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Contributing to Keras Deep Learning Course

Thank you for your interest in contributing to this course! Here are some guidelines to help you get started.

How to Contribute

1. Report Issues

If you find bugs, typos, or unclear explanations:

  • Open an issue with a clear description
  • Include relevant error messages and code snippets
  • Specify which notebook and module the issue is in

2. Improve Notebooks

To enhance existing notebooks:

  1. Fork the repository
  2. Create a branch: git checkout -b improve/notebook-name
  3. Make your changes
  4. Test the notebook end-to-end
  5. Create a pull request with a clear description

3. Add New Content

To contribute new notebooks or modules:

  1. Follow the existing naming convention: NN_descriptive_name.ipynb
  2. Include clear explanations and comments
  3. Add visualizations where appropriate
  4. Test with fresh kernel start
  5. Update the README with new notebook information

Notebook Guidelines

Structure

  • Clear title and description at the top
  • Markdown cells explaining concepts before code
  • Code cells with comments for complex operations
  • Visualizations and results clearly presented

Code Style

# Use clear variable names
learning_rate = 0.001  # Better than lr = 0.001

# Add comments for complex logic
# Normalize features to zero mean and unit variance
X_normalized = (X - X.mean()) / X.std()

# Use meaningful function names
def train_model(X_train, y_train, epochs=10):
    """Train a deep learning model."""
    pass

Requirements

  • Run the entire notebook from top to bottom
  • Clear any outputs before committing
  • Add markdown explanations for each section
  • Include learning objectives at the beginning
  • Add a summary or key takeaways at the end

Development Setup

# Install development dependencies
pip install -e ".[dev]"

# Format code with black
black Keras/

# Check code style with flake8
flake8 Keras/

# Run tests
pytest tests/

Code of Conduct

  • Be respectful and inclusive
  • Provide constructive feedback
  • Help others learn
  • No harassment or discrimination

Review Process

  1. All contributions go through peer review
  2. Maintainers will check:
    • Accuracy of content
    • Code quality
    • Notebook execution
    • Compliance with guidelines
  3. You may be asked to make revisions
  4. Once approved, your contribution will be merged

Questions?

  • Check existing issues and discussions first
  • Open a discussion for questions
  • Be clear and provide context

License

By contributing, you agree your work will be licensed under the same license as the project.

Thank you for helping make this course better! 🚀