GoKawiilExplainer revisits why backpropagation computes gradients backward, not forward
A technical blog post (originally from 2018) revisits a classic question about neural network training: why does the backpropagation algorithm invented by Rumelhart et al. in 1986 compute gradients by propagating errors backward through the network rather than forward, given that both directions are mathematically valid applications of the chain rule. The author walks through the notation and structure of a neural network node to set up an explanation of why forward-mode differentiation is computationally suboptimal compared to the standard backward approach.