大葉大學圖書館 |

Trends in deep learning methodologies[electronic resource] :algorithms, applications, and systems /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.3
書名/作者:	Trends in deep learning methodologies : algorithms, applications, and systems // edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava.
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.
標題:	Neural networks (Computer science).
ISBN:	9780128232682 (electronic bk.)
ISBN:	0128232684
ISBN:	9780128222263
ISBN:	0128222263
書目註:	Includes bibliographical references and index.
摘要、提要註:	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.
電子資源:	https://www.sciencedirect.com/science/book/9780128222263

Trends in deep learning methodologies[electronic resource] :algorithms, 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:

172060
Artificial intelligence.
Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: Q335 / .T74 2021

Dewey Class. No.: 006.3

Trends in deep learning methodologies[electronic resource] :algorithms, applications, and systems /
LDR:02660cmm a2200289 a 4500 001 522960
006 m o d
007 cr |n|||||||||
008 230416s2021 enka ob 001 0 eng d
015 $a GBC0I0460 $2 bnb
019 $a 1220827976 $a 1223025637
020 $a 9780128232682 (electronic bk.)
020 $a 0128232684
020 $a 9780128222263
020 $a 0128222263
035 $a (OCoLC)1230531334 $z (OCoLC)1220827976 $z (OCoLC)1223025637
035 $a 1230531334
040 $a UKMGB $b eng $c UKMGB $d OCLCO $d OCLCF $d OPELS $d YDXIT $d ABC $d YDX $d UKAHL $d WAU $d OCLCO $d ORMDA
041 0 $a eng
050 4 $a Q335 $b .T74 2021
082 0 4 $a 006.3 $2 23
245 0 0 $a Trends in deep learning methodologies $h [electronic resource] : $b algorithms, applications, and systems / $c edited by Vincenzo Piuri, Sandeep Raj, Angelo Genovese, Rajshree Srivastava.
260 $a London : $b Academic Press, $c 2021.
300 $a 1 online resource (xvii, 288 p.) : $b ill.
490 1 $a Hybrid computational intelligence for pattern analysis and understanding
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.
650 0 $a Artificial intelligence. $3 172060
650 0 $a Neural networks (Computer science). $3 381304
655 4 $a Electronic books. $2 local $3 336502
700 1 $a Piuri, Vincenzo, $e editor. $3 748499
700 1 $a Raj, Sandeep, $e editor. $3 748500
700 1 $a Genovese, Angelo, $d 1985- $e editor. $3 748501
700 1 $a Srivastava, Rajshree, $e editor. $3 748502
830 0 $a Hybrid computational intelligence for pattern analysis and understanding. $3 748503
856 4 0 $u https://www.sciencedirect.com/science/book/9780128222263