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

Apply Artificial Intelligence techniques in the browser or on resource-constrained computing devices. This book takes advantage of the intricacies of the Machine Learning (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, you'll 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.

Machine learning (ML) can be an intimidating subject until you know the essentials and for what applications it works. 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. You'll learn to work with ML models, calculations, and information gathering, and implement TensorFlow.js libraries for ML models. Additionally, you'll perform Human Gait Analysis using ML techniques in the browser.

This book is intended for computer science students and research scholars, as well as novice programmers and web developers in the domain of Internet Technologies. By mastering the techniques and concepts presented in this book, you'll be able to leverage the power of Artificial Intelligence and Machine Learning in your browser-based applications and resource-constrained computing devices.

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