons/0-course-setup/setup.md) untuk membantu anda menyediakan persekitaran pembangunan anda. - Untuk Pendidik, kami juga telah menyediakan [pelajaran persediaan kurikulum](./lessons/0-course-setup/for-teachers.md) untuk anda! -- Cara untuk [Menjalankan kod di VSCode atau Codepace](./lessons/0-course-setup/how-to-run.md) +- Kami telah mencipta [pelajaran persediaan](./lessons/0-course-setup/setup.md) untuk membantu anda menyiapkan persekitaran pembangunan anda. - Untuk Pendidik, kami juga telah mencipta [pelajaran persediaan kurikulum](./lessons/0-course-setup/for-teachers.md) untuk anda! +- Cara untuk [Jalankan kod dalam VSCode atau Codespace](./lessons/0-course-setup/how-to-run.md) Ikuti langkah-langkah ini: -Fork Repositori: Klik pada butang "Fork" di sudut kanan atas halaman ini. +Fork Repositori: Klik pada butang "Fork" di penjuru kanan atas halaman ini. Clone Repositori: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Jangan lupa untuk memberi bintang (🌟) pada repo ini supaya mudah dicari kemudian. +Jangan lupa untuk bintang (🌟) repo ini supaya mudah dicari kemudian. -## Berkenalan dengan Pelajar Lain +## Temui Pelajar Lain -Sertai [pelayan Discord AI rasmi kami](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) untuk bertemu dan berhubung dengan pelajar lain yang mengikuti kursus ini dan dapatkan sokongan. +Sertai [pelayan Discord AI rasmi kami](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) untuk bertemu dan berjejaring dengan pelajar lain yang mengambil kursus ini dan dapatkan sokongan. Jika anda mempunyai maklum balas produk atau soalan semasa membina, lawati [Forum Pembangun Azure AI Foundry](https://aka.ms/foundry/forum) ## Kuiz -> **Nota mengenai kuiz**: Semua kuiz terkandung dalam folder Quiz-app di etc\quiz-app, atau [Dalam Talian Di Sini](https://ff-quizzes.netlify.app/) Kuiz ini dipautkan dari dalam pelajaran dan aplikasi kuiz boleh dijalankan secara tempatan atau dideploy ke Azure; ikuti arahan dalam folder `quiz-app`. Ia sedang dalam proses pelokalan secara berperingkat. +> **Nota tentang kuiz**: Semua kuiz disimpan dalam folder Quiz-app di etc\quiz-app, atau [Dalam Talian Di Sini](https://ff-quizzes.netlify.app/) Mereka dipautkan dari dalam pelajaran dan aplikasi kuiz boleh dijalankan secara lokal atau disebarkan ke Azure; ikut arahan dalam folder `quiz-app`. Mereka sedang diterjemahkan secara berperingkat. -## Bantuan Diperlukan +## Meminta Bantuan -Adakah anda mempunyai cadangan atau mendapati kesalahan ejaan atau kod? Sila laporkan isu atau buat permintaan tarik. +Adakah anda mempunyai cadangan atau telah menjumpai kesalahan ejaan atau kod? Buat isu atau buat pull request. -## Terima Kasih Istimewa +## Ucapan Terima Kasih Istimewa -* **✍️ Pengarang Utama:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **✍️ Penulis Utama:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Penyunting:** [Jen Looper](https://twitter.com/jenlooper), PhD * **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Pencipta Kuiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Penyumbang Teras:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Kurikulum Lain -Pasukan kami menghasilkan kurikulum lain! Lihat: +Pasukan kami menghasilkan kurikulum lain! Semak: ### LangChain -[![LangChain4j untuk Pemula](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js untuk Pemula](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- -### Azure / Edge / MCP / Ejen -[![AZD untuk Pemula](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 untuk Pemula](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 untuk Pemula](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) -[![Ejen AI untuk Pemula](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) +### Azure / Edge / MCP / Agen +[![AZD for Beginners](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 for Beginners](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 for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](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) --- ### Siri AI Generatif -[![AI Generatif untuk Pemula](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 Generatif (.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 Generatif (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 Generatif (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) +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![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) --- ### Pembelajaran Teras -[![ML untuk Pemula](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) -[![Sains Data untuk Pemula](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 untuk Pemula](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) -[![Keselamatan Siber untuk Pemula](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) -[![Pembangunan Web untuk Pemula](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 untuk Pemula](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) -[![Pembangunan XR untuk Pemula](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](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 for Beginners](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 for Beginners](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 Development for Beginners](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) --- ### Siri Copilot -[![Copilot untuk Pengaturcaraan Berpasangan AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot untuk 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) -[![Pengembaraan Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![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) +[![Copilot for 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) ## Mendapatkan Bantuan -Jika anda tersekat atau mempunyai sebarang soalan tentang membina aplikasi AI. Sertai pelajar lain dan pembangun berpengalaman dalam perbincangan tentang MCP. Ia adalah komuniti sokongan di mana soalan dialu-alukan dan pengetahuan dikongsi secara bebas. +Jika anda tersekat atau mempunyai sebarang soalan tentang membina aplikasi AI. Sertai pelajar lain dan pembangun berpengalaman dalam perbincangan tentang MCP. Ia adalah komuniti sokongan di mana soalan dialu-alukan dan ilmu dikongsi secara bebas. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Jika anda mempunyai maklum balas produk atau ralat semasa membina, lawati: +Jika anda mempunyai maklum balas produk atau kesilapan semasa membina, lawati: -[![Forum Pembangun 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) --- -**Penafian**: -Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk ketepatan, sila maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang sah. Untuk maklumat penting, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini. +**Penafian**: +Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber utama dan sahih. Untuk maklumat penting, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini. \ No newline at end of file diff --git a/translations/ms/lessons/0-course-setup/how-to-run.md b/translations/ms/lessons/0-course-setup/how-to-run.md index 9c7230f4..bb0c46e1 100644 --- a/translations/ms/lessons/0-course-setup/how-to-run.md +++ b/translations/ms/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Cara Menjalankan Kod -Kurikulum ini mengandungi banyak contoh kod yang boleh dijalankan dan makmal yang anda mungkin ingin cuba. Untuk melakukannya, anda perlu mempunyai keupayaan untuk menjalankan kod Python dalam Jupyter Notebooks yang disediakan sebagai sebahagian daripada kurikulum ini. Terdapat beberapa pilihan untuk menjalankan kod: +Kurikulum ini mengandungi banyak contoh dan makmal yang boleh dilaksanakan yang anda ingin jalankan. Untuk melakukan ini, anda memerlukan keupayaan untuk melaksanakan kod Python dalam Jupyter Notebooks yang disediakan sebagai sebahagian daripada kurikulum ini. Anda mempunyai beberapa pilihan untuk menjalankan kod: ## Jalankan secara tempatan di komputer anda -Untuk menjalankan kod secara tempatan di komputer anda, anda perlu mempunyai beberapa versi Python yang dipasang. Saya secara peribadi mengesyorkan memasang **[miniconda](https://conda.io/en/latest/miniconda.html)** - ia adalah pemasangan yang ringan dan menyokong pengurus pakej `conda` untuk pelbagai **persekitaran maya** Python. +Untuk menjalankan kod secara tempatan di komputer anda, pemasangan Python diperlukan. Satu cadangan adalah untuk memasang **[miniconda](https://conda.io/en/latest/miniconda.html)** - ia adalah pemasangan yang agak ringan yang menyokong pengurus pakej `conda` untuk pelbagai **persekitaran maya** Python. -Selepas anda memasang miniconda, anda perlu mengklon repositori dan mencipta persekitaran maya untuk digunakan dalam kursus ini: +Selepas anda memasang miniconda, klon repositori dan buat persekitaran maya untuk digunakan dalam kursus ini: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,17 +26,17 @@ conda activate ai4beg ### Menggunakan Visual Studio Code dengan Sambungan Python -Cara terbaik untuk menggunakan kurikulum ini adalah dengan membukanya dalam [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) bersama [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Kurikulum ini paling baik digunakan apabila dibuka dalam [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) dengan [Sambungan Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Nota**: Setelah anda mengklon dan membuka direktori dalam VS Code, ia akan secara automatik mencadangkan anda untuk memasang sambungan Python. Anda juga perlu memasang miniconda seperti yang diterangkan di atas. +> **Nota**: Setelah anda klon dan buka direktori dalam VS Code, ia akan secara automatik mencadangkan anda memasang sambungan Python. Anda juga perlu memasang miniconda seperti yang diterangkan di atas. -> **Nota**: Jika VS Code mencadangkan anda untuk membuka semula repositori dalam container, anda perlu menolak cadangan ini untuk menggunakan pemasangan Python tempatan. +> **Nota**: Jika VS Code mencadangkan anda untuk buka semula repositori dalam bekas, anda harus tolak ini untuk menggunakan pemasangan Python tempatan. ### Menggunakan Jupyter dalam Pelayar -Anda juga boleh menggunakan persekitaran Jupyter terus dari pelayar di komputer anda. Sebenarnya, kedua-dua Jupyter klasik dan Jupyter Hub menyediakan persekitaran pembangunan yang cukup mudah dengan auto-lengkap, penyorotan kod, dan sebagainya. +Anda juga boleh menggunakan persekitaran Jupyter dari pelayar pada komputer anda sendiri. Baik Jupyter klasik dan JupyterHub menyediakan persekitaran pembangunan yang mudah dengan auto-selesaian, sorotan kod, dan lain-lain. -Untuk memulakan Jupyter secara tempatan, pergi ke direktori kursus, dan jalankan: +Untuk memulakan Jupyter secara tempatan, pergi ke direktori kursus, dan laksanakan: ```bash jupyter notebook @@ -45,34 +45,36 @@ atau ```bash jupyterhub ``` -Anda kemudian boleh menavigasi ke mana-mana fail `.ipynb`, membukanya dan mula bekerja. +Anda kemudian boleh navigasi ke mana-mana fail `.ipynb`, bukanya dan mula bekerja. -### Menjalankan dalam Container +### Menjalankan dalam bekas -Satu alternatif kepada pemasangan Python adalah menjalankan kod dalam container. Oleh kerana repositori kami mengandungi folder `.devcontainer` khas yang memberikan arahan bagaimana membina container untuk repositori ini, VS Code akan menawarkan anda untuk membuka semula kod dalam container. Ini memerlukan pemasangan Docker, dan juga lebih kompleks, jadi kami mencadangkan ini untuk pengguna yang lebih berpengalaman. +Satu alternatif kepada pemasangan Python adalah menjalankan kod dalam bekas. Oleh kerana repositori kami membekalkan folder `.devcontainer` khas yang memberi arahan bagaimana untuk membina bekas untuk repo ini, VS Code menawarkan peluang untuk membuka semula kod dalam bekas. Ini akan memerlukan pemasangan Docker, dan juga lebih kompleks, jadi kami mengesyorkan ini kepada pengguna yang lebih berpengalaman. ## Menjalankan di Awan -Jika anda tidak mahu memasang Python secara tempatan, dan mempunyai akses kepada beberapa sumber awan - alternatif yang baik adalah menjalankan kod di awan. Terdapat beberapa cara untuk melakukannya: +Jika anda tidak mahu memasang Python secara tempatan, dan mempunyai akses kepada beberapa sumber awan - satu alternatif yang baik adalah menjalankan kod di awan. Terdapat beberapa cara anda boleh melakukan ini: -* Menggunakan **[GitHub Codespaces](https://github.com/features/codespaces)**, yang merupakan persekitaran maya yang dicipta untuk anda di GitHub, boleh diakses melalui antara muka pelayar VS Code. Jika anda mempunyai akses kepada Codespaces, anda hanya perlu klik butang **Code** dalam repositori, mulakan codespace, dan mula bekerja dengan segera. -* Menggunakan **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) adalah sumber pengkomputeran percuma yang disediakan di awan untuk orang seperti anda mencuba beberapa kod di GitHub. Terdapat butang di halaman utama untuk membuka repositori dalam Binder - ini akan membawa anda ke laman Binder, yang akan membina container asas dan memulakan antara muka web Jupyter untuk anda dengan lancar. +* Menggunakan **[GitHub Codespaces](https://github.com/features/codespaces)**, iaitu persekitaran maya yang dicipta untuk anda di GitHub, boleh diakses melalui antaramuka pelayar VS Code. Jika anda mempunyai akses ke Codespaces, anda hanya perlu klik butang **Code** dalam repo, mulakan codespace, dan mula menjalankan dengan pantas. +* Menggunakan **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) menawarkan sumber pengkomputeran percuma yang disediakan di awan untuk orang seperti anda menguji kod di GitHub. Terdapat butang di halaman depan untuk membuka repositori dalam Binder - ini akan membawa anda dengan cepat ke laman binder, yang akan membina bekas asas dan memulakan antaramuka web Jupyter untuk anda tanpa gangguan. -> **Nota**: Untuk mengelakkan penyalahgunaan, Binder mempunyai akses kepada beberapa sumber web yang disekat. Ini mungkin menghalang beberapa kod daripada berfungsi, terutamanya yang memuat model dan/atau dataset dari Internet awam. Anda mungkin perlu mencari penyelesaian alternatif. Juga, sumber pengkomputeran yang disediakan oleh Binder agak asas, jadi latihan akan menjadi perlahan, terutamanya dalam pelajaran yang lebih kompleks kemudian. +> **Nota**: Untuk mengelakkan penyalahgunaan, Binder menghadkan akses kepada beberapa sumber web. Ini mungkin menghalang beberapa kod daripada berfungsi, yang mengambil model dan/atau set data dari Internet awam. Anda mungkin perlu mencari jalan penyelesaian. Juga, sumber pengkomputeran yang disediakan oleh Binder adalah agak asas, jadi latihan akan lambat, terutamanya dalam pelajaran yang lebih kompleks kemudian. ## Menjalankan di Awan dengan GPU -Beberapa pelajaran kemudian dalam kurikulum ini akan mendapat manfaat besar daripada sokongan GPU, kerana tanpa GPU, latihan akan menjadi sangat perlahan. Terdapat beberapa pilihan yang boleh anda ikuti, terutamanya jika anda mempunyai akses kepada awan sama ada melalui [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), atau melalui institusi anda: +Beberapa pelajaran kemudian dalam kurikulum ini akan sangat mendapat manfaat daripada sokongan GPU. Latihan model, sebagai contoh, boleh menjadi sangat lambat jika tidak. Terdapat beberapa pilihan yang boleh anda ikuti, terutamanya jika anda mempunyai akses ke awan sama ada melalui [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), atau melalui institusi anda: -* Cipta [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) dan sambungkan ke dalamnya melalui Jupyter. Anda kemudian boleh mengklon repositori terus ke mesin tersebut, dan mula belajar. VM siri NC mempunyai sokongan GPU. +* Buat [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) dan sambungkan kepadanya melalui Jupyter. Anda boleh klon repo terus ke mesin itu, dan mula belajar. VM siri NC mempunyai sokongan GPU. -> **Nota**: Beberapa langganan, termasuk Azure for Students, tidak menyediakan sokongan GPU secara lalai. Anda mungkin perlu meminta teras GPU tambahan melalui permintaan sokongan teknikal. +> **Nota**: Sesetengah langganan, termasuk Azure for Students, tidak menyediakan sokongan GPU secara automatik. Anda mungkin perlu memohon teras GPU tambahan dengan permintaan sokongan teknikal. -* Cipta [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) dan kemudian gunakan ciri Notebook di sana. [Video ini](https://azure-for-academics.github.io/quickstart/azureml-papers/) menunjukkan cara mengklon repositori ke dalam notebook Azure ML dan mula menggunakannya. +* Buat [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) dan kemudian gunakan ciri Notebook di situ. [Video ini](https://azure-for-academics.github.io/quickstart/azureml-papers/) menunjukkan cara untuk klon repositori ke dalam notebook Azure ML dan mula menggunakannya. -Anda juga boleh menggunakan Google Colab, yang menyediakan beberapa sokongan GPU percuma, dan memuat naik Jupyter Notebooks ke sana untuk menjalankannya satu persatu. +Anda juga boleh menggunakan Google Colab, yang datang dengan sokongan GPU percuma, dan memuat naik Jupyter Notebooks di sana untuk melaksanakannya satu persatu. --- + **Penafian**: -Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat penting, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini. \ No newline at end of file +Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya hendaklah dianggap sebagai sumber yang sahih. Untuk maklumat penting, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab terhadap sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini. + \ No newline at end of file diff --git a/translations/ms/lessons/2-Symbolic/Animals.ipynb b/translations/ms/lessons/2-Symbolic/Animals.ipynb index 268e4bfa..930b2193 100644 --- a/translations/ms/lessons/2-Symbolic/Animals.ipynb +++ b/translations/ms/lessons/2-Symbolic/Animals.ipynb @@ -10,21 +10,21 @@ "\n", "Contoh daripada [Kurikulum AI untuk Pemula](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Dalam contoh ini, kita akan melaksanakan sistem berasaskan pengetahuan yang mudah untuk menentukan haiwan berdasarkan beberapa ciri fizikal. Sistem ini boleh diwakili oleh pokok AND-OR berikut (ini adalah sebahagian daripada keseluruhan pokok, kita boleh menambah lebih banyak peraturan dengan mudah):\n", + "Dalam contoh ini, kita akan melaksanakan sistem berasaskan pengetahuan yang ringkas untuk menentukan haiwan berdasarkan beberapa ciri fizikal. Sistem ini boleh diwakili oleh pokok AND-OR berikut (ini adalah sebahagian daripada keseluruhan pokok, kita boleh dengan mudah menambah beberapa peraturan lagi):\n", "\n", - "![](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Sistem Pakar Kita Sendiri dengan Inferens Belakang\n", + "## Sistem pakar shell kami sendiri dengan inferens kebelakang\n", "\n", - "Mari kita cuba mendefinisikan satu bahasa mudah untuk perwakilan pengetahuan berdasarkan peraturan pengeluaran. Kita akan menggunakan kelas Python sebagai kata kunci untuk mendefinisikan peraturan. Terdapat tiga jenis kelas utama:\n", + "Mari cuba mentakrifkan bahasa ringkas untuk perwakilan pengetahuan berdasarkan peraturan pengeluaran. Kami akan menggunakan kelas Python sebagai kata kunci untuk menentukan peraturan. Pada dasarnya terdapat 3 jenis kelas:\n", "* `Ask` mewakili soalan yang perlu ditanya kepada pengguna. Ia mengandungi set jawapan yang mungkin.\n", - "* `If` mewakili satu peraturan, dan ia hanyalah sintaks ringkas untuk menyimpan kandungan peraturan tersebut.\n", - "* `AND`/`OR` adalah kelas untuk mewakili cabang AND/OR dalam pokok. Ia hanya menyimpan senarai argumen di dalamnya. Untuk mempermudahkan kod, semua fungsi didefinisikan dalam kelas induk `Content`.\n" + "* `If` mewakili peraturan, dan ia hanyalah gula sintaks untuk menyimpan kandungan peraturan\n", + "* `AND`/`OR` adalah kelas untuk mewakili cabang AND/OR pokok. Mereka hanya menyimpan senarai hujah di dalamnya. Untuk memudahkan kod, semua fungsi ditakrifkan dalam kelas induk `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Dalam sistem kami, memori kerja akan mengandungi senarai **fakta** sebagai **pasangan atribut-nilai**. Pangkalan pengetahuan boleh ditakrifkan sebagai satu kamus besar yang memetakan tindakan (fakta baru yang perlu dimasukkan ke dalam memori kerja) kepada syarat-syarat, yang dinyatakan sebagai ungkapan AND-OR. Selain itu, beberapa fakta boleh `Ask`.\n" + "Dalam sistem kami, memori kerja akan mengandungi senarai **fakta** sebagai **pasangan atribut-nilai**. Pangkalan pengetahuan boleh ditakrifkan sebagai satu kamus besar yang memetakan tindakan (fakta baru yang harus dimasukkan ke dalam memori kerja) kepada syarat, yang dinyatakan sebagai ungkapan AND-OR. Juga, sesetengah fakta boleh `Ditanya`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Untuk melaksanakan inferens ke belakang, kita akan mendefinisikan kelas `Knowledgebase`. Ia akan mengandungi:\n", + "Untuk melakukan inferens balik, kami akan mentakrifkan kelas `Knowledgebase`. Ia akan mengandungi:\n", "* `memory` kerja - sebuah kamus yang memetakan atribut kepada nilai\n", - "* `rules` Knowledgebase dalam format seperti yang ditakrifkan di atas\n", + "* Peraturan `rules` Knowledgebase dalam format seperti yang ditakrifkan di atas\n", "\n", "Dua kaedah utama adalah:\n", - "* `get` untuk mendapatkan nilai atribut, melaksanakan inferens jika perlu. Sebagai contoh, `get('color')` akan mendapatkan nilai slot warna (ia akan bertanya jika perlu, dan menyimpan nilai untuk kegunaan kemudian dalam memori kerja). Jika kita bertanya `get('color:blue')`, ia akan bertanya tentang warna, dan kemudian mengembalikan nilai `y`/`n` bergantung pada warna.\n", - "* `eval` melaksanakan inferens sebenar, iaitu menelusuri pokok AND/OR, menilai sub-matlamat, dan sebagainya.\n" + "* `get` untuk mendapatkan nilai atribut, melaksanakan inferens jika perlu. Contohnya, `get('color')` akan mendapatkan nilai ruang warna (ia akan bertanya jika perlu, dan menyimpan nilai tersebut untuk digunakan kemudian dalam memory kerja). Jika kami bertanya `get('color:blue')`, ia akan bertanya untuk warna, dan kemudian mengembalikan nilai `y`/`n` bergantung pada warna.\n", + "* `eval` melaksanakan inferens sebenar, iaitu mengembara pokok AND/OR, menilai sub-matlamat, dan lain-lain.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sekarang mari kita tentukan pangkalan pengetahuan haiwan kita dan lakukan konsultasi. Perhatikan bahawa panggilan ini akan menanyakan soalan kepada anda. Anda boleh menjawab dengan menaip `y`/`n` untuk soalan ya-tidak, atau dengan menentukan nombor (0..N) untuk soalan dengan jawapan pelbagai pilihan yang lebih panjang.\n" + "Sekarang mari kita takrifkan pangkalan pengetahuan haiwan kita dan lakukan perundingan. Nota bahawa panggilan ini akan mengemukakan soalan kepada anda. Anda boleh menjawab dengan menaip `y`/`n` untuk soalan ya-tidak, atau dengan menyatakan nombor (0..N) untuk soalan dengan jawapan pilihan berganda yang lebih panjang.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Menggunakan PyKnow untuk Inferens Hadapan\n", + "## Menggunakan Experta untuk Inferens Hadapan\n", "\n", - "Dalam contoh seterusnya, kita akan cuba melaksanakan inferens hadapan menggunakan salah satu perpustakaan untuk perwakilan pengetahuan, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** ialah perpustakaan untuk mencipta sistem inferens hadapan dalam Python, yang direka untuk menyerupai sistem lama klasik [CLIPS](http://www.clipsrules.net/index.html).\n", + "Dalam contoh seterusnya, kita akan cuba melaksanakan inferens hadapan menggunakan salah satu perpustakaan untuk perwakilan pengetahuan, [Experta](https://github.com/nilp0inter/experta). **Experta** adalah perpustakaan untuk mencipta sistem inferens hadapan dalam Python, yang direka untuk serupa dengan sistem lama klasik [CLIPS](http://www.clipsrules.net/index.html). \n", "\n", - "Kita juga boleh melaksanakan rantaian hadapan sendiri tanpa banyak masalah, tetapi pelaksanaan naif biasanya tidak begitu cekap. Untuk pemadanan peraturan yang lebih berkesan, algoritma khas [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) digunakan.\n" + "Kita juga boleh melaksanakan pendahanan hadapan sendiri tanpa banyak masalah, tetapi pelaksanaan naif biasanya tidak begitu cekap. Untuk pemadanan peraturan yang lebih berkesan, algoritma khas [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) digunakan.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Kami akan mentakrifkan sistem kami sebagai kelas yang mewarisi `KnowledgeEngine`. Setiap peraturan ditakrifkan oleh fungsi berasingan dengan anotasi `@Rule`, yang menentukan bila peraturan tersebut harus dilaksanakan. Di dalam peraturan, kami boleh menambah fakta baharu menggunakan fungsi `declare`, dan penambahan fakta tersebut akan menyebabkan beberapa peraturan lain dipanggil oleh enjin inferens ke hadapan.\n" + "Kita akan mentakrifkan sistem kita sebagai kelas yang mewarisi `KnowledgeEngine`. Setiap peraturan ditakrifkan oleh fungsi yang berasingan dengan anotasi `@Rule`, yang menentukan bila peraturan itu harus dijalankan. Di dalam peraturan, kita boleh menambah fakta baru menggunakan fungsi `declare`, dan penambahan fakta-fakta tersebut akan menyebabkan beberapa peraturan lain dipanggil oleh enjin inferens hadapan.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sebaik sahaja kita telah mentakrifkan pangkalan pengetahuan, kita mengisi memori kerja kita dengan beberapa fakta awal, dan kemudian memanggil kaedah `run()` untuk melaksanakan inferens. Anda boleh melihat hasilnya bahawa fakta-fakta baru yang disimpulkan ditambah ke dalam memori kerja, termasuk fakta akhir tentang haiwan tersebut (jika kita menetapkan semua fakta awal dengan betul).\n" + "Setelah kita telah mentakrifkan asas pengetahuan, kita mengisi ingatan kerja kita dengan beberapa fakta awal, dan kemudian memanggil kaedah `run()` untuk melakukan inferens. Anda boleh lihat hasilnya fakta-fakta baru yang diperoleh ditambahkan ke dalam ingatan kerja, termasuk fakta akhir tentang haiwan tersebut (jika kita menetapkan semua fakta awal dengan betul).