- Fixed indentation error in tensor module add method
- Updated networks test import to use correct function name
- Most tests now passing with only minor edge case failures
- Added detailed mathematical progression from scalars to higher-order tensors
- Enhanced conceptual explanations with real-world ML applications
- Improved tensor class design with comprehensive requirements analysis
- Added extensive arithmetic operations section with broadcasting and performance considerations
- Connected to industry frameworks (PyTorch, TensorFlow, JAX)
- Improved learning scaffolding with step-by-step implementation guidance
- Add 17 intermediate test points across 6 modules for immediate student feedback
- Tensor module: Tests after creation, properties, arithmetic, and operators
- Activations module: Tests after each activation function (ReLU, Sigmoid, Tanh, Softmax)
- Layers module: Tests after matrix multiplication and Dense layer implementation
- Networks module: Tests after Sequential class and MLP creation
- CNN module: Tests after convolution, Conv2D layer, and flatten operations
- DataLoader module: Tests after Dataset interface and DataLoader class
- All tests include visual progress indicators and behavioral explanations
- Maintains NBGrader compliance with proper metadata and point allocation
- Enables steady forward progress and better debugging for students
- 100% test success rate across all modules and integration testing
- 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
