CNN/RNN Assignment Help

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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

  • Facial recognition

  • Analysing documents

  • Understanding climate patterns

  • Video classification

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:

  • Speech recognition

  • Time series prediction

  • Music composition

  • Machine translation

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.