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.
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Backpropagation underlies virtually all modern deep learning, so understanding why it is structured the way it is helps practitioners and students build stronger intuition rather than treating it as a black-box algorithm. Clarifying the efficiency argument for backward over forward computation also illuminates broader tradeoffs in automatic differentiation techniques used across machine learning frameworks.
- Backpropagation applies the chain rule to compute gradients efficiently at each node using local calculations.
- The piece questions why gradients are computed backward instead of forward through the network's composite function.
- Understanding this distinction deepens intuition about automatic differentiation methods used in training neural networks.
Source: gregorygundersen.com — Gregory Gundersen, 2026-09-21
Published there as: “Why Backprop Goes Backward (2018)”
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