Enhanced all remaining modules with comprehensive educational content:
## Modules Updated
- ✅ 03_layers: Added NBGrader metadata, solution blocks for matmul_naive and Dense class
- ✅ 04_networks: Added NBGrader metadata, solution blocks for Sequential class and forward pass
- ✅ 05_cnn: Added NBGrader metadata, solution blocks for conv2d_naive function and Conv2D class
- ✅ 06_dataloader: Added NBGrader metadata, solution blocks for Dataset base class
## Key Features Added
- **NBGrader Metadata**: All cells properly tagged with grade, grade_id, locked, schema_version, solution, task flags
- **Solution Blocks**: All TODO sections now have ### BEGIN SOLUTION / ### END SOLUTION markers
- **Import Flexibility**: Robust import handling for development vs package usage
- **Educational Content**: Package structure documentation and mathematical foundations
- **Comprehensive Testing**: All modules run correctly as Python scripts
## Verification Results
- ✅ All modules execute without errors
- ✅ All solution blocks implemented correctly
- ✅ Export workflow works: tito export --all successfully exports all modules
- ✅ Package integration verified: all imports work correctly
- ✅ Educational content preserved and enhanced
## Ready for Production
- Complete NBGrader-compatible assignment system
- Streamlined tito export command with automatic .py → .ipynb conversion
- Comprehensive educational modules with real-world applications
- Robust testing infrastructure for all components
Total modules completed: 6/6 (setup, tensor, activations, layers, networks, cnn, dataloader)
- Added package structure documentation explaining modules/source/ vs tinytorch.core.
- Enhanced mathematical foundations with linear algebra refresher and Universal Approximation Theorem
- Added real-world applications for each activation function (ReLU, Sigmoid, Tanh, Softmax)
- Included mathematical properties, derivatives, ranges, and computational costs
- Added performance considerations and numerical stability explanations
- Connected to production ML systems (PyTorch, TensorFlow, JAX equivalents)
- Implemented streamlined 'tito export' command with automatic .py → .ipynb conversion
- All functionality preserved: scripts run correctly, tests pass, package integration works
- Ready to continue with remaining modules (layers, networks, cnn, dataloader)
- Remove unnecessary module_paths.txt file for cleaner architecture
- Update export command to discover modules dynamically from modules/source/
- Simplify nbdev command to support --all and module-specific exports
- Use single source of truth: nbdev settings.ini for module paths
- Clean up import structure in setup module for proper nbdev export
- Maintain clean separation between module discovery and export logic
This implements a proper software engineering approach with:
- Single source of truth (settings.ini)
- Dynamic discovery (no hardcoded paths)
- Clean CLI interface (tito package nbdev --export [--all|module])
- Robust error handling with helpful feedback