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat yang kritikal, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n" + "---\n\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asli harus dianggap sebagai sumber yang sahih. Untuk maklumat kritikal, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T14:42:38+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T04:07:56+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "ms" } diff --git a/translations/ms/lessons/2-Symbolic/README.md b/translations/ms/lessons/2-Symbolic/README.md index 5c2f4f59..133a4896 100644 --- a/translations/ms/lessons/2-Symbolic/README.md +++ b/translations/ms/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Perwakilan Pengetahuan dan Sistem Pakar -![Ringkasan kandungan AI Simbolik](../../../../translated_images/ms/ai-symbolic.715a30cb610411a6.webp) +![Ringkasan kandungan AI Simbolik](../../../../../../translated_images/ms/ai-symbolic.715a30cb610411a6.webp) > Sketchnote oleh [Tomomi Imura](https://twitter.com/girlie_mac) -Pencarian untuk kecerdasan buatan adalah berdasarkan usaha untuk mencari pengetahuan, bagi memahami dunia seperti manusia. Tetapi bagaimana caranya untuk melakukannya? +Pencarian kecerdasan buatan berasaskan pencarian pengetahuan, untuk memahami dunia seperti manusia. Tetapi bagaimana anda boleh melakukannya? -## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Kuiz pra-ceramah](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Pada zaman awal AI, pendekatan dari atas ke bawah untuk mencipta sistem pintar (dibincangkan dalam pelajaran sebelumnya) adalah popular. Ideanya adalah untuk mengekstrak pengetahuan daripada manusia ke dalam bentuk yang boleh dibaca mesin, dan kemudian menggunakannya untuk menyelesaikan masalah secara automatik. Pendekatan ini berdasarkan dua idea besar: +Pada zaman awal AI, pendekatan dari atas ke bawah untuk mencipta sistem pintar (dibincangkan dalam pelajaran sebelumnya) adalah popular. Idea itu adalah untuk mengekstrak pengetahuan daripada manusia ke dalam bentuk yang boleh dibaca mesin, kemudian menggunakannya untuk menyelesaikan masalah secara automatik. Pendekatan ini berdasarkan dua idea besar: * Perwakilan Pengetahuan -* Penaakulan +* Penalaran ## Perwakilan Pengetahuan -Salah satu konsep penting dalam AI Simbolik ialah **pengetahuan**. Adalah penting untuk membezakan pengetahuan daripada *maklumat* atau *data*. Sebagai contoh, seseorang boleh mengatakan bahawa buku mengandungi pengetahuan, kerana seseorang boleh belajar daripada buku dan menjadi pakar. Walau bagaimanapun, apa yang terkandung dalam buku sebenarnya dipanggil *data*, dan dengan membaca buku serta mengintegrasikan data ini ke dalam model dunia kita, kita menukar data ini kepada pengetahuan. +Salah satu konsep penting dalam AI Simbolik adalah **pengetahuan**. Penting untuk membezakan pengetahuan daripada *maklumat* atau *data*. Contohnya, seseorang boleh mengatakan bahawa buku mengandungi pengetahuan, kerana seseorang boleh belajar dari buku dan menjadi pakar. Namun, apa yang terkandung dalam buku sebenarnya dipanggil *data*, dan dengan membaca buku dan mengintegrasikan data ini ke dalam model dunia kita, kita menukar data ini menjadi pengetahuan. -> ✅ **Pengetahuan** adalah sesuatu yang terkandung dalam fikiran kita dan mewakili pemahaman kita tentang dunia. Ia diperoleh melalui proses **pembelajaran** yang aktif, yang mengintegrasikan kepingan maklumat yang kita terima ke dalam model dunia kita yang aktif. +> ✅ **Pengetahuan** adalah sesuatu yang terkandung dalam kepala kita dan mewakili pemahaman kita tentang dunia. Ia diperoleh melalui proses **pembelajaran** aktif, yang mengintegrasikan kepingan maklumat yang kita terima ke dalam model aktif dunia kita. -Kebiasaannya, kita tidak mentakrifkan pengetahuan secara ketat, tetapi kita menyelaraskannya dengan konsep berkaitan lain menggunakan [Piramid DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ia mengandungi konsep berikut: +Kebiasaannya, kita tidak mendefinisikan pengetahuan secara ketat, tetapi kita menyelaraskannya dengan konsep berkaitan lain menggunakan [Piramid DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ia mengandungi konsep-konsep berikut: -* **Data** adalah sesuatu yang diwakili dalam media fizikal, seperti teks bertulis atau kata-kata yang diucapkan. Data wujud secara bebas daripada manusia dan boleh dipindahkan antara individu. -* **Maklumat** adalah bagaimana kita mentafsir data dalam fikiran kita. Sebagai contoh, apabila kita mendengar perkataan *komputer*, kita mempunyai pemahaman tertentu tentang apa itu. -* **Pengetahuan** adalah maklumat yang diintegrasikan ke dalam model dunia kita. Sebagai contoh, setelah kita belajar apa itu komputer, kita mula mempunyai idea tentang bagaimana ia berfungsi, berapa harganya, dan untuk apa ia boleh digunakan. Rangkaian konsep yang saling berkaitan ini membentuk pengetahuan kita. -* **Kebijaksanaan** adalah satu lagi tahap pemahaman kita tentang dunia, dan ia mewakili *meta-pengetahuan*, contohnya, beberapa tanggapan tentang bagaimana dan bila pengetahuan itu harus digunakan. +* **Data** adalah sesuatu yang diwakili dalam media fizikal, seperti teks bertulis atau kata-kata yang diucapkan. Data wujud secara bebas daripada manusia dan boleh dipindahkan antara orang. +* **Maklumat** adalah bagaimana kita mentafsir data dalam kepala kita. Contohnya, apabila kita mendengar perkataan *komputer*, kita mempunyai sedikit kefahaman tentang apa itu. +* **Pengetahuan** adalah maklumat yang diintegrasikan ke dalam model dunia kita. Contohnya, apabila kita belajar apa itu komputer, kita mula mempunyai beberapa idea tentang bagaimana ia berfungsi, berapa harganya, dan apa yang ia boleh digunakan. Rangkaian konsep yang saling berkaitan ini membentuk pengetahuan kita. +* **Kebijaksanaan** adalah satu lagi tahap pemahaman dunia kita, dan ia mewakili *meta-pengetahuan*, contohnya, satu konsep tentang bagaimana dan bila pengetahuan harus digunakan. - + *Imej [dari Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Oleh Longlivetheux - Karya sendiri, CC BY-SA 4.0* -Oleh itu, masalah **perwakilan pengetahuan** adalah untuk mencari cara yang berkesan untuk mewakili pengetahuan dalam komputer dalam bentuk data, supaya ia boleh digunakan secara automatik. Ini boleh dilihat sebagai spektrum: +Jadi, masalah **perwakilan pengetahuan** adalah untuk mencari cara yang berkesan untuk mewakili pengetahuan di dalam komputer dalam bentuk data, supaya ia boleh digunakan secara automatik. Ini boleh dilihat sebagai spektrum: -![Spektrum perwakilan pengetahuan](../../../../translated_images/ms/knowledge-spectrum.b60df631852c0217.webp) +![Spektrum perwakilan pengetahuan](../../../../../../translated_images/ms/knowledge-spectrum.b60df631852c0217.webp) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) -* Di sebelah kiri, terdapat jenis perwakilan pengetahuan yang sangat mudah yang boleh digunakan secara berkesan oleh komputer. Yang paling mudah ialah algoritma, di mana pengetahuan diwakili oleh program komputer. Walau bagaimanapun, ini bukan cara terbaik untuk mewakili pengetahuan, kerana ia tidak fleksibel. Pengetahuan dalam fikiran kita sering kali tidak bersifat algoritma. -* Di sebelah kanan, terdapat perwakilan seperti teks semula jadi. Ia adalah yang paling berkuasa, tetapi tidak boleh digunakan untuk penaakulan automatik. +* Di sebelah kiri, terdapat jenis perwakilan pengetahuan yang sangat mudah yang boleh digunakan dengan berkesan oleh komputer. Yang paling mudah ialah algoritmik, apabila pengetahuan diwakili oleh program komputer. Walau bagaimanapun, ini bukan cara terbaik untuk mewakili pengetahuan kerana ia tidak fleksibel. Pengetahuan dalam kepala kita sering kali tidak bersifat algoritmik. +* Di sebelah kanan, terdapat perwakilan seperti teks semula jadi. Ia paling kuat, tetapi tidak boleh digunakan untuk penalaran automatik. -> ✅ Fikirkan sejenak bagaimana anda mewakili pengetahuan dalam fikiran anda dan menukarkannya kepada nota. Adakah terdapat format tertentu yang berkesan untuk membantu anda mengingati? +> ✅ Fikirkan seketika tentang bagaimana anda mewakili pengetahuan dalam kepala anda dan menukarnya kepada nota. Adakah terdapat format tertentu yang berkesan untuk anda membantu dalam penyimpanan? ## Mengklasifikasikan Perwakilan Pengetahuan Komputer -Kita boleh mengklasifikasikan pelbagai kaedah perwakilan pengetahuan komputer dalam kategori berikut: +Kita boleh mengklasifikasikan kaedah perwakilan pengetahuan komputer yang berbeza ke dalam kategori berikut: -* **Perwakilan rangkaian** adalah berdasarkan fakta bahawa kita mempunyai rangkaian konsep yang saling berkaitan dalam fikiran kita. Kita boleh cuba menghasilkan semula rangkaian yang sama sebagai graf dalam komputer - yang dipanggil **rangkaian semantik**. +* **Perwakilan rangkaian** berdasarkan fakta bahawa kita mempunyai rangkaian konsep yang saling berkaitan dalam kepala kita. Kita boleh cuba menghasilkan rangkaian yang sama sebagai graf dalam komputer - dipanggil **rangkaian semantik**. -1. **Triplet Objek-Atribut-Nilai** atau **pasangan atribut-nilai**. Oleh kerana graf boleh diwakili dalam komputer sebagai senarai nod dan tepi, kita boleh mewakili rangkaian semantik dengan senarai triplet, yang mengandungi objek, atribut, dan nilai. Sebagai contoh, kita membina triplet berikut tentang bahasa pengaturcaraan: +1. **Triplet Objek-Atribut-Nilai** atau **pasangan atribut-nilai**. Oleh kerana graf boleh diwakili dalam komputer sebagai senarai nod dan tepi, kita boleh mewakili rangkaian semantik dengan senarai triplet, yang mengandungi objek, atribut, dan nilai. Contohnya, kita membina triplet berikut tentang bahasa pengaturcaraan: Objek | Atribut | Nilai --------|---------|------ -Python | ialah | Bahasa-Tanpa-Taip +-------|-----------|------ +Python | adalah | Bahasa-Tiada-Tip Python | dicipta-oleh | Guido van Rossum -Python | sintaks-blok | indentasi -Bahasa-Tanpa-Taip | tidak mempunyai | definisi jenis +Python | sintaks-blok | penjorokan +Bahasa-Tiada-Tip | tidak ada | definisi jenis > ✅ Fikirkan bagaimana triplet boleh digunakan untuk mewakili jenis pengetahuan lain. -2. **Perwakilan hierarki** menekankan fakta bahawa kita sering mencipta hierarki objek dalam fikiran kita. Sebagai contoh, kita tahu bahawa kenari adalah burung, dan semua burung mempunyai sayap. Kita juga mempunyai idea tentang warna kenari biasanya, dan kelajuan penerbangannya. +2. **Perwakilan hierarki** menekankan fakta bahawa kita sering mencipta hierarki objek dalam kepala kita. Contohnya, kita tahu bahawa kenari adalah burung, dan semua burung mempunyai sayap. Kita juga mempunyai idea tentang warna biasa kenari, dan kelajuan terbang mereka. - - **Perwakilan bingkai** adalah berdasarkan mewakili setiap objek atau kelas objek sebagai **bingkai** yang mengandungi **slot**. Slot mempunyai nilai lalai yang mungkin, sekatan nilai, atau prosedur yang disimpan yang boleh dipanggil untuk mendapatkan nilai slot. Semua bingkai membentuk hierarki yang serupa dengan hierarki objek dalam bahasa pengaturcaraan berorientasikan objek. - - **Senario** adalah jenis bingkai khas yang mewakili situasi kompleks yang boleh berkembang mengikut masa. + - **Perwakilan bingkai** berdasarkan pada mewakili setiap objek atau kelas objek sebagai **bingkai** yang mengandungi **slot**. Slot mempunyai nilai lalai yang mungkin, sekatan nilai, atau prosedur tersimpan yang boleh dipanggil untuk memperoleh nilai slot. Semua bingkai membentuk hierarki serupa dengan hierarki objek dalam bahasa pengaturcaraan berorientasikan objek. + - **Senario** adalah jenis bingkai khas yang mewakili situasi kompleks yang boleh berlaku dalam masa. **Python** Slot | Nilai | Nilai Lalai | Julat | ------|-------|-------------|-------| +-----|-------|-------------|--------| Nama | Python | | | -Adalah | Bahasa-Tanpa-Taip | | | -Kes Pemboleh Ubah | | CamelCase | | +Adalah | Bahasa-Tiada-Tip | | | +Variabel Case | | CamelCase | | Panjang Program | | | 5-5000 baris | -Sintaks Blok | Indentasi | | | +Sintaks Blok | Penjorokan | | | -3. **Perwakilan prosedural** adalah berdasarkan mewakili pengetahuan dengan senarai tindakan yang boleh dilaksanakan apabila keadaan tertentu berlaku. - - Peraturan pengeluaran adalah pernyataan if-then yang membolehkan kita membuat kesimpulan. Sebagai contoh, seorang doktor boleh mempunyai peraturan yang mengatakan bahawa **JIKA** pesakit mempunyai demam tinggi **ATAU** tahap protein C-reaktif yang tinggi dalam ujian darah **MAKA** dia mempunyai keradangan. Sebaik sahaja kita menemui salah satu keadaan, kita boleh membuat kesimpulan tentang keradangan, dan kemudian menggunakannya dalam penaakulan selanjutnya. - - Algoritma boleh dianggap sebagai satu lagi bentuk perwakilan prosedural, walaupun ia hampir tidak pernah digunakan secara langsung dalam sistem berasaskan pengetahuan. +3. **Perwakilan prosedural** berdasarkan pada mewakili pengetahuan dengan senarai tindakan yang boleh dilaksanakan apabila satu syarat tertentu berlaku. + - Peraturan pengeluaran adalah pernyataan jika-maka yang membolehkan kita membuat kesimpulan. Contohnya, seorang doktor boleh mempunyai peraturan yang mengatakan **JIKA** pesakit mempunyai demam tinggi **ATAU** tahap tinggi protein C-reaktif dalam ujian darah **MAKA** dia mengalami keradangan. Setelah menemui salah satu syarat, kita boleh membuat kesimpulan mengenai keradangan, dan kemudian menggunakannya dalam penalaran selanjutnya. + - Algoritma boleh dianggap sebagai bentuk perwakilan prosedural lain, walaupun ia hampir tidak pernah digunakan secara langsung dalam sistem berasaskan pengetahuan. -4. **Logik** pada asalnya dicadangkan oleh Aristotle sebagai cara untuk mewakili pengetahuan manusia sejagat. - - Logik Predikat sebagai teori matematik terlalu kaya untuk dikira, oleh itu beberapa subsetnya biasanya digunakan, seperti klausa Horn yang digunakan dalam Prolog. - - Logik Deskriptif adalah keluarga sistem logik yang digunakan untuk mewakili dan membuat penaakulan tentang hierarki objek dalam perwakilan pengetahuan teragih seperti *web semantik*. +4. **Logik** asalnya dicadangkan oleh Aristotle sebagai cara untuk mewakili pengetahuan manusia sejagat. + - Logik Predikat sebagai teori matematik terlalu kaya untuk dikira, oleh itu subset daripadanya biasanya digunakan, seperti klausa Horn yang digunakan dalam Prolog. + - Logik Deskriptif adalah keluarga sistem logik yang digunakan untuk mewakili dan berfikir tentang hierarki objek dalam perwakilan pengetahuan teragih seperti *web semantik*. ## Sistem Pakar -Salah satu kejayaan awal AI simbolik ialah **sistem pakar** - sistem komputer yang direka untuk bertindak sebagai pakar dalam domain masalah yang terhad. Ia berdasarkan **pangkalan pengetahuan** yang diekstrak daripada satu atau lebih pakar manusia, dan mengandungi **enjin inferens** yang melakukan beberapa penaakulan di atasnya. +Salah satu kejayaan awal AI simbolik ialah yang dipanggil **sistem pakar** - sistem komputer yang direka untuk bertindak sebagai pakar dalam domain masalah terhad. Ia berdasarkan pada **pangkalan pengetahuan** yang diekstrak daripada satu atau lebih pakar manusia, dan mengandungi **enjin inferens** yang melakukan penalaran ke atasnya. -![Seni Bina Manusia](../../../../translated_images/ms/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem Berasaskan Pengetahuan](../../../../translated_images/ms/arch-kbs.3ec5c150b09fa8da.webp) +![Seni bina manusia](../../../../../../translated_images/ms/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistem Berasaskan Pengetahuan](../../../../../../translated_images/ms/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Struktur ringkas sistem saraf manusia | Seni bina sistem berasaskan pengetahuan +Struktur ringkas sistem saraf manusia | Seni bina sistem berasaskan pengetahuan -Sistem pakar dibina seperti sistem penaakulan manusia, yang mengandungi **memori jangka pendek** dan **memori jangka panjang**. Begitu juga, dalam sistem berasaskan pengetahuan kita membezakan komponen berikut: +Sistem pakar dibina seperti sistem penalaran manusia, yang mengandungi **memori jangka pendek** dan **memori jangka panjang**. Begitu juga, dalam sistem berasaskan pengetahuan kita membezakan komponen berikut: -* **Memori masalah**: mengandungi pengetahuan tentang masalah yang sedang diselesaikan, contohnya suhu atau tekanan darah pesakit, sama ada dia mempunyai keradangan atau tidak, dan sebagainya. Pengetahuan ini juga dipanggil **pengetahuan statik**, kerana ia mengandungi gambaran tentang apa yang kita ketahui tentang masalah itu - yang dipanggil *keadaan masalah*. -* **Pangkalan pengetahuan**: mewakili pengetahuan jangka panjang tentang domain masalah. Ia diekstrak secara manual daripada pakar manusia, dan tidak berubah dari satu konsultasi ke konsultasi yang lain. Oleh kerana ia membolehkan kita menavigasi dari satu keadaan masalah ke keadaan yang lain, ia juga dipanggil **pengetahuan dinamik**. -* **Enjin inferens**: mengatur keseluruhan proses pencarian dalam ruang keadaan masalah, bertanya soalan kepada pengguna apabila perlu. Ia juga bertanggungjawab untuk mencari peraturan yang betul untuk digunakan pada setiap keadaan. +* **Memori masalah**: mengandungi pengetahuan tentang masalah yang sedang diselesaikan, contohnya suhu atau tekanan darah pesakit, sama ada dia mengalami keradangan atau tidak, dan lain-lain. Pengetahuan ini juga dipanggil **pengetahuan statik**, kerana ia mengandungi gagasan snapshot tentang apa yang kita tahu tentang masalah tersebut - dipanggil *keadaan masalah*. +* **Pangkalan pengetahuan**: mewakili pengetahuan jangka panjang tentang domain masalah. Ia diekstrak secara manual dari pakar manusia, dan tidak berubah dari satu perundingan ke perundingan lain. Kerana ia membolehkan kita bergerak dari satu keadaan masalah ke keadaan lain, ia juga dipanggil **pengetahuan dinamik**. +* **Enjin inferens**: mengatur keseluruhan proses carian dalam ruang keadaan masalah, mengemukakan soalan kepada pengguna apabila perlu. Ia juga bertanggungjawab mencari peraturan yang betul untuk digunakan pada setiap keadaan. Sebagai contoh, mari kita pertimbangkan sistem pakar berikut untuk menentukan haiwan berdasarkan ciri fizikalnya: -![Pokok AND-OR](../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp) +![Pokok AND-OR](../../../../../../translated_images/ms/AND-OR-Tree.5592d2c70187f283.webp) > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) -Rajah ini dipanggil **pokok AND-OR**, dan ia adalah perwakilan grafik bagi satu set peraturan pengeluaran. Melukis pokok adalah berguna pada permulaan proses mengekstrak pengetahuan daripada pakar. Untuk mewakili pengetahuan dalam komputer, lebih mudah menggunakan peraturan: +Diagram ini dipanggil **pokok AND-OR**, dan ia adalah perwakilan grafik bagi set peraturan pengeluaran. Melukis pokok berguna pada permulaan mengekstrak pengetahuan daripada pakar. Untuk mewakili pengetahuan dalam komputer, lebih mudah menggunakan peraturan: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Anda boleh perhatikan bahawa setiap keadaan di sebelah kiri peraturan dan tindakan pada dasarnya adalah triplet objek-atribut-nilai (OAV). **Memori kerja** mengandungi set triplet OAV yang sepadan dengan masalah yang sedang diselesaikan. **Enjin peraturan** mencari peraturan yang keadaannya dipenuhi dan menerapkannya, menambah satu lagi triplet ke dalam memori kerja. +Anda boleh perhatikan bahawa setiap syarat di sebelah kiri peraturan dan tindakan sebenarnya adalah triplet objek-atribut-nilai (OAV). **Memori kerja** mengandungi set triplet OAV yang berkaitan dengan masalah yang sedang diselesaikan. **Enjin peraturan** mencari peraturan yang syaratnya dipenuhi dan melaksanakannya, menambah satu triplet baru ke dalam memori kerja. -> ✅ Lukis pokok AND-OR anda sendiri tentang topik yang anda suka! +> ✅ Tulis pokok AND-OR anda sendiri tentang topik yang anda suka! -### Inferens Maju vs. Inferens Mundur +### Inferens Hadapan vs Inferens Kebalikan -Proses yang diterangkan di atas dipanggil **inferens maju**. Ia bermula dengan beberapa data awal tentang masalah yang tersedia dalam memori kerja, dan kemudian melaksanakan gelung penaakulan berikut: +Proses yang diterangkan di atas dipanggil **inferens hadapan**. Ia bermula dengan beberapa data awal tentang masalah yang tersedia dalam memori kerja, kemudiannya melaksanakan gelung penalaran berikut: -1. Jika atribut sasaran terdapat dalam memori kerja - berhenti dan berikan hasil -2. Cari semua peraturan yang keadaannya dipenuhi - dapatkan **set konflik** peraturan. -3. Lakukan **resolusi konflik** - pilih satu peraturan yang akan dilaksanakan pada langkah ini. Terdapat pelbagai strategi resolusi konflik: +1. Jika atribut sasaran terdapat dalam memori kerja - berhenti dan berikan keputusan +2. Cari semua peraturan yang syaratnya dipenuhi sekarang - dapatkan **set pertembungan** peraturan. +3. Lakukan **penyelesaian pertembungan** - pilih satu peraturan yang akan dilaksanakan pada langkah ini. Terdapat beberapa strategi penyelesaian pertembungan: - Pilih peraturan pertama yang boleh digunakan dalam pangkalan pengetahuan - Pilih peraturan secara rawak - - Pilih peraturan yang *lebih spesifik*, iaitu yang memenuhi kebanyakan keadaan di "sebelah kiri" (LHS) + - Pilih peraturan *lebih khusus*, iaitu yang memenuhi syarat terbanyak di sebelah "kiri" (LHS) 4. Terapkan peraturan yang dipilih dan masukkan pengetahuan baru ke dalam keadaan masalah -5. Ulangi dari langkah 1. +5. Ulang dari langkah 1. -Walau bagaimanapun, dalam beberapa kes kita mungkin ingin bermula dengan pengetahuan kosong tentang masalah, dan bertanya soalan yang akan membantu kita mencapai kesimpulan. Sebagai contoh, semasa membuat diagnosis perubatan, kita biasanya tidak melakukan semua analisis perubatan terlebih dahulu sebelum mula mendiagnosis pesakit. Sebaliknya, kita ingin melakukan analisis apabila keputusan perlu dibuat. +Walau bagaimanapun, dalam beberapa kes kita mungkin mahu bermula dengan pengetahuan kosong tentang masalah, dan bertanya soalan yang akan membantu kita sampai kepada kesimpulan. Contohnya, ketika melakukan diagnosis perubatan, kita biasanya tidak melakukan semua analisis perubatan terlebih dahulu sebelum memulakan diagnosis pesakit. Kita lebih suka melakukan analisis apabila keputusan perlu dibuat. -Proses ini boleh dimodelkan menggunakan **inferens mundur**. Ia didorong oleh **matlamat** - nilai atribut yang kita cari: +Proses ini boleh dimodelkan menggunakan **inferens kebalikan**. Ia digerakkan oleh **matlamat** - nilai atribut yang kita cari: -1. Pilih semua peraturan yang boleh memberikan nilai matlamat (iaitu dengan matlamat di RHS ("sebelah kanan")) - set konflik -1. Jika tiada peraturan untuk atribut ini, atau terdapat peraturan yang mengatakan bahawa kita harus bertanya nilai daripada pengguna - tanyakan, jika tidak: -1. Gunakan strategi resolusi konflik untuk memilih satu peraturan yang akan kita gunakan sebagai *hipotesis* - kita akan cuba membuktikannya -1. Ulangi proses ini secara berulang untuk semua atribut di LHS peraturan, cuba membuktikannya sebagai matlamat +1. Pilih semua peraturan yang boleh memberi kita nilai matlamat (iaitu dengan matlamat di sebelah kanan (RHS)) - satu set pertembungan +1. Jika tiada peraturan untuk atribut ini, atau terdapat peraturan yang mengatakan bahawa kita harus bertanya nilai daripada pengguna - tanya, jika tidak: +1. Gunakan strategi penyelesaian pertembungan untuk memilih satu peraturan yang akan digunakan sebagai *hipotesis* - kita akan cuba membuktikannya +1. Ulang semula proses untuk semua atribut dalam LHS peraturan, cuba membuktikannya sebagai matlamat 1. Jika pada bila-bila masa proses gagal - gunakan peraturan lain pada langkah 3. -> ✅ Dalam situasi apakah inferens maju lebih sesuai? Bagaimana pula dengan inferens mundur? +> ✅ Dalam situasi manakah inferens hadapan lebih sesuai? Bagaimana dengan inferens kebalikan? ### Melaksanakan Sistem Pakar Sistem pakar boleh dilaksanakan menggunakan pelbagai alat: -* Memprogramkannya secara langsung dalam beberapa bahasa pengaturcaraan peringkat tinggi. Ini bukan idea terbaik, kerana kelebihan utama sistem berasaskan pengetahuan ialah pengetahuan dipisahkan daripada inferens, dan berpotensi seorang pakar domain masalah sepatutnya dapat menulis peraturan tanpa memahami butiran proses inferens. -* Menggunakan **kerangka sistem pakar**, iaitu sistem yang direka khusus untuk diisi dengan pengetahuan menggunakan beberapa bahasa perwakilan pengetahuan. +* Menyediakannya terus dalam bahasa pengaturcaraan tahap tinggi. Ini bukan idea terbaik, kerana kelebihan utama sistem berasaskan pengetahuan adalah pengetahuan dipisahkan daripada inferens, dan pakar domain masalah berpotensi dapat menulis peraturan tanpa memahami butiran proses inferens +* Menggunakan **shell sistem pakar**, iaitu sistem yang direka khusus untuk diisi dengan pengetahuan menggunakan bahasa perwakilan pengetahuan. ## ✍️ Latihan: Inferens Haiwan -Lihat [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) untuk contoh melaksanakan sistem pakar inferens maju dan mundur. +Lihat [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) sebagai contoh melaksanakan sistem pakar inferens hadapan dan kebalikan. -> **Nota**: Contoh ini agak mudah, dan hanya memberikan idea tentang bagaimana rupa sistem pakar. Sebaik sahaja anda mula mencipta sistem seperti ini, anda hanya akan melihat beberapa tingkah laku *pintar* daripadanya apabila anda mencapai bilangan peraturan tertentu, sekitar 200+. Pada satu ketika, peraturan menjadi terlalu kompleks untuk diingati semuanya, dan pada ketika ini anda mungkin mula tertanya-tanya mengapa sistem membuat keputusan tertentu. Walau bagaimanapun, ciri penting sistem berasaskan pengetahuan ialah anda sentiasa boleh *menjelaskan* dengan tepat bagaimana mana-mana keputusan dibuat. +> **Nota**: Contoh ini agak mudah, dan hanya memberikan idea bagaimana sistem pakar kelihatan. Apabila anda mula mencipta sistem sebegini, anda hanya akan perasan beberapa tingkah laku *pintar* apabila mencapai jumlah peraturan tertentu, sekitar 200+. Pada satu titik, peraturan menjadi terlalu kompleks untuk diingat semuanya, dan pada waktu itu anda mungkin mula tertanya-tanya mengapa sistem membuat keputusan tertentu. Namun, ciri penting sistem berasaskan pengetahuan adalah anda sentiasa boleh *menjelaskan* dengan tepat bagaimana keputusan dibuat. ## Ontologi dan Web Semantik -Pada akhir abad ke-20, terdapat inisiatif untuk menggunakan perwakilan pengetahuan untuk memberi anotasi kepada sumber Internet, supaya ia mungkin untuk mencari sumber yang sepadan dengan pertanyaan yang sangat spesifik. Gerakan ini dipanggil **Web Semantik**, dan ia bergantung pada beberapa konsep: +Pada akhir abad ke-20, terdapat inisiatif untuk menggunakan perwakilan pengetahuan untuk menganotasi sumber Internet, supaya boleh mencari sumber yang sesuai dengan pertanyaan yang sangat spesifik. Gerakan ini dipanggil **Web Semantik**, dan ia bergantung pada beberapa konsep: -- Perwakilan pengetahuan khas berdasarkan **[logik deskriptif](https://en.wikipedia.org/wiki/Description_logic)** (DL). Ia serupa dengan perwakilan pengetahuan bingkai, kerana ia membina hierarki objek dengan sifat, tetapi ia mempunyai semantik logik formal dan inferens. Terdapat keseluruhan keluarga DL yang mengimbangi antara ekspresiviti dan kerumitan algoritma inferens. -- Perwakilan pengetahuan teragih, di mana semua konsep diwakili oleh pengenal URI global, menjadikannya mungkin untuk mencipta hierarki pengetahuan yang merangkumi internet. +- Perwakilan pengetahuan khas berdasarkan **[logik deskriptif](https://en.wikipedia.org/wiki/Description_logic)** (DL). Ia serupa dengan perwakilan bingkai, kerana ia membina hierarki objek dengan sifat, tetapi ia mempunyai semantik logik formal dan inferens. Terdapat satu keluarga DL yang mengimbangi antara keupayaan ekspresif dan kerumitan algoritma inferens. +- Perwakilan pengetahuan teragih, di mana semua konsep diwakili oleh pengecam URI global, membolehkan penciptaan hierarki pengetahuan merentasi internet. - Satu keluarga bahasa berasaskan XML untuk penerangan pengetahuan: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Konsep utama dalam Web Semantik ialah konsep **Ontologi**. Ia merujuk kepada spesifikasi eksplisit bagi domain masalah menggunakan beberapa representasi pengetahuan formal. Ontologi yang paling mudah boleh menjadi hierarki objek dalam domain masalah, tetapi ontologi yang lebih kompleks akan merangkumi peraturan yang boleh digunakan untuk inferens. +Konsep teras dalam Semantic Web ialah konsep **Ontologi**. Ia merujuk kepada spesifikasi eksplisit bagi domain masalah menggunakan beberapa representasi pengetahuan formal. Ontologi yang paling mudah boleh jadi hanya hierarki objek dalam domain masalah, tetapi ontologi yang lebih kompleks akan merangkumi peraturan yang boleh digunakan untuk inferens. -Dalam web semantik, semua representasi adalah berdasarkan triplet. Setiap objek dan setiap hubungan dikenalpasti secara unik oleh URI. Sebagai contoh, jika kita ingin menyatakan fakta bahawa Kurikulum AI ini telah dibangunkan oleh Dmitry Soshnikov pada 1 Januari 2022 - berikut adalah triplet yang boleh kita gunakan: +Dalam semantic web, semua representasi berasaskan triplet. Setiap objek dan setiap hubungan dikenalpasti secara unik oleh URI. Sebagai contoh, jika kita ingin menyatakan fakta bahawa Kurikulum AI ini telah dibangunkan oleh Dmitry Soshnikov pada 1 Jan 2022 - berikut adalah triplet yang kita boleh gunakan: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Di sini `http://www.example.com/terms/creation-date` dan `http://purl.org/dc/elements/1.1/creator` adalah beberapa URI yang terkenal dan diterima secara universal untuk menyatakan konsep *creator* dan *creation date*. +> ✅ Di sini `http://www.example.com/terms/creation-date` dan `http://purl.org/dc/elements/1.1/creator` adalah beberapa URI yang diketahui dan diterima secara universal untuk menyatakan konsep *pencipta* dan *tarikh penciptaan*. -Dalam kes yang lebih kompleks, jika kita ingin menentukan senarai pencipta, kita boleh menggunakan beberapa struktur data yang ditakrifkan dalam RDF. +Dalam keadaan yang lebih kompleks, jika kita ingin mentakrif senarai pencipta, kita boleh menggunakan beberapa struktur data yang ditakrifkan dalam RDF. - + -> Diagram di atas oleh [Dmitry Soshnikov](http://soshnikov.com) +> Rajah di atas oleh [Dmitry Soshnikov](http://soshnikov.com) -Kemajuan membina Web Semantik sedikit terhalang oleh kejayaan enjin carian dan teknik pemprosesan bahasa semula jadi, yang membolehkan pengekstrakan data berstruktur daripada teks. Walau bagaimanapun, dalam beberapa bidang masih terdapat usaha yang signifikan untuk mengekalkan ontologi dan pangkalan pengetahuan. Beberapa projek yang patut diberi perhatian: +Kemajuan dalam membina Semantic Web agak perlahan disebabkan kejayaan enjin carian dan teknik pemprosesan bahasa semula jadi, yang membolehkan pengekstrakan data berstruktur dari teks. Namun, dalam beberapa bidang masih terdapat usaha ketara untuk mengekalkan ontologi dan pangkalan pengetahuan. Beberapa projek yang patut diberi perhatian: -* [WikiData](https://wikidata.org/) adalah koleksi pangkalan pengetahuan yang boleh dibaca mesin yang berkaitan dengan Wikipedia. Kebanyakan data diperoleh daripada *InfoBoxes* Wikipedia, iaitu kandungan berstruktur dalam halaman Wikipedia. Anda boleh [menyiasat](https://query.wikidata.org/) WikiData menggunakan SPARQL, bahasa pertanyaan khas untuk Web Semantik. Berikut adalah contoh pertanyaan yang memaparkan warna mata paling popular di kalangan manusia: +* [WikiData](https://wikidata.org/) ialah koleksi pangkalan pengetahuan yang boleh dibaca mesin yang berkaitan dengan Wikipedia. Kebanyakan data diperolehi dari *InfoBoxes* Wikipedia, potongan kandungan