TinyTorch/site/quickstart-guide.md

Quick Start Guide

From Zero to Building Neural Networks

Complete setup + first module in 15 minutes

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:

Step 1: One-Command Setup

# 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 for detailed workflow and troubleshooting.

Step 2: Verify Setup

# 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 for verification commands and troubleshooting.

🏗️ 15-Minute First Module Walkthrough

Let's build your first neural network component following the TinyTorch workflow:

1. Edit modules → 2. Export to package → 3. Validate with milestones

📖 See Student Workflow for the complete development cycle.

Module 01: Tensor Foundations

🎯 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.

# Step 1: Edit the module source
cd modules/source/01_tensor
jupyter lab 01_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

# Step 2: Export to package when ready
tito module complete 01

This makes your implementation importable: from tinytorch import Tensor

📖 See Student Workflow for the complete edit → export → validate cycle.

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

Next Step: Module 02 - Activations

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

⏱️ Time: 10 minutes

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

# Step 1: Edit the module
cd modules/source/02_activations
jupyter lab 02_activations_dev.py

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

# Step 2: Export when ready
tito module complete 02

📖 See Student Workflow for the complete edit → export → validate cycle.

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

📊 Track Your Progress

After completing your first modules:

Check your new capabilities: Use the optional checkpoint system to track your progress:

tito checkpoint status  # View your completion tracking

This is helpful for self-assessment but not required for the core workflow.

📖 See Student Workflow for the essential edit → export → validate cycle, and Track Your Progress** for detailed capability tracking.

🏆 Validate with Historical Milestones

After exporting your modules, prove what you've built by running milestone scripts:

After Module 07: Build Rosenblatt's 1957 Perceptron - the first trainable neural network After Module 07: 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 After Module 13: Generate text with 2017 Transformers After Module 18: Optimize for production with 2018 MLPerf

📖 See Journey Through ML History for complete timeline, requirements, and expected results.

The Workflow: Edit modules → Export with tito module complete N → Run milestone scripts to validate

📖 See Student Workflow for the complete cycle.

🎯 What You Just Accomplished

In 15 minutes, you've:

🔧 Setup Complete

Installed TinyTorch and verified your environment

🧱 Created Foundation

Implemented core tensor operations from scratch

🏆 First Capability

Earned your first ML systems capability checkpoint

🚀 Your Next Steps

Immediate Next Actions (Choose One):

🔥 Continue Building (Recommended): Begin Module 03 to add layers to your network.

📚 Master the Workflow:

• 📖 See Student Workflow for the complete edit → export → validate cycle
• 📖 See Essential Commands for complete TITO command reference
• 📖 See Track Your Progress for the full learning path

🎓 For Instructors:

• 📖 See Classroom Setup Guide for NBGrader integration (coming soon)

💡 Pro Tips for Continued Success

The TinyTorch Development Cycle:

1. Edit module sources in modules/source/
2. Export with tito module complete N
3. Validate by running milestone scripts

📖 See Student Workflow for detailed workflow guide and best practices.

🌟 You're Now a TinyTorch Builder!

Ready to Build Production ML Systems

You've proven you can build ML components from scratch. Time to keep going!

Continue Building → Master Commands →

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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!