{"owner":"microsoft","repo":"AI-For-Beginners","hasSkills":true,"hasMcp":false,"mcpConfig":null,"found":["AGENTS.md"],"skills":{"AGENTS.md":"# AGENTS.md\n\n## Project Overview\n\nAI for Beginners is a comprehensive 12-week, 24-lesson curriculum covering Artificial Intelligence fundamentals. This educational repository includes practical lessons using Jupyter Notebooks, quizzes, and hands-on labs. The curriculum covers:\n\n- Symbolic AI with Knowledge Representation and Expert Systems\n- Neural Networks and Deep Learning with TensorFlow and PyTorch\n- Computer Vision techniques and architectures\n- Natural Language Processing (NLP) including transformers and BERT\n- Specialized topics: Genetic Algorithms, Reinforcement Learning, Multi-Agent Systems\n- AI Ethics and Responsible AI principles\n\n**Key Technologies:** Python 3, Jupyter Notebooks, TensorFlow, PyTorch, Keras, OpenCV, Vue.js (for quiz app)\n\n**Architecture:** Educational content repository with Jupyter Notebooks organized by topic areas, supplemented by a Vue.js-based quiz application and extensive multi-language support.\n\n## Setup Commands\n\n### Primary Development Environment (Python/Jupyter)\n\nThe curriculum is designed to run with Python and Jupyter Notebooks. The recommended approach is using miniconda:\n\n```bash\n# Clone the repository\ngit clone https://github.com/microsoft/ai-for-beginners\ncd ai-for-beginners\n\n# Create and activate conda environment\nconda env create --name ai4beg --file environment.yml\nconda activate ai4beg\n\n# Start Jupyter Notebook\njupyter notebook\n# OR\njupyter lab\n```\n\n### Alternative: Using devcontainer\n\n```bash\n# Open in VS Code and select \"Reopen in Container\" when prompted\n# The devcontainer will automatically set up the environment\n```\n\n### Quiz Application Setup\n\nThe quiz app is a separate Vue.js application located in `etc/quiz-app/`:\n\n```bash\ncd etc/quiz-app\nnpm install\nnpm run serve  # Development server\nnpm run build  # Production build\nnpm run lint   # Lint and fix files\n```\n\n## Development Workflow\n\n### Working with Jupyter Notebooks\n\n1. **Local Development:**\n   - Activate conda environment: `conda activate ai4beg`\n   - Start Jupyter: `jupyter notebook` or `jupyter lab`\n   - Navigate to lesson folders and open `.ipynb` files\n   - Run cells interactively to follow lessons\n\n2. **VS Code with Python Extension:**\n   - Open repository in VS Code\n   - Install Python extension\n   - VS Code automatically detects and uses the conda environment\n   - Open `.ipynb` files directly in VS Code\n\n3. **Cloud Development:**\n   - **GitHub Codespaces:** Click \"Code\" → \"Codespaces\" → \"Create codespace on main\"\n   - **Binder:** Use the Binder badge on README to launch in browser\n   - Note: Binder has limited resources and some web access restrictions\n\n### GPU Support for Advanced Lessons\n\nLater lessons benefit significantly from GPU acceleration:\n\n- **Azure Data Science VM:** Use NC-series VMs with GPU support\n- **Azure Machine Learning:** Use notebook features with GPU compute\n- **Google Colab:** Upload notebooks individually (has free GPU support)\n\n### Quiz App Development\n\n```bash\ncd etc/quiz-app\nnpm run serve  # Hot-reload development server at http://localhost:8080\n```\n\n## Testing Instructions\n\nThis is an educational repository focused on learning content rather than software testing. There is no traditional test suite.