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

Why machine learning on the web?

Machine learning technology was invented in the 1950s. Back then, there was no such period where machine learning was the exciting field in computer science that it currently is. However, thanks to breakthroughs in areas of deep learning and artificial intelligence, a huge amount of resources in terms of money and manpower have been devoted to help research it. For example, it isn't unusual to use an extensive amount of computing power that's leveraged by GPUs in laboratories in universities. Nowadays, industries and academics are cooperating to make progress in the computer science field. We are living in an era that's creating and facing large-scale data like never before. The importance of machine learning mainly comes from the demand for providing value by making use of this large-scale data. Machine learning technology gives...