Building a Neural Network by Understanding How Autograd Systems Like PyTorch Work

A Step-by-Step Journey Into Backpropagation and Autograd

Modern deep learning frameworks like PyTorch make neural networks feel almost magical.

loss.backward()

One line.

That’s all it takes to compute gradients for millions of parameters.

But what actually happens inside?

How does a neural network learn?

What is backpropagation really doing?

What exactly is autograd?

This blog is a deep, step-by-step walkthrough inspired by Andrej Karpathy’s micrograd project, where we build a tinygrad (tiny autograd engine) from scratch and then use it to build a neural network.

The goal is not just to use neural networks.

The goal is to understand:

how neural networks actually learn under the hood

What We’re Going to Build

We will build:

1. A tiny autograd engine
2. Automatic backpropagation
3. A computation graph system
4. A neuron
5. A neural network
6. Gradient descent training

And by the end, you’ll understand what frameworks like PyTorch are doing internally.