Major changes:
- Moved TinyGPT from Module 16 to examples/tinygpt (capstone demo)
- Fixed Module 10 (optimizers) and Module 11 (training) bugs
- All 16 modules now passing tests (100% health)
- Added comprehensive testing with 'tito test --comprehensive'
- Renamed example files for clarity (train_xor_network.py, etc.)
- Created working TinyGPT example structure
- Updated documentation to reflect 15 core modules + examples
- Added KISS principle and testing framework documentation
- Remove redundant autograd_demo/ (covered by xor_network examples)
- Remove broken mnist_recognition/ (had CIFAR-10 data incorrectly)
- Streamline xor_network/ to single clean train.py
- Update examples README to reflect actual working examples
- Highlight 57.2% CIFAR-10 achievement and performance benchmarks
- Remove development artifacts and log files
Examples now showcase real ML capabilities:
- XOR Network: 100% accuracy
- CIFAR-10 MLP: 57.2% accuracy (exceeds course benchmarks)
- Clean, professional code patterns ready for students
- Create professional examples directory showcasing TinyTorch as real ML framework
- Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison
- Fix import paths in exported modules (training.py, dense.py)
- Update training module with autograd integration for loss functions
- Add progressive integration tests for all 16 modules
- Document framework capabilities and usage patterns
This commit establishes the examples gallery that demonstrates TinyTorch
works like PyTorch/TensorFlow, validating the complete framework.
