Hands-On Machine Learning with TensorFlow.js

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By Kai Sasaki
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    Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
About 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.

Publication date:
November 2019


Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js

In this section, readers will explore how machine learning applications work on the web platform. They will also learn how to set up an environment to run TensorFlow.js. Furthermore, readers will learn how to import pretrained models from Keras into TensorFlow.js. This section will also cover the ecosystem around TensorFlow.js.

This section contains the following chapters:

About the Author
  • Kai Sasaki

    Kai Sasaki works as a software engineer at Treasure Data. He engages in developing largescale distributed systems to make data valuable. His passion for creating artificial intelligence by processing large-scale data led him to the field of machine learning. He is one of the initial contributors to TensorFlow.js and keeps working to add new operators that are required for new types of machine learning models. Because of his work, he received the Google Open Source Peer Bonus in 2018.

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Hands-On Machine Learning with TensorFlow.js
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