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

Model visualization

Visualization is an efficient way of learning about what happens in the machine learning model. The progress of the training process can be tracked in terms of the accuracy or loss value of the target function. Seeing how the elements of a tensor are distributed can also provide us with some insight into how the machine learning algorithm runs. In this section, we are going to look at tfjs-vis, which is a visualization tool that's been designed especially for the TensorFlow.js framework.

As is often the case, tfjs-vis can be installed using npm. It provides UI components that can be easily and seamlessly rendered in our machine learning application. The tool has a pane on the right-hand side of the UI. Here, we can add any number of components to show the metrics of the machine learning model.

First, the layer inspection section provides information about...