Forward and Backward propagation of Max Pooling Layer in Convolutional Neural Networks
Theory and Code
Published in
4 min readFeb 21, 2022
Introduction
In the last article we saw how to do forward and backward propagation for convolution operations in CNNs. It was found that applying the pooling layer after the convolution layer improves performance helping the network to generalize better and reduce overfitting. This is because, given a certain grid (pooling height x pooling width) we sample only one value from it ignoring particular elements and suppressing…