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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

Further reading

  • Epanechnikov V A, Non-parametric estimation of a multivariate probability density, Theory of Probability and its Applications, 14, 1969

  • Parzen E, On Estimation of a Probability Density Function and Mode, The Annals of Mathematical Statistics, 1962

  • Sheather S J, The performance of six popular bandwidth selection methods on some real data sets (with discussion), Computational Statistics, 7, 1992

  • Schölkopf B, Platt J C, Shawe-Taylor J C, Smola A J, Williamson R C, Estimating the support of a high-dimensional distribution, Neural Computation, 13/7, 2001

  • Liu F T, Ting K M, Zhou Z, Isolation forest, ICDM 2008, Eighth IEEE International Conference on Data Mining, 2008

  • Dayan P, Abbott L F, Theoretical Neuroscience, The MIT Press, 2005

  • Machine Learning Algorithms Second Edition, Bonaccorso G., Packt Publishing, 2018

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