From f2a27efc74e71a8c8e3fc8c3ea73b1b32350038c Mon Sep 17 00:00:00 2001 From: "localizeflow[bot]" Date: Fri, 30 Jan 2026 02:22:28 +0000 Subject: [PATCH] chore(i18n): sync translations with latest source changes (chunk 1/1, 205 changes) --- translations/ms/.co-op-translator.json | 398 ++++++++++++++++++ translations/ms/AGENTS.md | 9 - translations/ms/README.md | 206 +++++---- translations/ms/SECURITY.md | 9 - translations/ms/etc/CODE_OF_CONDUCT.md | 9 - translations/ms/etc/CONTRIBUTING.md | 9 - translations/ms/etc/Mindmap.md | 9 - translations/ms/etc/SUPPORT.md | 9 - translations/ms/etc/TRANSLATIONS.md | 9 - translations/ms/etc/quiz-app/README.md | 9 - translations/ms/examples/README.md | 9 - .../ms/lessons/0-course-setup/for-teachers.md | 9 - .../ms/lessons/0-course-setup/how-to-run.md | 9 - .../ms/lessons/0-course-setup/setup.md | 9 - translations/ms/lessons/1-Intro/README.md | 9 - translations/ms/lessons/1-Intro/assignment.md | 9 - translations/ms/lessons/2-Symbolic/README.md | 17 +- .../ms/lessons/2-Symbolic/assignment.md | 9 - .../3-NeuralNetworks/03-Perceptron/README.md | 13 +- .../03-Perceptron/lab/README.md | 9 - .../04-OwnFramework/README.md | 11 +- .../04-OwnFramework/lab/README.md | 9 - .../3-NeuralNetworks/05-Frameworks/README.md | 9 - .../05-Frameworks/lab/README.md | 9 - .../ms/lessons/3-NeuralNetworks/README.md | 9 - .../4-ComputerVision/06-IntroCV/README.md | 11 +- .../4-ComputerVision/06-IntroCV/lab/README.md | 9 - .../07-ConvNets/CNN_Architectures.md | 13 +- .../4-ComputerVision/07-ConvNets/README.md | 11 +- .../07-ConvNets/lab/README.md | 9 - .../08-TransferLearning/README.md | 9 - .../08-TransferLearning/TrainingTricks.md | 9 - .../08-TransferLearning/lab/README.md | 9 - .../09-Autoencoders/README.md | 15 +- .../4-ComputerVision/10-GANs/README.md | 13 +- .../11-ObjectDetection/README.md | 9 - .../11-ObjectDetection/lab/README.md | 9 - .../12-Segmentation/README.md | 15 +- .../12-Segmentation/lab/README.md | 9 - .../ms/lessons/4-ComputerVision/README.md | 9 - .../ms/lessons/5-NLP/13-TextRep/README.md | 13 +- .../ms/lessons/5-NLP/13-TextRep/assignment.md | 9 - .../ms/lessons/5-NLP/14-Embeddings/README.md | 9 - .../lessons/5-NLP/14-Embeddings/assignment.md | 9 - .../5-NLP/15-LanguageModeling/README.md | 9 - .../5-NLP/15-LanguageModeling/lab/README.md | 9 - .../ms/lessons/5-NLP/16-RNN/README.md | 11 +- .../ms/lessons/5-NLP/16-RNN/assignment.md | 9 - .../5-NLP/17-GenerativeNetworks/README.md | 11 +- .../5-NLP/17-GenerativeNetworks/lab/README.md | 9 - .../lessons/5-NLP/18-Transformers/README.md | 11 +- .../18-Transformers/READMEtransformers.md | 112 ----- .../5-NLP/18-Transformers/assignment.md | 9 - .../ms/lessons/5-NLP/19-NER/README.md | 11 +- .../ms/lessons/5-NLP/19-NER/lab/README.md | 9 - .../ms/lessons/5-NLP/20-LangModels/README.md | 9 - .../5-NLP/20-LangModels/READMELargeLang.md | 56 --- translations/ms/lessons/5-NLP/README.md | 9 - .../6-Other/21-GeneticAlgorithms/README.md | 9 - .../ms/lessons/6-Other/22-DeepRL/README.md | 11 +- .../lessons/6-Other/22-DeepRL/lab/README.md | 9 - .../6-Other/23-MultiagentSystems/README.md | 11 +- .../23-MultiagentSystems/assignment.md | 9 - translations/ms/lessons/7-Ethics/README.md | 9 - translations/ms/lessons/README.md | 9 - .../lessons/X-Extras/X1-MultiModal/README.md | 9 - .../ms/lessons/sketchnotes/LICENSE.md | 9 - translations/ms/lessons/sketchnotes/README.md | 9 - translations/ms/troubleshoot.md | 9 - translations/sw/.co-op-translator.json | 398 ++++++++++++++++++ translations/sw/AGENTS.md | 9 - translations/sw/README.md | 171 ++++---- translations/sw/SECURITY.md | 9 - translations/sw/etc/CODE_OF_CONDUCT.md | 9 - translations/sw/etc/CONTRIBUTING.md | 9 - translations/sw/etc/Mindmap.md | 9 - translations/sw/etc/SUPPORT.md | 9 - translations/sw/etc/TRANSLATIONS.md | 9 - translations/sw/etc/quiz-app/README.md | 9 - translations/sw/examples/README.md | 9 - .../sw/lessons/0-course-setup/for-teachers.md | 9 - .../sw/lessons/0-course-setup/how-to-run.md | 9 - .../sw/lessons/0-course-setup/setup.md | 9 - translations/sw/lessons/1-Intro/README.md | 9 - translations/sw/lessons/1-Intro/assignment.md | 9 - translations/sw/lessons/2-Symbolic/README.md | 17 +- .../sw/lessons/2-Symbolic/assignment.md | 9 - .../3-NeuralNetworks/03-Perceptron/README.md | 13 +- .../03-Perceptron/lab/README.md | 9 - .../04-OwnFramework/README.md | 11 +- .../04-OwnFramework/lab/README.md | 9 - .../3-NeuralNetworks/05-Frameworks/README.md | 9 - .../05-Frameworks/lab/README.md | 9 - .../sw/lessons/3-NeuralNetworks/README.md | 9 - .../4-ComputerVision/06-IntroCV/README.md | 11 +- .../4-ComputerVision/06-IntroCV/lab/README.md | 9 - .../07-ConvNets/CNN_Architectures.md | 13 +- .../4-ComputerVision/07-ConvNets/README.md | 11 +- .../07-ConvNets/lab/README.md | 9 - .../08-TransferLearning/README.md | 9 - .../08-TransferLearning/TrainingTricks.md | 9 - .../08-TransferLearning/lab/README.md | 9 - .../09-Autoencoders/README.md | 15 +- .../4-ComputerVision/10-GANs/README.md | 13 +- .../11-ObjectDetection/README.md | 9 - .../11-ObjectDetection/lab/README.md | 9 - .../12-Segmentation/README.md | 15 +- .../12-Segmentation/lab/README.md | 9 - .../sw/lessons/4-ComputerVision/README.md | 9 - .../sw/lessons/5-NLP/13-TextRep/README.md | 13 +- .../sw/lessons/5-NLP/13-TextRep/assignment.md | 9 - .../sw/lessons/5-NLP/14-Embeddings/README.md | 9 - .../lessons/5-NLP/14-Embeddings/assignment.md | 9 - .../5-NLP/15-LanguageModeling/README.md | 9 - .../5-NLP/15-LanguageModeling/lab/README.md | 9 - .../sw/lessons/5-NLP/16-RNN/README.md | 11 +- .../sw/lessons/5-NLP/16-RNN/assignment.md | 9 - .../5-NLP/17-GenerativeNetworks/README.md | 11 +- .../5-NLP/17-GenerativeNetworks/lab/README.md | 9 - .../lessons/5-NLP/18-Transformers/README.md | 11 +- .../18-Transformers/READMEtransformers.md | 112 ----- .../5-NLP/18-Transformers/assignment.md | 9 - .../sw/lessons/5-NLP/19-NER/README.md | 11 +- .../sw/lessons/5-NLP/19-NER/lab/README.md | 9 - .../sw/lessons/5-NLP/20-LangModels/README.md | 9 - .../5-NLP/20-LangModels/READMELargeLang.md | 56 --- translations/sw/lessons/5-NLP/README.md | 9 - .../6-Other/21-GeneticAlgorithms/README.md | 9 - .../sw/lessons/6-Other/22-DeepRL/README.md | 11 +- .../lessons/6-Other/22-DeepRL/lab/README.md | 9 - .../6-Other/23-MultiagentSystems/README.md | 11 +- .../23-MultiagentSystems/assignment.md | 9 - translations/sw/lessons/7-Ethics/README.md | 9 - translations/sw/lessons/README.md | 9 - .../lessons/X-Extras/X1-MultiModal/README.md | 9 - .../sw/lessons/sketchnotes/LICENSE.md | 9 - translations/sw/lessons/sketchnotes/README.md | 9 - translations/sw/troubleshoot.md | 9 - translations/tl/.co-op-translator.json | 398 ++++++++++++++++++ translations/tl/AGENTS.md | 9 - translations/tl/README.md | 179 ++++---- translations/tl/SECURITY.md | 9 - translations/tl/etc/CODE_OF_CONDUCT.md | 9 - translations/tl/etc/CONTRIBUTING.md | 9 - translations/tl/etc/Mindmap.md | 9 - translations/tl/etc/SUPPORT.md | 9 - translations/tl/etc/TRANSLATIONS.md | 9 - translations/tl/etc/quiz-app/README.md | 9 - translations/tl/examples/README.md | 9 - .../tl/lessons/0-course-setup/for-teachers.md | 9 - .../tl/lessons/0-course-setup/how-to-run.md | 9 - .../tl/lessons/0-course-setup/setup.md | 9 - translations/tl/lessons/1-Intro/README.md | 9 - translations/tl/lessons/1-Intro/assignment.md | 9 - translations/tl/lessons/2-Symbolic/README.md | 17 +- .../tl/lessons/2-Symbolic/assignment.md | 9 - .../3-NeuralNetworks/03-Perceptron/README.md | 13 +- .../03-Perceptron/lab/README.md | 9 - .../04-OwnFramework/README.md | 11 +- .../04-OwnFramework/lab/README.md | 9 - .../3-NeuralNetworks/05-Frameworks/README.md | 9 - .../05-Frameworks/lab/README.md | 9 - .../tl/lessons/3-NeuralNetworks/README.md | 9 - .../4-ComputerVision/06-IntroCV/README.md | 11 +- .../4-ComputerVision/06-IntroCV/lab/README.md | 9 - .../07-ConvNets/CNN_Architectures.md | 13 +- .../4-ComputerVision/07-ConvNets/README.md | 11 +- .../07-ConvNets/lab/README.md | 9 - .../08-TransferLearning/README.md | 9 - .../08-TransferLearning/TrainingTricks.md | 9 - .../08-TransferLearning/lab/README.md | 9 - .../09-Autoencoders/README.md | 15 +- .../4-ComputerVision/10-GANs/README.md | 13 +- .../11-ObjectDetection/README.md | 9 - .../11-ObjectDetection/lab/README.md | 9 - .../12-Segmentation/README.md | 15 +- .../12-Segmentation/lab/README.md | 9 - .../tl/lessons/4-ComputerVision/README.md | 9 - .../tl/lessons/5-NLP/13-TextRep/README.md | 13 +- .../tl/lessons/5-NLP/13-TextRep/assignment.md | 9 - .../tl/lessons/5-NLP/14-Embeddings/README.md | 9 - .../lessons/5-NLP/14-Embeddings/assignment.md | 9 - .../5-NLP/15-LanguageModeling/README.md | 9 - .../5-NLP/15-LanguageModeling/lab/README.md | 9 - .../tl/lessons/5-NLP/16-RNN/README.md | 11 +- .../tl/lessons/5-NLP/16-RNN/assignment.md | 9 - .../5-NLP/17-GenerativeNetworks/README.md | 11 +- .../5-NLP/17-GenerativeNetworks/lab/README.md | 9 - .../lessons/5-NLP/18-Transformers/README.md | 11 +- .../5-NLP/18-Transformers/assignment.md | 9 - .../tl/lessons/5-NLP/19-NER/README.md | 11 +- .../tl/lessons/5-NLP/19-NER/lab/README.md | 9 - .../tl/lessons/5-NLP/20-LangModels/README.md | 9 - translations/tl/lessons/5-NLP/README.md | 9 - .../6-Other/21-GeneticAlgorithms/README.md | 9 - .../tl/lessons/6-Other/22-DeepRL/README.md | 11 +- .../lessons/6-Other/22-DeepRL/lab/README.md | 9 - .../6-Other/23-MultiagentSystems/README.md | 11 +- .../23-MultiagentSystems/assignment.md | 9 - translations/tl/lessons/7-Ethics/README.md | 9 - translations/tl/lessons/README.md | 9 - .../lessons/X-Extras/X1-MultiModal/README.md | 9 - .../tl/lessons/sketchnotes/LICENSE.md | 9 - translations/tl/lessons/sketchnotes/README.md | 9 - translations/tl/troubleshoot.md | 9 - 205 files changed, 1541 insertions(+), 2462 deletions(-) create mode 100644 translations/ms/.co-op-translator.json delete mode 100644 translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md delete mode 100644 translations/ms/lessons/5-NLP/20-LangModels/READMELargeLang.md create mode 100644 translations/sw/.co-op-translator.json delete mode 100644 translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md delete mode 100644 translations/sw/lessons/5-NLP/20-LangModels/READMELargeLang.md create mode 100644 translations/tl/.co-op-translator.json diff --git a/translations/ms/.co-op-translator.json b/translations/ms/.co-op-translator.json new file mode 100644 index 00000000..753ffb15 --- /dev/null +++ b/translations/ms/.co-op-translator.json @@ -0,0 +1,398 @@ +{ + "AGENTS.md": { + "original_hash": "6b11a37115944252ab3ed04e358d830d", + "translation_date": "2025-10-03T09:26:02+00:00", + "source_file": "AGENTS.md", + "language_code": "ms" + }, + "README.md": { + "original_hash": "1984fc89dd304a8a33ab5584691a99aa", + "translation_date": "2026-01-30T02:17:20+00:00", + "source_file": "README.md", + "language_code": "ms" + }, + "SECURITY.md": { + "original_hash": "a583f49d359c7ebba61433e4dfcd05a9", + "translation_date": "2025-08-29T11:44:21+00:00", + "source_file": "SECURITY.md", + "language_code": "ms" + }, + "etc/CODE_OF_CONDUCT.md": { + "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56", + "translation_date": "2025-08-29T12:02:57+00:00", + "source_file": "etc/CODE_OF_CONDUCT.md", + "language_code": "ms" + }, + "etc/CONTRIBUTING.md": { + "original_hash": "847a587aa1b83f4d00858183ff3ed18a", + "translation_date": "2025-08-29T12:02:48+00:00", + "source_file": "etc/CONTRIBUTING.md", + "language_code": "ms" + }, + "etc/Mindmap.md": { + "original_hash": "f2f88dbd2debd38e26149b27b1fd272d", + "translation_date": "2025-08-29T12:02:06+00:00", + "source_file": "etc/Mindmap.md", + "language_code": "ms" + }, + "etc/SUPPORT.md": { + "original_hash": "fdfc08baee91e402938a2b1f94fe0949", + "translation_date": "2025-08-29T12:01:29+00:00", + "source_file": "etc/SUPPORT.md", + "language_code": "ms" + }, + "etc/TRANSLATIONS.md": { + "original_hash": "62b3e3ad5182edb905eec649a87eeeb4", + "translation_date": "2025-08-29T12:02:39+00:00", + "source_file": "etc/TRANSLATIONS.md", + "language_code": "ms" + }, + "etc/quiz-app/README.md": { + "original_hash": "d699cf8509f74baa5b0b838de5cf0662", + "translation_date": "2025-08-29T12:03:02+00:00", + "source_file": "etc/quiz-app/README.md", + "language_code": "ms" + }, + "examples/README.md": { + "original_hash": "0d1babfdcbeb46525f2db3fbaaa54cd7", + "translation_date": "2025-10-03T11:32:48+00:00", + "source_file": "examples/README.md", + "language_code": "ms" + }, + "lessons/0-course-setup/for-teachers.md": { + "original_hash": "a094ef9927883de1cfcee51dbd143381", + "translation_date": "2025-08-29T11:46:15+00:00", + "source_file": "lessons/0-course-setup/for-teachers.md", + "language_code": "ms" + }, + "lessons/0-course-setup/how-to-run.md": { + "original_hash": "a4717bd9103b9f6cd84d534b83534689", + "translation_date": "2026-01-16T04:12:04+00:00", + "source_file": "lessons/0-course-setup/how-to-run.md", + "language_code": "ms" + }, + "lessons/0-course-setup/setup.md": { + "original_hash": "7b4e5b8956915870d0a0ed3cc5890042", + "translation_date": "2025-12-12T20:04:06+00:00", + "source_file": "lessons/0-course-setup/setup.md", + "language_code": "ms" + }, + "lessons/1-Intro/README.md": { + "original_hash": "f57e8aa46141fd220b16ffed8f11aec7", + "translation_date": "2025-11-18T21:50:31+00:00", + "source_file": "lessons/1-Intro/README.md", + "language_code": "ms" + }, + "lessons/1-Intro/assignment.md": { + "original_hash": "a334df77a82aaaf2a29c77065d3e481e", + "translation_date": "2025-11-18T21:51:38+00:00", + "source_file": "lessons/1-Intro/assignment.md", + "language_code": "ms" + }, + "lessons/2-Symbolic/README.md": { + "original_hash": "f9f06b266b8b2bfc6b8792ff2bb1bea4", + "translation_date": "2026-01-16T04:12:20+00:00", + "source_file": "lessons/2-Symbolic/README.md", + "language_code": "ms" + }, + "lessons/2-Symbolic/assignment.md": { + "original_hash": "a057a8604f3976c3e309884453f1fad0", + "translation_date": "2025-08-29T11:53:08+00:00", + "source_file": "lessons/2-Symbolic/assignment.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/03-Perceptron/README.md": { + "original_hash": "c34cbba802058b6fa267e1a294d4e510", + "translation_date": "2025-09-23T10:53:39+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md": { + "original_hash": "ba5d1eb353d20d3e7181066b3c424b99", + "translation_date": "2025-08-29T11:55:31+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/README.md": { + "original_hash": "789d6c3fb6fc7948a470b33078a5983a", + "translation_date": "2025-09-23T10:53:21+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md": { + "original_hash": "48fdd704d483e19bc3d7464074c9fcbe", + "translation_date": "2025-08-29T11:55:04+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/05-Frameworks/README.md": { + "original_hash": "ddd216f558a255260a9374008002c971", + "translation_date": "2025-09-23T10:53:58+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md": { + "original_hash": "e452d897efb9a89700f41021834cf6e5", + "translation_date": "2025-08-29T11:56:06+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md", + "language_code": "ms" + }, + "lessons/3-NeuralNetworks/README.md": { + "original_hash": "f862a99d88088163df12270e2f2ad6c3", + "translation_date": "2025-10-03T12:51:21+00:00", + "source_file": "lessons/3-NeuralNetworks/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/06-IntroCV/README.md": { + "original_hash": "feeca98225cb420afc89415f24f63d92", + "translation_date": "2025-09-23T10:49:13+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/06-IntroCV/lab/README.md": { + "original_hash": "3d53d6409f80970f7281a45dee35328a", + "translation_date": "2025-08-29T11:49:46+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/lab/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md": { + "original_hash": "53faab85adfcebd8c10bcd71dc2fa557", + "translation_date": "2025-09-23T10:48:36+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/07-ConvNets/README.md": { + "original_hash": "a560d5b845962cf33dc102266e409568", + "translation_date": "2025-09-23T10:48:21+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/07-ConvNets/lab/README.md": { + "original_hash": "b70fcf7fcee862990f848c679090943f", + "translation_date": "2025-10-03T14:56:27+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/08-TransferLearning/README.md": { + "original_hash": "178c0b5ee5395733eb18aec51e71a0a9", + "translation_date": "2025-09-23T10:48:52+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md": { + "original_hash": "ae074cd940fc2f4dc24fc07b66ccbd99", + "translation_date": "2025-08-29T11:48:38+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/08-TransferLearning/lab/README.md": { + "original_hash": "7765935c35fcee69b9fe2d0cfd6963e2", + "translation_date": "2025-08-29T11:49:04+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/lab/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/09-Autoencoders/README.md": { + "original_hash": "1b8d9e1b3a6f1daa864b1ff3dfc3076d", + "translation_date": "2025-09-23T10:50:16+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/10-GANs/README.md": { + "original_hash": "0ff65b4da07b23697235de2beb2a3c25", + "translation_date": "2025-09-23T10:51:00+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/11-ObjectDetection/README.md": { + "original_hash": "d76a7eda28de5210c8b1ba50a6216c69", + "translation_date": "2025-09-23T10:49:43+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md": { + "original_hash": "ad568d55ae65c856fe929fc2b278510a", + "translation_date": "2025-08-29T11:50:36+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/12-Segmentation/README.md": { + "original_hash": "6568aaae7e0e4afed4b5d74b5b223700", + "translation_date": "2025-09-23T10:50:44+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/12-Segmentation/lab/README.md": { + "original_hash": "365f0decfe0f47b460bbde8227c5009d", + "translation_date": "2025-08-29T11:51:33+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/lab/README.md", + "language_code": "ms" + }, + "lessons/4-ComputerVision/README.md": { + "original_hash": "58a52f000089c1d8906a4daa4ab1169b", + "translation_date": "2025-08-29T11:47:22+00:00", + "source_file": "lessons/4-ComputerVision/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/13-TextRep/README.md": { + "original_hash": "dbd3f73e4139f030ecb2e20387d70fee", + "translation_date": "2025-09-23T10:56:48+00:00", + "source_file": "lessons/5-NLP/13-TextRep/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/13-TextRep/assignment.md": { + "original_hash": "cdc1f2e631f055f3473b36d18e4760b3", + "translation_date": "2025-08-29T12:01:24+00:00", + "source_file": "lessons/5-NLP/13-TextRep/assignment.md", + "language_code": "ms" + }, + "lessons/5-NLP/14-Embeddings/README.md": { + "original_hash": "b708c9b85b833864c73c6281f1e6b96e", + "translation_date": "2025-09-23T10:56:27+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/14-Embeddings/assignment.md": { + "original_hash": "bc690ecf68b38d311cc9e12f3144a28c", + "translation_date": "2025-08-29T12:00:54+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/assignment.md", + "language_code": "ms" + }, + "lessons/5-NLP/15-LanguageModeling/README.md": { + "original_hash": "7ba20f54a5bfcd6521018cdfb17c7c57", + "translation_date": "2025-09-23T10:54:49+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/15-LanguageModeling/lab/README.md": { + "original_hash": "5130f01fdc5ebb83032b23d489027aac", + "translation_date": "2025-08-29T11:59:10+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/lab/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/16-RNN/README.md": { + "original_hash": "e2273cc150380a5e191903cea858f021", + "translation_date": "2025-09-23T10:55:59+00:00", + "source_file": "lessons/5-NLP/16-RNN/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/16-RNN/assignment.md": { + "original_hash": "47f7d3c6a5373543e051e4d1140ce898", + "translation_date": "2025-08-29T12:00:25+00:00", + "source_file": "lessons/5-NLP/16-RNN/assignment.md", + "language_code": "ms" + }, + "lessons/5-NLP/17-GenerativeNetworks/README.md": { + "original_hash": "51be6057374d01d70e07dd5ec88ebc0d", + "translation_date": "2025-09-23T10:54:23+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/17-GenerativeNetworks/lab/README.md": { + "original_hash": "439e12796197a90e7623d4c9c057b9c2", + "translation_date": "2025-08-29T11:58:52+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/lab/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/18-Transformers/README.md": { + "original_hash": "f335dfcb4a993920504c387973a36957", + "translation_date": "2025-09-23T10:54:57+00:00", + "source_file": "lessons/5-NLP/18-Transformers/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/18-Transformers/assignment.md": { + "original_hash": "177f3ea3995d725e6f9f5c66af16edcd", + "translation_date": "2025-08-29T11:59:17+00:00", + "source_file": "lessons/5-NLP/18-Transformers/assignment.md", + "language_code": "ms" + }, + "lessons/5-NLP/19-NER/README.md": { + "original_hash": "6522312ff835796ca34136a9462fafb2", + "translation_date": "2025-09-23T10:55:44+00:00", + "source_file": "lessons/5-NLP/19-NER/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/19-NER/lab/README.md": { + "original_hash": "032bda5068f543d6c1fcb30c34231461", + "translation_date": "2025-08-29T11:59:43+00:00", + "source_file": "lessons/5-NLP/19-NER/lab/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/20-LangModels/README.md": { + "original_hash": "97836d30a6bec736f8e3b4411c572bc2", + "translation_date": "2025-09-23T10:55:29+00:00", + "source_file": "lessons/5-NLP/20-LangModels/README.md", + "language_code": "ms" + }, + "lessons/5-NLP/README.md": { + "original_hash": "8ef02a9318257ea140ed3ed74442096d", + "translation_date": "2025-08-29T11:58:10+00:00", + "source_file": "lessons/5-NLP/README.md", + "language_code": "ms" + }, + "lessons/6-Other/21-GeneticAlgorithms/README.md": { + "original_hash": "6bbd632dfe6c62e5f66bb51fd78c174a", + "translation_date": "2025-09-23T10:46:49+00:00", + "source_file": "lessons/6-Other/21-GeneticAlgorithms/README.md", + "language_code": "ms" + }, + "lessons/6-Other/22-DeepRL/README.md": { + "original_hash": "04395657fc01648f8f70484d0e55ab67", + "translation_date": "2025-09-23T10:47:45+00:00", + "source_file": "lessons/6-Other/22-DeepRL/README.md", + "language_code": "ms" + }, + "lessons/6-Other/22-DeepRL/lab/README.md": { + "original_hash": "7bd8dc72040e98e35e7225e34058cd4e", + "translation_date": "2025-08-29T11:46:08+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/README.md", + "language_code": "ms" + }, + "lessons/6-Other/23-MultiagentSystems/README.md": { + "original_hash": "38a1185ae3d54b180378bbd71ae3ef16", + "translation_date": "2025-09-23T10:47:04+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/README.md", + "language_code": "ms" + }, + "lessons/6-Other/23-MultiagentSystems/assignment.md": { + "original_hash": "cf654ca60c7f86c8dad28596fb42994b", + "translation_date": "2025-08-29T11:45:37+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/assignment.md", + "language_code": "ms" + }, + "lessons/7-Ethics/README.md": { + "original_hash": "437c988596e751072e41a5aad3fcc5d9", + "translation_date": "2025-08-29T11:53:14+00:00", + "source_file": "lessons/7-Ethics/README.md", + "language_code": "ms" + }, + "lessons/README.md": { + "original_hash": "5fef1a0b22498d7188959e2a2cb08af7", + "translation_date": "2025-08-29T11:44:33+00:00", + "source_file": "lessons/README.md", + "language_code": "ms" + }, + "lessons/X-Extras/X1-MultiModal/README.md": { + "original_hash": "9c592c26aca16ca085d268c732284187", + "translation_date": "2025-08-29T11:46:59+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/README.md", + "language_code": "ms" + }, + "lessons/sketchnotes/LICENSE.md": { + "original_hash": "45ab63a2cd8f5faef6c9b150618837a4", + "translation_date": "2025-08-29T11:57:20+00:00", + "source_file": "lessons/sketchnotes/LICENSE.md", + "language_code": "ms" + }, + "lessons/sketchnotes/README.md": { + "original_hash": "050b8bddebafba55b129414e6ab096ab", + "translation_date": "2025-08-29T11:56:13+00:00", + "source_file": "lessons/sketchnotes/README.md", + "language_code": "ms" + }, + "troubleshoot.md": { + "original_hash": "8d9c5a4a7c7798d699672a22cb7fea86", + "translation_date": "2025-10-03T09:48:12+00:00", + "source_file": "troubleshoot.md", + "language_code": "ms" + } +} \ No newline at end of file diff --git a/translations/ms/AGENTS.md b/translations/ms/AGENTS.md index 5135038d..4d4c9430 100644 --- a/translations/ms/AGENTS.md +++ b/translations/ms/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Gambaran Projek diff --git a/translations/ms/README.md b/translations/ms/README.md index d9068ed2..c1bd6bc3 100644 --- a/translations/ms/README.md +++ b/translations/ms/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -23,11 +14,11 @@ CO_OP_TRANSLATOR_METADATA: # Kecerdasan Buatan untuk Pemula - Kurikulum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/ms/ai-overview.0857791951d19500.webp)| +|![Sketchnote oleh @girlie_mac https://twitter.com/girlie_mac](../../translated_images/ms/ai-overview.0857791951d19500.webp)| |:---:| -| AI For Beginners - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI Untuk Pemula - _Sketchnote oleh [@girlie_mac](https://twitter.com/girlie_mac)_ | -Terokai dunia **Kecerdasan Buatan** (AI) dengan kurikulum 12 minggu, 24 pelajaran kami! Ia termasuk pelajaran praktikal, kuiz, dan makmal. Kurikulum ini mesra pemula dan meliputi alat seperti TensorFlow dan PyTorch, serta etika dalam AI +Terokai dunia **Kecerdasan Buatan** (AI) dengan kurikulum 12-minggu, 24-pelajaran kami! Ia termasuk pelajaran praktikal, kuiz, dan makmal. Kurikulum ini mesra pemula dan merangkumi alat seperti TensorFlow dan PyTorch, serta etika dalam AI ### 🌐 Sokongan Pelbagai Bahasa @@ -35,199 +26,200 @@ Terokai dunia **Kecerdasan Buatan** (AI) dengan kurikulum 12 minggu, 24 pelajara #### Disokong melalui GitHub Action (Automatik & Sentiasa Dikemas Kini) -[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](./README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-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](./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)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) > **Lebih Suka Klon Secara Tempatan?** -> Repositori ini merangkumi 50+ terjemahan bahasa yang meningkatkan saiz muat turun dengan ketara. Untuk mengklon tanpa terjemahan, gunakan sparse checkout: +> Repositori ini merangkumi lebih 50 terjemahan bahasa yang secara signifikan meningkatkan saiz muat turun. Untuk klon tanpa terjemahan, gunakan 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' > ``` -> Ini memberikan anda segala yang anda perlukan untuk menamatkan kursus dengan muat turun yang lebih pantas. +> Ini memberikan anda semua yang anda perlukan untuk menyelesaikan kursus dengan muat turun yang lebih pantas. -**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)** +**Jika anda ingin agar bahasa terjemahan tambahan disokong disenaraikan [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 anda akan pelajari -**[Peta Fikiran Kursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Peta Minda Kursus](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** 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 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 yang berbeza untuk 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 merupakan teras AI moden. Kami akan menggambarkan konsep di sebalik topik penting ini menggunakan kod dalam dua kerangka kerja paling popular - [TensorFlow](http://Tensorflow.org) dan [PyTorch](http://pytorch.org). +* **Senibina Neural** untuk bekerja dengan imej dan teks. Kami akan merangkumi model terkini tetapi mungkin agak kekurangan dalam tahap terkini. * Pendekatan AI yang kurang popular, seperti **Algoritma Genetik** dan **Sistem Multi-Ejen**. -Apa yang tidak akan kami liputi dalam kurikulum ini: +Apa yang tidak akan kami bahas dalam kurikulum ini: -> [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) +> [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) -* 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/). +* Kes perniagaan penggunaan **AI dalam Perniagaan**. Pertimbangkan untuk 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), yang 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 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/). +* 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), **[Generative AI 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. +* Kerangka kerja **Cloud 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 [Bina dan operasi 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 [Bina dan Operasi 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 Bot**. Terdapat laluan pembelajaran [Cipta penyelesaian AI perbualan](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) yang berasingan, dan anda juga boleh merujuk kepada [pos blog ini](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) untuk maklumat lanjut. +* **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 tersedia dalam talian di [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -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). +Untuk pengenalan ringan kepada topik _AI dalam Awan_ anda boleh mempertimbangkan untuk 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 | -| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | -| 0 | [Persediaan Kursus](./lessons/0-course-setup/setup.md) | [Persiapkan Persekitaran Pembangunan Anda](./lessons/0-course-setup/how-to-run.md) | | +| | Pautan Pelajaran | PyTorch/Keras/TensorFlow | Makmal | +| :-: | :----------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | +| 0 | [Penyiapan Kursus](./lessons/0-course-setup/setup.md) | [Sediakan 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) | | +| 02 | [Perwakilan Pengetahuan dan Sistem Pakar](./lessons/2-Symbolic/README.md) | [Sistem Pakar](./lessons/2-Symbolic/Animals.ipynb) / [Ontology](./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 | [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) | +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Perceptron Berlapis dan Mewujudkan Rangka Kerja kami sendiri](./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 | [Pengenalan kepada Rangka 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) | [Lab](./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 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) | | +| 06 | [Pengenalan kepada Penglihatan Komputer. 