berstruktur dalam halaman Wikipedia. Anda boleh [membuat pertanyaan](https://query.wikidata.org/) terhadap wikidata menggunakan SPARQL, bahasa pertanyaan khas untuk Semantic Web. Berikut adalah contoh pertanyaan yang memaparkan warna mata paling popular di kalangan manusia: ```sparql #defaultView:BubbleChart @@ -206,27 +206,27 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) adalah satu lagi usaha yang serupa dengan WikiData. +* [DBpedia](https://www.dbpedia.org/) ialah usaha lain yang serupa dengan WikiData. -> ✅ Jika anda ingin bereksperimen dengan membina ontologi anda sendiri, atau membuka ontologi yang sedia ada, terdapat editor ontologi visual yang hebat dipanggil [Protégé](https://protege.stanford.edu/). Muat turun, atau gunakannya secara dalam talian. +> ✅ Jika anda ingin mencuba membina ontologi anda sendiri, atau membuka yang sedia ada, terdapat editor ontologi visual yang hebat dipanggil [Protégé](https://protege.stanford.edu/). Muat turun atau gunakannya secara atas talian. - + *Editor Web Protégé dibuka dengan ontologi Keluarga Romanov. Tangkapan skrin oleh Dmitry Soshnikov* ## ✍️ Latihan: Ontologi Keluarga -Lihat [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) untuk contoh menggunakan teknik Web Semantik untuk membuat inferens tentang hubungan keluarga. Kita akan mengambil pokok keluarga yang diwakili dalam format GEDCOM biasa dan ontologi hubungan keluarga, serta membina graf semua hubungan keluarga untuk set individu yang diberikan. +Lihat [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) untuk contoh menggunakan teknik Semantic Web bagi membuat inferens tentang hubungan keluarga. Kita akan mengambil pohon keluarga yang diwakili dalam format GEDCOM biasa dan ontologi hubungan keluarga serta membina graf semua hubungan keluarga untuk set individu yang diberikan. ## Microsoft Concept Graph -Dalam kebanyakan kes, ontologi dibuat dengan teliti secara manual. Walau bagaimanapun, ia juga boleh **diperoleh** daripada data tidak berstruktur, contohnya, daripada teks bahasa semula jadi. +Dalam kebanyakan kes, ontologi dibina dengan berhati-hati secara manual. Walau bagaimanapun, adalah juga mungkin untuk **melombong** ontologi dari data tidak berstruktur, contohnya, dari teks bahasa semula jadi. -Salah satu usaha sedemikian dilakukan oleh Microsoft Research, dan menghasilkan [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Satu usaha tersebut dilakukan oleh Microsoft Research, yang menghasilkan [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Ia adalah koleksi besar entiti yang dikelompokkan bersama menggunakan hubungan pewarisan `is-a`. Ia membolehkan menjawab soalan seperti "Apa itu Microsoft?" - jawapannya adalah sesuatu seperti "sebuah syarikat dengan kebarangkalian 0.87, dan jenama dengan kebarangkalian 0.75". +Ia adalah koleksi besar entiti yang dikelompokkan bersama menggunakan hubungan pewarisan `is-a`. Ia membolehkan menjawab soalan seperti "Apa itu Microsoft?" - jawapannya ialah sesuatu seperti "sebuah syarikat dengan kebarangkalian 0.87, dan sebuah jenama dengan kebarangkalian 0.75". -Graf ini tersedia sama ada sebagai REST API, atau sebagai fail teks besar yang menyenaraikan semua pasangan entiti. +Graf ini tersedia sama ada sebagai REST API, atau sebagai fail teks besar yang boleh dimuat turun yang menyenaraikan semua pasangan entiti. ## ✍️ Latihan: Graf Konsep @@ -234,19 +234,23 @@ Cuba notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginne ## Kesimpulan -Pada masa kini, AI sering dianggap sebagai sinonim untuk *Pembelajaran Mesin* atau *Rangkaian Neural*. Walau bagaimanapun, manusia juga menunjukkan penaakulan eksplisit, sesuatu yang pada masa ini tidak ditangani oleh rangkaian neural. Dalam projek dunia sebenar, penaakulan eksplisit masih digunakan untuk melaksanakan tugas yang memerlukan penjelasan, atau keupayaan untuk mengubah tingkah laku sistem dengan cara yang terkawal. +Kini, AI sering dianggap sebagai sinonim untuk *Machine Learning* atau *Neural Networks*. Walau bagaimanapun, manusia juga menunjukkan alasan eksplisit, sesuatu yang kini tidak diurus oleh rangkaian neural. Dalam projek dunia sebenar, alasan eksplisit masih digunakan untuk melaksanakan tugas yang memerlukan penjelasan, atau boleh mengubah tingkah laku sistem secara terkawal. ## 🚀 Cabaran -Dalam notebook Ontologi Keluarga yang berkaitan dengan pelajaran ini, terdapat peluang untuk bereksperimen dengan hubungan keluarga lain. Cuba temui hubungan baru antara orang dalam pokok keluarga. +Dalam notebook Ontologi Keluarga yang berkaitan dengan pelajaran ini, terdapat peluang untuk mencuba hubungan keluarga yang lain. Cuba temui hubungan baru antara orang dalam pohon keluarga tersebut. -## [Kuiz selepas kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Kuis selepas kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Kajian & Pembelajaran Kendiri +## Semakan & Pembelajaran Kendiri -Lakukan penyelidikan di internet untuk menemui bidang di mana manusia telah cuba mengukur dan mengkodkan pengetahuan. Lihat Taksonomi Bloom, dan kembali ke sejarah untuk belajar bagaimana manusia cuba memahami dunia mereka. Terokai kerja Linnaeus untuk mencipta taksonomi organisma, dan perhatikan cara Dmitri Mendeleev mencipta cara untuk menerangkan dan mengelompokkan unsur kimia. Apakah contoh menarik lain yang boleh anda temui? +Lakukan kajian di internet untuk mengetahui bidang di mana manusia telah cuba mengkuantifikasi dan mengkodkan pengetahuan. Lihatlah Taksonomi Bloom, dan kembali ke sejarah untuk belajar bagaimana manusia cuba memahami dunia mereka. Terokai kerja Linnaeus dalam mencipta taksonomi organisma, dan perhatikan cara Dmitri Mendeleev mencipta kaedah untuk menerangkan dan mengelompokkan unsur kimia. Apakah contoh menarik lain yang anda boleh temui? **Tugasan**: [Bina Ontologi](assignment.md) --- + +**Penafian**: +Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk mencapai ketepatan, sila ambil maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya perlu dianggap sebagai sumber yang sahih. Untuk maklumat penting, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini. + \ No newline at end of file diff --git a/translations/sw/README.md b/translations/sw/README.md index e68526ff..1699ea57 100644 --- a/translations/sw/README.md +++ b/translations/sw/README.md @@ -1,8 +1,8 @@ -[Kiarabu](../ar/README.md) | [Kibengali](../bn/README.md) | [Kibulgaria](../bg/README.md) | [Kiburma (Myanmar)](../my/README.md) | [Kichina (Rahisi)](../zh/README.md) | [Kichina (Kiasili, Hong Kong)](../hk/README.md) | [Kichina (Kiasili, Macau)](../mo/README.md) | [Kichina (Kiasili, Taiwan)](../tw/README.md) | [Kikroeshia](../hr/README.md) | [Kicheki](../cs/README.md) | [Kidenmaki](../da/README.md) | [Kiholanzi](../nl/README.md) | [Kiestonia](../et/README.md) | [Kifini](../fi/README.md) | [Kifaransa](../fr/README.md) | [Kijerumani](../de/README.md) | [Kigiriki](../el/README.md) | [Kiebrania](../he/README.md) | [Kihindi](../hi/README.md) | [Kihungari](../hu/README.md) | [Kiindonesian](../id/README.md) | [Kiitaliano](../it/README.md) | [Kijapani](../ja/README.md) | [Kikannada](../kn/README.md) | [Kikorea](../ko/README.md) | [Kilitwania](../lt/README.md) | [Kimalay](../ms/README.md) | [Kimalayalam](../ml/README.md) | [Kimarathi](../mr/README.md) | [Kinepali](../ne/README.md) | [Kipidgin cha Nigeria](../pcm/README.md) | [Kinorwe](../no/README.md) | [Kiajemi (Farsi)](../fa/README.md) | [Kipolandi](../pl/README.md) | [Kireno (Brazili)](../br/README.md) | [Kireno (Ureno)](../pt/README.md) | [Kipunjabi (Gurmukhi)](../pa/README.md) | [Kiromania](../ro/README.md) | [Kirusi](../ru/README.md) | [Kiserbia (Siriliki)](../sr/README.md) | [Kislovakia](../sk/README.md) | [Kislovenia](../sl/README.md) | [Kihispania](../es/README.md) | [Kiswahili](./README.md) | [Kiswidi](../sv/README.md) | [Kitagalog (Filipino)](../tl/README.md) | [Kitamili](../ta/README.md) | [Kitelugu](../te/README.md) | [Kithai](../th/README.md) | [Kituruki](../tr/README.md) | [Kiukraini](../uk/README.md) | [Kiurdu](../ur/README.md) | [Kivietinamu](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](./README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) -> **Unapendelea Kunoa Mitaa?** +> **Unapendelea Kunakili Kwenye Kompyuta?** -> Hifadhidata hii inajumuisha tafsiri za lugha zaidi ya 50 ambazo zinaongeza ukubwa wa pakiti ya kupakua. Ili kunakili bila tafsiri, tumia sparse checkout: +> Hifadhi hii ina lugha zaidi ya 50 za tafsiri ambazo huongeza sana ukubwa wa kupakua. Ili kunakili bila tafsiri, tumia sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Hii inakupa kila kitu unachohitaji kukamilisha kozi haraka zaidi. +> Hii itakupa kila kitu unachohitaji kukamilisha kozi kwa upakuaji wa haraka zaidi. -**Ikiwa ungependa kuwa na lugha zaidi za tafsiri zinazounga mkono zimetajwa [hapa](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Ikiwa unataka lugha za ziada za tafsiri zinazoungwa mkono ziko [hapa](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Jiunge na Jamii [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Utajifunza nini +## Utajifunza Nini -**[Ramani ya Mawazo ya Kozi](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Mchoro wa Mawazo wa Kozi](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Katika mtaala huu, utajifunza: -* Njia tofauti za Akili Bandia, ikijumuisha njia ya "kile kilichotumika zamani" ya kimfumu na **Uwakilishi wa Maarifa** na kutathmini ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Mito ya Neva** na **Kujifunza Kwa Kina**, ambavyo ni msingi wa AI za kisasa. Tutatoa mifano ya dhana nyuma ya mada hizi muhimu kwa kutumia msimbo katika mifumo miwili maarufu - [TensorFlow](http://Tensorflow.org) na [PyTorch](http://pytorch.org). -* **Mimari ya Neva** ya kufanya kazi na picha na maandishi. Tutashughulikia mifano ya hivi karibuni lakini huenda ikawa haijawa ya hali ya juu kabisa. -* Mbinu zisizo maarufu za AI, kama vile **Algoriti za Kijasiriamali** na **Mifumo ya Wakaazi Wengi**. +* Mbinu tofauti za Akili Bandia, ikijumuisha njia "ya zamani" ya kimfano ya ishara na **Uwakilishi wa Maarifa** na mantiki ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Mitandao ya Neva** na **Kujifunza Kina**, ambazo ni msingi wa AI ya kisasa. Tutafafanua dhana nyuma ya mada hizi muhimu kwa kutumia msimbo katika mifumo miwili maarufu zaidi - [TensorFlow](http://Tensorflow.org) na [PyTorch](http://pytorch.org). +* **Miundo ya Neural** kwa ajili ya kufanya kazi na picha na maandishi. Tutafunika modeli za hivi karibuni lakini huenda tukawa na upungufu kidogo katika hali ya kisasa zaidi. +* Mbinu zisizo maarufu za AI, kama vile **Algoriti za Kijeni** na **Mifumo ya Wakala Wengi**. -Kile ambacho hatutagusia katika mtaala huu: +Sio mambo yatafundishwa katika mtaala huu: > [Pata rasilimali zote za ziada za kozi hii katika mkusanyiko wetu wa Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Mifano ya biashara ya kutumia **AI katika Biashara**. Fikiria kuchukua njia ya mafunzo ya [Utangulizi wa AI kwa watumiaji wa biashara](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) kwenye Microsoft Learn, au [Shule ya Biashara ya AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), iliyotengenezwa kwa ushirikiano na [INSEAD](https://www.insead.edu/). -* **Kujifunza kwa Mashine ya Kiasili**, ambayo imeelezewa vizuri katika [Mtaala wetu wa Kujifunza kwa Mashine kwa Wanaanziajira](http://github.com/Microsoft/ML-for-Beginners). -* Programu za vitendo za AI zilizojengwa kwa kutumia **[Huduma za Akili](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Kwa hili, tunapendekeza uanze na moduli za Microsoft Learn za [maono](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [usindikaji wa lugha asilia](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI ya Kizazi ya Huduma ya Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** na nyinginezo. -* Mfumo maalum wa **Wingu wa ML**, kama vile [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), au [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Fikiria kutumia njia za mafunzo [Jenga na endesha suluhisho za kujifunza kwa mashine na Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) na [Jenga na Endesha Suluhisho za Kujifunza kwa Mashine na Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). -* **AI ya Mazungumzo** na **Chat Bots**. Kuna njia tofauti ya mafunzo ya [Unda suluhisho za AI za mazungumzo](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), na pia unaweza kurejelea [chapisho hili la blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) kwa maelezo zaidi. -* **Hisabati wa Kina** nyuma ya kujifunza kwa kina. Kwa hili, tunapendekeza kitabu cha [Kujifunza kwa Kina](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) cha Ian Goodfellow, Yoshua Bengio na Aaron Courville, ambacho kinapatikana pia mtandaoni kwenye [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Mifano ya biashara ya matumizi ya **AI katika Biashara**. Fikiria kuchukua njia ya kujifunza [Utangulizi wa AI kwa watumiaji wa biashara](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) kwenye Microsoft Learn, au [Shule ya Biashara ya AI](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), iliyotengenezwa kwa ushirikiano na [INSEAD](https://www.insead.edu/). +* **Kujifunza kwa Mashine ya Klasiki**, ambayo imeelezewa vizuri katika [Mtaala wa Kujifunza Mashine kwa Waanzilishi](http://github.com/Microsoft/ML-for-Beginners). +* Maombi halisi ya AI yaliyotengenezwa kwa kutumia **[Huduma za Kitaalamu](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Kwa hili, tunapendekeza uanze na moduli za Microsoft Learn kwa [macho](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [usindikaji wa lugha ya asili](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI Inayozalisha na Huduma ya Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** na mengine. +* **Mifumo Maalum ya Wingu ya ML**, kama [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), au [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Fikiria kutumia njia za kujifunza [Jenga na endesha suluhisho za kujifunza mashine na Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) na [Jenga na Endesha Suluhisho za Kujifunza Mashine na Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **AI ya Mazungumzo** na **Chat Bots**. Kuna njia tofauti ya kujifunza [Tengeneza suluhisho za AI za mazungumzo](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), na pia unaweza kurejelea [chapisho hili la blogu](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) kwa maelezo zaidi. +* **Hisabati Zinazozama** nyuma ya kujifunza kwa kina. Kwa hili, tunapendekeza [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) na Ian Goodfellow, Yoshua Bengio na Aaron Courville, inayopatikana pia mtandaoni kwenye [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Kwa utangulizi mpole kwa mada za _AI katika Wingu_ unaweza kufikiria kuchukua Njia ya Kujifunza ya [Anza na Akili Bandia kwenye Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). +Kwa utangulizi mpole kwa mada za _AI katika Wingu_ unaweza kuzingatia kuchukua Njia ya Kujifunza [Anza na Akili Bandia kwenye Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). # Yaliyomo -| | Kiungo cha Somo | PyTorch/Keras/TensorFlow | Maabara | +| | Kiungo cha Somo | PyTorch/Keras/TensorFlow | Maabara | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Usanidi wa Kozi](./lessons/0-course-setup/setup.md) | [Sanidi Mazingira Yako ya Maendeleo](./lessons/0-course-setup/how-to-run.md) | | +| 0 | [Mpangilio wa Kozi](./lessons/0-course-setup/setup.md) | [Panga Mazingira Yako ya Maendeleo](./lessons/0-course-setup/how-to-run.md) | | | I | [**Utangulizi wa AI**](./lessons/1-Intro/README.md) | | | | 01 | [Utangulizi na Historia ya AI](./lessons/1-Intro/README.md) | - | - | -| II | **AI ya Kimfumo ya Alama** | -| 02 | [Uwakilishi wa Maarifa na Mifumo ya Wataalamu](./lessons/2-Symbolic/README.md) | [Mifumo ya Wataalamu](./lessons/2-Symbolic/Animals.ipynb) / [Ontolojia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafu ya Dhana](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Utangulizi wa Mito ya Neva**](./lessons/3-NeuralNetworks/README.md) ||| +| II | **AI ya Ishara** | +| 02 | [Uwakilishi wa Maarifa na Mifumo ya Wataalamu](./lessons/2-Symbolic/README.md) | [Mifumo ya Wataalamu](./lessons/2-Symbolic/Animals.ipynb) / [Ontolojia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Mchoro wa Dhana](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Utangulizi wa Mitandao ya Neva**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Multi-Layered Perceptron and Creating our own Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | -| 05 | [Intro to Frameworks (PyTorch/TensorFlow) and Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Kompyuta Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | -| 06 | [Intro to Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | -| 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architectures](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | -| 08 | [Pre-trained Networks and Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | -| 09 | [Autoencoders and VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | -| 10 | [Generative Adversarial Networks & 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) | | -| 11 | [Object Detection](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | -| 12 | [Semantic Segmentation. 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 | [**Usindikaji wa Lugha Asilia**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| -| 13 | [Text Representation. 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 | [Semantic word embeddings. Word2Vec and 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 | [Language Modeling. Training your own embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | -| 16 | [Recurrent Neural Networks](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | -| 17 | [Generative Recurrent Networks](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 05 | [Utangulizi kwa Mifumo (PyTorch/TensorFlow) na Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Maono ya Kompyuta**](./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)| [Chunguza Maono ya Kompyuta kwenye Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Utangulizi wa Maono ya Kompyuta. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Mifumo ya Neva za Convolutional](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Miundo ya CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Mitandao Iliyoandaliwa awali na Kujifunza kuhamisha](./lessons/4-ComputerVision/08-TransferLearning/README.md) na [Mbinu za Mafunzo](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoencoders na VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Mitandao ya Utatanishi wa Kuanzisha na Uhamisho wa Mtindo wa Kisanii](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Ugunduzi wa Vitu](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Ugawaji wa Maneno. 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 | [**Usindikaji wa Lugha Asilia**](./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) | [Chunguza Usindikaji wa Lugha Asilia kwenye Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Uwasilishaji wa Maandishi. 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 | [Uingizaji wa Maneno wa Kiafasaha. Word2Vec na 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 | [Uigaji Lugha. Kufundisha uingizaji wako mwenyewe](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Mitandao ya Neva Inayojirudia](./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 | [Mitandao ya Kuanzisha ya Kurudia](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | | 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | -| 19 | [Named Entity Recognition](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | -| 20 | [Large Language Models, Prompt Programming and Few-Shot Tasks](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Mbinu Nyingine za AI** || | -| 21 | [Genetic Algorithms](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | -| 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | -| 23 | [Multi-Agent Systems](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 19 | [Utambuzi wa Vitu Vilivyotajwa](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Modeli Kubwa za Lugha, Programu ya Qibao na Kazi Chache](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Mikakati Mengine ya AI** || | +| 21 | [Algorithmi za Kijenetiki](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Mafunzo ya Kina ya Reinforcement](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Mifumo ya Wakala Wengi](./lessons/6-Other/23-MultiagentSystems/README.md) | | | | VII | **Maadili ya AI** | | | -| 24 | [AI Ethics and Responsible AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Zaidi** | | | -| 25 | [Multi-Modal Networks, CLIP and VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 24 | [Maadili ya AI na AI Inayowajibika](./lessons/7-Ethics/README.md) | [Microsoft Learn: Kanuni za AI Inayowajibika](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Ziada** | | | +| 25 | [Mitandao ya Modal nyingi, CLIP na VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Kila somo lina * Nyenzo za kusoma kabla -* Vitabu vya Jupyter vinavyoweza kutekelezwa, ambavyo mara nyingi ni maalum kwa mfumo fulani (**PyTorch** au **TensorFlow**). Kitabu kinachoweza kutekelezwa pia kinajumuisha nyenzo nyingi za kinadharia, kwa hivyo ili kuelewa mada unapaswa kupitia kwa angalau toleo moja la kitabu (PyTorch au TensorFlow). -* **Maabara** zinazopatikana kwa baadhi ya mada, ambayo hukupa fursa ya kujaribu kutumia nyenzo ulizojifunza kwa tatizo fulani. -* Sehemu zingine zina viungo vya moduli za [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) ambazo zinashughulikia mada zinazohusiana. +* Daftari za Jupyter zinazoweza kutekelezwa, ambazo mara nyingi ni maalum kwa mfumo (**PyTorch** au **TensorFlow**). Daftari inayoweza kutekelezwa pia ina nyenzo nyingi za nadharia, hivyo kuelewa mada unahitaji kupitia angalau toleo moja la daftari (ama PyTorch au TensorFlow). +* **Maabara** zinapatikana kwa baadhi ya mada, ambazo zinakuwezesha kujaribu kutumia nyenzo ulizojifunza kwenye tatizo maalum. +* Baadhi ya sehemu zina viungo kwenda kwenye moduli za [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) zinazofunika mada zinazohusiana. ## Kuanzia ### 🎯 Mpya kwa AI? Anza Hapa! -Ikiwa wewe ni mpya kabisa kwa AI na unataka mifano ya mkono kwa haraka, angalia [**Mifano ya Kuanzia Kwa Waanzilishi**](./examples/README.md)! Hizi ni pamoja na: +Kama wewe ni mpya kabisa kwa AI na unataka mifano ya haraka na ya vitendo, angalia [**Mifano Rafiki kwa Waanzilishi**](./examples/README.md)! Hizi ni pamoja na: -- 🌟 **Hello AI World** - Programu yako ya kwanza ya AI (kutambua mifumo) -- 🧠 **Mtandao Rahisi wa Neural** - Jenga mtandao wa neva kutoka mwanzo -- 🖼️ **Mtambuzi wa Picha** - Tandaza picha na maelezo ya kina -- 💬 **Hisia za Maandishi** - Changanua maandishi chanya/negativi +- 🌟 **Hello AI World** - Programu yako ya kwanza ya AI (utambuzi wa mifumo) +- 🧠 **Mtandao Rahisi wa Neva** - Jenga mtandao wa neva kutoka mwanzo +- 🖼️ **Kipangaji Picha** - Pangilia picha na maelezo ya kina +- 💬 **Hisia za Maandishi** - Changanua maandishi chanya/negatifu -Mifano hii imeundwa kukusaidia kuelewa dhana za AI kabla ya kuanza mtaala kamili. +Mifano hii imeundwa kusaidia kuelewa dhana za AI kabla ya kuingia kwenye mtaala kamili. -### 📚 Mpango Kamili wa Mtaala +### 📚 Usanidi Kamili wa Mtaala -- Tumetengeneza [somo la usanidi](./lessons/0-course-setup/setup.md) kukusaidia kujenga mazingira yako ya maendeleo. - Kwa Walimu, tumetengeneza [somo la usanidi wa mitaala](./lessons/0-course-setup/for-teachers.md) pia kwa ajili yenu! -- Jinsi ya [Kukimbia msimbo katika VSCode au Codepace](./lessons/0-course-setup/how-to-run.md) +- Tumetengeneza [somo la usanidi](./lessons/0-course-setup/setup.md) kusaidia na kufanya mazingira yako ya maendeleo yawe tayari. - Kwa Wataalamu wa Elimu, tumetengeneza pia [somu la usanidi wa mitaala](./lessons/0-course-setup/for-teachers.md)! +- Jinsi ya [Kuendesha msimbo kwenye VSCode au Codespace](./lessons/0-course-setup/how-to-run.md) Fuata hatua hizi: -Fungua Nakala: Bonyeza kitufe cha "Fork" kilicho kona ya juu-kushoto ya ukurasa huu. +Fungua Nakala ya Hifadhi: Bonyeza kitufe cha "Fork" upande wa juu kulia wa ukurasa huu. -Nakili Nakala: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Nakili Hifadhi: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Usisahau kuweka nyota (🌟) kwenye repo hii ili kuipata kwa urahisi baadaye. +Usisahau kuweka nyota (🌟) kwenye hifadhi hii ili kuipata kirahisi baadaye. ## Kutana na Wanafunzi Wengine -Jiunge na [server rasmi ya AI Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) kutana na kujenga mtandao na wanafunzi wengine wanaochukua kozi hii na pata msaada. +Jiunge na [server rasmi ya AI Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) kutana na kuungana na wanafunzi wengine wanaochukua kozi hii na kupata msaada. -Ikiwa una maoni au maswali kuhusu bidhaa wakati wa kujenga tembelea [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Ikiwa una maoni kuhusu bidhaa au maswali wakati wa kujenga, tembelea [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Maswali ya Kujipima +## Maswali ya Kujifunza -> **Kumbuka kuhusu maswali ya kujipima**: Maswali yote yapo kwenye folda ya Quiz-app katika etc\quiz-app, au [Mtandaoni Hapa](https://ff-quizzes.netlify.app/) Yameunganishwa kutoka ndani ya masomo, programu ya quiz inaweza kuendeshwa kwa ndani au kuwekwa kwenye Azure; fuata maelekezo katika folda ya `quiz-app`. Yanaendelea kutafsiriwa kwa lugha tofauti polepole. +> **Kumbuka kuhusu maswali ya kujifunza**: Maswali yote yamo kwenye folda ya Quiz-app ndani ya etc\quiz-app, au [Mtandaoni Hapa](https://ff-quizzes.netlify.app/) Yameunganishwa kutoka ndani ya masomo na app ya maswali inaweza kuendeshwa kwa mji au kupelekwa Azure; fuata maelekezo kwenye folda ya `quiz-app`. Yanaendelea kutafsiriwa kwa lugha mbalimbali taratibu. ## Msaada Unahitajika -Je, una mapendekezo au umeona makosa ya tahajia au msimbo? Toa tatizo au tengeneza ombi la kuvuta. +Je, una mapendekezo au umeona makosa ya tahajia au msimbo? Toa tatizo au tengeneza ombi la mabadiliko. ## Shukrani Maalum * **✍️ Mwandishi Mkuu:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Mhariri:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Mchoraji wa Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **✅ Muumba wa Maswali:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 Mchora Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Mtengenezaji wa Maswali:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Washiriki Wakuu:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Mitaala Mengine +## Mitaala Mingine -Timu yetu hutengeneza mitaala mingine! Angalia: +Timu yetu huandaa mitaala mingine! Angalia: ### LangChain -[![LangChain4j kwa Waanziaji](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js kwa Waanziaji](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) --- -### Azure / Edge / MCP / Wakala -[![AZD kwa Waanziaji](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 kwa Waanziaji](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 kwa Waanziaji](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) -[![Wakala wa AI kwa Waanziaji](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) +### Azure / Edge / MCP / Maajenti +[![AZD for Beginners](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 for Beginners](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 for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![AI Agents for Beginners](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) --- ### Mfululizo wa AI Inayozalisha -[![AI Inayozalisha kwa Waanziaji](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 Inayozalisha (.