\n\n### Validation Approaches:\n\n1. **Jupyter Notebooks:** Execute cells sequentially to verify code examples work\n2. **Quiz App Testing:** Manual testing via development server\n3. **Translation Validation:** Check translated content in `translations/` folder\n4. **Quiz App Linting:** `npm run lint` in `etc/quiz-app/`\n\n### Running Code Examples:\n\n```bash\n# Activate environment first\nconda activate ai4beg\n\n# Run Python scripts directly\npython lessons/4-ComputerVision/07-ConvNets/pytorchcv.py\n\n# Or execute notebooks\njupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb\n```\n\n## Code Style\n\n### Python Code Style\n\n- Standard Python conventions for educational code\n- Clear, readable code prioritizing learning over optimization\n- Comments explaining key concepts\n- Jupyter Notebook-friendly: cells should be self-contained where possible\n- No strict linting requirements for lesson content\n\n### JavaScript/Vue.js (Quiz App)\n\n- ESLint configuration in `etc/quiz-app/package.json`\n- Run `npm run lint` to check and auto-fix issues\n- Vue 2.x conventions\n- Component-based architecture\n\n### File Organization\n\n```\nlessons/\n  ├── 0-course-setup/          # Setup instructions\n  ├── 1-Intro/                 # Introduction to AI\n  ├── 2-Symbolic/              # Symbolic AI\n  ├── 3-NeuralNetworks/        # Neural Networks basics\n  ├── 4-ComputerVision/        # Computer Vision\n  ├── 5-NLP/                   # Natural Language Processing\n  ├── 6-Other/                 # Other AI techniques\n  ├── 7-Ethics/                # AI Ethics\n  └── X-Extras/                # Additional content\n\netc/\n  ├── quiz-app/                # Vue.js quiz application\n  └── quiz-src/                # Quiz source files\n\ntranslations/                  # Multi-language translations\n```\n\n## Build and Deployment\n\n### Jupyter Content\n\nNo build process required - Jupyter Notebooks are executed directly.\n\n### Quiz Application\n\n```bash\ncd etc/quiz-app\n\n# Development\nnpm run serve\n\n# Production build\nnpm run build  # Outputs to etc/quiz-app/dist/\n\n# Deploy to Azure Static Web Apps\n# Azure automatically creates GitHub Actions workflow\n# See etc/quiz-app/README.md for detailed deployment instructions\n```\n\n### Documentation Site\n\nThe repository uses Docsify for documentation:\n- `index.html` serves as entry point\n- No build required - served directly via GitHub Pages\n- Access at: https://microsoft.github.io/AI-For-Beginners/\n\n## Contributing Guidelines\n\n### Pull Request Process\n\n1. **Title Format:** Clear, descriptive titles describing the change\n2. **CLA Requirement:** Microsoft CLA must be signed (automated check)\n3. **Content Guidelines:**\n   - Maintain educational focus and beginner-friendly approach\n   - Test all code examples in notebooks\n   - Ensure notebooks run end-to-end\n   - Update translations if modifying English content\n4. **Quiz App Changes:** Run `npm run lint` before committing\n\n### Translation Contributions\n\n- Translations are automated via GitHub Actions using co-op-translator\n- Manual translations go in `translations/<language-code>/`\n- Quiz translations in `etc/quiz-app/src/assets/translations/`\n- Supported languages: 40+ languages (see README for full list)\n\n### Active Contribution Areas\n\nSee `etc/CONTRIBUTING.md` for current needs:\n- Deep Reinforcement Learning sections\n- Object Detection improvements\n- Named Entity Recognition examples\n- Custom embedding training samples\n\n## Environment Configuration\n\n### Required Dependencies\n\n```bash\n# Core Python packages (from requirements.txt)\ntensorflow==2.17.0\ntorch (via conda)\ntorchvision (via conda)\nkeras==3.5.0\nopencv (via conda)\nscikit-learn\nnumpy==1.26\npandas==2.2.2\nmatplotlib==3.9\njupyter\n```\n\n### Environment Variables\n\nNo special environment variables required for basic usage.