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Hands-On Machine Learning with TensorFlow.js

You're reading from   Hands-On Machine Learning with TensorFlow.js A guide to building ML applications integrated with web technology using the TensorFlow.js library

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838821739
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Kai Sasaki Kai Sasaki
Author Profile Icon Kai Sasaki
Kai Sasaki
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
2. Machine Learning for the Web FREE CHAPTER 3. Importing Pretrained Models into TensorFlow.js 4. TensorFlow.js Ecosystem 5. Section 2: Real-World Applications of TensorFlow.js
6. Polynomial Regression 7. Classification with Logistic Regression 8. Unsupervised Learning 9. Sequential Data Analysis 10. Dimensionality Reduction 11. Solving the Markov Decision Process 12. Section 3: Productionizing Machine Learning Applications with TensorFlow.js
13. Deploying Machine Learning Applications 14. Tuning Applications to Achieve High Performance 15. Future Work Around TensorFlow.js 16. Other Books You May Enjoy

Module bundler

A module bundler is a tool that bundles all the resources that are needed to run the application in a deployable format. Web applications depend on various kinds of resources and assets, such as HTML, stylesheets, JavaScript code, and images. They are essentially separate files. We need to build an artifact that contains all the necessary resources in order to deploy the application. The JavaScript community develops many bundle tools for this purpose. Naturally, we can make use of them when building machine learning applications on the web.

In this section, we are going to use some bundle tools to create packages for our machine learning application.

Parcel

We have already used Parcel in this book. The code...

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