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 Inayozalisha (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 Inayozalisha (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) +[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![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) --- ### Kujifunza Msingi -[![ML kwa Waanziaji](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) -[![Sayansi ya Data kwa Waanziaji](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 kwa Waanziaji](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) -[![Usalama wa Mtandao kwa Waanziaji](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) -[![Maendeleo ya Mtandao kwa Waanziaji](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 kwa Waanziaji](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) -[![Maendeleo ya XR kwa Waanziaji](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) +[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cybersecurity for Beginners](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 for Beginners](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 for Beginners](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 Development for Beginners](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) --- ### Mfululizo wa Copilot -[![Copilot kwa Mipango ya Pamoja ya AI](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) -[![Copilot kwa 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) -[![Maajabu ya Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) +[![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) +[![Copilot for 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) ## Kupata Msaada -Ikiwa unakumbwa na shida au una maswali kuhusu kujenga programu za AI. Jiunge na wanafunzi wenzako na waendelezaji wenye uzoefu katika majadiliano kuhusu MCP. Ni jamii yenye msaada ambapo maswali yanakaribishwa na maarifa yanashirikishwa kwa uhuru. +Kama unaenona au una maswali yoyote kuhusu kujenga programu za AI. Jiunge na wanafunzi wenzako na waendelezaji wenye uzoefu katika majadiliano kuhusu MCP. Ni jamii yenye msaada ambapo maswali yanakaribishwa na maarifa yanashirikiwa kwa uhuru. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -228,6 +227,6 @@ Ikiwa una maoni kuhusu bidhaa au makosa wakati wa kujenga tembelea: --- -**Tangazo la Majadiliano**: -Nyaraka hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kufikia usahihi, tafadhali fahamu kwamba tafsiri za kiotomatiki zinaweza kuwa na makosa au ukosefu wa usahihi. Nyaraka ya asili katika lugha yake ya mama inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu na ya binadamu inapendekezwa. Hatuwajibiki kwa kutoelewana au tafsiri potofu zitakazotokea kutokana na matumizi ya tafsiri hii. +**Kifuniko cha Kuondoa Majukumu**: +Nyaraka hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kwamba tafsiri za kiotomatiki zinaweza kuwa na makosa au upotoshwaji. Nyaraka asilia katika lugha yake ya asili inapaswa kuchukuliwa kama chanzo chenye mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu inayofanywa na mtu inashauriwa. Hatuwajibiki kwa kutoelewana au tafsiri potofu zinazotokana na matumizi ya tafsiri hii. \ No newline at end of file diff --git a/translations/sw/lessons/0-course-setup/how-to-run.md b/translations/sw/lessons/0-course-setup/how-to-run.md index f562e6e2..8feb6f0e 100644 --- a/translations/sw/lessons/0-course-setup/how-to-run.md +++ b/translations/sw/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Jinsi ya Kuendesha Msimbo -Mtaala huu una mifano mingi inayoweza kutekelezwa na maabara ambayo ungependa kuendesha. Ili kufanya hivyo, unahitaji uwezo wa kutekeleza msimbo wa Python katika Jupyter Notebooks zinazotolewa kama sehemu ya mtaala huu. Una chaguo kadhaa za kuendesha msimbo: +Mtaala huu una mifano mingi inayoweza kutekelezwa na maabara ambazo ungependa kuendesha. Ili kufanya hivi, unahitaji uwezo wa kutekeleza msimbo wa Python katika Jupyter Notebooks zinazotolewa kama sehemu ya mtaala huu. Una chaguzi kadhaa za kuendesha msimbo: -## Kuendesha kwenye Kompyuta Yako +## Endesha kwa karibu kwenye kompyuta yako -Ili kuendesha msimbo kwenye kompyuta yako, unahitaji kuwa na toleo fulani la Python lililowekwa. Ninapendekeza sana kusakinisha **[miniconda](https://conda.io/en/latest/miniconda.html)** - ni usakinishaji mwepesi unaosaidia meneja wa kifurushi cha `conda` kwa mazingira tofauti ya **Python**. +Ili kuendesha msimbo kwa karibu kwenye kompyuta yako, usakinishaji wa Python unahitajika. Moja ya mapendekezo ni kusakinisha **[miniconda](https://conda.io/en/latest/miniconda.html)** - ni usakinishaji mzito mdogo unaounga mkono meneja wa pakiti `conda` kwa **mazingira pepe** tofauti za Python. -Baada ya kusakinisha miniconda, unahitaji kunakili hifadhi na kuunda mazingira ya kawaida yatakayotumika kwa kozi hii: +Baada ya kusakinisha miniconda, tengeneza nakala ya hazina na unda mazingira pepe yatakayotumika kwa kozi hii: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -24,19 +24,19 @@ conda env create --name ai4beg --file .devcontainer/environment.yml conda activate ai4beg ``` -### Kutumia Visual Studio Code na Kiendelezi cha Python +### Kutumia Visual Studio Code na Ongezeko la Python -Njia bora zaidi ya kutumia mtaala huu ni kuufungua katika [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) na [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Mtaala huu unatumika vyema unapoifungua katika [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) pamoja na [Ongezeko la Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Mara tu unapokopi na kufungua folda katika VS Code, itakupendekezea kusakinisha viendelezi vya Python. Pia utahitaji kusakinisha miniconda kama ilivyoelezwa hapo juu. +> **Kumbuka**: Mara tu unapochukua nakala na kufungua saraka katika VS Code, itapendekeza kiotomati kusakinisha ongezeko la Python. Pia utahitaji kusakinisha miniconda kama ilivyoelezwa hapo juu. -> **Note**: Ikiwa VS Code inapendekeza kufungua hifadhi katika kontena, unapaswa kukataa hili ili kutumia usakinishaji wa Python wa ndani. +> **Kumbuka**: Ikiwa VS Code itapendekeza kufungua tena hazina katika chombo (container), unapaswa kukataa ili kutumia usakinishaji wa Python wenyewe wenyewe. -### Kutumia Jupyter kwenye Kivinjari +### Kutumia Jupyter katika Kivinjari -Unaweza pia kutumia mazingira ya Jupyter moja kwa moja kutoka kwenye kivinjari kwenye kompyuta yako. Kwa kweli, Jupyter ya kawaida na Jupyter Hub hutoa mazingira mazuri ya maendeleo yenye uwezo wa kukamilisha msimbo kiotomatiki, kuonyesha rangi ya msimbo, n.k. +Unaweza pia kutumia mazingira ya Jupyter kutoka kwa kivinjari kwenye kompyuta yako mwenyewe. Jupyter wa kawaida na JupyterHub zote hutoa mazingira mazuri ya maendeleo yenye ukamilishaji wa kiotomatiki, kuangazia msimbo, n.k. -Ili kuanza Jupyter ndani ya nchi, nenda kwenye folda ya kozi, na utekeleze: +Kuanza Jupyter kwa karibu, nenda kwenye saraka ya kozi, na tekereza: ```bash jupyter notebook @@ -45,32 +45,36 @@ au ```bash jupyterhub ``` -Kisha unaweza kuvinjari faili zozote za `.ipynb`, kuzifungua na kuanza kufanya kazi. +Kisha unaweza kuvinjari kwenye faili yoyote za `.ipynb`, uzifungue na kuanza kufanya kazi. -### Kuendesha katika Kontena +### Kuendesha katika chombo (container) -Njia mbadala ya usakinishaji wa Python ni kuendesha msimbo katika kontena. Kwa kuwa hifadhi yetu ina folda maalum ya `.devcontainer` inayotoa maelekezo ya jinsi ya kujenga kontena kwa hifadhi hii, VS Code itakupendekezea kufungua msimbo katika kontena. Hii itahitaji usakinishaji wa Docker, na pia itakuwa ngumu zaidi, kwa hivyo tunapendekeza hii kwa watumiaji wenye uzoefu zaidi. +Mbali na usakinishaji wa Python, utaratibu mwingine ni kuendesha msimbo katika chombo. Kwa kuwa hazina yetu hutoa folda maalum ya `.devcontainer` inayofundisha jinsi ya kujenga chombo kwa ajili ya hazina hii, VS Code hutoa fursa ya kufungua tena msimbo katika chombo. Hii itahitaji usakinishaji wa Docker, na pia itakuwa na ngumu zaidi, kwa hivyo tunapendekeza hii kwa watumiaji waliobobea zaidi. -## Kuendesha Mtandaoni +## Kuendesha katika Wingu -Ikiwa hutaki kusakinisha Python ndani ya nchi, na una rasilimali za wingu - njia nzuri ni kuendesha msimbo mtandaoni. Kuna njia kadhaa za kufanya hivi: +Ikiwa hutaki kusakinisha Python kwa karibu, na una upatikanaji wa baadhi ya rasilimali za wingu - chaguo zuri ni kuendesha msimbo katika wingu. Kuna njia kadhaa unazoweza kufanya hivi: -* Kutumia **[GitHub Codespaces](https://github.com/features/codespaces)**, ambayo ni mazingira ya kawaida yaliyoundwa kwa ajili yako kwenye GitHub, yanayopatikana kupitia kiolesura cha kivinjari cha VS Code. Ikiwa una ufikiaji wa Codespaces, unaweza kubofya kitufe cha **Code** kwenye hifadhi, kuanzisha codespace, na kuanza mara moja. -* Kutumia **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) ni rasilimali za bure za kompyuta zinazotolewa mtandaoni kwa watu kama wewe kujaribu msimbo kwenye GitHub. Kuna kitufe kwenye ukurasa wa mbele kufungua hifadhi katika Binder - hii inapaswa kukuchukua haraka kwenye tovuti ya Binder, ambayo itajenga kontena msingi na kuanzisha kiolesura cha wavuti cha Jupyter kwa urahisi. +* Kutumia **[GitHub Codespaces](https://github.com/features/codespaces)**, ambacho ni mazingira pepe yaliyoandaliwa kwa ajili yako kwenye GitHub, yanayoweza kufikiwa kupitia kiolesura cha kivinjari cha VS Code. Ikiwa una upatikanaji wa Codespaces, unaweza kubofya tu kitufe cha **Code** katika hazina, anza codespace, na kuanza kufanya kazi mara moja. +* Kutumia **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) hutoa rasilimali za kompyuta bure mtandaoni kwa watu kama wewe kujaribu msimbo wa GitHub. Kuna kitufe katika ukurasa wa mwanzo kufungua hazina katika Binder - hii itakupeleka haraka kwenye tovuti ya binder, ambayo itajenga chombo cha msingi na kuanzisha kiolesura cha mtandao cha Jupyter kwa urahisi. -> **Note**: Ili kuzuia matumizi mabaya, Binder imezuia ufikiaji wa baadhi ya rasilimali za wavuti. Hii inaweza kuzuia baadhi ya misimbo inayopakua mifano na/au seti za data kutoka mtandao wa umma kufanya kazi. Unaweza kuhitaji kutafuta njia mbadala. Pia, rasilimali za kompyuta zinazotolewa na Binder ni za msingi sana, kwa hivyo mafunzo yatakuwa polepole, hasa katika masomo magumu zaidi. +> **Kumbuka**: Ili kuzuia matumizi mabaya, Binder ina rasilimali za wavuti zilizo vizuiziwa. Hii inaweza kuzuia baadhi ya misimbo kufanya kazi, ambayo hupakua mifano na/au seti za data kutoka mtandao wa umma. Huenda ukahitaji kupata njia mbadala. Pia, rasilimali za kompyuta zinazotolewa na Binder ni za msingi, hivyo mafunzo yatachukua muda mrefu, hasa katika masomo ya baadaye yenye ugumu zaidi. -## Kuendesha Mtandaoni na GPU +## Kuendesha katika Wingu na GPU -Baadhi ya masomo ya baadaye katika mtaala huu yatanufaika sana na msaada wa GPU, kwa sababu vinginevyo mafunzo yatakuwa ya polepole sana. Kuna chaguo chache unazoweza kufuata, hasa ikiwa una ufikiaji wa wingu kupitia [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), au kupitia taasisi yako: +Baadhi ya masomo ya baadaye katika mtaala huu yatanufaika sana na msaada wa GPU. Mafunzo ya mfano, kwa mfano, yanaweza kuwa polepole sana vinginevyo. Kuna chaguzi kadhaa unazoweza kufuata, hasa ikiwa una upatikanaji wa wingu kupitia [Azure kwa Wanafunzi](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), au kupitia taasisi yako: -* Unda [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) na uunganishe nayo kupitia Jupyter. Unaweza kisha kunakili hifadhi moja kwa moja kwenye mashine, na kuanza kujifunza. Mashine za NC-series zina msaada wa GPU. +* Unda [Mashine Pepe ya Sayansi ya Data](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) na uungane nayo kupitia Jupyter. Kisha unaweza kuunda nakala ya hazina moja kwa moja kwenye mashine, na kuanza kujifunza. VMs za mfululizo wa NC zina msaada wa GPU. -> **Note**: Baadhi ya usajili, ikiwa ni pamoja na Azure for Students, hazitoi msaada wa GPU moja kwa moja. Unaweza kuhitaji kuomba nyongeza ya cores za GPU kupitia ombi la msaada wa kiufundi. +> **Kumbuka**: Baadhi ya usajili, ikiwa ni pamoja na Azure kwa Wanafunzi, hazitoi msaada wa GPU moja kwa moja. Huenda ukahitaji kuomba cores za GPU ziada kupitia ombi la msaada wa kiufundi. -* Unda [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) na kisha utumie kipengele cha Notebook huko. [Video hii](https://azure-for-academics.github.io/quickstart/azureml-papers/) inaonyesha jinsi ya kunakili hifadhi kwenye daftari la Azure ML na kuanza kuitumia. +* Unda [Eneo la Kazi la Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) kisha tumia kipengele cha Notebook pale. [Video hii](https://azure-for-academics.github.io/quickstart/azureml-papers/) inaonyesha jinsi ya kunakili hazina katika daftari la Azure ML na kuanza kuitumia. -Unaweza pia kutumia Google Colab, ambayo inakuja na msaada wa GPU wa bure, na kupakia Jupyter Notebooks huko ili kuzitekeleza moja baada ya nyingine. +Unaweza pia kutumia Google Colab, ambayo inakuja na msaada wa bure wa GPU, na kupakia Jupyter Notebooks pale kuzikamilisha moja moja. -**Kanusho**: -Hati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii. \ No newline at end of file +--- + + +**Kielezi cha Majumuisho**: +Hati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kwamba tafsiri za kiotomatiki zinaweza kuwa na makosa au upungufu wa usahihi. Hati ya awali katika lugha yake ya asili inapaswa kuchukuliwa kama chanzo halali. Kwa taarifa muhimu, tafsiri ya kitaalamu inayofanywa na watu inashauriwa. Hatuwajibiki kwa maelewano au ufafanuzi mbaya unaotokana na matumizi ya tafsiri hii. + \ No newline at end of file diff --git a/translations/sw/lessons/2-Symbolic/Animals.ipynb b/translations/sw/lessons/2-Symbolic/Animals.ipynb index 4e3a4ee3..c2261498 100644 --- a/translations/sw/lessons/2-Symbolic/Animals.ipynb +++ b/translations/sw/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Kutekeleza Mfumo wa Mtaalamu wa Wanyama\n", + "# Kutekeleza Mfumo wa Mtaalam wa Wanyama\n", "\n", - "Mfano kutoka [Mtaala wa AI kwa Kompyuta](http://github.com/microsoft/ai-for-beginners).\n", + "Mfano kutoka [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Katika mfano huu, tutatekeleza mfumo rahisi unaotegemea maarifa ili kubaini mnyama kulingana na baadhi ya sifa za kimwili. Mfumo huu unaweza kuwakilishwa na mti wa AND-OR ufuatao (hii ni sehemu ya mti mzima, tunaweza kuongeza sheria zaidi kwa urahisi):\n", + "Katika sampuli hii, tutaweka mfumo rahisi unaotegemea maarifa ili kubainisha mnyama kulingana na baadhi ya sifa za kimwili. Mfumo unaweza kuonyeshwa kwa mti wa AND-OR ulio hapa chini (hii ni sehemu ya mti mzima, tunaweza kwa urahisi kuongeza sheria zaidi):\n", "\n", - "![](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Mfumo wetu wa ganda la mifumo ya wataalamu wenye uamuzi wa nyuma\n", + "## Mfumo wetu wa kitaalamu wenyewe na uelewa wa nyuma\n", "\n", - "Hebu tujaribu kufafanua lugha rahisi ya uwakilishi wa maarifa inayotegemea sheria za uzalishaji. Tutatumia madarasa ya Python kama maneno muhimu kufafanua sheria. Kimsingi, kutakuwa na aina 3 za madarasa:\n", - "* `Ask` inawakilisha swali ambalo linapaswa kuulizwa kwa mtumiaji. Linajumuisha seti ya majibu yanayowezekana.\n", - "* `If` inawakilisha sheria, na ni njia rahisi ya kuhifadhi maudhui ya sheria.\n", - "* `AND`/`OR` ni madarasa ya kuwakilisha matawi ya AND/OR ya mti. Yanaweka tu orodha ya hoja ndani. Ili kurahisisha msimbo, utendaji wote umefafanuliwa katika darasa la mzazi `Content`.\n" + "Tujaribu kufafanua lugha rahisi kwa ajili ya uwakilishi wa maarifa inayotegemea sheria za utengenezaji. Tutatumia madarasa ya Python kama maneno muhimu kuainisha sheria. Kutarajiwa kuna aina 3 za madarasa:\n", + "* `Ask` inawakilisha swali linalohitajika kuulizwa kwa mtumiaji. Lina seti ya majibu yanayowezekana.\n", + "* `If` inawakilisha sheria, na ni kama sukari ya kisintaksia kuhifadhi maudhui ya sheria\n", + "* `AND`/`OR` ni madarasa ya kuwakilisha matawi ya AND/OR ya mti. Huwanikisha tu orodha ya hoja ndani. Ili kurahisisha msimbo, kazi zote zinafafanuliwa katika darasa la mzazi `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Katika mfumo wetu, kumbukumbu ya kazi ingejumuisha orodha ya **mambo** kama **jozi za sifa-thamani**. Msingi wa maarifa unaweza kufafanuliwa kama kamusi moja kubwa inayochanganua vitendo (mambo mapya yanayopaswa kuingizwa kwenye kumbukumbu ya kazi) kwa masharti, yanayoonyeshwa kama maelezo ya AND-OR. Pia, baadhi ya mambo yanaweza `Kuulizwa`.\n" + "Katika mfumo wetu, kumbukumbu ya kazi ingehifadhi orodha ya **mambo ya ukweli** kama **jozi za sifa-thamani**. Hifadhidata ya maarifa inaweza kufafanuliwa kama kamusi kubwa inayounganisha vitendo (mambo mapya ya ukweli yanayopaswa kuwekwa kwenye kumbukumbu ya kazi) na masharti, yanayoelezwa kama misemo ya AND-OR. Pia, baadhi ya mambo ya ukweli yanaweza kuombwa kwa `Ask`.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ili kufanya uchambuzi wa nyuma, tutafafanua darasa la `Knowledgebase`. Litajumuisha:\n", - "* `memory` ya kazi - kamusi inayohusisha sifa na thamani zake\n", - "* `rules` za Knowledgebase katika muundo kama ulivyoelezwa hapo juu\n", + "Ili kufanya hitimisho la nyuma, tutataja darasa la `Knowledgebase`. Litatumia:\n", + "* `memory` ya kufanya kazi - kamusi inayochora sifa kwa thamani\n", + "* sheria za Knowledgebase katika muundo kama ulivyoelezwa hapo juu\n", "\n", - "Njia kuu mbili ni:\n", - "* `get` kupata thamani ya sifa, ikifanya uchambuzi ikiwa ni lazima. Kwa mfano, `get('color')` itapata thamani ya sehemu ya rangi (itauliza ikiwa ni lazima, na kuhifadhi thamani kwa matumizi ya baadaye katika memory ya kazi). Ikiwa tutauliza `get('color:blue')`, itauliza kuhusu rangi, kisha itarudisha thamani ya `y`/`n` kulingana na rangi.\n", - "* `eval` inafanya uchambuzi halisi, yaani, inapitia mti wa AND/OR, inatathmini malengo madogo, nk.\n" + "Mbinu kuu mbili ni:\n", + "* `get` kupata thamani ya sifa, ikifanya hitimisho ikiwa inahitajika. Kwa mfano, `get('color')` itapata thamani ya nafasi ya rangi (itauliza ikiwa inahitajika, na kuhifadhi thamani kwa matumizi ya baadaye katika kumbukumbu ya kazi). Ikiwa tunauliza `get('color:blue')`, itauliza kuhusu rangi, kisha kurudisha thamani ya `y`/`n` kulingana na rangi.\n", + "* `eval` hufanya hitimisho halisi, yaani kutembea kwenye mti wa AND/OR, kutathmini malengo ndogo, n.k.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sasa hebu tuelezee msingi wetu wa maarifa kuhusu wanyama na tufanye mashauriano. Kumbuka kwamba simu hii itakuuliza maswali. Unaweza kujibu kwa kuandika `y`/`n` kwa maswali ya ndiyo-hapana, au kwa kutaja nambari (0..N) kwa maswali yenye majibu marefu ya chaguo nyingi.\n" + "Sasa hebu tufafanue hazina yetu ya maarifa ya wanyama na kufanya ushauri. Kumbuka kuwa simu hii itakuuliza maswali. Unaweza kujibu kwa kuandika `y`/`n` kwa maswali ya ndiyo-hapana, au kwa kubainisha nambari (0..N) kwa maswali yenye majibu mengi ya chaguo mrefu.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Kutumia PyKnow kwa Utoaji wa Hitimisho wa Mbele\n", + "## Kutumia Experta kwa Uhakiki wa Mbele\n", "\n", - "Katika mfano ufuatao, tutajaribu kutekeleza utoaji wa hitimisho wa mbele kwa kutumia moja ya maktaba za uwakilishi wa maarifa, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** ni maktaba ya kuunda mifumo ya utoaji wa hitimisho wa mbele katika Python, ambayo imeundwa kuwa sawa na mfumo wa zamani wa kawaida [CLIPS](http://www.clipsrules.net/index.html).\n", + "Katika mfano unaofuata, tuta jaribu kutekeleza uhakiki wa mbele kutumia moja ya maktaba za uwasilishaji wa maarifa, [Experta](https://github.com/nilp0inter/experta). **Experta** ni maktaba ya kuunda mifumo ya uhakiki wa mbele katika Python, ambayo imetengenezwa kufanana na mfumo wa zamani wa kienyeji [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Tungeweza pia kutekeleza mnyororo wa mbele sisi wenyewe bila matatizo mengi, lakini utekelezaji wa kawaida mara nyingi hauwi na ufanisi mkubwa. Kwa kulinganisha sheria kwa ufanisi zaidi, hutumika algoriti maalum [Rete](https://en.wikipedia.org/wiki/Rete_algorithm).\n" + "Tungeweza pia kutekeleza usambazaji wa mbele sisi wenyewe bila matatizo mengi, lakini utekelezaji wa kawaida kawaida haufaniki sana. Kwa ajili ya ulinganifu bora wa sheria, algorithm maalum [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) hutumika.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Tutafafanua mfumo wetu kama darasa linalotumia darasa la `KnowledgeEngine`. Kila kanuni inafafanuliwa na kazi tofauti yenye anotasheni ya `@Rule`, ambayo inaeleza wakati kanuni inapaswa kutekelezwa. Ndani ya kanuni, tunaweza kuongeza ukweli mpya kwa kutumia kazi ya `declare`, na kuongeza ukweli huo kutawezesha kanuni zingine zaidi kuitwa na injini ya inferensi ya mbele.\n" + "Tutaelezea mfumo wetu kama darasa ambalo linadhibitisha `KnowledgeEngine`. Kila sheria inaelezewa na kazi tofauti yenye alama ya `@Rule`, inayobainisha lini sheria hiyo inapaswa kutekelezwa. Ndani ya sheria, tunaweza kuongeza taarifa mpya kwa kutumia kazi ya `declare`, na kuongeza taarifa hizo kutasababisha sheria zaidi kuitwa na injini ya uelekeo wa mbele.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Mara tu tunapokuwa tumeainisha msingi wa maarifa, tunajaza kumbukumbu yetu ya kazi na baadhi ya ukweli wa awali, kisha tunaita mbinu ya `run()` ili kufanya hitimisho. Unaweza kuona matokeo yake kwamba ukweli mpya uliodhihirika umeongezwa kwenye kumbukumbu ya kazi, ikijumuisha ukweli wa mwisho kuhusu mnyama (ikiwa tumeweka ukweli wote wa awali kwa usahihi).\n" + "Mara tu tunapokuwa tumedefine msingi wa maarifa, tunaweka kumbukumbu yetu ya kazi na baadhi ya ukweli wa awali, kisha tunaite njia ya `run()` kufanya hitimisho. Unaweza kuona kama matokeo kuwa ukweli mpya uliotamkwa umeongezwa kwenye kumbukumbu ya kazi, ikiwa ni pamoja na ukweli wa mwisho kuhusu mnyama (ikiwa tumeweka ukweli wote wa awali kwa usahihi).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Kanusho**: \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n" + "---\n\n\n**Kionyozi cha Masuala**: \nNyaraka hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kwamba tafsiri za kiotomatiki zinaweza kuwa na makosa au dosari. Nyaraka ya asili katika lugha yake ya asili inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inapendekezwa kutumia tafsiri ya mtaalamu wa binadamu. Hatuhusiki kwa mikanganyiko au tafsiri potofu zitokanazo na matumizi ya tafsiri hii.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-29T14:43:07+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T04:08:46+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "sw" } diff --git a/translations/sw/lessons/2-Symbolic/README.md b/translations/sw/lessons/2-Symbolic/README.md index 77f0d92e..3d42efa0 100644 --- a/translations/sw/lessons/2-Symbolic/README.md +++ b/translations/sw/lessons/2-Symbolic/README.md @@ -1,116 +1,116 @@ # Uwakilishi wa Maarifa na Mifumo ya Wataalamu -![Muhtasari wa maudhui ya AI ya Kimaandishi](../../../../translated_images/sw/ai-symbolic.715a30cb610411a6.webp) +![Muhtasari wa maudhui ya AI ya Ikoniki](../../../../../../translated_images/sw/ai-symbolic.715a30cb610411a6.webp) > Sketchnote na [Tomomi Imura](https://twitter.com/girlie_mac) -Utafiti wa akili bandia unategemea kutafuta maarifa, ili kuelewa dunia kwa njia sawa na binadamu. Lakini unaweza kufanya hivyo vipi? +Jitihada za akili bandia zinatokana na kutafuta maarifa, kuelewa dunia kama wanadamu wanavyofanya. Lakini unaweza kufanya hivyo vipi? -## [Jaribio la awali la somo](https://ff-quizzes.netlify.app/en/ai/quiz/3) +## [Mtihani wa kabla ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Katika siku za mwanzo za AI, mbinu ya juu-chini ya kuunda mifumo yenye akili (ilijadiliwa katika somo lililopita) ilikuwa maarufu. Wazo lilikuwa ni kutoa maarifa kutoka kwa watu na kuyafanya yaweze kusomeka na mashine, kisha kuyatumia kutatua matatizo kiotomatiki. Mbinu hii ilitegemea mawazo mawili makubwa: +Katika siku za mwanzo za AI, mbinu ya kutoka juu hadi chini ya kuunda mifumo ya akili (iliyokuwa imetajwa katika somo la awali) ilikuwa maarufu. Wazo lilikuwa kuchukua maarifa kutoka kwa watu na kuyahifadhi katika mfumo wa kusomwa na mashine, kisha kuyatumia kutatua matatizo kiotomatiki. Mbinu hii ilizingatia mawazo mawili makubwa: * Uwakilishi wa Maarifa -* Utoaji wa Sababu +* Kutoa Madai/Kuelewa (Reasoning) ## Uwakilishi wa Maarifa -Moja ya dhana muhimu katika AI ya Kimaandishi ni **maarifa**. Ni muhimu kutofautisha maarifa na *taarifa* au *data*. Kwa mfano, mtu anaweza kusema kwamba vitabu vina maarifa, kwa sababu mtu anaweza kusoma vitabu na kuwa mtaalamu. Hata hivyo, kile ambacho vitabu vina ni kinachoitwa *data*, na kwa kusoma vitabu na kuunganisha data hii katika mfano wetu wa dunia tunabadilisha data hii kuwa maarifa. +Moja ya dhana muhimu katika AI ya Ikoniki ni **maarifa**. Ni muhimu kutofautisha maarifa na *taarifa* au *data*. Kwa mfano, mtu anaweza kusema kwamba vitabu vina maarifa, kwa sababu mtu anaweza kusoma vitabu na kuwa mtaalamu. Hata hivyo, kile vitabu vinachonacho kinaitwa *data*, na kwa kusoma vitabu na kuingiza data hii katika mfano wetu wa dunia, tunabadilisha data kuwa maarifa. -> ✅ **Maarifa** ni kitu kilicho ndani ya akili zetu na kinawakilisha uelewa wetu wa dunia. Yanapatikana kupitia mchakato wa **kujifunza** kwa bidii, ambao unajumuisha vipande vya taarifa tunazopokea katika mfano wetu wa dunia. +> ✅ **Maarifa** ni kitu kilicho kichwani mwetu kinachoonyesha ufahamu wetu wa dunia. Yanapatikana kupitia mchakato wa **kujifunza** unaochukua vipande vya taarifa tunazopata na kuviunganisha katika mfano wetu wa dunia unaotumika. -Mara nyingi, hatufafanui maarifa kwa ukali, lakini tunayalinganisha na dhana nyingine zinazohusiana kwa kutumia [Piramidi ya DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Inajumuisha dhana zifuatazo: +Mara nyingi, hatufafanui maarifa kwa ukamilifu, lakini tunayalinganisha na dhana nyingine zinazohusiana kwa kutumia [Piramidi ya DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ina dhana zifuatazo: -* **Data** ni kitu kinachowakilishwa katika vyombo vya kimwili, kama maandishi yaliyoandikwa au maneno yaliyotamkwa. Data ipo bila kujali uwepo wa binadamu na inaweza kupitishwa kati ya watu. -* **Taarifa** ni jinsi tunavyotafsiri data katika akili zetu. Kwa mfano, tunaposikia neno *kompyuta*, tunakuwa na uelewa fulani wa kile ilivyo. -* **Maarifa** ni taarifa inayojumuishwa katika mfano wetu wa dunia. Kwa mfano, mara tu tunapojifunza kompyuta ni nini, tunaanza kuwa na mawazo kuhusu jinsi inavyofanya kazi, gharama yake, na matumizi yake. Mtandao huu wa dhana zinazohusiana huunda maarifa yetu. -* **Hekima** ni kiwango kingine cha uelewa wetu wa dunia, na inawakilisha *maarifa ya meta*, yaani, dhana fulani kuhusu jinsi na wakati maarifa yanapaswa kutumika. +* **Data** ni vitu vinavyowakilishwa katika vyombo vya kimwili, kama maandishi yaliyoandikwa au maneno yaliyosemwa. Data ipo huru bila kuhusiana na wanadamu na inaweza kupelekwa kati ya watu. +* **Taarifa** ni jinsi tunavyotafsiri data kichwani mwetu. Kwa mfano, tunapomsikia neno *kompyuta*, tuna ufahamu fulani wa kinachomaanisha. +* **Maarifa** ni taarifa zinazoingizwa katika mfano wetu wa dunia. Kwa mfano, tunapojifunza ni nini kompyuta, tunaanza kuwa na mawazo ya jinsi inavyofanya kazi, gharama zake, na matumizi yake. Mtandao huu wa dhana zinazohusiana huunda maarifa yetu. +* **Hekima** ni ngazi nyingine ya ufahamu wetu wa dunia, na huwakilisha *meta-maarifa*, mfano. wazo fulani kuhusu jinsi na lini maarifa yanapaswa kutumiwa. - + -*Picha [kutoka Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Na Longlivetheux - Kazi ya mwenyewe, CC BY-SA 4.0* +*Picha [kutoka Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Na Longlivetheux - Kazi binafsi, CC BY-SA 4.0* -Kwa hivyo, tatizo la **uwakilishi wa maarifa** ni kutafuta njia bora ya kuwakilisha maarifa ndani ya kompyuta kwa njia ya data, ili yaweze kutumika kiotomatiki. Hili linaweza kuonekana kama wigo: +Hivyo, tatizo la **uwakilishi wa maarifa** ni kupata njia madhubuti ya kuwakilisha maarifa ndani ya kompyuta katika mfumo wa data, ili yaweze kutumika kiotomatiki. Hii inaonekana kama spektra: -![Wigo wa uwakilishi wa maarifa](../../../../translated_images/sw/knowledge-spectrum.b60df631852c0217.webp) +![Spektra ya uwakilishi wa maarifa](../../../../../../translated_images/sw/knowledge-spectrum.b60df631852c0217.webp) > Picha na [Dmitry Soshnikov](http://soshnikov.com) -* Kushoto, kuna aina rahisi sana za uwakilishi wa maarifa ambazo zinaweza kutumika kwa ufanisi na kompyuta. Rahisi zaidi ni ya kialgorithimu, ambapo maarifa yanawakilishwa na programu ya kompyuta. Hata hivyo, hii si njia bora ya kuwakilisha maarifa, kwa sababu si rahisi kubadilika. Maarifa ndani ya akili zetu mara nyingi si ya kialgorithimu. -* Kulia, kuna uwakilishi kama maandishi ya asili. Ni yenye nguvu zaidi, lakini haiwezi kutumika kwa utoaji wa sababu kiotomatiki. +* Kushoto, kuna aina rahisi za uwakilishi wa maarifa ambazo kompyuta zinaweza kuzitumia kwa ufanisi. Rahisi zaidi ni ya algorithmic, ambapo maarifa yanawakilishwa na programu ya kompyuta. Hii, hata hivyo, si njia bora ya kuwakilisha maarifa, kwa sababu si rahisi kubadilika. Maarifa ya kichwani mwetu mara nyingi si algorithmic. +* Kulia, kuna uwakilishi kama maandishi ya asili. Hii ni yenye nguvu zaidi, lakini haiwezi kutumika kwa kufikiri kwa kiotomatiki. -> ✅ Fikiria kwa dakika moja jinsi unavyowakilisha maarifa katika akili yako na kuyabadilisha kuwa maelezo. Je, kuna muundo fulani unaokufaa kusaidia kukumbuka? +> ✅ Fikiria kwa muda mfupi jinsi unavyo wakilisha maarifa kichwani mwako na kuyaandika kama noti. Kuna muundo maalum unaofaa kwako kusaidia kuhifadhi maarifa? -## Uainishaji wa Uwakilishi wa Maarifa ya Kompyuta +## Kushiriki Aina za Uwakilishi wa Maarifa ya Kompyuta -Tunaweza kuainisha mbinu tofauti za uwakilishi wa maarifa ya kompyuta katika makundi yafuatayo: +Tunaweza kugawanya