\n\nFor Azure deployments (quiz app):\n- `AZURE_STATIC_WEB_APPS_API_TOKEN` (set automatically by Azure)\n\n## Debugging and Troubleshooting\n\n### Common Issues\n\n**Issue:** Conda environment creation fails\n- **Solution:** Update conda first: `conda update conda -y`\n- Ensure sufficient disk space (50GB recommended)\n\n**Issue:** Jupyter kernel not found\n- **Solution:** \n  ```bash\n  conda activate ai4beg\n  python -m ipykernel install --user --name ai4beg\n  ```\n\n**Issue:** GPU not detected in notebooks\n- **Solution:** \n  - Verify CUDA installation: `nvidia-smi`\n  - Check PyTorch GPU: `python -c \"import torch; print(torch.cuda.is_available())\"`\n  - Check TensorFlow GPU: `python -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\n\n**Issue:** Quiz app won't start\n- **Solution:**\n  ```bash\n  cd etc/quiz-app\n  rm -rf node_modules package-lock.json\n  npm install\n  npm run serve\n  ```\n\n**Issue:** Binder times out or blocks downloads\n- **Solution:** Use GitHub Codespaces or local setup for better resource access\n\n### Memory Issues\n\nSome lessons require significant RAM (8GB+ recommended):\n- Use cloud VMs for resource-intensive lessons\n- Close other applications when training models\n- Reduce batch sizes in notebooks if running out of memory\n\n## Additional Notes\n\n### For Course Instructors\n\n- See `lessons/0-course-setup/for-teachers.md` for teaching guidance\n- Lessons are self-contained and can be taught in sequence or selected individually\n- Estimated time: 12 weeks at 2 lessons per week\n\n### Cloud Resources\n\n- **Azure for Students:** Free credits available for students\n- **Microsoft Learn:** Supplementary learning paths linked throughout\n- **Binder:** Free but limited resources and some network restrictions\n\n### Code Execution Options\n\n1. **Local (Recommended):** Full control, best performance, GPU support\n2. **GitHub Codespaces:** Cloud-based VS Code, good for quick access\n3. **Binder:** Browser-based Jupyter, free but limited\n4. **Azure ML Notebooks:** Enterprise option with GPU support\n5. **Google Colab:** Upload notebooks individually, free GPU tier available\n\n### Working with Notebooks\n\n- Notebooks are designed to be run cell-by-cell for learning\n- Many notebooks download datasets on first run (may take time)\n- Some models require GPU for reasonable training times\n- Pre-trained models are used where possible to reduce compute requirements\n\n### Performance Considerations\n\n- Later computer vision lessons (CNNs, GANs) benefit from GPU\n- NLP transformer lessons may require significant RAM\n- Training from scratch is educational but time-consuming\n- Transfer learning examples minimize training time\n"},"files":{"AGENTS.md":"# AGENTS.md\n\n## Project Overview\n\nAI for Beginners is a comprehensive 12-week, 24-lesson curriculum covering Artificial Intelligence fundamentals. This educational repository includes practical lessons using Jupyter Notebooks, quizzes, and hands-on labs. The curriculum covers:\n\n- Symbolic AI with Knowledge Representation and Expert Systems\n- Neural Networks and Deep Learning with TensorFlow and PyTorch\n- Computer Vision techniques and architectures\n- Natural Language Processing (NLP) including transformers and BERT\n- Specialized topics: Genetic Algorithms, Reinforcement Learning, Multi-Agent Systems\n- AI Ethics and Responsible AI principles\n\n**Key Technologies:** Python 3, Jupyter Notebooks, TensorFlow, PyTorch, Keras, OpenCV, Vue.js (for quiz app)\n\n**Architecture:** Educational content repository with Jupyter Notebooks organized by topic areas, supplemented by a Vue.js-based quiz application and extensive multi-language support.