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 | [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) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Rangkaian Pra-latih dan Pembelajaran Pemindahan](./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) | [Lab](./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) | | | 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) | | +| 11 | [Pengesanan Objek](./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 | [Segementasi 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 | [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 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) | +| 14 | [Embedding 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 embedding 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) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Rangkaian Neural Rekuren](./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 Rekuren 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) | [Lab](./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 | [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) | +| 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) | [Lab](./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 Agen Berbilang](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| 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) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Sistem Agen Pelbagai](./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) | | +| 24 | [Etika AI dan AI yang Bertanggungjawab](./lessons/7-Ethics/README.md) | [Microsoft Learn: Prinsip AI yang Bertanggungjawab](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | | IX | **Tambahan** | | | -| 25 | [Rangkaian Multi-Modal, CLIP dan VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| 25 | [Rangkaian Multi-Mod, CLIP dan VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Setiap pelajaran mengandungi -* Bahan pra-bacaan -* 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. +* Bahan pra-pembacaan +* Jupyter Notebook yang boleh dilaksanakan, yang sering khusus untuk rangka kerja (**PyTorch** atau **TensorFlow**). Notebook yang boleh dilaksanakan juga mengandungi banyak bahan teori, jadi untuk memahami topik anda perlu melalui sekurang-kurangnya satu versi notebook (sama ada PyTorch atau TensorFlow). +* **Makmal** tersedia untuk beberapa topik, yang memberi peluang kepada anda untuk mencuba menerapkan bahan yang telah anda pelajari pada 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. ## Memulakan -### 🎯 Baru dalam AI? Mula di Sini! +### 🎯 Baru kepada AI? Bermula di sini! -Jika anda benar-benar baru dalam AI dan mahukan contoh pantas dan praktikal, lihat [**Contoh Mesra Pemula**](./examples/README.md) kami! Contoh ini termasuk: +Jika anda benar-benar baru dalam AI dan mahukan contoh praktikal yang cepat, lihat [**Contoh Mesra Pemula**](./examples/README.md) kami! Ini termasuk: -- 🌟 **Hello AI World** - Program AI pertama anda (pengenalan pola) -- 🧠 **Rangkaian Neural Mudah** - Membina rangkaian neural dari awal -- πŸ–ΌοΈ **Pengelasan Imej** - Mengelas imej dengan komen terperinci -- πŸ’¬ **Sentimen Teks** - Analisis teks positif/negatif +- 🌟 **Hello AI World** - Program AI pertama anda (pengenalan corak) +- 🧠 **Rangkaian Neural Mudah** - Bina rangkaian neural dari mula -Contoh-contoh ini direka untuk membantu anda memahami konsep AI sebelum meneroka keseluruhan kurikulum. +- πŸ–ΌοΈ **Pengelasan Imej** - Klasifikasikan imej dengan ulasan terperinci +- πŸ’¬ **Sentimen Teks** - Analisis teks positif/negatif + +Contoh-contoh ini direka untuk membantu anda memahami konsep AI sebelum menyelami kurikulum penuh. ### πŸ“š Persediaan Kurikulum Penuh -- 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) +- 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 [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 penjuru kanan atas halaman ini. +Fork Repositori: Klik butang "Fork" di penjuru kanan atas halaman ini. -Clone Repositori: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Clone Repositori: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Jangan lupa untuk bintang (🌟) repo ini supaya mudah dicari kemudian. +Jangan lupa untuk membintangi (🌟) repo ini supaya anda dapat mencarinya dengan lebih mudah kemudian. -## Temui Pelajar Lain +## Berjumpa dengan Pelajar Lain -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. +Sertai [pelayan Discord AI rasmi kami](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) untuk berjumpa dan berhubung dengan pelajar lain yang mengikuti kursus ini serta mendapatkan sokongan. -Jika anda mempunyai maklum balas produk atau soalan semasa membina, lawati [Forum Pembangun Azure AI Foundry](https://aka.ms/foundry/forum) +Sekiranya anda mempunyai maklum balas produk atau soalan semasa membina, kunjungi [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Kuiz -> **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. +> **Nota tentang kuiz**: Semua kuiz terkandung dalam folder Quiz-app di etc\quiz-app, atau [Dalam Talian Di Sini](https://ff-quizzes.netlify.app/) Kuiz-kuiz tersebut dipautkan dari dalam pelajaran, aplikasi kuiz boleh dijalankan secara tempatan atau dideploy ke Azure; ikut arahan dalam folder `quiz-app`. Ia sedang diperingkatkan untuk pelokalan. -## Meminta Bantuan +## Bantuan Diperlukan -Adakah anda mempunyai cadangan atau telah menjumpai kesalahan ejaan atau kod? Buat isu atau buat pull request. +Ada cadangan atau menjumpai kesilapan ejaan atau kod? Buka isu atau buat pull request. -## Ucapan Terima Kasih Istimewa +## Terima Kasih Khas -* **✍️ Penulis Utama:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **πŸ”₯ Penyunting:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **βœ… Pencipta Kuiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **πŸ™ Penyumbang Teras:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Pengarang Utama:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **πŸ”₯ Penyunting:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **βœ… Pencipta Kuiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **πŸ™ Penyumbang Teras:** [Evgenii Pishchik](https://github.com/Pe4enIks) ## Kurikulum Lain Pasukan kami menghasilkan kurikulum lain! Semak: -### LangChain -[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) -[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) +### 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) --- -### 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) +### 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) --- -### Siri AI Generatif -[![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) +### 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) --- -### Pembelajaran Teras -[![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) +### 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) --- -### Siri Copilot -[![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) +### 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) ## 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 ilmu dikongsi secara bebas. +Jika anda tersekat atau mempunyai soalan tentang membina aplikasi AI, sertai pelajar lain dan pembangun berpengalaman dalam perbincangan mengenai MCP. Ia adalah komuniti yang menyokong di mana soalan dialu-alukan dan pengetahuan dikongsi secara bebas. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -Jika anda mempunyai maklum balas produk atau kesilapan semasa membina, lawati: +Jika anda mempunyai maklum balas produk atau ralat semasa membina, lawati: [![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 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. +**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 maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang sahih. Untuk maklumat penting, terjemahan profesional oleh manusia adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau penafsiran yang salah yang timbul daripada penggunaan terjemahan ini. \ No newline at end of file diff --git a/translations/ms/SECURITY.md b/translations/ms/SECURITY.md index 6d1fcbf9..cd090f2c 100644 --- a/translations/ms/SECURITY.md +++ b/translations/ms/SECURITY.md @@ -1,12 +1,3 @@ - ## Keselamatan Microsoft mengambil serius keselamatan produk dan perkhidmatan perisian kami, termasuk semua repositori kod sumber yang diuruskan melalui organisasi GitHub kami, yang merangkumi [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), dan [organisasi GitHub kami](https://opensource.microsoft.com/). diff --git a/translations/ms/etc/CODE_OF_CONDUCT.md b/translations/ms/etc/CODE_OF_CONDUCT.md index 83f2cac5..ab45940b 100644 --- a/translations/ms/etc/CODE_OF_CONDUCT.md +++ b/translations/ms/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Kod Etika Sumber Terbuka Microsoft Projek ini telah mengguna pakai [Kod Etika Sumber Terbuka Microsoft](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/ms/etc/CONTRIBUTING.md b/translations/ms/etc/CONTRIBUTING.md index 158cb01a..4538edc1 100644 --- a/translations/ms/etc/CONTRIBUTING.md +++ b/translations/ms/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Menyumbang Projek ini mengalu-alukan sumbangan dan cadangan. Kebanyakan sumbangan memerlukan anda diff --git a/translations/ms/etc/Mindmap.md b/translations/ms/etc/Mindmap.md index 405bb962..1b53f909 100644 --- a/translations/ms/etc/Mindmap.md +++ b/translations/ms/etc/Mindmap.md @@ -1,12 +1,3 @@ - # AI ## [Pengenalan kepada AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/ms/etc/SUPPORT.md b/translations/ms/etc/SUPPORT.md index 5574c638..85dd0ec2 100644 --- a/translations/ms/etc/SUPPORT.md +++ b/translations/ms/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # Sokongan ## Cara melaporkan isu dan mendapatkan bantuan diff --git a/translations/ms/etc/TRANSLATIONS.md b/translations/ms/etc/TRANSLATIONS.md index 7a5c0522..dd1576c7 100644 --- a/translations/ms/etc/TRANSLATIONS.md +++ b/translations/ms/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # Menyumbang dengan menterjemah pelajaran Kami mengalu-alukan terjemahan untuk pelajaran dalam kurikulum ini! diff --git a/translations/ms/etc/quiz-app/README.md b/translations/ms/etc/quiz-app/README.md index 2504c153..cb455ff5 100644 --- a/translations/ms/etc/quiz-app/README.md +++ b/translations/ms/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # Kuiz Kuiz-kuiz ini adalah kuiz sebelum dan selepas kuliah untuk kurikulum AI di https://aka.ms/ai-beginners diff --git a/translations/ms/examples/README.md b/translations/ms/examples/README.md index b1d72a54..eacc111e 100644 --- a/translations/ms/examples/README.md +++ b/translations/ms/examples/README.md @@ -1,12 +1,3 @@ - # Contoh AI Mesra Pemula Selamat datang! Direktori ini mengandungi contoh mudah dan berdiri sendiri untuk membantu anda memulakan dengan AI dan pembelajaran mesin. Setiap contoh direka untuk mesra pemula dengan komen terperinci dan penjelasan langkah demi langkah. diff --git a/translations/ms/lessons/0-course-setup/for-teachers.md b/translations/ms/lessons/0-course-setup/for-teachers.md index aaf8bf25..2e317cfa 100644 --- a/translations/ms/lessons/0-course-setup/for-teachers.md +++ b/translations/ms/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # Untuk Pendidik Adakah anda ingin menggunakan kurikulum ini di dalam kelas anda? Jangan ragu untuk mencubanya! 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 bb0c46e1..96ee57b7 100644 --- a/translations/ms/lessons/0-course-setup/how-to-run.md +++ b/translations/ms/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # Cara 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: diff --git a/translations/ms/lessons/0-course-setup/setup.md b/translations/ms/lessons/0-course-setup/setup.md index 8bd30668..c7c6fb5a 100644 --- a/translations/ms/lessons/0-course-setup/setup.md +++ b/translations/ms/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # Memulakan dengan Kurikulum ini ## Adakah anda seorang pelajar? diff --git a/translations/ms/lessons/1-Intro/README.md b/translations/ms/lessons/1-Intro/README.md index 7a04ef46..2a5d3b42 100644 --- a/translations/ms/lessons/1-Intro/README.md +++ b/translations/ms/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # Pengenalan kepada AI ![Ringkasan kandungan Pengenalan AI dalam bentuk doodle](../../../../translated_images/ms/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/ms/lessons/1-Intro/assignment.md b/translations/ms/lessons/1-Intro/assignment.md index e294f1e8..b7f518a3 100644 --- a/translations/ms/lessons/1-Intro/assignment.md +++ b/translations/ms/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam Permainan adalah satu bidang yang telah banyak dipengaruhi oleh perkembangan AI dan ML. Dalam tugasan ini, tulis sebuah kertas pendek mengenai permainan yang anda suka yang telah dipengaruhi oleh evolusi AI. Ia haruslah permainan yang cukup lama untuk telah dipengaruhi oleh beberapa jenis sistem pemprosesan komputer. Contoh yang baik adalah Catur atau Go, tetapi juga lihat permainan video seperti pong atau Pac-Man. Tulis sebuah esei yang membincangkan masa lalu, masa kini, dan masa depan AI dalam permainan tersebut. diff --git a/translations/ms/lessons/2-Symbolic/README.md b/translations/ms/lessons/2-Symbolic/README.md index 133a4896..f82cd890 100644 --- a/translations/ms/lessons/2-Symbolic/README.md +++ b/translations/ms/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # 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) @@ -41,7 +32,7 @@ Kebiasaannya, kita tidak mendefinisikan pengetahuan secara ketat, tetapi kita me 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) @@ -94,7 +85,7 @@ Sintaks Blok | Penjorokan | | | 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 @@ -106,7 +97,7 @@ Sistem pakar dibina seperti sistem penalaran manusia, yang mengandungi **memori 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) diff --git a/translations/ms/lessons/2-Symbolic/assignment.md b/translations/ms/lessons/2-Symbolic/assignment.md index b8f925c9..c867b86a 100644 --- a/translations/ms/lessons/2-Symbolic/assignment.md +++ b/translations/ms/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # Membina Ontologi Membina pangkalan pengetahuan adalah tentang mengkategorikan model yang mewakili fakta mengenai sesuatu topik. Pilih satu topik - seperti seseorang, tempat, atau benda - dan kemudian bina model bagi topik tersebut. Gunakan beberapa teknik dan strategi pembinaan model yang diterangkan dalam pelajaran ini. Contohnya adalah mencipta ontologi bagi ruang tamu dengan perabot, lampu, dan sebagainya. Bagaimana ruang tamu berbeza daripada dapur? Bilik mandi? Bagaimana anda tahu ia adalah ruang tamu dan bukan ruang makan? Gunakan [ProtΓ©gΓ©](https://protege.stanford.edu/) untuk membina ontologi anda. diff --git a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/README.md index eac2abab..abbfc794 100644 --- a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # Pengenalan kepada Rangkaian Neural: Perceptron ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ Salah satu usaha pertama untuk melaksanakan sesuatu yang serupa dengan rangkaian | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > Imej [dari Wikipedia](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +25,7 @@ y(x) = f(wTx) di mana f adalah fungsi pengaktifan langkah - + ## Melatih Perceptron diff --git a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index 5c5a9cb5..dde5ca23 100644 --- a/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Pengelasan Pelbagai Kelas dengan Perceptron Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 12df2e29..62a036fe 100644 --- a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # Pengenalan kepada Rangkaian Neural. Multi-Layered Perceptron Dalam bahagian sebelumnya, anda telah mempelajari model rangkaian neural yang paling mudah - perceptron satu lapisan, iaitu model klasifikasi linear dua kelas. @@ -65,7 +56,7 @@ Algoritma penurunan gradien akan kekal sama, tetapi ia akan menjadi lebih sukar Perhatikan bahawa bahagian paling kiri semua ungkapan tersebut adalah sama, dan oleh itu kita boleh mengira derivatif dengan berkesan bermula daripada fungsi kehilangan dan bergerak "ke belakang" melalui graf pengiraan. Oleh itu, kaedah latihan perceptron berbilang lapisan dipanggil **backpropagation**, atau 'backprop'. -compute graph +compute graph > TODO: rujukan imej diff --git a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index f82f0af5..07b4d1bc 100644 --- a/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # Pengelasan MNIST dengan Rangka Kerja Kita Sendiri Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md index 32eb8a0a..4e5bdaf8 100644 --- a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Rangka Kerja Rangkaian Neural Seperti yang telah kita pelajari, untuk melatih rangkaian neural dengan cekap, kita perlu melakukan dua perkara: diff --git a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index c54b1334..bcab99cf 100644 --- a/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # Pengelasan dengan PyTorch/TensorFlow Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/3-NeuralNetworks/README.md b/translations/ms/lessons/3-NeuralNetworks/README.md index 3f796a16..f8a4c34f 100644 --- a/translations/ms/lessons/3-NeuralNetworks/README.md +++ b/translations/ms/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # Pengenalan kepada Rangkaian Neural ![Ringkasan kandungan Pengenalan Rangkaian Neural dalam bentuk doodle](../../../../translated_images/ms/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md index 18caaf63..f5b48f98 100644 --- a/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/ms/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # Pengenalan kepada Penglihatan Komputer [Penglihatan Komputer](https://wikipedia.org/wiki/Computer_vision) adalah satu bidang yang bertujuan untuk membolehkan komputer memahami imej digital pada tahap tinggi. Definisi ini agak luas kerana *memahami* boleh membawa pelbagai maksud, termasuk mencari objek dalam gambar (**pengesanan objek**), memahami apa yang sedang berlaku (**pengesanan peristiwa**), menerangkan gambar dalam bentuk teks, atau membina semula pemandangan dalam 3D. Terdapat juga tugas-tugas khas berkaitan imej manusia: anggaran umur dan emosi, pengesanan dan pengenalan wajah, serta anggaran pose 3D, antara lain. @@ -115,7 +106,7 @@ Baca lebih lanjut tentang optical flow [dalam tutorial hebat ini](https://learno Dalam makmal ini, anda akan mengambil video dengan gerakan mudah, dan matlamat anda adalah untuk mengekstrak pergerakan atas/bawah/kiri/kanan menggunakan optical flow. -Bingkai Pergerakan Tapak Tangan +Bingkai Pergerakan Tapak Tangan --- diff --git a/translations/ms/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/ms/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 1b02d53b..c1dfd386 100644 --- a/translations/ms/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Mengesan Pergerakan menggunakan Optical Flow Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://aka.ms/ai-beginners). diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 23c5de5e..f687d5a2 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # Senibina CNN Terkenal ### VGG-16 @@ -25,7 +16,7 @@ Seperti yang anda lihat, VGG mengikuti senibina piramid tradisional, iaitu uruta ResNet adalah keluarga model yang dicadangkan oleh Microsoft Research pada tahun 2015. Idea utama ResNet adalah menggunakan **blok residual**: - + > Imej daripada [kertas ini](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +28,7 @@ Anda juga boleh menganggap rangkaian ini sebagai mampu menyesuaikan kerumitannya Senibina Google Inception membawa idea ini satu langkah lebih jauh, dan membina setiap lapisan rangkaian sebagai gabungan beberapa laluan yang berbeza: - + > Imej daripada [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md index f2144572..6d586267 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Rangkaian Neural Konvolusi Kita telah melihat sebelum ini bahawa rangkaian neural sangat baik dalam mengendalikan imej, malah perceptron satu lapisan mampu mengenali angka tulisan tangan daripada dataset MNIST dengan ketepatan yang munasabah. Walau bagaimanapun, dataset MNIST sangat istimewa, di mana semua angka berada di tengah imej, menjadikan tugas ini lebih mudah. @@ -24,7 +15,7 @@ Untuk mengekstrak pola, kita akan menggunakan konsep **penapis konvolusi**. Sepe Sebagai contoh, jika kita menggunakan penapis tepi menegak dan mendatar 3x3 pada angka MNIST, kita boleh mendapatkan sorotan (contohnya, nilai tinggi) di mana terdapat tepi menegak dan mendatar dalam imej asal kita. Oleh itu, kedua-dua penapis ini boleh digunakan untuk "mencari" tepi. Begitu juga, kita boleh mereka bentuk penapis yang berbeza untuk mencari pola tahap rendah yang lain: - + > Imej daripada [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md index ed0b513f..0b3850a0 100644 --- a/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # Pengelasan Wajah Haiwan Peliharaan Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md index 3c63f087..0b0591c5 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Rangkaian Pra-latih dan Pembelajaran Pemindahan Melatih CNN boleh mengambil masa yang lama, dan memerlukan banyak data untuk tugas tersebut. Walau bagaimanapun, sebahagian besar masa dihabiskan untuk mempelajari penapis tahap rendah terbaik yang boleh digunakan oleh rangkaian untuk mengekstrak corak daripada imej. Satu persoalan semula jadi timbul - bolehkah kita menggunakan rangkaian neural yang telah dilatih pada satu dataset dan menyesuaikannya untuk mengklasifikasikan imej yang berbeza tanpa memerlukan proses latihan penuh? diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index 0649175e..dd4aa3e6 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # Helah Latihan Pembelajaran Mendalam Apabila rangkaian neural menjadi semakin mendalam, proses latihannya menjadi semakin mencabar. Salah satu masalah utama ialah apa yang dipanggil [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) atau [exploding gradients](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Pos ini](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) memberikan pengenalan yang baik tentang masalah tersebut. diff --git a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index f6a4e84d..68c0a37e 100644 --- a/translations/ms/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Pengelasan Haiwan Peliharaan Oxford menggunakan Pembelajaran Pindahan Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md index d0f5eaf6..e310493c 100644 --- a/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/ms/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # Autoencoders Semasa melatih CNN, salah satu masalahnya ialah kita memerlukan banyak data yang berlabel. Dalam kes klasifikasi imej, kita perlu memisahkan imej ke dalam kelas yang berbeza, yang memerlukan usaha manual. @@ -46,7 +37,7 @@ Ringkasnya: * Kita mengambil sampel vektor `sample` daripada taburan N(zmean,exp(zlog\_sigma)) * Penyahkod cuba menyahkod imej asal menggunakan `sample` sebagai vektor input - + > Imej daripada [blog post ini](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) oleh Isaak Dykeman @@ -57,13 +48,13 @@ Variational auto-encoders menggunakan fungsi kehilangan kompleks yang terdiri da Satu kelebihan penting VAE ialah ia membolehkan kita menjana imej baharu dengan agak mudah, kerana kita tahu taburan mana yang perlu diambil sampel vektor laten. Sebagai contoh, jika kita melatih VAE dengan vektor laten 2D pada MNIST, kita boleh mengubah komponen vektor laten untuk mendapatkan digit yang berbeza: -vaemnist +vaemnist > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) Perhatikan bagaimana imej bercampur antara satu sama lain, apabila kita mula mendapatkan vektor laten daripada bahagian yang berbeza dalam ruang parameter laten. Kita juga boleh memvisualisasikan ruang ini dalam 2D: -vaemnist cluster +vaemnist cluster > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ms/lessons/4-ComputerVision/10-GANs/README.md b/translations/ms/lessons/4-ComputerVision/10-GANs/README.md index 1667a2e3..d6050d04 100644 --- a/translations/ms/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/ms/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Generative Adversarial Networks Dalam bahagian sebelumnya, kita telah mempelajari tentang **model generatif**: model yang boleh menghasilkan imej baru yang serupa dengan imej dalam dataset latihan. VAE adalah contoh yang baik bagi model generatif. @@ -17,7 +8,7 @@ Namun, jika kita cuba menghasilkan sesuatu yang benar-benar bermakna, seperti lu Idea utama GAN adalah mempunyai dua rangkaian neural yang dilatih saling bersaing: - + > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +32,7 @@ Generator sedikit lebih rumit. Anda boleh menganggapnya sebagai discriminator ya > βœ… Oleh kerana lapisan konvolusi dilaksanakan sebagai penapis linear yang melintasi imej, dekonvolusi pada dasarnya serupa dengan konvolusi dan boleh dilaksanakan menggunakan logik lapisan yang sama. - + > Imej oleh [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md index 26e9813d..6378ae68 100644 --- a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Pengesanan Objek Model klasifikasi imej yang telah kita pelajari sebelum ini mengambil imej dan menghasilkan keputusan kategori, seperti kelas 'nombor' dalam masalah MNIST. Walau bagaimanapun, dalam banyak kes, kita bukan sahaja ingin mengetahui bahawa gambar menggambarkan objek - kita juga ingin menentukan lokasi tepatnya. Inilah tujuan utama **pengesanan objek**. diff --git a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index f32c2e4b..b3036cc4 100644 --- a/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Pengesanan Kepala menggunakan Dataset Hollywood Heads Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/ms/lessons/4-ComputerVision/12-Segmentation/README.md index 94cd7a8e..3d643931 100644 --- a/translations/ms/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/ms/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # Segmentasi Kita telah mempelajari tentang Pengesanan Objek sebelum ini, yang membolehkan kita mencari objek dalam imej dengan meramalkan *kotak sempadan* mereka. Walau bagaimanapun, untuk sesetengah tugas, kita bukan sahaja memerlukan kotak sempadan tetapi juga penempatan objek yang lebih tepat. Tugas ini dipanggil **segmentasi**. @@ -20,7 +11,7 @@ Segmentasi boleh dilihat sebagai **klasifikasi piksel**, di mana untuk **setiap* Sebagai contoh, dalam segmentasi instans, kambing biri-biri ini adalah objek yang berbeza, tetapi dalam segmentasi semantik semua kambing biri-biri diwakili oleh satu kelas. - + > Imej daripada [blog ini](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +20,7 @@ Terdapat pelbagai seni bina neural untuk segmentasi, tetapi semuanya mempunyai s * **Encoder** mengekstrak ciri daripada imej input. * **Decoder** mengubah ciri-ciri tersebut menjadi **imej mask**, dengan saiz dan bilangan saluran yang sama yang sepadan dengan bilangan kelas. - + > Imej daripada [penerbitan ini](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +34,7 @@ Dalam pelajaran ini, kita akan melihat segmentasi dalam tindakan dengan melatih > βœ… Teknik ini sangat sesuai untuk jenis pengimejan perubatan ini, tetapi apakah aplikasi dunia sebenar lain yang boleh anda bayangkan? -navi +navi > Imej daripada Pangkalan Data PH2 diff --git a/translations/ms/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/ms/lessons/4-ComputerVision/12-Segmentation/lab/README.md index 1d92316d..95481171 100644 --- a/translations/ms/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/ms/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # Segmentasi Tubuh Manusia Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/4-ComputerVision/README.md b/translations/ms/lessons/4-ComputerVision/README.md index 11705437..3f05cd20 100644 --- a/translations/ms/lessons/4-ComputerVision/README.md +++ b/translations/ms/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # Penglihatan Komputer ![Ringkasan kandungan Penglihatan Komputer dalam bentuk doodle](../../../../translated_images/ms/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/ms/lessons/5-NLP/13-TextRep/README.md b/translations/ms/lessons/5-NLP/13-TextRep/README.md index b0cc35ed..449ceea7 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/README.md +++ b/translations/ms/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # Mewakili Teks sebagai Tensor ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ Matlamat kita adalah untuk mengelaskan item berita ke dalam salah satu kategori Jika kita ingin menyelesaikan tugas Pemprosesan Bahasa Semula Jadi (NLP) dengan rangkaian neural, kita memerlukan cara untuk mewakili teks sebagai tensor. Komputer sudah mewakili watak teks sebagai nombor yang memetakan kepada fon pada skrin anda menggunakan pengekodan seperti ASCII atau UTF-8. -Imej menunjukkan diagram pemetaan watak kepada perwakilan ASCII dan binari +Imej menunjukkan diagram pemetaan watak kepada perwakilan ASCII dan binari > [Sumber imej](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +39,7 @@ Dalam beberapa kes, kita mungkin mempertimbangkan untuk menggunakan tri-gram -- Apabila menyelesaikan tugas seperti pengelasan teks, kita perlu dapat mewakili teks dengan satu vektor bersaiz tetap, yang akan kita gunakan sebagai input kepada pengelas padat akhir. Salah satu cara paling mudah untuk melakukannya adalah dengan menggabungkan semua perwakilan perkataan individu, contohnya dengan menambahnya. Jika kita menambah pengekodan satu-haba setiap perkataan, kita akan berakhir dengan vektor frekuensi, menunjukkan berapa kali setiap perkataan muncul dalam teks. Perwakilan teks seperti ini dipanggil **bag of words** (BoW). - + > Imej oleh penulis diff --git a/translations/ms/lessons/5-NLP/13-TextRep/assignment.md b/translations/ms/lessons/5-NLP/13-TextRep/assignment.md index 87e3e8a4..00e4b483 100644 --- a/translations/ms/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/ms/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # Tugasan: Buku Nota Menggunakan buku nota yang berkaitan dengan pelajaran ini (sama ada versi PyTorch atau TensorFlow), jalankan semula menggunakan dataset anda sendiri, mungkin salah satu daripada Kaggle, dengan memberikan kredit yang sewajarnya. Tulis semula buku nota tersebut untuk menonjolkan penemuan anda sendiri. Cuba beberapa dataset yang inovatif yang mungkin memberikan kejutan, seperti [dataset tentang penampakan UFO ini](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) daripada NUFORC. diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/README.md b/translations/ms/lessons/5-NLP/14-Embeddings/README.md index c46cec26..898cea1c 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/ms/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Pembenaman ## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/ms/lessons/5-NLP/14-Embeddings/assignment.md b/translations/ms/lessons/5-NLP/14-Embeddings/assignment.md index 067f48ed..3b3c6b8b 100644 --- a/translations/ms/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/ms/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # Tugasan: Buku Nota Menggunakan buku nota yang berkaitan dengan pelajaran ini (sama ada versi PyTorch atau TensorFlow), jalankan semula buku nota tersebut menggunakan dataset anda sendiri, mungkin dari Kaggle, dengan memberikan penghargaan yang sewajarnya. Tulis semula buku nota tersebut untuk menekankan penemuan anda sendiri. Cuba gunakan jenis dataset yang berbeza dan dokumentasikan penemuan anda, menggunakan teks seperti [lirik lagu Beatles ini](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics). diff --git a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md index 9d8f4eaf..ca223496 100644 --- a/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/ms/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Pemodelan Bahasa Pemerangkapan semantik, seperti Word2Vec dan GloVe, sebenarnya adalah langkah pertama ke arah **pemodelan bahasa** - mencipta model yang dapat *memahami* (atau *mewakili*) sifat bahasa. diff --git a/translations/ms/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/ms/lessons/5-NLP/15-LanguageModeling/lab/README.md index 1aa2e5d4..dcdf3bdd 100644 --- a/translations/ms/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/ms/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Melatih Model Skip-Gram Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/5-NLP/16-RNN/README.md b/translations/ms/lessons/5-NLP/16-RNN/README.md index 9f10f3f1..693014af 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/README.md +++ b/translations/ms/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Rangkaian Neural Berulang ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ Mari kita lihat bagaimana sel RNN ringkas diatur. Ia menerima keadaan sebelumnya Sel RNN ringkas mempunyai dua matriks berat di dalamnya: satu mengubah simbol input (kita panggil ia W), dan satu lagi mengubah keadaan input (H). Dalam kes ini, output rangkaian dikira sebagai σ(W×Xi+H×Si-1+b), di mana σ adalah fungsi pengaktifan dan b adalah bias tambahan. -Anatomi Sel RNN +Anatomi Sel RNN > Gambar oleh penulis diff --git a/translations/ms/lessons/5-NLP/16-RNN/assignment.md b/translations/ms/lessons/5-NLP/16-RNN/assignment.md index a918144e..e6434e6f 100644 --- a/translations/ms/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/ms/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # Tugasan: Buku Nota Menggunakan buku nota yang berkaitan dengan pelajaran ini (sama ada versi PyTorch atau TensorFlow), jalankan semula menggunakan dataset anda sendiri, mungkin salah satu daripada Kaggle, dengan memberikan kredit kepada sumber. Tulis semula buku nota tersebut untuk menekankan penemuan anda sendiri. Cuba jenis dataset yang berbeza dan dokumentasikan penemuan anda, menggunakan teks seperti [dataset pertandingan Kaggle tentang tweet cuaca ini](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv). diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md index 025af31f..a05cd269 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Rangkaian Generatif ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ Kita akan melatih RNN ini untuk menjana teks langkah demi langkah. Pada setiap l Semasa menjana teks (semasa inferens), kita bermula dengan beberapa **prompt**, yang dilalui melalui sel RNN untuk menghasilkan keadaan perantaraannya, dan kemudian daripada keadaan ini penjanaan bermula. Kita menjana satu aksara pada satu masa, dan menghantar keadaan dan aksara yang dijana kepada sel RNN lain untuk menjana aksara seterusnya, sehingga kita menjana aksara yang mencukupi. - + > Imej oleh penulis diff --git a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index 5318b7fb..209c9489 100644 --- a/translations/ms/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/ms/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # Penjana Teks Tahap Perkataan menggunakan RNN Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/5-NLP/18-Transformers/README.md b/translations/ms/lessons/5-NLP/18-Transformers/README.md index 257a47ca..f6814b89 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/README.