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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

Self-organizing maps

A self-organizing map is a model that was proposed for the first time by Willshaw and Von Der Malsburg (in How Patterned Neural Connections Can Be Set Up by Self- Organization, Willshaw, D. J. and Von Der Malsburg, C., Proceedings of the Royal Society of London, B/194, N. 1117, 1976), with the goal of finding a way to describe different phenomena that happen in the brains of many animals. In fact, they observed that some areas of the brain can develop internally organized structures whose subcomponents are selectively receptive, with respect to specific input patterns (for example, some visual cortex areas are very responsive to vertical or horizontal bands). The central idea of an SOM can be synthesized by thinking about a clustering procedure aimed at finding out the low-level properties of a sample, thanks to its assignment to a cluster. The main practical...

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