Beginning Machine Learning in the Browser by Nagender Kumar Suryadevara
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Beginning Machine Learning in the Browser

Beginning Machine Learning in the Browser


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About the Book

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
About the Author: Nagender Kumar Suryadevara received his Ph.D. from the School of Engineering and Advanced Technology, Massey University, New Zealand, in 2014. He has authored two books and over 45 publications in different international journals, conferences, and book chapters. His research interests lie in the domains of wireless sensor networks, Internet of Things technologies, and time-series data mining.


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Product Details
  • ISBN-13: 9781484268421
  • Publisher: Apress
  • Publisher Imprint: Apress
  • Height: 234 mm
  • No of Pages: 182
  • Spine Width: 11 mm
  • Weight: 335 gr
  • ISBN-10: 1484268423
  • Publisher Date: 17 Mar 2021
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Quick-Start Guide to Gait Analysis with JavaScript and Tensorflow.Js
  • Width: 156 mm


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