AutoGrad + Neural Network + Optimizer

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

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

fromAutoGradimportValuea=Value(2.0)
b=Value(-3.0)
c=Value(10.0)
d=a*be=d+cL=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