- ✅ tito system info/doctor: Full system health check working
- ✅ tito module status: Shows all 14 modules with proper status
- ✅ tito export --all: Successfully exports all modules to tinytorch package
- ✅ tito test --all: Runs all inline tests (65/66 tests passing)
- ✅ tito nbgrader: All assignment management commands available
- ✅ tito package nbdev: NBDev integration working
- ✅ Global PATH: Added bin/ to PATH for global tito access
Only minor issue: 1 MLOps test failing due to script execution
All core functionality working perfectly for educational use
- Remove all .ipynb files from modules/source/ directories
- Follow Python-first development workflow where .py files are source of truth
- .ipynb files should be temporary outputs generated only for NBGrader work
- Keeps repository clean and follows project conventions
Removed notebooks:
- modules/source/00_setup/setup_dev.ipynb
- modules/source/01_tensor/tensor_dev.ipynb
- modules/source/03_layers/layers_dev.ipynb
- modules/source/04_networks/networks_dev.ipynb
- modules/source/05_cnn/cnn_dev.ipynb
- modules/source/06_dataloader/dataloader_dev.ipynb
- modules/source/07_autograd/autograd_dev.ipynb
- 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)
