Hands-On Machine Learning with TensorFlow.js

Summary

In this chapter, we looked at several techniques we can use to improve the performance and stability of machine learning applications that are written in TensorFlow.js. Since TensorFlow.js is a framework that accelerates various kinds of runtime systems, such as WebGL, understanding its internal structure and implementation is the key to creating a performant application.

It is also important to profile our application's execution. Without complete knowledge of bottleneck and performance characteristics, we may end up with misplaced optimization. We can make use of the profiler that TensorFlow.js implements, as well as the Chrome profiler, to do this since the machine learning application in TensorFlow.js is just a web application. ...