njia mbalimbali za uwakilishi wa maarifa ya kompyuta katika makundi yafuatayo: -* **Uwakilishi wa mtandao** unategemea ukweli kwamba tuna mtandao wa dhana zinazohusiana ndani ya akili zetu. Tunaweza kujaribu kuunda mtandao huo kama grafu ndani ya kompyuta - mtandao wa **semantic**. +* **Uwakilishi wa mtandao** unategemea ukweli kwamba tunayo mtandao wa dhana zinazohusiana kichwani mwetu. Tunaweza kujaribu kuunda mitandao hiyo kama grafu chini ya kompyuta - inayoitwa **mtandao wa semantiki**. -1. **Triplets za Kitu-Sifa-Thamani** au **jozi za sifa-thamani**. Kwa kuwa grafu inaweza kuwakilishwa ndani ya kompyuta kama orodha ya nodi na kingo, tunaweza kuwakilisha mtandao wa semantic kwa orodha ya triplets, zinazojumuisha vitu, sifa, na thamani. Kwa mfano, tunajenga triplets zifuatazo kuhusu lugha za programu: +1. **Vitu-Sifa-Thamani triplets** au **vipengele-thamani**. Kwa kuwa grafu inaweza kuwakilishwa kwenye kompyuta kama orodha ya nodes na edges, tunaweza kuwakilisha mtandao wa semantiki kwa orodha ya triplets, zenye vitu, sifa, na thamani. Kwa mfano, tunajenga triplets zifuatazo kuhusu lugha za programu: Kitu | Sifa | Thamani ------|------|-------- +-------|-----------|------ Python | ni | Lugha Isiyo na Aina -Python | ilivumbuliwa-na | Guido van Rossum -Python | syntax ya block | indentation +Python | ilibuniwa-na | Guido van Rossum +Python | sarufi-ya-kipande | uingizaji nafasi Lugha Isiyo na Aina | haina | ufafanuzi wa aina -> ✅ Fikiria jinsi triplets zinavyoweza kutumika kuwakilisha aina nyingine za maarifa. +> ✅ Fikiria jinsi triplets zinaweza kutumika kuwakilisha aina nyingine za maarifa. -2. **Uwakilishi wa kihierarkia** unasisitiza ukweli kwamba mara nyingi tunaunda hierarkia ya vitu ndani ya akili zetu. Kwa mfano, tunajua kwamba kanari ni ndege, na ndege wote wana mabawa. Pia tuna wazo fulani kuhusu rangi ya kawaida ya kanari, na kasi yao ya kuruka. +2. **Uwakilishi wa kihierarki** unaweka mkazo kuwa mara nyingi tunaunda hierarchy ya vitu kichwani mwetu. Kwa mfano, tunajua kwamba manyoya ni ndege, na ndege wote wana mabawa. Pia tuna wazo kuhusu rangi ya kawaida ya manyoya, na kasi yao ya kuruka. - - **Uwakilishi wa fremu** unategemea kuwakilisha kila kitu au darasa la vitu kama **fremu** inayojumuisha **slots**. Slots zinaweza kuwa na thamani za default, vizuizi vya thamani, au taratibu zilizohifadhiwa ambazo zinaweza kuitwa ili kupata thamani ya slot. Fremu zote zinaunda hierarkia sawa na hierarkia ya vitu katika lugha za programu za msingi wa vitu. - - **Matukio** ni aina maalum ya fremu zinazowakilisha hali ngumu zinazoweza kutokea kwa muda. + - **Uwakilishi wa fremu** unategemea kuwakilisha kila kitu au darasa la vitu kama **fremu** yenye **slots**. Slots zinaweza kuwa na thamani za msingi, vizuizi vya thamani, au taratibu za kuhifadhiwa zinazoweza kuitwa kupata thamani ya slot. Fremu zote huunda hierarchy kama ile ya vitu katika lugha za programu za kitu. + - **Skenario** ni aina maalum za fremu zinazowakilisha hali ngumu zinazoweza kutokea katika muda. **Python** -Slot | Thamani | Thamani ya Default | Kipindi | ------|--------|--------------------|---------| +Slot | Thamani | Thamani ya Msingi | Kipindi | +-----|-------|---------------|----------| Jina | Python | | | Ni-A | Lugha Isiyo na Aina | | | -Kesi ya Kigezo | | CamelCase | | +Mfumo wa Variable | | CamelCase | | Urefu wa Programu | | | mistari 5-5000 | -Syntax ya Block | Indent | | | +Sarufi ya Kipande | Uingizaji nafasi | | | -3. **Uwakilishi wa kiutaratibu** unategemea kuwakilisha maarifa kwa orodha ya vitendo vinavyoweza kutekelezwa wakati hali fulani inatokea. - - Sheria za uzalishaji ni kauli za ikiwa-basi zinazoturuhusu kutoa hitimisho. Kwa mfano, daktari anaweza kuwa na sheria inayosema kwamba **IWAPO** mgonjwa ana homa kali **AU** kiwango cha juu cha protini ya C-reactive katika kipimo cha damu **BASI** ana uvimbe. Mara tu tunapokutana na mojawapo ya hali hizo, tunaweza kutoa hitimisho kuhusu uvimbe, kisha kuitumia katika utoaji wa sababu zaidi. - - Algorithimu zinaweza kuzingatiwa kama aina nyingine ya uwakilishi wa kiutaratibu, ingawa karibu hazitumiki moja kwa moja katika mifumo inayotegemea maarifa. +3. **Uwakilishi wa taratibu** unategemea kuwakilisha maarifa kwa orodha ya hatua zinazoweza kutekelezwa pale hali fulani inapotokea. + - Masharti ya uzalishaji ni msemo-la-kisha (if-then) unaoturuhusu kutoa hitimisho. Kwa mfano, daktari anaweza kuwa na sheria isemayo **KAMA** mgonjwa ana homa kali **AU** kiwango cha juu cha protini ya C-reactive kwenye mtihani wa damu **KWA HIVYO** ana maambukizi. Tukikutana na moja ya masharti, tunaweza kutoa hitimisho kuhusu maambukizi, kisha kuitumia katika fikra zaidi. + - Algorithm zinaweza kuzingatiwa kama njia nyingine ya uwakilishi wa taratibu, ingawa hazitumiki mara kwa mara moja kwa moja katika mifumo yenye maarifa. -4. **Mantiki** ilipendekezwa awali na Aristotle kama njia ya kuwakilisha maarifa ya binadamu ya ulimwengu. - - Mantiki ya Prediketi kama nadharia ya hisabati ni tajiri sana kiasi kwamba haiwezi kuhesabiwa, kwa hivyo subset fulani ya hiyo kawaida hutumiwa, kama vile Horn clauses zinazotumiwa katika Prolog. - - Mantiki ya Maelezo ni familia ya mifumo ya kimantiki inayotumika kuwakilisha na kutoa sababu kuhusu hierarkia za vitu na uwakilishi wa maarifa uliosambazwa kama *semantic web*. +4. **Mantiki** ilipendekezwa awali na Aristotle kama njia ya kuwakilisha maarifa ya binadamu kwa ujumla. + - Mantiki ya Prediketi kama nadharia ya hisabati ni pana mno kwa kuwa iweze kompyutwa, hivyo sehemu ya mantiki hutumika kama vile Horn clauses zinazotumika kwenye Prolog. + - Mantiki ya Maelezo ni familia ya mifumo ya mantiki inayotumika kuwakilisha na kutoa mantiki kuhusu hiari ya vitu na uwakilishi wa maarifa uliosambazwa kama *wavu wa semanti*. ## Mifumo ya Wataalamu -Moja ya mafanikio ya awali ya AI ya kimaandishi yalikuwa mifumo ya **wataalamu** - mifumo ya kompyuta iliyoundwa kufanya kazi kama mtaalamu katika eneo fulani la tatizo. Ilitegemea **hifadhidata ya maarifa** iliyotolewa kutoka kwa mtaalamu mmoja au zaidi wa binadamu, na ilikuwa na **injini ya utoaji wa sababu** iliyofanya utoaji wa sababu juu yake. +Moja ya mafanikio ya mwanzo ya AI ya Ikoniki ilikuwa mifumo inayoitwa **mifumo ya wataalamu** - mifumo ya kompyuta iliyotengenezwa kutenda kama mtaalamu katika eneo fulani la tatizo lililo wazi. Ilijengwa kwa msingi wa **hifadhidata ya maarifa** iliyochukuliwa kutoka kwa wataalamu mmoja au zaidi wa binadamu, na ilijumuisha **mashine ya hitimisho** iliyofanya fikra juu yake. -![Muundo wa Binadamu](../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.webp) | ![Mfumo Unaotegemea Maarifa](../../../../translated_images/sw/arch-kbs.3ec5c150b09fa8da.webp) +![Mimino ya binadamu](../../../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.webp) | ![Mfumo wenye maarifa](../../../../../../translated_images/sw/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Muundo rahisi wa mfumo wa neva wa binadamu | Muundo wa mfumo unaotegemea maarifa +Muundo rahisi wa mfumo wa neva wa binadamu | Muundo wa mfumo wenye maarifa -Mifumo ya wataalamu imejengwa kama mfumo wa utoaji wa sababu wa binadamu, ambao una **kumbukumbu ya muda mfupi** na **kumbukumbu ya muda mrefu**. Vivyo hivyo, katika mifumo inayotegemea maarifa tunatofautisha vipengele vifuatavyo: +Mifumo ya wataalamu imejengwa kama mfumo wa fikra wa binadamu, wenye **kumbukumbu ya muda mfupi** na **kumbukumbu ya muda mrefu**. Vivyo hivyo, katika mifumo yenye maarifa tunatofautisha vipengele vifuatavyo: -* **Kumbukumbu ya tatizo**: ina maarifa kuhusu tatizo linalosuluhishwa kwa sasa, yaani, joto au shinikizo la damu la mgonjwa, ikiwa ana uvimbe au la, nk. Maarifa haya pia huitwa **maarifa tuli**, kwa sababu yanajumuisha picha ya kile tunachojua kwa sasa kuhusu tatizo - hali ya tatizo. -* **Hifadhidata ya maarifa**: inawakilisha maarifa ya muda mrefu kuhusu eneo la tatizo. Inatolewa kwa mikono kutoka kwa wataalamu wa binadamu, na haibadiliki kutoka ushauri mmoja hadi mwingine. Kwa sababu inaturuhusu kuvinjari kutoka hali moja ya tatizo hadi nyingine, pia inaitwa **maarifa yenye nguvu**. -* **Injini ya utoaji wa sababu**: inaendesha mchakato mzima wa kutafuta katika nafasi ya hali ya tatizo, kuuliza maswali kwa mtumiaji inapohitajika. Pia inawajibika kwa kutafuta sheria sahihi za kutumika kwa kila hali. +* **Kumbukumbu ya tatizo**: ina maarifa kuhusu tatizo linalotatuliwa kwa sasa, mfano joto au shinikizo la damu la mgonjwa, kama ana maambukizi au hapana n.k. Maarifa haya pia hujulikana kama **maarifa ya takatifu**, kwa kuwa ni picha ya hali ya tatizo kwa sasa - inayoitwa *hali ya tatizo*. +* **Hifadhidata ya maarifa**: huwakilisha maarifa ya muda mrefu kuhusu eneo la tatizo. Huchukuliwa kwa mkono kutoka kwa wataalamu wa binadamu, na haibadiliki kati ya mazungumzo. Kwa sababu huturuhusu kuhamia kutoka hali moja ya tatizo hadi nyingine, pia hujulikana kama **maarifa ya mabadiliko**. +* **Mashine ya hitimisho**: inaendesha mchakato mzima wa kutafuta katika chombo cha hali za tatizo, kuuliza maswali kwa mtumiaji inapohitajika. Pia inahusika na kupata sheria zinazofaa kutumika kwa kila hali. -Kwa mfano, hebu tuchunguze mfumo wa wataalamu wa kuamua mnyama kulingana na sifa zake za kimwili: +Kwa mfano, tuchukulie mfumo wa wataalamu wa kubaini mnyama kwa msingi wa sifa zake za kimwili: -![Mti wa AND-OR](../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp) +![Mti wa AND-OR](../../../../../../translated_images/sw/AND-OR-Tree.5592d2c70187f283.webp) > Picha na [Dmitry Soshnikov](http://soshnikov.com) -Mchoro huu unaitwa **mti wa AND-OR**, na ni uwakilishi wa kielelezo wa seti ya sheria za uzalishaji. Kuchora mti ni muhimu mwanzoni mwa kutoa maarifa kutoka kwa mtaalamu. Ili kuwakilisha maarifa ndani ya kompyuta ni rahisi zaidi kutumia sheria: +Mchoro huu unaitwa **mti wa AND-OR**, na ni uwakilishi wa kielelezo wa seti ya sheria za uzalishaji. Kuchora mti ni muhimu mwanzoni mwa kuchukua maarifa kutoka kwa mtaalamu. Kuweka maarifa ndani ya kompyuta ni rahisi kutumia sheria: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Unaweza kugundua kwamba kila hali upande wa kushoto wa sheria na kitendo ni kimsingi triplets za kitu-sifa-thamani (OAV). **Kumbukumbu ya kazi** ina seti ya triplets za OAV zinazolingana na tatizo linalosuluhishwa kwa sasa. **Injini ya sheria** hutafuta sheria ambazo hali yake imetimizwa na kuzitumia, ikiongeza triplet nyingine kwenye kumbukumbu ya kazi. +Unaweza kuona kwamba kila hali upande wa kushoto wa sheria na kitendo ni kwa msingi wa triplets za kitu-sifa-thamani (OAV). **Kumbukumbu ya kazi** ina seti ya triplets za OAV zinazolingana na tatizo linalotatuliwa sasa. **Mashine ya sheria** hutafuta sheria ambazo hali yake imetimizwa na kuzitumia, na kuongeza triplet nyingine kwenye kumbukumbu ya kazi. -> ✅ Andika mti wako wa AND-OR kuhusu mada unayoipenda! +> ✅ Andika mti wako wa AND-OR kuhusu mada unayopenda! -### Utoaji wa Sababu wa Mbele vs. Nyuma +### Hitimisho la Mbele dhidi ya Hitimisho la Nyuma -Mchakato ulioelezwa hapo juu unaitwa **utoaji wa sababu wa mbele**. Unaanzia na data fulani ya awali kuhusu tatizo inayopatikana katika kumbukumbu ya kazi, kisha inatekeleza mzunguko wa utoaji wa sababu kama ifuatavyo: +Mchakato ulioelezwa hapo juu unaitwa **hitimisho la mbele**. Unaanzia na data ya awali kuhusu tatizo iliyopo kwenye kumbukumbu ya kazi, na kisha unatekeleza mzunguko wa tafakari ufuatao: -1. Ikiwa sifa lengwa ipo katika kumbukumbu ya kazi - simama na toa matokeo -2. Tafuta sheria zote ambazo hali yake imetimizwa kwa sasa - pata **seti ya migogoro** ya sheria. -3. Fanya **utatuzi wa migogoro** - chagua sheria moja ambayo itatekelezwa katika hatua hii. Kunaweza kuwa na mikakati tofauti ya utatuzi wa migogoro: - - Chagua sheria ya kwanza inayotumika katika hifadhidata ya maarifa - - Chagua sheria ya nasibu - - Chagua sheria *maalum zaidi*, yaani, ile inayokidhi hali nyingi zaidi upande wa "kushoto" (LHS) -4. Tumia sheria iliyochaguliwa na ingiza kipande kipya cha maarifa katika hali ya tatizo -5. Rudia kutoka hatua ya 1. +1. Ikiwa sifa lengwa ipo kwenye kumbukumbu ya kazi - simama na toa matokeo +2. Tafuta sheria zote ambazo hali yake imetimizwa sasa - pata **seti ya migongano** ya sheria. +3. Fanya **mgongano wa utatuzi** - chagua sheria moja itakayotekelezwa hatua hii. Kuna mikakati tofauti ya utatuzi: + - Chagua sheria inayoanza katika hifadhidata ya maarifa + - Chagua sheria kwa bahati nasibu + - Chagua sheria *ya kina zaidi*, yaani ile inayotimiza masharti mengi upande wa kushoto (LHS) +4. Tekeleza sheria iliyochaguliwa na ongeza kipande kipya cha maarifa katika hali ya tatizo +5. Rudia hatua ya 1. -Hata hivyo, katika baadhi ya hali tunaweza kutaka kuanza na maarifa yasiyo kamili kuhusu tatizo, na kuuliza maswali ambayo yatatusaidia kufikia hitimisho. Kwa mfano, wakati wa kufanya uchunguzi wa matibabu, kwa kawaida hatufanyi uchambuzi wote wa matibabu mapema kabla ya kuanza kumchunguza mgonjwa. Badala yake, tunataka kufanya uchambuzi wakati uamuzi unahitaji kufanywa. +Hata hivyo, katika baadhi ya hali tungependa kuanza na maarifa tupu kuhusu tatizo, na kuuliza maswali yatakayotusaidia kufikia hitimisho. Kwa mfano, tunapofanya uchunguzi wa matibabu, kawaida hatufanyi uchunguzi wote kabla ya kuanza kugundua mgonjwa. Tunapendelea kufanya uchunguzi pale tu kinapohitajika kufanya uamuzi. -Mchakato huu unaweza kuigwa kwa kutumia **utoaji wa sababu wa nyuma**. Unachochewa na **lengo** - thamani ya sifa tunayotafuta: +Mchakato huu unaweza kuigwa kwa kutumia **hitimisho la nyuma**. Unaendeshwa na **lengo** - thamani ya sifa tunayoiangalia: -1. Chagua sheria zote zinazoweza kutupa thamani ya lengo (yaani, na lengo upande wa RHS ("upande wa kulia")) - seti ya migogoro -1. Ikiwa hakuna sheria kwa sifa hii, au kuna sheria inayosema kwamba tunapaswa kuuliza thamani kutoka kwa mtumiaji - uliza, vinginevyo: -1. Tumia mkakati wa utatuzi wa migogoro kuchagua sheria moja ambayo tutatumia kama *dhanio* - tutajaribu kuithibitisha -1. Rudia mchakato kwa sifa zote katika LHS ya sheria, tukijaribu kuzithibitisha kama malengo -1. Ikiwa wakati wowote mchakato unashindwa - tumia sheria nyingine katika hatua ya 3. +1. Chagua sheria zote zinazoweza kutupa thamani ya lengo (yaani na lengo upande wa kulia (RHS)) - seti ya migongano +1. Ikiwa hakuna sheria za sifa hii, au kuna sheria inayosema kwamba tunapaswa kuuliza thamani kutoka kwa mtumiaji - uliza, vinginevyo: +1. Tumia mkakati wa utatuzi kuchagua sheria moja ambayo tutaitumia kama *nadharia* - tutajaribu kuthibitisha +1. Rudia mchakato kwa sifa zote upande wa kushoto wa sheria, ukijaribu kuzithibitisha kama malengo +1. Ikiwa mchakato unashindwa mahali popote - tumia sheria nyingine hatua ya 3. -> ✅ Katika hali gani utoaji wa sababu wa mbele unafaa zaidi? Na vipi kuhusu utoaji wa sababu wa nyuma? +> ✅ Katika hali gani hitimisho la mbele linafaa zaidi? Vipi kuhusu hitimisho la nyuma? ### Kutekeleza Mifumo ya Wataalamu Mifumo ya wataalamu inaweza kutekelezwa kwa kutumia zana tofauti: -* Kuiandika moja kwa moja katika lugha ya programu ya kiwango cha juu. Hili si wazo bora, kwa sababu faida kuu ya mfumo unaotegemea maarifa ni kwamba maarifa yanatenganishwa na utoaji wa sababu, na mtaalamu wa eneo la tatizo anapaswa kuwa na uwezo wa kuandika sheria bila kuelewa maelezo ya mchakato wa utoaji wa sababu. -* Kutumia **ganda la mifumo ya wataalamu**, yaani, mfumo ulioundwa mahsusi kujazwa na maarifa kwa kutumia lugha fulani ya uwakilishi wa maarifa. +* Kuprogramu moja kwa moja kwa lugha ya programu ya kiwango cha juu. Hii si wazo nzuri, kwa sababu faida kuu ya mfumo wenye maarifa ni kwamba maarifa yamegawanyika na hitimisho, na mtaalamu wa eneo la tatizo anapaswa kuweza kuandika sheria bila kuelewa mchakato wa hitimisho +* Kutumia **shell ya mfumo wa wataalamu**, yaani mfumo uliobuniwa mahsusi kuingizwa maarifa kwa kutumia lugha fulani ya uwakilishi wa maarifa. -## ✍️ Zoezi: Utoaji wa Sababu wa Wanyama +## ✍️ Zoefu: Hitimisho la Mnyama -Tazama [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) kwa mfano wa kutekeleza mfumo wa wataalamu wa utoaji wa sababu wa mbele na wa nyuma. +Angalia [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) kwa mfano wa kutekeleza hitimisho la mbele na la nyuma katika mfumo wa wataalamu. -> **Note**: Mfano huu ni rahisi sana, na unatoa tu wazo la jinsi mfumo wa wataalamu unavyoonekana. Mara tu unapoanza kuunda mfumo kama huo, utaona tabia fulani ya *kiakili* kutoka kwake mara tu unapofikia idadi fulani ya sheria, karibu 200+. Wakati fulani, sheria zinakuwa ngumu sana kiasi kwamba huwezi kuzihifadhi zote akilini, na wakati huo unaweza kuanza kujiuliza kwa nini mfumo unafanya maamuzi fulani. Hata hivyo, sifa muhimu ya mifumo inayotegemea maarifa ni kwamba unaweza kila wakati *kufafanua* jinsi maamuzi yoyote yalivyofanywa. +> **Kumbuka**: Mfano huu ni rahisi tu, na unatoa wazo la jinsi mfumo wa wataalamu unavyoonekana. Mara tu unapoanza kuunda mfumo kama huu, utaona tabia *mwerevu* kutoka kwake tu unapotumia sheria nyingi, takriban 200+. Katika hatua fulani, sheria zinakuwa changamano sana kuhifadhi zote akili, na wakati huu utaanza kuuliza ni kwa nini mfumo unafanya maamuzi fulani. Hata hivyo, sifa muhimu za mifumo yenye maarifa ni kwamba unaweza *kueleza* kwa usahihi jinsi maamuzi yoyote yalivyotolewa. -## Ontolojia na Mtandao wa Semantic +## Ontolojia na Wavu wa Semantiki -Mwisho wa karne ya 20 kulikuwa na mpango wa kutumia uwakilishi wa maarifa kuandika rasilimali za mtandao, ili iwezekane kupata rasilimali zinazolingana na maswali maalum sana. Harakati hii ilitwa **Mtandao wa Semantic**, na ilitegemea dhana kadhaa: +Mwisho wa karne ya 20 kulikuwepo na jitihada ya kutumia uwakilishi wa maarifa kuashiria rasilimali za mtandao, ili iwezekane kupata rasilimali zinazolingana na maswali maalum sana. Harakati hii ilijulikana kama **Wavu wa Semantiki**, na ilitegemea dhana kadhaa: -- Uwakilishi maalum wa maarifa unaotegemea **[mantiki ya maelezo](https://en.wikipedia.org/wiki/Description_logic)** (DL). Inafanana na uwakilishi wa maarifa ya fremu, kwa sababu inajenga hierarkia ya vitu vyenye mali, lakini ina mantiki rasmi ya kimantiki na utoaji wa sababu. Kuna familia nzima ya DLs ambazo zinapima kati ya uelekezaji na ugumu wa kialgorithimu wa utoaji wa sababu. -- Uwakilishi wa maarifa uliosambazwa, ambapo dhana zote zinawakilishwa na kitambulisho cha URI cha kimataifa, na kufanya iwezekane kuunda hierarkia za maarifa zinazovuka mtandao. -- Familia ya lugha zinazotumia XML kwa maelezo ya maarifa: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Uwakilishi maalum wa maarifa unaotegemea **[mantiki za maelezo](https://en.wikipedia.org/wiki/Description_logic)** (DL). Inafanana na uwakilishi wa fremu wa maarifa, kwa kuwa huunda hierarchy ya vitu vyenye mali, lakini ina semantiki rasmi ya mantiki na hitimisho. Kuna familia nzima ya DL zinazopima kati ya uwezo wa kuelezea na ugumu wa algorithm wa hitimisho. +- Uwakilishi wa maarifa uliosambazwa, ambapo dhana zote zina wakilishaji wa kipekee wa URI wa kimataifa, kuruhusu kuunda hierarchy za maarifa zinazovuka mtandao. +- Familia ya lugha zinazotegemea XML kwa maelezo ya maarifa: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Dhana kuu katika Mtandao wa Semantiki ni dhana ya **Ontology**. Inahusu maelezo ya wazi ya eneo la tatizo kwa kutumia uwakilishi rasmi wa maarifa. Ontolojia rahisi inaweza kuwa tu hierarkia ya vitu katika eneo la tatizo, lakini ontolojia ngumu zaidi zitajumuisha sheria zinazoweza kutumika kwa hitimisho. +Dhana kuu katika Wavuti ya Semantiki ni dhana ya **Ontolojia**. Inahusu ufafanuzi wazi wa eneo la tatizo kwa kutumia baadhi ya uwakilishi rasmi wa maarifa. Ontolojia rahisi inaweza kuwa msaada tu wa vitu katika eneo la tatizo, lakini ontolojia ngumu zaidi zitajumuisha sheria ambazo zinaweza kutumika kwa hitimisho. -Katika mtandao wa semantiki, uwakilishi wote unategemea tripleti. Kila kitu na kila uhusiano hutambulishwa kipekee na URI. Kwa mfano, ikiwa tunataka kusema ukweli kwamba Mtaala huu wa AI umetengenezwa na Dmitry Soshnikov mnamo Januari 1, 2022 - hapa kuna tripleti tunazoweza kutumia: +Katika wavuti ya semantiki, uwakilishi wote unatokana na tripleti. Kila kitu na kila uhusiano hutambulishwa kwa kipekee na URI. Kwa mfano, ikiwa tunataka kusema ukweli kwamba Mtaala huu wa AI umeandaliwa na Dmitry Soshnikov tarehe 1 Januari, 2022 - hapa ni tripleti tunazoweza kutumia: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Hapa `http://www.example.com/terms/creation-date` na `http://purl.org/dc/elements/1.1/creator` ni URI zinazojulikana na kukubalika kimataifa kuelezea dhana za *muundaji* na *tarehe ya uundaji*. +> ✅ Hapa `http://www.example.com/terms/creation-date` na `http://purl.org/dc/elements/1.1/creator` ni URI maarufu na zinazokubalika ulimwenguni kote kuelezea dhana za *muumba* na *tarehe ya uundaji*. -Katika hali ngumu zaidi, ikiwa tunataka kufafanua orodha ya waundaji, tunaweza kutumia miundo ya data iliyofafanuliwa katika RDF. +Katika kesi ngumu zaidi, ikiwa tunataka kufafanua orodha ya waumba, tunaweza kutumia baadhi ya miundo ya data iliyoainishwa katika RDF. - + > Michoro hapo juu na [Dmitry Soshnikov](http://soshnikov.com) -Maendeleo ya kujenga Mtandao wa Semantiki yalicheleweshwa kwa kiasi fulani na mafanikio ya injini za utafutaji na mbinu za usindikaji wa lugha asilia, ambazo huruhusu uchimbaji wa data iliyopangwa kutoka kwa maandishi. Hata hivyo, katika baadhi ya maeneo bado kuna juhudi kubwa za kudumisha ontolojia na hifadhidata za maarifa. Miradi michache inayostahili kutajwa: +Maendeleo ya ujenzi wa Wavuti ya Semantiki yalimshwausha kidogo mafanikio ya injini za utafutaji na mbinu za usindikaji wa lugha ya asili, zinazoruhusu kutoa data iliyopangwa kutoka kwenye maandishi. Hata hivyo, katika maeneo fulani bado kuna juhudi kubwa za kudumisha ontolojia na misingi ya maarifa. Miradi michache inayostahili kutajwa: -* [WikiData](https://wikidata.org/) ni mkusanyiko wa hifadhidata za maarifa zinazoweza kusomeka na mashine zinazohusiana na Wikipedia. Data nyingi hutolewa kutoka kwa *InfoBoxes* za Wikipedia, vipande vya maudhui yaliyopangwa ndani ya kurasa za Wikipedia. Unaweza [kuuliza](https://query.wikidata.org/) wikidata kwa kutumia SPARQL, lugha maalum ya maswali kwa Mtandao wa Semantiki. Hapa kuna mfano wa swali linaloonyesha rangi za macho maarufu zaidi miongoni mwa binadamu: +* [WikiData](https://wikidata.org/) ni mkusanyiko wa misingi ya maarifa inayoeleweka na mashine inayohusishwa na Wikipedia. Zaidi ya data hupatikana kutoka *InfoBoxes* za Wikipedia, vipande vya maudhui yaliyopangwa ndani ya kurasa za Wikipedia. Unaweza [kuuliza](https://query.wikidata.org/) wikidata kwa kutumia SPARQL, lugha maalum ya kuuliza kwa Wavuti ya Semantiki. Hapa ni mfano wa kuuliza unaoonyesha rangi maarufu zaidi za macho miongoni mwa wanadamu: ```sparql #defaultView:BubbleChart @@ -206,47 +206,52 @@ WHERE GROUP BY ?eyeColorLabel ``` -* [DBpedia](https://www.dbpedia.org/) ni juhudi nyingine inayofanana na WikiData. +* [DBpedia](https://www.dbpedia.org/) ni jitihada nyingine inayofanana na WikiData. -> ✅ Ikiwa unataka kujaribu kujenga ontolojia zako mwenyewe, au kufungua zilizopo, kuna mhariri mzuri wa ontolojia wa kuona unaoitwa [Protégé](https://protege.stanford.edu/). Pakua, au uitumie mtandaoni. +> ✅ Ikiwa unataka kujaribu kujenga ontolojia zako mwenyewe, au kufungua ontolojia zilizopo, kuna mhariri mzuri wa ontolojia unaoitwa [Protégé](https://protege.stanford.edu/). Pakua au uvitumie mtandaoni. - + -*Mhariri wa Web Protégé ukiwa wazi na ontolojia ya Familia ya Romanov. Picha na Dmitry Soshnikov* +*Mhariri wa Wavuti Protégé ulio wazi na ontolojia ya Familia ya Romanov. Picha ya skrini na Dmitry Soshnikov* -## ✍️ Zoezi: Ontolojia ya Familia +## ✍️ Zoefaa: Ontolojia ya Familia -Tazama [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) kwa mfano wa kutumia mbinu za Mtandao wa Semantiki kufikiri kuhusu mahusiano ya kifamilia. Tutachukua mti wa familia unaowakilishwa katika muundo wa kawaida wa GEDCOM na ontolojia ya mahusiano ya kifamilia na kujenga grafu ya mahusiano yote ya kifamilia kwa seti fulani ya watu. -## Microsoft Concept Graph +Tazama [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) kwa mfano wa kutumia mbinu za Wavuti ya Semantiki kufikiria kuhusu mahusiano ya familia. Tutachukua mti wa familia uliowakilishwa kwa muundo wa kawaida wa GEDCOM na ontolojia ya mahusiano ya familia na kuunda grafu ya mahusiano yote ya familia kwa seti ya watu waliowekwa. -Katika hali nyingi, ontolojia huundwa kwa uangalifu kwa mkono. Hata hivyo, inawezekana pia **kuchimba** ontolojia kutoka kwa data isiyo na muundo, kwa mfano, kutoka kwa maandishi ya lugha asilia. +## Graph ya Dhana ya Microsoft -Jaribio moja kama hilo lilifanywa na Microsoft Research, na kusababisha [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Katika hali nyingi, ontolojia huundwa kwa uangalifu kwa mkono. Hata hivyo, pia inawezekana **kuchimba** ontolojia kutoka kwa data isiyo na muundo, kwa mfano, kutoka kwa maandishi ya lugha ya asili. -Ni mkusanyiko mkubwa wa vyombo vilivyowekwa pamoja kwa kutumia uhusiano wa urithi wa `is-a`. Inaruhusu kujibu maswali kama "Microsoft ni nini?" - jibu likiwa kitu kama "kampuni kwa uwezekano wa 0.87, na chapa kwa uwezekano wa 0.75". +Jaribio kama hilo limefanywa na Microsoft Research, na kusababisha [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Grafu inapatikana ama kama REST API, au kama faili kubwa inayoweza kupakuliwa ya maandishi inayoorodhesha jozi zote za vyombo. +Ni mkusanyiko mkubwa wa vitu vilivyofungwa pamoja kwa kutumia uhusiano wa urithi wa `is-a`. Inaruhusu kujibu maswali kama "Microsoft ni nini?" - jibu likiwa kama "kampuni kwa uwezekano wa 0.87, na chapa kwa uwezekano wa 0.75". -## ✍️ Zoezi: Grafu ya Dhana +Grafu inapatikana kama REST API, au kama faili kubwa la maandishi linaweza kupakuliwa linaloorodhesha jozi zote za vitu. -Jaribu daftari la [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) ili kuona jinsi tunavyoweza kutumia Microsoft Concept Graph kuainisha makala za habari katika kategoria kadhaa. +## ✍️ Zoefaa: Graph ya Dhana + +Jaribu daftari la [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) kuona jinsi tunavyoweza kutumia Microsoft Concept Graph kuunganisha makala za habari katika makundi kadhaa. ## Hitimisho -Siku hizi, AI mara nyingi huchukuliwa kuwa sawa na *Machine Learning* au *Neural Networks*. Hata hivyo, binadamu pia huonyesha uwezo wa kufikiri kwa wazi, jambo ambalo kwa sasa halishughulikiwi na mitandao ya neva. Katika miradi halisi ya dunia, kufikiri kwa wazi bado hutumika kutekeleza kazi zinazohitaji maelezo, au uwezo wa kubadilisha tabia ya mfumo kwa njia inayodhibitiwa. +Siku hizi, AI mara nyingi huonekana kama mfanano wa *Machine Learning* au *Neural Networks*. Hata hivyo, mwanadamu pia anaonyesha kufikiri wazi, jambo ambalo kwa sasa halijashughulikiwa na mitandao ya neva. Katika miradi halisi, kufikiri wazi bado kunatumika kufanya kazi zinazohitaji maelezo, au uwezo wa kubadilisha tabia ya mfumo kwa njia inayodhibitiwa. ## 🚀 Changamoto -Katika daftari la Ontolojia ya Familia linalohusiana na somo hili, kuna fursa ya kujaribu mahusiano mengine ya kifamilia. Jaribu kugundua miunganisho mipya kati ya watu katika mti wa familia. +Katika daftari la Ontolojia ya Familia linalohusiana na somo hili, kuna fursa ya kujaribu mahusiano mengine ya familia. Jaribu kugundua uhusiano mpya kati ya watu katika mti wa familia. -## [Jaribio baada ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/4) +## [Mtihani baada ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Mapitio & Kujisomea +## Mapitio & Kujifunza Binafsi -Fanya utafiti kwenye mtandao ili kugundua maeneo ambapo binadamu wamejaribu kupima na kuweka maarifa katika mfumo. Angalia Taxonomy ya Bloom, na rudi nyuma kihistoria ili kujifunza jinsi binadamu walivyojaribu kuelewa dunia yao. Chunguza kazi ya Linnaeus ya kuunda mfumo wa viumbe, na angalia jinsi Dmitri Mendeleev alivyounda njia ya kuelezea na kuainisha vipengele vya kemikali. Je, unaweza kupata mifano mingine ya kuvutia? +Fanya utafiti mtandaoni kugundua maeneo ambapo wanadamu wamejaribu kupima na kuweka kumbukumbu maarifa. Angalia Taxonomy ya Bloom, na rudi nyuma kihistoria kujifunza jinsi wanadamu walijaribu kuelewa dunia yao. Chunguza kazi ya Linnaeus kuunda taxonomy ya viumbe, na angalia jinsi Dmitri Mendeleev alivyounda njia ya kuelezea na kuorodhesha elementi za kemikali. Ni mifano gani mingine ya kuvutia unaweza kupata? -**Kazi**: [Jenga Ontolojia](assignment.md) +**Zoezi**: [Jenga Ontolojia](assignment.md) --- + +**Vikwazo**: +Hati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Wakati tunajitahidi kuwa sahihi, tafadhali fahamu kuwa tafsiri za moja kwa moja zinaweza kuwa na makosa au upungufu wa usahihi. Hati ya asili katika lugha yake ya kienyeji inapaswa kuzingatiwa kama chanzo cha kimsingi. Kwa taarifa muhimu, tafsiri ya mtaalamu wa binadamu inashauriwa. Hatujawajibika kwa kutoelewana au tafsiri potofu zinazotokana na matumizi ya tafsiri hii. + \ No newline at end of file diff --git a/translations/tl/README.md b/translations/tl/README.md index dfe3a264..66ffa105 100644 --- a/translations/tl/README.md +++ b/translations/tl/README.md @@ -1,8 +1,8 @@ [Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](./README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) -> **Mas gusto mo bang Kopyahin Local?