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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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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js FREE CHAPTER
2. Machine Learning for the Web 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

Experimental backend implementations

We have introduced several backend implementations. They are Vanilla CPU, WebGL, and Node.js. These backends were provided from the very beginning of TensorFlow.js, but the community keeps developing new backend implementations to support many more use cases and platforms. Bear in mind that most of them are still experimental and they do not fully support all operations. Let's take a look at how and when to use these backends in our applications.

WebGPU – a new standard for accelerated graphics and computations

WebGPU is a new web standard for accelerated graphics processing and computation. It provides a brand-new JavaScript API that allows us to flexibly achieve higher-performance...

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