Book Image

Hands-On Machine Learning with TensorFlow.js

By : Kai Sasaki
Book Image

Hands-On Machine Learning with TensorFlow.js

By: Kai Sasaki

Overview of this book

TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
5
Section 2: Real-World Applications of TensorFlow.js
12
Section 3: Productionizing Machine Learning Applications with TensorFlow.js

Summary

In this chapter, we covered the basic tools and software that support web platforms. Web platforms are supported by numerous kinds of ecosystems, including programming languages that alternate between the de facto standard and package managers. While JavaScript is the primary language that's used in web applications, TypeScript is gaining popularity rapidly because of its safety and scalability.

Due to the way web platforms work, bundling all the resources into a portable format is required if we wish to publish the application. For machine learning applications in particular, it is common to have multiple types of resources in an application, such as images, audio, and movies, that can be used for training and prediction. Module bundlers can help us build the final artifacts. We looked at Parcel and Webpack so that we can use them to build a publishable format for...