Hands-On Machine Learning with TensorFlow js [kornbolt]

Hands-On Machine Learning with TensorFlow.js [kornbolt]





Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively



Key Features

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

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