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Difference between CNN and 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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