Difference Between CNN And RNN Architecture In Deep Learning

Hello guys, welcome back to my blog. In this article, I will discuss the difference between CNN and RNN architecture in deep learning, advantages and disadvantages of the convolutional neural network, advantages, and disadvantages of the recurrent neural network, applications of the convolutional neural network, applications of the recurrent neural network, etc.

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Difference Between CNN And RNN Architecture

Recurrent neural network

A recurrent neural network will have a recurrent connection to the hidden state. This looping concentrate will ensure that the sequential information will be captured in the input data. Recurrent neural network solves the problem related to time series data, text data, audio data.

Architecture of recurrent neural network

Working-of-recurrent-Neural-network-RNN-1

The architecture of the recurrent neural network looks like above.

The recurrent neural network scans through the data from left towards right. All weights remain the same for each step in the forward propagation. These weights should be operated in backward propagation.

Advantages of recurrent neural network

01. A recurrent neural network will capture the sequential information which will be present in the input data.

02. In a recurrent neural network, the output of each step will be always depending upon not only the current input but also on the previous inputs.

03. The recurrent neural network will share the parameters across different time steps, which will be termed as parameter sharing, due to this the fewer parameters to train and reduces the computational cost.

Disadvantage of recurrent neural network

Recurrent neural networks suffer from the varnishing and exploding gradient problems. This is a common problem in all the types of neural networks.

Applications of recurrent neural network

01. It can be used in image classification based on daytime and night time, which is the example for one to one mapping.

02. Image captioning where the caption can be given based on the vote is being shown in the image. These types of tasks require one-to-many recurrent neural networks in which only one image is input and the output will be consisting of several words.

03. This can be used in automatic language translation in which the written or spoken words of one language is input and the output represents the different language to the same text.

04. A recurrent neural network can be used in time series prediction like forecasting of a stock price, which gives a history of prices. This is an example of a many-to-one recurrent neural network. In this many past prices will be used to predict the single and future prices.

Convolutional neural network

A convolutional neural network is a deep learning network that is used for computer vision or image recognition. It processes on image data.

The convolutional neural network models are used across various applications and domains. Specially convolutional neural networks are prevalent in image and video processing projects. Filter a.k.a kernels are the building blocks of convolutional neural networks. These are used to extract the relevant features from the input using the convolution operation. These are introduced to solve problems that are related to image data, but these convolutional neural networks perform well on sequential input data also.

Read complete CNN article – What Is CNN Or Convolution Neural Network, Classification Of X And O.

Architecture of convolutional neural network

A convolutional neural network will be made up of three type of layers,

Convolution layer
Pooling layer
Fully connected layer

Convolution layer will be the 1st layer, which extracts features from the input image. pooling layers will be added in between the convolution layers which helps us to simplify the data by decreasing its dimensionality. This layer shortens the time that is required for training and helps us to curb the problem of overfitting. After this convolution layer and pooling layers, fully connected layers will be present. In the fully connected layer, every neuron in the first will be connected to every neuron in the next. For example, in a vehicle recognition system, there are various features that have to be considered. The side we offer car shows only two wheels which exactly looks like a motorcycle.

As such there will be a non-zero probability, albeit small, that a car is classified as a motorcycle or vice versa, and the extra features such as the presence of windows or doors will help us to determine the vehicle type more accurately. The output layer will generate the probability which corresponds to each class. In the example of identifying a card the motorcycle will have the lower probability because among other things there are no visible doors.

Advantages of convolutional neural network

01. Convolutional neural networks have the capacity to learn the philter automatically without mentioning it explicitly. These filters will be helpful in extracting good and relevant features from the input data.

02. A convolutional neural network is able to capture special features from a particular image. Special features in the arrangement of pixels and relation in between pixels in an image. These pixels help us to identify the object accurately and we can identify the location of an object and also its relationship with other objects in an image.

Consider, one image of a person we can easily predict that this is a human’s face by analyzing some specific features such as nose, eyes, etc. We can also predict how these specific features are arranged in a particular image with the help of a convolutional neural network.

03. A convolutional neural network can share the parameters. A single philter can be applied across various parts of input just to produce a feature map.

Disadvantages of convolutional neural network

01. CNN doesn’t give the clarity about position and orientation of object.

02. They need a lot of training data.

Applications of convolutional neural networks

Used in decoding facial recognition.
Used in historic and environmental collections.
Used in image recognition.
Used in drug discovery.
Used in checkers game.
Used in time series forecasting.

Difference Between CNN And RNN Architecture

01. Recurrent neural network process on the sequence of data. While the convolutional neural network process on image data.

02. RNN has recurrent connections but CNN doesn’t having recurrent connections.

03. Recurrent neural networks share the parameters. While the convolutional neural network doesn’t share parameters.

04. There is no spatial relationship in recurrent neural network but there is spatial relationship in convolutional neural network.

05. Recurrent neural network varnish’s and explodes gradient, but CNN doesn’t vanish and explode.

06. For recurrent neural networks, a set of weights applied temporarily, But for the convolutional neural network, it saves a set of weights and applies them spatially.

07. Recurrent neural networks are good for natural language processing. While the convolutional neural networks are good for computer vision.

08. A recurrent neural network learns to recognize patterns across time. CNN learns to recognize patterns across the space.

09. A recurrent neural network solves the temporal data problems. But the convolutional neural network tries to recognize lines and curves, then it combines these small structures to analyze large structures (faces, objects).

10. Recurrent neural network contains present input and the previous input . But the convolutional neural network contains only current input.

11. Recurrent neural network processes sequence of data but convolutional neural network processes image data.

I hope this article may help you all a lot. Thank you for reading. If you have any doubts related to this article “difference between CNN and RNN”, then comment below.

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Chetan Shidling

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