Beginning Machine Learning in the Browser: Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js
Category
Author
Publication
Apress
From the Back Cover
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, youll learn about the classification of normal and abnormal Gait patterns.
With Beginning Machine Learning in the Browser, youll be on your way to becoming an experienced Machine Learning developer.
You will:
- 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
