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Review of Deep Learning: Architectures, Applications and Challenges

by Ankit Sirmorya, Milind Chaudhari, Suhail Balasinor
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 18
Year of Publication: 2022
Authors: Ankit Sirmorya, Milind Chaudhari, Suhail Balasinor
10.5120/ijca2022922164

Ankit Sirmorya, Milind Chaudhari, Suhail Balasinor . Review of Deep Learning: Architectures, Applications and Challenges. International Journal of Computer Applications. 184, 18 ( Jun 2022), 1-13. DOI=10.5120/ijca2022922164

@article{ 10.5120/ijca2022922164,
author = { Ankit Sirmorya, Milind Chaudhari, Suhail Balasinor },
title = { Review of Deep Learning: Architectures, Applications and Challenges },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2022 },
volume = { 184 },
number = { 18 },
month = { Jun },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number18/32415-2022922164/ },
doi = { 10.5120/ijca2022922164 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:44.923579+05:30
%A Ankit Sirmorya
%A Milind Chaudhari
%A Suhail Balasinor
%T Review of Deep Learning: Architectures, Applications and Challenges
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 18
%P 1-13
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Deep Learning is a continuously evolving subset of machine learning techniques. New technology has provided solutions to a wide range of complex problems that were once unsolvable due to limitations in human intelligence. Since its conception, several DL architectures have been developed, including recursive neural networks, recurrent neural networks, artificial neural networks, and convolution neural networks. Many of their contributions have been in the area of computer vision, natural language processing, sequence generation, etc. Despite their increasing popularity, many individuals cannot see the bigger picture or comprehend these techniques. In this paper, the various deep learning models are described, as well as how they work. In addition, the article explains a few prominent DL models and their relevance in contemporary technology. As with every rapidly changing technology, DL has some limitations. These limitations are mitigated to some extent in this paper. Further, it emphasizes their continued development, the challenges they face, and the possibilities for future research in their fields.

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Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Machine Learning

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