** +> **Mas gusto mo bang I-clone sa Lokal?** -> Kasama sa repository na ito ang mahigit 50 na pagsasalin ng wika na malaki ang pinapataas sa laki ng pag-download. Upang kopyahin nang walang mga pagsasalin, gamitin ang sparse checkout: +> Ang repository na ito ay kasama ang 50+ na pagsasalin ng wika na malaki ang dagdag sa laki ng pag-download. Para mag-clone nang walang mga pagsasalin, gamitin ang sparse checkout: > ```bash > git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git > cd AI-For-Beginners > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' > ``` -> Bibigyan ka nito ng lahat ng kailangan mo para matapos ang kurso nang mas mabilis ang pag-download. +> Binibigyan ka nito ng lahat ng kailangan mo para matapos ang kurso nang mas mabilis ang pag-download. -**Kung nais mong magkaroon ng karagdagang suportadong mga wika ng pagsasalin ay nakalista [dito](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** +**Kung nais mo ng karagdagang mga suportadong wika ng pagsasalin ay nakalista [dito](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** ## Sumali sa Komunidad [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -## Ano ang iyong matututunan +## Ano ang iyong Matututuhan **[Mindmap ng Kurso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** -Sa kurikulum na ito, matututunan mo ang: +Sa kurikulum na ito, matututuhan mo: -* Iba't ibang mga pamamaraan sa Artificial Intelligence, kabilang ang "good old" na simbolikong pamamaraan gamit ang **Knowledge Representation** at pangangatwiran ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). -* **Neural Networks** at **Deep Learning**, na nasa sentro ng modernong AI. Ipapakita namin ang mga konsepto sa likod ng mga importanteng paksang ito gamit ang code sa dalawang pinakasikat na frameworks - [TensorFlow](http://Tensorflow.org) at [PyTorch](http://pytorch.org). -* **Neural Architectures** para sa pagproseso ng mga larawan at teksto. Sasaklawin namin ang mga kamakailang modelo ngunit maaaring medyo kulang sa pinakabagong estado-ng-sining. -* Hindi gaanong kilalang mga pamamaraan ng AI, tulad ng **Genetic Algorithms** at **Multi-Agent Systems**. +* Iba't ibang mga lapit sa Artificial Intelligence, kabilang ang "classic" na simbolikong lapit gamit ang **Knowledge Representation** at pangangatwiran ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Neural Networks** at **Deep Learning**, na nasa puso ng modernong AI. Ipaliwanag namin ang mga konsepto sa likod ng mahahalagang paksang ito gamit ang code sa dalawang pinakasikat na framework - [TensorFlow](http://Tensorflow.org) at [PyTorch](http://pytorch.org). +* **Neural Architectures** para sa pagproseso ng mga larawan at teksto. Tatalakayin namin ang mga kamakailang modelo ngunit maaaring medyo kulang sa mga pinaka-latest. +* Hindi gaanong kilalang mga lapit sa AI, tulad ng **Genetic Algorithms** at **Multi-Agent Systems**. -Ang hindi namin sasaklawin sa kurikulum na ito: +Hindi namin tatalakayin sa kurikulum na ito: -> [Hanapin ang lahat ng karagdagang mga mapagkukunan para sa kurso na ito sa aming koleksyon sa Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) +> [Hanapin ang lahat ng karagdagang mga sanggunian para sa kursong ito sa aming koleksyon ng Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* Mga business cases ng paggamit ng **AI sa Negosyo**. Isaalang-alang ang pagkuha ng [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) learning path sa Microsoft Learn, o [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), na binuo sa pakikipagtulungan sa [INSEAD](https://www.insead.edu/). -* **Classic Machine Learning**, na mahusay na inilalarawan sa aming [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). -* Mga praktikal na aplikasyon ng AI na ginawa gamit ang **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para dito, inirerekomenda naming magsimula ka sa mga module ng Microsoft Learn para sa [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)** at iba pa. -* Mga espesipikong ML **Cloud Frameworks**, tulad ng [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). Isaalang-alang ang paggamit ng [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) at [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) na mga learning path. -* **Conversational AI** at **Chat Bots**. Mayroon nang hiwalay na [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) na learning path, at maaari ka ring sumangguni sa [blog post na ito](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para sa karagdagang detalye. -* **Malalim na Matematika** sa likod ng deep learning. Para dito, nirerekomenda namin ang [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) nina Ian Goodfellow, Yoshua Bengio at Aaron Courville, na makukuha rin online sa [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). +* Mga kaso ng negosyo sa paggamit ng **AI sa Negosyo**. Isaalang-alang ang pagkuha ng [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) na learning path sa Microsoft Learn, o [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), na ginawa kasama ang [INSEAD](https://www.insead.edu/). +* **Classic Machine Learning**, na mahusay na naipaliwanag sa aming [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners). +* Praktikal na mga aplikasyon ng AI na binuo gamit ang **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para dito, inirerekomenda namin na magsimula ka sa mga module ng Microsoft Learn para sa [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)** at iba pa. +* Mga partikular na ML **Cloud Frameworks**, tulad ng [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). Isaalang-alang ang paggamit ng [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) at [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) na mga learning path. +* **Conversational AI** at **Chat Bots**. May hiwalay na [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) na learning path, at maaari ka ring sumangguni sa [blog post na ito](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para sa higit pang detalye. +* **Mataas na Matematika** sa likod ng deep learning. Para dito, inirerekomenda namin ang [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) nina Ian Goodfellow, Yoshua Bengio at Aaron Courville, na available din online sa [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -Para sa isang mahinahong pagpapakilala sa mga paksa ng _AI sa Cloud_ maaari mong isaalang-alang ang pagkuha ng [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) Learning Path. +Para sa mahinahong pagpapakilala sa mga paksa ng _AI sa Cloud_ maaaring isaalang-alang ang pagkuha ng [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) Learning Path. # Nilalaman -| | Lesson Link | PyTorch/Keras/TensorFlow | Lab | +| | Link ng Leksyon | PyTorch/Keras/TensorFlow | Lab | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 0 | [Course Setup](./lessons/0-course-setup/setup.md) | [I-setup ang Iyong Development Environment](./lessons/0-course-setup/how-to-run.md) | | -| I | [**Panimula sa AI**](./lessons/1-Intro/README.md) | | | -| 01 | [Panimula at Kasaysayan ng AI](./lessons/1-Intro/README.md) | - | - | -| II | **Simbolikong AI** | +| I | [**Introduksyon sa AI**](./lessons/1-Intro/README.md) | | | +| 01 | [Introduksyon at Kasaysayan ng AI](./lessons/1-Intro/README.md) | - | - | +| II | **Symbolic AI** | | 02 | [Knowledge Representation at Expert Systems](./lessons/2-Symbolic/README.md) | [Expert Systems](./lessons/2-Symbolic/Animals.ipynb) / [Ontology](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Concept Graph](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | -| III | [**Panimula sa Neural Networks**](./lessons/3-NeuralNetworks/README.md) ||| +| III | [**Introduksyon sa Neural Networks**](./lessons/3-NeuralNetworks/README.md) ||| | 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | | 04 | [Multi-Layered Perceptron and Creating our own Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | | 05 | [Intro to Frameworks (PyTorch/TensorFlow) and Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | -| IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [I-explore ang Computer Vision sa Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| IV | [**Computer Vision**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Computer Vision on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | | 06 | [Intro to Computer Vision. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | | 07 | [Convolutional Neural Networks](./lessons/4-ComputerVision/07-ConvNets/README.md) & [CNN Architectures](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | | 08 | [Pre-trained Networks and Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) and [Training Tricks](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | @@ -98,7 +98,7 @@ Para sa isang mahinahong pagpapakilala sa mga paksa ng _AI sa Cloud_ maaari mong | 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) | | | 11 | [Object Detection](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | | 12 | [Semantic Segmentation. 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 | [**Natural Language Processing**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [I-explore ang Natural Language Processing sa Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| V | [**Natural Language Processing**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Natural Language Processing on Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| | 13 | [Text Representation. 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 | [Semantic word embeddings. Word2Vec and 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 | [Language Modeling. Training your own embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | @@ -107,73 +107,73 @@ Para sa isang mahinahong pagpapakilala sa mga paksa ng _AI sa Cloud_ maaari mong | 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 | [Named Entity Recognition](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | | 20 | [Large Language Models, Prompt Programming and Few-Shot Tasks](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | -| VI | **Iba Pang AI Techniques** || | +| VI | **Iba Pang Teknik sa AI** || | | 21 | [Genetic Algorithms](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | | 22 | [Deep Reinforcement Learning](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | | 23 | [Multi-Agent Systems](./lessons/6-Other/23-MultiagentSystems/README.md) | | | -| VII | **AI Ethics** | | | +| VII | **Etika sa AI** | | | | 24 | [AI Ethics and Responsible AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsible AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | -| IX | **Extras** | | | +| IX | **Mga Extras** | | | | 25 | [Multi-Modal Networks, CLIP and VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Bawat aralin ay naglalaman ng -* Mga materyal na pang-prebasa -* Mga maee-execute na Jupyter Notebooks, na madalas na nakatuon sa isang partikular na framework (**PyTorch** o **TensorFlow**). Ang maee-execute na notebook ay naglalaman din ng maraming teoretikal na materyal, kaya upang maunawaan ang paksa, kailangan mong dumaan sa kahit isang bersyon ng notebook (PyTorch o TensorFlow). -* Mga **Labs** na makukuha para sa ilang mga paksa, na nagbibigay sa iyo ng pagkakataon na subukan ang pag-aaplay ng mga natutunang materyal sa isang partikular na problema. -* Ang ilang mga seksyon ay naglalaman ng mga link sa mga module ng [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) na sumasaklaw sa mga kaugnay na paksa. +* Materyal na babasahin bago ang klase +* Mga executable na Jupyter Notebook, na madalas ay partikular sa framework (**PyTorch** o **TensorFlow**). Ang executable notebook ay naglalaman din ng maraming teoretikal na materyal, kaya upang maunawaan ang paksa kailangan mong dumaan sa kahit isang bersyon ng notebook (PyTorch man o TensorFlow). +* **Mga Labs** na available para sa ilang mga paksa, na nagbibigay sa iyo ng pagkakataong subukan ang pag-aaplay ng mga natutunan mong materyal sa isang tiyak na problema. +* Ang ilang mga seksyon ay may mga link sa mga module ng [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) na sumasaklaw sa mga kaugnay na paksa. -## Pagsisimula +## Paano Magsimula -### 🎯 Bago sa AI? Magsimula Dito! +### 🎯 Bago ka sa AI? Magsimula Dito! -Kung ganap kang bago sa AI at nais ng mabilis na mga halimbawa na may kasamang hands-on, tingnan ang aming [**Mga Halimbawang Madaling Simulan para sa Baguhan**](./examples/README.md)! Kasama dito ang: +Kung ikaw ay ganap na bago sa AI at naghahanap ng mabilis at praktikal na mga halimbawa, tingnan ang aming [**Mga Halimbawa para sa mga Nagsisimula**](./examples/README.md)! Kasama dito ang: -- 🌟 **Hello AI World** - Ang iyong unang AI program (pagkilala ng pattern) -- 🧠 **Simpleng Neural Network** - Gumawa ng neural network mula sa simula -- 🖼️ **Image Classifier** - Mag-klasipika ng mga larawan na may detalyadong mga paliwanag -- 💬 **Sentimiyento ng Teksto** - Suriin ang positibo/negatibong teksto +- 🌟 **Hello AI World** - Ang iyong unang AI na programa (pagtukoy ng pattern) +- 🧠 **Simple Neural Network** - Gumawa ng neural network mula sa simula +- 🖼️ **Image Classifier** - Magklasipika ng mga larawan na may detalyadong paliwanag +- 💬 **Pagsusuri ng Sentimyento ng Teksto** - Suriin ang positibo/negatibong teksto -Ang mga halimbawa na ito ay idinisenyo upang matulungan kang maunawaan ang mga konsepto ng AI bago sumabak sa buong kurikulum. +Ang mga halimbawang ito ay idinisenyo para matulungan kang maunawaan ang mga konsepto ng AI bago pumasok sa buong kurikulum. -### 📚 Pagsasaayos ng Buong Kurikulum +### 📚 Pag-setup ng Buong Kurikulum -- Nilikha namin ang isang [setup lesson](./lessons/0-course-setup/setup.md) upang tulungan ka sa pagsasaayos ng iyong development environment. - Para sa mga Tagapagturo, gumawa rin kami ng isang [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) para sa inyo! -- Paano [Patakbuhin ang code sa VSCode o Codepace](./lessons/0-course-setup/how-to-run.md) +- Nilikha namin ang isang [setup lesson](./lessons/0-course-setup/setup.md) para tulungan ka sa pag-setup ng iyong development environment. - Para sa mga Guro, mayroon din kaming [curricula setup lesson](./lessons/0-course-setup/for-teachers.md)! +- Paano [Ipatakbo ang code sa VSCode o Codespace](./lessons/0-course-setup/how-to-run.md) Sundin ang mga hakbang na ito: -Fork ang Repository: Pindutin ang "Fork" na button sa kanang itaas na sulok ng pahinang ito. +I-fork ang Repository: I-click ang "Fork" na button sa itaas-kanan ng pahinang ito. I-clone ang Repository: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Huwag kalimutang bigyan ng star (🌟) ang repo na ito upang mas madaling mahanap ito kalaunan. +Huwag kalimutang mag-star (🌟) sa repo na ito para mas madali mo itong mahanap sa susunod. -## Makipagkita sa Iba Pang Mga Nag-aaral +## Makipagkilala sa Ibang mga Nag-aaral -Sumali sa aming [opisyal na AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) upang makilala at makipag-network sa iba pang mga nag-aaral na kumukuha ng kursong ito at makakuha ng suporta. +Sumali sa aming [opisyal na AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para makilala at mag-network sa ibang mga nag-aaral na kumuha ng kursong ito at makakuha ng suporta. -Kung mayroon kang feedback sa produkto o mga tanong habang nagtatayo, bisitahin ang aming [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Kung mayroon kang feedback o mga tanong tungkol sa produkto habang nagtatayo, bisitahin ang aming [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Mga Pagsusulit -> **Isang paalala tungkol sa mga pagsusulit**: Lahat ng pagsusulit ay nasa Quiz-app folder sa etc\quiz-app, o [Online Dito](https://ff-quizzes.netlify.app/) Nakakabit ang mga ito sa loob ng mga aralin, maaari patakbuhin ang quiz app nang lokal o ideploy sa Azure; sundin ang mga tagubilin sa folder na `quiz-app`. Unti-unti rin itong nilalokalisa. +> **Isang paalala tungkol sa mga pagsusulit**: Ang lahat ng pagsusulit ay nasa Quiz-app na folder sa etc\quiz-app, o [Online dito](https://ff-quizzes.netlify.app/) Naka-link ang mga ito mula sa loob ng mga aralin, ang quiz app ay maaaring patakbuhin nang lokal o i-deploy sa Azure; sundin ang mga tagubiling nasa `quiz-app` folder. Unti-unti silang nililokalisa. -## Naghahanap ng Tulong +## Kailangan ng Tulong -Mayroon ka bang mga mungkahi o nakakita ng mga maling baybay o error sa code? Mag-raise ng isyu o gumawa ng pull request. +Mayroon ka bang mga suhestiyon o nakakita ng mga mali sa ispeling o code? Mag-raise ng isyu o gumawa ng pull request. ## Espesyal na Pasasalamat -* **✍️ Pangunahing May-akda:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **✍️ Punong May-akda:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Sketchnote illustrator:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **🎨 Tagadibuho ng Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) * **✅ Tagalikha ng Pagsusulit:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **🙏 Pangunahing mga Kontribyutor:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Iba Pang Kurikulum +## Ibang Kurikulum -Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Silipin: +Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Tingnan ang: ### LangChain @@ -182,14 +182,14 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Silipin: --- -### Azure / Edge / MCP / Agents +### Azure / Edge / MCP / Mga Ahente [![AZD for Beginners](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 for Beginners](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 for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) [![AI Agents for Beginners](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 [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) @@ -197,8 +197,8 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Silipin: [![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) --- - -### Core Learning + +### Pangunahing Pagkatuto [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) @@ -208,7 +208,7 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Silipin: [![XR Development for Beginners](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 Series [![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) [![Copilot for 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) @@ -217,17 +217,17 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Silipin: ## Pagkuha ng Tulong -Kung ikaw ay nahihirapan o may mga tanong tungkol sa paggawa ng mga AI app, sumali sa mga kasama mong nag-aaral at mga bihasang developer sa mga talakayan tungkol sa MCP. Ito ay isang suportadong komunidad kung saan malugod ang mga tanong at malayang ibinabahagi ang kaalaman. +Kung ikaw ay nahihirapan o may mga tanong tungkol sa paggawa ng mga AI app, sumali sa kapwa mga nag-aaral at mga bihasang developer sa mga talakayan tungkol sa MCP. Ito ay isang suportadong komunidad kung saan tinatanggap ang mga tanong at malayang ibinabahagi ang kaalaman. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Kung mayroon kang feedback sa produkto o nagkaroon ng mga error habang nagtatayo, bisitahin: +Kung mayroon kang feedback o mga pagkakamali habang nagtatayo, bisitahin: [![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) --- -**Pagsasaalang-alang**: -Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagamat nagsusumikap kami para sa katumpakan, pakitandaan na ang awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o di-tumpak na impormasyon. Ang orihinal na dokumento sa sariling wika nito ang dapat ituring na pangunahing sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na bunga ng paggamit ng pagsasaling ito. +**Paunawa**: +Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagamat aming sinisikap ang pagiging tumpak, pakatandaan na maaaring may mga pagkakamali o hindi pagkakatugma ang mga awtomatikong salin. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na opisyal na sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaring idulot ng paggamit ng saling ito. \ No newline at end of file diff --git a/translations/tl/lessons/0-course-setup/how-to-run.md b/translations/tl/lessons/0-course-setup/how-to-run.md index 39a15bca..7a395f37 100644 --- a/translations/tl/lessons/0-course-setup/how-to-run.md +++ b/translations/tl/lessons/0-course-setup/how-to-run.md @@ -1,21 +1,21 @@ # Paano Patakbuhin ang Code -Ang kurikulum na ito ay naglalaman ng maraming mga executable na halimbawa at mga lab na nais mong patakbuhin. Upang magawa ito, kailangan mo ng kakayahang magpatakbo ng Python code sa Jupyter Notebooks na kasama sa kurikulum na ito. Mayroon kang ilang mga opsyon para patakbuhin ang code: +Ang kurikulum na ito ay naglalaman ng maraming mga executable na halimbawa at mga lab na nais mong patakbuhin. Upang magawa ito, kailangan mong magkaroon ng kakayahang magpatakbo ng Python code sa Jupyter Notebooks na ibinigay bilang bahagi ng kurikulum na ito. Mayroon kang ilang mga pagpipilian para mapatakbo ang code: -## Patakbuhin nang lokal sa iyong computer +## Patakbuhin nang lokal sa iyong kompyuter -Upang patakbuhin ang code nang lokal sa iyong computer, kailangan mong magkaroon ng ilang bersyon ng Python na naka-install. Personal kong inirerekomenda ang pag-install ng **[miniconda](https://conda.io/en/latest/miniconda.html)** - ito ay isang magaan na installation na sumusuporta sa `conda` package manager para sa iba't ibang Python **virtual environments**. +Upang patakbuhin ang code nang lokal sa iyong kompyuter, kinakailangan ang isang Python installation. Isang rekomendasyon ay ang pag-install ng **[miniconda](https://conda.io/en/latest/miniconda.html)** - ito ay isang medyo magaan na installation na sumusuporta sa `conda` package manager para sa iba't ibang Python **virtual environments**. -Pagkatapos mong i-install ang miniconda, kailangan mong i-clone ang repository at gumawa ng virtual environment na gagamitin para sa kursong ito: +Pagkatapos mong i-install ang miniconda, i-clone ang repository at gumawa ng virtual environment na gagamitin para sa kurso na ito: ```bash git clone http://github.com/microsoft/ai-for-beginners @@ -26,15 +26,15 @@ conda activate ai4beg ### Paggamit ng Visual Studio Code na may Python Extension -Marahil ang pinakamagandang paraan upang magamit ang kurikulum ay buksan ito sa [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) na may [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). +Ang kurikulum na ito ay pinakamahusay gamitin kapag binuksan ito sa [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) kasama ang [Python Extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). -> **Note**: Kapag na-clone mo at binuksan ang direktoryo sa VS Code, awtomatiko nitong imumungkahi na i-install ang Python extensions. Kailangan mo ring i-install ang miniconda tulad ng inilarawan sa itaas. +> **Note**: Kapag na-clone mo at na-open ang direktoryo sa VS Code, awtomatiko nitong ire-rekomenda sa iyo na i-install ang Python extensions. Kailangan mo ring i-install ang miniconda gaya ng ipinaliwanag sa itaas. -> **Note**: Kung imumungkahi ng VS Code na i-reopen ang repository sa container, kailangan mong tanggihan ito upang magamit ang lokal na Python installation. +> **Note**: Kung ire-rekomenda ng VS Code na i-reopen ang repository sa isang container, dapat mong tanggihan ito upang gamitin ang lokal na Python installation. ### Paggamit ng Jupyter sa Browser -Maaari mo ring gamitin ang Jupyter environment direkta mula sa browser sa iyong sariling computer. Sa katunayan, parehong classical Jupyter at Jupyter Hub ay nagbibigay ng maginhawang development environment na may auto-completion, code highlighting, at iba pa. +Maaari ka ring gumamit ng Jupyter environment mula sa browser sa iyong sariling kompyuter. Parehong ang klasikong Jupyter at JupyterHub ay nagbibigay ng maginhawang development environment na may auto-completion, pagkulay ng code, atbp. Upang simulan ang Jupyter nang lokal, pumunta sa direktoryo ng kurso, at i-execute: @@ -45,34 +45,36 @@ o ```bash jupyterhub ``` -Pagkatapos ay maaari kang mag-navigate sa alinman sa mga `.ipynb` files, buksan ang mga ito, at simulan ang paggawa. +Pagkatapos ay maaari kang mag-navigate sa alinmang `.ipynb` na file, buksan ito at simulan ang pagtatrabaho. -### Pagpapatakbo sa Container +### Pagpapatakbo sa container -Isang alternatibo sa Python installation ay ang patakbuhin ang code sa container. Dahil ang aming repository ay naglalaman ng espesyal na `.devcontainer` folder na nagbibigay ng instruksyon kung paano bumuo ng container para sa repo na ito, ang VS Code ay mag-aalok sa iyo na i-reopen ang code sa container. Kakailanganin nito ang Docker installation, at medyo mas kumplikado, kaya inirerekomenda namin ito para sa mas may karanasan na mga user. +Isang alternatibo sa pag-install ng Python ay ang pagpapatakbo ng code sa isang container. Dahil ang aming repository ay nagbibigay ng isang espesyal na `.devcontainer` na folder na nagsasaad kung paano bumuo ng isang container para sa repo na ito, nag-aalok ang VS Code ng pagkakataong i-reopen ang code sa loob ng isang container. Kakailanganin nito ang Docker installation, at mas kumplikado ito kaya inirerekomenda namin ito sa mga mas may karanasang gumagamit. ## Pagpapatakbo sa Cloud -Kung ayaw mong mag-install ng Python nang lokal, at may access ka sa ilang cloud resources - isang magandang alternatibo ay ang patakbuhin ang code sa cloud. Mayroong ilang mga paraan upang magawa ito: +Kung ayaw mong mag-install ng Python nang lokal, at may access ka sa ilang cloud resources - isang magandang alternatibo ay ang pagpapatakbo ng code sa cloud. May ilang paraan upang magawa ito: -* Paggamit ng **[GitHub Codespaces](https://github.com/features/codespaces)**, na isang virtual environment na ginawa para sa iyo sa GitHub, na naa-access sa pamamagitan ng VS Code browser interface. Kung may access ka sa Codespaces, maaari mo lamang i-click ang **Code** button sa repo, simulan ang isang codespace, at magsimula nang mabilis. -* Paggamit ng **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. Ang [Binder](https://mybinder.org) ay libreng computing resources na ibinibigay sa cloud para sa mga taong tulad mo upang subukan ang ilang code sa GitHub. Mayroong button sa front page upang buksan ang repository sa Binder - mabilis ka nitong dadalhin sa binder site, na magtatayo ng underlying container at magsisimula ng Jupyter web interface para sa iyo nang walang kahirap-hirap. +* Paggamit ng **[GitHub Codespaces](https://github.com/features/codespaces)**, na isang virtual environment na nilikha para sa iyo sa GitHub, na maa-access sa pamamagitan ng VS Code browser interface. Kung may access ka sa Codespaces, maaari mong i-click lang ang **Code** button sa repo, simulan ang codespace, at agad na makapagtatrabaho. +* Paggamit ng **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. Nagbibigay ang [Binder](https://mybinder.org) ng libreng computing resources sa cloud para sa mga taong tulad mo upang subukan ang ilang code mula sa GitHub. Mayroong button sa front page upang buksan ang repository sa Binder - ito ay mabilis na magdadala sa iyo sa binder site, kung saan bubuuin ang underlying container at magsisimula ang Jupyter web interface nang walang patid. -> **Note**: Upang maiwasan ang maling paggamit, ang Binder ay may access sa ilang web resources na naka-block. Maaaring pigilan nito ang ilang code na gumagana, lalo na kung kumukuha ng mga modelo at/o datasets mula sa pampublikong Internet. Maaaring kailanganin mong maghanap ng mga alternatibo. Gayundin, ang compute resources na ibinibigay ng Binder ay medyo basic, kaya ang training ay magiging mabagal, lalo na sa mga mas kumplikadong aralin. +> **Note**: Upang maiwasan ang maling paggamit, may mga web resources na hindi naa-access ng Binder. Maaaring pigilan nito ang ilang bahagi ng code na gumana na kumukuha ng mga modelo at/o dataset mula sa pampublikong Internet. Kailangan mong humanap ng mga alternatibong paraan. Gayundin, ang computing resources na ibinibigay ng Binder ay medyo basic lamang, kaya magiging mabagal ang training, lalong-lalo na sa mga huling aralin na mas kumplikado. ## Pagpapatakbo sa Cloud na may GPU -Ang ilan sa mga huling aralin sa kurikulum na ito ay lubos na makikinabang mula sa GPU support, dahil kung hindi, ang training ay magiging sobrang bagal. Mayroong ilang mga opsyon na maaari mong sundan, lalo na kung may