||CNN stands for Convolutional Neural Network.
||RNN stands for Recurrent Neural Network.
||CNN is considered to be more potent than RNN.
||RNN includes less feature compatibility when compared to CNN.
||CNN is ideal for images and video processing.
||RNN is ideal for text and speech Analysis.
||It is suitable for spatial data like images.
||RNN is used for temporal data, also called sequential data.
||The network takes fixed-size inputs and generates fixed size outputs.
||RNN can handle arbitrary input/ output lengths.
||CNN is a type of feed-forward artificial neural network with variations of multilayer perceptron's designed to use minimal amounts of preprocessing.
||RNN, unlike feed-forward neural networks- can use their internal memory to process arbitrary sequences of inputs.
||CNN's use of connectivity patterns between the neurons. CNN is affected by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they can respond to overlapping regions in the visual field.
||Recurrent neural networks use time-series information- what a user spoke last would impact what he will speak next.