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Development and analysis of deep lea...

Chen, Shyi-Ming.

Development and analysis of deep learning architectures

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development and analysis of deep learning architecturesedited by Witold Pedrycz, Shyi-Ming Chen.
other author:	Pedrycz, Witold.
Published:	Cham :Springer International Publishing :2020.
Description:	xi, 292 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-31764-5
ISBN:	9783030317645$q(electronic bk.)

Development and analysis of deep learning architectures
Development and analysis of deep learning architectures[electronic resource] /edited by Witold Pedrycz, Shyi-Ming Chen. - Cham :Springer International Publishing :2020. - xi, 292 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8671860-949X ;. - Studies in computational intelligence ;v. 216..

Preface -- Chapter 1. Direct Error Driven Learning for Classification in Applications Generating Big-Data -- Chapter 2. Deep Learning for Soft Sensor Design -- Chapter 3. Case Study: Deep Convolutional Networks in Healthcare, etc.

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

ISBN: 9783030317645$q(electronic bk.)

Standard No.: 10.1007/978-3-030-31764-5doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Development and analysis of deep learning architectures
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245 0 0 $a Development and analysis of deep learning architectures $h [electronic resource] / $c edited by Witold Pedrycz, Shyi-Ming Chen.
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520 $a This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
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