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"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"
|
||||
}
|
||||
}
|
||||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "6b11a37115944252ab3ed04e358d830d",
|
||||
"translation_date": "2025-10-03T09:26:02+00:00",
|
||||
"source_file": "AGENTS.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# AGENTS.md
|
||||
|
||||
## Gambaran Projek
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "85102ce4bfab31103e99dc8ca2e2f181",
|
||||
"translation_date": "2026-01-16T04:10:15+00:00",
|
||||
"source_file": "README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
[](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE)
|
||||
[](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/)
|
||||
[](https://GitHub.com/microsoft/AI-For-Beginners/issues/)
|
||||
|
|
@ -23,11 +14,11 @@ CO_OP_TRANSLATOR_METADATA:
|
|||
|
||||
# Kecerdasan Buatan untuk Pemula - Kurikulum
|
||||
|
||||
||
|
||||
||
|
||||
|:---:|
|
||||
| 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)
|
||||
|
||||
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
|
||||
[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.
|
||||
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
|
||||
|
||||
**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
|
||||
[](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
|
||||
- 🖼️ **Pengelasan Imej** - Mengelas imej dengan komen terperinci
|
||||
- 💬 **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.
|
||||
- 🖼️ **Pengelasan Imej** - Klasifikasikan imej dengan ulasan terperinci
|
||||
- 💬 **Sentimen Teks** - Analisis teks positif/negatif
|
||||
|
||||
Contoh-contoh ini direka untuk membantu anda memahami konsep AI sebelum menyelami kurikulum penuh.
|
||||
|
||||
### 📚 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
|
||||
* **🔥 Penyunting:** [Jen Looper](https://twitter.com/jenlooper), PhD
|
||||
* **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac)
|
||||
* **✅ Pencipta Kuiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
|
||||
* **🙏 Penyumbang Teras:** [Evgenii Pishchik](https://github.com/Pe4enIks)
|
||||
* **✍️ Pengarang Utama:** [Dmitry Soshnikov](http://soshnikov.com), PhD
|
||||
* **🔥 Penyunting:** [Jen Looper](https://twitter.com/jenlooper), PhD
|
||||
* **🎨 Ilustrator Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac)
|
||||
* **✅ Pencipta Kuiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
|
||||
* **🙏 Penyumbang Teras:** [Evgenii Pishchik](https://github.com/Pe4enIks)
|
||||
|
||||
## Kurikulum Lain
|
||||
|
||||
Pasukan kami menghasilkan kurikulum lain! Semak:
|
||||
|
||||
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
|
||||
### LangChain
|
||||
[](https://aka.ms/langchain4j-for-beginners)
|
||||
[](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
|
||||
### LangChain
|
||||
[](https://aka.ms/langchain4j-for-beginners)
|
||||
[](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
|
||||
|
||||
---
|
||||
|
||||
### Azure / Edge / MCP / Agen
|
||||
[](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
### Azure / Edge / MCP / Ejen
|
||||
[](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
|
||||
---
|
||||
|
||||
### Siri AI Generatif
|
||||
[](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
|
||||
[-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
|
||||
[-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
|
||||
[](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
|
||||
[-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
|
||||
[-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
|
||||
[](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
|
||||
[](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
### Pembelajaran Teras
|
||||
[](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
|
||||
[](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
|
||||
---
|
||||
|
||||
### Siri Copilot
|
||||
[](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
|
||||
### Siri Copilot
|
||||
[](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
|
||||
<!-- CO-OP TRANSLATOR OTHER COURSES END -->
|
||||
|
||||
## 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.
|
||||
|
||||
[](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:
|
||||
|
||||
[](https://aka.ms/foundry/forum)
|
||||
|
||||
---
|
||||
|
||||
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
|
||||
**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.
|
||||
<!-- CO-OP TRANSLATOR DISCLAIMER END -->
|
||||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a583f49d359c7ebba61433e4dfcd05a9",
|
||||
"translation_date": "2025-08-29T11:44:21+00:00",
|
||||
"source_file": "SECURITY.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
## 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/).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "c06b12caf3c901eb3156e3dd5b0aea56",
|
||||
"translation_date": "2025-08-29T12:02:57+00:00",
|
||||
"source_file": "etc/CODE_OF_CONDUCT.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Kod Etika Sumber Terbuka Microsoft
|
||||
|
||||
Projek ini telah mengguna pakai [Kod Etika Sumber Terbuka Microsoft](https://opensource.microsoft.com/codeofconduct/).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
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"original_hash": "847a587aa1b83f4d00858183ff3ed18a",
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"translation_date": "2025-08-29T12:02:48+00:00",
|
||||
"source_file": "etc/CONTRIBUTING.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Menyumbang
|
||||
|
||||
Projek ini mengalu-alukan sumbangan dan cadangan. Kebanyakan sumbangan memerlukan anda
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
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"original_hash": "f2f88dbd2debd38e26149b27b1fd272d",
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||||
"translation_date": "2025-08-29T12:02:06+00:00",
|
||||
"source_file": "etc/Mindmap.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# AI
|
||||
|
||||
## [Pengenalan kepada AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md)
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "fdfc08baee91e402938a2b1f94fe0949",
|
||||
"translation_date": "2025-08-29T12:01:29+00:00",
|
||||
"source_file": "etc/SUPPORT.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Sokongan
|
||||
|
||||
## Cara melaporkan isu dan mendapatkan bantuan
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "62b3e3ad5182edb905eec649a87eeeb4",
|
||||
"translation_date": "2025-08-29T12:02:39+00:00",
|
||||
"source_file": "etc/TRANSLATIONS.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Menyumbang dengan menterjemah pelajaran
|
||||
|
||||
Kami mengalu-alukan terjemahan untuk pelajaran dalam kurikulum ini!
