# 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

```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 [TITO CLI Reference](tito/overview.md) for detailed workflow and troubleshooting.

Step 2: Verify Setup

```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 [Module Workflow](tito/modules.md) for detailed commands and [Troubleshooting](tito/troubleshooting.md) if needed.

Step 3: Join the Community & Benchmark

After setup, join the global TinyTorch community and validate your setup: ```bash # Join the community (optional) tito community join # Run baseline benchmark to validate setup tito benchmark baseline ``` **Community Features:** - Join with optional information (country, institution, course type) - Track your progress automatically - See your cohort (Fall 2024, Spring 2025, etc.) - All data stored locally in `.tinytorch/` directory **Baseline Benchmark:** - Quick validation that everything works - Your "Hello World" moment! - Generates score and saves results locally See [Community Guide](community.md) for complete features.

## 15-Minute First Module Walkthrough Let's build your first neural network component following the **TinyTorch workflow**: ```{mermaid} graph TD Start[Clone & Setup] --> Edit[Edit Module
01_tensor.ipynb] Edit --> Export[Export to Package
tito module complete 01] Export --> Test[Test Import
from tinytorch import Tensor] Test --> Next[Continue to Module 02] style Start fill:#e3f2fd style Edit fill:#fffbeb style Export fill:#f0fdf4 style Test fill:#fef3c7 style Next fill:#f3e5f5 ``` See [Student Workflow](student-workflow.md) 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. ```bash # Step 1: Edit the module source cd modules/01_tensor jupyter lab tensor_dev.ipynb ``` 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 ```bash # Step 2: Export to package when ready tito module complete 01 ``` This makes your implementation importable: `from tinytorch import Tensor` See [Student Workflow](student-workflow.md) 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. ```bash # Step 1: Edit the module cd modules/02_activations jupyter lab activations_dev.ipynb ``` 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 ```bash # Step 2: Export when ready tito module complete 02 ``` See [Student Workflow](student-workflow.md) 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: ```bash tito checkpoint status # View your completion tracking ``` This is helpful for self-assessment but not required for the core workflow. See [Student Workflow](student-workflow.md) for the essential edit → export → validate cycle.

## 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 Torch Olympics** See [Journey Through ML History](chapters/milestones.md) 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](student-workflow.md) 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](student-workflow.md) for the complete edit → export → validate cycle - See [TITO CLI Reference](tito/overview.md) for complete command reference **For Instructors:** - See [Classroom Setup Guide](usage-paths/classroom-use.md) for [NBGrader](https://nbgrader.readthedocs.io/) integration **Notebook Platforms:** - **Online (Viewing)**: Jupyter/MyBinder, Google Colab, Marimo - great for exploring notebooks - **⚠️ Important**: Online notebooks are for **viewing only**. For full package experiments, milestone validation, and CLI tools, you need **local installation** (see [Student Workflow](student-workflow.md))

## Pro Tips for Continued Success

**The TinyTorch Development Cycle:** 1. Edit module sources in `modules/NN_name/` (e.g., `modules/01_tensor/tensor_dev.ipynb`) 2. Export with `tito module complete N` 3. Validate by running milestone scripts See [Student Workflow](student-workflow.md) 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 → TITO CLI Reference →

--- **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!