Major changes:
- Moved TinyGPT from Module 16 to examples/tinygpt (capstone demo)
- Fixed Module 10 (optimizers) and Module 11 (training) bugs
- All 16 modules now passing tests (100% health)
- Added comprehensive testing with 'tito test --comprehensive'
- Renamed example files for clarity (train_xor_network.py, etc.)
- Created working TinyGPT example structure
- Updated documentation to reflect 15 core modules + examples
- Added KISS principle and testing framework documentation
- Remove 00_introduction module (meta-content, not substantive learning)
- Remove 16_capstone_backup backup directory
- Remove utilities directory from modules/source
- Clean up generated book chapters for removed modules
Result: Clean 16-module progression (01_setup → 16_tinygpt) focused on
hands-on ML systems implementation without administrative overhead.
- Replace ugly gray background with clean white theme
- Add proper logo styling and configuration
- Update book chapters from module READMEs
- Add educational-ml-docs-architect agent
- Clean up custom CSS for better readability
- Configure logo.png in correct location
- Update tito book command with proper chapters
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.
- Replace hardcoded module names array with dynamic reading from module.yaml files
- Add get_module_names() function to read actual module structure
- Fix IndexError in get_prev_module_name() and get_next_module_name() functions
- Update navigation logic to use actual module count instead of hardcoded assumptions
- Successfully converts all 16 modules to chapters with proper navigation
- Book build now completes without errors
🔄 Chapter File Reorganization:
- Renamed 05-networks.md → 05-dense.md
- Renamed 06-cnn.md → 06-spatial.md
- Created 07-attention.md with transformer-focused content
- Renumbered all subsequent chapters (7→8, 8→9, 9→10, etc.)
- Updated final module: 15-capstone.md → 16-capstone.md
📚 Attention Chapter Content:
- Added comprehensive attention module introduction
- Covers self-attention, multi-head attention, transformer foundations
- Explains Query-Key-Value mechanism and scaled dot-product attention
- Connects to previous modules (tensors, activations, layers, dense)
- Positions attention as foundation for modern AI (GPT, BERT, ViTs)
✅ Build Verification:
- Jupyter Book builds successfully with no missing file errors
- All 16 chapters now properly indexed in table of contents
- New structure: Foundation (1-3), Building Blocks (4-7), Training (8-11),
Inference & Serving (12-16)
Result: Complete alignment between repository structure, book chapters,
and table of contents. Students can now navigate the full 16-module course
with proper attention coverage and updated section organization.
- Regenerated all chapters with YAML-based difficulty ratings
- Updated book with improved navigation and fixed appendix links
- Applied copyright year 2025 across all pages
- Integrated inclusive language changes throughout generated content
- Book now reflects all UX and consistency improvements
- Updated book generation to include 15_capstone with 5-star difficulty rating
- Changed time estimate from '20-40 hours' to 'Capstone Project' for better visitor experience
- Removed specific week references from project phases for more encouraging presentation
- Maintained detailed project structure while making timeline more flexible
- Ensures consistent 5-star rating for expert-level modules across the framework
- Removed 'Home → Module Name' breadcrumbs that added clutter without value
- Chapters now start cleanly with title and difficulty/time badges
- Maintains the useful difficulty stars and time estimates
- Improves visual hierarchy and reduces interface noise
Result: Cleaner, more focused chapter headers
Problem: Grid cards were showing raw HTML code instead of rendering properly
Root cause: README converter was adding new grid cards while preserving
original ones, creating duplicate/conflicting grid sections
Solution:
- Modified book/convert_readmes.py to remove existing grid cards from
source READMEs before adding new interactive elements
- Added regex patterns to clean up grid-related markup
- Regenerated all 14 book chapters with fixed converter
- Grid cards now render properly as interactive buttons
Result: All chapters now have clean, properly formatted grid cards
that render correctly in Jupyter Book
Standardize module endings with motivational section + grid cards:
Added to 4 key modules:
- 01_setup: Foundation workflow mastery message
- 03_activations: Neural networks come alive message
- 06_cnn: Computer vision implementation message
- 09_optimizers: Learning algorithms message
Standard Format:
## 🎉 Ready to Build?
[Module-specific motivational content about what they're building]
Take your time, test thoroughly, and enjoy building something that really works! 🔥
[Grid cards automatically follow via converter]
Progress: 6/14 modules now have consistent endings
- ✅ 01_setup, 02_tensor, 03_activations, 06_cnn, 07_dataloader, 09_optimizers
- 🔄 8 more modules to standardize
Result: Better user experience with consistent motivation + clear next steps
Key Improvements:
1. **Meaningful titles**: Keep 'Module: CNN' format instead of just 'CNN'
2. **Clean breadcrumbs**: 'Home → CNN' instead of 'Home → Module 3: 03 Activations'
3. **Remove duplicate info**: Stop generating redundant Module Info boxes
4. **Use source formatting**: Let READMEs control their own presentation
5. **Enhanced README**: Added Jupyter Book admonition formatting to CNN module info
Results:
- More logical navigation and titles
- Single source of truth for module information
- Better formatted content boxes (CNN example with admonitions)
- Eliminated confusing duplicate content
- Cleaner, more professional presentation
README Updates:
- All modules now use consistent '🔥 Module: [Name]' format
- Removed inconsistent emojis (🧠, 🚀, 📊, 🧱, 🏋️)
- Removed module numbers and descriptive subtitles
- Clean, consistent branding across all 14 modules
Converter Updates:
- Added header cleaning logic to strip module prefixes from chapter titles
- Chapters now show clean names: 'CNN', 'Tensor', 'Setup', etc.
- No emoji or module numbers in final website headers
- Maintains clean, professional appearance
Result: Consistent source files + clean website presentation
- Keep the '🎉 Ready to Build?' motivational section
- Keep the grid cards for Binder/Colab/Source access
- Remove the redundant '🚀 Interactive Learning' section header
- Content now flows naturally: motivation → 'Choose your preferred way...' → grid cards
- Eliminates unnecessary section break and improves reading flow
- All chapters updated with cleaner, more streamlined format
- Updated book/convert_readmes.py to use Jupyter Book grid cards instead of Bootstrap buttons
- Format matches what was in main branch at merge commit 3a687aa
- Three interactive options in clean grid layout:
🚀 Launch Binder - Interactive browser environment
⚡ Open in Colab - GPU access and cloud compute
📖 View Source - Browse Python source code
- Added helpful 'Save Your Progress' tip about Binder sessions
- All chapters regenerated with proper grid card format
- Tested successful book build
- Updated book/convert_readmes.py to use original format from git history
- Three environment options: Builder (blue), Jupyter (green), Colab (light blue)
- Each option has descriptive admonition box explaining its purpose
- Bootstrap-style anchor buttons with proper CSS classes
- Matches original commit abac2b7 format exactly
- All chapters regenerated with proper interactive elements
✅ 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
✅ Clean source file headers: 'Module X:' → clean descriptive titles
✅ Regenerate overview pages with clean headers
✅ More flexible content that works in any context
✅ Numbers still provided by book TOC structure
Changes:
- Remove 'Module X: ' prefix from all source file headers
- Headers now focus on descriptive content titles
- Book maintains proper chapter ordering via _toc.yml
- Content is more reusable across different presentations
✅ 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