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "d699cf8509f74baa5b0b838de5cf0662",
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||||
"translation_date": "2025-08-29T12:03:02+00:00",
|
||||
"source_file": "etc/quiz-app/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Kuiz
|
||||
|
||||
Kuiz-kuiz ini adalah kuiz sebelum dan selepas kuliah untuk kurikulum AI di https://aka.ms/ai-beginners
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
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||||
"original_hash": "0d1babfdcbeb46525f2db3fbaaa54cd7",
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||||
"translation_date": "2025-10-03T11:32:48+00:00",
|
||||
"source_file": "examples/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
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||||
"original_hash": "a094ef9927883de1cfcee51dbd143381",
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||||
"translation_date": "2025-08-29T11:46:15+00:00",
|
||||
"source_file": "lessons/0-course-setup/for-teachers.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Untuk Pendidik
|
||||
|
||||
Adakah anda ingin menggunakan kurikulum ini di dalam kelas anda? Jangan ragu untuk mencubanya!
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a4717bd9103b9f6cd84d534b83534689",
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||||
"translation_date": "2026-01-16T04:12:04+00:00",
|
||||
"source_file": "lessons/0-course-setup/how-to-run.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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:
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "7b4e5b8956915870d0a0ed3cc5890042",
|
||||
"translation_date": "2025-12-12T20:04:06+00:00",
|
||||
"source_file": "lessons/0-course-setup/setup.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Memulakan dengan Kurikulum ini
|
||||
|
||||
## Adakah anda seorang pelajar?
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "f57e8aa46141fd220b16ffed8f11aec7",
|
||||
"translation_date": "2025-11-18T21:50:31+00:00",
|
||||
"source_file": "lessons/1-Intro/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Pengenalan kepada AI
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
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|
||||
"translation_date": "2025-11-18T21:51:38+00:00",
|
||||
"source_file": "lessons/1-Intro/assignment.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,15 +1,6 @@
|
|||
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|
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|
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|
||||
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|
||||
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|
||||
-->
|
||||
# Perwakilan Pengetahuan dan Sistem Pakar
|
||||
|
||||
|
||||
|
||||
|
||||
> 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:
|
||||
|
||||
|
||||
|
||||
|
||||
> 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.
|
||||
|
||||
 | 
|
||||
 | 
|
||||
---------------------------------------------|------------------------------------------------
|
||||
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:
|
||||
|
||||
|
||||
|
||||
|
||||
> Imej oleh [Dmitry Soshnikov](http://soshnikov.com)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
}
|
||||
-->
|
||||
# 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 [Protégé](https://protege.stanford.edu/) untuk membina ontologi anda.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
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"source_file": "lessons/3-NeuralNetworks/03-Perceptron/README.md",
|
||||
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|
||||
}
|
||||
-->
|
||||
# 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
|
|||
|
||||
| | |
|
||||
|--------------|-----------|
|
||||
|<img src='images/Rosenblatt-wikipedia.jpg' alt='Frank Rosenblatt'/> | <img src='images/Mark_I_perceptron_wikipedia.jpg' alt='The Mark 1 Perceptron' />|
|
||||
|<img src='../../../../../translated_images/ms/Rosenblatt-wikipedia.294821b285ac796d.webp' alt='Frank Rosenblatt'/> | <img src='../../../../../translated_images/ms/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp' alt='The Mark 1 Perceptron' />|
|
||||
|
||||
> Imej [dari Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
|
||||
|
||||
|
|
@ -34,7 +25,7 @@ y(x) = f(w<sup>T</sup>x)
|
|||
di mana f adalah fungsi pengaktifan langkah
|
||||
|
||||
<!-- img src="http://www.sciweavers.org/tex2img.php?eq=f%28x%29%20%3D%20%5Cbegin%7Bcases%7D%0A%20%20%20%20%20%20%20%20%20%2B1%20%26%20x%20%5Cgeq%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20-1%20%26%20x%20%3C%200%0A%20%20%20%20%20%20%20%5Cend%7Bcases%7D%20%5C%5C%0A&bc=White&fc=Black&im=jpg&fs=12&ff=arev&edit=0" align="center" border="0" alt="f(x) = \begin{cases} +1 & x \geq 0 \\ -1 & x < 0 \end{cases} \\" width="154" height="50" / -->
|
||||
<img src="images/activation-func.png"/>
|
||||
<img src="../../../../../translated_images/ms/activation-func.b4924007c7ce7764.webp"/>