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # Mekanisme Perhatian dan Transformer ## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Idea pengekodan kedudukan adalah seperti berikut. * Pemadanan yang boleh dilatih, serupa dengan pemadanan token. Ini adalah pendekatan yang kita pertimbangkan di sini. Kita menggunakan lapisan pemadanan di atas kedua-dua token dan kedudukan mereka, menghasilkan vektor pemadanan dengan dimensi yang sama, yang kemudian kita tambahkan bersama. * Fungsi pengekodan kedudukan tetap, seperti yang dicadangkan dalam kertas asal. - + > Imej oleh penulis diff --git a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md deleted file mode 100644 index 9e2e4fa1..00000000 --- a/translations/ms/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ /dev/null @@ -1,112 +0,0 @@ -# Mekanisme Perhatian dan Transformer - -## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/35) - -Salah satu masalah terpenting dalam domain NLP adalah **penerjemahan mesin**, sebuah tugas penting yang menjadi dasar alat seperti Google Translate. Di bagian ini, kita akan fokus pada penerjemahan mesin, atau, lebih umum, pada setiap tugas *urutan-ke-urutan* (yang juga disebut **transduksi kalimat**). - -Dengan RNN, urutan-ke-urutan diimplementasikan oleh dua jaringan berulang, di mana satu jaringan, **encoder**, mengompresi urutan masukan menjadi keadaan tersembunyi, sementara jaringan lainnya, **decoder**, mengubah keadaan tersembunyi ini menjadi hasil terjemahan. Ada beberapa masalah dengan pendekatan ini: - -* Keadaan akhir dari jaringan encoder kesulitan mengingat awal kalimat, sehingga menyebabkan kualitas model yang buruk untuk kalimat panjang. -* Semua kata dalam urutan memiliki dampak yang sama pada hasil. Namun, dalam kenyataannya, kata-kata tertentu dalam urutan masukan seringkali memiliki dampak yang lebih besar pada keluaran urutan daripada yang lain. - -**Mekanisme Perhatian** menyediakan cara untuk memberikan bobot pada dampak kontekstual dari setiap vektor masukan terhadap setiap prediksi keluaran dari RNN. Cara ini diimplementasikan dengan membuat jalur pendek antara keadaan sementara dari RNN masukan dan RNN keluaran. Dengan cara ini, saat menghasilkan simbol keluaran yt, kita akan mempertimbangkan semua keadaan tersembunyi masukan hi, dengan koefisien bobot yang berbeda Ξ±t,i. - -![Gambar menunjukkan model encoder/decoder dengan lapisan perhatian aditif](../../../../../translated_images/ms/encoder-decoder-attention.7a726296894fb567.webp) - -> Model encoder-decoder dengan mekanisme perhatian aditif dalam [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), dikutip dari [posting blog ini](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) - -Matriks perhatian {Ξ±i,j} akan mewakili sejauh mana kata-kata masukan tertentu berperan dalam penghasilan kata tertentu dalam urutan keluaran. Di bawah ini adalah contoh matriks semacam itu: - -![Gambar menunjukkan contoh keselarasan yang ditemukan oleh RNNsearch-50, diambil dari Bahdanau - arviz.org](../../../../../translated_images/ms/bahdanau-fig3.09ba2d37f202a6af.webp) - -> Gambar dari [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) - -Mekanisme perhatian bertanggung jawab atas banyak keadaan terkini atau hampir terkini dalam NLP. Namun, menambahkan perhatian sangat meningkatkan jumlah parameter model yang menyebabkan masalah skala dengan RNN. Salah satu batasan utama dalam menskalakan RNN adalah bahwa sifat berulang dari model membuatnya sulit untuk melakukan pelatihan secara batch dan paralel. Dalam RNN, setiap elemen dari urutan perlu diproses dalam urutan sekuensial yang berarti tidak dapat dengan mudah diparalelkan. - -![Encoder Decoder dengan Perhatian](../../../../../lessons/5-NLP/18-Transformers/images/EncDecAttention.gif) - -> Gambar dari [Blog Google](https://research.googleblog.com/2016/09/a-neural-network-for-machine.html) - -Adopsi mekanisme perhatian yang dikombinasikan dengan batasan ini menyebabkan penciptaan Model Transformer yang kini menjadi Standar Terkini yang kita kenal dan gunakan saat ini seperti BERT hingga Open-GPT3. - -## Model Transformer - -Salah satu ide utama di balik transformer adalah menghindari sifat sekuensial dari RNN dan menciptakan model yang dapat diparalelkan selama pelatihan. Ini dicapai dengan menerapkan dua ide: - -* pengkodean posisi -* menggunakan mekanisme perhatian diri untuk menangkap pola alih-alih RNN (atau CNN) (itulah sebabnya makalah yang memperkenalkan transformer disebut *[Attention is all you need](https://arxiv.org/abs/1706.03762)*) - -### Pengkodean/Embedding Posisi - -Ide pengkodean posisi adalah sebagai berikut. -1. Ketika menggunakan RNN, posisi relatif dari token diwakili oleh jumlah langkah, dan oleh karena itu tidak perlu diwakili secara eksplisit. -2. Namun, setelah kita beralih ke perhatian, kita perlu mengetahui posisi relatif dari token dalam urutan. -3. Untuk mendapatkan pengkodean posisi, kita menambah urutan token kita dengan urutan posisi token dalam urutan (yaitu, urutan angka 0,1, ...). -4. Kita kemudian mencampurkan posisi token dengan vektor embedding token. Untuk mengubah posisi (bilangan bulat) menjadi vektor, kita dapat menggunakan berbagai pendekatan: - -* Embedding yang dapat dilatih, mirip dengan embedding token. Ini adalah pendekatan yang kita pertimbangkan di sini. Kita menerapkan lapisan embedding di atas baik token maupun posisi mereka, menghasilkan vektor embedding dengan dimensi yang sama, yang kemudian kita tambahkan bersama. -* Fungsi pengkodean posisi tetap, seperti yang diusulkan dalam makalah asli. - - - -> Gambar oleh penulis - -Hasil yang kita dapatkan dengan embedding posisi menggabungkan baik token asli maupun posisinya dalam urutan. - -### Perhatian Diri Multi-Kepala - -Selanjutnya, kita perlu menangkap beberapa pola dalam urutan kita. Untuk melakukan ini, transformer menggunakan mekanisme **perhatian diri**, yang pada dasarnya adalah perhatian yang diterapkan pada urutan yang sama sebagai masukan dan keluaran. Menerapkan perhatian diri memungkinkan kita untuk mempertimbangkan **konteks** dalam kalimat, dan melihat kata-kata mana yang saling terkait. Misalnya, ini memungkinkan kita untuk melihat kata-kata mana yang dirujuk oleh ko-referensi, seperti *itu*, dan juga mempertimbangkan konteks: - -![](../../../../../translated_images/ms/CoreferenceResolution.861924d6d384a7d6.webp) - -> Gambar dari [Blog Google](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) - -Dalam transformer, kita menggunakan **Multi-Head Attention** untuk memberikan kekuatan kepada jaringan untuk menangkap berbagai jenis ketergantungan, misalnya hubungan kata jangka panjang vs. jangka pendek, ko-referensi vs. hal lain, dll. - -[Notebook TensorFlow](../../../../../lessons/5-NLP/18-Transformers/TransformersTF.ipynb) berisi lebih banyak rincian tentang implementasi lapisan transformer. - -### Perhatian Encoder-Decoder - -Dalam transformer, perhatian digunakan di dua tempat: - -* Untuk menangkap pola dalam teks masukan menggunakan perhatian diri -* Untuk melakukan penerjemahan urutan - ini adalah lapisan perhatian antara encoder dan decoder. - -Perhatian encoder-decoder sangat mirip dengan mekanisme perhatian yang digunakan dalam RNN, seperti yang dijelaskan di awal bagian ini. Diagram animasi ini menjelaskan peran perhatian encoder-decoder. - -![GIF Animasi menunjukkan bagaimana evaluasi dilakukan dalam model transformer.](../../../../../lessons/5-NLP/18-Transformers/images/transformer-animated-explanation.gif) - -Karena setiap posisi masukan dipetakan secara independen ke setiap posisi keluaran, transformer dapat melakukan paralelisasi lebih baik daripada RNN, yang memungkinkan model bahasa yang jauh lebih besar dan lebih ekspresif. Setiap kepala perhatian dapat digunakan untuk mempelajari berbagai hubungan antara kata yang meningkatkan tugas Pemrosesan Bahasa Alami yang lebih lanjut. - -## BERT - -**BERT** (Bidirectional Encoder Representations from Transformers) adalah jaringan transformer multi-layer yang sangat besar dengan 12 lapisan untuk *BERT-base*, dan 24 untuk *BERT-large*. Model ini pertama kali dilatih pada korpus data teks yang besar (WikiPedia + buku) menggunakan pelatihan tanpa pengawasan (memprediksi kata-kata yang disembunyikan dalam kalimat). Selama pelatihan awal, model menyerap tingkat pemahaman bahasa yang signifikan yang kemudian dapat dimanfaatkan dengan dataset lain menggunakan penyempurnaan. Proses ini disebut **transfer learning**. - -![gambar dari http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/ms/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) - -> Gambar [sumber](http://jalammar.github.io/illustrated-bert/) - -## ✍️ Latihan: Transformers - -Lanjutkan pembelajaran Anda di notebook berikut: - -* [Transformers di PyTorch](../../../../../lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) -* [Transformers di TensorFlow](../../../../../lessons/5-NLP/18-Transformers/TransformersTF.ipynb) - -## Kesimpulan - -Dalam pelajaran ini, Anda belajar tentang Transformers dan Mekanisme Perhatian, semua alat penting dalam kotak alat NLP. Ada banyak variasi arsitektur Transformer termasuk BERT, DistilBERT, BigBird, OpenGPT3, dan lebih banyak lagi yang dapat disempurnakan. Paket [HuggingFace](https://github.com/huggingface/) menyediakan repositori untuk melatih banyak arsitektur ini dengan PyTorch dan TensorFlow. - -## πŸš€ Tantangan - -## [Kuiz pasca-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/36) - -## Tinjauan & Studi Mandiri - -* [Posting blog](https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/), menjelaskan makalah klasik [Attention is all you need](https://arxiv.org/abs/1706.03762) tentang transformer. -* [Seri posting blog](https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452) tentang transformer, menjelaskan arsitektur secara rinci. - -## [Tugas](assignment.md) - -**Penafian**: -Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan berasaskan AI. Walaupun kami berusaha untuk ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa ibunda harus dianggap sebagai sumber yang berautoriti. Untuk maklumat penting, terjemahan manusia yang 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 diff --git a/translations/ms/lessons/5-NLP/18-Transformers/assignment.md b/translations/ms/lessons/5-NLP/18-Transformers/assignment.md index 6c77b3ee..3a52dd04 100644 --- a/translations/ms/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/ms/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # Tugasan: Transformers Cuba bereksperimen dengan Transformers di HuggingFace! Cuba beberapa skrip yang mereka sediakan untuk bekerja dengan pelbagai model yang terdapat di laman mereka: https://huggingface.co/docs/transformers/run_scripts. Cuba salah satu set data mereka, kemudian import salah satu set data anda sendiri daripada kurikulum ini atau daripada Kaggle dan lihat jika anda boleh menghasilkan teks yang menarik. Hasilkan sebuah notebook dengan penemuan anda. diff --git a/translations/ms/lessons/5-NLP/19-NER/README.md b/translations/ms/lessons/5-NLP/19-NER/README.md index 1a60b6c8..6a266cdb 100644 --- a/translations/ms/lessons/5-NLP/19-NER/README.md +++ b/translations/ms/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Pengenalan Entiti Bernama Sehingga kini, kita kebanyakannya menumpukan pada satu tugas NLP - klasifikasi. Walau bagaimanapun, terdapat juga tugas NLP lain yang boleh diselesaikan dengan rangkaian neural. Salah satu tugas tersebut ialah **[Pengenalan Entiti Bernama](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), yang berkaitan dengan mengenal pasti entiti tertentu dalam teks, seperti tempat, nama orang, selang masa, formula kimia, dan sebagainya. @@ -17,7 +8,7 @@ Sehingga kini, kita kebanyakannya menumpukan pada satu tugas NLP - klasifikasi. Bayangkan anda ingin membangunkan bot sembang bahasa semula jadi, seperti Amazon Alexa atau Google Assistant. Cara bot sembang pintar berfungsi adalah dengan *memahami* apa yang pengguna mahukan melalui klasifikasi teks pada ayat input. Hasil klasifikasi ini dikenali sebagai **niat**, yang menentukan apa yang bot sembang perlu lakukan. -Bot NER +Bot NER > Imej oleh penulis diff --git a/translations/ms/lessons/5-NLP/19-NER/lab/README.md b/translations/ms/lessons/5-NLP/19-NER/lab/README.md index 4de5d1b5..0952f519 100644 --- a/translations/ms/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/ms/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/ms/lessons/5-NLP/20-LangModels/README.md b/translations/ms/lessons/5-NLP/20-LangModels/README.md index 453fbd64..008cc322 100644 --- a/translations/ms/lessons/5-NLP/20-LangModels/README.md +++ b/translations/ms/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # Model Bahasa Besar yang Telah Dilatih Dalam semua tugasan sebelumnya, kita melatih rangkaian neural untuk melaksanakan tugas tertentu menggunakan dataset berlabel. Dengan model transformer besar seperti BERT, kita menggunakan pemodelan bahasa secara kendiri untuk membina model bahasa, yang kemudian disesuaikan untuk tugas tertentu dengan latihan tambahan yang lebih spesifik kepada domain. Walau bagaimanapun, telah dibuktikan bahawa model bahasa besar juga boleh menyelesaikan banyak tugas tanpa latihan khusus domain. Keluarga model yang mampu melakukan ini dipanggil **GPT**: Generative Pre-Trained Transformer. diff --git a/translations/ms/lessons/5-NLP/20-LangModels/READMELargeLang.md b/translations/ms/lessons/5-NLP/20-LangModels/READMELargeLang.md deleted file mode 100644 index ab372809..00000000 --- a/translations/ms/lessons/5-NLP/20-LangModels/READMELargeLang.md +++ /dev/null @@ -1,56 +0,0 @@ -# Model Bahasa Besar yang Ditraining Sebelumnya - -Dalam semua tugas sebelumnya, kami melatih jaringan saraf untuk melakukan tugas tertentu menggunakan dataset yang dilabeli. Dengan model transformer besar, seperti BERT, kami menggunakan pemodelan bahasa dengan cara yang diawasi sendiri untuk membangun model bahasa, yang kemudian disesuaikan untuk tugas hilir tertentu dengan pelatihan spesifik domain lebih lanjut. Namun, telah dibuktikan bahwa model bahasa besar juga dapat menyelesaikan banyak tugas tanpa pelatihan spesifik domain. Keluarga model yang mampu melakukan hal itu disebut **GPT**: Generative Pre-Trained Transformer. - -## [Kuis Pra-perkuliahan](https://ff-quizzes.netlify.app/en/ai/quiz/39) - -## Generasi Teks dan Perplexity - -Ide tentang jaringan saraf yang dapat melakukan tugas umum tanpa pelatihan hilir disajikan dalam makalah [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). Ide utamanya adalah banyak tugas lain dapat dimodelkan menggunakan **generasi teks**, karena memahami teks pada dasarnya berarti mampu memproduksinya. Karena model dilatih pada sejumlah besar teks yang mencakup pengetahuan manusia, ia juga menjadi mengetahui berbagai subjek. - -> Memahami dan mampu memproduksi teks juga berarti mengetahui sesuatu tentang dunia di sekitar kita. Orang juga belajar dengan membaca dalam jumlah besar, dan jaringan GPT serupa dalam hal ini. - -Jaringan generasi teks bekerja dengan memprediksi probabilitas kata berikutnya $$P(w_N)$$ Namun, probabilitas tanpa syarat dari kata berikutnya sama dengan frekuensi kata ini dalam korpus teks. GPT mampu memberikan **probabilitas bersyarat** dari kata berikutnya, mengingat kata-kata sebelumnya: $$P(w_N | w_{n-1}, ..., w_0)$$ - -> Anda dapat membaca lebih lanjut tentang probabilitas dalam [Kurikulum Data Science untuk Pemula kami](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/04-stats-and-probability) - -Kualitas model penghasil bahasa dapat didefinisikan menggunakan **perplexity**. Ini adalah metrik intrinsik yang memungkinkan kita mengukur kualitas model tanpa dataset spesifik tugas. Ini didasarkan pada pengertian *probabilitas sebuah kalimat* - model memberikan probabilitas tinggi pada kalimat yang kemungkinan besar nyata (yaitu model tidak **perplexed** olehnya), dan probabilitas rendah pada kalimat yang kurang masuk akal (misalnya *Bisakah itu melakukan apa?*). Ketika kami memberikan kalimat dari korpus teks nyata kepada model kami, kami berharap kalimat tersebut memiliki probabilitas tinggi, dan **perplexity** rendah. Secara matematis, ini didefinisikan sebagai probabilitas invers ternormalisasi dari set uji: -$$ -\mathrm{Perplexity}(W) = \sqrt[N]{1\over P(W_1,...,W_N)} -$$ - -**Anda dapat bereksperimen dengan generasi teks menggunakan [editor teks bertenaga GPT dari Hugging Face](https://transformer.huggingface.co/doc/gpt2-large)**. Di editor ini, Anda mulai menulis teks Anda, dan menekan **[TAB]** akan menawarkan beberapa opsi penyelesaian. Jika opsi tersebut terlalu pendek, atau Anda tidak puas dengan mereka - tekan [TAB] lagi, dan Anda akan mendapatkan lebih banyak opsi, termasuk potongan teks yang lebih panjang. - -## GPT adalah Sebuah Keluarga - -GPT bukanlah model tunggal, melainkan kumpulan model yang dikembangkan dan dilatih oleh [OpenAI](https://openai.com). - -Di bawah model GPT, kami memiliki: - -| [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2#openai-gpt2) | [GPT 3](https://openai.com/research/language-models-are-few-shot-learners) | [GPT-4](https://openai.com/gpt-4) | -| -- | -- | -- | -|Model bahasa dengan hingga 1,5 miliar parameter. | Model bahasa dengan hingga 175 miliar parameter | 100T parameter dan menerima input serta output teks dari gambar. | - - -Model GPT-3 dan GPT-4 tersedia [sebagai layanan kognitif dari Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/openai-service/#overview?WT.mc_id=academic-77998-cacaste), dan sebagai [API OpenAI](https://openai.com/api/). - -## Rekayasa Prompt - -Karena GPT telah dilatih pada volume data yang sangat besar untuk memahami bahasa dan kode, mereka memberikan keluaran sebagai respons terhadap masukan (prompt). Prompt adalah masukan atau kueri GPT di mana seseorang memberikan instruksi kepada model tentang tugas yang akan mereka selesaikan selanjutnya. Untuk mendapatkan hasil yang diinginkan, Anda perlu menggunakan prompt yang paling efektif yang melibatkan pemilihan kata, format, frasa, atau bahkan simbol yang tepat. Pendekatan ini adalah [Rekayasa Prompt](https://learn.microsoft.com/en-us/shows/ai-show/the-basics-of-prompt-engineering-with-azure-openai-service?WT.mc_id=academic-77998-bethanycheum) - -[Dokumentasi ini](https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/?WT.mc_id=academic-77998-bethanycheum) memberikan Anda informasi lebih lanjut tentang rekayasa prompt. - -## ✍️ Contoh Notebook: [Bermain dengan OpenAI-GPT](../../../../../lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) - -Lanjutkan pembelajaran Anda di notebook berikut: - -* [Menghasilkan teks dengan OpenAI-GPT dan Hugging Face Transformers](../../../../../lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) - -## Kesimpulan - -Model bahasa umum yang baru dilatih sebelumnya tidak hanya memodelkan struktur bahasa, tetapi juga mengandung sejumlah besar bahasa alami. Dengan demikian, mereka dapat digunakan secara efektif untuk menyelesaikan beberapa tugas NLP dalam pengaturan zero-shot atau few-shot. - -## [Kuis Pasca-perkuliahan](https://ff-quizzes.netlify.app/en/ai/quiz/40) - -**Penafian**: -Dokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI berasaskan mesin. Walaupun kami berusaha untuk ketepatan, sila sedar bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang sah. 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. \ No newline at end of file diff --git a/translations/ms/lessons/5-NLP/README.md b/translations/ms/lessons/5-NLP/README.md index d9409206..a09c1c67 100644 --- a/translations/ms/lessons/5-NLP/README.md +++ b/translations/ms/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # Pemprosesan Bahasa Semula Jadi ![Ringkasan tugas NLP dalam bentuk doodle](../../../../translated_images/ms/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/ms/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/ms/lessons/6-Other/21-GeneticAlgorithms/README.md index a86a172a..01bf6235 100644 --- a/translations/ms/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/ms/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # Algoritma Genetik ## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/ms/lessons/6-Other/22-DeepRL/README.md b/translations/ms/lessons/6-Other/22-DeepRL/README.md index 155bac73..7e41afd0 100644 --- a/translations/ms/lessons/6-Other/22-DeepRL/README.md +++ b/translations/ms/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # Pembelajaran Pengukuhan Mendalam Pembelajaran pengukuhan (RL) dianggap sebagai salah satu paradigma asas pembelajaran mesin, selain pembelajaran diselia dan pembelajaran tanpa penyeliaan. Dalam pembelajaran diselia, kita bergantung pada dataset dengan hasil yang diketahui, manakala RL berasaskan **belajar melalui pengalaman**. Sebagai contoh, apabila kita pertama kali melihat permainan komputer, kita mula bermain walaupun tanpa mengetahui peraturannya, dan tidak lama kemudian kita dapat meningkatkan kemahiran kita hanya melalui proses bermain dan menyesuaikan tingkah laku kita. @@ -34,7 +25,7 @@ Anda mungkin pernah melihat alat keseimbangan moden seperti *Segway* atau *Gyros Versi ringkas keseimbangan dikenali sebagai masalah **CartPole**. Dalam dunia cartpole, kita mempunyai slider mendatar yang boleh bergerak ke kiri atau kanan, dan matlamatnya adalah untuk menyeimbangkan tiang menegak di atas slider semasa ia bergerak. -a cartpole +a cartpole Untuk mencipta dan menggunakan persekitaran ini, kita memerlukan beberapa baris kod Python: diff --git a/translations/ms/lessons/6-Other/22-DeepRL/lab/README.md b/translations/ms/lessons/6-Other/22-DeepRL/lab/README.md index f68feb88..8af41f63 100644 --- a/translations/ms/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/ms/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - ## Persekitaran Persekitaran Mountain Car terdiri daripada sebuah kereta yang terperangkap di dalam lembah. Matlamat anda adalah untuk melompat keluar dari lembah dan mencapai bendera. Tindakan yang boleh anda lakukan adalah mempercepat ke kiri, ke kanan, atau tidak melakukan apa-apa. Anda boleh memerhatikan kedudukan kereta di sepanjang paksi-x, dan kelajuannya. diff --git a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md index 0d59f248..fdb3f585 100644 --- a/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/ms/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Sistem Multi-Ejen Salah satu cara untuk mencapai kecerdasan adalah melalui pendekatan **emergent** (atau **sinergi**), yang berdasarkan fakta bahawa gabungan tingkah laku banyak ejen yang agak mudah boleh menghasilkan tingkah laku sistem keseluruhan yang lebih kompleks (atau pintar). Secara teori, ini berdasarkan prinsip [Kecerdasan Kolektif](https://en.wikipedia.org/wiki/Collective_intelligence), [Emergentisme](https://en.wikipedia.org/wiki/Global_brain) dan [Sibernetik Evolusi](https://en.wikipedia.org/wiki/Global_brain), yang menyatakan bahawa sistem tahap tinggi memperoleh nilai tambah apabila digabungkan dengan betul daripada sistem tahap rendah (dikenali sebagai *prinsip peralihan metasistem*). @@ -60,7 +51,7 @@ Anda boleh [muat turun](https://ccl.northwestern.edu/netlogo/download.shtml) dan Satu perkara hebat tentang NetLogo ialah ia mengandungi perpustakaan model yang berfungsi yang boleh anda cuba. Pergi ke **File → Models Library**, dan anda mempunyai banyak kategori model untuk dipilih. -NetLogo Models Library +NetLogo Models Library > Tangkapan skrin perpustakaan model oleh Dmitry Soshnikov diff --git a/translations/ms/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/ms/lessons/6-Other/23-MultiagentSystems/assignment.md index 01ebcf8e..ffff110e 100644 --- a/translations/ms/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/ms/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # Tugasan NetLogo Ambil salah satu model dalam perpustakaan NetLogo dan gunakannya untuk mensimulasikan situasi kehidupan sebenar sebaik mungkin. Contoh yang baik adalah mengubah suai model Virus dalam folder Alternative Visualizations untuk menunjukkan bagaimana ia boleh digunakan untuk memodelkan penyebaran COVID-19. Bolehkah anda membina model yang meniru penyebaran virus dalam kehidupan sebenar? diff --git a/translations/ms/lessons/7-Ethics/README.md b/translations/ms/lessons/7-Ethics/README.md index 79e6535a..c36cc61f 100644 --- a/translations/ms/lessons/7-Ethics/README.md +++ b/translations/ms/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # AI Beretika dan Bertanggungjawab Anda hampir menyelesaikan kursus ini, dan saya berharap pada tahap ini anda sudah jelas bahawa AI berdasarkan beberapa kaedah matematik formal yang membolehkan kita mencari hubungan dalam data dan melatih model untuk meniru beberapa aspek tingkah laku manusia. Pada masa ini dalam sejarah, kita menganggap AI sebagai alat yang sangat berkuasa untuk mengekstrak corak daripada data, dan menerapkan corak tersebut untuk menyelesaikan masalah baharu. diff --git a/translations/ms/lessons/README.md b/translations/ms/lessons/README.md index 42acfb74..4c3f7927 100644 --- a/translations/ms/lessons/README.md +++ b/translations/ms/lessons/README.md @@ -1,12 +1,3 @@ - # Gambaran Keseluruhan ![Gambaran Keseluruhan dalam bentuk lakaran](../../../translated_images/ms/ai-overview.0857791951d19500.webp) diff --git a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md index ee9b98ee..dc239843 100644 --- a/translations/ms/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/ms/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Rangkaian Multi-Mod Selepas kejayaan model transformer dalam menyelesaikan tugas NLP, seni bina yang sama atau serupa telah digunakan untuk tugas penglihatan komputer. Terdapat minat yang semakin meningkat untuk membina model yang dapat *menggabungkan* keupayaan penglihatan dan bahasa semula jadi. Salah satu usaha tersebut dilakukan oleh OpenAI, dan ia dikenali sebagai CLIP dan DALL.E. diff --git a/translations/ms/lessons/sketchnotes/LICENSE.md b/translations/ms/lessons/sketchnotes/LICENSE.md index 2eb76f61..3b81978e 100644 --- a/translations/ms/lessons/sketchnotes/LICENSE.md +++ b/translations/ms/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Hak Cipta Creative Commons Attribution-ShareAlike 4.0 Antarabangsa ======================================================================= diff --git a/translations/ms/lessons/sketchnotes/README.md b/translations/ms/lessons/sketchnotes/README.md index 303c574f..64491f0b 100644 --- a/translations/ms/lessons/sketchnotes/README.md +++ b/translations/ms/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - Semua sketchnote kurikulum boleh dimuat turun di sini. 