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✅ Phase 1-2 Complete: Modules 1-10 aligned with tutorial master plan ✅ CNN Training Pipeline: Autograd → Spatial → Optimizers → DataLoader → Training ✅ Technical Validation: All modules import and function correctly ✅ CIFAR-10 Ready: Multi-channel Conv2D, BatchNorm, MaxPool2D, complete pipeline Key Achievements: - Fixed module sequence alignment (spatial now Module 7, not 6) - Updated tutorial master plan for logical pedagogical flow - Phase 2 milestone achieved: Students can train CNNs on CIFAR-10 - Complete systems engineering focus throughout all modules - Production-ready CNN pipeline with memory profiling Next Phase: Language models (Modules 11-15) for TinyGPT milestone
Module 00: Hello - Personalized Setup
🎯 Learning Objectives
- Set up personalized TinyTorch environment
- Understand system requirements and capabilities
- Create interactive first experience with ML systems
- Configure development environment for success
📚 What You'll Build
- Interactive Rich CLI setup experience
- Personalized configuration system
- System capability detection
- First successful ML computation
🎓 By the End You'll Be Able To
- Run TinyTorch commands
- Have personalized system configuration
- Understand your hardware capabilities
- Be ready to start building ML systems
🚀 Example Unlocked
After completing this module, you'll have a working TinyTorch environment ready for Module 01!
📊 ML Systems Concepts
- System profiling and resource detection
- Environment configuration best practices
- Development tool setup for ML workflows
🔑 Key Takeaways
- Personalized learning experience from the start
- Understanding your system's ML capabilities
- Ready to build production ML systems