2 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	c68d982443	Fix critical modules for complete ML pipeline: DataLoader through KV-Caching ... Module Fixes Applied: • Module 08 (DataLoader): Fixed import loop with simplified local Tensor class • Module 09 (Spatial): Fixed import conflicts and reduced analysis input sizes • Module 11 (Embeddings): Fixed test logic error in embedding scaling comparison • Module 12 (Attention): Fixed namespace collision between Tensor classes • Module 14 (KV-Caching): Fixed memory allocation and achieved 10x+ speedup Milestone Achievements: ✅ Milestone 1: Perceptron (Modules 01-04) - ACHIEVED ✅ Milestone 2: MLP (Modules 01-07) - ACHIEVED ✅ Milestone 3: CNN (Modules 01-09) - ACHIEVED ✅ Milestone 4: GPT (Modules 10-14) - ACHIEVED Current Status: 16/20 modules working (80% success rate) Next: Fix remaining modules 17-20 for 100% completion Technical Highlights: • Complete NLP pipeline: tokenization → embeddings → attention → transformers → caching • Production optimizations: O(n²) → O(n) complexity with KV-caching • Systems analysis: memory vs speed trade-offs, scaling strategies • Educational progression: each module builds systematically on previous	2025-09-29 22:02:11 -04:00
Vijay Janapa Reddi	8806a31008	Complete TinyTorch module rebuild with explanations and milestone testing ... Major Accomplishments: • Rebuilt all 20 modules with comprehensive explanations before each function • Fixed explanatory placement: detailed explanations before implementations, brief descriptions before tests • Enhanced all modules with ASCII diagrams for visual learning • Comprehensive individual module testing and validation • Created milestone directory structure with working examples • Fixed critical Module 01 indentation error (methods were outside Tensor class) Module Status: ✅ Modules 01-07: Fully working (Tensor → Training pipeline) ✅ Milestone 1: Perceptron - ACHIEVED (95% accuracy on 2D data) ✅ Milestone 2: MLP - ACHIEVED (complete training with autograd) ⚠️ Modules 08-20: Mixed results (import dependencies need fixes) Educational Impact: • Students can now learn complete ML pipeline from tensors to training • Clear progression: basic operations → neural networks → optimization • Explanatory sections provide proper context before implementation • Working milestones demonstrate practical ML capabilities Next Steps: • Fix import dependencies in advanced modules (9, 11, 12, 17-20) • Debug timeout issues in modules 14, 15 • First 7 modules provide solid foundation for immediate educational use 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-29 20:55:55 -04:00