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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Mehdi Gheisari Orcid Logo, Fereshteh Ebrahimzadeh, Mohamadtaghi Rahimi, Mahdieh Moazzamigodarzi, Yang Liu Orcid Logo, Pijush Kanti Dutta Pramanik, Mohammad Ali Heravi, Abolfazl Mehbodniya Orcid Logo, Mustafa Ghaderzadeh Orcid Logo, Mohammad Reza Feylizadeh, Saeed Kosari

CAAI Transactions on Intelligence Technology, Volume: 8, Issue: 3, Pages: 581 - 606

Swansea University Author: Yang Liu Orcid Logo

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DOI (Published version): 10.1049/cit2.12180

Abstract

Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising...

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Published in: CAAI Transactions on Intelligence Technology
ISSN: 2468-2322 2468-2322
Published: Institution of Engineering and Technology (IET) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa67387
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Abstract: Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state-of-the-art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on.
Keywords: data mining; data privacy; deep learning
College: Faculty of Science and Engineering
Issue: 3
Start Page: 581
End Page: 606