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Differences from Other Frameworks

This framework distinguishes itself from both industrial and educational frameworks in several key aspects:

Compared to Industrial Frameworks (PyTorch, TensorFlow, etc.)​

  • Prioritizes educational value over production performance
  • Simpler implementation with minimal dependencies
  • More transparent and easier to understand internals
  • Focused feature set without overwhelming complexity

Compared to Educational Frameworks​

Micrograd​

  • Supports full tensor operations (not just scalar computations)
  • Includes complete set of optimizers and loss functions
  • Maintains practical usability while being educational

TinyFlow​

  • Pure Python implementation (vs C++)
  • Modern dynamic computation approach
  • Actively maintained and documented
  • Lower barrier to entry for beginners

DeZero​

  • Simpler architecture without higher-order derivatives
  • PyTorch-like API for easier industry transition
  • Includes practical features like ONNX export
  • Focus on essential concepts without excessive complexity

Unique Features​

  • Balanced approach between education and practicality
  • GPU acceleration support for real-world applications (coming soon)
  • ONNX export capability for deployment learning
  • Comprehensive documentation focused on learning
  • Smooth transition path to industrial frameworks