🎨 Dicipta oleh: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/ms/troubleshoot.md b/translations/ms/troubleshoot.md index f8f3cc60..50d5551a 100644 --- a/translations/ms/troubleshoot.md +++ b/translations/ms/troubleshoot.md @@ -1,12 +1,3 @@ - # Panduan Penyelesaian Masalah AI-For-Beginners Panduan ini membantu anda menyelesaikan masalah biasa yang dihadapi semasa menggunakan atau menyumbang kepada repositori [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners). Setiap masalah disertakan dengan latar belakang, simptom, penjelasan, dan langkah-langkah penyelesaian. diff --git a/translations/sw/.co-op-translator.json b/translations/sw/.co-op-translator.json new file mode 100644 index 00000000..10ac3926 --- /dev/null +++ b/translations/sw/.co-op-translator.json @@ -0,0 +1,398 @@ +{ + "AGENTS.md": { + "original_hash": "6b11a37115944252ab3ed04e358d830d", + "translation_date": "2025-10-03T09:26:55+00:00", + "source_file": "AGENTS.md", + "language_code": "sw" + }, + "README.md": { + "original_hash": "1984fc89dd304a8a33ab5584691a99aa", + "translation_date": "2026-01-30T02:22:16+00:00", + "source_file": "README.md", + "language_code": "sw" + }, + "SECURITY.md": { + "original_hash": "a583f49d359c7ebba61433e4dfcd05a9", + "translation_date": "2025-08-25T20:47:30+00:00", + "source_file": "SECURITY.md", + "language_code": "sw" + }, + "etc/CODE_OF_CONDUCT.md": { + "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56", + "translation_date": "2025-08-25T21:06:19+00:00", + "source_file": "etc/CODE_OF_CONDUCT.md", + "language_code": "sw" + }, + "etc/CONTRIBUTING.md": { + "original_hash": "847a587aa1b83f4d00858183ff3ed18a", + "translation_date": "2025-08-25T21:06:33+00:00", + "source_file": "etc/CONTRIBUTING.md", + "language_code": "sw" + }, + "etc/Mindmap.md": { + "original_hash": "f2f88dbd2debd38e26149b27b1fd272d", + "translation_date": "2025-08-25T21:06:41+00:00", + "source_file": "etc/Mindmap.md", + "language_code": "sw" + }, + "etc/SUPPORT.md": { + "original_hash": "fdfc08baee91e402938a2b1f94fe0949", + "translation_date": "2025-08-25T21:06:13+00:00", + "source_file": "etc/SUPPORT.md", + "language_code": "sw" + }, + "etc/TRANSLATIONS.md": { + "original_hash": "62b3e3ad5182edb905eec649a87eeeb4", + "translation_date": "2025-08-25T21:06:24+00:00", + "source_file": "etc/TRANSLATIONS.md", + "language_code": "sw" + }, + "etc/quiz-app/README.md": { + "original_hash": "d699cf8509f74baa5b0b838de5cf0662", + "translation_date": "2025-08-25T21:07:07+00:00", + "source_file": "etc/quiz-app/README.md", + "language_code": "sw" + }, + "examples/README.md": { + "original_hash": "0d1babfdcbeb46525f2db3fbaaa54cd7", + "translation_date": "2025-10-03T11:33:09+00:00", + "source_file": "examples/README.md", + "language_code": "sw" + }, + "lessons/0-course-setup/for-teachers.md": { + "original_hash": "a094ef9927883de1cfcee51dbd143381", + "translation_date": "2025-08-25T21:05:48+00:00", + "source_file": "lessons/0-course-setup/for-teachers.md", + "language_code": "sw" + }, + "lessons/0-course-setup/how-to-run.md": { + "original_hash": "a4717bd9103b9f6cd84d534b83534689", + "translation_date": "2026-01-16T04:20:07+00:00", + "source_file": "lessons/0-course-setup/how-to-run.md", + "language_code": "sw" + }, + "lessons/0-course-setup/setup.md": { + "original_hash": "7b4e5b8956915870d0a0ed3cc5890042", + "translation_date": "2025-12-12T20:07:50+00:00", + "source_file": "lessons/0-course-setup/setup.md", + "language_code": "sw" + }, + "lessons/1-Intro/README.md": { + "original_hash": "f57e8aa46141fd220b16ffed8f11aec7", + "translation_date": "2025-11-18T21:54:15+00:00", + "source_file": "lessons/1-Intro/README.md", + "language_code": "sw" + }, + "lessons/1-Intro/assignment.md": { + "original_hash": "a334df77a82aaaf2a29c77065d3e481e", + "translation_date": "2025-11-18T21:55:40+00:00", + "source_file": "lessons/1-Intro/assignment.md", + "language_code": "sw" + }, + "lessons/2-Symbolic/README.md": { + "original_hash": "f9f06b266b8b2bfc6b8792ff2bb1bea4", + "translation_date": "2026-01-16T04:21:13+00:00", + "source_file": "lessons/2-Symbolic/README.md", + "language_code": "sw" + }, + "lessons/2-Symbolic/assignment.md": { + "original_hash": "a057a8604f3976c3e309884453f1fad0", + "translation_date": "2025-08-25T21:05:21+00:00", + "source_file": "lessons/2-Symbolic/assignment.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/03-Perceptron/README.md": { + "original_hash": "c34cbba802058b6fa267e1a294d4e510", + "translation_date": "2025-09-23T11:04:25+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md": { + "original_hash": "ba5d1eb353d20d3e7181066b3c424b99", + "translation_date": "2025-08-29T12:04:56+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/README.md": { + "original_hash": "789d6c3fb6fc7948a470b33078a5983a", + "translation_date": "2025-09-23T11:04:05+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md": { + "original_hash": "48fdd704d483e19bc3d7464074c9fcbe", + "translation_date": "2025-08-25T21:00:40+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/05-Frameworks/README.md": { + "original_hash": "ddd216f558a255260a9374008002c971", + "translation_date": "2025-09-23T11:04:47+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md": { + "original_hash": "e452d897efb9a89700f41021834cf6e5", + "translation_date": "2025-08-25T21:01:18+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md", + "language_code": "sw" + }, + "lessons/3-NeuralNetworks/README.md": { + "original_hash": "f862a99d88088163df12270e2f2ad6c3", + "translation_date": "2025-10-03T12:51:53+00:00", + "source_file": "lessons/3-NeuralNetworks/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/06-IntroCV/README.md": { + "original_hash": "feeca98225cb420afc89415f24f63d92", + "translation_date": "2025-09-23T10:59:47+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/06-IntroCV/lab/README.md": { + "original_hash": "3d53d6409f80970f7281a45dee35328a", + "translation_date": "2025-08-25T20:56:28+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/lab/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md": { + "original_hash": "53faab85adfcebd8c10bcd71dc2fa557", + "translation_date": "2025-09-23T10:59:06+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/07-ConvNets/README.md": { + "original_hash": "a560d5b845962cf33dc102266e409568", + "translation_date": "2025-09-23T10:58:45+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/07-ConvNets/lab/README.md": { + "original_hash": "b70fcf7fcee862990f848c679090943f", + "translation_date": "2025-10-03T14:56:44+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/08-TransferLearning/README.md": { + "original_hash": "178c0b5ee5395733eb18aec51e71a0a9", + "translation_date": "2025-09-23T10:59:22+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md": { + "original_hash": "ae074cd940fc2f4dc24fc07b66ccbd99", + "translation_date": "2025-08-25T20:57:06+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/08-TransferLearning/lab/README.md": { + "original_hash": "7765935c35fcee69b9fe2d0cfd6963e2", + "translation_date": "2025-08-25T20:57:37+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/lab/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/09-Autoencoders/README.md": { + "original_hash": "1b8d9e1b3a6f1daa864b1ff3dfc3076d", + "translation_date": "2025-09-23T11:01:02+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/10-GANs/README.md": { + "original_hash": "0ff65b4da07b23697235de2beb2a3c25", + "translation_date": "2025-09-23T11:01:45+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/11-ObjectDetection/README.md": { + "original_hash": "d76a7eda28de5210c8b1ba50a6216c69", + "translation_date": "2025-09-23T11:00:17+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md": { + "original_hash": "ad568d55ae65c856fe929fc2b278510a", + "translation_date": "2025-08-25T20:54:58+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/12-Segmentation/README.md": { + "original_hash": "6568aaae7e0e4afed4b5d74b5b223700", + "translation_date": "2025-09-23T11:01:28+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/12-Segmentation/lab/README.md": { + "original_hash": "365f0decfe0f47b460bbde8227c5009d", + "translation_date": "2025-08-25T20:53:51+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/lab/README.md", + "language_code": "sw" + }, + "lessons/4-ComputerVision/README.md": { + "original_hash": "58a52f000089c1d8906a4daa4ab1169b", + "translation_date": "2025-08-25T20:53:04+00:00", + "source_file": "lessons/4-ComputerVision/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/13-TextRep/README.md": { + "original_hash": "dbd3f73e4139f030ecb2e20387d70fee", + "translation_date": "2025-09-23T11:07:54+00:00", + "source_file": "lessons/5-NLP/13-TextRep/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/13-TextRep/assignment.md": { + "original_hash": "cdc1f2e631f055f3473b36d18e4760b3", + "translation_date": "2025-08-25T20:51:08+00:00", + "source_file": "lessons/5-NLP/13-TextRep/assignment.md", + "language_code": "sw" + }, + "lessons/5-NLP/14-Embeddings/README.md": { + "original_hash": "b708c9b85b833864c73c6281f1e6b96e", + "translation_date": "2025-09-23T11:07:35+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/14-Embeddings/assignment.md": { + "original_hash": "bc690ecf68b38d311cc9e12f3144a28c", + "translation_date": "2025-08-25T20:50:01+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/assignment.md", + "language_code": "sw" + }, + "lessons/5-NLP/15-LanguageModeling/README.md": { + "original_hash": "7ba20f54a5bfcd6521018cdfb17c7c57", + "translation_date": "2025-09-23T11:05:48+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/15-LanguageModeling/lab/README.md": { + "original_hash": "5130f01fdc5ebb83032b23d489027aac", + "translation_date": "2025-08-25T20:51:28+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/lab/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/16-RNN/README.md": { + "original_hash": "e2273cc150380a5e191903cea858f021", + "translation_date": "2025-09-23T11:07:06+00:00", + "source_file": "lessons/5-NLP/16-RNN/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/16-RNN/assignment.md": { + "original_hash": "47f7d3c6a5373543e051e4d1140ce898", + "translation_date": "2025-08-25T20:49:36+00:00", + "source_file": "lessons/5-NLP/16-RNN/assignment.md", + "language_code": "sw" + }, + "lessons/5-NLP/17-GenerativeNetworks/README.md": { + "original_hash": "51be6057374d01d70e07dd5ec88ebc0d", + "translation_date": "2025-09-23T11:05:23+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/17-GenerativeNetworks/lab/README.md": { + "original_hash": "439e12796197a90e7623d4c9c057b9c2", + "translation_date": "2025-08-25T20:50:34+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/lab/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/18-Transformers/README.md": { + "original_hash": "f335dfcb4a993920504c387973a36957", + "translation_date": "2025-09-23T11:06:01+00:00", + "source_file": "lessons/5-NLP/18-Transformers/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/18-Transformers/assignment.md": { + "original_hash": "177f3ea3995d725e6f9f5c66af16edcd", + "translation_date": "2025-08-25T20:51:36+00:00", + "source_file": "lessons/5-NLP/18-Transformers/assignment.md", + "language_code": "sw" + }, + "lessons/5-NLP/19-NER/README.md": { + "original_hash": "6522312ff835796ca34136a9462fafb2", + "translation_date": "2025-09-23T11:06:45+00:00", + "source_file": "lessons/5-NLP/19-NER/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/19-NER/lab/README.md": { + "original_hash": "032bda5068f543d6c1fcb30c34231461", + "translation_date": "2025-08-25T20:52:02+00:00", + "source_file": "lessons/5-NLP/19-NER/lab/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/20-LangModels/README.md": { + "original_hash": "97836d30a6bec736f8e3b4411c572bc2", + "translation_date": "2025-09-23T11:06:28+00:00", + "source_file": "lessons/5-NLP/20-LangModels/README.md", + "language_code": "sw" + }, + "lessons/5-NLP/README.md": { + "original_hash": "8ef02a9318257ea140ed3ed74442096d", + "translation_date": "2025-08-25T20:48:39+00:00", + "source_file": "lessons/5-NLP/README.md", + "language_code": "sw" + }, + "lessons/6-Other/21-GeneticAlgorithms/README.md": { + "original_hash": "6bbd632dfe6c62e5f66bb51fd78c174a", + "translation_date": "2025-09-23T10:57:11+00:00", + "source_file": "lessons/6-Other/21-GeneticAlgorithms/README.md", + "language_code": "sw" + }, + "lessons/6-Other/22-DeepRL/README.md": { + "original_hash": "04395657fc01648f8f70484d0e55ab67", + "translation_date": "2025-09-23T10:58:17+00:00", + "source_file": "lessons/6-Other/22-DeepRL/README.md", + "language_code": "sw" + }, + "lessons/6-Other/22-DeepRL/lab/README.md": { + "original_hash": "7bd8dc72040e98e35e7225e34058cd4e", + "translation_date": "2025-08-25T20:59:25+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/README.md", + "language_code": "sw" + }, + "lessons/6-Other/23-MultiagentSystems/README.md": { + "original_hash": "38a1185ae3d54b180378bbd71ae3ef16", + "translation_date": "2025-09-23T10:57:32+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/README.md", + "language_code": "sw" + }, + "lessons/6-Other/23-MultiagentSystems/assignment.md": { + "original_hash": "cf654ca60c7f86c8dad28596fb42994b", + "translation_date": "2025-08-25T20:58:38+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/assignment.md", + "language_code": "sw" + }, + "lessons/7-Ethics/README.md": { + "original_hash": "437c988596e751072e41a5aad3fcc5d9", + "translation_date": "2025-08-25T20:47:57+00:00", + "source_file": "lessons/7-Ethics/README.md", + "language_code": "sw" + }, + "lessons/README.md": { + "original_hash": "5fef1a0b22498d7188959e2a2cb08af7", + "translation_date": "2025-08-25T20:47:52+00:00", + "source_file": "lessons/README.md", + "language_code": "sw" + }, + "lessons/X-Extras/X1-MultiModal/README.md": { + "original_hash": "9c592c26aca16ca085d268c732284187", + "translation_date": "2025-08-25T20:59:33+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/README.md", + "language_code": "sw" + }, + "lessons/sketchnotes/LICENSE.md": { + "original_hash": "45ab63a2cd8f5faef6c9b150618837a4", + "translation_date": "2025-08-25T21:03:06+00:00", + "source_file": "lessons/sketchnotes/LICENSE.md", + "language_code": "sw" + }, + "lessons/sketchnotes/README.md": { + "original_hash": "050b8bddebafba55b129414e6ab096ab", + "translation_date": "2025-08-25T21:01:58+00:00", + "source_file": "lessons/sketchnotes/README.md", + "language_code": "sw" + }, + "troubleshoot.md": { + "original_hash": "8d9c5a4a7c7798d699672a22cb7fea86", + "translation_date": "2025-10-03T09:49:04+00:00", + "source_file": "troubleshoot.md", + "language_code": "sw" + } +} \ No newline at end of file diff --git a/translations/sw/AGENTS.md b/translations/sw/AGENTS.md index f55720a2..887d8efa 100644 --- a/translations/sw/AGENTS.md +++ b/translations/sw/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Muhtasari wa Mradi diff --git a/translations/sw/README.md b/translations/sw/README.md index 1699ea57..9f913d93 100644 --- a/translations/sw/README.md +++ b/translations/sw/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -23,156 +14,158 @@ CO_OP_TRANSLATOR_METADATA: # Akili Bandia kwa Waanzilishi - Mtaala -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/sw/ai-overview.0857791951d19500.webp)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/sw/ai-overview.0857791951d19500.webp)| |:---:| -| AI Kwa Waanzilishi - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI Kwa Waanzilishi - _Sketchnote na [@girlie_mac](https://twitter.com/girlie_mac)_ | + +Chunguza ulimwengu wa **Akili Bandia** (AI) na mtaala wetu wa wiki 12, masomo 24! Unajumuisha masomo ya vitendo, maswali na maabara. Mtaala huu ni rafiki kwa waanzilishi na unashughulikia zana kama TensorFlow na PyTorch, pamoja na maadili katika AI -Chunguza ulimwengu wa **Akili Bandia** (AI) kupitia mtaala wetu wa wiki 12 wenye masomo 24! Unajumuisha masomo ya vitendo, mitihani, na maabara. Mtaala ni rafiki kwa wanaoanza na unahusisha zana kama TensorFlow na PyTorch, pamoja na maadili katika AI ### 🌐 Msaada wa Lugha Nyingi -#### Imesaidiwa kupitia Kitendo cha GitHub (Kiotomatiki & Kila Wakati Kisasishwa) +#### Unaungwa mkono kupitia Hatua ya GitHub (Moja kwa moja & Daima Imeboreshwa) -[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) +[Kiarabu](../ar/README.md) | [Kibangla](../bn/README.md) | [Ki Bulgaria](../bg/README.md) | [Kiburma (Myanmar)](../my/README.md) | [Kichina (Rahisi)](../zh-CN/README.md) | [Kichina (Kiasili, Hong Kong)](../zh-HK/README.md) | [Kichina (Kiasili, Macau)](../zh-MO/README.md) | [Kichina (Kiasili, Taiwan)](../zh-TW/README.md) | [Kikroeshia](../hr/README.md) | [Kicheki](../cs/README.md) | [Kidanishi](../da/README.md) | [Kiholanzi](../nl/README.md) | [Kiestonia](../et/README.md) | [Kifinlandi](../fi/README.md) | [Kifaransa](../fr/README.md) | [Kijerumani](../de/README.md) | [Kigiriki](../el/README.md) | [Kiebrania](../he/README.md) | [Kihindi](../hi/README.md) | [Kihangari](../hu/README.md) | [Kiindonesia](../id/README.md) | [Kiitaliano](../it/README.md) | [Kijapani](../ja/README.md) | [Kikannada](../kn/README.md) | [Kikorea](../ko/README.md) | [Kilithuanian](../lt/README.md) | [Kimelayu](../ms/README.md) | [Kimalayalam](../ml/README.md) | [Kimarathi](../mr/README.md) | [Kinepali](../ne/README.md) | [Kipidgin cha Nijeria](../pcm/README.md) | [Kinorwe](../no/README.md) | [Kifarsi (Persia)](../fa/README.md) | [Kipolishi](../pl/README.md) | [Kireno (Brazil)](../pt-BR/README.md) | [Kireno (Ureno)](../pt-PT/README.md) | [Kipunjabi (Gurmukhi)](../pa/README.md) | [Kiromania](../ro/README.md) | [Kirusi](../ru/README.md) | [Kiserbia (Cyrillic)](../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) | [Kiteleu](../te/README.md) | [Kithai](../th/README.md) | [Kituruki](../tr/README.md) | [Kiukreni](../uk/README.md) | [Kiurdu](../ur/README.md) | [Kivietinamu](../vi/README.md) -> **Unapendelea Kunakili Kwenye Kompyuta?** +> **Unapendelea Kukopa Kwenye Kompyuta Binafsi?** -> Hifadhi hii ina lugha zaidi ya 50 za tafsiri ambazo huongeza sana ukubwa wa kupakua. Ili kunakili bila tafsiri, tumia sparse checkout: +> Hifadhi hii ina tafsiri zaidi ya lugha 50 ambazo huongeza sana ukubwa wa kupakua. Ili kukopa 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 itakupa kila kitu unachohitaji kukamilisha kozi kwa upakuaji wa haraka zaidi. +> Hii inakupa kila unachohitaji kukamilisha kozi kwa upakuaji wa kasi zaidi. -**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)** +**Ikiwa unataka kuongeza lugha nyingine 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 +## Kile utakachojifunza -**[Mchoro wa Mawazo wa Kozi](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** +**[Ramani ya Akili ya Kozi](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Katika mtaala huu, utajifunza: -* 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**. +* Mbinu tofauti za Akili Bandia, ikiwa ni pamoja na mbinu ya "kale nzuri" ya alama na **Uwakilishi wa Maarifa** na hoja ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Mitandao ya Neva** na **Kujifunza Kina**, ambazo ni msingi wa AI ya kisasa. Tutataja dhana za mada hizi muhimu kwa kutumia msimbo katika mifumo miwili maarufu - [TensorFlow](http://Tensorflow.org) na [PyTorch](http://pytorch.org). +* **Mibinu ya Neural** kwa kazi na picha na maandishi. Tutashughulikia mifano ya hivi karibuni lakini inaweza kuwa na upungufu kidogo katika hali ya kisasa zaidi. +* Mbinu za AI zisizo maarufu sana, kama vile **Algorithmi za Kijenetiki** na **Mifumo ya Wakala Wengi**. -Sio mambo yatafundishwa katika mtaala huu: +Sio tutakachoshughulikia 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 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/). +* Masuala ya biashara ya kutumia **AI katika Biashara**. Fikiria kuchukua njia ya kujifunza 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 Klasiki**, ambacho kimeelezwa vizuri katika [Mtaala wa Kujifunza kwa Mashine kwa Waanzilishi](http://github.com/Microsoft/ML-for-Beginners). +* Programu halisi za AI zilizoanzishwa kwa kutumia **[Huduma za Kitalamu](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 asili](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[AI ya Kizazi na Huduma za Azure OpenAI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** na zingine. +* **Mifumo Mahususi ya Mawingu ya 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 kujifunza [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 ya kujifunza tofauti 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 Nzito** nyuma ya kujifunza 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, ambayo pia inapatikana mtandaoni kwenye [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -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). +Kwa utangulizi mwepesi wa mada za _AI katika Mwingu_ unaweza kufikiria 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 | [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 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 | [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) | +| II | **AI ya Alama** | +| 02 | [Uwakilishi wa Maarifa na Mifumo ya Wataalamu](./lessons/2-Symbolic/README.md) | [Mifumo ya Wataalamu](./lessons/2-Symbolic/Animals.ipynb) / [Ontagolojia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafu ya Dhana](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Utangulizi wa Mitandao ya Neuro**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Daftari](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Maabara](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Multilayered Perceptron na Kuunda Mfumo Wetu Mwenyewe](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Daftari](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Maabara](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Utangulizi kwa Mifumo (PyTorch/TensorFlow) na Kuvaa Mzigo Zaidi](./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) | [Maabara](./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) | +| 06 | [Utangulizi kwa Maono ya Kompyuta. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Daftari](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Maabara](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Mitandao ya Neuron ya Kukunja](./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) | [Maabara](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Mitandao Iliyopangwa awali na Kujifunza Uhamisho](./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) | [Maabara](./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) | | +| 10 | [Mitandao ya Kupagawana Kama Washindani & Uhamisho wa Mtindo wa Sanaa](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Utambuzi wa Vitu](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Maabara](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Ugawaji wa Kimsamiati. 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) | +| 13 | [Uwakilishi 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 | [Embedding za maneno za Kimsamiati. 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. Mafunzo ya embeddings zako binafsi](./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) | [Maabara](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Mitandao ya Neuron 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 Kurudiarudia Zaizozalisha](./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) | [Maabara](./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 | [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) | +| 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) | [Maabara](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Mifano Mikubwa ya Lugha, Programu ya Prompt na Kazi za 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 | **Mbinu Nyingine za AI** || | +| 21 | [Algoritmi za Jenetiki](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Daftari](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Mafunzo ya Kina ya Kuimarisha](./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) | [Maabara](./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 | [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) | | +| 24 | [Maadili ya AI na AI yenye Uwajibikaji](./lessons/7-Ethics/README.md) | [Microsoft Learn: Kanuni za AI yenye Uwajibikaji](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Nyongeza** | | | +| 25 | [Mitandao ya Modal Wingi, CLIP na VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Daftari](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Kila somo lina * Nyenzo za kusoma kabla -* 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. +* Dainamiki za Jupyter zinazoendeshwa, mara nyingi maalum kwa mfumo (**PyTorch** au **TensorFlow**). Daftari linaloendeshwa pia lina nyenzo nyingi za nadharia, hivyo kuelewa mada unahitaji kupitia angalau toleo moja la daftari (yaani PyTorch au TensorFlow). +* **Maabara** zinapatikana kwa mada fulani, zinazokupa fursa ya kujaribu kutumia nyenzo ulizojifunza kwa tatizo fulani. +* Sehemu zingine zina viungo kwa moduli za [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) zinazofunika mada zinazohusiana. -## Kuanzia +## Kuanzisha ### 🎯 Mpya kwa AI? Anza Hapa! -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: +Kama wewe ni mpya kabisa kwa AI na unataka mifano ya haraka, ya mikono, angalia [**Mifano Rahisi Kwa Waanzilishi**](./examples/README.md)! Hizi ni pamoja na: - 🌟 **Hello AI World** - Programu yako ya kwanza ya AI (utambuzi wa mifumo) -- 🧠 **Mtandao Rahisi wa Neva** - Jenga mtandao wa neva kutoka mwanzo -- πŸ–ΌοΈ **Kipangaji Picha** - Pangilia picha na maelezo ya kina -- πŸ’¬ **Hisia za Maandishi** - Changanua maandishi chanya/negatifu +- 🧠 **Mtandao Rahisi wa Neuron** - Tengeneza mtandao wa neuron kutoka mwanzoni -Mifano hii imeundwa kusaidia kuelewa dhana za AI kabla ya kuingia kwenye mtaala kamili. +- πŸ–ΌοΈ **Mfafanuzi wa Picha** - Tafsiri picha kwa maelezo ya kina +- πŸ’¬ **Hisia za Maandishi** - Changanua maandishi chanya/negativi -### πŸ“š Usanidi Kamili wa Mtaala +Mifano hii imeundwa kukusaidia kuelewa dhana za AI kabla ya kuingia kwenye mtaala kamili. -- 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) +### πŸ“š Mipangilio ya Mtaala Kamili + +- Tumeunda [somu ya kuweka](./lessons/0-course-setup/setup.md) kusaidia kwa kuweka mazingira yako ya maendeleo. - Kwa Walimu, tumeunda [somu ya kuweka mitaala](./lessons/0-course-setup/for-teachers.md) pia kwa ajili yenu! +- Jinsi ya [Kuendesha msimbo katika VSCode au Codespace](./lessons/0-course-setup/how-to-run.md) Fuata hatua hizi: -Fungua Nakala ya Hifadhi: Bonyeza kitufe cha "Fork" upande wa juu kulia wa ukurasa huu. +Fikisha Hifadhidata: Bonyeza kitufe cha "Fikisha" juu-kushoto mwa ukurasa huu. -Nakili Hifadhi: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Nakili Hifadhidata: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Usisahau kuweka nyota (🌟) kwenye hifadhi hii ili kuipata kirahisi baadaye. +Usisahau kuweka nyota (🌟) kwenye repo hii ili kuipata kwa urahisi 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 kuungana na wanafunzi wengine wanaochukua kozi hii na kupata 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 upokee msaada. -Ikiwa una maoni kuhusu bidhaa au maswali wakati wa kujenga, tembelea [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) +Ikiwa una maoni au maswali kuhusu bidhaa wakati wa kujenga tembelea [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) -## Maswali ya Kujifunza +## Mtihani -> **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. +> **Kumbuka kuhusu mitihani**: Mitihani yote iko ndani ya folda ya Quiz-app katika etc\quiz-app, au [Mtandaoni Hapa](https://ff-quizzes.netlify.app/) Imeunganishwa kutoka ndani ya masomo, programu ya mtihani inaweza kuendeshwa kwa ndani au kuwekwa Azure; fuata maelekezo katika folda ya `quiz-app`. Inatafsiriwa polepole. -## Msaada Unahitajika +## Kuhitaji Msaada -Je, una mapendekezo au umeona makosa ya tahajia au msimbo? Toa tatizo au tengeneza ombi la mabadiliko. +Je, una mapendekezo au umepata makosa ya tahajia au ya msimbo? Fungua suala au tengeneza ombi la kuvuta. ## Shukrani Maalum * **✍️ Mwandishi Mkuu:** [Dmitry Soshnikov](http://soshnikov.com), PhD * **πŸ”₯ Mhariri:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Mchora Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **βœ… Mtengenezaji wa Maswali:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🎨 Mchoraji wa Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **βœ… Muumbaji wa Mtihani:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) * **πŸ™ Washiriki Wakuu:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Mitaala Mingine +## Mitaala Mengine -Timu yetu huandaa mitaala mingine! Angalia: +Timu yetu hutengeneza mitaala mingine! Angalia: ### LangChain @@ -181,7 +174,7 @@ Timu yetu huandaa mitaala mingine! Angalia: --- -### Azure / Edge / MCP / Maajenti +### Azure / Edge / MCP / Wakala [![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) @@ -189,7 +182,7 @@ Timu yetu huandaa mitaala mingine! Angalia: --- -### Mfululizo wa AI Inayozalisha +### Mfululizo wa AI wa Kizazi [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) @@ -197,7 +190,7 @@ Timu yetu huandaa mitaala mingine! Angalia: --- -### Kujifunza Msingi +### Mafunzo ya Msingi [![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) @@ -216,7 +209,7 @@ Timu yetu huandaa mitaala mingine! Angalia: ## Kupata Msaada -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. +Ikiwa umekwama au una maswali kuhusu kujenga programu za AI. Jiunge na wanafunzi wenzao na watengenezaji wenye uzoefu katika majadiliano kuhusu MCP. Ni jamii yenye msaada ambapo maswali yanakaribishwa na maarifa hushirikiwa kwa huru. [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) @@ -227,6 +220,6 @@ Ikiwa una maoni kuhusu bidhaa au makosa wakati wa kujenga tembelea: --- -**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. +**Kauli ya Kutegemea**: +Hati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuwa sahihi, 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 cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu inayofanywa na binadamu inapendekezwa. Hatuhusiki kwa maelewano mabaya au tafsiri isiyo sahihi inayotokana na matumizi ya tafsiri hii. \ No newline at end of file diff --git a/translations/sw/SECURITY.md b/translations/sw/SECURITY.md index bfb17e96..1124d5cd 100644 --- a/translations/sw/SECURITY.md +++ b/translations/sw/SECURITY.md @@ -1,12 +1,3 @@ - ## Usalama Microsoft inachukulia usalama wa bidhaa na huduma zetu za programu kwa uzito, ikijumuisha hazina zote za msimbo wa chanzo zinazodhibitiwa kupitia mashirika yetu ya GitHub, ambayo ni pamoja na [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), na [mashirika yetu ya GitHub](https://opensource.microsoft.com/). diff --git a/translations/sw/etc/CODE_OF_CONDUCT.md b/translations/sw/etc/CODE_OF_CONDUCT.md index 0af22182..245a1ebd 100644 --- a/translations/sw/etc/CODE_OF_CONDUCT.md +++ b/translations/sw/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Kanuni za Maadili ya Microsoft Open Source Mradi huu umechukua [Kanuni za Maadili za Microsoft Open Source](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/sw/etc/CONTRIBUTING.md b/translations/sw/etc/CONTRIBUTING.md index d1ab84c6..29a73f01 100644 --- a/translations/sw/etc/CONTRIBUTING.md +++ b/translations/sw/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Kuchangia Mradi huu unakaribisha michango na mapendekezo. Michango mingi inahitaji wewe diff --git a/translations/sw/etc/Mindmap.md b/translations/sw/etc/Mindmap.md index af3cf1e2..591c0306 100644 --- a/translations/sw/etc/Mindmap.md +++ b/translations/sw/etc/Mindmap.md @@ -1,12 +1,3 @@ - # AI ## [Utangulizi wa AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/sw/etc/SUPPORT.md b/translations/sw/etc/SUPPORT.md index 70eb6e5d..9b1f7d27 100644 --- a/translations/sw/etc/SUPPORT.md +++ b/translations/sw/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # Msaada ## Jinsi ya kuripoti matatizo na kupata msaada diff --git a/translations/sw/etc/TRANSLATIONS.md b/translations/sw/etc/TRANSLATIONS.md index 9a48ee96..33d570fe 100644 --- a/translations/sw/etc/TRANSLATIONS.md +++ b/translations/sw/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # Changia kwa kutafsiri masomo Tunakaribisha tafsiri za masomo katika mtaala huu! diff --git a/translations/sw/etc/quiz-app/README.md b/translations/sw/etc/quiz-app/README.md index 12b21ab2..d45ab227 100644 --- a/translations/sw/etc/quiz-app/README.md +++ b/translations/sw/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # Maswali ya Mitihani Maswali haya ni ya kabla na baada ya mihadhara kwa mtaala wa AI unaopatikana kwenye https://aka.ms/ai-beginners diff --git a/translations/sw/examples/README.md b/translations/sw/examples/README.md index 2c86c784..83ea0067 100644 --- a/translations/sw/examples/README.md +++ b/translations/sw/examples/README.md @@ -1,12 +1,3 @@ - # Mifano ya AI kwa Wanaoanza Karibu! Hii ni orodha ya mifano rahisi, inayojitegemea ili kukusaidia kuanza na AI na ujifunzaji wa mashine. Kila mfano umeundwa kuwa rafiki kwa wanaoanza, ukiwa na maelezo ya kina na maelekezo ya hatua kwa hatua. diff --git a/translations/sw/lessons/0-course-setup/for-teachers.md b/translations/sw/lessons/0-course-setup/for-teachers.md index 6956fc49..01600d38 100644 --- a/translations/sw/lessons/0-course-setup/for-teachers.md +++ b/translations/sw/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # Kwa Walimu Je, ungependa kutumia mtaala huu darasani kwako? Tafadhali jisikie huru! 