Hands-On Machine Learning with TensorFlow.js: A guide to building ML applications integrated with web technology using the TensorFlow.js library

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Machine Learning for the Web

In this book, we will learn how to use TensorFlow.js to create machine learning applications. You'll need to be familiar with the following in order to get started:

Web-based programming languages, such as JavaScript and TypeScript
Web platform technology stacks (only a basic knowledge is required)
The fundamentals of machine learning algorithms

In this chapter, we are going to clarify why machine learning on the web is crucial in modern machine learning use cases and when to use web technology so that you can run your applications. You will also be introduced to the basic APIs of TensorFlow.js so that you can construct machine learning models. These topics act as the basis for the chapters that follow.

In this chapter, we will cover the following topics:

Why machine learning on the web?
Operation graphs
What is TensorFlow.js?
Installing TensorFlow...

Key benefits

Build, train and run machine learning models in the browser using TensorFlow.js

Create smart web applications from scratch with the help of useful examples

Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function

Description

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.

Who is this book for?

This book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.

What you will learn

Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset

Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js

Apply the Bellman equation to solve MDP problems

Use the k-means algorithm in TensorFlow.js to visualize prediction results

Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps

Implement tf.js backend frameworks to tune and accelerate app performance

What do you get with Print?