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Beginning machine learning in the browser: quick-start guide to gait analysis with JavaScript and TensorFlow.js
Author
Publisher
Apress
Publication Date
2021
Language
English
Description
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ISBN
9781484268438
Table of Contents
From the eBook
Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Preface
Chapter 1: Web Development
Machine Learning Overview
Web Communication
Organizing the Web with HTML
Web Development Using IDEs/Editors
Building Blocks of Web Development
HTML and CSS Programming
Dynamic HTML
Cascading Style Sheets
Inline Style Sheets
Embedded Style Sheets
External Style Sheets
JavaScript Basics
Including the JavaScript
Where to Insert JS Scripts
JavaScript for an Event-Driven Process
Document Object Model Manipulation
Introduction to jQuery
Summary
References
Chapter 2: Browser-Based Data Processing
JavaScript Libraries and API for ML on the Web
W3C WebML CG (Community Group)
Manipulating HTML Elements Using JS Libraries
p5.js
Drawing Graphical Objects
Manipulating DOM Objects
DOM onEvent(mousePressed) Handling
Multiple DOM Objects onEvent Handling
HTML Interactive Elements
Interaction with HTML and CSS Elements
Hierarchical (Parent-Child) Interaction of DOM Elements
Accessing DOM Parent-Child Elements Using Variables
Graphics and Interactive Processing in the Browser Using p5.js
Interactive Graphics Application
Object Instance, Storage of Multiple Values, and Loop Through Object
Getting Started with Machine Learning in the Browser Using ml5.js and p5.js
Design, Develop, and Execute Programs Locally
Method 1: Using Python
HTTP Server
Method 2: Using Visual Studio Code Editor with Node.js Live Server
Summary
References
Chapter 3: Human Pose Estimation in the Browser
Human Pose at a Glance
PoseNet vs. OpenPose
Human Pose Estimation Using Neural Networks
DeepPose: Human Pose Estimation via Deep Neural Networks
Efficient Object Localization Using Convolutional Networks
Convolutional Pose Machines
Human Pose Estimation with Iterative Error Feedback
Stacked Hourglass Networks for Human Pose Estimation
Simple Baselines for Human Pose Estimation and Tracking
Deep High-Resolution Representation Learning for Human Pose Estimation
Using the ml5.js:posenet() Method
Input, Output, and Data Structure of the PoseNet Model
Input
Output
on() Function
Summary
References
Chapter 4: Human Pose Classification
Need for Human Pose Estimation in the Browser
ML Classification Techniques in the Browser
ML Using TensorFlow.js
Changing Flat File Data into TensorFlow.js Format
Artificial Neural Network Model in the Browser Using TensorFlow.js
Trivial Neural Network
Example 1: Neural Network Model in TensorFlow.js
Example 2: A Simple ANN to Realize the "Not AND" (NAND) Boolean Operation
Human Pose Classification Using PoseNet
Setting Up a PoseNet Project
Step 1: Including TensorFlow.js and PoseNet Libraries in the HTML Program (Main File)
Subjects
Subjects
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