|
||||
|
||||
## Melatih Perceptron
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
}
|
||||
-->
|
||||
# Pengelasan Pelbagai Kelas dengan Perceptron
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
}
|
||||
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|
||||
# 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'.
|
||||
|
||||
<img alt="compute graph" src="images/ComputeGraphGrad.png"/>
|
||||
<img alt="compute graph" src="../../../../../translated_images/ms/ComputeGraphGrad.4626252c0de03507.webp"/>
|
||||
|
||||
> TODO: rujukan imej
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
}
|
||||
-->
|
||||
# Pengelasan MNIST dengan Rangka Kerja Kita Sendiri
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
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|
||||
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|
||||
# Rangka Kerja Rangkaian Neural
|
||||
|
||||
Seperti yang telah kita pelajari, untuk melatih rangkaian neural dengan cekap, kita perlu melakukan dua perkara:
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
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|
||||
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|
||||
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|
||||
# Pengelasan dengan PyTorch/TensorFlow
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
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|
||||
# Pengenalan kepada Rangkaian Neural
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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"source_file": "lessons/4-ComputerVision/06-IntroCV/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img src="images/palm-movement.png" width="30%" alt="Bingkai Pergerakan Tapak Tangan"/>
|
||||
<img src="../../../../../translated_images/ms/palm-movement.341495f0e9c47da3.webp" width="30%" alt="Bingkai Pergerakan Tapak Tangan"/>
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
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|
||||
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|
||||
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|
||||
# Mengesan Pergerakan menggunakan Optical Flow
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://aka.ms/ai-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
"source_file": "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md",
|
||||
"language_code": "ms"
|
||||
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|
||||
-->
|
||||
# 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**:
|
||||
|
||||
<img src="images/resnet-block.png" width="300"/>
|
||||
<img src="../../../../../translated_images/ms/resnet-block.aba4ccbcc0944434.webp" width="300"/>
|
||||
|
||||
> 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:
|
||||
|
||||
<img src="images/inception.png" width="400"/>
|
||||
<img src="../../../../../translated_images/ms/inception.a6605b85bcbc6f52.webp" width="400"/>
|
||||
|
||||
> Imej daripada [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
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|
||||
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|
||||
-->
|
||||
# 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:
|
||||
|
||||
<img src="images/lmfilters.jpg" width="500" align="center"/>
|
||||
<img src="../../../../../translated_images/ms/lmfilters.ea9e4868a82cf74c.webp" width="500" align="center"/>
|
||||
|
||||
> Imej daripada [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
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|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Pengelasan Wajah Haiwan Peliharaan
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
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|
||||
-->
|
||||
# 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?
|
||||
|
|
|
|||
|
|
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|
|||
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|
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||||
"translation_date": "2025-08-29T11:48:38+00:00",
|
||||
"source_file": "lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
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|
|||
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|
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|
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|
||||
"language_code": "ms"
|
||||
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|
||||
-->
|
||||
# Pengelasan Haiwan Peliharaan Oxford menggunakan Pembelajaran Pindahan
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
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|
|||
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|
||||
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||||
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|
||||
"source_file": "lessons/4-ComputerVision/09-Autoencoders/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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(z<sub>mean</sub>,exp(z<sub>log\_sigma</sub>))
|
||||
* Penyahkod cuba menyahkod imej asal menggunakan `sample` sebagai vektor input
|
||||
|
||||
<img src="images/vae.png" width="50%">
|
||||
<img src="../../../../../translated_images/ms/vae.464c465a5b6a9e25.webp" width="50%">
|
||||
|
||||
> 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:
|
||||
|
||||
<img alt="vaemnist" src="images/vaemnist.png" width="50%"/>
|
||||
<img alt="vaemnist" src="../../../../../translated_images/ms/vaemnist.cab9e602dc08dc50.webp" width="50%"/>
|
||||
|
||||
> 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:
|
||||
|
||||
<img alt="vaemnist cluster" src="images/vaemnist-diag.png" width="50%"/>
|
||||
<img alt="vaemnist cluster" src="../../../../../translated_images/ms/vaemnist-diag.694315f775d5d666.webp" width="50%"/>
|
||||
|
||||
> Imej oleh [Dmitry Soshnikov](http://soshnikov.com)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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||||
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|
||||
"source_file": "lessons/4-ComputerVision/10-GANs/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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:
|
||||
|
||||
<img src="images/gan_architecture.png" width="70%"/>
|
||||
<img src="../../../../../translated_images/ms/gan_architecture.8f3a5ab62b8d5d69.webp" width="70%"/>
|
||||
|
||||
> Imej oleh [Dmitry Soshnikov](http://soshnikov.com)
|
||||
|
||||
|
|
@ -41,7 +32,7 @@ Generator sedikit lebih rumit. Anda boleh menganggapnya sebagai discriminator ya
|
|||
|
||||
> ✅ 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.
