Updated March 22, 2023

Difference Between CNN and RNN

In this article, we will discuss the major differences between CNN vs RNN. Convolutional neural networks are one of the special editions in the neural network family in the field of information technology. It extracts its name from the underlying hidden layer, which consists of pooling layers, convolutional layers, complete, interconnected layers, and normalization layers. It is designed using normal activation methods; convolution, pooling functions are used as the activation functions. Recurrent Neural Network is a defined variance that is mainly employed for natural language processing. In a common neural network, the input is processed through a finite input layer and generated output with the assumption of completely independent input layers.

Head to Head Comparison between CNN and RNN (Infographics)

Below are the top 6 comparisons between CNN vs RNN:

Key Differences Between CNN and RNN

Let us discuss the top comparison between CNN vs RNN:

Mathematically, convolution is a grouping formula. In CNN’s convolution occurs between two matrices to deliver a third output matrix. Matrix is nothing but a rectangular array of numbers stored in columns and rows. A CNN utilizes the convolution in the convolution layers to segregate the input information and find the actual one.
The convolutional layer is engaged in a computational activity like high complicated in a Convolutional neural network which acts as a numerical filter that helps the computer to find corners of pictures, concentrated and faded areas, color contractions, and other attributes like the height of the pictures, depth and pixels scattered, size and weight of the image.
The pooling layer is often built in between the convolution layers, which are used to decrease the structure of representation designed by convolutional layers used to decrease the memory components that allow many convolutional layers.
Normalization is to enhance the productivity and Constancy of neural networks. It tends to make more adaptable inputs of the individual layer by changing all the given inputs to a corresponding mean value zero and a variant of one in which these inputs are considered as regularized data. The fully interconnected layers help to link every neuron from one layer to another layer.
CNN’s are specially designed for computer vision, but guiding them with required data can modify them to get an advanced form of images, music, speech, videos, and text.
CNN contains innumerable layers of filters or neuron layers which is hidden and optimizes, giving high efficiency in detecting an image, and the process happens from interconnected layers. Because of this popular feature, they are called a feedforward loop.
RNN has the same traditional structure as artificial neuron networks and CNN. They have another partition of memory which can work as feedback loops. Similarly, like a human brain, especially in conversations, high weight is given to data redundancy to relate and understand the sentences and meaning behind them. This unique feature of RNN is used to predict the next set or sequence of words. RNN can also be fed a sequence of data that have varying lengths and sizes, where CNN operates only with the fixed input data.
Now the example of CNN is image recognition. The computer can read numbers. But with the picture representation of 1 and 0 and many layers of CNN. The peek deep of the Convolutional neuron network helps to learn more techniques.
By analyzing each layer of mathematical calculations and helping computers to define the details of images in bits at a time in an eventual effort. This helps to identify particular objects by reading one by one of the layer.
An RNN is a neural network with an active data memory popularly known as LSTM, which can be applied to a sequence of input data that helps the system predict the next step. The output of some interconnected layers is fed back again to the prior layer’s inputs by creating a feedback loop. The best scenario for RNN is explained below.
Tracking of main dishes in the hotel which the dish should not be repeated in a week like tacos on Monday, burgers on Tuesday, pasta on Wednesday, pizza on Thursday, sushi on Friday. With the help of RNN, if the output “pizza” is fed again into the network to determine Friday’s dish, then the RNN will let us know about the next main dish is sushi, because of the event which has been carried out periodically in the past days.
In these modern days, the dubbed KITT would feature deep learning from convolutional networks and recurrent neural networks to see, talk and hear, which is made possible with CNN as image crunchers used for vision and RNN the mathematical engines which are ears and mouth to implement the language patterns.

Comparison Table of CNN vs RNN

The below table below summarizes the comparisons between CNN vs RNN:

CNN	RNN
CNN is applicable for sparse data like images.	RNN is applicable for temporary data and sequential data.
CNN is considered a more powerful tool than RNN.	RNN has fewer features and low capabilities compared to CNN.
The interconnection consumes a finite set of input and generates a finite set of output according to the input.	RNN can allow arbitrary input length and output length.
CNN is a clockwise type of feed-forward artificial neural network with a variety of multiple layers of perception, which is specially designed to utilize the minimum amount of pre-processing.	RNN works on a loop network that uses their internal memory to handle the arbitrary input sequences.
CNN’s are special for video processing and image processing.	RNN works primarily on time series information on the past influence of the consumer. Analyzing if the user is going to talk next or not.
CNN follows interconnectivity patterns between the neurons which is inspired by the animal visual cortex, where the individual neurons are organized in a way that they respond to overlapping areas tilling the visual field.	RNN works primarily on speech analysis and text analysis.

Conclusion

CNN is the vision of autonomous vehicles, fusion energy research and oil exploration. It is also more helpful in diagnosing diseases faster than medical imaging. RNN is applied as voice control of Amazon Alexa, Apple’s Siri, and Google’s assistant, which understands human language processing and works on the voice-based computing revolution principle. Today autonomous cars can be tested before hitting them to the road. AI-based machines and technologies are setting the future trend with CNN and RNN.