\n\n## Setup Commands\n\n### Primary Development Environment (Python/Jupyter)\n\nThe curriculum is designed to run with Python and Jupyter Notebooks. The recommended approach is using miniconda:\n\n```bash\n# Clone the repository\ngit clone https://github.com/microsoft/ai-for-beginners\ncd ai-for-beginners\n\n# Create and activate conda environment\nconda env create --name ai4beg --file environment.yml\nconda activate ai4beg\n\n# Start Jupyter Notebook\njupyter notebook\n# OR\njupyter lab\n```\n\n### Alternative: Using devcontainer\n\n```bash\n# Open in VS Code and select \"Reopen in Container\" when prompted\n# The devcontainer will automatically set up the environment\n```\n\n### Quiz Application Setup\n\nThe quiz app is a separate Vue.js application located in `etc/quiz-app/`:\n\n```bash\ncd etc/quiz-app\nnpm install\nnpm run serve  # Development server\nnpm run build  # Production build\nnpm run lint   # Lint and fix files\n```\n\n## Development Workflow\n\n### Working with Jupyter Notebooks\n\n1. **Local Development:**\n   - Activate conda environment: `conda activate ai4beg`\n   - Start Jupyter: `jupyter notebook` or `jupyter lab`\n   - Navigate to lesson folders and open `.ipynb` files\n   - Run cells interactively to follow lessons\n\n2. **VS Code with Python Extension:**\n   - Open repository in VS Code\n   - Install Python extension\n   - VS Code automatically detects and uses the conda environment\n   - Open `.ipynb` files directly in VS Code\n\n3. **Cloud Development:**\n   - **GitHub Codespaces:** Click \"Code\" → \"Codespaces\" → \"Create codespace on main\"\n   - **Binder:** Use the Binder badge on README to launch in browser\n   - Note: Binder has limited resources and some web access restrictions\n\n### GPU Support for Advanced Lessons\n\nLater lessons benefit significantly from GPU acceleration:\n\n- **Azure Data Science VM:** Use NC-series VMs with GPU support\n- **Azure Machine Learning:** Use notebook features with GPU compute\n- **Google Colab:** Upload notebooks individually (has free GPU support)\n\n### Quiz App Development\n\n```bash\ncd etc/quiz-app\nnpm run serve  # Hot-reload development server at http://localhost:8080\n```\n\n## Testing Instructions\n\nThis is an educational repository focused on learning content rather than software testing. There is no traditional test suite.\n\n### Validation Approaches:\n\n1. **Jupyter Notebooks:** Execute cells sequentially to verify code examples work\n2. **Quiz App Testing:** Manual testing via development server\n3. **Translation Validation:** Check translated content in `translations/` folder\n4. **Quiz App Linting:** `npm run lint` in `etc/quiz-app/`\n\n### Running Code Examples:\n\n```bash\n# Activate environment first\nconda activate ai4beg\n\n# Run Python scripts directly\npython lessons/4-ComputerVision/07-ConvNets/pytorchcv.py\n\n# Or execute notebooks\njupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb\n```\n\n## Code Style\n\n### Python Code Style\n\n- Standard Python conventions for educational code\n- Clear, readable code prioritizing learning over optimization\n- Comments explaining key concepts\n- Jupyter Notebook-friendly: cells should be self-contained where possible\n- No strict linting requirements for lesson content\n\n### JavaScript/Vue.js (Quiz App)\n\n- ESLint configuration in `etc/quiz-app/package.json`\n- Run `npm run lint` to check and auto-fix issues\n- Vue 2.x conventions\n- Component-based architecture\n\n### File Organization\n\n```\nlessons/\n  ├── 0-course-setup/          # Setup instructions\n  ├── 1-Intro/                 # Introduction to AI\n  ├── 2-Symbolic/              # Symbolic AI\n  ├── 3-NeuralNetworks/        # Neural Networks basics\n  ├── 4-ComputerVision/        # Computer Vision\n  ├── 5-NLP/                   # Natural Language Processing\n  ├── 6-Other/                 # Other AI techniques\n  ├── 7-Ethics/                # AI Ethics\n  └── X-Extras/                # Additional content\n\netc/\n  ├── quiz-app/                # Vue.js quiz application\n  └── quiz-src/                # Quiz source files\n\ntranslations/                  # Multi-language translations\n```\n\n## Build and Deployment\n\n### Jupyter Content\n\nNo build process required - Jupyter Notebooks are executed directly.