|
||||
|
||||
<img src="images/gan_arch_detail.png" width="70%"/>
|
||||
<img src="../../../../../translated_images/ms/gan_arch_detail.46b95fd366f8e543.webp" width="70%"/>
|
||||
|
||||
> Imej oleh [Dmitry Soshnikov](http://soshnikov.com)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
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|
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||||
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|
||||
"source_file": "lessons/4-ComputerVision/11-ObjectDetection/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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**.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
CO_OP_TRANSLATOR_METADATA:
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||||
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|
||||
"source_file": "lessons/4-ComputerVision/11-ObjectDetection/lab/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Pengesanan Kepala menggunakan Dataset Hollywood Heads
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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||||
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|
||||
"source_file": "lessons/4-ComputerVision/12-Segmentation/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img src="images/instance_vs_semantic.jpeg" width="50%">
|
||||
<img src="../../../../../translated_images/ms/instance_vs_semantic.eee9812bebf8cd45.webp" width="50%">
|
||||
|
||||
> 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.
|
||||
|
||||
<img src="images/segm.png" width="80%">
|
||||
<img src="../../../../../translated_images/ms/segm.92442f2cb42ff4fa.webp" width="80%">
|
||||
|
||||
> 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
|
|||
|
||||
> ✅ Teknik ini sangat sesuai untuk jenis pengimejan perubatan ini, tetapi apakah aplikasi dunia sebenar lain yang boleh anda bayangkan?
|
||||
|
||||
<img alt="navi" src="images/navi.png"/>
|
||||
<img alt="navi" src="../../../../../translated_images/ms/navi.2f20b727910110ea.webp"/>
|
||||
|
||||
> Imej daripada Pangkalan Data PH<sup>2</sup>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
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|
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"source_file": "lessons/4-ComputerVision/12-Segmentation/lab/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Segmentasi Tubuh Manusia
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Penglihatan Komputer
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
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"source_file": "lessons/5-NLP/13-TextRep/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img alt="Imej menunjukkan diagram pemetaan watak kepada perwakilan ASCII dan binari" src="images/ascii-character-map.png" width="50%"/>
|
||||
<img alt="Imej menunjukkan diagram pemetaan watak kepada perwakilan ASCII dan binari" src="../../../../../translated_images/ms/ascii-character-map.18ed6aa7f3b0a7ff.webp" width="50%"/>
|
||||
|
||||
> [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).
|
||||
|
||||
<img src="images/bow.png" width="90%"/>
|
||||
<img src="../../../../../translated_images/ms/bow.3811869cff59368d.webp" width="90%"/>
|
||||
|
||||
> Imej oleh penulis
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
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"source_file": "lessons/5-NLP/13-TextRep/assignment.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
CO_OP_TRANSLATOR_METADATA:
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|
||||
"source_file": "lessons/5-NLP/14-Embeddings/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Pembenaman
|
||||
|
||||
## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/27)
|
||||
|
|
|
|||
|
|
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|
|||
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|
||||
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|
||||
"source_file": "lessons/5-NLP/14-Embeddings/assignment.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
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||||
"translation_date": "2025-09-23T10:54:49+00:00",
|
||||
"source_file": "lessons/5-NLP/15-LanguageModeling/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
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||||
"translation_date": "2025-08-29T11:59:10+00:00",
|
||||
"source_file": "lessons/5-NLP/15-LanguageModeling/lab/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Melatih Model Skip-Gram
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
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||||
"translation_date": "2025-09-23T10:55:59+00:00",
|
||||
"source_file": "lessons/5-NLP/16-RNN/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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×X<sub>i</sub>+H×S<sub>i-1</sub>+b), di mana σ adalah fungsi pengaktifan dan b adalah bias tambahan.
