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

Summary

In this chapter, we introduced how the regression problem can be solved by TensorFlow.js. The regression problem is common in any machine learning application. Now, you know how to write an application that solves this type of problem.

You have learned what the regression problem is and how the polynomial regression model can be applied to solve the problem. Polynomial regression is a simple mathematical model that predicts the continuous target value. However, it can show a pretty good result if we use a well-tuned optimizer such as Adam. Therefore, you should also learn how iterative optimization works. We will look at this continuously throughout this book.

Regression problems can appear in any kind of format, but for simplicity, we tried to solve the sine curve fitting problem as an example of a regression problem. You saw how your model fit the target curve properly...