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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

Exercise

  1. Come up with a situation around you that can be defined as an MDP problem.
  2. Do you think we can use the state-value function to solve the MDP problem in the same way as we use the action-value function?
  3. Explore the active-function result by changing the following hyperparameters for the four-states MDP introduced here:
    1. Discount ratio
    2. Learning rate
    3. Reward in the transition from state 2 to 3
  1. Use the following policy for the four-states MDP introduced in the chapter:
    1. Always choose action 1.
    2. Always choose action 2.
    3. Choosing the action maximizing the action value.
  2. Try to run the CartPole example in the example code and see how the behavior is changed.
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