Can you walk through the basic procedural steps of backpropagation in neural networks?

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Anonymous · Accepted Answer

Backpropagation is an optimization technique used to find out the best
parameters for every layer. We once first do a complete forward pass and find
the loss function. Then for each precedding layer according to dependency, we
apply chain rule to find gradinet of loss function, and update the parametrs for
that layer's neurons according to update rule. This goes on till we have
exhausted epochs or we have reached a satisfactory accuracy or threshold.

Can you walk through the basic procedural steps of backpropagation in neural networks?

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