AutoGrad + Neural Network + Optimizer

Tiny from-scratch autograd + neural net playground in pure Python.

Quickstart (Karpathy-style scalar graph) - click for online demo

from AutoGrad import Value

a = Value(2.0)
b = Value(-3.0)
c = Value(10.0)

d = a * b
e = d + c
L = e.relu()

L.backward()

print("L:", L.value)
print("a.grad:", a.grad)
print("b.grad:", b.grad)
print("c.grad:", c.grad)

This is the same learning flow as micrograd examples:

• build a tiny computation graph by hand
• call backward() once on the final node
• inspect gradients on leaf nodes

Train the tiny network

python3 optimizer.py

That runs one small demo loop with:

• Network from NeuralNetwork.py
• GD_Optimizer from optimizer.py
• cross-entropy over integer labels