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 sim...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Suryadevara, Nagender Kumar
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress, 2021.
Temas:
Machine learning.
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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)

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