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Generalization with deep learningfor improvement on sensing capability /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generalization with deep learningeditors, Zhenghua Chen, Min Wu, Xiaoli Li.
Reminder of title:	for improvement on sensing capability /
other author:	Chen, Zhenghua.
Published:	Singapore :World Scientific,c2021.
Description:	1 online resource (xii, 314 p.)
Subject:	Electronic surveillanceData processing.
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/11784#t=toc
ISBN:	9789811218842$q(ebook)

Generalization with deep learningfor improvement on sensing capability /
Generalization with deep learningfor improvement on sensing capability /[electronic resource] :editors, Zhenghua Chen, Min Wu, Xiaoli Li. - Singapore :World Scientific,c2021. - 1 online resource (xii, 314 p.)

Includes bibliographical references and index.

"Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"--Publisher's website.

Mode of access: World Wide Web.

ISBN: 9789811218842$q(ebook)Subjects--Topical Terms:

931243
Electronic surveillance
--Data processing.

LC Class. No.: TK7882.E2 / G44 2021

Dewey Class. No.: 006.3/1

Generalization with deep learningfor improvement on sensing capability /
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020 $a 9789811218842$q(ebook)
020 $z 9789811218835$q(hardback)
035 $a 00011784
040 $a WSPC $b eng $c WSPC
041 0 $a eng
050 0 4 $a TK7882.E2 $b G44 2021
082 0 4 $a 006.3/1 $2 23
245 0 0 $a Generalization with deep learning $h [electronic resource] : $b for improvement on sensing capability / $c editors, Zhenghua Chen, Min Wu, Xiaoli Li.
260 $a Singapore : $b World Scientific, $c c2021.
300 $a 1 online resource (xii, 314 p.)
504 $a Includes bibliographical references and index.
520 $a "Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities. In this edited volume, we aim to narrow the gap between human and machine by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data"--Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Electronic surveillance $x Data processing. $3 931243
650 0 $a Remote sensing $x Data processing. $3 226296
650 0 $a Diagnostic imaging $x Data processing. $3 276544
650 0 $a Machine learning. $3 188639
700 1 $a Chen, Zhenghua. $3 931240
700 1 $a Wu, Min, $d 1974- $3 931241
700 1 $a Li, Xiao-Li, $d 1969- $3 931242
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11784#t=toc