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88 lines
2.5 KiB
Markdown
88 lines
2.5 KiB
Markdown
# Quick Exploration Path
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**Perfect for:** "I want to see what this is about" • "Can I try this without installing anything?"
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---
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## Launch Immediately (0 Setup Required)
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Click the **Launch Binder** button on any chapter to get:
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- Live Jupyter environment in your browser
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- Pre-configured TinyTorch development setup
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- Ability to run and modify all code immediately
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- No installation, no account creation needed
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```{admonition} What You'll Experience
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:class: tip
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**5 minutes from now**, you'll be implementing real ML components:
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- **ReLU activation function** from scratch
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- **Tensor operations** that power neural networks
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- **Dense layers** that transform data
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- **Complete neural networks** for image classification
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- **🔥 Language models** built from the same foundations
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All running live in your browser!
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```
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---
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## Recommended Exploration Path
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### Start Here: Chapter 1 - Setup
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- Understand the TinyTorch development workflow
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- Get familiar with the educational approach
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- See how components fit together
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**[Launch Setup Chapter](../chapters/01-setup.md)**
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### Then Try: Chapter 3 - Activations
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- Implement your first ML function (ReLU)
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- See immediate visual results
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- Understand why nonlinearity matters
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**[Launch Activations Chapter](../chapters/03-activations.md)**
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### Build Up: Chapter 4 - Layers
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- Create the building blocks of neural networks
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- Combine your ReLU with matrix operations
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- See how simple math becomes powerful AI
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**[Launch Layers Chapter](../chapters/04-layers.md)**
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---
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## Important Limitations
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**Sessions are temporary:**
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- Binder sessions timeout after ~20 minutes of inactivity
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- Your work is **not saved** when the session ends
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- Great for exploration, not for ongoing projects
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**For persistent work:** Ready to build your own TinyTorch? → **[Serious Development Path](serious-development.md)**
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---
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## What You'll Understand
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After exploring 2-3 chapters, you'll have hands-on understanding of:
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- **How ML frameworks work under the hood**
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- **Why activation functions are crucial**
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- **How matrix multiplication powers neural networks**
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- **The relationship between layers, networks, and learning**
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- **Real implementation vs. high-level APIs**
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- **Why vision and language models share the same foundations**
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---
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## Next Steps
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**Satisfied with exploration?** You've gained valuable insight into ML systems!
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**Want to build more?** → **[Fork the repo and work locally](serious-development.md)**
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**Teaching a class?** → **[Classroom setup guide](classroom-use.md)**
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---
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*No commitment required - just click and explore!* |