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✅ 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
2.5 KiB
🔬 Quick Exploration Path
Perfect for: "I want to see what this is about" • "Can I try this without installing anything?"
🚀 Launch Immediately (0 Setup Required)
Click the 🚀 Launch Binder button on any chapter to get:
- Live Jupyter environment in your browser
- Pre-configured TinyTorch development setup
- Ability to run and modify all code immediately
- No installation, no account creation needed
:class: tip
**5 minutes from now**, you'll be implementing real ML components:
- **ReLU activation function** from scratch
- **Tensor operations** that power neural networks
- **Dense layers** that transform data
- **Complete neural networks** for image classification
All running live in your browser!
📚 Recommended Exploration Path
Start Here: Chapter 1 - Setup
- Understand the TinyTorch development workflow
- Get familiar with the educational approach
- See how components fit together
Then Try: Chapter 3 - Activations
- Implement your first ML function (ReLU)
- See immediate visual results
- Understand why nonlinearity matters
Build Up: Chapter 4 - Layers
- Create the building blocks of neural networks
- Combine your ReLU with matrix operations
- See how simple math becomes powerful AI
⚠️ Important Limitations
Sessions are temporary:
- Binder sessions timeout after ~20 minutes of inactivity
- Your work is not saved when the session ends
- Great for exploration, not for ongoing projects
For persistent work: Ready to build your own TinyTorch? → Serious Development Path
🎯 What You'll Understand
After exploring 2-3 chapters, you'll have hands-on understanding of:
✅ How ML frameworks work under the hood
✅ Why activation functions are crucial
✅ How matrix multiplication powers neural networks
✅ The relationship between layers, networks, and learning
✅ Real implementation vs. high-level APIs
🔄 Next Steps
Satisfied with exploration? You've gained valuable insight into ML systems!
Want to build more? → Fork the repo and work locally
Teaching a class? → Classroom setup guide
🎉 No commitment required - just click and explore!