Address practical concerns about running milestone examples:
DATASET MANAGEMENT:
- Add data_manager.py for automatic dataset downloading
- Support MNIST, CIFAR-10, XOR, and Perceptron datasets
- Handle download with progress bars and caching
- Clear error handling and fallback options
STANDARDIZED TEMPLATE:
- Create MILESTONE_TEMPLATE.py showing standard structure
- Emphasize "YOU BUILT THIS" throughout code comments
- Include historical context and educational rationale
- Add systems analysis (memory, performance, scaling)
- Clear module prerequisite mapping
RUNNING INSTRUCTIONS:
- Comprehensive troubleshooting section in README
- Performance expectations and timing estimates
- Command-line options (--test-only, --demo-mode)
- Clear dataset logistics explanation
EXAMPLE IMPLEMENTATION:
- Update perceptron_1957 to follow new template
- Demonstrate "YOUR TinyTorch" emphasis throughout
- Show proper dataset integration and systems analysis
- Include command-line interface for different modes
Students now have clear, practical milestone examples that:
- Handle all dataset logistics automatically
- Emphasize their own implementations throughout
- Provide historical context and educational value
- Include troubleshooting and performance guidance