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 8feb6f0e..66841141 100644 --- a/translations/sw/lessons/0-course-setup/how-to-run.md +++ b/translations/sw/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # Jinsi ya 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: diff --git a/translations/sw/lessons/0-course-setup/setup.md b/translations/sw/lessons/0-course-setup/setup.md index 34f28b44..5ecf8861 100644 --- a/translations/sw/lessons/0-course-setup/setup.md +++ b/translations/sw/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # Kuanza na Mtaala huu ## Je, wewe ni mwanafunzi? diff --git a/translations/sw/lessons/1-Intro/README.md b/translations/sw/lessons/1-Intro/README.md index f6317c72..53d1ef40 100644 --- a/translations/sw/lessons/1-Intro/README.md +++ b/translations/sw/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # Utangulizi wa AI ![Muhtasari wa maudhui ya Utangulizi wa AI katika mchoro](../../../../translated_images/sw/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/sw/lessons/1-Intro/assignment.md b/translations/sw/lessons/1-Intro/assignment.md index 037e4ea4..8ccbe205 100644 --- a/translations/sw/lessons/1-Intro/assignment.md +++ b/translations/sw/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam Michezo ni eneo ambalo limeathiriwa sana na maendeleo ya AI na ML. Katika kazi hii, andika karatasi fupi kuhusu mchezo unaoupenda ambao umeathiriwa na mabadiliko ya AI. Unapaswa kuwa mchezo wa zamani wa kutosha kuathiriwa na aina kadhaa za mifumo ya usindikaji wa kompyuta. Mfano mzuri ni Chess au Go, lakini pia angalia michezo ya video kama pong au Pac-Man. Andika insha inayojadili historia ya mchezo huo, hali yake ya sasa, na mustakabali wake wa AI. diff --git a/translations/sw/lessons/2-Symbolic/README.md b/translations/sw/lessons/2-Symbolic/README.md index 3d42efa0..ddcd883d 100644 --- a/translations/sw/lessons/2-Symbolic/README.md +++ b/translations/sw/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # Uwakilishi wa Maarifa na Mifumo ya Wataalamu -![Muhtasari wa maudhui ya AI ya Ikoniki](../../../../../../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) @@ -41,7 +32,7 @@ Mara nyingi, hatufafanui maarifa kwa ukamilifu, lakini tunayalinganisha na dhana 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: -![Spektra ya 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) @@ -94,7 +85,7 @@ Sarufi ya Kipande | Uingizaji nafasi | | | 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. -![Mimino ya binadamu](../../../../../../translated_images/sw/arch-human.5d4d35f1bba3ab1c.webp) | ![Mfumo wenye 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 wenye maarifa @@ -106,7 +97,7 @@ Mifumo ya wataalamu imejengwa kama mfumo wa fikra wa binadamu, wenye **kumbukumb 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) diff --git a/translations/sw/lessons/2-Symbolic/assignment.md b/translations/sw/lessons/2-Symbolic/assignment.md index b2174190..f3ba7efb 100644 --- a/translations/sw/lessons/2-Symbolic/assignment.md +++ b/translations/sw/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # Jenga Ontolojia Kujenga msingi wa maarifa kunahusu kuainisha mfano unaowakilisha ukweli kuhusu mada fulani. Chagua mada - kama mtu, mahali, au kitu - kisha jenga mfano wa mada hiyo. Tumia baadhi ya mbinu na mikakati ya kujenga mifano iliyoelezewa katika somo hili. Mfano unaweza kuwa kuunda ontolojia ya sebule yenye fanicha, taa, na kadhalika. Sebule inatofautianaje na jikoni? Bafuni? Unajuaje kuwa ni sebule na si chumba cha kulia chakula? Tumia [ProtΓ©gΓ©](https://protege.stanford.edu/) kujenga ontolojia yako. diff --git a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/README.md index 0d1dbdc1..2d11657d 100644 --- a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # Utangulizi wa Mitandao ya Neva: Perceptron ## [Jaribio la awali ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ Moja ya majaribio ya kwanza ya kutekeleza kitu kinachofanana na mtandao wa neva | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > Picha [kutoka Wikipedia](https://en.wikipedia.org/wiki/Perceptron) @@ -34,7 +25,7 @@ y(x) = f(wTx) ambapo f ni kazi ya hatua ya uanzishaji - + ## Mafunzo ya Perceptron diff --git a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index a19bf107..56084fb0 100644 --- a/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Uainishaji wa Darasa Nyingi kwa Kutumia Perceptron Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/README.md index 963b99c6..af90f248 100644 --- a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # Utangulizi wa Mitandao ya Neural. Multi-Layered Perceptron Katika sehemu iliyopita, ulijifunza kuhusu mfano rahisi wa mtandao wa neural - perceptron ya tabaka moja, mfano wa uainishaji wa tabaka mbili wa mstari. @@ -65,7 +56,7 @@ Algorithimu ya gradient descent ingesalia ile ile, lakini ingekuwa ngumu zaidi k Kumbuka kwamba sehemu ya kushoto kabisa ya maelezo haya yote ni sawa, na hivyo tunaweza kuhesabu derivatives kwa ufanisi kuanzia kazi ya hasara na kwenda "nyuma" kupitia grafu ya hesabu. Hivyo mbinu ya kufundisha perceptron ya tabaka nyingi inaitwa **backpropagation**, au 'backprop'. -compute graph +compute graph > TODO: rejea ya picha diff --git a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index 99f9aab4..158de4f9 100644 --- a/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # Uainishaji wa MNIST kwa Mfumo Wetu Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md index c8e1ebef..6bc75bdf 100644 --- a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Mfumo wa Mitandao ya Neural Kama tulivyojifunza tayari, ili kuweza kufundisha mitandao ya neural kwa ufanisi tunahitaji kufanya mambo mawili: diff --git a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index 0acc598d..f6abab58 100644 --- a/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # Uainishaji kwa kutumia PyTorch/TensorFlow Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/3-NeuralNetworks/README.md b/translations/sw/lessons/3-NeuralNetworks/README.md index 6d4545ed..4412cdfb 100644 --- a/translations/sw/lessons/3-NeuralNetworks/README.md +++ b/translations/sw/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # Utangulizi wa Mitandao ya Neva ![Muhtasari wa maudhui ya Utangulizi wa Mitandao ya Neva katika mchoro](../../../../translated_images/sw/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md index b606d8e2..af630bdb 100644 --- a/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/sw/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # Utangulizi wa Uelewa wa Picha na Kompyuta [Computer Vision](https://wikipedia.org/wiki/Computer_vision) ni taaluma inayolenga kuwezesha kompyuta kupata uelewa wa kiwango cha juu wa picha za kidijitali. Hii ni tafsiri pana, kwa sababu *uelewa* unaweza kumaanisha mambo mengi tofauti, ikiwa ni pamoja na kutambua kitu kwenye picha (**utambuzi wa vitu**), kuelewa kinachotokea (**utambuzi wa matukio**), kuelezea picha kwa maandishi, au kujenga upya mandhari kwa 3D. Pia kuna kazi maalum zinazohusiana na picha za binadamu: makadirio ya umri na hisia, utambuzi wa uso na utambulisho, na makadirio ya mkao wa 3D, miongoni mwa mengine. @@ -115,7 +106,7 @@ Soma zaidi kuhusu optical flow [katika mafunzo haya mazuri](https://learnopencv. Katika maabara hii, utachukua video yenye ishara rahisi, na lengo lako ni kutoa harakati za juu/chini/kushoto/kulia kwa kutumia optical flow. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/sw/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/sw/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 959eef58..d74201ed 100644 --- a/translations/sw/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Kugundua Harakati kwa Kutumia Optical Flow Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://aka.ms/ai-beginners). diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index 09db4c41..8fc89678 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # Miundo Maarufu ya CNN ### VGG-16 @@ -25,7 +16,7 @@ Kama unavyoona, VGG inafuata muundo wa jadi wa piramidi, ambao ni mfululizo wa t ResNet ni familia ya miundo iliyopendekezwa na Microsoft Research mwaka 2015. Wazo kuu la ResNet ni kutumia **residual blocks**: - + > Picha kutoka [karatasi hii](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +28,7 @@ Unaweza pia kufikiria mtandao huu kama unaoweza kurekebisha ugumu wake kulingana Muundo wa Google Inception unachukua wazo hili hatua moja mbele, na hujenga kila tabaka la mtandao kama mchanganyiko wa njia kadhaa tofauti: - + > Picha kutoka [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md index 9901dea2..ef73701c 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Mitandao ya Neural ya Convolutional Tumeona awali kwamba mitandao ya neural ni nzuri sana katika kushughulikia picha, na hata perceptron ya tabaka moja inaweza kutambua namba zilizoandikwa kwa mkono kutoka kwenye seti ya data ya MNIST kwa usahihi wa kuridhisha. Hata hivyo, seti ya data ya MNIST ni maalum sana, na namba zote ziko katikati ya picha, jambo ambalo hufanya kazi kuwa rahisi. @@ -24,7 +15,7 @@ Ili kutoa mifumo, tutatumia dhana ya **vichujio vya convolutional**. Kama unavyo Kwa mfano, tukitumia vichujio vya mstari wima na mstari mlalo vya 3x3 kwenye namba za MNIST, tunaweza kupata sehemu zenye mwangaza (mfano, thamani za juu) ambapo kuna mistari wima na mlalo kwenye picha yetu ya awali. Kwa hivyo vichujio hivyo viwili vinaweza kutumika "kutafuta" mistari. Vivyo hivyo, tunaweza kubuni vichujio tofauti kutafuta mifumo mingine ya kiwango cha chini: - + > Picha ya [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md index be14092c..53655f69 100644 --- a/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # Uainishaji wa Nyuso za Wanyama Kipenzi Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md index ea939869..89f7571b 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Mitandao Iliyojifunza Kabla na Kujifunza kwa Uhamisho Kufundisha CNNs inaweza kuchukua muda mwingi, na data nyingi inahitajika kwa kazi hiyo. Hata hivyo, muda mwingi hutumika kujifunza vichujio vya kiwango cha chini ambavyo mtandao unaweza kutumia kutoa mifumo kutoka kwa picha. Swali la asili linatokea - je, tunaweza kutumia mtandao wa neva uliyojifunza kwenye seti moja ya data na kuubadilisha ili kuainisha picha tofauti bila kuhitaji mchakato kamili wa mafunzo? diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index 335e0296..2b63ddf2 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # Mbinu za Mafunzo ya Kujifunza Kina Kadri mitandao ya neva inavyozidi kuwa na kina, mchakato wa mafunzo yake unazidi kuwa changamoto. Tatizo moja kubwa ni kinachojulikana kama [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) au [exploding gradients](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Chapisho hili](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) linatoa utangulizi mzuri kuhusu matatizo haya. diff --git a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index 0d098ad3..c6822fc5 100644 --- a/translations/sw/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Uainishaji wa Wanyama wa Oxford kwa Kutumia Kujifunza kwa Uhamisho Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md index 39c63075..6b030df2 100644 --- a/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/sw/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # Autoencoders Wakati wa kufundisha CNNs, mojawapo ya changamoto ni kwamba tunahitaji data nyingi yenye lebo. Katika hali ya uainishaji wa picha, tunahitaji kutenganisha picha katika madarasa tofauti, jambo ambalo linahitaji juhudi za mikono. @@ -46,7 +37,7 @@ Kwa muhtasari: * Tunachukua vector `sample` kutoka kwenye usambazaji N(zmean,exp(zlog\_sigma)) * Decoder hujaribu kurejesha picha ya asili kwa kutumia `sample` kama vector ya pembejeo - + > Picha kutoka [blog post hii](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) na Isaak Dykeman @@ -57,13 +48,13 @@ Variational auto-encoders hutumia loss function ngumu inayojumuisha sehemu mbili Faida moja muhimu ya VAEs ni kwamba zinaturuhusu kuunda picha mpya kwa urahisi, kwa sababu tunajua usambazaji wa kuchukua latent vectors. Kwa mfano, tukifundisha VAE na latent vector ya 2D kwenye MNIST, tunaweza kubadilisha vipengele vya latent vector ili kupata namba tofauti: -vaemnist +vaemnist > Picha na [Dmitry Soshnikov](http://soshnikov.com) Angalia jinsi picha zinavyoungana, tunapoanza kuchukua latent vectors kutoka sehemu tofauti za latent parameter space. Tunaweza pia kuona anga hii kwa 2D: -vaemnist cluster +vaemnist cluster > Picha na [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sw/lessons/4-ComputerVision/10-GANs/README.md b/translations/sw/lessons/4-ComputerVision/10-GANs/README.md index 23602d99..d14bcc66 100644 --- a/translations/sw/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/sw/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Mitandao ya Kizazi ya Kihasama Katika sehemu iliyopita, tulijifunza kuhusu **miundo ya kizazi**: miundo inayoweza kuzalisha picha mpya zinazofanana na zile zilizopo kwenye seti ya mafunzo. VAE ilikuwa mfano mzuri wa muundo wa kizazi. @@ -17,7 +8,7 @@ Hata hivyo, tukijaribu kuzalisha kitu chenye maana zaidi, kama mchoro wa ubora w Wazo kuu la GAN ni kuwa na mitandao miwili ya neva ambayo itafundishwa dhidi ya kila mmoja: - + > Picha na [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +32,7 @@ Generator ni ngumu kidogo. Unaweza kuichukulia kama discriminator iliyogeuzwa. K > βœ… Kwa sababu tabaka ya convolution inatekelezwa kama kichujio cha mstari kinachopita kwenye picha, deconvolution kimsingi ni sawa na convolution, na inaweza kutekelezwa kwa kutumia mantiki sawa ya tabaka. - + > Picha na [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md index eed4d7e8..30aae572 100644 --- a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Utambuzi wa Vitu Mifano ya uainishaji wa picha tuliyojifunza hadi sasa ilichukua picha na kutoa matokeo ya kategoria, kama vile darasa 'namba' katika tatizo la MNIST. Hata hivyo, mara nyingi hatutaki tu kujua kwamba picha inaonyesha vitu - tunataka pia kujua mahali vilipo kwa usahihi. Hii ndiyo hasa dhumuni la **utambuzi wa vitu**. diff --git a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index fb9d308e..d265c4b2 100644 --- a/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Utambuzi wa Vichwa kwa Kutumia Hollywood Heads Dataset Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/sw/lessons/4-ComputerVision/12-Segmentation/README.md index 276c046e..ee7e085d 100644 --- a/translations/sw/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/sw/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # Ugawaji Tumejifunza awali kuhusu Utambuzi wa Vitu, ambao hutuwezesha kutambua vitu kwenye picha kwa kutabiri *maboksi ya mipaka*. Hata hivyo, kwa baadhi ya kazi hatuhitaji tu maboksi ya mipaka, bali pia utambuzi wa vitu kwa usahihi zaidi. Kazi hii inaitwa **ugawaji**. @@ -20,7 +11,7 @@ Ugawaji unaweza kuonekana kama **uainishaji wa pikseli**, ambapo kwa **kila** pi Kwa ugawaji wa mfano, kondoo hawa ni vitu tofauti, lakini kwa ugawaji wa kisemantiki kondoo wote wanawakilishwa na darasa moja. - + > Picha kutoka [makala hii ya blogu](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +20,7 @@ Kuna usanifu tofauti wa neva kwa ugawaji, lakini zote zina muundo sawa. Kwa namn * **Encoder** huchukua vipengele kutoka kwenye picha ya ingizo. * **Decoder** hubadilisha vipengele hivyo kuwa **picha ya maski**, yenye ukubwa sawa na idadi ya njia zinazolingana na idadi ya madarasa. - + > Picha kutoka [chapisho hili](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +34,7 @@ Katika somo hili, tutaona ugawaji ukifanya kazi kwa kufundisha mtandao kutambua > βœ… Mbinu hii inafaa hasa kwa aina hii ya picha za matibabu, lakini ni matumizi gani mengine ya ulimwengu halisi unayoweza kufikiria? -navi +navi > Picha kutoka Hifadhidata ya PH2 diff --git a/translations/sw/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/sw/lessons/4-ComputerVision/12-Segmentation/lab/README.md index ee65176f..35a62eb3 100644 --- a/translations/sw/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/sw/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # Usegaji wa Mwili wa Binadamu Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/4-ComputerVision/README.md b/translations/sw/lessons/4-ComputerVision/README.md index 1bf68b23..207ff0dc 100644 --- a/translations/sw/lessons/4-ComputerVision/README.md +++ b/translations/sw/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # Maono ya Kompyuta ![Muhtasari wa maudhui ya Maono ya Kompyuta katika mchoro](../../../../translated_images/sw/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/sw/lessons/5-NLP/13-TextRep/README.md b/translations/sw/lessons/5-NLP/13-TextRep/README.md index b9f10b5f..b150744c 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/README.md +++ b/translations/sw/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # Kuwakilisha Maandishi kama Tensors ## [Jaribio la kabla ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ Lengo letu litakuwa kuainisha kipengele cha habari katika mojawapo ya kategoria Ikiwa tunataka kutatua kazi za Usindikaji wa Lugha Asilia (NLP) kwa kutumia mitandao ya neva, tunahitaji njia ya kuwakilisha maandishi kama tensors. Kompyuta tayari zinawakilisha herufi za maandishi kama namba zinazolingana na fonti kwenye skrini yako kwa kutumia encodings kama ASCII au UTF-8. -Picha inayoonyesha mchoro wa kuonyesha ramani ya herufi kwa uwakilishi wa ASCII na binary +Picha inayoonyesha mchoro wa kuonyesha ramani ya herufi kwa uwakilishi wa ASCII na binary > [Chanzo cha Picha](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +39,7 @@ Katika baadhi ya matukio, tunaweza kuzingatia kutumia tri-grams -- mchanganyiko Tunapokuwa tunatatua kazi kama uainishaji wa maandishi, tunahitaji kuwakilisha maandishi kwa vector ya ukubwa wa kudumu, ambayo tutatumia kama ingizo kwa classifier ya mwisho yenye dense. Njia rahisi zaidi ya kufanya hivyo ni kuunganisha uwakilishi wa maneno ya mtu binafsi, kwa mfano kwa kuyaongeza. Ikiwa tunaongeza one-hot encodings za kila neno, tutapata vector ya frequencies, inayoonyesha mara ngapi kila neno linatokea ndani ya maandishi. Uwakilishi wa maandishi kama huu unaitwa **bag of words** (BoW). - + > Picha na mwandishi diff --git a/translations/sw/lessons/5-NLP/13-TextRep/assignment.md b/translations/sw/lessons/5-NLP/13-TextRep/assignment.md index 6af53006..ff159df1 100644 --- a/translations/sw/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/sw/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # Kazi: Daftari Kutumia daftari zinazohusiana na somo hili (iwe ni toleo la PyTorch au TensorFlow), ziendeshe tena ukitumia seti yako ya data, labda moja kutoka Kaggle, ukitumia kwa kutaja chanzo. Andika upya daftari ili kuonyesha matokeo yako mwenyewe. Jaribu seti za data za ubunifu ambazo zinaweza kushangaza, kama [hii kuhusu matukio ya UFO](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) kutoka NUFORC. diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/README.md b/translations/sw/lessons/5-NLP/14-Embeddings/README.md index fb4be33f..0e878e95 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/sw/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Embeddings ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/sw/lessons/5-NLP/14-Embeddings/assignment.md b/translations/sw/lessons/5-NLP/14-Embeddings/assignment.md index 28c45891..f921445d 100644 --- a/translations/sw/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/sw/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # Kazi: Notebooks Kutumia notebooks zinazohusiana na somo hili (iwe ni toleo la PyTorch au TensorFlow), ziendeshe tena ukitumia seti yako ya data, labda moja kutoka Kaggle, ukitumia kwa kutambua chanzo. Andika upya notebook ili kuonyesha matokeo yako mwenyewe. Jaribu aina tofauti ya seti ya data na uandike matokeo yako, ukitumia maandishi kama [maneno ya nyimbo za Beatles](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics). diff --git a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md index 46360ec0..5f065f09 100644 --- a/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/sw/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Uundaji wa Lugha Uwakilishi wa semantiki, kama Word2Vec na GloVe, ni hatua ya kwanza kuelekea **uundaji wa lugha** - kuunda mifano inayoweza *kuelewa* (au *kuwakilisha*) asili ya lugha. diff --git a/translations/sw/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/sw/lessons/5-NLP/15-LanguageModeling/lab/README.md index b27bcb00..a3f3b322 100644 --- a/translations/sw/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/sw/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Kufundisha Mfano wa Skip-Gram Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/5-NLP/16-RNN/README.md b/translations/sw/lessons/5-NLP/16-RNN/README.md index f9aa6ad7..7b8adb41 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/README.md +++ b/translations/sw/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Mitandao ya Neural Inayojirudia ## [Jaribio la Kabla ya Somo](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ Tuangalie jinsi kiini rahisi cha RNN kinavyopangwa. Kinapokea hali ya awali Si+H×Si-1+b), ambapo σ ni kazi ya uanzishaji na b ni upendeleo wa ziada. -RNN Cell Anatomy +RNN Cell Anatomy > Picha na mwandishi diff --git a/translations/sw/lessons/5-NLP/16-RNN/assignment.md b/translations/sw/lessons/5-NLP/16-RNN/assignment.md index a301c69b..eb5d3b55 100644 --- a/translations/sw/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/sw/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # Kazi: Daftari Kutumia daftari zinazohusiana na somo hili (iwe ni toleo la PyTorch au TensorFlow), ziendeshe tena ukitumia seti yako ya data, labda moja kutoka Kaggle, ukitumia kwa kutaja chanzo. Andika upya daftari ili kuonyesha matokeo yako mwenyewe. Jaribu aina tofauti ya seti ya data na uandike matokeo yako, ukitumia maandishi kama [seti ya data ya mashindano ya Kaggle kuhusu tweets za hali ya hewa](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv). diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md index ab01f0c8..ffbb1dcc 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Mitandao ya Kizazi ## [Jaribio la awali la somo](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ Tutafundisha RNN hii kuzalisha maandishi hatua kwa hatua. Katika kila hatua, tut Wakati wa kuzalisha maandishi (wakati wa utabiri), tunaanza na **msukumo fulani**, ambao unapitia seli za RNN ili kuzalisha hali yake ya kati, na kisha kutoka hali hii kizazi kinaanza. Tunazalisha herufi moja kwa wakati, na kupitisha hali na herufi iliyozalishwa kwa seli nyingine ya RNN ili kuzalisha inayofuata, hadi tutakapozalisha herufi za kutosha. - + > Picha na mwandishi diff --git a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index 8e95da08..2dacbf1e 100644 --- a/translations/sw/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/sw/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # Uzalishaji wa Maandishi kwa Ngazi ya Neno kwa kutumia RNNs Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta Wanaoanza](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/5-NLP/18-Transformers/README.md b/translations/sw/lessons/5-NLP/18-Transformers/README.md index f3cfe922..76a8b539 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/README.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # Mbinu za Uangalizi na Transformers ## [Jaribio la awali ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Wazo la usimbaji wa nafasi ni kama ifuatavyo. * Embedding inayoweza kufundishwa, sawa na embedding ya tokeni. Hii ndiyo mbinu tunayoangalia hapa. Tunatumia safu za embedding juu ya tokeni na nafasi zao, na kusababisha vector za embedding za vipimo sawa, ambazo tunaziongeza pamoja. * Kazi ya usimbaji wa nafasi isiyobadilika, kama ilivyopendekezwa katika karatasi ya awali. - + > Picha na mwandishi diff --git a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md b/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md deleted file mode 100644 index 9e1ca89d..00000000 --- a/translations/sw/lessons/5-NLP/18-Transformers/READMEtransformers.md +++ /dev/null @@ -1,112 +0,0 @@ -# Mekanismer fΓΆr uppmΓ€rksamhet och Transformatorer - -## [FΓΆr-lΓ€rare quiz](https://ff-quizzes.netlify.app/en/ai/quiz/35) - -Ett av de mest betydelsefulla problemen inom NLP-omrΓ₯det Γ€r **maskinΓΆversΓ€ttning**, en grundlΓ€ggande uppgift som ligger till grund fΓΆr verktyg som Google Translate. I denna sektion kommer vi att fokusera pΓ₯ maskinΓΆversΓ€ttning, eller mer generellt, pΓ₯ vilken *sekvens-till-sekvens* uppgift som helst (vilket ocksΓ₯ kallas **meningstransduktion**). - -Med RNN:er implementeras sekvens-till-sekvens av tvΓ₯ Γ₯terkommande nΓ€tverk, dΓ€r ett nΓ€tverk, **kodaren**, komprimerar en ingΓ₯ngssekvens till ett dolt tillstΓ₯nd, medan ett annat nΓ€tverk, **avkodaren**, utvecklar detta dolda tillstΓ₯nd till ett ΓΆversatt resultat. Det finns ett par problem med denna metod: - -* Det slutliga tillstΓ₯ndet fΓΆr kodarnΓ€tverket har svΓ₯rt att komma ihΓ₯g bΓΆrjan av en mening, vilket leder till dΓ₯lig kvalitet pΓ₯ modellen fΓΆr lΓ₯nga meningar. -* Alla ord i en sekvens har samma inverkan pΓ₯ resultatet. I verkligheten har dock specifika ord i ingΓ₯ngssekvensen ofta mer inverkan pΓ₯ sekventiella utdata Γ€n andra. - -**UppmΓ€rksamhetsmekanismer** ger ett sΓ€tt att vikta den kontextuella pΓ₯verkan av varje ingΓ₯ngsvektor pΓ₯ varje utdatafΓΆrutsΓ€gelse av RNN. SΓ€ttet det implementeras pΓ₯ Γ€r genom att skapa genvΓ€gar mellan mellanliggande tillstΓ₯nd av ingΓ₯ngs-RNN och utgΓ₯ngs-RNN. PΓ₯ detta sΓ€tt, nΓ€r vi genererar utdata symbol yt, kommer vi att ta hΓ€nsyn till alla ingΓ₯ngs dolda tillstΓ₯nd hi, med olika viktkoefficienter Ξ±t,i. - -![Bild som visar en kodare/avkodare-modell med ett additivt uppmΓ€rksamhetslager](../../../../../translated_images/sw/encoder-decoder-attention.7a726296894fb567.webp) - -> Kodare-avkodare-modell med additiv uppmΓ€rksamhetsmekanism i [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf), citerad frΓ₯n [denna bloggpost](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) - -UppmΓ€rksamhetsmatrisen {Ξ±i,j} skulle representera graden av att vissa ingΓ₯ngsord spelar en roll i generationen av ett givet ord i utgΓ₯ngssekvensen. Nedan Γ€r ett exempel pΓ₯ en sΓ₯dan matris: - -![Bild som visar en exempeljustering som hittats av RNNsearch-50, tagen frΓ₯n Bahdanau - arviz.org](../../../../../translated_images/sw/bahdanau-fig3.09ba2d37f202a6af.webp) - -> Figur frΓ₯n [Bahdanau et al., 2015](https://arxiv.org/pdf/1409.0473.pdf) (Fig.3) - -UppmΓ€rksamhetsmekanismer Γ€r ansvariga fΓΆr mycket av den nuvarande eller nΓ€ra nuvarande toppmoderna inom NLP. Att lΓ€gga till uppmΓ€rksamhet ΓΆkar dock kraftigt antalet modellparametrar vilket ledde till skalningsproblem med RNN:er. En viktig begrΓ€nsning av att skala RNN:er Γ€r att den Γ₯terkommande naturen av modellerna gΓΆr det utmanande att batcha och parallellisera trΓ€ning. I en RNN mΓ₯ste varje element i en sekvens bearbetas i sekventiell ordning, vilket innebΓ€r att det inte kan parallelliseras enkelt. - -![Kodare Avkodare med UppmΓ€rksamhet](../../../../../lessons/5-NLP/18-Transformers/images/EncDecAttention.gif) - -> Figur frΓ₯n [Google's Blog](https://research.googleblog.com/2016/09/a-neural-network-for-machine.html) - -Antagandet av uppmΓ€rksamhetsmekanismer kombinerat med denna begrΓ€nsning ledde till skapandet av de nuvarande toppmoderna transformatormodellerna som vi kΓ€nner och anvΓ€nder idag, sΓ₯som BERT till Open-GPT3. - -## Transformatormodeller - -En av de huvudsakliga idΓ©erna bakom transformatorer Γ€r att undvika den sekventiella naturen av RNN:er och att skapa en modell som Γ€r parallelliserbar under trΓ€ning. Detta uppnΓ₯s genom att implementera tvΓ₯ idΓ©er: - -* positionskodning -* anvΓ€nda sjΓ€lvuppmΓ€rksamhetsmekanism fΓΆr att fΓ₯nga mΓΆnster istΓ€llet fΓΆr RNN:er (eller CNN:er) (det Γ€r dΓ€rfΓΆr artikeln som introducerar transformatorer kallas *[Attention is all you need](https://arxiv.org/abs/1706.03762)*) - -### Positionskodning/Embedding - -IdΓ©n med positionskodning Γ€r fΓΆljande. -1. NΓ€r man anvΓ€nder RNN:er representeras den relativa positionen av token av antalet steg, och behΓΆver dΓ€rfΓΆr inte uttryckligen representeras. -2. Men nΓ€r vi vΓ€xlar till uppmΓ€rksamhet, behΓΆver vi veta de relativa positionerna fΓΆr token inom en sekvens. -3. FΓΆr att fΓ₯ positionskodning, kompletterar vi vΓ₯r sekvens av token med en sekvens av tokenpositioner i sekvensen (dvs. en sekvens av siffror 0,1, ...). -4. Vi blandar sedan tokenpositionen med en tokeninbΓ€ddningsvektor. FΓΆr att omvandla positionen (heltal) till en vektor kan vi anvΓ€nda olika tillvΓ€gagΓ₯ngssΓ€tt: - -* TrΓ€ningsbar inbΓ€ddning, liknande tokeninbΓ€ddning. Detta Γ€r den metod vi ΓΆvervΓ€ger hΓ€r. Vi tillΓ€mpar inbΓ€ddningslager ovanpΓ₯ bΓ₯de token och deras positioner, vilket resulterar i inbΓ€ddningsvektorer av samma dimensioner, som vi sedan lΓ€gger ihop. -* Fast positionskodningsfunktion, som fΓΆreslagits i den ursprungliga artikeln. - - - -> Bild av fΓΆrfattaren - -Resultatet vi fΓ₯r med positionsinbΓ€ddning inbΓ€ddas bΓ₯de den ursprungliga token och dess position inom en sekvens. - -### Multi-Head SjΓ€lv-UppmΓ€rksamhet - -NΓ€sta steg Γ€r att fΓ₯nga vissa mΓΆnster inom vΓ₯r sekvens. FΓΆr att gΓΆra detta anvΓ€nder transformatorer en **sjΓ€lvuppmΓ€rksamhets**mekanism, som i grunden Γ€r uppmΓ€rksamhet tillΓ€mpad pΓ₯ samma sekvens som ingΓ₯ng och utgΓ₯ng. TillΓ€mpning av sjΓ€lvuppmΓ€rksamhet gΓΆr att vi kan ta hΓ€nsyn till **kontext** inom meningen och se vilka ord som Γ€r relaterade. Till exempel gΓΆr det att vi kan se vilka ord som hΓ€nvisas till av referenser, sΓ₯som *det*, och Γ€ven ta kontexten i beaktande: - -![](../../../../../translated_images/sw/CoreferenceResolution.861924d6d384a7d6.webp) - -> Bild frΓ₯n [Google Blog](https://research.googleblog.com/2017/08/transformer-novel-neural-network.html) - -I transformatorer anvΓ€nder vi **Multi-Head Attention** fΓΆr att ge nΓ€tverket kraften