# Quick Start Guide

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<h2 style="margin: 0 0 1rem 0; color: #495057;">From Zero to Building Neural Networks</h2>
<p style="margin: 0; font-size: 1.1rem; color: #6c757d;">Complete setup + first module in 15 minutes</p>
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**Purpose**: Get hands-on experience building ML systems in 15 minutes. Complete setup verification and build your first neural network component from scratch.

## ⚡ 2-Minute Setup

Let's get you ready to build ML systems:

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<h4 style="margin: 0 0 1rem 0; color: #1976d2;">Step 1: One-Command Setup</h4>

```bash
# Clone repository
git clone https://github.com/mlsysbook/TinyTorch.git
cd TinyTorch

# Automated setup (handles everything!)
./setup-environment.sh

# Activate environment
source activate.sh
```

**What this does:**
- ✅ Creates optimized virtual environment (arm64 on Apple Silicon)
- ✅ Installs all dependencies (NumPy, Jupyter, Rich, PyTorch for validation)
- ✅ Configures TinyTorch in development mode
- ✅ Verifies installation

**📖 See [Essential Commands](tito-essentials.html)** for detailed workflow and troubleshooting.

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<h4 style="margin: 0 0 1rem 0; color: #15803d;">Step 2: Verify Setup</h4>

```bash
# Run system diagnostics
tito system doctor
```

You should see all green checkmarks! This confirms your environment is ready for hands-on ML systems building.

**📖 See [Essential Commands](tito-essentials.html)** for verification commands and troubleshooting.

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## 🏗️ 15-Minute First Module Walkthrough

Let's build your first neural network component and unlock your first capability:

### Module 01: Tensor Foundations

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**🎯 Learning Goal:** Build N-dimensional arrays - the foundation of all neural networks

**⏱️ Time:** 15 minutes

**💻 Action:** Start with Module 01 to build tensor operations from scratch.

```bash
# Navigate to the tensor module
cd modules/01_tensor
jupyter lab tensor_dev.py
```

You'll implement core tensor operations:
- N-dimensional array creation
- Basic mathematical operations (add, multiply, matmul)
- Shape manipulation (reshape, transpose)
- Memory layout understanding

**Key Implementation:** Build the `Tensor` class that forms the foundation of all neural networks

**📖 See [Essential Commands](tito-essentials.html)** for module workflow commands.

**✅ Achievement Unlocked:** Foundation capability - "Can I create and manipulate the building blocks of ML?"

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### Next Step: Module 02 - Activations

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**🎯 Learning Goal:** Add nonlinearity - the key to neural network intelligence

**⏱️ Time:** 10 minutes

**💻 Action:** Continue with Module 02 to add activation functions.

You'll implement essential activation functions:
- ReLU (Rectified Linear Unit) - the workhorse of deep learning
- Softmax - for probability distributions
- Understand gradient flow and numerical stability
- Learn why nonlinearity enables learning

**Key Implementation:** Build activation functions that allow neural networks to learn complex patterns

**📖 See [Essential Commands](tito-essentials.html)** for module development workflow.

**✅ Achievement Unlocked:** Intelligence capability - "Can I add nonlinearity to enable learning?"

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## 📊 Track Your Progress

After completing your first modules:

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**Check your new capabilities:** Track your progress through the 21-checkpoint system to see your growing ML systems expertise.

**📖 See [Track Your Progress](learning-progress.html)** for detailed capability tracking and [Essential Commands](tito-essentials.html)** for progress monitoring commands.

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## 🏆 Unlock Historical Milestones

As you progress, **prove what you've built** by recreating history's greatest ML breakthroughs:

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**After Module 04**: Build **Rosenblatt's 1957 Perceptron** - the first trainable neural network  
**After Module 06**: Solve the **1969 XOR Crisis** with multi-layer networks  
**After Module 08**: Achieve **95%+ accuracy on MNIST** with 1986 backpropagation  
**After Module 09**: Hit **75%+ on CIFAR-10** with 1998 CNNs - your North Star goal! 🎯

**📖 See [Journey Through ML History](chapters/milestones.html)** for complete milestone demonstrations.

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**Why Milestones Matter**: These aren't toy demos - they're historically significant achievements proving YOUR implementations work at production scale!

## 🎯 What You Just Accomplished

In 15 minutes, you've:

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<h4 style="margin: 0 0 0.5rem 0; color: #0d9488;">🔧 Setup Complete</h4>
<p style="margin: 0; font-size: 0.9rem;">Installed TinyTorch and verified your environment</p>
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<h4 style="margin: 0 0 0.5rem 0; color: #1d4ed8;">🧱 Created Foundation</h4>
<p style="margin: 0; font-size: 0.9rem;">Implemented core tensor operations from scratch</p>
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<h4 style="margin: 0 0 0.5rem 0; color: #a16207;">🏆 First Capability</h4>
<p style="margin: 0; font-size: 0.9rem;">Earned your first ML systems capability checkpoint</p>
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## 🚀 Your Next Steps

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### Immediate Next Actions (Choose One):

**🔥 Continue Building (Recommended):** Begin Module 03 to add intelligence to your network with nonlinear activation functions.

**📚 Learn the Workflow:**
- **📖 See [Essential Commands](tito-essentials.html)** for complete TITO command guide
- **📖 See [Track Your Progress](learning-progress.html)** for the full learning path

**🎓 For Instructors:**
- **📖 See [Classroom Setup Guide](usage-paths/classroom-use.html)** for NBGrader integration and grading workflow

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## 💡 Pro Tips for Continued Success

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**Essential Development Practices:**
- Always verify your environment before starting
- Track your progress through capability checkpoints
- Follow the standard module development workflow
- Use diagnostic commands when debugging issues

**📖 See [Essential Commands](tito-essentials.html)** for complete workflow commands and troubleshooting guide.

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## 🌟 You're Now a TinyTorch Builder!

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<h3 style="margin: 0 0 1rem 0; color: #495057;">Ready to Build Production ML Systems</h3>
<p style="margin: 0 0 1.5rem 0; color: #6c757d;">You've proven you can build ML components from scratch. Time to keep going!</p>
<a href="chapters/03-activations.html" style="display: inline-block; background: #007bff; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500; margin-right: 1rem;">Continue Building →</a>
<a href="tito-essentials.html" style="display: inline-block; background: #28a745; color: white; padding: 0.75rem 1.5rem; border-radius: 0.25rem; text-decoration: none; font-weight: 500;">Master Commands →</a>
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---

**What makes TinyTorch different:** You're not just learning *about* neural networks—you're building them from fundamental mathematical operations. Every line of code you write builds toward complete ML systems mastery.

**Next milestone:** After Module 08, you'll train real neural networks on actual datasets using 100% your own code!