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. |