國立高雄大學圖資館 |

Language: English

Back

Deep learningtheory, architectures and applications in speech, image and language processing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learningedited by Gyanendra Verma & Rajesh Doriya.
Reminder of title:	theory, architectures and applications in speech, image and language processing /
other author:	Verma, Gyanendra.
Published:	Singapore :Bentham Science,2023.
Description:	1 online resource (270 p.)
Subject:	Machine learning.
Online resource:	https://www.eurekaselect.com/ebook_volume/3557
ISBN:	9789815079210$q(Online)

Deep learningtheory, architectures and applications in speech, image and language processing /
Deep learningtheory, architectures and applications in speech, image and language processing /[electronic resource] :edited by Gyanendra Verma & Rajesh Doriya. - 1st ed. - Singapore :Bentham Science,2023. - 1 online resource (270 p.)

Includes bibliographical references and index.

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

ISBN: 9789815079210$q(Online)Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325

Dewey Class. No.: 006.31

Deep learningtheory, architectures and applications in speech, image and language processing /
LDR:02620nmm a2200265 a 4500 001 663839
005 20240923072503.0
006 m o d
007 cr cnu---unuuu
008 241219s2023 si ob 001 0 eng d
020 $a 9789815079210$q(Online)
020 $z 9789815079234$q(paperback)
020 $z 9789815079227$q(Print)
035 $a 9789815079210
040 $a MiAaPQ $b eng $c MiAaPQ $d MiAaPQ
041 0 $a eng
050 0 0 $a Q325
082 0 0 $a 006.31 $2 23
245 0 0 $a Deep learning $h [electronic resource] : $b theory, architectures and applications in speech, image and language processing / $c edited by Gyanendra Verma & Rajesh Doriya.
250 $a 1st ed.
260 $a Singapore : $b Bentham Science, $c 2023.
300 $a 1 online resource (270 p.)
504 $a Includes bibliographical references and index.
520 $a This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.
588 $a Description based on print version record.
650 0 $a Machine learning. $3 188639
650 0 $a Artificial intelligence. $3 194058
700 1 $a Verma, Gyanendra. $3 976276
700 1 $a Doriya, Rajesh. $3 953321
856 4 0 $u https://www.eurekaselect.com/ebook_volume/3557