A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be ap...

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Vydané v:	Archives of computational methods in engineering Ročník 27; číslo 4; s. 1071 - 1092
Hlavní autori:	Dargan, Shaveta, Kumar, Munish, Ayyagari, Maruthi Rohit, Kumar, Gulshan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.09.2020 Springer Nature B.V
Predmet:	Algorithms Computer vision Deep learning Engineering Face recognition Handwriting recognition Image classification Machine learning Mathematical and Computational Engineering Natural language processing Object recognition Original Paper Speech recognition
ISSN:	1134-3060, 1886-1784
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Abstract	Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.
AbstractList	Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.
Author	Ayyagari, Maruthi Rohit Dargan, Shaveta Kumar, Munish Kumar, Gulshan
Author_xml	– sequence: 1 givenname: Shaveta surname: Dargan fullname: Dargan, Shaveta organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 2 givenname: Munish orcidid: 0000-0003-0115-1620 surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University – sequence: 3 givenname: Maruthi Rohit surname: Ayyagari fullname: Ayyagari, Maruthi Rohit organization: College of Business, University of Dallas – sequence: 4 givenname: Gulshan surname: Kumar fullname: Kumar, Gulshan organization: Department of Computer Applications, Shaheed Bhagat Singh State Technical Campus
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Snippet	Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient...
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SubjectTerms	Algorithms Computer vision Deep learning Engineering Face recognition Handwriting recognition Image classification Machine learning Mathematical and Computational Engineering Natural language processing Object recognition Original Paper Speech recognition
Title	A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning
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