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Difference between CNN and RNN

S.no	CNN	RNN
1	CNN stands for Convolutional Neural Network.	RNN stands for Recurrent Neural Network.
2	CNN is considered to be more potent than RNN.	RNN includes less feature compatibility when compared to CNN.
3	CNN is ideal for images and video processing.	RNN is ideal for text and speech Analysis.
4	It is suitable for spatial data like images.	RNN is used for temporal data, also called sequential data.
5	The network takes fixed-size inputs and generates fixed size outputs.	RNN can handle arbitrary input/ output lengths.
6	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.
7	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.

Following are the diagram shows the schematic representation of CNN and RNN

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