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Design Philosophy

Our educational deep learning framework is built upon several core design principles:

Education-First Approach​

  • Prioritizes clarity and understanding over performance
  • Every component is designed to be easily understood and debugged
  • Comprehensive documentation explaining implementation details and design choices

Simplicity and Readability​

  • Written in pure Python with minimal dependencies (primarily NumPy)
  • Clean, well-structured code that's easy to follow
  • Avoids unnecessary complexity while maintaining essential functionality

Practical Learning Path​

  • PyTorch-like API to ensure familiar patterns and easy transition
  • Compact yet complete implementation of core deep learning concepts
  • Balance between educational value and practical utility

Transparency​

  • Open implementation of all components
  • Clear documentation of design decisions
  • No "magic" or hidden complexity

Accessibility​

  • Low barrier to entry for beginners
  • Gradual progression from basic to advanced concepts
  • Focus on debuggability and understanding