Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works

Calculating the gradients needed to train a neural network reveals a major inefficiency, as many parameters require re-computing the same intermediate values. Backpropagation's core idea is to eliminate this waste by systematically reusing these shared calculations instead of performing them repeatedly. The process works by propagating the error backward from the final output layer to the input layer, efficiently determining how each parameter contributes to the overall error. To accomplish this, the algorithm leverages the intermediate values, like neuron activations, that were calculated and stored during the initial forward pass.