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

TensorFlow.js Ecosystem

Just like TensorFlow, TensorFlow.js has a bunch of ecosystem libraries. These libraries help us to build applications quickly and efficiently because some of them are designed to allow us to develop machine learning applications intuitively. In this chapter, we are going to introduce tools and libraries built on top of TensorFlow.js that we can use to accelerate the development of our application. Because these libraries are available as open source software, you can customize and contribute to them if necessary to meet your requirements.

The following topics will be covered in this chapter:

  • Why high-level libraries?
  • Using existing machine learning models
  • MobileNet in tfjs-models
  • Supported models by tfjs-models
  • Image classification application
  • Example applications in the community
  • Loading the data from various kinds of storage
  • Data sources
  • Webcam
  • Pose...