Introduction
In a neural network, a parameter affects the loss through a long chain of intermediate quantities, often along several paths at once. The multivariate chain rule tells us how to combine all those influences. Drawing the computation as a graph makes the rule mechanical — and that is exactly how automatic differentiation in PyTorch and JAX works.