BEGINNING MACHINE LEARNING IN THE BROWSER : quick -start guide to gait analysis with javascript... and tensorflow.js

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. What You'll 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 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) Step 2: Single-Person Pose Estimation Using a Browser Webcam PoseNet Model Confidence Values Summary References Chapter 5: Gait Analysis Gait Measurement Techniques Gait Cycle Measurement Parameters and Terminology Web User Interface for Monitoring Gait Parameters index.html Real-Time Data Visualization of the Gait Parameters (Patterns) on the Browser Determining Gait Patterns Using Threshold Values Summary References Chapter 6: Future Possibilities for Running AI Methods in a Browser Introduction Additional Machine Learning Applications with TensorFlow Face Recognition Using face-api.js Hand Pose Estimation Summary References Conclusion Index 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.