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Trends in deep learning methodologiesalgorithms, applications, and systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Trends in deep learning methodologiesedited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava.
其他題名:	algorithms, applications, and systems /
其他作者:	Piuri, Vincenzo,
出版者:	London :Academic Press,2021.
面頁冊數:	1 online resource (xvii, 288 p.) :ill.
標題:	Artificial intelligence.
電子資源:	https://www.sciencedirect.com/science/book/9780128222263
ISBN:	9780128232682 (electronic bk.)

Trends in deep learning methodologiesalgorithms, applications, and systems /
Trends in deep learning methodologiesalgorithms, applications, and systems /[electronic resource] :edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava. - London :Academic Press,2021. - 1 online resource (xvii, 288 p.) :ill. - Hybrid computational intelligence for pattern analysis and understanding. - Hybrid computational intelligence for pattern analysis and understanding..

Includes bibliographical references and index.

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto- encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning- based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

ISBN: 9780128232682 (electronic bk.)

Nat. Bib. No.: GBC0I0460bnbSubjects--Topical Terms:

194058
Artificial intelligence.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: Q335 / .T74 2021

Dewey Class. No.: 006.3

Trends in deep learning methodologiesalgorithms, applications, and systems /
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504 $a Includes bibliographical references and index.
520 $a Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto- encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning- based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.
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