3 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	04cbc65724	Fix training pipeline: Parameter class, Variable.sum(), gradient handling ... Major fixes for complete training pipeline functionality: Core Components Fixed: - Parameter class: Now wraps Variables with requires_grad=True for proper gradient tracking - Variable.sum(): Essential for scalar loss computation from multi-element tensors - Gradient handling: Fixed memoryview issues in autograd and activations - Tensor indexing: Added __getitem__ support for weight inspection Training Results: - XOR learning: 100% accuracy (4/4) - network successfully learns XOR function - Linear regression: Weight=1.991 (target=2.0), Bias=0.980 (target=1.0) - Integration tests: 21/22 passing (95.5% success rate) - Module tests: All individual modules passing - General functionality: 4/5 tests passing with core training working Technical Details: - Fixed gradient data access patterns throughout activations.py - Added safe memoryview handling in Variable.backward() - Implemented proper Parameter-Variable delegation - Added Tensor subscripting for debugging access 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-28 19:14:11 -04:00
Vijay Janapa Reddi	01192b9749	Prepare for v0.1 release ... Documentation: • Add comprehensive student quickstart guide • Create instructor guide with grading workflow • Update README with v0.1 features and capabilities • Document interactive ML Systems questions • Add tito grade command documentation Cleanup: • Remove __pycache__ directories (1073 removed) • Clean .ipynb_checkpoints • Remove experimental Python files • Clean up temporary files (.pyc, .DS_Store) Features in v0.1: • 17 educational modules from tensors to transformers • Interactive ML Systems thinking questions (NBGrader) • TinyGPT demonstrating 70% framework reuse • 16-checkpoint capability progression system • Simplified tito CLI wrapping all functionality • Complete instructor grading workflow Ready for v0.1 release tag.	2025-09-17 19:29:16 -04:00
Vijay Janapa Reddi	8410023864	Implement comprehensive checkpoint system with CLI integration ... Features: - 16 checkpoint test suite validating ML systems capabilities - Integration tests covering complete learning progression - Rich CLI progress tracking with visual timelines - Capability-driven assessment from environment to production Checkpoints: - Environment setup through full ML system deployment - Each checkpoint validates integrated functionality - Progressive capability building with clear success criteria - Professional CLI interface with status/timeline/test commands	2025-09-16 21:02:11 -04:00