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

Classification with Logistic Regression

Logistic regression is one of the most commonly used linear classification models. Although it was developed a long time ago, it is still widely used in the practical field of industries. It is not only powerful but also simple enough that it can be a good resource when it comes to understanding the classification problem of machine learning.

This classification problem is categorized as a supervised learning-type problem and it is the most common setting in the machine learning field. It aims to attach the label to a new incoming instance by using information from past observations. These labels can be interpreted as non-ordered discrete values, unlike continuous values, which we learned about in the previous chapter. In this chapter, we are going to learn about a powerful traditional algorithm called logistic regression by trying to solve...