Why back propagation goes backward

Backpropagation computes gradients by propagating error signals from output nodes back toward the inputs, using the chain rule to combine local derivatives. A forward-only approach would require repeatedly transmitting the same intermediate terms to every downstream node, causing exponential growth in computation. By passing these terms backward, each node needs only its local information, yielding a linear-time algorithm.