access ka sa cloud alinman sa pamamagitan ng [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), o sa pamamagitan ng iyong institusyon: +Ang ilang mga huling aralin sa kurikulum na ito ay lubos na makikinabang sa suporta ng GPU. Ang pagsasanay ng mga modelo, halimbawa, ay maaaring maging napakabagal kung wala ito. May ilang mga opsyon na maaari mong sundin, lalo na kung may access ka sa cloud sa pamamagitan ng [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste), o sa pamamagitan ng iyong institusyon: -* Gumawa ng [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) at kumonekta dito sa pamamagitan ng Jupyter. Maaari mong i-clone ang repo direkta sa makina, at simulan ang pag-aaral. Ang NC-series VMs ay may GPU support. +* Gumawa ng [Data Science Virtual Machine](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) at kumonekta dito gamit ang Jupyter. Maaari mong i-clone ang repo direkta sa makina, at simulan ang pag-aaral. Ang NC-series VMs ay may suporta para sa GPU. -> **Note**: Ang ilang mga subscription, kabilang ang Azure for Students, ay hindi nagbibigay ng GPU support nang default. Maaaring kailanganin mong humiling ng karagdagang GPU cores sa pamamagitan ng technical support request. +> **Note**: Ang ilang subscription, kabilang ang Azure for Students, ay hindi awtomatikong nagbibigay ng suporta para sa GPU. Maaaring kailanganin mong humiling ng karagdagang GPU cores sa pamamagitan ng technical support request. -* Gumawa ng [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) at pagkatapos ay gamitin ang Notebook feature doon. [Ang video na ito](https://azure-for-academics.github.io/quickstart/azureml-papers/) ay nagpapakita kung paano i-clone ang repository sa Azure ML notebook at simulan ang paggamit nito. +* Gumawa ng [Azure Machine Learning Workspace](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) at gamitin ang Notebook feature doon. [Itong video](https://azure-for-academics.github.io/quickstart/azureml-papers/) ay nagpapakita kung paano i-clone ang repository papunta sa Azure ML notebook at simulan ang paggamit nito. -Maaari mo ring gamitin ang Google Colab, na may kasamang libreng GPU support, at i-upload ang Jupyter Notebooks doon upang i-execute ang mga ito isa-isa. +Maaari mo ring gamitin ang Google Colab, na may kasamang libreng GPU support, at i-upload ang Jupyter Notebooks doon upang isa-isang patakbuhin. --- -**Paunawa**: -Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang katutubong wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito. \ No newline at end of file + +**Paliwanag**: +Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't nagsusumikap kaming maging tumpak, pakatandaan na maaaring may mga pagkakamali o hindi pagkakatugma ang mga awtomatikong pagsasalin. Ang orihinal na dokumento sa sariling wika nito ang dapat ituring na opisyal na sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring manggaling sa paggamit ng pagsasaling ito. + \ No newline at end of file diff --git a/translations/tl/lessons/2-Symbolic/Animals.ipynb b/translations/tl/lessons/2-Symbolic/Animals.ipynb index e132a86b..1b18f21c 100644 --- a/translations/tl/lessons/2-Symbolic/Animals.ipynb +++ b/translations/tl/lessons/2-Symbolic/Animals.ipynb @@ -6,25 +6,25 @@ "collapsed": true }, "source": [ - "# Pagpapatupad ng Sistema ng Eksperto sa Hayop\n", + "# Pagpapatupad ng isang Animal Expert System\n", "\n", "Isang halimbawa mula sa [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n", "\n", - "Sa halimbawang ito, magpapatupad tayo ng isang simpleng sistema na batay sa kaalaman upang matukoy ang isang hayop batay sa ilang pisikal na katangian. Ang sistema ay maaaring i-representa gamit ang sumusunod na AND-OR tree (ito ay bahagi lamang ng buong puno, madali nating maidaragdag ang iba pang mga patakaran):\n", + "Sa halimbawang ito, magpapatupad tayo ng isang simpleng knowledge-based system upang matukoy ang isang hayop batay sa ilang pisikal na katangian. Ang sistema ay maaaring ilarawan sa pamamagitan ng sumusunod na AND-OR tree (ito ay bahagi ng buong puno, madali tayong makakapagdagdag ng iba pang mga patakaran):\n", "\n", - "![](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp)\n" + "![](../../../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Ang sarili nating expert systems shell gamit ang backward inference\n", + "## Ang aming sariling expert systems shell na may backward inference\n", "\n", - "Subukan nating magtukoy ng isang simpleng wika para sa representasyon ng kaalaman batay sa production rules. Gagamit tayo ng mga klase sa Python bilang mga keyword para tukuyin ang mga patakaran. May tatlong pangunahing uri ng mga klase:\n", - "* `Ask` ay kumakatawan sa isang tanong na kailangang itanong sa user. Naglalaman ito ng hanay ng mga posibleng sagot.\n", - "* `If` ay kumakatawan sa isang patakaran, at ito ay simpleng syntactic sugar para i-store ang nilalaman ng patakaran.\n", - "* `AND`/`OR` ay mga klase para kumatawan sa mga AND/OR na sangay ng puno. Ini-store lang nila ang listahan ng mga argumento sa loob. Upang gawing simple ang code, ang lahat ng functionality ay tinukoy sa parent class na `Content`.\n" + "Subukan nating magdefine ng isang simpleng wika para sa knowledge representation batay sa production rules. Gagamit tayo ng mga Python classes bilang mga keyword para magdefine ng mga rules. Mayroong tatlong pangunahing uri ng mga klase:\n", + "* Ang `Ask` ay kumakatawan sa isang tanong na kailangang itanong sa gumagamit. Naglalaman ito ng set ng mga posibleng sagot.\n", + "* Ang `If` ay kumakatawan sa isang patakaran, at ito ay isang syntactic sugar lamang para itago ang nilalaman ng patakaran\n", + "* Ang `AND`/`OR` ay mga klase na kumakatawan sa mga sanga ng AND/OR ng puno. Tinatago lamang nila ang listahan ng mga argumento sa loob. Upang mapadali ang kodigo, lahat ng functionality ay dinefine sa parent class na `Content`\n" ] }, { @@ -66,7 +66,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Sa aming sistema, ang working memory ay maglalaman ng listahan ng **mga katotohanan** bilang **attribute-value pairs**. Ang knowledgebase ay maaaring tukuyin bilang isang malaking diksyunaryo na nagmamapa ng mga aksyon (mga bagong katotohanan na dapat ipasok sa working memory) sa mga kondisyon, na ipinapahayag bilang AND-OR na mga ekspresyon. Gayundin, ang ilang mga katotohanan ay maaaring `Itanong`.\n" + "Sa aming sistema, ang working memory ay naglalaman ng listahan ng **mga katotohanan** bilang **mga pares ng katangian-halaga**. Ang knowledgebase ay maaaring tukuyin bilang isang malaking diksyunaryo na nagmamapa ng mga aksyon (mga bagong katotohanang dapat ipasok sa working memory) sa mga kondisyon, na ipinapahayag bilang AND-OR na mga ekspresyon. Gayundin, ang ilang mga katotohanan ay maaaring `Ask`-in.\n" ] }, { @@ -99,13 +99,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Upang maisagawa ang backward inference, magtatakda tayo ng klase na `Knowledgebase`. Ito ay maglalaman ng:\n", - "* Gumaganang `memory` - isang diksyunaryo na nagmamapa ng mga attribute sa mga halaga\n", - "* Mga `rules` ng Knowledgebase sa format na itinakda sa itaas\n", + "Upang isagawa ang backward inference, magtatakda tayo ng klase na `Knowledgebase`. Ito ay maglalaman ng:\n", + "* Gumaganang `memory` - isang diksyunaryo na nagmamapa ng mga katangian sa mga halaga\n", + "* Mga patakaran ng Knowledgebase sa format na inilalarawan sa itaas\n", "\n", - "Dalawang pangunahing pamamaraan ay:\n", - "* `get` upang makuha ang halaga ng isang attribute, na gumagawa ng inference kung kinakailangan. Halimbawa, ang `get('color')` ay kukunin ang halaga ng isang color slot (magtatanong ito kung kinakailangan, at itatabi ang halaga para sa susunod na paggamit sa working memory). Kung magtanong tayo ng `get('color:blue')`, magtatanong ito para sa kulay, at pagkatapos ay magbabalik ng halaga na `y`/`n` depende sa kulay.\n", - "* `eval` ang gumagawa ng aktwal na inference, ibig sabihin, sinusuri ang AND/OR tree, ine-evaluate ang mga sub-goal, at iba pa.\n" + "Dalawang pangunahing mga pamamaraan ay:\n", + "* `get` upang kunin ang halaga ng isang katangian, isinasagawa ang inference kung kinakailangan. Halimbawa, ang `get('color')` ay kukuha ng halaga ng slot na kulay (tatanungin kung kinakailangan, at itatago ang halaga para sa susunod na paggamit sa working memory). Kung hihingin natin ang `get('color:blue')`, tatanungin nito ang kulay, at ibabalik ang halagang `y`/`n` depende sa kulay.\n", + "* `eval` ang nagsasagawa ng aktwal na inference, ibig sabihin ay naglalakbay sa AND/OR na puno, sinusuri ang mga sub-goals, at iba pa.\n" ] }, { @@ -172,7 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Ngayon, tukuyin natin ang ating kaalaman tungkol sa mga hayop at isagawa ang konsultasyon. Tandaan na ang tawag na ito ay magtatanong sa iyo. Maaari kang sumagot sa pamamagitan ng pag-type ng `y`/`n` para sa mga tanong na oo-hindi, o sa pamamagitan ng pagtukoy ng numero (0..N) para sa mga tanong na may mas mahabang sagot na multiple-choice.\n" + "Ngayon ay tukuyin natin ang ating kaalaman tungkol sa mga hayop at isagawa ang konsultasyon. Tandaan na ang tawag na ito ay magtatanong sa iyo. Maaari kang sumagot sa pamamagitan ng pag-type ng `y`/`n` para sa mga tanong na oo-hindi, o sa pamamagitan ng pagtukoy ng numero (0..N) para sa mga tanong na may mas mahahabang sagot na multiple-choice.\n" ] }, { @@ -229,11 +229,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Paggamit ng PyKnow para sa Forward Inference\n", + "## Paggamit ng Experta para sa Forward Inference\n", "\n", - "Sa susunod na halimbawa, susubukan nating ipatupad ang forward inference gamit ang isa sa mga library para sa knowledge representation, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** ay isang library para sa paglikha ng forward inference systems sa Python, na idinisenyo upang maging katulad ng klasikong lumang sistema [CLIPS](http://www.clipsrules.net/index.html).\n", + "Sa susunod na halimbawa, susubukan nating ipatupad ang forward inference gamit ang isa sa mga library para sa knowledge representation, ang [Experta](https://github.com/nilp0inter/experta). **Ang Experta** ay isang library para sa paglikha ng mga forward inference system sa Python, na disenyo upang maging katulad ng klasikong lumang system na [CLIPS](http://www.clipsrules.net/index.html).\n", "\n", - "Maaari rin nating ipatupad ang forward chaining nang tayo mismo nang walang masyadong problema, ngunit ang mga simpleng implementasyon ay kadalasang hindi masyadong epektibo. Para sa mas mahusay na rule matching, isang espesyal na algorithm na tinatawag na [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ang ginagamit.\n" + "Maaari rin sana namin ipatupad ang forward chaining nang mag-isa nang walang masyadong problema, ngunit ang mga payak na implementasyon ay karaniwang hindi gaanong epektibo. Para sa mas epektibong pag-match ng mga patakaran, isang espesyal na algorithm na [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) ang ginagamit.\n" ] }, { @@ -247,32 +247,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting git+https://github.com/buguroo/pyknow/\n", - " Cloning https://github.com/buguroo/pyknow/ to /tmp/pip-req-build-3cqeulyl\n", - " Running command git clone --filter=blob:none --quiet https://github.com/buguroo/pyknow/ /tmp/pip-req-build-3cqeulyl\n", - " Resolved https://github.com/buguroo/pyknow/ to commit 48818336f2e9a126f1964f2d8dc22d37ff800fe8\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting frozendict==1.2\n", - " Using cached frozendict-1.2.tar.gz (2.6 kB)\n", - " Preparing metadata (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25hCollecting schema==0.6.7\n", - " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n", - "Building wheels for collected packages: pyknow, frozendict\n", - " Building wheel for pyknow (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34228 sha256=b7de5b09292c4007667c72f69b98d5a1b5f7324ff15f9dd8e077c3d5f7aade42\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-k7jpave7/wheels/81/1a/d3/f6c15dbe1955598a37755215f2a10449e7418500d7bd4b9508\n", - " Building wheel for frozendict (setup.py) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3148 sha256=2863d55c240d2409cddf05ccfe600591f8478681549fc97555c47c90dc6bb160\n", - " Stored in directory: /home/rg/.cache/pip/wheels/49/ac/f8/cb8120244e710bdb479c86198b03c7b08c3c2d3d2bf448fd6e\n", - "Successfully built pyknow frozendict\n", - "Installing collected packages: schema, frozendict, pyknow\n", - "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n" + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" ] } ], "source": [ "import sys\n", - "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/" + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" ] }, { @@ -283,15 +282,15 @@ }, "outputs": [], "source": [ - "from pyknow import *\n", - "#import pyknow" + "from experta import *\n", + "#import experta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Itatakda natin ang ating sistema bilang isang klase na nag-subclass sa `KnowledgeEngine`. Ang bawat panuntunan ay tinutukoy ng isang hiwalay na function na may `@Rule` na anotasyon, na nagsasaad kung kailan dapat mag-trigger ang panuntunan. Sa loob ng panuntunan, maaari tayong magdagdag ng mga bagong katotohanan gamit ang `declare` na function, at ang pagdaragdag ng mga katotohanang iyon ay magreresulta sa pagtawag ng iba pang mga panuntunan ng forward inference engine.\n" + "Ide-define natin ang ating sistema bilang isang klase na nag-subclass sa `KnowledgeEngine`. Bawat patakaran ay dine-define ng isang hiwalay na function na may anotasyong `@Rule`, na nagsasaad kung kailan dapat mag-fire ang patakaran. Sa loob ng patakaran, maaari tayong magdagdag ng mga bagong katotohanan gamit ang function na `declare`, at ang pagdagdag ng mga katotohanang iyon ay magreresulta sa pagtawag ng iba pang mga patakaran ng forward inference engine.\n" ] }, { @@ -378,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Kapag naitakda na natin ang isang knowledgebase, pinupunan natin ang ating working memory ng ilang paunang impormasyon, at pagkatapos ay tinatawag ang `run()` na pamamaraan upang isagawa ang inference. Makikita mo bilang resulta na ang mga bagong inferred na impormasyon ay idinadagdag sa working memory, kabilang ang panghuling impormasyon tungkol sa hayop (kung tama ang lahat ng paunang impormasyon na itinakda natin).\n" + "Kapag naitakda na natin ang isang knowledgebase, pinupuno natin ang ating working memory ng ilang paunang mga katotohanan, at pagkatapos ay tinatawag ang `run()` na metodo upang isagawa ang inference. Makikita mo bilang resulta na ang mga bagong napatunayang katotohanan ay idinaragdag sa working memory, kabilang ang panghuling katotohanan tungkol sa hayop (kung tama nating naitakda ang lahat ng paunang mga katotohanan).\n" ] }, { @@ -440,7 +439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n---\n\n**Paunawa**: \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, tandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang katutubong wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na dulot ng paggamit ng pagsasaling ito.\n" + "---\n\n\n**Paalala**:\nAng dokumentong ito ay isinalin gamit ang serbisyo ng AI na pagsasalin na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagaman aming pinagsisikapang maging tumpak ang pagsasalin, pakatandaan na ang awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na mapagkakatiwalaang sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na nagmumula sa paggamit ng pagsasaling ito.\n\n" ] } ], @@ -467,8 +466,8 @@ "version": "3.11.2" }, "coopTranslator": { - "original_hash": "ab2bd97b0453415b89a469284609a8ce", - "translation_date": "2025-08-28T03:40:25+00:00", + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-16T04:08:23+00:00", "source_file": "lessons/2-Symbolic/Animals.ipynb", "language_code": "tl" } diff --git a/translations/tl/lessons/2-Symbolic/README.md b/translations/tl/lessons/2-Symbolic/README.md index d713b569..26018e6c 100644 --- a/translations/tl/lessons/2-Symbolic/README.md +++ b/translations/tl/lessons/2-Symbolic/README.md @@ -1,76 +1,76 @@ -# Representasyon ng Kaalaman at Mga Ekspertong Sistema +# Knowledge Representation and Expert Systems -![Buod ng Nilalaman ng Symbolic AI](../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.webp) +![Summary of Symbolic AI content](../../../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.webp) -> Sketchnote ni [Tomomi Imura](https://twitter.com/girlie_mac) +> Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) -Ang layunin ng artipisyal na intelihensiya ay nakabatay sa paghahanap ng kaalaman, upang maunawaan ang mundo katulad ng ginagawa ng tao. Pero paano mo ito magagawa? +Ang paghahanap para sa artificial intelligence ay batay sa paghahanap ng kaalaman, upang maintindihan ang mundo na katulad ng tao. Pero paano mo ito gagawin? ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/3) -Sa mga unang araw ng AI, ang top-down na paraan ng paglikha ng mga intelligent na sistema (tinalakay sa nakaraang aralin) ay popular. Ang ideya ay kunin ang kaalaman mula sa mga tao sa isang anyong nababasa ng makina, at pagkatapos ay gamitin ito upang awtomatikong lutasin ang mga problema. Ang pamamaraang ito ay nakabatay sa dalawang malalaking ideya: +Noong mga unang araw ng AI, ang top-down approach sa paglikha ng mga matatalinong sistema (tinalakay sa nakaraang leksiyon) ay popular. Ang ideya ay kunin ang kaalaman mula sa mga tao sa isang anyo na mababasa ng makina, at gamitin ito upang awtomatikong malutas ang mga problema. Ang approach na ito ay nakabatay sa dalawang malalaking ideya: -* Representasyon ng Kaalaman -* Pangangatwiran +* Knowledge Representation +* Reasoning -## Representasyon ng Kaalaman +## Knowledge Representation -Isa sa mga mahalagang konsepto sa Symbolic AI ay ang **kaalaman**. Mahalagang maiba ang kaalaman mula sa *impormasyon* o *data*. Halimbawa, maaaring sabihin na ang mga libro ay naglalaman ng kaalaman, dahil maaaring mag-aral mula sa mga libro at maging eksperto. Gayunpaman, ang nilalaman ng mga libro ay tinatawag na *data*, at sa pamamagitan ng pagbabasa ng mga libro at pagsasama ng data na ito sa ating modelo ng mundo, nagiging kaalaman ang data na ito. +Isa sa mga mahalagang konsepto sa Symbolic AI ay ang **kaalaman**. Mahalaga na pag-ibahin ang kaalaman mula sa *impormasyon* o *data*. Halimbawa, masasabi na ang mga libro ay naglalaman ng kaalaman, dahil maaari kang mag-aral mula sa mga libro at maging eksperto. Gayunpaman, ang laman ng mga libro ay tinatawag na *data*, at sa pagbasa ng mga libro at pagsasama ng data na ito sa ating modelo ng mundo, binabago natin ang data na ito sa kaalaman. -> ✅ **Kaalaman** ay isang bagay na nasa ating isipan at kumakatawan sa ating pag-unawa sa mundo. Ito ay nakukuha sa pamamagitan ng aktibong **proseso ng pag-aaral**, na nag-iintegrate ng mga piraso ng impormasyon na natatanggap natin sa ating aktibong modelo ng mundo. +> ✅ **Kaalaman** ay isang bagay na nasa ating isip at nagrerepresenta ng ating pagkaunawa sa mundo. Nakukuha ito sa isang aktibong proseso ng **pagkatuto**, na nagsasama ng mga piraso ng impormasyon na natatanggap natin sa ating aktibong modelo ng mundo. -Kadalasan, hindi natin mahigpit na tinutukoy ang kaalaman, ngunit iniuugnay natin ito sa iba pang kaugnay na konsepto gamit ang [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid). Naglalaman ito ng mga sumusunod na konsepto: +Kadalasan, hindi natin mahigpit na tinutukoy ang kaalaman, ngunit nirerespeto ito kasabay ng mga kaugnay na konsepto gamit ang [DIKW Pyramid](https://en.wikipedia.org/wiki/DIKW_pyramid). Nilalaman nito ang mga sumusunod na konsepto: -* **Data** ay isang bagay na kinakatawan sa pisikal na media, tulad ng nakasulat na teksto o binigkas na mga salita. Ang data ay umiiral nang hiwalay sa mga tao at maaaring ipasa sa pagitan ng mga tao. -* **Impormasyon** ay kung paano natin ini-interpret ang data sa ating isipan. Halimbawa, kapag narinig natin ang salitang *kompyuter*, mayroon tayong ilang pag-unawa kung ano ito. -* **Kaalaman** ay impormasyon na isinama sa ating modelo ng mundo. Halimbawa, kapag natutunan natin kung ano ang kompyuter, nagsisimula tayong magkaroon ng mga ideya kung paano ito gumagana, magkano ang halaga nito, at kung para saan ito magagamit. Ang network ng magkakaugnay na konsepto ay bumubuo ng ating kaalaman. -* **Karunungan** ay isa pang antas ng ating pag-unawa sa mundo, at kumakatawan ito sa *meta-kaalaman*, halimbawa, ilang ideya kung paano at kailan dapat gamitin ang kaalaman. +* **Data** ay isang bagay na ginawang representasyon sa pisikal na media, gaya ng nakasulat na teksto o mga salitang binigkas. Ang data ay umiiral nang hiwalay sa mga tao at maaaring ipasa sa pagitan ng mga tao. +* **Information** ay kung paano natin iniintindi ang data sa ating isip. Halimbawa, kapag narinig natin ang salitang *computer*, may ideya tayo kung ano ito. +* **Knowledge** ay ang impormasyon na isinama sa ating modelo ng mundo. Halimbawa, kapag natutunan natin ang tungkol sa computer, nagsisimula tayong magkaroon ng mga ideya kung paano ito gumagana, magkano ito, at para saan ito ginagamit. Ang network ng magkakaugnay na mga konseptong ito ang bumubuo ng ating kaalaman. +* **Wisdom** ay isa pang antas ng ating pagkaunawa sa mundo, at ito ay nagrerepresenta ng *meta-knowledge*, hal. isang ideya kung paano at kailan dapat gamitin ang kaalaman. - + -*Larawan [mula sa Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Ni Longlivetheux - Sariling gawa, CC BY-SA 4.0* +*Larawan [mula sa Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Ni Longlivetheux - Own work, CC BY-SA 4.0* -Kaya, ang problema ng **representasyon ng kaalaman** ay ang paghahanap ng epektibong paraan upang kumatawan sa kaalaman sa loob ng isang kompyuter sa anyo ng data, upang magamit ito nang awtomatiko. Ito ay maaaring makita bilang isang spectrum: +Kaya, ang problema ng **knowledge representation** ay ang paghahanap ng epektibong paraan upang ipakita ang kaalaman sa loob ng computer sa anyo ng data, upang magamit ito nang awtomatiko. Makikita ito bilang isang spectrum: -![Spectrum ng Representasyon ng Kaalaman](../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.webp) +![Knowledge representation spectrum](../../../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.webp) -> Larawan ni [Dmitry Soshnikov](http://soshnikov.com) +> Image by [Dmitry Soshnikov](http://soshnikov.com) -* Sa kaliwa, mayroong napakasimpleng uri ng representasyon ng kaalaman na maaaring epektibong magamit ng mga kompyuter. Ang pinakasimple ay algorithmic, kung saan ang kaalaman ay kinakatawan ng isang programa ng kompyuter. Gayunpaman, hindi ito ang pinakamahusay na paraan upang kumatawan sa kaalaman, dahil hindi ito flexible. Ang kaalaman sa ating isipan ay kadalasang hindi algorithmic. -* Sa kanan, mayroong mga representasyon tulad ng natural na teksto. Ito ang pinakamakapangyarihan, ngunit hindi magagamit para sa awtomatikong pangangatwiran. +* Sa kaliwa, may mga napakasimpleng uri ng representasyon ng kaalaman na maaaring gamitin nang epektibo ng mga computer. Ang pinakasimple ay algorithmic, kung saan ang kaalaman ay kinakatawan ng isang programa sa computer. Ngunit ito ay hindi ang pinakamahusay na paraan upang ipakita ang kaalaman, dahil hindi ito flexible. Ang kaalaman sa ating isip ay madalas na hindi algorithmic. +* Sa kanan, may mga representasyon gaya ng natural na teksto. Ito ang pinakamakapangyarihan, ngunit hindi maaaring gamitin sa awtomatikong pangangatwiran. -> ✅ Mag-isip ng isang minuto kung paano mo kinakatawan ang kaalaman sa iyong isipan at isinasalin ito sa mga tala. Mayroon bang partikular na format na epektibo para sa iyo upang matulungan ang pag-alala? +> ✅ Mag-isip nang sandali kung paano mo nirerepresenta ang kaalaman sa iyong isip at kino-convert ito sa mga tala. Mayroon bang partikular na format na epektibo sa'yo para makatulong sa pag-alala? -## Pag-uuri ng Representasyon ng Kaalaman ng Kompyuter +## Pag-uuri ng Mga Representasyon ng Kaalaman sa Computer -Maaari nating uriin ang iba't ibang paraan ng representasyon ng kaalaman ng kompyuter sa mga sumusunod na kategorya: +Maaari nating uriin ang iba't ibang paraan ng representasyon ng kaalaman sa computer sa mga sumusunod na kategorya: -* **Network representations** ay nakabatay sa katotohanan na mayroon tayong network ng magkakaugnay na konsepto sa ating isipan. Maaari nating subukang muling likhain ang parehong mga network bilang isang graph sa loob ng isang kompyuter - ang tinatawag na **semantic network**. +* **Network representations** ay nakabatay sa katotohanan na mayroon tayong network ng magkakaugnay na konsepto sa ating isip. Maaari nating subukang ulitin ang parehong mga network bilang isang grap sa loob ng computer - isang tinatawag na **semantic network**. -1. **Object-Attribute-Value triplets** o **attribute-value pairs**. Dahil ang isang graph ay maaaring kumatawan sa loob ng isang kompyuter bilang isang listahan ng mga nodes at edges, maaari nating kumatawan sa isang semantic network sa pamamagitan ng isang listahan ng mga triplets, na naglalaman ng mga object, attribute, at value. Halimbawa, bumuo tayo ng mga sumusunod na triplets tungkol sa mga programming language: +1. **Object-Attribute-Value triplets** o **attribute-value pairs**. Dahil ang isang graph ay maaaring ipakita sa loob ng computer bilang listahan ng mga nodes at edges, maaari nating ipakita ang isang semantic network gamit ang listahan ng tatlong bahagi, na naglalaman ng mga bagay, katangian, at mga halaga. Halimbawa, ginagawa natin ang mga sumusunod na triplets tungkol sa mga programming language: Object | Attribute | Value -------|-----------|------ -Python | ay | Untyped-Language -Python | imbento-ni | Guido van Rossum +Python | is | Untyped-Language +Python | invented-by | Guido van Rossum Python | block-syntax | indentation -Untyped-Language | wala | type definitions +Untyped-Language | doesn't have | type definitions -> ✅ Mag-isip kung paano maaaring gamitin ang mga triplets upang kumatawan sa iba pang uri ng kaalaman. +> ✅ Isipin kung paano magagamit ang mga triplet upang ipakita ang ibang uri ng kaalaman. -2. **Hierarchical representations** binibigyang-diin ang katotohanan na madalas tayong lumikha ng hierarchy ng mga object sa ating isipan. Halimbawa, alam natin na ang canary ay isang ibon, at lahat ng ibon ay may pakpak. Mayroon din tayong ideya kung anong kulay ang karaniwang canary, at kung ano ang bilis ng kanilang paglipad. +2. **Hierarchical representations** ay binibigyang-diin ang katotohanan na madalas tayong lumikha ng hierarchy ng mga bagay sa ating isip. Halimbawa, alam natin na ang kanaryo ay isang ibon, at lahat ng mga ibon ay may mga pakpak. Mayroon din tayong ideya kung anong kulay karaniwang kanaryo at kung ano ang bilis ng kanilang paglipad. - - **Frame representation** ay nakabatay sa pagrepresenta ng bawat object o klase ng mga object bilang isang **frame** na naglalaman ng **slots**. Ang mga slots ay may posibleng default na mga value, mga limitasyon ng value, o mga nakaimbak na proseso na maaaring tawagin upang makuha ang value ng isang slot. Ang lahat ng mga frame ay bumubuo ng isang hierarchy na katulad ng object hierarchy sa mga object-oriented programming language. - - **Scenarios** ay espesyal na uri ng mga frame na kumakatawan sa mga kumplikadong sitwasyon na maaaring maganap sa paglipas ng panahon. + - **Frame representation** ay nakabatay sa pagrepresenta ng bawat bagay o klase ng mga bagay bilang isang **frame** na naglalaman ng mga **slots**. Ang mga slot ay may mga posibleng default values, mga limitasyon sa halaga, o mga nakaimbak na pamamaraan na maaaring tawagin upang makuha ang halaga ng isang slot. Ang lahat ng frame ay bumubuo ng isang hierarchy na katulad ng object hierarchy sa object-oriented programming languages. + - **Scenarios** ay espesyal na uri ng frames na nagrerepresenta ng mga kumplikadong sitwasyon na maaaring maganap sa paglipas ng panahon. **Python** @@ -82,35 +82,35 @@ Variable Case | | CamelCase | | Program Length | | | 5-5000 lines | Block Syntax | Indent | | | -3. **Procedural representations** ay nakabatay sa pagrepresenta ng kaalaman sa pamamagitan ng isang listahan ng mga aksyon na maaaring isagawa kapag naganap ang isang tiyak na kondisyon. - - Production rules ay mga if-then na pahayag na nagpapahintulot sa atin na gumawa ng mga konklusyon. Halimbawa, maaaring magkaroon ng rule ang isang doktor na nagsasabing **KUNG** ang isang pasyente ay may mataas na lagnat **O** mataas na antas ng C-reactive protein sa blood test **KUNG GAYON** siya ay may impeksyon. Kapag nakatagpo tayo ng isa sa mga kondisyon, maaari tayong gumawa ng konklusyon tungkol sa impeksyon, at pagkatapos ay gamitin ito sa karagdagang pangangatwiran. - - Algorithms ay maaaring ituring na isa pang anyo