Beginning Machine Learning in the Browser: Quick-Start Guide to Gait Analysis with JavaScript and Tensorflow.Js

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Beginning Machine Learning in the Browser: Quick-Start Guide to Gait Analysis with JavaScript and Tensorflow.Js

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內容描述

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

作者介紹

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.