|
||||
|
||||
<img alt="Anatomi Sel RNN" src="images/rnn-anatomy.png" width="50%"/>
|
||||
<img alt="Anatomi Sel RNN" src="../../../../../translated_images/ms/rnn-anatomy.79ee3f3920b3294b.webp" width="50%"/>
|
||||
|
||||
> Gambar oleh penulis
|
||||
|
||||
|
|
|
|||
|
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|
|||
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|
||||
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|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
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||||
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||||
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|
||||
"source_file": "lessons/5-NLP/17-GenerativeNetworks/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img src="images/rnn-generate-inf.png" width="60%"/>
|
||||
<img src="../../../../../translated_images/ms/rnn-generate-inf.5168dc65e0370eea.webp" width="60%"/>
|
||||
|
||||
> Imej oleh penulis
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
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||||
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|
||||
"source_file": "lessons/5-NLP/17-GenerativeNetworks/lab/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Penjana Teks Tahap Perkataan menggunakan RNN
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
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|
||||
"source_file": "lessons/5-NLP/18-Transformers/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img src="images/pos-embedding.png" width="50%"/>
|
||||
<img src="../../../../../translated_images/ms/pos-embedding.e41ce9b6cf6078af.webp" width="50%"/>
|
||||
|
||||
> Imej oleh penulis
|
||||
|
||||
|
|
|
|||
|
|
@ -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 y<sub>t</sub>, kita akan mempertimbangkan semua keadaan tersembunyi masukan h<sub>i</sub>, dengan koefisien bobot yang berbeda α<sub>t,i</sub>.
|
||||
|
||||
|
||||
|
||||
> 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 {α<sub>i,j</sub>} 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 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.
|
||||
|
||||
|
||||
|
||||
> 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.
|
||||
|
||||
<img src="images/pos-embedding.png" width="50%"/>
|
||||
|
||||
> 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:
|
||||
|
||||
|
||||
|
||||
> 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.
|
||||
|
||||
|
||||
|
||||
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 [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.
|
||||
|
||||
## 🚀 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.
|
||||
|
|
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|
|||
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|
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|
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|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
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|
|||
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|
||||
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|
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"source_file": "lessons/5-NLP/19-NER/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img alt="Bot NER" src="images/bot-ner.png" width="50%"/>
|
||||
<img alt="Bot NER" src="../../../../../translated_images/ms/bot-ner.4b09235dbb0ad275.webp" width="50%"/>
|
||||
|
||||
> Imej oleh penulis
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
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|
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"original_hash": "032bda5068f543d6c1fcb30c34231461",
|
||||
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|
||||
"source_file": "lessons/5-NLP/19-NER/lab/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# NER
|
||||
|
||||
Tugasan Makmal daripada [Kurikulum AI untuk Pemula](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
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||||
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|
||||
"source_file": "lessons/5-NLP/20-LangModels/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
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|
|||
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|
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|
||||
"source_file": "lessons/5-NLP/README.md",
|
||||
"language_code": "ms"
|
||||
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|
||||
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|
||||
# Pemprosesan Bahasa Semula Jadi
|
||||
|
||||
|
||||
|
|
|
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|
|
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|
|||
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|
||||
"source_file": "lessons/6-Other/21-GeneticAlgorithms/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# Algoritma Genetik
|
||||
|
||||
## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ai/quiz/41)
|
||||
|
|
|
|||
|
|
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|
|||
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"source_file": "lessons/6-Other/22-DeepRL/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img alt="a cartpole" src="images/cartpole.png" width="200"/>
|
||||
<img alt="a cartpole" src="../../../../../translated_images/ms/cartpole.f52a67f27e058170.webp" width="200"/>
|
||||
|
||||
Untuk mencipta dan menggunakan persekitaran ini, kita memerlukan beberapa baris kod Python:
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
## 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.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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||||
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|
||||
"source_file": "lessons/6-Other/23-MultiagentSystems/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img alt="NetLogo Models Library" src="images/NetLogo-ModelLib.png" width="60%"/>
|
||||
<img alt="NetLogo Models Library" src="../../../../../translated_images/ms/NetLogo-ModelLib.efe023afb4763c05.webp" width="60%"/>
|
||||
|
||||
> Tangkapan skrin perpustakaan model oleh Dmitry Soshnikov
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
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|
||||
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|
||||
}
|
||||
-->
|
||||
# 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?
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
"source_file": "lessons/7-Ethics/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
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|
||||
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|
||||
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|
||||
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|
||||
# Gambaran Keseluruhan
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
"source_file": "lessons/X-Extras/X1-MultiModal/README.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
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|
|||
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|
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||||
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|
||||
"source_file": "lessons/sketchnotes/LICENSE.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
Hak Cipta Creative Commons Attribution-ShareAlike 4.0 Antarabangsa
|
||||
|
||||
=======================================================================
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
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|
||||
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|
||||
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|
||||
}
|
||||
-->
|
||||
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))
|
||||
|
|
|
|||
|
|
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|
|||
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|
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||||
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|
||||
"source_file": "troubleshoot.md",
|
||||
"language_code": "ms"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -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",
|
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"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"
|
||||
}
|
||||
}
|
||||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "6b11a37115944252ab3ed04e358d830d",
|
||||
"translation_date": "2025-10-03T09:26:55+00:00",
|
||||
"source_file": "AGENTS.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# AGENTS.md
|
||||
|
||||
## Muhtasari wa Mradi
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "85102ce4bfab31103e99dc8ca2e2f181",
|
||||
"translation_date": "2026-01-16T04:18:33+00:00",
|
||||
"source_file": "README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
[](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE)
|
||||
[](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/)
|
||||
[](https://GitHub.com/microsoft/AI-For-Beginners/issues/)
|
||||
|
|
@ -23,156 +14,158 @@ CO_OP_TRANSLATOR_METADATA:
|
|||
|
||||
# Akili Bandia kwa Waanzilishi - Mtaala
|
||||
|
||||
||
|
||||
||
|
||||
|:---:|
|
||||
| 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)
|
||||
|
||||
<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
|
||||
[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.