\n\n### Quiz Application\n\n```bash\ncd etc/quiz-app\n\n# Development\nnpm run serve\n\n# Production build\nnpm run build  # Outputs to etc/quiz-app/dist/\n\n# Deploy to Azure Static Web Apps\n# Azure automatically creates GitHub Actions workflow\n# See etc/quiz-app/README.md for detailed deployment instructions\n```\n\n### Documentation Site\n\nThe repository uses Docsify for documentation:\n- `index.html` serves as entry point\n- No build required - served directly via GitHub Pages\n- Access at: https://microsoft.github.io/AI-For-Beginners/\n\n## Contributing Guidelines\n\n### Pull Request Process\n\n1. **Title Format:** Clear, descriptive titles describing the change\n2. **CLA Requirement:** Microsoft CLA must be signed (automated check)\n3. **Content Guidelines:**\n   - Maintain educational focus and beginner-friendly approach\n   - Test all code examples in notebooks\n   - Ensure notebooks run end-to-end\n   - Update translations if modifying English content\n4. **Quiz App Changes:** Run `npm run lint` before committing\n\n### Translation Contributions\n\n- Translations are automated via GitHub Actions using co-op-translator\n- Manual translations go in `translations/<language-code>/`\n- Quiz translations in `etc/quiz-app/src/assets/translations/`\n- Supported languages: 40+ languages (see README for full list)\n\n### Active Contribution Areas\n\nSee `etc/CONTRIBUTING.md` for current needs:\n- Deep Reinforcement Learning sections\n- Object Detection improvements\n- Named Entity Recognition examples\n- Custom embedding training samples\n\n## Environment Configuration\n\n### Required Dependencies\n\n```bash\n# Core Python packages (from requirements.txt)\ntensorflow==2.17.0\ntorch (via conda)\ntorchvision (via conda)\nkeras==3.5.0\nopencv (via conda)\nscikit-learn\nnumpy==1.26\npandas==2.2.2\nmatplotlib==3.9\njupyter\n```\n\n### Environment Variables\n\nNo special environment variables required for basic usage.\n\nFor Azure deployments (quiz app):\n- `AZURE_STATIC_WEB_APPS_API_TOKEN` (set automatically by Azure)\n\n## Debugging and Troubleshooting\n\n### Common Issues\n\n**Issue:** Conda environment creation fails\n- **Solution:** Update conda first: `conda update conda -y`\n- Ensure sufficient disk space (50GB recommended)\n\n**Issue:** Jupyter kernel not found\n- **Solution:** \n  ```bash\n  conda activate ai4beg\n  python -m ipykernel install --user --name ai4beg\n  ```\n\n**Issue:** GPU not detected in notebooks\n- **Solution:** \n  - Verify CUDA installation: `nvidia-smi`\n  - Check PyTorch GPU: `python -c \"import torch; print(torch.cuda.is_available())\"`\n  - Check TensorFlow GPU: `python -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\n\n**Issue:** Quiz app won't start\n- **Solution:**\n  ```bash\n  cd etc/quiz-app\n  rm -rf node_modules package-lock.json\n  npm install\n  npm run serve\n  ```\n\n**Issue:** Binder times out or blocks downloads\n- **Solution:** Use GitHub Codespaces or local setup for better resource access\n\n### Memory Issues\n\nSome lessons require significant RAM (8GB+ recommended):\n- Use cloud VMs for resource-intensive lessons\n- Close other applications when training models\n- Reduce batch sizes in notebooks if running out of memory\n\n## Additional Notes\n\n### For Course Instructors\n\n- See `lessons/0-course-setup/for-teachers.md` for teaching guidance\n- Lessons are self-contained and can be taught in sequence or selected