Beginning Machine Learning in the Browser: Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js

Book Description

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

中文：

书名：Beginning Machine Learning in the Browser: Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js

在浏览器或资源受限的计算设备上应用人工智能技术。机器学习(ML)可能是一个令人生畏的学科，直到你知道它的本质以及它对哪些应用程序有效。这本书通过使用简单、灵活和可移植的编程语言(如JavaScript)来利用ML过程的复杂性，从而使用更容易接近的基本编码思想。

使用JavaScript编程特性和标准库，您将首先学习设计和开发交互式图形应用程序。然后进一步研究神经系统和人体姿势估计策略。为了在浏览器中培训和部署ML模型，将重点介绍TensorFlow.js库。

在掌握了基础知识之后，您将深入了解ML的荒野。使用ML和处理(P5)库进行人体步态分析。构建具有主题的步态识别，您将逐渐了解各种ML实现问题。例如，您将了解正常步态模式和异常步态模式的分类。

With在浏览器中开始机器学习，您将成为一名经验丰富的机器学习开发人员。

What You’ll Learn

• 使用ML模型、计算和信息收集
• 为ML模型实现TensorFlow.js库
• 在浏览器中使用ML技术执行人体步态分析

这本书是为谁写的

计算机科学专业的学生和研究学者，以及互联网技术领域的新手程序员/网站开发人员