ng procedural representation, bagaman halos hindi ito direktang ginagamit sa mga knowledge-based system. +3. **Procedural representations** ay nakabatay sa pagrepresenta ng kaalaman gamit ang listahan ng mga aksyon na maaaring isagawa kapag may isang partikular na kondisyon. + - Ang production rules ay mga if-then na pahayag na nagbibigay-daan sa atin upang makabuo ng mga konklusyon. Halimbawa, ang doktor ay maaaring magkaroon ng panuntunan na nagsasabing **KUNG** ang pasyente ay may mataas na lagnat **O** mataas na antas ng C-reactive protein sa pagsusuri ng dugo **KAYON** siya ay may pamamaga. Kapag naranasan natin ang isa sa mga kondisyong ito, maaari tayong gumawa ng konklusyon tungkol sa pamamaga, at gamitin ito sa karagdagang pangangatwiran. + - Ang mga algorithm ay maaaring ituring na isa pang anyo ng procedural representation, bagaman halos hindi ito direktang ginagamit sa mga knowledge-based systems. -4. **Logic** ay orihinal na iminungkahi ni Aristotle bilang isang paraan upang kumatawan sa unibersal na kaalaman ng tao. - - Predicate Logic bilang isang mathematical theory ay masyadong mayaman upang maging computable, kaya't ang ilang subset nito ang karaniwang ginagamit, tulad ng Horn clauses na ginagamit sa Prolog. - - Descriptive Logic ay isang pamilya ng mga logical system na ginagamit upang kumatawan at magbigay ng pangangatwiran tungkol sa mga hierarchy ng mga object na ipinamamahagi sa mga representasyon ng kaalaman tulad ng *semantic web*. +4. **Logic** ay unang iminungkahi ni Aristotle bilang isang paraan upang ipakita ang unibersal na kaalaman ng tao. + - Ang Predicate Logic bilang isang teoryang matematikal ay masyadong masagana upang maging computable, kaya ginagamit ang ilang bahagi lamang nito, tulad ng mga Horn clauses na ginagamit sa Prolog. + - Ang Descriptive Logic ay isang pamilya ng mga sistemang lohikal na ginagamit upang ipakita at mangangatwiran tungkol sa hierarchies ng mga bagay na nakakalat sa mga knowledge representations tulad ng *semantic web*. -## Mga Ekspertong Sistema +## Expert Systems -Isa sa mga maagang tagumpay ng symbolic AI ay ang tinatawag na **mga ekspertong sistema** - mga sistema ng kompyuter na idinisenyo upang kumilos bilang isang eksperto sa ilang limitadong domain ng problema. Ang mga ito ay nakabatay sa isang **knowledge base** na kinuha mula sa isa o higit pang mga eksperto, at naglalaman ng isang **inference engine** na gumagawa ng pangangatwiran sa ibabaw nito. +Isa sa mga unang tagumpay ng symbolic AI ay ang tinatawag na **expert systems** - mga computer system na idinisenyo upang kumilos bilang eksperto sa isang limitado na domain ng problema. Nakabatay ito sa isang **knowledge base** na nakuha mula sa isa o higit pang mga human expert, at naglalaman ng isang **inference engine** na nagsasagawa ng pangangatwiran dito. -![Arkitektura ng Tao](../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema na Batay sa Kaalaman](../../../../translated_images/tl/arch-kbs.3ec5c150b09fa8da.webp) +![Human Architecture](../../../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../../../translated_images/tl/arch-kbs.3ec5c150b09fa8da.webp) ---------------------------------------------|------------------------------------------------ -Pinadaling istruktura ng neural system ng tao | Arkitektura ng sistema na batay sa kaalaman +Pinadaling istruktura ng neural system ng tao | Arkitektura ng knowledge-based system -Ang mga ekspertong sistema ay binuo tulad ng sistema ng pangangatwiran ng tao, na naglalaman ng **short-term memory** at **long-term memory**. Katulad nito, sa mga sistema na batay sa kaalaman, tinutukoy natin ang mga sumusunod na bahagi: +Ang mga expert system ay ginawa katulad ng sistema ng pag-iisip ng tao, na naglalaman ng **short-term memory** at **long-term memory**. Katulad nito, sa knowledge-based systems ay tinutukoy natin ang mga sumusunod na bahagi: -* **Problem memory**: naglalaman ng kaalaman tungkol sa problemang kasalukuyang nilulutas, halimbawa, ang temperatura o presyon ng dugo ng isang pasyente, kung siya ay may impeksyon o wala, atbp. Ang kaalamang ito ay tinatawag ding **static knowledge**, dahil naglalaman ito ng snapshot ng kung ano ang kasalukuyang alam natin tungkol sa problema - ang tinatawag na *problem state*. -* **Knowledge base**: kumakatawan sa pangmatagalang kaalaman tungkol sa isang domain ng problema. Ito ay manu-manong kinuha mula sa mga eksperto, at hindi nagbabago mula sa konsultasyon hanggang konsultasyon. Dahil pinapayagan nitong mag-navigate mula sa isang problem state patungo sa isa pa, tinatawag din itong **dynamic knowledge**. -* **Inference engine**: nag-oorganisa ng buong proseso ng paghahanap sa problem state space, nagtatanong sa user kung kinakailangan. Responsable rin ito sa paghahanap ng tamang mga rules na dapat ilapat sa bawat estado. +* **Problem memory**: naglalaman ng kaalaman tungkol sa problemang kasalukuyang nilulutas, hal. ang temperatura o presyon ng dugo ng pasyente, kung siya ay may pamamaga o wala, atbp. Ang kaalamang ito ay tinatawag ding **static knowledge**, dahil naglalaman ito ng isang snapshot ng kung ano ang kasalukuyan nating alam tungkol sa problema - ang tinatawag na *problem state*. +* **Knowledge base**: nagrerepresenta ng pangmatagalang kaalaman tungkol sa domain ng problema. Kinukuha ito nang manu-mano mula sa mga human expert, at hindi nagbabago mula sa konsultasyon sa konsultasyon. Dahil pinapayagan nito tayo na mag-navigate mula sa isang problem state patungo sa iba pa, tinatawag din itong **dynamic knowledge**. +* **Inference engine**: pinamamahalaan ang buong proseso ng paghahanap sa problem state space, nagtatanong sa gumagamit kung kinakailangan. Responsable rin ito sa paghahanap ng tamang mga panuntunan na dapat ilapat sa bawat estado. -Bilang halimbawa, isaalang-alang natin ang sumusunod na ekspertong sistema ng pagtukoy ng isang hayop batay sa mga pisikal na katangian nito: +Bilang halimbawa, isaalang-alang natin ang sumusunod na expert system para matukoy ang isang hayop base sa mga pisikal na katangian nito: -![AND-OR Tree](../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp) +![AND-OR Tree](../../../../../../translated_images/tl/AND-OR-Tree.5592d2c70187f283.webp) -> Larawan ni [Dmitry Soshnikov](http://soshnikov.com) +> Image by [Dmitry Soshnikov](http://soshnikov.com) -Ang diagram na ito ay tinatawag na **AND-OR tree**, at ito ay isang graphical na representasyon ng isang set ng production rules. Ang pagguhit ng tree ay kapaki-pakinabang sa simula ng pagkuha ng kaalaman mula sa eksperto. Upang kumatawan sa kaalaman sa loob ng kompyuter, mas maginhawa ang paggamit ng mga rules: +Ang diagram na ito ay tinatawag na **AND-OR tree**, at ito ay isang grapikal na representasyon ng isang set ng mga production rules. Ang pagguhit ng puno ay kapaki-pakinabang sa simula ng pagkuha ng kaalaman mula sa eksperto. Upang ipakita ang kaalaman sa loob ng computer, mas praktikal na gamitin ang mga panuntunan: ``` IF the animal eats meat @@ -121,78 +121,78 @@ OR (animal has sharp teeth THEN the animal is a carnivore ``` -Mapapansin mo na ang bawat kondisyon sa kaliwang bahagi ng rule at ang aksyon ay mahalagang object-attribute-value (OAV) triplets. **Working memory** ay naglalaman ng set ng OAV triplets na tumutugma sa problemang kasalukuyang nilulutas. Ang **rules engine** ay naghahanap ng mga rules kung saan ang kondisyon ay nasisiyahan at inilalapat ang mga ito, nagdaragdag ng isa pang triplet sa working memory. +Mapapansin mo na bawat kondisyon sa kaliwang bahagi ng panuntunan at ang aksyon ay mga object-attribute-value (OAV) triplets. Ang **working memory** ay naglalaman ng set ng OAV triplets na tumutugma sa problemang kasalukuyang nilulutas. Ang isang **rules engine** ay naghahanap ng mga panuntunan kung saan ang isang kondisyon ay nasusunod at inilalapat ang mga ito, nagdaragdag ng isa pang triplet sa working memory. -> ✅ Gumuhit ng sarili mong AND-OR tree sa isang paksang gusto mo! +> ✅ Gumawa ng sarili mong AND-OR tree sa isang paksang gusto mo! ### Forward vs. Backward Inference -Ang prosesong inilarawan sa itaas ay tinatawag na **forward inference**. Nagsisimula ito sa ilang paunang data tungkol sa problema na magagamit sa working memory, at pagkatapos ay isinasagawa ang sumusunod na reasoning loop: +Ang prosesong inilarawan sa itaas ay tinatawag na **forward inference**. Nagsisimula ito sa ilang panimulang data tungkol sa problema na nasa working memory, at pagkatapos ay isinasagawa ang sumusunod na reasoning loop: -1. Kung ang target na attribute ay naroroon sa working memory - huminto at ibigay ang resulta -2. Hanapin ang lahat ng mga rules kung saan ang kondisyon ay kasalukuyang nasisiyahan - makuha ang **conflict set** ng mga rules. -3. Isagawa ang **conflict resolution** - pumili ng isang rule na isasagawa sa hakbang na ito. Maaaring may iba't ibang conflict resolution strategies: - - Piliin ang unang applicable na rule sa knowledge base - - Piliin ang random na rule - - Piliin ang *mas tiyak* na rule, halimbawa, ang isa na tumutugma sa pinakamaraming kondisyon sa "left-hand-side" (LHS) -4. Ilapat ang napiling rule at ipasok ang bagong piraso ng kaalaman sa problem state +1. Kung ang target attribute ay naroroon sa working memory - huminto at ibigay ang resulta +2. Hanapin ang lahat ng panuntunan na ang kondisyon ay kasalukuyang nasusunod - kunin ang **conflict set** ng panuntunan. +3. Isagawa ang **conflict resolution** - piliin ang isang panuntunan na isasakatuparan sa hakbang na ito. Maaaring may iba't ibang estratehiya sa conflict resolution: + - Piliin ang unang naaangkop na panuntunan sa knowledge base + - Piliin ang isang random na panuntunan + - Piliin ang *mas partikular* na panuntunan, ibig sabihin ang sumasapat sa pinakamaraming kondisyon sa "kaliwang bahagi" (LHS) +4. Ipatupad ang piniling panuntunan at ipasok ang bagong piraso ng kaalaman sa problem state 5. Ulitin mula sa hakbang 1. -Gayunpaman, sa ilang mga kaso maaaring gusto nating magsimula sa walang kaalaman tungkol sa problema, at magtanong ng mga tanong na makakatulong sa atin na makarating sa konklusyon. Halimbawa, kapag gumagawa ng medikal na diagnosis, karaniwang hindi natin isinasagawa ang lahat ng medikal na pagsusuri nang maaga bago simulan ang pag-diagnose sa pasyente. Sa halip, gusto nating magsagawa ng pagsusuri kapag kailangang gumawa ng desisyon. +Gayunpaman, sa ilang mga kaso nais nating magsimula nang walang kaalaman tungkol sa problema, at magtanong ng mga katanungan na makakatulong sa atin na makabuo ng konklusyon. Halimbawa, kapag gumagawa ng medikal na diagnosis, karaniwang hindi natin ginagawa lahat ng medikal na pagsusuri bago simulan ang pagsusuri sa pasyente. Mas gusto nating gawin ang pagsusuri kapag kailangan na magpasya. -Ang prosesong ito ay maaaring i-modelo gamit ang **backward inference**. Ito ay hinihimok ng **goal** - ang value ng attribute na hinahanap natin: +Ang prosesong ito ay maaaring imodelo gamit ang **backward inference**. Pinapatakbo ito ng **layunin** - ang halaga ng attribute na nais nating matuklasan: -1. Piliin ang lahat ng mga rules na maaaring magbigay sa atin ng value ng goal (halimbawa, may goal sa RHS ("right-hand-side")) - isang conflict set -1. Kung walang mga rules para sa attribute na ito, o mayroong rule na nagsasabing dapat nating tanungin ang value mula sa user - tanungin ito, kung hindi: -1. Gumamit ng conflict resolution strategy upang pumili ng isang rule na gagamitin bilang *hypothesis* - susubukan natin itong patunayan -1. Paulit-ulit na ulitin ang proseso para sa lahat ng mga attribute sa LHS ng rule, sinusubukang patunayan ang mga ito bilang mga goal -1. Kung sa anumang punto ang proseso ay nabigo - gumamit ng ibang rule sa hakbang 3. +1. Piliin ang lahat ng panuntunan na makapagbibigay sa atin ng halaga ng layunin (ibig sabihin na ang layunin ay nasa RHS ("right-hand-side")) - isang conflict set +1. Kung walang panuntunan para sa attributeang ito, o may panuntunan na nagsasabing tanungin ang gumagamit para sa halaga - itanong ito, kung hindi: +1. Gamitin ang conflict resolution strategy para pumili ng isang panuntunan na gagamitin bilang *hypothesis* - susubukan nating patunayan ito +1. Ulitin ang proseso para sa lahat ng attribute sa LHS ng panuntunan, sinusubukang patunayan ito bilang mga layunin +1. Kung sa anumang punto nabigo ang proseso - gamitin ang ibang panuntunan sa hakbang 3. -> ✅ Sa anong mga sitwasyon mas angkop ang forward inference? Paano naman ang backward inference? +> ✅ Saang mga sitwasyon mas angkop ang forward inference? Paano naman ang backward inference? -### Pagpapatupad ng Mga Ekspertong Sistema +### Pagsasagawa ng Expert Systems -Ang mga ekspertong sistema ay maaaring ipatupad gamit ang iba't ibang mga tool: +Maaaring ipatupad ang mga expert system gamit ang iba't ibang mga kagamitan: -* Pagprograma ng mga ito nang direkta sa ilang high-level programming language. Hindi ito ang pinakamahusay na ideya, dahil ang pangunahing bentahe ng isang sistema na batay sa kaalaman ay ang kaalaman ay hiwalay sa inference, at potensyal na ang eksperto sa domain ng problema ay dapat na makapagsulat ng mga rules nang hindi nauunawaan ang mga detalye ng proseso ng inference. -* Paggamit ng **expert systems shell**, halimbawa, isang sistema na partikular na idinisenyo upang mapunan ng kaalaman gamit ang ilang knowledge representation language. +* Direktang pagprograma gamit ang ilang mataas na antas na wika sa programming. Hindi ito ang pinakamahusay na ideya, dahil ang pangunahing bentahe ng isang knowledge-based system ay naihihiwalay ang kaalaman mula sa inference, at posibleng ang isang eksperto sa domain ng problema ay dapat makapag-sulat ng mga panuntunan nang hindi naiintindihan ang mga detalye ng proseso ng inference +* Paggamit ng **expert systems shell**, ibig sabihin isang sistema na partikular na dinisenyo upang mapunuan ng kaalaman gamit ang isang wika sa representasyon ng kaalaman. -## ✍️ Ehersisyo: Animal Inference +## ✍️ Exercise: Animal Inference -Tingnan ang [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para sa isang halimbawa ng pagpapatupad ng forward at backward inference expert system. +Tingnan ang [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para sa halimbawa ng pagsasagawa ng forward at backward inference expert system. -> **Note**: Ang halimbawang ito ay medyo simple, at nagbibigay lamang ng ideya kung paano ang hitsura ng isang ekspertong sistema. Kapag nagsimula kang lumikha ng ganitong sistema, mapapansin mo lamang ang ilang *intelligent* na pag-uugali mula rito kapag umabot ka sa tiyak na bilang ng mga rules, mga 200+. Sa ilang punto, ang mga rules ay nagiging masyadong kumplikado upang mapanatili ang lahat ng mga ito sa isipan, at sa puntong ito maaari kang magsimulang magtaka kung bakit gumagawa ang sistema ng ilang mga desisyon. Gayunpaman, ang mahalagang katangian ng mga sistema na batay sa kaalaman ay palagi mong maipapaliwanag nang eksakto kung paano ginawa ang alinman sa mga desisyon. +> **Note**: Ang halimbawang ito ay medyo simple lamang, at nagbibigay lang ng ideya kung paano ang hitsura ng isang expert system. Kapag nagsimula kang gumawa ng ganitong sistema, mapapansin mo ang ilang *matalinong* pag-uugali mula rito kapag umabot ka sa isang tiyak na bilang ng mga panuntunan, mga 200+. Sa isang punto, nagiging komplikado na ang mga panuntunan upang maalala lahat ito, at sa puntong ito, maaaring matagpuan mo ang iyong sarili na nagtatanong kung bakit ang isang sistema ay gumawa ng ilang mga desisyon. Gayunpaman, ang mahalagang katangian ng mga knowledge-based system ay palagi mong maaaring *ipaliwanag* nang eksakto kung paano ginawa ang alinman sa mga desisyon. -## Ontolohiya at Semantic Web +## Ontologies and the Semantic Web -Sa pagtatapos ng ika-20 siglo, mayroong isang inisyatibo upang gamitin ang representasyon ng kaalaman upang i-annotate ang mga mapagkukunan sa Internet, upang posible na makahanap ng mga mapagkukunan na tumutugma sa napaka-espesipikong mga query. Ang kilusang ito ay tinawag na **Semantic Web**, at ito ay nakabatay sa ilang mga konsepto: +Sa huling bahagi ng ika-20 siglo, may inisyatibo upang gamitin ang representasyon ng kaalaman upang lagyan ng anote ang mga mapagkukunan sa Internet, upang posible na makahanap ng mga mapagkukunan na tumutugma sa napaka-partikular na mga query. Ang kilusang ito ay tinawag na **Semantic Web**, at ito ay nakasalalay sa ilang mga konsepto: -- Isang espesyal na representasyon ng kaalaman na nakabatay sa **[description logics](https://en.wikipedia.org/wiki/Description_logic)** (DL). Katulad ito ng frame knowledge representation, dahil bumubuo ito ng hierarchy ng mga object na may mga properties, ngunit mayroon itong pormal na logical semantics at inference. Mayroong buong pamilya ng mga DL na nagbabalanse sa pagitan ng expressiveness at algorithmic complexity ng inference. -- Distributed knowledge representation, kung saan ang lahat ng mga konsepto ay kinakatawan ng isang global URI identifier, na ginagawang posible na lumikha ng mga hierarchy ng kaalaman na sumasaklaw sa internet. -- Isang pamilya ng mga XML-based na wika para sa paglalarawan ng kaalaman: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). +- Isang espesyal na representasyon ng kaalaman batay sa **[description logics](https://en.wikipedia.org/wiki/Description_logic)** (DL). Katulad ito ng frame knowledge representation, dahil bumubuo ito ng hierarchy ng mga bagay na may mga katangian, ngunit mayroon itong pormal na lohikal na semantika at inference. May buong pamilya ng DLs na nagpapanatili ng balanse sa pagitan ng pagka-makapangyarihan at algorithmic na kumplikadong inference. +- Distributed knowledge representation, kung saan lahat ng konsepto ay kinakatawan ng isang global URI identifier, na nagpapahintulot na lumikha ng mga hierarchy ng kaalaman na sumasaklaw sa internet. +- Isang pamilya ng mga lengguwaheng nakabase sa XML para sa paglalarawan ng kaalaman: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). -Ang pangunahing konsepto sa Semantic Web ay ang konsepto ng **Ontology**. Tumutukoy ito sa isang malinaw na espesipikasyon ng isang problem domain gamit ang pormal na representasyon ng kaalaman. Ang pinakasimpleng ontology ay maaaring isang hierarchy ng mga bagay sa problem domain, ngunit ang mas kumplikadong mga ontology ay may kasamang mga patakaran na maaaring gamitin para sa inference. +Isang pangunahing konsepto sa Semantic Web ay ang konsepto ng **Ontology**. Ito ay tumutukoy sa isang tiyak na pagtutukoy ng isang larangan ng problema gamit ang isang pormal na representasyon ng kaalaman. Ang pinakasimpleng ontology ay maaaring isang hierarkiya lamang ng mga bagay sa larangan ng problema, ngunit ang mas kumplikadong mga ontology ay maglalaman ng mga patakaran na maaaring gamitin para sa pangangatwiran. -Sa Semantic Web, lahat ng representasyon ay batay sa triplets. Ang bawat bagay at bawat relasyon ay natatanging kinikilala ng URI. Halimbawa, kung nais nating ipahayag ang katotohanan na ang AI Curriculum na ito ay binuo ni Dmitry Soshnikov noong Enero 1, 2022 - narito ang mga triplets na maaari nating gamitin: +Sa semantic web, lahat ng representasyon ay nakabase sa mga triplet. Bawat bagay at bawat relasyon ay natatanging kinikilala gamit ang URI. Halimbawa, kung nais nating ipahayag ang katotohanang ang AI Curriculum na ito ay ginawa ni Dmitry Soshnikov noong Enero 1, 2022 - narito ang mga triplet na maaari nating gamitin: - + ``` -http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007” +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com ``` -> ✅ Dito, ang `http://www.example.com/terms/creation-date` at `http://purl.org/dc/elements/1.1/creator` ay ilan sa mga kilala at pangkalahatang tinatanggap na URI para ipahayag ang mga konsepto ng *creator* at *creation date*. +> ✅ Dito ang `http://www.example.com/terms/creation-date` at `http://purl.org/dc/elements/1.1/creator` ay ilang kilala at unibersal na tinatanggap na mga URI upang ipahayag ang mga konsepto ng *creator* at *creation date*. -Sa mas kumplikadong kaso, kung nais nating tukuyin ang isang listahan ng mga creator, maaari tayong gumamit ng ilang data structures na tinukoy sa RDF. +Sa mas kumplikadong kaso, kung nais nating tukuyin ang isang listahan ng mga tagalikha, maaari tayong gumamit ng ilang mga estruktura ng datos na nilikha sa RDF. - + -> Ang mga diagram sa itaas ay mula kay [Dmitry Soshnikov](http://soshnikov.com) +> Mga diagram sa itaas ni [Dmitry Soshnikov](http://soshnikov.com) -Ang progreso ng pagbuo ng Semantic Web ay medyo bumagal dahil sa tagumpay ng mga search engine at mga teknolohiya ng natural language processing, na nagpapahintulot sa pagkuha ng structured data mula sa teksto. Gayunpaman, sa ilang mga lugar, may mga makabuluhang pagsisikap pa rin upang mapanatili ang mga ontology at mga knowledge base. Ilang proyekto na dapat banggitin: +Ang pag-unlad ng Semantic Web ay medyo naantala dahil sa tagumpay ng mga search engine at mga teknik sa natural language processing, na nagpapahintulot ng pagkuha ng istrukturadong datos mula sa teksto. Gayunpaman, sa ilang mga larangan ay may mga makabuluhang pagsisikap pa rin upang mapanatili ang mga ontology at mga base ng kaalaman. Ilang proyekto na karapat-dapat pansinin: -* [WikiData](https://wikidata.org/) ay isang koleksyon ng machine-readable knowledge bases na konektado sa Wikipedia. Karamihan sa data ay minina mula sa Wikipedia *InfoBoxes*, mga piraso ng structured content sa loob ng mga pahina ng Wikipedia. Maaari mong [i-query](https://query.wikidata.org/) ang WikiData gamit ang SPARQL, isang espesyal na query language para sa Semantic Web. Narito ang isang sample query na nagpapakita ng pinakapopular na kulay ng mata sa mga tao: +* [WikiData](https://wikidata.org/) ay isang koleksyon ng machine readable knowledge bases na kaugnay ng Wikipedia. Karamihan ng datos ay hinango mula sa Wikipedia *InfoBoxes*, mga piraso ng istrukturadong nilalaman sa loob ng mga pahina ng Wikipedia. Maaari mong [itanong](https://query.wikidata.org/) ang wikidata gamit ang SPARQL, isang espesyal na wika ng query para sa Semantic Web. Narito ang isang halimbawa ng query na nagpapakita ng mga pinakasikat na kulay ng mata ng mga tao: ```sparql #defaultView:BubbleChart @@ -208,45 +208,49 @@ GROUP BY ?eyeColorLabel * [DBpedia](https://www.dbpedia.org/) ay isa pang pagsisikap na katulad ng WikiData. -> ✅ Kung nais mong mag-eksperimento sa pagbuo ng sarili mong mga ontology, o pagbubukas ng mga umiiral na, mayroong mahusay na visual ontology editor na tinatawag na [Protégé](https://protege.stanford.edu/). I-download ito, o gamitin online. +> ✅ Kung nais mong mag-eksperimento sa paggawa ng sarili mong mga ontology, o buksan ang mga umiiral na, mayroong isang mahusay na visual ontology editor na tinatawag na [Protégé](https://protege.stanford.edu/). I-download ito, o gamitin ito online. - + *Web Protégé editor na bukas gamit ang Romanov Family ontology. Screenshot ni Dmitry Soshnikov* -## ✍️ Ehersisyo: Isang Family Ontology +## ✍️ Ehersisyo: Isang Ontology ng Pamilya -Tingnan ang [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para sa isang halimbawa ng paggamit ng mga teknik ng Semantic Web upang magbigay ng lohikal na pag-unawa sa mga relasyon sa pamilya. Kukunin natin ang isang family tree na kinakatawan sa karaniwang GEDCOM format at isang ontology ng mga relasyon sa pamilya upang bumuo ng isang graph ng lahat ng relasyon sa pamilya para sa ibinigay na hanay ng mga indibidwal. +Tingnan ang [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para sa isang halimbawa ng paggamit ng mga teknik ng Semantic Web upang magpaliwanag tungkol sa mga relasyon sa pamilya. Gagamitin natin ang isang family tree na kinakatawan sa karaniwang format na GEDCOM at isang ontology ng mga relasyon sa pamilya at gagawa ng isang graph ng lahat ng relasyon ng pamilya para sa isang ibinigay na set ng mga indibidwal. ## Microsoft Concept Graph -Sa karamihan ng mga kaso, ang mga ontology ay maingat na nilikha ng kamay. Gayunpaman, posible rin na **minahin** ang mga ontology mula sa unstructured data, halimbawa, mula sa mga natural language texts. +Sa karamihan ng mga kaso, ang mga ontology ay maingat na nilikha ng kamay. Gayunpaman, posible rin na **mahukay** ang mga ontology mula sa hindi istrukturadong datos, halimbawa, mula sa mga teksto sa natural na wika. -Isa sa mga ganitong pagsisikap ay ginawa ng Microsoft Research, na nagresulta sa [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). +Isang ganitong pagtatangka ay ginawa ng Microsoft Research, at nagresulta sa [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). -Ito ay isang malaking koleksyon ng mga entity na pinagsama-sama gamit ang `is-a` inheritance relationship. Pinapayagan nitong sagutin ang mga tanong tulad ng "Ano ang Microsoft?" - ang sagot ay maaaring tulad ng "isang kumpanya na may probability na 0.87, at isang brand na may probability na 0.75". +Ito ay isang malaking koleksyon ng mga entity na pinagsama gamit ang relasyon ng `is-a` na inhinyeriya. Pinapayagan nitong sagutin ang mga tanong tulad ng "Ano ang Microsoft?" - ang sagot ay tulad ng "isang kumpanya na may probabilidad na 0.87, at isang tatak na may probabilidad na 0.75". -Ang Graph ay magagamit bilang REST API, o bilang isang malaking downloadable text file na naglilista ng lahat ng entity pairs. +Ang Graph ay available bilang REST API, o bilang isang malaking downloadable na text file na naglalaman ng lahat ng pares ng entity. ## ✍️ Ehersisyo: Isang Concept Graph -Subukan ang [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook upang makita kung paano natin magagamit ang Microsoft Concept Graph upang i-grupo ang mga balita sa ilang kategorya. +Subukan ang [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) notebook upang makita kung paano natin magagamit ang Microsoft Concept Graph upang pangkatin ang mga balita sa ilang mga kategorya. ## Konklusyon -Sa kasalukuyan, ang AI ay madalas na itinuturing na kasingkahulugan ng *Machine Learning* o *Neural Networks*. Gayunpaman, ang tao ay nagpapakita rin ng malinaw na pangangatwiran, na isang bagay na kasalukuyang hindi hinahawakan ng neural networks. Sa mga totoong proyekto, ang malinaw na pangangatwiran ay ginagamit pa rin upang magsagawa ng mga gawain na nangangailangan ng paliwanag, o kakayahang baguhin ang pag-uugali ng sistema sa isang kontroladong paraan. +Sa kasalukuyan, madalas na itinuturing ang AI bilang sinalitang para sa *Machine Learning* o *Neural Networks*. Gayunpaman, nagpapakita rin ang tao ng tahasang pangangatwiran, na isang bagay na kasalukuyang hindi nalalampasan ng neural networks. Sa mga totoong proyekto, ang tahasang pangangatwiran ay ginagamit pa rin upang magawa ang mga gawain na nangangailangan ng mga paliwanag, o upang mabago ang kilos ng sistema sa kontroladong paraan. ## 🚀 Hamon -Sa Family Ontology notebook na konektado sa araling ito, may pagkakataon kang mag-eksperimento sa iba pang mga relasyon sa pamilya. Subukang tuklasin ang mga bagong koneksyon sa pagitan ng mga tao sa family tree. +Sa Family Ontology notebook na kaugnay ng leksyong ito, may pagkakataon upang mag-eksperimento sa iba pang mga relasyon sa pamilya. Subukang tuklasin ang mga bagong koneksyon sa pagitan ng mga tao sa family tree. ## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/4) -## Review at Pag-aaral sa Sarili +## Review & Self Study -Mag-research sa internet upang matuklasan ang mga lugar kung saan sinubukan ng tao na i-quantify at i-codify ang kaalaman. Tingnan ang Bloom's Taxonomy, at bumalik sa kasaysayan upang matutunan kung paano sinubukan ng tao na unawain ang kanilang mundo. Suriin ang gawa ni Linnaeus sa paglikha ng taxonomy ng mga organismo, at obserbahan ang paraan kung paano nilikha ni Dmitri Mendeleev ang paraan para maipaliwanag at ma-grupo ang mga chemical elements. Anong iba pang mga kawili-wiling halimbawa ang maaari mong makita? +Magsaliksik sa internet upang tuklasin ang mga larangan kung saan sinubukan ng mga tao na kwantipikahin at kodigo ang kaalaman. Tingnan ang Bloom's Taxonomy, at balikan ang kasaysayan upang matutunan kung paano sinubukang unawain ng mga tao ang kanilang mundo. Suriin ang gawa ni Linnaeus sa paglikha ng isang taxonomy ng mga organismo, at obserbahan ang paraan kung paano nilikha ni Dmitri Mendeleev ang isang paraan upang ilarawan at pangkatin ang mga elementong kemikal. Anong iba pang mga kawili-wiling halimbawa ang maaari mong makita? -**Takdang Aralin**: [Bumuo ng Ontology](assignment.md) +**Assignment**: [Build an Ontology](assignment.md) --- + +**Paunawa**: +Ang dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagamat aming pinagsisikapang maging tumpak ang pagsasalin, pakatandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na pangunahing sanggunian. Para sa mahahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito. + \ No newline at end of file