- Remove excessive emojis while maintaining strategic usage
- Update CSS with academic typography (Source Sans Pro, Source Serif Pro)
- Professional color scheme with academic blues (#2c3e50, #3498db)
- Clean navigation without emoji decorations
- Enhanced visual hierarchy with professional spacing
- University-level styling consistent with Harvard standards
- Maintained pedagogical effectiveness and engagement
- Improved readability with clean, accessible design
- Professional tone throughout all content
- Academic credibility without sacrificing approachability
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
- Create comprehensive introduction module (00-introduction.md) for Jupyter Book
- Add visual system overview and architecture documentation
- Update TOC to include introduction as module 0 in Foundation section
- Refactor classroom-use.md to be high-level overview pointing to instructor guide
- Eliminate duplication between classroom-use and instructor guide
- Ensure all 17 modules (00-16) are properly documented
Features:
- Introduction module provides system overview and dependency visualizations
- Clear separation: classroom-use = overview, instructor-guide = detailed workflow
- Professional navigation structure with all modules properly ordered
- Cross-references between related documentation sections
Successfully built and tested with jupyter-book build.
- Create complete instructor guide with user journey from setup to course completion
- Cover all phases: setup, course prep, assignment management, grading workflow
- Include weekly routines, troubleshooting, and student guidance
- Add quick reference card for daily commands
- Update Jupyter Book TOC to include instructor documentation
- Update classroom-use guide to reference comprehensive documentation
Features documented:
- 30-minute initial setup process
- Weekly assignment workflow (generate -> release -> grade -> feedback)
- Batch operations for efficiency
- System monitoring and analytics
- End-to-semester procedures
- Student support guidelines
- Common troubleshooting scenarios
Provides complete user journey for instructors and TAs using NBGrader + TinyTorch.
- Updated all module references to start from 01 instead of 00
- Changed tagline to 'Build your own ML framework. Start small. Go deep.'
- Added educational foundation section linking to ML Systems book
- Updated README, documentation, CLI examples, and prerequisites
- Regenerated book content with consistent numbering throughout
- Maintains 14 modules total but with natural numbering (01-14)
✅ Rename all module directories: 00_setup → 01_setup, etc.
✅ Update convert_modules.py mappings for new directory names
✅ Update _toc.yml file paths and titles (1-14 instead of 0-13)
✅ Regenerate all overview pages with new numbering
✅ Fix all broken references in usage-paths and intro
✅ Update chapter references to use natural numbering
Benefits:
- More intuitive course progression starting from 1
- Matches academic course numbering conventions
- Eliminates confusion about 'Module 0' concept
- Cleaner mental model for students and instructors
- All references and links properly updated
Complete transformation: 14 modules now numbered 01-14
✅ Remove unnecessary nesting: book/tinytorch-course/ → book/
✅ Update all path references in scripts and workflows
✅ Cleaner development experience with shorter paths
✅ Book builds successfully with simplified structure
Changes:
- Move all book files up one directory level
- Update convert_modules.py paths
- Update GitHub Actions workflow paths
- Update book configuration paths
- Test confirms everything works correctly
