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Hands-On Unsupervised Learning with Python

You're reading from   Hands-On Unsupervised Learning with Python Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789348279
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Unsupervised Learning FREE CHAPTER 2. Clustering Fundamentals 3. Advanced Clustering 4. Hierarchical Clustering in Action 5. Soft Clustering and Gaussian Mixture Models 6. Anomaly Detection 7. Dimensionality Reduction and Component Analysis 8. Unsupervised Neural Network Models 9. Generative Adversarial Networks and SOMs 10. Assessments 11. Other Books You May Enjoy

Chapter 9

  1. No; the generator and discriminator are functionally different.
  2. No, it can't, because the output of a discriminator must be a probability (that is, pi ∈ (0, 1)).
  3. Yes; it's correct. The discriminator can learn to output different probabilities very quickly, and the gradients of its loss function can become close to 0, reducing the magnitude of the correction feedback provided to the generator.
  4. Yes; it's normally quite slower.
  5. The critic is slower, because the variables are clipped after every update.
  6. As the supports are disjointed, the Jensen-Shannon divergence is equal to log(2).
  7. The goal is to develop highly selective units whose responses are only elicited by a specific feature set.
  8. It's impossible to know the final organization during the early stages of the training process; therefore, it's not a good practice to force the premature...
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