Beginner's Guide to Gait Analysis with JavaScript and TensorFlow.js

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Delve into the world of Artificial Intelligence (AI) and explore its applications within the browser or on resource-constrained computing devices. Machine learning (ML), a subfield of AI, can initially appear intimidating, but this book aims to demystify the essentials and showcase the diverse applications it offers.

Using the simple, flexible, and portable programming language of JavaScript, this book takes you on a journey through the intricacies of ML processes. You'll begin by learning to design and develop interactive graphics applications, leveraging JavaScript programming features and standard libraries. From there, you'll venture deeper into neural systems and human pose estimation strategies.

To facilitate the training and deployment of your ML models within the browser, the book will place a strong emphasis on the TensorFlow.js libraries. These powerful tools will enable you to bring the power of ML to your web-based projects.

After mastering the fundamentals, you'll delve into the broader realm of ML. The book will guide you through the integration of ML and Processing (P5) libraries for Human Gait analysis. By building up Gait recognition with various themes, you'll gain an understanding of a variety of ML implementation challenges. This includes learning about the classification of normal and abnormal Gait patterns.

By the end of "Beginning Machine Learning in the Browser," you'll be well on your way to becoming an experienced Machine Learning developer, equipped with the knowledge and skills to apply AI techniques in the browser or on resource-constrained devices.

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 - Novice programmers/web developers in the domain of Internet Technologies

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