att fΓ₯nga flera olika typer av beroenden, t.ex. lΓ₯ngsiktiga vs. kortsiktiga ordfΓΆrhΓ₯llanden, medreferens vs. nΓ₯got annat, osv. - -[TensorFlow Notebook](../../../../../lessons/5-NLP/18-Transformers/TransformersTF.ipynb) innehΓ₯ller mer information om implementeringen av transformatorlager. - -### Kodare-Avkodare UppmΓ€rksamhet - -I transformatorer anvΓ€nds uppmΓ€rksamhet pΓ₯ tvΓ₯ stΓ€llen: - -* FΓΆr att fΓ₯nga mΓΆnster inom ingΓ₯ngstexten med hjΓ€lp av sjΓ€lvuppmΓ€rksamhet -* FΓΆr att utfΓΆra sekvensΓΆversΓ€ttning - det Γ€r uppmΓ€rksamhetslagret mellan kodaren och avkodaren. - -Kodare-avkodare uppmΓ€rksamhet Γ€r mycket lik den uppmΓ€rksamhetsmekanism som anvΓ€nds i RNN:er, som beskrivits i bΓΆrjan av denna sektion. Detta animerade diagram fΓΆrklarar rollen av kodare-avkodare uppmΓ€rksamhet. - -![Animerad GIF som visar hur utvΓ€rderingarna utfΓΆrs i transformatormodeller.](../../../../../lessons/5-NLP/18-Transformers/images/transformer-animated-explanation.gif) - -Eftersom varje ingΓ₯ngsposition mappas oberoende till varje utgΓ₯ngsposition kan transformatorer parallellisera bΓ€ttre Γ€n RNN:er, vilket mΓΆjliggΓΆr mycket stΓΆrre och mer uttrycksfulla sprΓ₯kmodeller. Varje uppmΓ€rksamhetshuvud kan anvΓ€ndas fΓΆr att lΓ€ra sig olika relationer mellan ord som fΓΆrbΓ€ttrar efterfΓΆljande NLP-uppgifter. - -## BERT - -**BERT** (Bidirectional Encoder Representations from Transformers) Γ€r ett mycket stort flerlagers transformatornΓ€tverk med 12 lager fΓΆr *BERT-base*, och 24 fΓΆr *BERT-large*. Modellen fΓΆrtrΓ€nas fΓΆrst pΓ₯ en stor korpus av textdata (WikiPedia + bΓΆcker) med hjΓ€lp av osupervised trΓ€ning (fΓΆrutsΓ€ga maskerade ord i en mening). Under fΓΆrtrΓ€ningen absorberar modellen betydande nivΓ₯er av sprΓ₯kfΓΆrstΓ₯else som sedan kan utnyttjas med andra dataset genom finjustering. Denna process kallas **ΓΆverfΓΆringsinlΓ€rning**. - -![Bild frΓ₯n http://jalammar.github.io/illustrated-bert/](../../../../../translated_images/sw/jalammarBERT-language-modeling-masked-lm.34f113ea5fec4362.webp) - -> Bild [kΓ€lla](http://jalammar.github.io/illustrated-bert/) - -## ✍️ Γ–vningar: Transformatorer - -FortsΓ€tt din inlΓ€rning i fΓΆljande anteckningsbΓΆcker: - -* [Transformatorer i PyTorch](../../../../../lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) -* [Transformatorer i TensorFlow](../../../../../lessons/5-NLP/18-Transformers/TransformersTF.ipynb) - -## Slutsats - -I denna lektion lΓ€rde du dig om transformatorer och uppmΓ€rksamhetsmekanismer, alla viktiga verktyg i NLP-verktygslΓ₯dan. Det finns mΓ₯nga varianter av transformatorarkitekturer inklusive BERT, DistilBERT, BigBird, OpenGPT3 och mer som kan finjusteras. [HuggingFace-paketet](https://github.com/huggingface/) tillhandahΓ₯ller ett fΓΆrrΓ₯d fΓΆr trΓ€ning av mΓ₯nga av dessa arkitekturer med bΓ₯de PyTorch och TensorFlow. - -## πŸš€ Utmaning - -## [Efter-lΓ€rare quiz](https://ff-quizzes.netlify.app/en/ai/quiz/36) - -## Granskning & SjΓ€lvstudie - -* [Bloggpost](https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/), som fΓΆrklarar den klassiska [Attention is all you need](https://arxiv.org/abs/1706.03762) artikeln om transformatorer. -* [En serie bloggposter](https://towardsdatascience.com/transformers-explained-visually-part-1-overview-of-functionality-95a6dd460452) om transformatorer, som fΓΆrklarar arkitekturen i detalj. - -## [Uppgift](assignment.md) - -**Ansvarsfriskrivning**: -Detta dokument har ΓΆversatts med hjΓ€lp av maskinbaserade AI-ΓΆversΓ€ttningstjΓ€nster. Γ„ven om vi strΓ€var efter noggrannhet, vΓ€nligen var medveten om att automatiska ΓΆversΓ€ttningar kan innehΓ₯lla fel eller inkonsekvenser. Det ursprungliga dokumentet pΓ₯ sitt modersmΓ₯l bΓΆr betraktas som den auktoritativa kΓ€llan. FΓΆr kritisk information rekommenderas professionell mΓ€nsklig ΓΆversΓ€ttning. Vi ansvarar inte fΓΆr nΓ₯gra missfΓΆrstΓ₯nd eller feltolkningar som uppstΓ₯r till fΓΆljd av anvΓ€ndningen av denna ΓΆversΓ€ttning. \ No newline at end of file diff --git a/translations/sw/lessons/5-NLP/18-Transformers/assignment.md b/translations/sw/lessons/5-NLP/18-Transformers/assignment.md index e90541cd..505e20c9 100644 --- a/translations/sw/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/sw/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # Kazi: Transformers Fanya majaribio na Transformers kwenye HuggingFace! Jaribu baadhi ya skripti wanazotoa ili kufanya kazi na mifano mbalimbali inayopatikana kwenye tovuti yao: https://huggingface.co/docs/transformers/run_scripts. Jaribu mojawapo ya seti zao za data, kisha leta moja yako kutoka kwenye mtaala huu au kutoka Kaggle na uone kama unaweza kuzalisha maandishi ya kuvutia. Tengeneza daftari la maelezo na matokeo yako. diff --git a/translations/sw/lessons/5-NLP/19-NER/README.md b/translations/sw/lessons/5-NLP/19-NER/README.md index 00bc442c..b0c9607a 100644 --- a/translations/sw/lessons/5-NLP/19-NER/README.md +++ b/translations/sw/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Utambuzi wa Viumbe Vilivyotajwa Hadi sasa, tumekuwa tukijikita zaidi kwenye kazi moja ya NLP - uainishaji. Hata hivyo, kuna kazi nyingine za NLP ambazo zinaweza kufanikishwa kwa kutumia mitandao ya neva. Mojawapo ya kazi hizo ni **[Utambuzi wa Viumbe Vilivyotajwa](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), ambayo inahusika na kutambua viumbe maalum ndani ya maandishi, kama vile maeneo, majina ya watu, vipindi vya tarehe na muda, fomula za kemikali, na kadhalika. @@ -17,7 +8,7 @@ Hadi sasa, tumekuwa tukijikita zaidi kwenye kazi moja ya NLP - uainishaji. Hata Tuseme unataka kuunda roboti ya mazungumzo ya lugha asilia, sawa na Amazon Alexa au Google Assistant. Njia ambayo roboti za mazungumzo za akili hufanya kazi ni *kuelewa* kile mtumiaji anataka kwa kufanya uainishaji wa maandishi kwenye sentensi ya pembejeo. Matokeo ya uainishaji huu ni kile kinachoitwa **nia**, ambayo huamua roboti ya mazungumzo inapaswa kufanya nini. -Bot NER +Bot NER > Picha na mwandishi diff --git a/translations/sw/lessons/5-NLP/19-NER/lab/README.md b/translations/sw/lessons/5-NLP/19-NER/lab/README.md index cef894c3..430d924b 100644 --- a/translations/sw/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/sw/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/5-NLP/20-LangModels/README.md b/translations/sw/lessons/5-NLP/20-LangModels/README.md index 0a850afb..50a98c2d 100644 --- a/translations/sw/lessons/5-NLP/20-LangModels/README.md +++ b/translations/sw/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # Miundo Mikubwa ya Lugha Iliyofunzwa Kabla Katika kazi zetu zote za awali, tulikuwa tukifundisha mtandao wa neva kufanya kazi fulani kwa kutumia seti ya data yenye lebo. Kwa miundo mikubwa ya transformer, kama BERT, tunatumia uundaji wa lugha kwa njia ya kujifunza binafsi ili kujenga mfano wa lugha, ambao baadaye unataalamishwa kwa kazi maalum za chini kwa mafunzo zaidi ya kikoa maalum. Hata hivyo, imeonyeshwa kuwa miundo mikubwa ya lugha inaweza pia kutatua kazi nyingi bila mafunzo yoyote maalum ya kikoa. Familia ya miundo inayoweza kufanya hivyo inaitwa **GPT**: Generative Pre-Trained Transformer. diff --git a/translations/sw/lessons/5-NLP/20-LangModels/READMELargeLang.md b/translations/sw/lessons/5-NLP/20-LangModels/READMELargeLang.md deleted file mode 100644 index 02e76a95..00000000 --- a/translations/sw/lessons/5-NLP/20-LangModels/READMELargeLang.md +++ /dev/null @@ -1,56 +0,0 @@ -# FΓΆrtrΓ€nade Stora SprΓ₯kmodeller - -I alla vΓ₯ra tidigare uppgifter har vi trΓ€nat ett neuralt nΓ€tverk fΓΆr att utfΓΆra en viss uppgift med hjΓ€lp av en mΓ€rkt dataset. Med stora transformer-modeller, sΓ₯som BERT, anvΓ€nder vi sprΓ₯kmodellering pΓ₯ ett sjΓ€lvΓΆvervakat sΓ€tt fΓΆr att bygga en sprΓ₯kmodell, som sedan specialiseras fΓΆr specifika nedstrΓΆmsuppgifter med ytterligare domΓ€nspecifik trΓ€ning. Det har emellertid visat sig att stora sprΓ₯kmodeller ocksΓ₯ kan lΓΆsa mΓ₯nga uppgifter utan NΓ…GON domΓ€nspecifik trΓ€ning. En familj av modeller som kan gΓΆra detta kallas **GPT**: Generative Pre-Trained Transformer. - -## [FΓΆr-fΓΆrelΓ€sningsquiz](https://ff-quizzes.netlify.app/en/ai/quiz/39) - -## Textgenerering och Perplexitet - -IdΓ©n om att ett neuralt nΓ€tverk kan utfΓΆra allmΓ€nna uppgifter utan nedstrΓΆms trΓ€ning presenteras i artikeln [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). HuvudidΓ©n Γ€r att mΓ₯nga andra uppgifter kan modelleras med hjΓ€lp av **textgenerering**, eftersom fΓΆrstΓ₯else av text i grunden innebΓ€r att kunna producera den. Eftersom modellen trΓ€nas pΓ₯ en enorm mΓ€ngd text som omfattar mΓ€nsklig kunskap, blir den ocksΓ₯ kunnig om en mΓ€ngd olika Γ€mnen. - -> Att fΓΆrstΓ₯ och kunna producera text innebΓ€r ocksΓ₯ att veta nΓ₯got om vΓ€rlden omkring oss. MΓ€nniskor lΓ€r sig ocksΓ₯ i stor utstrΓ€ckning genom att lΓ€sa, och GPT-nΓ€tverket Γ€r liknande i detta avseende. - -TextgenereringsnΓ€tverk fungerar genom att fΓΆrutsΓ€ga sannolikheten fΓΆr nΓ€sta ord $$P(w_N)$$. Men den ovillkorliga sannolikheten fΓΆr nΓ€sta ord Γ€r lika med frekvensen av detta ord i textkorpuset. GPT kan ge oss **villkorlig sannolikhet** fΓΆr nΓ€sta ord, givet de fΓΆregΓ₯ende: $$P(w_N | w_{n-1}, ..., w_0)$$ - -> Du kan lΓ€sa mer om sannolikheter i vΓ₯r [Data Science for Beginners Curriculum](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/04-stats-and-probability) - -Kvaliteten pΓ₯ en sprΓ₯kgenererande modell kan definieras med hjΓ€lp av **perplexitet**. Det Γ€r en inneboende metrik som gΓΆr att vi kan mΓ€ta modellens kvalitet utan nΓ₯got uppgiftsspecifikt dataset. Den baseras pΓ₯ begreppet *sannolikheten fΓΆr en mening* - modellen tilldelar hΓΆg sannolikhet till en mening som troligtvis Γ€r verklig (dvs. modellen Γ€r inte **fΓΆrvirrad** av den), och lΓ₯g sannolikhet till meningar som Γ€r mindre meningsfulla (t.ex. *Kan den gΓΆra vad?*). NΓ€r vi ger vΓ₯r modell meningar frΓ₯n ett verkligt textkorpus fΓΆrvΓ€ntar vi oss att de har hΓΆg sannolikhet och lΓ₯g **perplexitet**. Matematisk definieras det som normaliserad invers sannolikhet fΓΆr testuppsΓ€ttningen: -$$ -\mathrm{Perplexity}(W) = \sqrt[N]{1\over P(W_1,...,W_N)} -$$ - -**Du kan experimentera med textgenerering med hjΓ€lp av [GPT-drivet textredigerare frΓ₯n Hugging Face](https://transformer.huggingface.co/doc/gpt2-large)**. I denna redigerare bΓΆrjar du skriva din text, och genom att trycka pΓ₯ **[TAB]** erbjuds du flera alternativ fΓΆr avslutning. Om de Γ€r fΓΆr korta, eller om du inte Γ€r nΓΆjd med dem - tryck [TAB] igen, sΓ₯ fΓ₯r du fler alternativ, inklusive lΓ€ngre texter. - -## GPT Γ€r en Familj - -GPT Γ€r inte en enda modell, utan snarare en samling modeller som utvecklats och trΓ€nats av [OpenAI](https://openai.com). - -Under GPT-modellerna har vi: - -| [GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2#openai-gpt2) | [GPT 3](https://openai.com/research/language-models-are-few-shot-learners) | [GPT-4](https://openai.com/gpt-4) | -| -- | -- | -- | -|SprΓ₯kmodell med upp till 1,5 miljarder parametrar. | SprΓ₯kmodell med upp till 175 miljarder parametrar | 100T parametrar och accepterar bΓ₯de bild- och textinmatningar och producerar text. | - - -GPT-3 och GPT-4-modellerna Γ€r tillgΓ€ngliga [som en kognitiv tjΓ€nst frΓ₯n Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/openai-service/#overview?WT.mc_id=academic-77998-cacaste), och som [OpenAI API](https://openai.com/api/). - -## Prompt Engineering - -Eftersom GPT har trΓ€nats pΓ₯ stora mΓ€ngder data fΓΆr att fΓΆrstΓ₯ sprΓ₯k och kod, ger de utdata som svar pΓ₯ indata (prompter). Prompter Γ€r GPT-inmatningar eller frΓ₯gor dΓ€r man ger instruktioner till modellerna om uppgifter de ska slutfΓΆra. FΓΆr att framkalla ett ΓΆnskat resultat behΓΆver du den mest effektiva prompten, vilket innebΓ€r att vΓ€lja rΓ€tt ord, format, fraser eller till och med symboler. Denna metod kallas [Prompt Engineering](https://learn.microsoft.com/en-us/shows/ai-show/the-basics-of-prompt-engineering-with-azure-openai-service?WT.mc_id=academic-77998-bethanycheum) - -[Denna dokumentation](https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/?WT.mc_id=academic-77998-bethanycheum) ger dig mer information om prompt engineering. - -## ✍️ Exempel Notbok: [Leka med OpenAI-GPT](../../../../../lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) - -FortsΓ€tt ditt lΓ€rande i fΓΆljande notbΓΆcker: - -* [Generera text med OpenAI-GPT och Hugging Face Transformers](../../../../../lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) - -## Slutsats - -Nya allmΓ€nna fΓΆrtrΓ€nade sprΓ₯kmodeller modellerar inte bara sprΓ₯kstruktur, utan innehΓ₯ller ocksΓ₯ stora mΓ€ngder naturligt sprΓ₯k. DΓ€rfΓΆr kan de effektivt anvΓ€ndas fΓΆr att lΓΆsa vissa NLP-uppgifter i zero-shot eller few-shot instΓ€llningar. - -## [Efter-fΓΆrelΓ€sningsquiz](https://ff-quizzes.netlify.app/en/ai/quiz/40) - -**Ansvarsfriskrivning**: -Detta dokument har ΓΆversatts med hjΓ€lp av maskinbaserade AI-ΓΆversΓ€ttningstjΓ€nster. Γ„ven om vi strΓ€var efter noggrannhet, vΓ€nligen var medveten om att automatiska ΓΆversΓ€ttningar kan innehΓ₯lla fel eller brister. Det ursprungliga dokumentet pΓ₯ sitt modersmΓ₯l bΓΆr betraktas som den auktoritativa kΓ€llan. FΓΆr kritisk information rekommenderas professionell mΓ€nsklig ΓΆversΓ€ttning. Vi ansvarar inte fΓΆr eventuella missfΓΆrstΓ₯nd eller feltolkningar som uppstΓ₯r frΓ₯n anvΓ€ndningen av denna ΓΆversΓ€ttning. \ No newline at end of file diff --git a/translations/sw/lessons/5-NLP/README.md b/translations/sw/lessons/5-NLP/README.md index 885d1108..a2e828da 100644 --- a/translations/sw/lessons/5-NLP/README.md +++ b/translations/sw/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # Usindikaji wa Lugha Asilia ![Muhtasari wa kazi za NLP katika mchoro](../../../../translated_images/sw/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/sw/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/sw/lessons/6-Other/21-GeneticAlgorithms/README.md index 25747a6f..36eacaed 100644 --- a/translations/sw/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/sw/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # Algorithms za Kijenetiki ## [Maswali ya awali ya somo](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/sw/lessons/6-Other/22-DeepRL/README.md b/translations/sw/lessons/6-Other/22-DeepRL/README.md index afc5fc1f..43ec06b9 100644 --- a/translations/sw/lessons/6-Other/22-DeepRL/README.md +++ b/translations/sw/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # Kujifunza Kuimarisha Kina (Deep Reinforcement Learning) Kujifunza kuimarisha (Reinforcement Learning - RL) ni mojawapo ya mifumo ya msingi ya kujifunza kwa mashine, sambamba na kujifunza kwa usimamizi (supervised learning) na kujifunza bila usimamizi (unsupervised learning). Wakati katika kujifunza kwa usimamizi tunategemea seti ya data yenye matokeo yanayojulikana, RL inategemea **kujifunza kwa kufanya**. Kwa mfano, tunapoona mchezo wa kompyuta kwa mara ya kwanza, tunaanza kucheza hata bila kujua sheria, na hivi karibuni tunaweza kuboresha ujuzi wetu kupitia mchakato wa kucheza na kurekebisha tabia zetu. @@ -34,7 +25,7 @@ Labda umewahi kuona vifaa vya kisasa vya kusawazisha kama *Segway* au *Gyroscoot Toleo rahisi la kusawazisha linajulikana kama tatizo la **CartPole**. Katika ulimwengu wa CartPole, tuna slider ya mlalo inayoweza kusogea kushoto au kulia, na lengo ni kusawazisha nguzo wima juu ya slider inaposogea. -a cartpole +a cartpole Ili kuunda na kutumia mazingira haya, tunahitaji mistari michache ya msimbo wa Python: diff --git a/translations/sw/lessons/6-Other/22-DeepRL/lab/README.md b/translations/sw/lessons/6-Other/22-DeepRL/lab/README.md index 4eb2e1ab..a60ae52e 100644 --- a/translations/sw/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/sw/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - # Kufundisha Gari la Mlima Kutoroka Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md index fd1b0141..891d3e5b 100644 --- a/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/sw/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Mfumo wa Wakala Wengi Njia mojawapo ya kufanikisha akili ni mbinu inayoitwa **emergent** (au **synergetic**), ambayo inategemea ukweli kwamba tabia ya pamoja ya mawakala wengi wa kawaida inaweza kusababisha tabia ya jumla ya mfumo kuwa ngumu zaidi (au yenye akili). Kimsingi, hii inategemea kanuni za [Akili ya Pamoja](https://en.wikipedia.org/wiki/Collective_intelligence), [Emergentism](https://en.wikipedia.org/wiki/Global_brain) na [Evolutionary Cybernetics](https://en.wikipedia.org/wiki/Global_brain), ambazo zinasema kwamba mifumo ya kiwango cha juu hupata aina fulani ya thamani ya ziada inapounganishwa vizuri kutoka kwa mifumo ya kiwango cha chini (kanuni inayoitwa *metasystem transition*). @@ -60,7 +51,7 @@ Unaweza [kupakua](https://ccl.northwestern.edu/netlogo/download.shtml) na kusaki Jambo zuri kuhusu NetLogo ni kwamba ina maktaba ya miundo inayofanya kazi ambayo unaweza kuijaribu. Nenda kwa **File → Models Library**, na una kategoria nyingi za miundo za kuchagua. -NetLogo Models Library +NetLogo Models Library > Picha ya skrini ya maktaba ya miundo na Dmitry Soshnikov diff --git a/translations/sw/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/sw/lessons/6-Other/23-MultiagentSystems/assignment.md index 9c1e0784..b0efc9a8 100644 --- a/translations/sw/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/sw/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # Kazi ya NetLogo Chukua mojawapo ya mifano katika maktaba ya NetLogo na uitumie kuiga hali halisi ya maisha kwa ukaribu iwezekanavyo. Mfano mzuri ungekuwa kurekebisha mfano wa Virus katika folda ya Alternative Visualizations ili kuonyesha jinsi unavyoweza kutumika kuiga kuenea kwa COVID-19. Je, unaweza kujenga mfano unaoiga kuenea kwa virusi halisi? diff --git a/translations/sw/lessons/7-Ethics/README.md b/translations/sw/lessons/7-Ethics/README.md index 093a893e..1c6487a4 100644 --- a/translations/sw/lessons/7-Ethics/README.md +++ b/translations/sw/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # AI ya Kimaadili na ya Kuwajibika U karibu kumaliza kozi hii, na natumai kwamba kufikia sasa unaelewa wazi kuwa AI inategemea mbinu kadhaa za kihisabati rasmi ambazo hutuwezesha kupata uhusiano katika data na kufundisha mifano kuiga baadhi ya tabia za binadamu. Katika hatua hii ya historia, tunachukulia AI kama chombo chenye nguvu sana cha kutoa mifumo kutoka kwa data, na kutumia mifumo hiyo kutatua matatizo mapya. diff --git a/translations/sw/lessons/README.md b/translations/sw/lessons/README.md index 0f6b8659..aa453bf4 100644 --- a/translations/sw/lessons/README.md +++ b/translations/sw/lessons/README.md @@ -1,12 +1,3 @@ - # Muhtasari ![Muhtasari katika mchoro](../../../translated_images/sw/ai-overview.0857791951d19500.webp) diff --git a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md index d8644ad4..b854ccc6 100644 --- a/translations/sw/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/sw/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Mitandao ya Multi-Modal Baada ya mafanikio ya mifano ya transformer katika kutatua kazi za NLP, usanifu sawa au unaofanana umetumika katika kazi za maono ya kompyuta. Kuna shauku inayoongezeka ya kujenga mifano ambayo inaweza *kuunganisha* uwezo wa maono na lugha ya asili. Mojawapo ya majaribio hayo yalifanywa na OpenAI, na inaitwa CLIP na DALL.E. diff --git a/translations/sw/lessons/sketchnotes/LICENSE.md b/translations/sw/lessons/sketchnotes/LICENSE.md index dd79738a..6a1c6760 100644 --- a/translations/sw/lessons/sketchnotes/LICENSE.md +++ b/translations/sw/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Hati ya Utoaji wa Haki ya Kushiriki na Kurekebisha 4.0 Kimataifa Creative Commons Corporation ("Creative Commons") si kampuni ya sheria na haitoi huduma za kisheria au ushauri wa kisheria. Usambazaji wa leseni za umma za Creative Commons hauundi uhusiano wa wakili-mteja au uhusiano mwingine wowote. Creative Commons hutoa leseni zake na taarifa zinazohusiana kwa msingi wa "kama ilivyo". Creative Commons haitoi dhamana yoyote kuhusu leseni zake, nyenzo zozote zilizopewa leseni chini ya masharti na masharti yake, au taarifa zinazohusiana. Creative Commons inakanusha dhima yote kwa uharibifu unaotokana na matumizi yao kwa kiwango cha juu kinachowezekana. diff --git a/translations/sw/lessons/sketchnotes/README.md b/translations/sw/lessons/sketchnotes/README.md index d8753c6a..b48974a0 100644 --- a/translations/sw/lessons/sketchnotes/README.md +++ b/translations/sw/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - Sketchnoti zote za mtaala zinaweza kupakuliwa hapa. 🎨 Imeundwa na: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/sw/troubleshoot.md b/translations/sw/troubleshoot.md index a0d0f4cc..f1b28a68 100644 --- a/translations/sw/troubleshoot.md +++ b/translations/sw/troubleshoot.md @@ -1,12 +1,3 @@ - # Mwongozo wa Kutatua Matatizo ya AI-For-Beginners Mwongozo huu unakusaidia kutatua matatizo ya kawaida unayokutana nayo unapotumia au kuchangia kwenye [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) hifadhi. Kila tatizo lina maelezo ya msingi, dalili, maelezo, na hatua za kutatua. diff --git a/translations/tl/.co-op-translator.json b/translations/tl/.co-op-translator.json new file mode 100644 index 00000000..7afa99af --- /dev/null +++ b/translations/tl/.co-op-translator.json @@ -0,0 +1,398 @@ +{ + "AGENTS.md": { + "original_hash": "6b11a37115944252ab3ed04e358d830d", + "translation_date": "2025-10-03T09:26:26+00:00", + "source_file": "AGENTS.md", + "language_code": "tl" + }, + "README.md": { + "original_hash": "1984fc89dd304a8a33ab5584691a99aa", + "translation_date": "2026-01-30T02:19:02+00:00", + "source_file": "README.md", + "language_code": "tl" + }, + "SECURITY.md": { + "original_hash": "a583f49d359c7ebba61433e4dfcd05a9", + "translation_date": "2025-08-28T02:26:01+00:00", + "source_file": "SECURITY.md", + "language_code": "tl" + }, + "etc/CODE_OF_CONDUCT.md": { + "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56", + "translation_date": "2025-08-28T02:46:56+00:00", + "source_file": "etc/CODE_OF_CONDUCT.md", + "language_code": "tl" + }, + "etc/CONTRIBUTING.md": { + "original_hash": "847a587aa1b83f4d00858183ff3ed18a", + "translation_date": "2025-08-28T02:46:47+00:00", + "source_file": "etc/CONTRIBUTING.md", + "language_code": "tl" + }, + "etc/Mindmap.md": { + "original_hash": "f2f88dbd2debd38e26149b27b1fd272d", + "translation_date": "2025-08-28T02:46:13+00:00", + "source_file": "etc/Mindmap.md", + "language_code": "tl" + }, + "etc/SUPPORT.md": { + "original_hash": "fdfc08baee91e402938a2b1f94fe0949", + "translation_date": "2025-08-28T02:45:42+00:00", + "source_file": "etc/SUPPORT.md", + "language_code": "tl" + }, + "etc/TRANSLATIONS.md": { + "original_hash": "62b3e3ad5182edb905eec649a87eeeb4", + "translation_date": "2025-08-28T02:46:37+00:00", + "source_file": "etc/TRANSLATIONS.md", + "language_code": "tl" + }, + "etc/quiz-app/README.md": { + "original_hash": "d699cf8509f74baa5b0b838de5cf0662", + "translation_date": "2025-08-28T02:47:03+00:00", + "source_file": "etc/quiz-app/README.md", + "language_code": "tl" + }, + "examples/README.md": { + "original_hash": "0d1babfdcbeb46525f2db3fbaaa54cd7", + "translation_date": "2025-10-03T11:32:56+00:00", + "source_file": "examples/README.md", + "language_code": "tl" + }, + "lessons/0-course-setup/for-teachers.md": { + "original_hash": "a094ef9927883de1cfcee51dbd143381", + "translation_date": "2025-08-28T02:28:13+00:00", + "source_file": "lessons/0-course-setup/for-teachers.md", + "language_code": "tl" + }, + "lessons/0-course-setup/how-to-run.md": { + "original_hash": "a4717bd9103b9f6cd84d534b83534689", + "translation_date": "2026-01-16T04:17:01+00:00", + "source_file": "lessons/0-course-setup/how-to-run.md", + "language_code": "tl" + }, + "lessons/0-course-setup/setup.md": { + "original_hash": "7b4e5b8956915870d0a0ed3cc5890042", + "translation_date": "2025-12-12T20:05:47+00:00", + "source_file": "lessons/0-course-setup/setup.md", + "language_code": "tl" + }, + "lessons/1-Intro/README.md": { + "original_hash": "f57e8aa46141fd220b16ffed8f11aec7", + "translation_date": "2025-11-18T21:52:52+00:00", + "source_file": "lessons/1-Intro/README.md", + "language_code": "tl" + }, + "lessons/1-Intro/assignment.md": { + "original_hash": "a334df77a82aaaf2a29c77065d3e481e", + "translation_date": "2025-11-18T21:54:06+00:00", + "source_file": "lessons/1-Intro/assignment.md", + "language_code": "tl" + }, + "lessons/2-Symbolic/README.md": { + "original_hash": "f9f06b266b8b2bfc6b8792ff2bb1bea4", + "translation_date": "2026-01-16T04:17:22+00:00", + "source_file": "lessons/2-Symbolic/README.md", + "language_code": "tl" + }, + "lessons/2-Symbolic/assignment.md": { + "original_hash": "a057a8604f3976c3e309884453f1fad0", + "translation_date": "2025-08-28T02:35:50+00:00", + "source_file": "lessons/2-Symbolic/assignment.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/03-Perceptron/README.md": { + "original_hash": "c34cbba802058b6fa267e1a294d4e510", + "translation_date": "2025-09-23T06:58:42+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md": { + "original_hash": "ba5d1eb353d20d3e7181066b3c424b99", + "translation_date": "2025-08-29T06:43:14+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/README.md": { + "original_hash": "789d6c3fb6fc7948a470b33078a5983a", + "translation_date": "2025-09-23T06:58:19+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md": { + "original_hash": "48fdd704d483e19bc3d7464074c9fcbe", + "translation_date": "2025-08-28T02:38:12+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/05-Frameworks/README.md": { + "original_hash": "ddd216f558a255260a9374008002c971", + "translation_date": "2025-09-23T06:58:58+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md": { + "original_hash": "e452d897efb9a89700f41021834cf6e5", + "translation_date": "2025-08-28T02:39:11+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md", + "language_code": "tl" + }, + "lessons/3-NeuralNetworks/README.md": { + "original_hash": "f862a99d88088163df12270e2f2ad6c3", + "translation_date": "2025-10-03T12:51:38+00:00", + "source_file": "lessons/3-NeuralNetworks/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/06-IntroCV/README.md": { + "original_hash": "feeca98225cb420afc89415f24f63d92", + "translation_date": "2025-09-23T06:53:33+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/06-IntroCV/lab/README.md": { + "original_hash": "3d53d6409f80970f7281a45dee35328a", + "translation_date": "2025-08-28T02:31:52+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/lab/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md": { + "original_hash": "53faab85adfcebd8c10bcd71dc2fa557", + "translation_date": "2025-09-23T06:52:55+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/07-ConvNets/README.md": { + "original_hash": "a560d5b845962cf33dc102266e409568", + "translation_date": "2025-09-23T06:52:36+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/07-ConvNets/lab/README.md": { + "original_hash": "b70fcf7fcee862990f848c679090943f", + "translation_date": "2025-10-03T14:56:35+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/lab/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/08-TransferLearning/README.md": { + "original_hash": "178c0b5ee5395733eb18aec51e71a0a9", + "translation_date": "2025-09-23T06:53:09+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md": { + "original_hash": "ae074cd940fc2f4dc24fc07b66ccbd99", + "translation_date": "2025-08-28T02:30:43+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/08-TransferLearning/lab/README.md": { + "original_hash": "7765935c35fcee69b9fe2d0cfd6963e2", + "translation_date": "2025-08-28T02:31:15+00:00", + "source_file": "lessons/4-ComputerVision/08-TransferLearning/lab/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/09-Autoencoders/README.md": { + "original_hash": "1b8d9e1b3a6f1daa864b1ff3dfc3076d", + "translation_date": "2025-09-23T06:54:49+00:00", + "source_file": "lessons/4-ComputerVision/09-Autoencoders/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/10-GANs/README.md": { + "original_hash": "0ff65b4da07b23697235de2beb2a3c25", + "translation_date": "2025-09-23T06:55:42+00:00", + "source_file": "lessons/4-ComputerVision/10-GANs/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/11-ObjectDetection/README.md": { + "original_hash": "d76a7eda28de5210c8b1ba50a6216c69", + "translation_date": "2025-09-23T06:54:08+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md": { + "original_hash": "ad568d55ae65c856fe929fc2b278510a", + "translation_date": "2025-08-28T02:32:40+00:00", + "source_file": "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/12-Segmentation/README.md": { + "original_hash": "6568aaae7e0e4afed4b5d74b5b223700", + "translation_date": "2025-09-23T06:55:26+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/12-Segmentation/lab/README.md": { + "original_hash": "365f0decfe0f47b460bbde8227c5009d", + "translation_date": "2025-08-28T02:33:38+00:00", + "source_file": "lessons/4-ComputerVision/12-Segmentation/lab/README.md", + "language_code": "tl" + }, + "lessons/4-ComputerVision/README.md": { + "original_hash": "58a52f000089c1d8906a4daa4ab1169b", + "translation_date": "2025-08-28T02:29:29+00:00", + "source_file": "lessons/4-ComputerVision/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/13-TextRep/README.md": { + "original_hash": "dbd3f73e4139f030ecb2e20387d70fee", + "translation_date": "2025-09-23T07:01:59+00:00", + "source_file": "lessons/5-NLP/13-TextRep/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/13-TextRep/assignment.md": { + "original_hash": "cdc1f2e631f055f3473b36d18e4760b3", + "translation_date": "2025-08-28T02:45:36+00:00", + "source_file": "lessons/5-NLP/13-TextRep/assignment.md", + "language_code": "tl" + }, + "lessons/5-NLP/14-Embeddings/README.md": { + "original_hash": "b708c9b85b833864c73c6281f1e6b96e", + "translation_date": "2025-09-23T07:01:37+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/14-Embeddings/assignment.md": { + "original_hash": "bc690ecf68b38d311cc9e12f3144a28c", + "translation_date": "2025-08-28T02:44:58+00:00", + "source_file": "lessons/5-NLP/14-Embeddings/assignment.md", + "language_code": "tl" + }, + "lessons/5-NLP/15-LanguageModeling/README.md": { + "original_hash": "7ba20f54a5bfcd6521018cdfb17c7c57", + "translation_date": "2025-09-23T06:59:53+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/15-LanguageModeling/lab/README.md": { + "original_hash": "5130f01fdc5ebb83032b23d489027aac", + "translation_date": "2025-08-28T02:42:18+00:00", + "source_file": "lessons/5-NLP/15-LanguageModeling/lab/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/16-RNN/README.md": { + "original_hash": "e2273cc150380a5e191903cea858f021", + "translation_date": "2025-09-23T07:01:08+00:00", + "source_file": "lessons/5-NLP/16-RNN/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/16-RNN/assignment.md": { + "original_hash": "47f7d3c6a5373543e051e4d1140ce898", + "translation_date": "2025-08-28T02:44:32+00:00", + "source_file": "lessons/5-NLP/16-RNN/assignment.md", + "language_code": "tl" + }, + "lessons/5-NLP/17-GenerativeNetworks/README.md": { + "original_hash": "51be6057374d01d70e07dd5ec88ebc0d", + "translation_date": "2025-09-23T06:59:32+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/17-GenerativeNetworks/lab/README.md": { + "original_hash": "439e12796197a90e7623d4c9c057b9c2", + "translation_date": "2025-08-28T02:41:56+00:00", + "source_file": "lessons/5-NLP/17-GenerativeNetworks/lab/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/18-Transformers/README.md": { + "original_hash": "f335dfcb4a993920504c387973a36957", + "translation_date": "2025-09-23T07:00:04+00:00", + "source_file": "lessons/5-NLP/18-Transformers/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/18-Transformers/assignment.md": { + "original_hash": "177f3ea3995d725e6f9f5c66af16edcd", + "translation_date": "2025-08-28T02:42:56+00:00", + "source_file": "lessons/5-NLP/18-Transformers/assignment.md", + "language_code": "tl" + }, + "lessons/5-NLP/19-NER/README.md": { + "original_hash": "6522312ff835796ca34136a9462fafb2", + "translation_date": "2025-09-23T07:00:49+00:00", + "source_file": "lessons/5-NLP/19-NER/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/19-NER/lab/README.md": { + "original_hash": "032bda5068f543d6c1fcb30c34231461", + "translation_date": "2025-08-28T02:43:43+00:00", + "source_file": "lessons/5-NLP/19-NER/lab/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/20-LangModels/README.md": { + "original_hash": "97836d30a6bec736f8e3b4411c572bc2", + "translation_date": "2025-09-23T07:00:29+00:00", + "source_file": "lessons/5-NLP/20-LangModels/README.md", + "language_code": "tl" + }, + "lessons/5-NLP/README.md": { + "original_hash": "8ef02a9318257ea140ed3ed74442096d", + "translation_date": "2025-08-28T02:41:08+00:00", + "source_file": "lessons/5-NLP/README.md", + "language_code": "tl" + }, + "lessons/6-Other/21-GeneticAlgorithms/README.md": { + "original_hash": "6bbd632dfe6c62e5f66bb51fd78c174a", + "translation_date": "2025-09-23T06:51:07+00:00", + "source_file": "lessons/6-Other/21-GeneticAlgorithms/README.md", + "language_code": "tl" + }, + "lessons/6-Other/22-DeepRL/README.md": { + "original_hash": "04395657fc01648f8f70484d0e55ab67", + "translation_date": "2025-09-23T06:52:08+00:00", + "source_file": "lessons/6-Other/22-DeepRL/README.md", + "language_code": "tl" + }, + "lessons/6-Other/22-DeepRL/lab/README.md": { + "original_hash": "7bd8dc72040e98e35e7225e34058cd4e", + "translation_date": "2025-08-28T02:28:06+00:00", + "source_file": "lessons/6-Other/22-DeepRL/lab/README.md", + "language_code": "tl" + }, + "lessons/6-Other/23-MultiagentSystems/README.md": { + "original_hash": "38a1185ae3d54b180378bbd71ae3ef16", + "translation_date": "2025-09-23T06:51:21+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/README.md", + "language_code": "tl" + }, + "lessons/6-Other/23-MultiagentSystems/assignment.md": { + "original_hash": "cf654ca60c7f86c8dad28596fb42994b", + "translation_date": "2025-08-28T02:27:33+00:00", + "source_file": "lessons/6-Other/23-MultiagentSystems/assignment.md", + "language_code": "tl" + }, + "lessons/7-Ethics/README.md": { + "original_hash": "437c988596e751072e41a5aad3fcc5d9", + "translation_date": "2025-08-28T02:35:56+00:00", + "source_file": "lessons/7-Ethics/README.md", + "language_code": "tl" + }, + "lessons/README.md": { + "original_hash": "5fef1a0b22498d7188959e2a2cb08af7", + "translation_date": "2025-08-28T02:26:15+00:00", + "source_file": "lessons/README.md", + "language_code": "tl" + }, + "lessons/X-Extras/X1-MultiModal/README.md": { + "original_hash": "9c592c26aca16ca085d268c732284187", + "translation_date": "2025-08-28T02:29:00+00:00", + "source_file": "lessons/X-Extras/X1-MultiModal/README.md", + "language_code": "tl" + }, + "lessons/sketchnotes/LICENSE.md": { + "original_hash": "45ab63a2cd8f5faef6c9b150618837a4", + "translation_date": "2025-08-28T02:40:23+00:00", + "source_file": "lessons/sketchnotes/LICENSE.md", + "language_code": "tl" + }, + "lessons/sketchnotes/README.md": { + "original_hash": "050b8bddebafba55b129414e6ab096ab", + "translation_date": "2025-08-28T02:39:18+00:00", + "source_file": "lessons/sketchnotes/README.md", + "language_code": "tl" + }, + "troubleshoot.md": { + "original_hash": "8d9c5a4a7c7798d699672a22cb7fea86", + "translation_date": "2025-10-03T09:48:38+00:00", + "source_file": "troubleshoot.md", + "language_code": "tl" + } +} \ No newline at end of file diff --git a/translations/tl/AGENTS.md b/translations/tl/AGENTS.md index 5c9cae77..8a37408b 100644 --- a/translations/tl/AGENTS.md +++ b/translations/tl/AGENTS.md @@ -1,12 +1,3 @@ - # AGENTS.md ## Pangkalahatang-ideya ng Proyekto diff --git a/translations/tl/README.md b/translations/tl/README.md index 66ffa105..d9aaed84 100644 --- a/translations/tl/README.md +++ b/translations/tl/README.md @@ -1,12 +1,3 @@ - [![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) [![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) @@ -21,13 +12,13 @@ CO_OP_TRANSLATOR_METADATA: [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) -# Artificial Intelligence for Beginners - A Curriculum +# Artificial Intelligence para sa Mga Nagsisimula - Isang Kurikulum -|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../../../translated_images/tl/ai-overview.0857791951d19500.webp)| +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/tl/ai-overview.0857791951d19500.webp)| |:---:| -| AI For Beginners - _Sketchnote ni [@girlie_mac](https://twitter.com/girlie_mac)_ | +| AI Para sa Mga Nagsisimula - _Sketchnote ni [@girlie_mac](https://twitter.com/girlie_mac)_ | -Suriin ang mundo ng **Artificial Intelligence** (AI) gamit ang aming 12-linggong, 24-leksyong kurikulum! Kasama dito ang praktikal na mga leksyon, kuis, at mga laboratoryo. Ang kurikulum ay madaling sundan para sa mga nagsisimula at sumasaklaw ng mga kasangkapan tulad ng TensorFlow at PyTorch, pati na rin ang etika sa AI +Siyasatin ang mundo ng **Artificial Intelligence** (AI) gamit ang aming 12-linggong, 24-pangangaraling kurikulum! Kasama dito ang mga praktikal na aralin, pagsusulit, at mga laboratoryo. Ang kurikulum ay angkop para sa mga nagsisimula at sumasaklaw sa mga kasangkapan tulad ng TensorFlow at PyTorch, pati na rin ang etika sa AI ### 🌐 Suporta sa Maramihang Wika @@ -35,145 +26,145 @@ Suriin ang mundo ng **Artificial Intelligence** (AI) gamit ang aming 12-linggong #### Sinusuportahan sa pamamagitan ng GitHub Action (Awtomatiko at Laging Napapanahon) -[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) +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-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)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-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 I-clone sa Lokal?** +> **Mas gusto mo bang I-clone Lokal?** -> 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: +> Kasama sa repositoryong ito ang 50+ na pagsasalin ng wika na lubos na nagpapalaki ng 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' > ``` -> Binibigyan 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 upang matapos ang kurso nang mas mabilis ang pag-download. -**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)** +**Kung nais mong magkaroon ng karagdagang mga sinusuportahang 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 Matututuhan +## Ano ang iyong matututunan **[Mindmap ng Kurso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** Sa kurikulum na ito, matututuhan mo: -* 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**. +* Iba’t ibang mga pamamaraan sa Artificial Intelligence, kabilang ang "magandang luma" 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 puso ng modernong AI. Ilalarawan namin ang mga konsepto sa likod ng mga mahahalagang paksang ito gamit ang code sa dalawang pinakapopular na frameworks - [TensorFlow](http://Tensorflow.org) at [PyTorch](http://pytorch.org). +* **Neural Architectures** para sa pagtatrabaho gamit ang mga imahe at teksto. Tatalakayin namin ang mga kamakailang modelo ngunit maaaring may kaunting kakulangan sa labing-huling mga estado ng teknolohiya. +* Hindi gaanong popular na mga pamamaraan sa AI, tulad ng **Genetic Algorithms** at **Multi-Agent Systems**. -Hindi namin tatalakayin sa kurikulum na ito: +Hindi namin saklawin sa kurikulung ito: -> [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) +> [Hanapin ang lahat ng karagdagang mapagkukunan para sa kursong ito sa aming koleksyon sa Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) -* 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/). +* Mga kaso sa negosyo ng paggamit ng **AI sa Negosyo**. Isaalang-alang ang pagkuha ng [Panimula sa AI para sa mga gumagamit sa negosyo](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 kasama ang [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). +* Praktikal na mga aplikasyon ng AI na ginawa gamit ang **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para dito, inirerekumenda 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 tiyak 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) learning paths. +* **Conversational AI** at **Chat Bots**. Mayroong hiwalay na [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum) learning path, at maaari ka ring tumukoy sa [blog post na ito](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para sa higit pang detalye. +* **Malalim na Matematika** sa likod ng deep learning. Para dito, inirerekumenda namin ang [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) ni Ian Goodfellow, Yoshua Bengio at Aaron Courville, na makukuha rin online sa [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). -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. +Para sa banayad na 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. # Nilalaman -| | Link ng Leksyon | PyTorch/Keras/TensorFlow | Lab | +| | Lesson Link | 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 | [**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 | [**Introduksyon sa Neural Networks**](./lessons/3-NeuralNetworks/README.md) ||| +| 0 | [Pagsasaayos ng Kurso](./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** | +| 02 | [Pagpapakita ng Kaalaman at Mga Expert System](./lessons/2-Symbolic/README.md) | [Mga Expert System](./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) ||| | 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)| [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) | +| 04 | [Multi-Layered Perceptron at Paglikha ng Sariling 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 | [Panimula sa Frameworks (PyTorch/TensorFlow) at 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)| [Siyasatin ang Computer Vision sa Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Panimula sa 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) | | +| 08 | [Pre-trained Networks at Transfer Learning](./lessons/4-ComputerVision/08-TransferLearning/README.md) at [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 at 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 at 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) | [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) | +| 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) | [Siyasatin ang Natural Language Processing sa Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Representasyon ng Teksto. 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 at 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. Pagsasanay ng sariling 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) | | 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 Teknik sa AI** || | +| 20 | [Malalaking Language Models, Prompt Programming at 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 | **Ibang 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 | **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 | **Mga Extras** | | | -| 25 | [Multi-Modal Networks, CLIP and VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | +| VII | **AI Ethics** | | | +| 24 | [AI Ethics at Responsable AI](./lessons/7-Ethics/README.md) | [Microsoft Learn: Responsable AI Principles](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Extras** | | | +| 25 | [Multi-Modal Networks, CLIP at VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | ## Bawat aralin ay naglalaman ng -* 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. +* Material na dapat basahin bago ang aralin +* Mga executable na Jupyter Notebooks, na kadalasan ay partikular sa framework (**PyTorch** o **TensorFlow**). Ang executable notebook ay naglalaman din ng maraming teoretikal na materyal, kaya para maintindihan ang paksa kailangan mong dumaan sa kahit isang bersyon ng notebook (PyTorch o TensorFlow). +* **Labs** na available para sa ilang mga paksa, na nagbibigay sa iyo ng pagkakataon na subukan ang paggamit ng natutunang materyal sa isang partikular na problema. +* Ang ilang mga seksyon ay may mga link sa [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) na mga module na sumasaklaw sa mga kaugnay na paksa. ## Paano Magsimula -### 🎯 Bago ka sa AI? Magsimula Dito! +### 🎯 Bago ka sa AI? Magsimula dito! -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: +Kung ikaw ay ganap na bago sa AI at gusto mo ng mabilis na mga hands-on na halimbawa, tingnan ang aming [**Beginner-Friendly Examples**](./examples/README.md)! Kasama dito: -- 🌟 **Hello AI World** - Ang iyong unang AI na programa (pagtukoy ng pattern) -- 🧠 **Simple Neural Network** - Gumawa ng neural network mula sa simula -- πŸ–ΌοΈ **Image Classifier** - Magklasipika ng mga larawan na may detalyadong paliwanag -- πŸ’¬ **Pagsusuri ng Sentimyento ng Teksto** - Suriin ang positibo/negatibong teksto +- 🌟 **Hello AI World** - Ang iyong unang programa sa AI (pagnilala ng pattern) +- 🧠 **Simple Neural Network** - Bumuo ng neural network mula sa simula +- πŸ–ΌοΈ **Tagapag-uri ng Imahe** - Uriin ang mga imahe na may detalyadong mga paliwanag +- πŸ’¬ **Sentimyento ng Teksto** - Suriin ang positibo/negatibong teksto -Ang mga halimbawang ito ay idinisenyo para matulungan kang maunawaan ang mga konsepto ng AI bago pumasok sa buong kurikulum. +Ang mga halimbawa na ito ay idinisenyo upang tulungan kang maunawaan ang mga konsepto ng AI bago pumasok sa buong kurikulum. ### πŸ“š Pag-setup ng Buong Kurikulum -- 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) +- Nilikha namin ang isang [setup lesson](./lessons/0-course-setup/setup.md) upang matulungan ka sa pag-set up ng iyong development environment. - Para sa mga Guro, nagsagawa kami ng isang [curricula setup lesson](./lessons/0-course-setup/for-teachers.md) para din sa inyo! +- Paano [Patakbuhin ang code sa isang VSCode o Codespace](./lessons/0-course-setup/how-to-run.md) Sundin ang mga hakbang na ito: -I-fork ang Repository: I-click ang "Fork" na button sa itaas-kanan ng pahinang ito. +Fork ang Repositoryo: I-click ang "Fork" button sa kanang itaas ng pahinang ito. -I-clone ang Repository: `git clone https://github.com/microsoft/AI-For-Beginners.git` +Clone ang Repositoryo: `git clone https://github.com/microsoft/AI-For-Beginners.git` -Huwag kalimutang mag-star (🌟) sa repo na ito para mas madali mo itong mahanap sa susunod. +Huwag kalimutang bigyan ng star (🌟) ang repo na ito para mas madali mo itong mahanap sa susunod. -## Makipagkilala sa Ibang mga Nag-aaral +## Kilalanin ang Iba pang mga Nag-aaral -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. +Sumali sa aming [opisyal na AI Discord server](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para makipagkilala at makipag-network sa ibang mga nag-aaral na kumukuha ng kursong ito at makakuha ng suporta. -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) +Kung mayroon kang feedback sa produkto o mga tanong habang nagbuo, bisitahin ang aming [Azure AI Foundry Developer Forum](https://aka.ms/foundry/forum) ## Mga Pagsusulit -> **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. +> **Isang paalala tungkol sa mga pagsusulit**: Lahat ng pagsusulit ay matatagpuan sa Quiz-app folder sa etc\quiz-app, o [Online Dito](https://ff-quizzes.netlify.app/) Nakalink ito mula sa loob ng mga lessons, maaaring patakbuhin ang quiz app nang lokal o i-deploy ito sa Azure; sundin ang mga tagubilin sa `quiz-app` folder. Unti-unti itong nilalagyan ng lokal na wika. ## Kailangan ng Tulong -Mayroon ka bang mga suhestiyon o nakakita ng mga mali sa ispeling o code? Mag-raise ng isyu o gumawa ng pull request. +May mga suhestiyon ka ba o nakakita ng mga maling baybay o pagkakamali sa code? Mag-raise ng isyu o gumawa ng pull request. ## Espesyal na Pasasalamat -* **✍️ Punong May-akda:** [Dmitry Soshnikov](http://soshnikov.com), PhD -* **πŸ”₯ Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD -* **🎨 Tagadibuho ng Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) -* **βœ… Tagalikha ng Pagsusulit:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) -* **πŸ™ Pangunahing mga Kontribyutor:** [Evgenii Pishchik](https://github.com/Pe4enIks) +* **✍️ Pangunahing May-akda:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **πŸ”₯ Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Tagapag-illustrate ng Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **βœ… Tagalikha ng Pagsusulit:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **πŸ™ Mga Pangunahing Kontribyutor:** [Evgenii Pishchik](https://github.com/Pe4enIks) -## Ibang Kurikulum +## Iba pang mga Kurikulum -Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Tingnan ang: +Ang aming koponan ay gumagawa rin ng iba pang mga kurikulum! Tingnan ang: ### LangChain @@ -182,14 +173,14 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Tingnan ang: --- -### Azure / Edge / MCP / Mga Ahente +### Azure / Edge / MCP / Agents [![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 +188,8 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Tingnan ang: [![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) --- - -### Pangunahing Pagkatuto + +### Pangunahing Pag-aaral [![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) @@ -208,26 +199,26 @@ Ang aming koponan ay gumagawa ng iba pang mga kurikulum! Tingnan ang: [![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) [![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) -## Pagkuha ng Tulong +## Paghingi ng Tulong -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. +Kung ikaw ay naipit o may mga tanong tungkol sa paggawa ng AI apps. Sumali sa kapwa nag-aaral at mga bihasang developer sa mga talakayan tungkol sa MCP. Isang suportadong komunidad kung saan malugod 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 o mga pagkakamali habang nagtatayo, bisitahin: +Kung mayroon kang feedback sa produkto o mga error habang nagbuo, 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) --- -**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. +**Pagsasakatwiran**: +Ang dokumentong ito ay isinalin gamit ang serbisyo ng AI na pagsasalin na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagamat kami ay nagsusumikap para sa katumpakan, pakatandaan na ang awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o di-tumpak na bahagi. Ang orihinal na dokumento sa orihinal nitong wika ang dapat ituring na pangunahing sanggunian. Para sa mga 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. \ No newline at end of file diff --git a/translations/tl/SECURITY.md b/translations/tl/SECURITY.md index d19e6698..b913d19e 100644 --- a/translations/tl/SECURITY.md +++ b/translations/tl/SECURITY.md @@ -1,12 +1,3 @@ - ## Seguridad Sineseryoso ng Microsoft ang seguridad ng aming mga produkto at serbisyo, kabilang na ang lahat ng source code repositories na pinamamahalaan sa pamamagitan ng aming mga organisasyon sa GitHub, tulad ng [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), at [aming mga organisasyon sa GitHub](https://opensource.microsoft.com/). diff --git a/translations/tl/etc/CODE_OF_CONDUCT.md b/translations/tl/etc/CODE_OF_CONDUCT.md index 4f7bda2f..1c7ecd9b 100644 --- a/translations/tl/etc/CODE_OF_CONDUCT.md +++ b/translations/tl/etc/CODE_OF_CONDUCT.md @@ -1,12 +1,3 @@ - # Microsoft Open Source Code of Conduct Ang proyektong ito ay sumunod sa [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). diff --git a/translations/tl/etc/CONTRIBUTING.md b/translations/tl/etc/CONTRIBUTING.md index 0bf41d2d..6ab07a29 100644 --- a/translations/tl/etc/CONTRIBUTING.md +++ b/translations/tl/etc/CONTRIBUTING.md @@ -1,12 +1,3 @@ - # Pag-aambag Ang proyektong ito ay tumatanggap ng mga ambag at mungkahi. Karamihan sa mga ambag ay nangangailangan sa iyo na sumang-ayon sa isang Contributor License Agreement (CLA) na nagsasaad na mayroon kang karapatan, at talagang ginagawa mo, na bigyan kami ng mga karapatan upang gamitin ang iyong ambag. Para sa mga detalye, bisitahin ang https://cla.microsoft.com. diff --git a/translations/tl/etc/Mindmap.md b/translations/tl/etc/Mindmap.md index a30b34b3..e11c7da0 100644 --- a/translations/tl/etc/Mindmap.md +++ b/translations/tl/etc/Mindmap.md @@ -1,12 +1,3 @@ - # AI ## [Panimula sa AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) diff --git a/translations/tl/etc/SUPPORT.md b/translations/tl/etc/SUPPORT.md index c9f71ef9..60a6ade0 100644 --- a/translations/tl/etc/SUPPORT.md +++ b/translations/tl/etc/SUPPORT.md @@ -1,12 +1,3 @@ - # Suporta ## Paano maghain ng mga isyu at humingi ng tulong diff --git a/translations/tl/etc/TRANSLATIONS.md b/translations/tl/etc/TRANSLATIONS.md index d512f54e..de09ba23 100644 --- a/translations/tl/etc/TRANSLATIONS.md +++ b/translations/tl/etc/TRANSLATIONS.md @@ -1,12 +1,3 @@ - # Mag-ambag sa pamamagitan ng pagsasalin ng mga aralin Malugod naming tinatanggap ang mga pagsasalin para sa mga aralin sa kurikulum na ito! diff --git a/translations/tl/etc/quiz-app/README.md b/translations/tl/etc/quiz-app/README.md index 22f41cb9..6032e959 100644 --- a/translations/tl/etc/quiz-app/README.md +++ b/translations/tl/etc/quiz-app/README.md @@ -1,12 +1,3 @@ - # Mga Pagsusulit Ang mga pagsusulit na ito ay pre- at post-lecture quizzes para sa AI curriculum sa https://aka.ms/ai-beginners diff --git a/translations/tl/examples/README.md b/translations/tl/examples/README.md index 22f1a241..ae91299e 100644 --- a/translations/tl/examples/README.md +++ b/translations/tl/examples/README.md @@ -1,12 +1,3 @@ - # Mga Halimbawa ng AI para sa Baguhan Maligayang pagdating! Ang direktoryong ito ay naglalaman ng mga simpleng halimbawa na standalone upang matulungan kang magsimula sa AI at machine learning. Ang bawat halimbawa ay idinisenyo para sa mga baguhan na may detalyadong mga komento at hakbang-hakbang na paliwanag. diff --git a/translations/tl/lessons/0-course-setup/for-teachers.md b/translations/tl/lessons/0-course-setup/for-teachers.md index e22aab5a..ca3e032a 100644 --- a/translations/tl/lessons/0-course-setup/for-teachers.md +++ b/translations/tl/lessons/0-course-setup/for-teachers.md @@ -1,12 +1,3 @@ - # Para sa mga Guro Gusto mo bang gamitin ang kurikulum na ito sa iyong klase? Huwag mag-atubiling gamitin ito! 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 7a395f37..8f409c02 100644 --- a/translations/tl/lessons/0-course-setup/how-to-run.md +++ b/translations/tl/lessons/0-course-setup/how-to-run.md @@ -1,12 +1,3 @@ - # 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 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: diff --git a/translations/tl/lessons/0-course-setup/setup.md b/translations/tl/lessons/0-course-setup/setup.md index 0df74375..89b3b16b 100644 --- a/translations/tl/lessons/0-course-setup/setup.md +++ b/translations/tl/lessons/0-course-setup/setup.md @@ -1,12 +1,3 @@ - # Pagsisimula sa Kurikulum na Ito ## Ikaw ba ay isang mag-aaral? diff --git a/translations/tl/lessons/1-Intro/README.md b/translations/tl/lessons/1-Intro/README.md index b7aa88fc..5e88e6db 100644 --- a/translations/tl/lessons/1-Intro/README.md +++ b/translations/tl/lessons/1-Intro/README.md @@ -1,12 +1,3 @@ - # Panimula sa AI ![Buod ng nilalaman ng Panimula sa AI sa isang doodle](../../../../translated_images/tl/ai-intro.bf28d1ac4235881c.webp) diff --git a/translations/tl/lessons/1-Intro/assignment.md b/translations/tl/lessons/1-Intro/assignment.md index 25f34a54..13c42bda 100644 --- a/translations/tl/lessons/1-Intro/assignment.md +++ b/translations/tl/lessons/1-Intro/assignment.md @@ -1,12 +1,3 @@ - # Game Jam Ang mga laro ay isang larangan na malaki ang naging impluwensya ng mga pag-unlad sa AI at ML. Sa gawaing ito, magsulat ng maikling papel tungkol sa isang larong gusto mo na naapektuhan ng ebolusyon ng AI. Dapat itong isang lumang laro na naapektuhan ng iba't ibang uri ng mga sistema ng pagpoproseso ng kompyuter. Isang magandang halimbawa ay Chess o Go, ngunit maaari ring tingnan ang mga video game tulad ng Pong o Pac-Man. Sumulat ng sanaysay na tatalakay sa nakaraan, kasalukuyan, at hinaharap ng AI sa larong ito. diff --git a/translations/tl/lessons/2-Symbolic/README.md b/translations/tl/lessons/2-Symbolic/README.md index 26018e6c..0bb59da0 100644 --- a/translations/tl/lessons/2-Symbolic/README.md +++ b/translations/tl/lessons/2-Symbolic/README.md @@ -1,15 +1,6 @@ - # Knowledge Representation and Expert Systems -![Summary of Symbolic AI content](../../../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.webp) +![Summary of Symbolic AI content](../../../../translated_images/tl/ai-symbolic.715a30cb610411a6.webp) > Sketchnote by [Tomomi Imura](https://twitter.com/girlie_mac) @@ -41,7 +32,7 @@ Kadalasan, hindi natin mahigpit na tinutukoy ang kaalaman, ngunit nirerespeto it 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: -![Knowledge representation spectrum](../../../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.webp) +![Knowledge representation spectrum](../../../../translated_images/tl/knowledge-spectrum.b60df631852c0217.webp) > Image by [Dmitry Soshnikov](http://soshnikov.com) @@ -94,7 +85,7 @@ Block Syntax | Indent | | | 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. -![Human Architecture](../../../../../../translated_images/tl/arch-human.5d4d35f1bba3ab1c.webp) | ![Knowledge-Based System](../../../../../../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 knowledge-based system @@ -106,7 +97,7 @@ Ang mga expert system ay ginawa katulad ng sistema ng pag-iisip ng tao, na nagla 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) > Image by [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tl/lessons/2-Symbolic/assignment.md b/translations/tl/lessons/2-Symbolic/assignment.md index d83c56f7..d3801998 100644 --- a/translations/tl/lessons/2-Symbolic/assignment.md +++ b/translations/tl/lessons/2-Symbolic/assignment.md @@ -1,12 +1,3 @@ - # Gumawa ng Ontolohiya Ang paggawa ng knowledge base ay tungkol sa pag-uuri ng isang modelo na kumakatawan sa mga katotohanan tungkol sa isang paksa. Pumili ng paksa - tulad ng isang tao, lugar, o bagay - at pagkatapos ay bumuo ng isang modelo ng paksang iyon. Gamitin ang ilan sa mga teknik at estratehiya sa paggawa ng modelo na tinalakay sa araling ito. Halimbawa, maaaring gumawa ng ontolohiya ng isang sala na may kasangkapan, ilaw, at iba pa. Paano naiiba ang sala sa kusina? Sa banyo? Paano mo malalaman na ito ay isang sala at hindi isang silid-kainan? Gamitin ang [ProtΓ©gΓ©](https://protege.stanford.edu/) upang bumuo ng iyong ontolohiya. diff --git a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/README.md index a7d5bf28..f30343cf 100644 --- a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -1,12 +1,3 @@ - # Panimula sa Neural Networks: Perceptron ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/5) @@ -15,7 +6,7 @@ Isa sa mga unang pagsubok na lumikha ng isang bagay na katulad ng modernong neur | | | |--------------|-----------| -|Frank Rosenblatt | The Mark 1 Perceptron| +|Frank Rosenblatt | The Mark 1 Perceptron| > Mga larawan [mula sa Wikipedia](https://en.wikipedia.org/wiki/Perceptron) @@ -33,7 +24,7 @@ y(x) = f(wTx) kung saan ang f ay isang step activation function - + ## Pagsasanay ng Perceptron diff --git a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md index 10d2c0fe..1a0f5ca2 100644 --- a/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -1,12 +1,3 @@ - # Multi-Class Classification gamit ang Perceptron Gawain mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/README.md index e5fcaaa0..52bdca98 100644 --- a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -1,12 +1,3 @@ - # Panimula sa Neural Networks. Multi-Layered Perceptron Sa nakaraang seksyon, natutunan mo ang tungkol sa pinakasimpleng modelo ng neural network - ang one-layered perceptron, isang linear na modelo para sa two-class classification. @@ -65,7 +56,7 @@ Mananatili ang gradient descent algorithm, ngunit magiging mas mahirap kalkulahi Pansinin na ang kaliwang bahagi ng lahat ng mga ekspresyon ay pareho, kaya't maaari nating epektibong kalkulahin ang derivatives simula sa loss function at magpatuloy "pabalik" sa computational graph. Kaya't ang paraan ng pag-train ng multi-layered perceptron ay tinatawag na **backpropagation**, o 'backprop'. -compute graph +compute graph > TODO: citation ng imahe diff --git a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md index 5227b263..d70b148c 100644 --- a/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -1,12 +1,3 @@ - # MNIST Classification gamit ang Sariling Framework Gawain mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md index ca2b586c..65ac9912 100644 --- a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/README.md @@ -1,12 +1,3 @@ - # Neural Network Frameworks Tulad ng natutunan na natin, upang ma-train ang neural networks nang epektibo, kailangan nating gawin ang dalawang bagay: diff --git a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md index 910f38a1..ac5baa57 100644 --- a/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -1,12 +1,3 @@ - # Pag-uuri gamit ang PyTorch/TensorFlow Takdang-Aralin mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/3-NeuralNetworks/README.md b/translations/tl/lessons/3-NeuralNetworks/README.md index a63cb08d..fdd4d9fb 100644 --- a/translations/tl/lessons/3-NeuralNetworks/README.md +++ b/translations/tl/lessons/3-NeuralNetworks/README.md @@ -1,12 +1,3 @@ - # Panimula sa Neural Networks ![Buod ng nilalaman ng Intro Neural Networks sa isang doodle](../../../../translated_images/tl/ai-neuralnetworks.1c687ae40bc86e83.webp) diff --git a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md index 3f090226..aa110158 100644 --- a/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md +++ b/translations/tl/lessons/4-ComputerVision/06-IntroCV/README.md @@ -1,12 +1,3 @@ - # Panimula sa Computer Vision [Computer Vision](https://wikipedia.org/wiki/Computer_vision) ay isang disiplina na ang layunin ay bigyan ang mga computer ng kakayahang maunawaan ang mga digital na imahe sa mataas na antas. Medyo malawak ang depinisyon na ito dahil ang *pag-unawa* ay maaaring mangahulugan ng iba't ibang bagay, tulad ng paghahanap ng isang bagay sa larawan (**object detection**), pag-unawa sa nangyayari (**event detection**), paglalarawan ng larawan gamit ang teksto, o pagbuo ng eksena sa 3D. Mayroon ding mga espesyal na gawain na may kaugnayan sa mga larawan ng tao: pagtatantiya ng edad at emosyon, pagtuklas at pagkilala sa mukha, at pagtatantiya ng 3D pose, upang pangalanan ang ilan. @@ -115,7 +106,7 @@ Magbasa pa tungkol sa optical flow [sa mahusay na tutorial na ito](https://learn Sa lab na ito, kukuha