Backpropagation
The algorithm that calculates loss gradients backwards through neural layers to update model weights.
Backpropagation uses the calculus chain rule to calculate the gradient of a loss function relative to each network weight. Optimizers use these gradients to adjust weights, reducing training loss.
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