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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
32 lines
1.0 KiB
Markdown
32 lines
1.0 KiB
Markdown
# Module 00: Hello - Personalized Setup
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## 🎯 Learning Objectives
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- Set up personalized TinyTorch environment
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- Understand system requirements and capabilities
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- Create interactive first experience with ML systems
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- Configure development environment for success
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## 📚 What You'll Build
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- Interactive Rich CLI setup experience
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- Personalized configuration system
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- System capability detection
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- First successful ML computation
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## 🎓 By the End You'll Be Able To
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- Run TinyTorch commands
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- Have personalized system configuration
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- Understand your hardware capabilities
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- Be ready to start building ML systems
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## 🚀 Example Unlocked
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After completing this module, you'll have a working TinyTorch environment ready for Module 01!
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## 📊 ML Systems Concepts
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- System profiling and resource detection
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- Environment configuration best practices
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- Development tool setup for ML workflows
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## 🔑 Key Takeaways
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- Personalized learning experience from the start
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- Understanding your system's ML capabilities
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- Ready to build production ML systems |