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3. Convolutional Neural Network

A Convolutional neural network, also called ConvNet or CNN,
have several layers in which data is sorted into categories. These
networks have an input layer, an output layer, and a hidden
multitude of convolutional layers in between. The layers create
feature maps that record areas of an image that are broken down
further until they generate valuable outputs. These layers can be
pooled or entirely connected, and these networks are especially
beneficial for image recognition applications.

4. Deconvolutional Neural Network

A Deconvolutional neural network works in reverse of a convolutional neural network. The application
of the network is to detect items that might have been recognised as important under a convolutional
neural network. These items would likely have been discarded during the convolutional neural network
execution process. This type of neural network is also widely used for image analysis or processing.

5. Modular Neural Network
A Modular neural network contains several networks that
work independently from one another. These networks
do not interact with each other during an analysis process.
Instead, these processes are done to allow complex, elaborate
computing processes to be done more efficiently. Similar

to other modular industries, such as modular real estate,
the goal of network independence is to have each module
responsible for a particular part of an overall bigger picture.

Application of Neural Networks

Neural networks have several uses across several industries, such as the following:

1. Computer Vision
Computer vision is the ability of computers to extract information and insights from images and videos.
With neural networks, computers can distinguish and recognise images similar to humans. Computer
vision has several applications, such as the following:

• Visual recognition is needed in self-driving cars so they can recognise road signs and other road
users.

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