-
The chain rule at scale: from calc-101 to backprop through a million parameters
A neural network with a million trainable parameters sounds as though it should require a million separate calculus problems.
-
Backpropagation demystified: hand-compute the gradients, then verify with autograd
Backpropagation is the engine behind modern deep learning. Whether you’re training a tiny multilayer perceptron or a frontier-scale language model, every optimization step depends on efficiently compu