|
||||
<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
|
||||
|
||||
**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
|
||||
[](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
|
||||
- 🖼️ **Kipangaji Picha** - Pangilia picha na maelezo ya kina
|
||||
- 💬 **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.
|
||||
- 🖼️ **Mfafanuzi wa Picha** - Tafsiri picha kwa maelezo ya kina
|
||||
- 💬 **Hisia za Maandishi** - Changanua maandishi chanya/negativi
|
||||
|
||||
### 📚 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)
|
||||
### 📚 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
|
||||
* **🔥 Mhariri:** [Jen Looper](https://twitter.com/jenlooper), PhD
|
||||
* **🎨 Mchora Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac)
|
||||
* **✅ Mtengenezaji wa Maswali:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
|
||||
* **🎨 Mchoraji wa Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac)
|
||||
* **✅ Muumbaji wa Mtihani:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
|
||||
* **🙏 Washiriki Wakuu:** [Evgenii Pishchik](https://github.com/Pe4enIks)
|
||||
|
||||
## Mitaala Mingine
|
||||
## Mitaala Mengine
|
||||
|
||||
Timu yetu huandaa mitaala mingine! Angalia:
|
||||
Timu yetu hutengeneza mitaala mingine! Angalia:
|
||||
|
||||
<!-- CO-OP TRANSLATOR OTHER COURSES START -->
|
||||
### LangChain
|
||||
|
|
@ -181,7 +174,7 @@ Timu yetu huandaa mitaala mingine! Angalia:
|
|||
|
||||
---
|
||||
|
||||
### Azure / Edge / MCP / Maajenti
|
||||
### Azure / Edge / MCP / Wakala
|
||||
[](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](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
|
||||
[](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
|
||||
[-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
|
||||
[](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
|
||||
[](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.
|
||||
|
||||
[](https://discord.gg/nTYy5BXMWG)
|
||||
|
||||
|
|
@ -227,6 +220,6 @@ Ikiwa una maoni kuhusu bidhaa au makosa wakati wa kujenga tembelea:
|
|||
---
|
||||
|
||||
<!-- CO-OP TRANSLATOR DISCLAIMER START -->
|
||||
**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.
|
||||
<!-- CO-OP TRANSLATOR DISCLAIMER END -->
|
||||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a583f49d359c7ebba61433e4dfcd05a9",
|
||||
"translation_date": "2025-08-25T20:47:30+00:00",
|
||||
"source_file": "SECURITY.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
## 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/).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "c06b12caf3c901eb3156e3dd5b0aea56",
|
||||
"translation_date": "2025-08-25T21:06:19+00:00",
|
||||
"source_file": "etc/CODE_OF_CONDUCT.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Kanuni za Maadili ya Microsoft Open Source
|
||||
|
||||
Mradi huu umechukua [Kanuni za Maadili za Microsoft Open Source](https://opensource.microsoft.com/codeofconduct/).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "847a587aa1b83f4d00858183ff3ed18a",
|
||||
"translation_date": "2025-08-25T21:06:33+00:00",
|
||||
"source_file": "etc/CONTRIBUTING.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Kuchangia
|
||||
|
||||
Mradi huu unakaribisha michango na mapendekezo. Michango mingi inahitaji wewe
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "f2f88dbd2debd38e26149b27b1fd272d",
|
||||
"translation_date": "2025-08-25T21:06:41+00:00",
|
||||
"source_file": "etc/Mindmap.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# AI
|
||||
|
||||
## [Utangulizi wa AI](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md)
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "fdfc08baee91e402938a2b1f94fe0949",
|
||||
"translation_date": "2025-08-25T21:06:13+00:00",
|
||||
"source_file": "etc/SUPPORT.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Msaada
|
||||
|
||||
## Jinsi ya kuripoti matatizo na kupata msaada
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "62b3e3ad5182edb905eec649a87eeeb4",
|
||||
"translation_date": "2025-08-25T21:06:24+00:00",
|
||||
"source_file": "etc/TRANSLATIONS.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Changia kwa kutafsiri masomo
|
||||
|
||||
Tunakaribisha tafsiri za masomo katika mtaala huu!
