🔬 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
What You’ll Experience
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!