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

Book 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.

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
Who this book is 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.

Table of Contents
Machine Learning for the Web
Importing Pre-trained Models into TensorFlow.js
TensorFlow.js Ecosystem
Polynomial Regression
Classification with Logistic Regression
Unsupervised Learning
Sequential Data Analysis
Dimensionality Reduction
Solving Markov decision problems
Deploying Machine Learning Applications
Tuning applications to achieve high performance
Future Works around TensorFlow.js