國立高雄大學圖資館 |

Language: English

Back

Beginning machine learning in the br...

SpringerLink (Online service)

Beginning machine learning in the browserquick-start guide to gait analysis with JavaScript and TensorFlow.js /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginning machine learning in the browserby Nagender Kumar Suryadevara.
Reminder of title:	quick-start guide to gait analysis with JavaScript and TensorFlow.js /
Author:	Suryadevara, Nagender Kumar.
Published:	Berkeley, CA :Apress :2021.
Description:	xiv, 182 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-6843-8
ISBN:	9781484268438$q(electronic bk.)

Beginning machine learning in the browserquick-start guide to gait analysis with JavaScript and TensorFlow.js /
Suryadevara, Nagender Kumar.

Beginning machine learning in the browserquick-start guide to gait analysis with JavaScript and TensorFlow.js /[electronic resource] :by Nagender Kumar Suryadevara. - Berkeley, CA :Apress :2021. - xiv, 182 p. :ill., digital ;24 cm.

Chapter 1: Web Development -- Chapter 2: Browser- based Data Processing -- Chapter 3: Human Pose -- Chapter 4: Human Pose Classification -- Chapter 5: Gait Analysis -- Chapter 6: Future Possibilities for Running AI Methods in a Browser.

Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, you'll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, you'll be on your way to becoming an experienced Machine Learning developer. You will: Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser.

ISBN: 9781484268438$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6843-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .S879 2021

Dewey Class. No.: 006.31

Beginning machine learning in the browserquick-start guide to gait analysis with JavaScript and TensorFlow.js /
LDR:02698nmm a2200325 a 4500 001 599892
003 DE-He213
005 20210719143120.0
006 m d
007 cr nn 008maaau
008 211027s2021 cau s 0 eng d
020 $a 9781484268438$q(electronic bk.)
020 $a 9781484268421$q(paper)
024 7 $a 10.1007/978-1-4842-6843-8 $2 doi
035 $a 978-1-4842-6843-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .S879 2021
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 .S963 2021
100 1 $a Suryadevara, Nagender Kumar. $3 713599
245 1 0 $a Beginning machine learning in the browser $h [electronic resource] : $b quick-start guide to gait analysis with JavaScript and TensorFlow.js / $c by Nagender Kumar Suryadevara.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xiv, 182 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Web Development -- Chapter 2: Browser- based Data Processing -- Chapter 3: Human Pose -- Chapter 4: Human Pose Classification -- Chapter 5: Gait Analysis -- Chapter 6: Future Possibilities for Running AI Methods in a Browser.
520 $a Apply Artificial Intelligence techniques in the browser or on resource constrained computing devices. Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. This book takes advantage of the intricacies of the ML processes by using a simple, flexible and portable programming language such as JavaScript to work with more approachable, fundamental coding ideas. Using JavaScript programming features along with standard libraries, you'll first learn to design and develop interactive graphics applications. Then move further into neural systems and human pose estimation strategies. For training and deploying your ML models in the browser, TensorFlow.js libraries will be emphasized. After conquering the fundamentals, you'll dig into the wilderness of ML. Employ the ML and Processing (P5) libraries for Human Gait analysis. Building up Gait recognition with themes, you'll come to understand a variety of ML implementation issues. For example, you'll learn about the classification of normal and abnormal Gait patterns. With Beginning Machine Learning in the Browser, you'll be on your way to becoming an experienced Machine Learning developer. You will: Work with ML models, calculations, and information gathering Implement TensorFlow.js libraries for ML models Perform Human Gait Analysis using ML techniques in the browser.
650 0 $a Machine learning. $3 188639
650 1 4 $a Artificial Intelligence. $3 212515
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6843-8
950 $a Professional and Applied Computing (SpringerNature-12059)