# Quick Exploration Path — TinyTorch

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# 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

- 🔥 Language models built from the same foundations

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

Launch Setup Chapter

### Then Try: Chapter 3 - Activations

- Implement your first ML function (ReLU)

- See immediate visual results

- Understand why nonlinearity matters

Launch Activations Chapter

### 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

Launch Layers Chapter

## 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!