Beginning Machine Learning in the Browser

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

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.

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