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Beginning Machine Learning in the Browser - Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js (Paperback, 1st ed.)
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Beginning Machine Learning in the Browser - Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js (Paperback, 1st ed.)
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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
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