Deep Learning

Backpropagation

Computing gradients by applying the chain rule in reverse topological order over the computation graph, caching intermediates.

Why interviewers ask about it

∂L/∂W = xᵀg needs the forward activation, which is exactly why activation memory dominates training and why gradient checkpointing exists.

Discarding intermediate activations during the forward pass and recomputing them during backward.

This term is part of the free AI/ML Engineer interview preparation module - browse the full glossary for every definition.