ka ng video na may simpleng gestures, at ang layunin mo ay kunin ang mga galaw na pataas/pababa/kaliwa/kanan gamit ang optical flow. -Palm Movement Frame +Palm Movement Frame --- diff --git a/translations/tl/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/tl/lessons/4-ComputerVision/06-IntroCV/lab/README.md index 4c8e1708..c77be7cd 100644 --- a/translations/tl/lessons/4-ComputerVision/06-IntroCV/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -1,12 +1,3 @@ - # Pagtukoy ng Galaw gamit ang Optical Flow Takdang-Aralin mula sa [AI for Beginners Curriculum](https://aka.ms/ai-beginners). diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md index fb782369..1ea34ebb 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -1,12 +1,3 @@ - # Mga Kilalang Arkitektura ng CNN ### VGG-16 @@ -25,7 +16,7 @@ Tulad ng nakikita mo, sinusunod ng VGG ang tradisyunal na pyramid architecture, Ang ResNet ay isang pamilya ng mga modelo na iminungkahi ng Microsoft Research noong 2015. Ang pangunahing ideya ng ResNet ay ang paggamit ng **residual blocks**: - + > Larawan mula sa [papel na ito](https://arxiv.org/pdf/1512.03385.pdf) @@ -37,7 +28,7 @@ Maaari mo ring isipin ang network na ito bilang may kakayahang i-adjust ang comp Ang arkitektura ng Google Inception ay nagdadala ng ideyang ito sa mas mataas na antas, at binubuo ang bawat layer ng network bilang kombinasyon ng iba't ibang paths: - + > Larawan mula sa [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md index bada001d..884416cc 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/README.md @@ -1,12 +1,3 @@ - # Convolutional Neural Networks Nakita na natin dati na ang neural networks ay mahusay sa pagproseso ng mga imahe, at kahit ang isang layer na perceptron ay kayang kilalanin ang mga handwritten digits mula sa MNIST dataset na may makatwirang katumpakan. Gayunpaman, ang MNIST dataset ay espesyal, dahil lahat ng digits ay nakasentro sa loob ng imahe, na nagpapadali sa gawain. @@ -24,7 +15,7 @@ Upang makuha ang mga pattern, gagamit tayo ng konsepto ng **convolutional filter Halimbawa, kung mag-aapply tayo ng 3x3 vertical edge at horizontal edge filters sa MNIST digits, makakakuha tayo ng mga highlight (hal. mataas na values) kung saan may mga vertical at horizontal edges sa orihinal na imahe. Kaya ang dalawang filters na ito ay maaaring gamitin upang "hanapin" ang mga edges. Katulad nito, maaari tayong magdisenyo ng iba't ibang filters upang hanapin ang iba pang low-level patterns: - + > Larawan ng [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) diff --git a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md index f1ae7865..4abb58cd 100644 --- a/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/07-ConvNets/lab/README.md @@ -1,12 +1,3 @@ - # Pag-uuri ng Mukha ng Alagang Hayop Takdang-Aralin mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md index 4a2b3dc0..965fd277 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/README.md @@ -1,12 +1,3 @@ - # Pre-trained Networks at Transfer Learning Ang pag-train ng CNNs ay maaaring tumagal ng mahabang oras, at nangangailangan ng maraming data para sa gawain na ito. Gayunpaman, karamihan sa oras ay ginugugol sa pag-aaral ng pinakamahusay na low-level filters na magagamit ng network upang makuha ang mga pattern mula sa mga imahe. Isang natural na tanong ang lumalabas - maaari ba nating gamitin ang isang neural network na na-train na sa isang dataset at i-adapt ito upang mag-classify ng ibang mga imahe nang hindi kinakailangan ang buong proseso ng pag-train? diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md index f6deef21..1f272dcb 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md @@ -1,12 +1,3 @@ - # Mga Teknik sa Pagsasanay ng Deep Learning Habang lumalalim ang mga neural network, nagiging mas mahirap ang proseso ng kanilang pagsasanay. Isa sa mga pangunahing problema ay ang tinatawag na [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) o [exploding gradients](https://deepai.org/machine-learning-glossary-and-terms/exploding-gradient-problem#:~:text=Exploding%20gradients%20are%20a%20problem,updates%20are%20small%20and%20controlled.). [Ang post na ito](https://towardsdatascience.com/the-vanishing-exploding-gradient-problem-in-deep-neural-networks-191358470c11) ay nagbibigay ng magandang pagpapakilala sa mga problemang ito. diff --git a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md index c3f49999..b0a4ad66 100644 --- a/translations/tl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/08-TransferLearning/lab/README.md @@ -1,12 +1,3 @@ - # Pag-uuri ng Oxford Pets gamit ang Transfer Learning Takdang-Aralin mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md index a6ab2b81..f0a35ccd 100644 --- a/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md +++ b/translations/tl/lessons/4-ComputerVision/09-Autoencoders/README.md @@ -1,12 +1,3 @@ - # Autoencoders Kapag nagte-train ng CNNs, isa sa mga problema ay kailangan natin ng maraming labeled na data. Sa kaso ng image classification, kailangan nating paghiwalayin ang mga imahe sa iba't ibang klase, na isang manu-manong gawain. @@ -46,7 +37,7 @@ Sa kabuuan: * Kumukuha tayo ng vector `sample` mula sa distribution N(zmean,exp(zlog\_sigma)) * Sinusubukan ng decoder na i-decode ang orihinal na imahe gamit ang `sample` bilang input vector - + > Imahe mula sa [blog post na ito](https://ijdykeman.github.io/ml/2016/12/21/cvae.html) ni Isaak Dykeman @@ -57,13 +48,13 @@ Ang Variational auto-encoders ay gumagamit ng isang komplikadong loss function n Isang mahalagang bentahe ng VAEs ay pinapayagan tayo nitong madaling makabuo ng mga bagong imahe, dahil alam natin kung aling distribution ang dapat pagkunan ng latent vectors. Halimbawa, kung magte-train tayo ng VAE na may 2D latent vector sa MNIST, maaari nating baguhin ang mga component ng latent vector upang makakuha ng iba't ibang digit: -vaemnist +vaemnist > Imahe ni [Dmitry Soshnikov](http://soshnikov.com) Pansinin kung paano nagbiblend ang mga imahe sa isa't isa, habang nagsisimula tayong kumuha ng latent vectors mula sa iba't ibang bahagi ng latent parameter space. Maaari rin nating i-visualize ang space na ito sa 2D: -vaemnist cluster +vaemnist cluster > Imahe ni [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tl/lessons/4-ComputerVision/10-GANs/README.md b/translations/tl/lessons/4-ComputerVision/10-GANs/README.md index 35ccc19c..43c997eb 100644 --- a/translations/tl/lessons/4-ComputerVision/10-GANs/README.md +++ b/translations/tl/lessons/4-ComputerVision/10-GANs/README.md @@ -1,12 +1,3 @@ - # Generative Adversarial Networks Sa nakaraang seksyon, natutunan natin ang tungkol sa **generative models**: mga modelo na maaaring lumikha ng mga bagong imahe na kahawig ng mga nasa training dataset. Ang VAE ay isang magandang halimbawa ng generative model. @@ -17,7 +8,7 @@ Gayunpaman, kung susubukan nating lumikha ng isang bagay na talagang makabuluhan Ang pangunahing ideya ng GAN ay ang pagkakaroon ng dalawang neural networks na magsasanay laban sa isa't isa: - + > Larawan mula kay [Dmitry Soshnikov](http://soshnikov.com) @@ -41,7 +32,7 @@ Ang Generator ay medyo mas komplikado. Maaari mo itong ituring na baliktad na di > βœ… Dahil ang convolution layer ay ipinatutupad bilang isang linear filter na naglalakbay sa imahe, ang deconvolution ay mahalagang katulad ng convolution, at maaaring ipatupad gamit ang parehong layer logic. - + > Larawan mula kay [Dmitry Soshnikov](http://soshnikov.com) diff --git a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md index c3c4badd..587a09e2 100644 --- a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md +++ b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/README.md @@ -1,12 +1,3 @@ - # Pag-detect ng Objekto Ang mga modelo ng image classification na ating tinalakay hanggang ngayon ay tumatanggap ng isang imahe at nagbibigay ng resulta na kategorya, tulad ng klase na 'numero' sa problema ng MNIST. Gayunpaman, sa maraming pagkakataon, hindi lang natin nais malaman na ang isang larawan ay nagpapakita ng mga bagay - nais din nating matukoy ang eksaktong lokasyon ng mga ito. Ito ang layunin ng **pag-detect ng objekto**. diff --git a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md index 7f68218a..ea14cb4f 100644 --- a/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/11-ObjectDetection/lab/README.md @@ -1,12 +1,3 @@ - # Pagtukoy ng Ulo gamit ang Hollywood Heads Dataset Gawain sa Lab mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/4-ComputerVision/12-Segmentation/README.md b/translations/tl/lessons/4-ComputerVision/12-Segmentation/README.md index 7ef496bc..802b9dd5 100644 --- a/translations/tl/lessons/4-ComputerVision/12-Segmentation/README.md +++ b/translations/tl/lessons/4-ComputerVision/12-Segmentation/README.md @@ -1,12 +1,3 @@ - # Segmentation Natutuhan na natin ang tungkol sa Object Detection, na nagbibigay-daan sa atin upang matukoy ang mga bagay sa larawan sa pamamagitan ng pag-predict ng kanilang *bounding boxes*. Gayunpaman, para sa ilang mga gawain, hindi lang bounding boxes ang kailangan natin, kundi mas tiyak na lokasyon ng mga bagay. Ang gawaing ito ay tinatawag na **segmentation**. @@ -20,7 +11,7 @@ Ang segmentation ay maaaring tingnan bilang **pixel classification**, kung saan Halimbawa, sa instance segmentation, ang mga tupa ay magkakaibang bagay, ngunit sa semantic segmentation, ang lahat ng tupa ay kinakatawan ng isang klase. - + > Larawan mula sa [blog post na ito](https://nirmalamurali.medium.com/image-classification-vs-semantic-segmentation-vs-instance-segmentation-625c33a08d50) @@ -29,7 +20,7 @@ May iba't ibang neural architectures para sa segmentation, ngunit pare-pareho an * **Encoder** na kumukuha ng mga features mula sa input image * **Decoder** na nagta-transform ng mga features na iyon sa **mask image**, na may parehong laki at bilang ng channels na tumutugma sa bilang ng mga klase. - + > Larawan mula sa [publikasyong ito](https://arxiv.org/pdf/2001.05566.pdf) @@ -43,7 +34,7 @@ Sa araling ito, makikita natin ang segmentation sa aksyon sa pamamagitan ng pag- > βœ… Ang teknik na ito ay partikular na angkop para sa ganitong uri ng medical imaging, ngunit anong iba pang mga aplikasyon sa totoong mundo ang naiisip mo? -navi +navi > Larawan mula sa PH2 Database diff --git a/translations/tl/lessons/4-ComputerVision/12-Segmentation/lab/README.md b/translations/tl/lessons/4-ComputerVision/12-Segmentation/lab/README.md index ae1eec2b..7f9fcee6 100644 --- a/translations/tl/lessons/4-ComputerVision/12-Segmentation/lab/README.md +++ b/translations/tl/lessons/4-ComputerVision/12-Segmentation/lab/README.md @@ -1,12 +1,3 @@ - # Paghiwa-hiwalay ng Katawan ng Tao Gawain sa Lab mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/4-ComputerVision/README.md b/translations/tl/lessons/4-ComputerVision/README.md index 117c6ef5..135ee248 100644 --- a/translations/tl/lessons/4-ComputerVision/README.md +++ b/translations/tl/lessons/4-ComputerVision/README.md @@ -1,12 +1,3 @@ - # Computer Vision ![Buod ng nilalaman ng Computer Vision sa isang doodle](../../../../translated_images/tl/ai-computervision.6506ebebac3fbf76.webp) diff --git a/translations/tl/lessons/5-NLP/13-TextRep/README.md b/translations/tl/lessons/5-NLP/13-TextRep/README.md index cf57df9f..775b161b 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/README.md +++ b/translations/tl/lessons/5-NLP/13-TextRep/README.md @@ -1,12 +1,3 @@ - # Pagsasalarawan ng Teksto bilang Tensors ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/25) @@ -25,7 +16,7 @@ Ang layunin natin ay uriin ang balita sa isa sa mga kategorya batay sa teksto. Kung nais nating lutasin ang mga gawain sa Natural Language Processing (NLP) gamit ang neural networks, kailangan natin ng paraan upang maipakita ang teksto bilang tensors. Ang mga computer ay kumakatawan na sa mga karakter ng teksto bilang mga numero na tumutugma sa mga font sa iyong screen gamit ang mga encoding tulad ng ASCII o UTF-8. -Larawan na nagpapakita ng diagram na nagmamapa ng isang karakter sa ASCII at binary na representasyon +Larawan na nagpapakita ng diagram na nagmamapa ng isang karakter sa ASCII at binary na representasyon > [Pinagmulan ng Larawan](https://www.seobility.net/en/wiki/ASCII) @@ -48,7 +39,7 @@ Sa ilang mga kaso, maaari rin nating isaalang-alang ang paggamit ng tri-grams -- Kapag nilulutas ang mga gawain tulad ng pag-uuri ng teksto, kailangan nating maipakita ang teksto sa pamamagitan ng isang fixed-size na vector, na gagamitin natin bilang input sa panghuling dense classifier. Isa sa mga pinakasimpleng paraan upang gawin ito ay pagsamahin ang lahat ng indibidwal na representasyon ng salita, halimbawa sa pamamagitan ng pagdaragdag ng mga ito. Kung idaragdag natin ang one-hot encodings ng bawat salita, magkakaroon tayo ng vector ng mga frequency, na nagpapakita kung ilang beses lumitaw ang bawat salita sa loob ng teksto. Ang ganitong representasyon ng teksto ay tinatawag na **bag of words** (BoW). - + > Larawan mula sa may-akda diff --git a/translations/tl/lessons/5-NLP/13-TextRep/assignment.md b/translations/tl/lessons/5-NLP/13-TextRep/assignment.md index b3d22fd4..ff76f91c 100644 --- a/translations/tl/lessons/5-NLP/13-TextRep/assignment.md +++ b/translations/tl/lessons/5-NLP/13-TextRep/assignment.md @@ -1,12 +1,3 @@ - # Takdang-Aralin: Mga Notebook Gamitin ang mga notebook na kaugnay ng araling ito (maaari ang bersyon ng PyTorch o TensorFlow), at patakbuhin muli ang mga ito gamit ang sarili mong dataset, marahil mula sa Kaggle, na ginamit nang may tamang pagkilala. Isulat muli ang notebook upang maipakita ang sarili mong mga natuklasan. Subukan ang ilang kakaibang dataset na maaaring magbigay ng nakakagulat na resulta, tulad ng [dataset na ito tungkol sa mga ulat ng UFO sightings](https://www.kaggle.com/datasets/NUFORC/ufo-sightings) mula sa NUFORC. diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/README.md b/translations/tl/lessons/5-NLP/14-Embeddings/README.md index 0ca8beca..c4129e80 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/README.md +++ b/translations/tl/lessons/5-NLP/14-Embeddings/README.md @@ -1,12 +1,3 @@ - # Embeddings ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/27) diff --git a/translations/tl/lessons/5-NLP/14-Embeddings/assignment.md b/translations/tl/lessons/5-NLP/14-Embeddings/assignment.md index 1060d32b..9347e70c 100644 --- a/translations/tl/lessons/5-NLP/14-Embeddings/assignment.md +++ b/translations/tl/lessons/5-NLP/14-Embeddings/assignment.md @@ -1,12 +1,3 @@ - # Asaynment: Mga Notebook Gamitin ang mga notebook na kaugnay ng araling ito (maaaring ang bersyon ng PyTorch o TensorFlow), at patakbuhin muli ang mga ito gamit ang sarili mong dataset, marahil mula sa Kaggle, na ginamit nang may tamang pagkilala. Isulat muli ang notebook upang maipakita ang sarili mong mga natuklasan. Subukan ang ibang uri ng dataset at idokumento ang iyong mga natuklasan, gamit ang teksto tulad ng [mga liriko ng Beatles na ito](https://www.kaggle.com/datasets/jenlooper/beatles-lyrics). diff --git a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md index 4c5e370b..0bb8166e 100644 --- a/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md +++ b/translations/tl/lessons/5-NLP/15-LanguageModeling/README.md @@ -1,12 +1,3 @@ - # Pagmomodelo ng Wika Ang mga semantic embeddings, tulad ng Word2Vec at GloVe, ay isang unang hakbang patungo sa **pagmomodelo ng wika** - ang paggawa ng mga modelo na sa isang paraan ay *nakakaintindi* (o *nagre-representa*) sa kalikasan ng wika. diff --git a/translations/tl/lessons/5-NLP/15-LanguageModeling/lab/README.md b/translations/tl/lessons/5-NLP/15-LanguageModeling/lab/README.md index d6137d94..9b67ca21 100644 --- a/translations/tl/lessons/5-NLP/15-LanguageModeling/lab/README.md +++ b/translations/tl/lessons/5-NLP/15-LanguageModeling/lab/README.md @@ -1,12 +1,3 @@ - # Pagsasanay sa Skip-Gram Model Takdang-Aralin mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/5-NLP/16-RNN/README.md b/translations/tl/lessons/5-NLP/16-RNN/README.md index 25ffaec5..466431c5 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/README.md +++ b/translations/tl/lessons/5-NLP/16-RNN/README.md @@ -1,12 +1,3 @@ - # Recurrent Neural Networks ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/31) @@ -31,7 +22,7 @@ Tingnan natin kung paano nakaayos ang isang simpleng RNN cell. Tumatanggap ito n Ang isang simpleng RNN cell ay may dalawang weight matrices sa loob: ang isa ay nagta-transform ng input symbol (tawagin natin itong W), at ang isa ay nagta-transform ng input state (H). Sa kasong ito, ang output ng network ay kinakalkula bilang σ(W×Xi+H×Si-1+b), kung saan ang σ ay ang activation function at ang b ay karagdagang bias. -RNN Cell Anatomy +RNN Cell Anatomy > Larawan mula sa may-akda diff --git a/translations/tl/lessons/5-NLP/16-RNN/assignment.md b/translations/tl/lessons/5-NLP/16-RNN/assignment.md index 42c2c3de..7a953b89 100644 --- a/translations/tl/lessons/5-NLP/16-RNN/assignment.md +++ b/translations/tl/lessons/5-NLP/16-RNN/assignment.md @@ -1,12 +1,3 @@ - # Takdang-Aralin: Mga Notebook Gamitin ang mga notebook na kaugnay ng araling ito (maaari ang bersyon ng PyTorch o TensorFlow), at patakbuhin muli ang mga ito gamit ang sarili mong dataset, marahil mula sa Kaggle, na ginamit nang may tamang pagkilala. Isulat muli ang notebook upang maipakita ang sarili mong mga natuklasan. Subukan ang ibang uri ng dataset at idokumento ang iyong mga natuklasan, gamit ang teksto tulad ng [dataset ng kompetisyon sa Kaggle tungkol sa mga tweet ng panahon](https://www.kaggle.com/competitions/crowdflower-weather-twitter/data?select=train.csv). diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md index 24c3c8bd..3b07807e 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/README.md @@ -1,12 +1,3 @@ - # Mga Generative na Network ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/33) @@ -36,7 +27,7 @@ Sasanayin natin ang RNN na ito upang mag-generate ng teksto hakbang-hakbang. Sa Kapag nag-generate ng teksto (sa panahon ng inference), magsisimula tayo sa isang **prompt**, na ipapasa sa RNN cells upang mag-generate ng intermediate state nito, at pagkatapos mula sa state na ito magsisimula ang generation. Mag-generate tayo ng isang character sa bawat pagkakataon, at ipapasa ang state at ang generated character sa isa pang RNN cell upang mag-generate ng susunod, hanggang sa makabuo tayo ng sapat na mga character. - + > Larawan ng may-akda diff --git a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md index 42cec307..b3b547f9 100644 --- a/translations/tl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md +++ b/translations/tl/lessons/5-NLP/17-GenerativeNetworks/lab/README.md @@ -1,12 +1,3 @@ - # Pagbuo ng Teksto sa Antas ng Salita gamit ang RNNs Gawain sa Lab mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/5-NLP/18-Transformers/README.md b/translations/tl/lessons/5-NLP/18-Transformers/README.md index 97c37f4e..77849a63 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/README.md +++ b/translations/tl/lessons/5-NLP/18-Transformers/README.md @@ -1,12 +1,3 @@ - # Mga Mekanismo ng Atensyon at Transformers ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/35) @@ -56,7 +47,7 @@ Ang ideya ng positional encoding ay ang mga sumusunod. * Trainable embedding, katulad ng token embedding. Ito ang pamamaraang isinasaalang-alang natin dito. Nag-a-apply tayo ng embedding layers sa parehong mga token at kanilang mga posisyon, na nagreresulta sa embedding vectors na may parehong dimensyon, na pagkatapos ay pinagsasama natin. * Fixed position encoding function, tulad ng iminungkahi sa orihinal na papel. - + > Larawan ng may-akda diff --git a/translations/tl/lessons/5-NLP/18-Transformers/assignment.md b/translations/tl/lessons/5-NLP/18-Transformers/assignment.md index 6ef691cc..b52bf592 100644 --- a/translations/tl/lessons/5-NLP/18-Transformers/assignment.md +++ b/translations/tl/lessons/5-NLP/18-Transformers/assignment.md @@ -1,12 +1,3 @@ - # Asaynment: Transformers Subukan ang Transformers sa HuggingFace! Gumamit ng ilan sa mga script na kanilang ibinibigay upang magamit ang iba't ibang modelo na makikita sa kanilang site: https://huggingface.co/docs/transformers/run_scripts. Subukan ang isa sa kanilang mga dataset, pagkatapos ay mag-import ng isa mula sa kurikulum na ito o mula sa Kaggle at tingnan kung makakagawa ka ng mga kawili-wiling teksto. Gumawa ng notebook na naglalaman ng iyong mga natuklasan. diff --git a/translations/tl/lessons/5-NLP/19-NER/README.md b/translations/tl/lessons/5-NLP/19-NER/README.md index 2f8f4f6c..e7e601fb 100644 --- a/translations/tl/lessons/5-NLP/19-NER/README.md +++ b/translations/tl/lessons/5-NLP/19-NER/README.md @@ -1,12 +1,3 @@ - # Named Entity Recognition Hanggang ngayon, karamihan sa ating pokus ay nasa isang NLP task - ang classification. Gayunpaman, may iba pang mga NLP task na maaaring magawa gamit ang neural networks. Isa sa mga ito ay ang **[Named Entity Recognition](https://wikipedia.org/wiki/Named-entity_recognition)** (NER), na tumutukoy sa pagkilala ng mga partikular na entidad sa loob ng teksto, tulad ng mga lugar, pangalan ng tao, mga petsa, chemical formula, at iba pa. @@ -17,7 +8,7 @@ Hanggang ngayon, karamihan sa ating pokus ay nasa isang NLP task - ang classific Halimbawa, nais mong gumawa ng natural language chat bot, katulad ng Amazon Alexa o Google Assistant. Ang paraan ng paggana ng mga intelligent chat bots ay ang *pag-unawa* sa nais ng user sa pamamagitan ng text classification sa input na pangungusap. Ang resulta ng classification na ito ay tinatawag na **intent**, na tumutukoy kung ano ang dapat gawin ng chat bot. -Bot NER +Bot NER > Larawan mula sa may-akda diff --git a/translations/tl/lessons/5-NLP/19-NER/lab/README.md b/translations/tl/lessons/5-NLP/19-NER/lab/README.md index 7c6ce9eb..00945cbd 100644 --- a/translations/tl/lessons/5-NLP/19-NER/lab/README.md +++ b/translations/tl/lessons/5-NLP/19-NER/lab/README.md @@ -1,12 +1,3 @@ - # NER Gawain mula sa [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners). diff --git a/translations/tl/lessons/5-NLP/20-LangModels/README.md b/translations/tl/lessons/5-NLP/20-LangModels/README.md index 6ce7aec7..a637b64d 100644 --- a/translations/tl/lessons/5-NLP/20-LangModels/README.md +++ b/translations/tl/lessons/5-NLP/20-LangModels/README.md @@ -1,12 +1,3 @@ - # Mga Pre-Trained na Malalaking Language Models Sa lahat ng ating mga nakaraang gawain, nagte-train tayo ng neural network upang maisagawa ang isang partikular na gawain gamit ang labeled dataset. Sa malalaking transformer models, tulad ng BERT, ginagamit natin ang language modelling sa self-supervised na paraan upang makabuo ng isang language model, na pagkatapos ay isinasapersonal para sa partikular na downstream task gamit ang karagdagang domain-specific na training. Gayunpaman, napatunayan na ang malalaking language models ay maaari ring magsagawa ng maraming gawain nang WALANG anumang domain-specific na training. Ang pamilya ng mga modelong may kakayahang gawin ito ay tinatawag na **GPT**: Generative Pre-Trained Transformer. diff --git a/translations/tl/lessons/5-NLP/README.md b/translations/tl/lessons/5-NLP/README.md index 420154f6..ce80ac6f 100644 --- a/translations/tl/lessons/5-NLP/README.md +++ b/translations/tl/lessons/5-NLP/README.md @@ -1,12 +1,3 @@ - # Natural Language Processing ![Buod ng mga gawain sa NLP sa isang doodle](../../../../translated_images/tl/ai-nlp.b22dcb8ca4707cea.webp) diff --git a/translations/tl/lessons/6-Other/21-GeneticAlgorithms/README.md b/translations/tl/lessons/6-Other/21-GeneticAlgorithms/README.md index 84f108fd..339c8393 100644 --- a/translations/tl/lessons/6-Other/21-GeneticAlgorithms/README.md +++ b/translations/tl/lessons/6-Other/21-GeneticAlgorithms/README.md @@ -1,12 +1,3 @@ - # Mga Genetic Algorithm ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ai/quiz/41) diff --git a/translations/tl/lessons/6-Other/22-DeepRL/README.md b/translations/tl/lessons/6-Other/22-DeepRL/README.md index daf2aa67..2b78c474 100644 --- a/translations/tl/lessons/6-Other/22-DeepRL/README.md +++ b/translations/tl/lessons/6-Other/22-DeepRL/README.md @@ -1,12 +1,3 @@ - # Deep Reinforcement Learning Ang Reinforcement Learning (RL) ay isa sa mga pangunahing paradigms ng machine learning, katabi ng supervised learning at unsupervised learning. Habang sa supervised learning ay umaasa tayo sa dataset na may mga kilalang resulta, ang RL ay nakabatay sa **pagkatuto sa pamamagitan ng paggawa**. Halimbawa, kapag unang beses nating nakita ang isang computer game, nagsisimula tayong maglaro kahit hindi alam ang mga patakaran, at sa kalaunan ay napapabuti natin ang ating kakayahan sa pamamagitan ng paglalaro at pag-aadjust ng ating mga kilos. @@ -34,7 +25,7 @@ Marahil ay nakita na ninyo ang mga modernong balancing devices tulad ng *Segway* Ang isang pinasimpleng bersyon ng balancing ay kilala bilang **CartPole** problem. Sa mundo ng cartpole, mayroon tayong horizontal slider na maaaring gumalaw pakaliwa o pakanan, at ang layunin ay mag-balanse ng vertical pole sa ibabaw ng slider habang ito ay gumagalaw. -a cartpole +a cartpole Para gumawa at gumamit ng environment na ito, kailangan natin ng ilang linya ng Python code: diff --git a/translations/tl/lessons/6-Other/22-DeepRL/lab/README.md b/translations/tl/lessons/6-Other/22-DeepRL/lab/README.md index 1ab33114..e5e14fed 100644 --- a/translations/tl/lessons/6-Other/22-DeepRL/lab/README.md +++ b/translations/tl/lessons/6-Other/22-DeepRL/lab/README.md @@ -1,12 +1,3 @@ - ## Ang Kapaligiran Ang Mountain Car environment ay binubuo ng isang kotse na na-trap sa loob ng isang lambak. Ang layunin mo ay makalabas sa lambak at maabot ang bandila. Ang mga aksyon na maaari mong gawin ay magpabilis papunta sa kaliwa, sa kanan, o walang gawin. Maaari mong obserbahan ang posisyon ng kotse sa x-axis, at ang bilis nito. diff --git a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md index a239669a..1b5ed07b 100644 --- a/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md +++ b/translations/tl/lessons/6-Other/23-MultiagentSystems/README.md @@ -1,12 +1,3 @@ - # Multi-Agent Systems Isa sa mga posibleng paraan ng pag-abot sa katalinuhan ay ang tinatawag na **emergent** (o **synergetic**) na pamamaraan, na nakabatay sa ideya na ang pinagsamang kilos ng maraming simpleng ahente ay maaaring magresulta sa mas kumplikado (o matalino) na kilos ng sistema bilang kabuuan. Teoretikal, ito ay nakabatay sa mga prinsipyo ng [Collective Intelligence](https://en.wikipedia.org/wiki/Collective_intelligence), [Emergentism](https://en.wikipedia.org/wiki/Global_brain), at [Evolutionary Cybernetics](https://en.wikipedia.org/wiki/Global_brain), na nagsasabing ang mas mataas na antas ng mga sistema ay nakakakuha ng karagdagang halaga kapag maayos na pinagsama mula sa mas mababang antas ng mga sistema (tinatawag na *principle of metasystem transition*). @@ -60,7 +51,7 @@ Maaari mong [i-download](https://ccl.northwestern.edu/netlogo/download.shtml) at Ang maganda sa NetLogo ay mayroon itong library ng mga gumaganang modelo na maaari mong subukan. Pumunta sa **File → Models Library**, at mayroon kang maraming kategorya ng mga modelo na mapagpipilian. -NetLogo Models Library +NetLogo Models Library > Screenshot ng models library ni Dmitry Soshnikov diff --git a/translations/tl/lessons/6-Other/23-MultiagentSystems/assignment.md b/translations/tl/lessons/6-Other/23-MultiagentSystems/assignment.md index 3fa4e335..ec9acfdc 100644 --- a/translations/tl/lessons/6-Other/23-MultiagentSystems/assignment.md +++ b/translations/tl/lessons/6-Other/23-MultiagentSystems/assignment.md @@ -1,12 +1,3 @@ - # Gawain sa NetLogo Pumili ng isa sa mga modelo mula sa library ng NetLogo at gamitin ito upang gayahin ang isang totoong sitwasyon nang mas malapit hangga't maaari. Isang magandang halimbawa ay ang pag-aayos ng Virus model sa folder na Alternative Visualizations upang ipakita kung paano ito magagamit sa pagmomodelo ng pagkalat ng COVID-19. Kaya mo bang gumawa ng isang modelo na ginagaya ang totoong pagkalat ng virus? diff --git a/translations/tl/lessons/7-Ethics/README.md b/translations/tl/lessons/7-Ethics/README.md index b792d7dd..37ae8268 100644 --- a/translations/tl/lessons/7-Ethics/README.md +++ b/translations/tl/lessons/7-Ethics/README.md @@ -1,12 +1,3 @@ - # Etikal at Responsableng AI Malapit mo nang matapos ang kursong ito, at sana sa puntong ito ay malinaw na sa iyo na ang AI ay nakabatay sa iba't ibang pormal na matematikal na pamamaraan na nagbibigay-daan sa atin upang matuklasan ang mga relasyon sa datos at sanayin ang mga modelo upang gayahin ang ilang aspeto ng kilos ng tao. Sa kasalukuyang panahon, itinuturing natin ang AI bilang isang napakalakas na kasangkapan upang kumuha ng mga pattern mula sa datos, at gamitin ang mga pattern na ito upang lutasin ang mga bagong problema. diff --git a/translations/tl/lessons/README.md b/translations/tl/lessons/README.md index 3918e50d..35662c8f 100644 --- a/translations/tl/lessons/README.md +++ b/translations/tl/lessons/README.md @@ -1,12 +1,3 @@ - # Pangkalahatang-ideya ![Pangkalahatang-ideya sa isang doodle](../../../translated_images/tl/ai-overview.0857791951d19500.webp) diff --git a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md index 503e432f..bcdc0706 100644 --- a/translations/tl/lessons/X-Extras/X1-MultiModal/README.md +++ b/translations/tl/lessons/X-Extras/X1-MultiModal/README.md @@ -1,12 +1,3 @@ - # Multi-Modal Networks Matapos ang tagumpay ng mga transformer model sa paglutas ng mga gawain sa NLP, ang parehong arkitektura o mga katulad nito ay inilapat sa mga gawain sa computer vision. Lumalago ang interes sa paggawa ng mga modelong *pinagsasama* ang kakayahan sa vision at natural language. Isa sa mga ganitong pagsubok ay ginawa ng OpenAI, na tinawag na CLIP at DALL.E. diff --git a/translations/tl/lessons/sketchnotes/LICENSE.md b/translations/tl/lessons/sketchnotes/LICENSE.md index 94481434..49a306d7 100644 --- a/translations/tl/lessons/sketchnotes/LICENSE.md +++ b/translations/tl/lessons/sketchnotes/LICENSE.md @@ -1,12 +1,3 @@ - Attribution-ShareAlike 4.0 International ======================================================================= diff --git a/translations/tl/lessons/sketchnotes/README.md b/translations/tl/lessons/sketchnotes/README.md index 71f75b97..8e58037e 100644 --- a/translations/tl/lessons/sketchnotes/README.md +++ b/translations/tl/lessons/sketchnotes/README.md @@ -1,12 +1,3 @@ - Ang lahat ng sketchnotes ng kurikulum ay maaaring ma-download dito. 🎨 Ginawa ni: Tomomi Imura (Twitter: [@girlie_mac](https://twitter.com/girlie_mac), GitHub: [girliemac](https://github.com/girliemac)) diff --git a/translations/tl/troubleshoot.md b/translations/tl/troubleshoot.md index 08e05fc7..f7c47556 100644 --- a/translations/tl/troubleshoot.md +++ b/translations/tl/troubleshoot.md @@ -1,12 +1,3 @@ - # Gabay sa Pag-aayos ng AI-For-Beginners Ang gabay na ito ay tumutulong sa iyo na lutasin ang mga karaniwang problema na nararanasan habang ginagamit o nag-aambag sa [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) repository. Ang bawat problema ay may kasamang background, sintomas, paliwanag, at mga hakbang sa solusyon.