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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 6

  1. As the random variables are clearly independent, P(Tall, Rain) = P(Tall)P(Rain) = 0.75 • 0.2 = 0.15.
  2. One of the main drawbacks of histograms is that when the number of bins is too large, many of them start to be empty, because there are no samples in all of the value ranges. In this case, either the cardinality of X can be smaller than 1,000, or, even with more than 1,000 samples, the relative frequencies can be concentrated in a number of bins smaller than 1,000.
  3. The total number of samples is 75, and the bins have equal lengths. Hence, P(0 < x < 2) = 20/75 ≈ 0.27, P(2 < x < 4) = 30/75 = 0.4, and P(4 < x < 6) = 25/75 ≈ 0.33. As we don't have any samples, we can assume that P(x > 6) = 0; therefore, P(x > 2) = P(2 < x < 4) + P(4 < x < 6) ≈ 0.73. We have an immediate confirmation, considering that 0...
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