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Neural Network and Neural Computing

Neural Network and Neural Computing
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Author(s): Partha Ghosh (Government College of Engineering and Ceramic Technology, Kolkata, India)and Suradhuni Ghosh (Government Girls General Degree College, Kolkata, India)
Copyright: 2024
Pages: 45
Source title: Intelligent Decision Making Through Bio-Inspired Optimization
Source Author(s)/Editor(s): Ramkumar Jaganathan (Sri Krishna Arts and Science College, India), Shilpa Mehta (Auckland University of Technology, New Zealand)and Ram Krishan (Mata Sundri University Girls College, Mansa, India)
DOI: 10.4018/979-8-3693-2073-0.ch009

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Abstract

Deep learning, a subset of AI, has gained popularity in various fields, including computer vision and NLP. It is based on artificial neural networks, which process multiple layers of data and extract high-level features automatically. Unlike traditional ML algorithms, deep learning can process large unstructured data and complex algorithms better than traditional methods. The human brain inspires neural networks, which contain artificial neurons similar to biological neurons. These networks are made up of three layers: input, hidden, and output. Deep learning maps inputs to outputs and finds correlations, making it a “universal approximator.” It can be combined with other AI methods to perform more complex tasks, such as deep reinforcement learning. Top companies using ANN include Nvidia Corp., Alphabet, Salesforce.com, Amazon.com, Microsoft Corp., Twilio, IBM, and Facebook. Deep learning uses ANN to analyze data and make predictions and has found applications in almost every business sector.

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