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

Solving the Markov Decision Process

Reinforcement learning is one of the most exciting fields in machine learning. There are many machine learning systems leveraged by reinforcement learning algorithms. AlphaGo is the famous example for this type of application. So far, we have seen applications using supervised learning and unsupervised learning techniques. While they are powerful in specific fields, they do not provide a distinct solution where even a human does not know the correct answer. In this chapter, we are going to introduce reinforcement learning, which has become one of the most prominent fields recently. This technology has shown outstanding performance when there is no complete knowledge about the target, such as in TV games. We will also learn how to implement the Bellman equation for solving Markov decision process (MDP) problems and how it relates to reinforcement...