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "d699cf8509f74baa5b0b838de5cf0662",
|
||||
"translation_date": "2025-08-25T21:07:07+00:00",
|
||||
"source_file": "etc/quiz-app/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Maswali ya Mitihani
|
||||
|
||||
Maswali haya ni ya kabla na baada ya mihadhara kwa mtaala wa AI unaopatikana kwenye https://aka.ms/ai-beginners
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "0d1babfdcbeb46525f2db3fbaaa54cd7",
|
||||
"translation_date": "2025-10-03T11:33:09+00:00",
|
||||
"source_file": "examples/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a094ef9927883de1cfcee51dbd143381",
|
||||
"translation_date": "2025-08-25T21:05:48+00:00",
|
||||
"source_file": "lessons/0-course-setup/for-teachers.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Kwa Walimu
|
||||
|
||||
Je, ungependa kutumia mtaala huu darasani kwako? Tafadhali jisikie huru!
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"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"
|
||||
}
|
||||
-->
|
||||
# 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:
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "7b4e5b8956915870d0a0ed3cc5890042",
|
||||
"translation_date": "2025-12-12T20:07:50+00:00",
|
||||
"source_file": "lessons/0-course-setup/setup.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Kuanza na Mtaala huu
|
||||
|
||||
## Je, wewe ni mwanafunzi?
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "f57e8aa46141fd220b16ffed8f11aec7",
|
||||
"translation_date": "2025-11-18T21:54:15+00:00",
|
||||
"source_file": "lessons/1-Intro/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Utangulizi wa AI
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a334df77a82aaaf2a29c77065d3e481e",
|
||||
"translation_date": "2025-11-18T21:55:40+00:00",
|
||||
"source_file": "lessons/1-Intro/assignment.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
|
|
|||
|
|
@ -1,15 +1,6 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "f9f06b266b8b2bfc6b8792ff2bb1bea4",
|
||||
"translation_date": "2026-01-16T04:21:13+00:00",
|
||||
"source_file": "lessons/2-Symbolic/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Uwakilishi wa Maarifa na Mifumo ya Wataalamu
|
||||
|
||||
|
||||
|
||||
|
||||
> 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:
|
||||
|
||||
|
||||
|
||||
|
||||
> 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.
|
||||
|
||||
 | 
|
||||
 | 
|
||||
---------------------------------------------|------------------------------------------------
|
||||
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:
|
||||
|
||||
|
||||
|
||||
|
||||
> Picha na [Dmitry Soshnikov](http://soshnikov.com)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
|
||||
"original_hash": "a057a8604f3976c3e309884453f1fad0",
|
||||
"translation_date": "2025-08-25T21:05:21+00:00",
|
||||
"source_file": "lessons/2-Symbolic/assignment.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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 [Protégé](https://protege.stanford.edu/) kujenga ontolojia yako.
|
||||
|
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|||
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||||
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||||
"source_file": "lessons/3-NeuralNetworks/03-Perceptron/README.md",
|
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"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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
|
|||
|
||||
| | |
|
||||
|--------------|-----------|
|
||||
|<img src='images/Rosenblatt-wikipedia.jpg' alt='Frank Rosenblatt'/> | <img src='images/Mark_I_perceptron_wikipedia.jpg' alt='The Mark 1 Perceptron' />|
|
||||
|<img src='../../../../../translated_images/sw/Rosenblatt-wikipedia.294821b285ac796d.webp' alt='Frank Rosenblatt'/> | <img src='../../../../../translated_images/sw/Mark_I_perceptron_wikipedia.1f84eaa2d4b76ec9.webp' alt='The Mark 1 Perceptron' />|
|
||||
|
||||
> Picha [kutoka Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
|
||||
|
||||
|
|
@ -34,7 +25,7 @@ y(x) = f(w<sup>T</sup>x)
|
|||
ambapo f ni kazi ya hatua ya uanzishaji
|
||||
|
||||
<!-- img src="http://www.sciweavers.org/tex2img.php?eq=f%28x%29%20%3D%20%5Cbegin%7Bcases%7D%0A%20%20%20%20%20%20%20%20%20%2B1%20%26%20x%20%5Cgeq%200%20%5C%5C%0A%20%20%20%20%20%20%20%20%20-1%20%26%20x%20%3C%200%0A%20%20%20%20%20%20%20%5Cend%7Bcases%7D%20%5C%5C%0A&bc=White&fc=Black&im=jpg&fs=12&ff=arev&edit=0" align="center" border="0" alt="f(x) = \begin{cases} +1 & x \geq 0 \\ -1 & x < 0 \end{cases} \\" width="154" height="50" / -->
|
||||
<img src="images/activation-func.png"/>
|
||||
<img src="../../../../../translated_images/sw/activation-func.b4924007c7ce7764.webp"/>
|
||||
|
||||
## Mafunzo ya Perceptron
|
||||
|
||||
|
|
|
|||
|
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@ -1,12 +1,3 @@
|
|||
<!--
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||||
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|
||||
"source_file": "lessons/3-NeuralNetworks/03-Perceptron/lab/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Uainishaji wa Darasa Nyingi kwa Kutumia Perceptron
|
||||
|
||||
Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
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@ -1,12 +1,3 @@
|
|||
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|
||||
CO_OP_TRANSLATOR_METADATA:
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||||
{
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"translation_date": "2025-09-23T11:04:05+00:00",
|
||||
"source_file": "lessons/3-NeuralNetworks/04-OwnFramework/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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'.
