Project Tenets

MLAI is guided by a set of core principles that inform all development decisions and code design. These tenets ensure that the project remains true to its educational mission while maintaining high code quality.

Core Principles

Our project tenets are documented in detail in the tenets directory. Here’s a summary of the key principles:

  1. Clarity Over Cleverness Code should be easy to read and understand, especially for those new to machine learning or Python.

  2. Mathematical Transparency Mathematical concepts should be explicit in both code and documentation.

  3. Educational Focus Every module, function, and class should serve a clear pedagogical purpose.

  4. Good Python Practices Follow PEP 8 and PEP 257 for code and docstring style.

  5. Reproducibility Ensure that all examples and tutorials can be run end-to-end by users.

  6. Inclusivity and Accessibility Documentation and code should be accessible to a diverse audience.

  7. Open Science and Sharing Prefer open-source tools and datasets.

Applying the Tenets

These tenets are applied throughout the project:

  • Code Review: All contributions are evaluated against these principles

  • Documentation: Docstrings and tutorials reflect our educational focus

  • API Design: Functions and classes are designed for clarity and learning

  • Testing: Tests serve as executable documentation (see CIP-0002: Comprehensive Test Framework with pytest)

For Contributors

When contributing to MLAI, please:

  • Review the full tenets document before starting work

  • Consider how your changes align with our principles

  • Prioritize clarity and educational value over optimization

  • Include mathematical explanations where relevant

  • Write code that serves as a learning resource

The tenets are living documents that may evolve as the project grows. Major changes to the tenets are discussed through the CIP process (see Code Improvement Proposals (CIPs)).