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SESK: Stuff Everyone Should Know · sesk.info/ai-backprop-enables-gradient-descent

Which algorithm makes gradient descent practical for multilayer neural networks?

Backpropagation efficiently supplies the needed gradients.

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  1. Backpropagation ✔
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Why: Backpropagation applies the chain rule from the loss backward through the computation graph. Those gradients tell an optimizer how parameters affect the loss.

Huh, didn't know that: Modern autodifferentiation libraries usually perform the backward calculations automatically.

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Backpropagation learned from its mistakes, in reverse order.
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References

Answer: Backpropagation. Backpropagation applies the chain rule from the loss backward through the computation graph. Those gradients tell an optimizer how parameters affect the loss.

  1. Google Machine Learning Crash Course: Backpropagation developers.google.com/machine-learning/crash-course/neural-networks/backpropagation
    It makes gradient descent feasible for multi-layer neural networks.
    Checked 2026-10-10.

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