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What is CNN?
Convolutional neural networks (CNN) are one of the most popular models used today. This neural network computational model uses a variation of multilayer perceptrons and contains one or more convolutional layers that can be either entirely connected or pooled. These convolutional layers create feature maps that record a region of image which is ultimately broken into rectangles and sent out for nonlinear processing.
Some of the many applications of CNN are
Understanding climate patterns
What is RNN?
RNN or recurrent neural network is a class of artificial neural networks that processes information sequences like temperatures, daily stock prices, and sentences. These algorithms are designed to take a series of inputs without any predetermined size limit.
The many applications of RNNs include:
Time series prediction
Which is better?
There are different important factor by which we can understand the difference between RNN/CNN:
Type of input data: While RNNs are suitable for handling temporal or sequential data, CNNs are suitable for handling spatial data (images).
Computing power: Since both RNN and CNN are used for different purposes, it might not be appropriate to compare their computational ability.
Architecture: Convolutional neural networks use the connectivity patterns available in neurons. Inspired by the visual cortex of the brain, CNNs have numerous layers and each one is responsible for detecting a specific set of features in the image. The combined output of all the layers helps CNNs identify and classify images.