Quick Start Guide

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 Verification#

Let’s make sure you’re ready to build ML systems:

Step 1: Install & Verify

# Clone and install
git clone https://github.com/veekaybee/tinytorch.git
cd tinytorch
pip install -e .

Expected output: A working TinyTorch development environment ready for hands-on building.

📖 See Essential Commands for complete setup verification and troubleshooting.

Step 2: Verify Your Starting Point

Confirm you’re ready to begin building ML systems from scratch. Your development environment should be configured and ready for hands-on implementation.

📖 See Essential Commands for verification commands and troubleshooting.

🏗️ 15-Minute First Module Walkthrough#

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

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.

# 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 for module workflow commands.

✅ 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.

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 for module development workflow.

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

📊 Track Your Progress#

After completing your first modules:

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

📖 See Track Your Progress for detailed capability tracking and Essential Commands** for progress monitoring commands.

🎯 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 intelligence to your network with nonlinear activation functions.

📚 Learn the Workflow:

🎓 For Instructors:

💡 Pro Tips for Continued Success#

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 for complete workflow commands and troubleshooting guide.

🌟 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 →

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!

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