Backpropagation computes neural network gradients efficiently by propagating error terms backward through the network, rather than forward. A forward pass would require repeatedly computing the same intermediate terms for each weight, while a backward pass leverages the chain rule to pass shared terms once per node, achieving linear time complexity.