convolutional neural networks (though it is common to use onebased on the use casewe can choose more appropriate number larger strides often help in reducing computationgeneralizing learning of featuresetc padding we also saw that applying convolution reduces the size of the feature map when compared to the size of the input image zero-padding is generic way to control the shrinkage of the dimension after applying filters larger than and avoiding information loss at the boundaries batch normalization batch normalization is technique that helps to train very deep neural networks by standardizing the inputs to layer for each mini-batch standardizing the inputs helps to stabilize the learning process and thereby dramatically reduces the number of training epochs required to train deep networks the batch normalization layer is added after the convolutional layer and usually is part of standard block of convolutional operation that isa combination of convolutional layerbatch normalization layeractivationand max pooling operation together in the same sequence is defined as convolutional unit we typically add several such units in cnn filter filters are analogous to kernels in recent implementations (including pytorchand academiathe term filter is more common than kernel in generalfor convolution operationswe use filters of size and earlier implementations also favored filters