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https://github.com/MLSysBook/TinyTorch.git
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a21a006603
- Reorganized chapter structure with new numbering system - Added new chapters: introduction, tokenization, embeddings, profiling, quantization, caching - Removed obsolete chapters (15-mlops) and consolidated content - Updated table of contents and navigation structure - Enhanced visual design with new logos and favicon - Added comprehensive documentation (FAQ, user manual, command reference, competitions) - Improved theme design and custom CSS styling - Added QUICKSTART.md for rapid onboarding - Updated all chapter cross-references and links
2.7 KiB
2.7 KiB
Quick Exploration Path
Perfect for: "I want to see what this is about" • "Can I try this without installing anything?"
Time Commitment: 5-30 minutes • Setup Required: None
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
**Immediate implementation experience** with real ML components:
- **5 min**: ReLU activation function from scratch
- **10 min**: Tensor operations that power neural networks
- **15 min**: Dense layers that transform data
- **20 min**: Complete neural networks for image classification
- **30 min**: See how language models use the same foundations
All running live in your browser with zero setup!
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
- Why vision and language models share the same foundations
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!