individually\n- Estimated time: 12 weeks at 2 lessons per week\n\n### Cloud Resources\n\n- **Azure for Students:** Free credits available for students\n- **Microsoft Learn:** Supplementary learning paths linked throughout\n- **Binder:** Free but limited resources and some network restrictions\n\n### Code Execution Options\n\n1. **Local (Recommended):** Full control, best performance, GPU support\n2. **GitHub Codespaces:** Cloud-based VS Code, good for quick access\n3. **Binder:** Browser-based Jupyter, free but limited\n4. **Azure ML Notebooks:** Enterprise option with GPU support\n5. **Google Colab:** Upload notebooks individually, free GPU tier available\n\n### Working with Notebooks\n\n- Notebooks are designed to be run cell-by-cell for learning\n- Many notebooks download datasets on first run (may take time)\n- Some models require GPU for reasonable training times\n- Pre-trained models are used where possible to reduce compute requirements\n\n### Performance Considerations\n\n- Later computer vision lessons (CNNs, GANs) benefit from GPU\n- NLP transformer lessons may require significant RAM\n- Training from scratch is educational but time-consuming\n- Transfer learning examples minimize training time\n"},"items":[{"name":"AGENTS.md","path":"AGENTS.md","title":"AGENTS.md","content":"# AGENTS.md\n\n## Project Overview\n\nAI for Beginners is a comprehensive 12-week, 24-lesson curriculum covering Artificial Intelligence fundamentals. This educational repository includes practical lessons using Jupyter Notebooks, quizzes, and hands-on labs. The curriculum covers:\n\n- Symbolic AI with Knowledge Representation and Expert Systems\n- Neural Networks and Deep Learning with TensorFlow and PyTorch\n- Computer Vision techniques and architectures\n- Natural Language Processing (NLP) including transformers and BERT\n- Specialized topics: Genetic Algorithms, Reinforcement Learning, Multi-Agent Systems\n- AI Ethics and Responsible AI principles\n\n**Key Technologies:** Python 3, Jupyter Notebooks, TensorFlow, PyTorch, Keras, OpenCV, Vue.js (for quiz app)\n\n**Architecture:** Educational content repository with Jupyter Notebooks organized by topic areas, supplemented by a Vue.js-based quiz application and extensive multi-language support.\n\n## Setup Commands\n\n### Primary Development Environment (Python/Jupyter)\n\nThe curriculum is designed to run with Python and Jupyter Notebooks. The recommended approach is using miniconda:\n\n```bash\n# Clone the repository\ngit clone https://github.com/microsoft/ai-for-beginners\ncd ai-for-beginners\n\n# Create and activate conda environment\nconda env create --name ai4beg --file environment.yml\nconda activate ai4beg\n\n# Start Jupyter Notebook\njupyter notebook\n# OR\njupyter lab\n```\n\n### Alternative: Using devcontainer\n\n```bash\n# Open in VS Code and select \"Reopen in Container\" when prompted\n# The devcontainer will automatically set up the environment\n```\n\n### Quiz Application Setup\n\nThe quiz app is a separate Vue.js application located in `etc/quiz-app/`:\n\n```bash\ncd etc/quiz-app\nnpm install\nnpm run serve  # Development server\nnpm run build  # Production build\nnpm run lint   # Lint and fix files\n```\n\n## Development Workflow\n\n### Working with Jupyter Notebooks\n\n1. **Local Development:**\n   - Activate conda environment: `conda activate ai4beg`\n   - Start Jupyter: `jupyter notebook` or `jupyter lab`\n   - Navigate to lesson folders and open `.ipynb` files\n   - Run cells interactively to follow lessons\n\n2. **VS Code with Python Extension:**\n   - Open repository in VS Code\n   - Install Python extension\n   - VS Code automatically detects and uses the conda environment\n   - Open `.ipynb` files directly in VS Code\n\n3. **Cloud