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

AutoML edge helper

Automated machine learning (AutoML) has been getting hugely popular in recent years due to the ease and speed with which the machine learning technology can be applied to real services.

Cloud ML is a service to provide the functionality to train a machine learning model without much knowledge or experience in the machine learning field. All we need to have is the data and labels to be predicted. The system automatically finds the best model and tunes the hyperparameters. We can use the time saved, to build an adequately good model and apply it in our application.

tfjs-automl is a library to make the application easily integrate with the AutoML Vision Edge API. It allows us to import the model trained in the AutoML Vision Edge service. We can use the library using a npm package:

$ npm i @tensorflow/tfjs-automl

Alternatively, CDN can be used:

<script src=...