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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

Product type Paperback

Published in Nov 2019

Publisher Packt

ISBN-13 9781838821739

Length 296 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Kai Sasaki

View More author details

Table of Contents (17) Chapters

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

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Questions

Which backend implementations enable us to make use of GPU resources on the web platform?
Which web browser does not support the following new standards respectively?
1. WebGPU
2. WebAssembly
What is gl_GlobalInvocationID in the WebGPU context?
What's one benefit of using WebAssembly in our application?
Are the following platforms supported by React Native?
1. Windows
2. macOS
3. tvOS
Can we make use of hardware acceleration such as the GPU in the Electron application?

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Authors (1)

Sasaki

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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