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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

Questions

  1. How many backend implementations are supported by TensorFlow.js?
  2. Which executes MNIST prediction the fastest: MobileNet, WebGL, or Node.js?
  3. Write a benchmark that shows the poor performance of a WebGL backend compared to a vanilla CPU backend.
  4. What compatibility can we use to reshape tensors? For example, we can reshape a tensor with the shape [2, 3] and make it [1, 2, 3], but we cannot make it [2, 2, 3]. Why?
  1. What is the main disadvantage of using the asynchronous API to download data from a tensor?
  2. How can we track training progress using tf-vis? What type of API can we use?
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