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
- Combine venv setup and tito execution in same step
- Add pytest installation for tito environment validation
- Add explanatory comments about GitHub Actions shell behavior
- Remove environment skipping hack in favor of proper setup
- Workflow now uses tito CLI consistently for book generation
- Updated title to match new tagline format
- Added humble educational foundation section referencing CS249r course
- Confirmed result-oriented 'What You'll Achieve' section works well
- All branding now consistent across book and documentation
- Clean author attribution without unnecessary copyright notices
- 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
✅ Show actual implementation code instead of vague descriptions
✅ Contrast 'import torch' with 'class Tensor:' implementations
✅ Display real function definitions students will write
✅ Make clear students build every component from scratch
Changes:
- Replace vague 'Build your own tensors' with 'class Tensor:'
- Show actual method signatures: __add__, backward, forward
- Include concrete loss function: mse_loss implementation
- Display real optimizer logic: param.data -= lr * param.grad
- Change ending: 'I built this!' → 'I implemented every line!'
✅ 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
✅ Add learning goals extraction with beautiful admonition blocks
✅ Create hybrid book approach (overview pages + interactive buttons)
✅ Generate both notebooks (Binder/Colab) and book pages
✅ Update GitHub Actions for main branch deployment
✅ All 14 modules now have consistent formatting
Features:
- Extract content from source files (no duplication)
- Interactive launch buttons (GitHub/Binder/Colab)
- Learning objectives as 🎯 tip admonitions
- Notebooks generated in-place for direct access
- Clean separation: dev → main → website deployment
- Fix repository URL and directory structure
- Add prominent Jupyter Book documentation link
- List all 14 complete modules with proper organization
- Update installation and workflow instructions
- Add dev/main branch git workflow documentation
- Include modern badges and three user onboarding paths
- Emphasize production ML and inline testing approach
- Reflect current tech stack and learning outcomes
- Changed trigger from main to dev branch (repository default)
- Updated deploy condition to check for dev branch
- Ensures workflow runs on correct branch for this repository
- Add module conversion step before book building
- Install jupytext dependency for conversion script
- Maintains existing GitHub Pages deployment pipeline
- Automatically converts modules/source/ to notebooks on deploy
- Created convert_modules.py for raw source-to-notebook conversion
- Configured Jupyter Book with execution disabled for performance
- Removed NBGrader solution stripping to preserve complete source code
- Cleaned up all built artifacts (_build/, chapters/*.ipynb) from version control
- Updated book configuration for pure source-based builds
- Single source of truth: all content generated from modules/source/ only
✅ Converted all TinyTorch modules to interactive student notebooks:
- Foundation: Setup, Tensors, Activations (3 modules)
- Building Blocks: Layers, Networks, CNNs (3 modules)
- Training Systems: DataLoader, Autograd, Optimizers, Training (4 modules)
- Production: Compression, Kernels, Benchmarking, MLOps (4 modules)
🚀 Complete student learning experience:
- Big picture landing page with clear usage paths
- 3 dedicated path guides (exploration, development, classroom)
- Tiny🔥Torch branding with logo integration
- Student notebooks with solutions stripped but educational content preserved
- Interactive Binder integration ready for deployment
📊 Course statistics:
- 14 progressive modules building from CLI to production MLOps
- ~800KB total content across all interactive notebooks
- Professional development workflow with automated testing
- Proven pedagogical outcomes with Build → Use → Understand pattern
Ready for GitHub Pages deployment and student use