Development:**\n   - **GitHub Codespaces:** Click \"Code\" → \"Codespaces\" → \"Create codespace on main\"\n   - **Binder:** Use the Binder badge on README to launch in browser\n   - Note: Binder has limited resources and some web access restrictions\n\n### GPU Support for Advanced Lessons\n\nLater lessons benefit significantly from GPU acceleration:\n\n- **Azure Data Science VM:** Use NC-series VMs with GPU support\n- **Azure Machine Learning:** Use notebook features with GPU compute\n- **Google Colab:** Upload notebooks individually (has free GPU support)\n\n### Quiz App Development\n\n```bash\ncd etc/quiz-app\nnpm run serve  # Hot-reload development server at http://localhost:8080\n```\n\n## Testing Instructions\n\nThis is an educational repository focused on learning content rather than software testing. There is no traditional test suite.\n\n### Validation Approaches:\n\n1. **Jupyter Notebooks:** Execute cells sequentially to verify code examples work\n2. **Quiz App Testing:** Manual testing via development server\n3. **Translation Validation:** Check translated content in `translations/` folder\n4. **Quiz App Linting:** `npm run lint` in `etc/quiz-app/`\n\n### Running Code Examples:\n\n```bash\n# Activate environment first\nconda activate ai4beg\n\n# Run Python scripts directly\npython lessons/4-ComputerVision/07-ConvNets/pytorchcv.py\n\n# Or execute notebooks\njupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb\n```\n\n## Code Style\n\n### Python Code Style\n\n- Standard Python conventions for educational code\n- Clear, readable code prioritizing learning over optimization\n- Comments explaining key concepts\n- Jupyter Notebook-friendly: cells should be self-contained where possible\n- No strict linting requirements for lesson content\n\n### JavaScript/Vue.js (Quiz App)\n\n- ESLint configuration in `etc/quiz-app/package.json`\n- Run `npm run lint` to check and auto-fix issues\n- Vue 2.x conventions\n- Component-based architecture\n\n### File Organization\n\n```\nlessons/\n  ├── 0-course-setup/          # Setup instructions\n  ├── 1-Intro/                 # Introduction to AI\n  ├── 2-Symbolic/              # Symbolic AI\n  ├── 3-NeuralNetworks/        # Neural Networks basics\n  ├── 4-ComputerVision/        # Computer Vision\n  ├── 5-NLP/                   # Natural Language Processing\n  ├── 6-Other/                 # Other AI techniques\n  ├── 7-Ethics/                # AI Ethics\n  └── X-Extras/                # Additional content\n\netc/\n  ├── quiz-app/                # Vue.js quiz application\n  └── quiz-src/                # Quiz source files\n\ntranslations/                  # Multi-language translations\n```\n\n## Build and Deployment\n\n### Jupyter Content\n\nNo build process required - Jupyter Notebooks are executed directly.\n\n### Quiz Application\n\n```bash\ncd etc/quiz-app\n\n# Development\nnpm run serve\n\n# Production build\nnpm run build  # Outputs to etc/quiz-app/dist/\n\n# Deploy to Azure Static Web Apps\n# Azure automatically creates GitHub Actions workflow\n# See etc/quiz-app/README.md for detailed deployment instructions\n```\n\n### Documentation Site\n\nThe repository uses Docsify for documentation:\n- `index.html` serves as entry point\n- No build required - served directly via GitHub Pages\n- Access at: https://microsoft.github.io/AI-For-Beginners/\n\n## Contributing Guidelines\n\n### Pull Request Process\n\n1. **Title Format:** Clear, descriptive titles describing the change\n2. **CLA Requirement:** Microsoft CLA must be signed (automated check)\n3. **Content Guidelines:**\n   - Maintain educational focus and beginner-friendly approach\n   - Test all code examples in notebooks\n   - Ensure notebooks run end-to-end\n   - Update translations