convolutional neural networks filter depth filter depth usually refers to the depth corresponding to the number of color channels in the input image for the filters in the later layersthe depth corresponds to the number of filters in the previous layers for regular image with three color channels ( rgand )we use filter with depth of number of filters filters act as feature extractorhenceit is common to have several filters within each convolutional block of the network sample arrangement would be convolutional block with filters of size (and of depth followed by activation/batch normalization and pooling blocksfollowed by another block with filters (now having depth of )and so on summarizing key learnings from cnns so farwe have covered the key constituent concepts behind cnnthe convolution operation and the pooling operationand how they are used in conjunction let' now take step back to internalize the ideas behind cnns using these building blocks the first idea to consider is the capacity of cnns cnns that replace at least one of fully connected layers of neural network with the convolution operation have less capacity than that of fully connected network that isthere exists datasets that fully connected network will be able to model that cnn will not be able to sothe first point to note is that cnns achieve more by limiting the capacity and hence making the training efficient