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Micrograd in PyTorch Style

To understand autograd, build it yourself.

Key Insight​

PyTorch's autograd is powered by a dynamic computation graph (DAG). Every time you perform an operation on a tensor with requires_grad=True, PyTorch records it as a node in this graph. By recreating a simplified educational engine like micrograd, you learn exactly how the forward pass builds the graph and how the backward pass uses the chain rule to calculate gradients.

Why This Matters​

It is easy to use loss.backward() as a magic black box, but understanding the underlying graph is the only way to debug vanishing gradients, detached tensors, and memory leaks caused by holding onto graph references.