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Handbook of deep learning applications

Balas, Valentina Emilia.

Handbook of deep learning applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Handbook of deep learning applicationsedited by Valentina Emilia Balas ... [et al.].
其他作者:	Balas, Valentina Emilia.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	vi, 383 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learningCongresses.
電子資源:	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

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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.
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