The term backpropagation, short for “backward propagation of errors,” is a supervised learning algorithm used to minimize errors in predictions made by neural networks.
In principle, Backpropagation is a chain-rule application that can be used to compute gradients of loss functions in relation to model parameters.
The mechanism operates in two main phases:
The forward pass and the backward pass.
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