Can you describe the fundamental steps involved in the backpropagation process within neural networks?

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Anonymous · Accepted Answer

backpropagation is a step in estimating the parameters of a model numerically
using an optimization algorithm such as gradient decsent or adam. The
optimization algorithm involves applying chain rule to the model,  it then makes
a forward pass to calculate the gradient of the data for the calculated
gradient, and then during the backpropagation, the optimizer updates the
parameters of a model using the calculated gradient, a learning rate and the
specific formulation of the optimizer. For Stochasting gradient decsent the
delta is simply the learning rate multiplied by the gradient.

Can you describe the fundamental steps involved in the backpropagation process within neural networks?

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