pooling layerswhich are basically layers that perform non-linear down sampling to reduce the input size and number of parameters from the convolutional layer output to generalize the model moreprevent overfitting and reduce computation time filters go through the heights and width of the input and reduce it by taking an aggregate like sumaverageor max typical pooling components are average or max pooling fully connected mlps to perform tasks such as image classification and object recognition typical cnn architecture with all the components is depicted as follows in figure - which is lenet cnn model (sourcedeeplearning netfigure - lenet cnn model (sourcedeeplearning netrecurrent neural networks recurrent neural networkalso known as rnnis special type of an artificial neural network that allows persisting information based on past knowledge by using special type of looped architecture they are used lot in areas related to data with sequences like predicting the next word of sentence these looped networks are called recurrent because they perform the same operations and computation for each and every element in sequence of input data rnns have memory that helps in capturing information from past sequences figure - (sourcecolah' blog at the typical structure of rnn and how it works by unrolling the network based on input sequence length to be fed at any point in time figure - recurrent neural network (sourcecolah' blog