|
||||
|
||||
<img alt="compute graph" src="images/ComputeGraphGrad.png"/>
|
||||
<img alt="compute graph" src="../../../../../translated_images/sw/ComputeGraphGrad.4626252c0de03507.webp"/>
|
||||
|
||||
> TODO: rejea ya picha
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
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|
||||
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|
||||
"original_hash": "48fdd704d483e19bc3d7464074c9fcbe",
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"translation_date": "2025-08-25T21:00:40+00:00",
|
||||
"source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Uainishaji wa MNIST kwa Mfumo Wetu
|
||||
|
||||
Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
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{
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"original_hash": "ddd216f558a255260a9374008002c971",
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||||
"translation_date": "2025-09-23T11:04:47+00:00",
|
||||
"source_file": "lessons/3-NeuralNetworks/05-Frameworks/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Mfumo wa Mitandao ya Neural
|
||||
|
||||
Kama tulivyojifunza tayari, ili kuweza kufundisha mitandao ya neural kwa ufanisi tunahitaji kufanya mambo mawili:
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
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"original_hash": "e452d897efb9a89700f41021834cf6e5",
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"translation_date": "2025-08-25T21:01:18+00:00",
|
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"source_file": "lessons/3-NeuralNetworks/05-Frameworks/lab/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Uainishaji kwa kutumia PyTorch/TensorFlow
|
||||
|
||||
Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
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|
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"original_hash": "f862a99d88088163df12270e2f2ad6c3",
|
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"translation_date": "2025-10-03T12:51:53+00:00",
|
||||
"source_file": "lessons/3-NeuralNetworks/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Utangulizi wa Mitandao ya Neva
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
||||
{
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"original_hash": "feeca98225cb420afc89415f24f63d92",
|
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"translation_date": "2025-09-23T10:59:47+00:00",
|
||||
"source_file": "lessons/4-ComputerVision/06-IntroCV/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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.
|
||||
|
||||
<img src="images/palm-movement.png" width="30%" alt="Palm Movement Frame"/>
|
||||
<img src="../../../../../translated_images/sw/palm-movement.341495f0e9c47da3.webp" width="30%" alt="Palm Movement Frame"/>
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
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|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Kugundua Harakati kwa Kutumia Optical Flow
|
||||
|
||||
Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://aka.ms/ai-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
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|
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"source_file": "lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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**:
|
||||
|
||||
<img src="images/resnet-block.png" width="300"/>
|
||||
<img src="../../../../../translated_images/sw/resnet-block.aba4ccbcc0944434.webp" width="300"/>
|
||||
|
||||
> 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:
|
||||
|
||||
<img src="images/inception.png" width="400"/>
|
||||
<img src="../../../../../translated_images/sw/inception.a6605b85bcbc6f52.webp" width="400"/>
|
||||
|
||||
> Picha kutoka [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
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|
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"source_file": "lessons/4-ComputerVision/07-ConvNets/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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:
|
||||
|
||||
<img src="images/lmfilters.jpg" width="500" align="center"/>
|
||||
<img src="../../../../../translated_images/sw/lmfilters.ea9e4868a82cf74c.webp" width="500" align="center"/>
|
||||
|
||||
> Picha ya [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
||||
CO_OP_TRANSLATOR_METADATA:
|
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{
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"original_hash": "b70fcf7fcee862990f848c679090943f",
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|
||||
"source_file": "lessons/4-ComputerVision/07-ConvNets/lab/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# Uainishaji wa Nyuso za Wanyama Kipenzi
|
||||
|
||||
Kazi ya Maabara kutoka [Mtaala wa AI kwa Kompyuta](https://github.com/microsoft/ai-for-beginners).
|
||||
|
|
|
|||
|
|
@ -1,12 +1,3 @@
|
|||
<!--
|
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CO_OP_TRANSLATOR_METADATA:
|
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{
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"original_hash": "178c0b5ee5395733eb18aec51e71a0a9",
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"translation_date": "2025-09-23T10:59:22+00:00",
|
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"source_file": "lessons/4-ComputerVision/08-TransferLearning/README.md",
|
||||
"language_code": "sw"
|
||||
}
|
||||
-->
|
||||
# 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?
|
||||
|
|
|
|||
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Loading…
Reference in New Issue