- Remove generic learning communities section
- Remove vague 'next steps' career advice
- Remove fluffy usage instructions
- Keep focused: academic courses, books, alternative implementations, production internals
- Result: curated reference for students who built ML systems from scratch
✂️ Reduced MLOps Focus:
- Renamed 'MLOps & Production' → 'Development Tools'
- Removed redundant 'MLOps Community' link
- Focuses on practical development tools instead
🎯 Made Framework Differentiations Distinct:
- Micrograd: 'shows you the math, TinyTorch shows you the systems'
- Tinygrad: 'optimizes for speed, TinyTorch optimizes for learning'
- NNFS: 'focuses on algorithms, TinyTorch focuses on complete systems engineering'
💡 Benefits:
- Each differentiation now highlights specific strengths vs repetitive vehicle analogy
- Less MLOps emphasis (appears in course already)
- More concise and memorable comparisons
Result: Cleaner resource organization with unique, specific differentiations
that avoid repetition and over-emphasis on any single topic.
🔥 Major Improvements:
- Removed research papers section (belongs in specific labs as context)
- Added clear differentiation for alternative implementations with vehicle analogy
- Moved ML Systems book to books section with prominent positioning
- Added actual book links (O'Reilly, deeplearningbook.org) where available
- Focused on maintainable, stable resources
🎯 Key Differentiations Added:
- 'Micrograd teaches engine parts, TinyTorch teaches you to design the whole vehicle'
- 'NNFS teaches engine parts, TinyTorch teaches the whole vehicle and drive it'
- 'Tinygrad optimizes for speed, TinyTorch optimizes for learning systems thinking'
🏭 Production Focus:
- Added industrial tools: W&B, MLOps Community, Papers with Code
- Reorganized into: Courses, Books, Alternative Implementations, Production Tools
- Removed quickly-outdated content, kept stable educational resources
📖 ML Systems Book Positioning:
- Moved Vijay's book from courses to books section
- Positioned as 'the perfect companion to TinyTorch'
- Added proper book links for maintainability
Result: Much more focused, maintainable resource page that complements
TinyTorch without duplicating content that belongs in specific labs.
🎓 Course Additions:
- Added CS 249r: Tiny Machine Learning (Harvard) to course list
- Covers TinyML systems, edge AI, and resource-constrained machine learning
- Complements existing MIT TinyML course with Harvard perspective
📖 Section Naming Fix:
- Changed 'Essential Books' → 'Recommended Books'
- Avoids prescriptive language and duplication issues
- More inclusive and less hierarchical phrasing
🔄 Organization Benefits:
- Eliminates potential confusion with ML Systems book already in courses
- Creates clearer separation between course materials and supplementary books
- Better reflects that these are helpful additions, not requirements
Result: More thoughtful resource organization with key Harvard tinyML
course addition and improved section naming.
📖 New Resources Page:
- Created book/resources.md with curated external learning materials
- Academic courses: Stanford CS329S, Harvard ML Systems, MIT TinyML
- Essential books: Chip Huyen, Andriy Burkov, Deep Learning textbook
- Framework deep dives: PyTorch/TensorFlow internals and architecture
- Research papers: Autograd, Adam, Attention, TensorFlow/PyTorch papers
- Implementation guides: micrograd, tinygrad, Neural Networks from Scratch
- Communities: MLOps, r/MachineLearning, technical blogs
- Next steps: Post-TinyTorch learning paths and advanced specializations
🔄 Updated Table of Contents:
- Fixed module names: networks → dense, cnn → spatial
- Added 07_attention to Building Blocks section
- Updated all numbering to reflect 16-module structure
- Renamed 'Production & Performance' → 'Inference & Serving'
- Added new 'Additional Resources' section with 📚 Learning Resources
🎯 Educational Value:
- Provides context for TinyTorch implementations
- Bridges from educational framework to production systems
- Offers multiple learning paths for different interests
- Connects TinyTorch concepts to broader ML systems ecosystem
Result: Students now have comprehensive resources to deepen their
understanding and apply TinyTorch knowledge to real-world systems.
