國立高雄大學圖資館 |

Language: English

Back

Handbook of deep learning applications

Balas, Valentina Emilia.

Handbook of deep learning applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of deep learning applicationsedited by Valentina Emilia Balas ... [et al.].
other author:	Balas, Valentina Emilia.
Published:	Cham :Springer International Publishing :2019.
Description:	vi, 383 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-3-030-11479-4
ISBN:	9783030114794$q(hardback)

Handbook of deep learning applications
Handbook of deep learning applications[electronic resource] /edited by Valentina Emilia Balas ... [et al.]. - Cham :Springer International Publishing :2019. - vi, 383 p. :ill., digital ;24 cm. - Smart innovation, systems and technologies,v.1362190-3018 ;. - Smart innovation, systems and technologies ;v.12..

Designing a Neural Network from scratch for Big Data powered by Multi-node GPUs -- Deep Learning for Scene Understanding -- Deep Learning for Driverless Vehicles -- Deep Learning for Document Representation -- Deep learning for marine species recognition -- Deep molecular representation in Cheminformatics -- Deep Learning in eHealth -- Deep Learning for Brain Computer Interfaces -- Deep Learning in Gene Expression Modeling.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

ISBN: 9783030114794$q(hardback)

Standard No.: 10.1007/978-3-030-11479-4doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .H363 2019

Dewey Class. No.: 006.31

Handbook of deep learning applications
LDR:02433nmm a2200337 a 4500 001 553806
003 DE-He213
005 20190905141533.0
006 m d
007 cr nn 008maaau
008 191112s2019 gw s 0 eng d
020 $a 9783030114794$q(hardback)
020 $a 9783030114787$q(paper)
024 7 $a 10.1007/978-3-030-11479-4 $2 doi
035 $a 978-3-030-11479-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .H363 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H236 2019
245 0 0 $a Handbook of deep learning applications $h [electronic resource] / $c edited by Valentina Emilia Balas ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a vi, 383 p. : $b ill., digital ; $c 24 cm.
490 1 $a Smart innovation, systems and technologies, $x 2190-3018 ; $v v.136
505 0 $a Designing a Neural Network from scratch for Big Data powered by Multi-node GPUs -- Deep Learning for Scene Understanding -- Deep Learning for Driverless Vehicles -- Deep Learning for Document Representation -- Deep learning for marine species recognition -- Deep molecular representation in Cheminformatics -- Deep Learning in eHealth -- Deep Learning for Brain Computer Interfaces -- Deep Learning in Gene Expression Modeling.
520 $a This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
650 0 $a Machine learning $v Congresses. $3 384498
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 567118
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Balas, Valentina Emilia. $3 339595
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Smart innovation, systems and technologies ; $v v.12. $3 559898
856 4 0 $u https://doi.org/10.1007/978-3-030-11479-4
950 $a Intelligent Technologies and Robotics (Springer-42732)