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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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Toc

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 1

  1. Unsupervised learning can be applied independently from supervised approaches, because its goal is different. If a problem requires a supervised approach, often unsupervised learning cannot be employed as an alternative solution. In general, unsupervised methods try to extract pieces of information from a dataset (for example, clustering) without any external hint (such as the prediction error). Conversely, supervised methods require hints in order to correct their parameters.
  2. As the goal is finding the causes of the trend, it's necessary to perform a diagnostic analysis.
  3. No; the likelihood of n independent samples being drawn from the same distribution is obtained as a product of the single probabilities (see question 4 for the main assumption).
  4. The main hypothesis is that the samples are independent and identically distributed (IID).
  5. The gender can be encoded...
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