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correct outcome occurred and adjust accordingly. This type of neural network is often used in text-
to-speech applications.

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. 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. 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.
• Visual recognition is needed in self-driving cars so they can recognise road signs and other
road users.
• Content moderation to automatically remove unsafe or inappropriate content from image and
video archives.
• Image labelling to identify brand logos, clothing, safety gear, and other image details.
2. Speech Recognition
Neural networks can analyse human speech despite varying speech patterns, pitch, tone, language,
and accent. Virtual assistants like Amazon Alexa and automatic transcription software use speech
recognition to do tasks like these:
• Convert digital conversations into documentation in real time.
• Accurately subtitle videos and meeting recordings for wider content reach.

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