Description

Book Synopsis


Table of Contents
Chapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS References
Chapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data structures of Posenet model References
Chapter 3: Human Pose Classification Classification techniques using ML Neural Network in the browser Human Pose classification based on the outputs of Posenet model Consideration of poses using Confidence scores of Posenet model Storage of data using JSON formats related to the outputs of Posenet model References
Chapter 4: Gait Analysis Normal vs Abnormal Gait patterns Determination of Gait patterns using threshold values of the models User Interface design and development for monitoring of Gait patterns Real-Time data visualization of the Gait patterns on the browser References
Chapter 5: Future Possible Applications of Key Concepts

Beginning Machine Learning in the Browser

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    A Paperback / softback by Nagender Kumar Suryadevara

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      View other formats and editions of Beginning Machine Learning in the Browser by Nagender Kumar Suryadevara

      Publisher: APress
      Publication Date: 02/04/2021
      ISBN13: 9781484268421, 978-1484268421
      ISBN10: 1484268423

      Description

      Book Synopsis


      Table of Contents
      Chapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS References
      Chapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data structures of Posenet model References
      Chapter 3: Human Pose Classification Classification techniques using ML Neural Network in the browser Human Pose classification based on the outputs of Posenet model Consideration of poses using Confidence scores of Posenet model Storage of data using JSON formats related to the outputs of Posenet model References
      Chapter 4: Gait Analysis Normal vs Abnormal Gait patterns Determination of Gait patterns using threshold values of the models User Interface design and development for monitoring of Gait patterns Real-Time data visualization of the Gait patterns on the browser References
      Chapter 5: Future Possible Applications of Key Concepts

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