How Backpropagation Actually Unlocks Neural Networks

Logosofia no Dia a Dia · Apr 25, 2026 · 25m

What actually happens inside a neural network when it learns from its mistakes? This episode breaks down backpropagation — the algorithm that computes gradients for every weight in a network by propagating error signals backward through the same connections that carried data forward. We walk through a concrete example using the chain rule, explain the credit-assignment problem that kept neural networks shallow for decades, and trace the history from Rumelhart, Hinton, and Williams's landmark 1986 paper to the vanishing gradient crisis that nearly killed deep learning. Along the way, we cover…