Build the autograd engine

What you’ll build

A working automatic-differentiation engine — the same idea behind PyTorch — in about 100 lines. By the end, a real trainable neuron whose gradients you can prove are correct.

a = Value(2.0)
b = Value(-3.0)
L = (a * b + Value(10.0)).tanh()

L.backward()      # fills in every gradient automatically
a.grad            # how much L moves when you nudge a

The tests decide when you’re done — not the tutor. When they pass, you’ve built it.

What you’ll learn

• Implement a Value class that records every operation into a computation graph
• Write the backward pass — reverse-mode autodiff — so gradients fill in automatically
• Derive and apply the chain rule by hand through +, *, and tanh
• Check your gradients against numerical estimates, so you know they're correct
• Wire it into a single neuron and watch the gradients flow end to end

Before you start: Comfortable writing Python classes. A faint memory of derivatives is plenty — you re-derive the chain rule here by building it.

Meet your tutor

It won’t hand you the answer. It asks the one question that makes the next line obvious — and checks your work by running the tests.