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E-raamat: Beginning Machine Learning in the Browser: Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 01-Apr-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484268438
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Beginning Machine Learning in the Browser: Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.jsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 01-Apr-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484268438
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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 MLimplementation issues. For example, youll learn about the classification of normal and abnormal Gait patterns.





With Beginning Machine Learning in the Browser, youll be on your way to becoming an experienced Machine Learning developer.





What Youll Learn









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













Who This Book Is For





Computer science students and research scholars, and novice programmers/web developers in the domain of Internet Technologies
About the Author vii
About the Technical Reviewer ix
Acknowledgments xi
Preface xiii
Chapter 1 Web Development
1(30)
Machine Learning Overview
1(3)
Web Communication
4(2)
Organizing the Web with HTML
6(1)
Web Development Using IDEs/Editors
6(3)
Building Blocks of Web Development
9(1)
HTML and CSS Programming
9(9)
JavaScript Basics
18(1)
Including the JavaScript
18(1)
Where to Insert JS Scripts
19(3)
JavaScript for an Event-Driven Process
22(1)
Document Object Model Manipulation
23(3)
Introduction to jQuery
26(2)
Summary
28(1)
References
29(2)
Chapter 2 Browser-Based Data Processing
31(34)
JavaScript Libraries and API for ML on the Web
31(1)
W3C WebML CG (Community Group)
32(1)
Manipulating HTML Elements Using JS Libraries
33(1)
P5.js
34(1)
Drawing Graphical Objects
35(1)
Manipulating DOM Objects
36(2)
DOM onEvent(mousePressed) Handling
38(1)
Multiple DOM Objects onEvent Handling
39(2)
HTML Interactive Elements
41(4)
Hierarchical (Parent-Child) Interaction of DOM Elements
45(2)
Accessing DOM Parent-Child Elements Using Variables
47(2)
Graphics and Interactive Processing in the Browser Using p5.js
49(2)
Interactive Graphics Application
51(2)
Object Instance, Storage of Multiple Values, and Loop Through Object
53(3)
Getting Started with Machine Learning in the Browser Using ml5.js and p5.js
56(1)
Design, Develop, and Execute Programs Locally
56(1)
Method 1 Using Python - HTTP Server
56(2)
Method 2 Using Visual Studio Code Editor with Node.js Live Server
58(5)
Summary
63(1)
References
63(2)
Chapter 3 Human Pose Estimation in the Browser
65(30)
Human Pose at a Glance
66(1)
PoseNetvs. OpenPose
66(1)
Human Pose Estimation Using Neural Networks
67(1)
DeepPose: Human Pose Estimation via Deep Neural Networks
67(1)
Efficient Object Localization Using Convolutional Networks
68(1)
Convolutional Pose Machines
68(1)
Human Pose Estimation with Iterative Error Feedback
69(1)
Stacked Hourglass Networks for Human Pose Estimation
69(1)
Simple Baselines for Human Pose Estimation and Tracking
69(1)
Deep High-Resolution Representation Learning for Human Pose Estimation
70(1)
Using the ml5.js:posenet() Method
70(20)
Input, Output, and Data Structure of the PoseNet Model
90(1)
Input
90(2)
Output
92(1)
.On(j Function
92(1)
Summary
92(1)
References
93(2)
Chapter 4 Human Pose Classification
95(40)
Need for Human Pose Estimation in the Browser
96(1)
ML Classification Techniques in the Browser
97(3)
ML Using TensorFlow.js
100(6)
Changing Flat File Data into TensorFlow.js Format
106(7)
Artificial Neural Network Model in the Browser Using TensorFlow.js
113(1)
Trivial Neural Network
114(1)
Example 1 Neural Network Model in TensorFlow.js
115(2)
Example 2 A Simple ANN to Realize the "Not AND" (NAND) Boolean Operation
117(4)
Human Pose Classification Using PoseNet
121(2)
Setting Up a PoseNet Project
123(1)
Step 1 Including TensorFlow.js and PoseNet Libraries in the HTML Program (Main File)
123(1)
Step 2 Single-Person Pose Estimation Using a Browser Webcam
124(5)
PoseNet Model Confidence Values
129(4)
Summary
133(1)
References
134(1)
Chapter 5 Gait Analysis
135(28)
Gait Measurement Techniques
135(2)
Gait Cycle Measurement Parameters and Terminology
137(1)
Web User Interface for Monitoring Gait Parameters
138(2)
Index.html
140(12)
Real-Time Data Visualization of the Gait Parameters (Patterns) on the Browser
152(8)
Determining Gait Patterns Using Threshold Values
160(1)
Summary
161(1)
References
162(1)
Chapter 6 Future Possibilities for Running Al Methods in a Browser
163(14)
Introduction
163(2)
Additional Machine Learning Applications with TensorFlow
165(1)
Face Recognition Using face-api.js
165(2)
Hand Pose Estimation
167(8)
Summary
175(1)
References
175(2)
Conclusion 177(2)
Index 179
Nagender Kumar Suryadevara received his Ph.D. from the School of Engineering and Advanced Technology, Massey University, New Zealand, in 2014. He has authored two books and over 45 publications in different international journals, conferences, and book chapters. His research interests lie in the domains of wireless sensor networks, Internet of Things technologies, and time-series data mining.
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