A comprehensive review of deep learning algorithms
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Authors
Varastehpour, Soheil
Sharifzadeh, Hamid
Ardekani, Iman
Sharifzadeh, Hamid
Ardekani, Iman
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Date
2021-11-26
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Journal Article
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Keyword
artificial intelligence (AI)
deep-learning algorithms
Convolutional Neural Network (CNN)
autoencoders
restricted Boltzmann machine
sparse coding
literature reviews
deep-learning algorithms
Convolutional Neural Network (CNN)
autoencoders
restricted Boltzmann machine
sparse coding
literature reviews
ANZSRC Field of Research Code (2020)
Citation
Varastehpour, S., Sharifzadeh, H., Ardekani, I. (2021). A Comprehensive Review of Deep Learning Algorithms. (Unitec ePress Occasional and Discussion Paper Series 2021/4). 1-29, Unitec ePress . ISSN 2324-3635. http://www.unitec.ac.nz/epress.
Abstract
Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed
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Unitec ePress
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A Comprehensive Review of Deep Learning Algorithms by Dr Soheil Varastehpour, Dr Hamid Sharifzadeh and Dr Iman
Ardekani is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Attribution-NonCommercial-NoDerivatives 4.0 International
Attribution-NonCommercial-NoDerivatives 4.0 International