LEADER 05033nam 2200445 450 001 9910483611403321 005 20211020172746.0 010 $a1-4842-6843-1 035 $a(CKB)4100000011867188 035 $a(MiAaPQ)EBC6533448 035 $a(Au-PeEL)EBL6533448 035 $a(OCoLC)1245672692 035 $a(CaSebORM)9781484268438 035 $a(PPN)255295154 035 $a(EXLCZ)994100000011867188 100 $a20211020d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBeginning machine learning in the browser $equick-start guide to gait analysis with JavaScript and TensorFlow.js /$fNagender Kumar Suryadevara 210 1$a[Place of publication not identified] :$cAPress,$d[2021] 210 4$d©2021 215 $a1 online resource (xiv, 182 pages) 311 $a1-4842-6842-3 320 $aIncludes bibliographical references and index. 327 $aIntro -- 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. 327 $aConvolutional 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. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aSuryadevara$b Nagender Kumar$0739737 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483611403321 996 $aBeginning machine learning in the browser$92854688 997 $aUNINA