3 Commits

Include renames

Author	SHA1	Message	Date
Vijay Janapa Reddi	8a101cf52d	Add tito grade command for simplified NBGrader interface ... Implement comprehensive grading workflow wrapped behind tito CLI: • tito grade setup - Initialize NBGrader course structure • tito grade generate - Create instructor version with solutions • tito grade release - Create student version without solutions • tito grade collect - Collect student submissions • tito grade autograde - Automatically grade submissions • tito grade manual - Open manual grading interface • tito grade feedback - Generate student feedback • tito grade export - Export grades to CSV This allows users to only learn tito commands without needing to understand NBGrader's complex interface. All grading functionality is accessible through simple, consistent tito commands.	2025-09-17 19:22:02 -04:00
Vijay Janapa Reddi	0c24d77a86	Fix module structure ordering across all modules ... Standardize module structure to ensure correct section ordering: - if __name__ block → ML Systems Thinking → Module Summary (always last) Fixed 10 modules with incorrect ordering: • 02_tensor, 04_layers, 05_dense, 06_spatial • 08_dataloader, 09_autograd, 10_optimizers, 11_training • 12_compression (consolidated 3 scattered if blocks) • 15_mlops (consolidated 6 scattered if blocks) All 17 modules now follow consistent structure: 1. Content and implementations 2. Main execution block (if __name__) 3. ML Systems Thinking Questions 4. Module Summary (always last section) Updated CLAUDE.md with explicit ordering requirements to prevent future issues.	2025-09-17 17:33:09 -04:00
Vijay Janapa Reddi	d04d66a716	Implement interactive ML Systems questions and standardize module structure ... Major Educational Framework Enhancements: • Deploy interactive NBGrader text response questions across ALL modules • Replace passive question lists with active 150-300 word student responses • Enable comprehensive ML Systems learning assessment and grading TinyGPT Integration (Module 16): • Complete TinyGPT implementation showing 70% component reuse from TinyTorch • Demonstrates vision-to-language framework generalization principles • Full transformer architecture with attention, tokenization, and generation • Shakespeare demo showing autoregressive text generation capabilities Module Structure Standardization: • Fix section ordering across all modules: Tests → Questions → Summary • Ensure Module Summary is always the final section for consistency • Standardize comprehensive testing patterns before educational content Interactive Question Implementation: • 3 focused questions per module replacing 10-15 passive questions • NBGrader integration with manual grading workflow for text responses • Questions target ML Systems thinking: scaling, deployment, optimization • Cumulative knowledge building across the 16-module progression Technical Infrastructure: • TPM agent for coordinated multi-agent development workflows • Enhanced documentation with pedagogical design principles • Updated book structure to include TinyGPT as capstone demonstration • Comprehensive QA validation of all module structures Framework Design Insights: • Mathematical unity: Dense layers power both vision and language models • Attention as key innovation for sequential relationship modeling • Production-ready patterns: training loops, optimization, evaluation • System-level thinking: memory, performance, scaling considerations Educational Impact: • Transform passive learning to active engagement through written responses • Enable instructors to assess deep ML Systems understanding • Provide clear progression from foundations to complete language models • Demonstrate real-world framework design principles and trade-offs	2025-09-17 14:42:24 -04:00