if modifying English content\n4. **Quiz App Changes:** Run `npm run lint` before committing\n\n### Translation Contributions\n\n- Translations are automated via GitHub Actions using co-op-translator\n- Manual translations go in `translations/<language-code>/`\n- Quiz translations in `etc/quiz-app/src/assets/translations/`\n- Supported languages: 40+ languages (see README for full list)\n\n### Active Contribution Areas\n\nSee `etc/CONTRIBUTING.md` for current needs:\n- Deep Reinforcement Learning sections\n- Object Detection improvements\n- Named Entity Recognition examples\n- Custom embedding training samples\n\n## Environment Configuration\n\n### Required Dependencies\n\n```bash\n# Core Python packages (from requirements.txt)\ntensorflow==2.17.0\ntorch (via conda)\ntorchvision (via conda)\nkeras==3.5.0\nopencv (via conda)\nscikit-learn\nnumpy==1.26\npandas==2.2.2\nmatplotlib==3.9\njupyter\n```\n\n### Environment Variables\n\nNo special environment variables required for basic usage.\n\nFor Azure deployments (quiz app):\n- `AZURE_STATIC_WEB_APPS_API_TOKEN` (set automatically by Azure)\n\n## Debugging and Troubleshooting\n\n### Common Issues\n\n**Issue:** Conda environment creation fails\n- **Solution:** Update conda first: `conda update conda -y`\n- Ensure sufficient disk space (50GB recommended)\n\n**Issue:** Jupyter kernel not found\n- **Solution:** \n  ```bash\n  conda activate ai4beg\n  python -m ipykernel install --user --name ai4beg\n  ```\n\n**Issue:** GPU not detected in notebooks\n- **Solution:** \n  - Verify CUDA installation: `nvidia-smi`\n  - Check PyTorch GPU: `python -c \"import torch; print(torch.cuda.is_available())\"`\n  - Check TensorFlow GPU: `python -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"`\n\n**Issue:** Quiz app won't start\n- **Solution:**\n  ```bash\n  cd etc/quiz-app\n  rm -rf node_modules package-lock.json\n  npm install\n  npm run serve\n  ```\n\n**Issue:** Binder times out or blocks downloads\n- **Solution:** Use GitHub Codespaces or local setup for better resource access\n\n### Memory Issues\n\nSome lessons require significant RAM (8GB+ recommended):\n- Use cloud VMs for resource-intensive lessons\n- Close other applications when training models\n- Reduce batch sizes in notebooks if running out of memory\n\n## Additional Notes\n\n### For Course Instructors\n\n- See `lessons/0-course-setup/for-teachers.md` for teaching guidance\n- Lessons are self-contained and can be taught in sequence or selected individually\n- Estimated time: 12 weeks at 2 lessons per week\n\n### Cloud Resources\n\n- **Azure for Students:** Free credits available for students\n- **Microsoft Learn:** Supplementary learning paths linked throughout\n- **Binder:** Free but limited resources and some network restrictions\n\n### Code Execution Options\n\n1. **Local (Recommended):** Full control, best performance, GPU support\n2. **GitHub Codespaces:** Cloud-based VS Code, good for quick access\n3. **Binder:** Browser-based Jupyter, free but limited\n4. **Azure ML Notebooks:** Enterprise option with GPU support\n5. **Google Colab:** Upload notebooks individually, free GPU tier available\n\n### Working with Notebooks\n\n- Notebooks are designed to be run cell-by-cell for learning\n- Many notebooks download datasets on first run (may take time)\n- Some models require GPU for reasonable training times\n- Pre-trained models are used where possible to reduce compute requirements\n\n### Performance Considerations\n\n- Later computer vision lessons (CNNs, GANs) benefit from GPU\n- NLP transformer lessons may require significant RAM\n- Training from scratch is educational but time-consuming\n- Transfer learning examples minimize training time\n","category":"root","tokens":2405}]}