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Applied Unsupervised Learning with Python

You're reading from   Applied Unsupervised Learning with Python Discover hidden patterns and relationships in unstructured data with Python

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781789952292
Length 482 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Table of Contents (12) Chapters Close

Applied Unsupervised Learning with Python
Preface
1. Introduction to Clustering FREE CHAPTER 2. Hierarchical Clustering 3. Neighborhood Approaches and DBSCAN 4. Dimension Reduction and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding (t-SNE) 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

Chapter 5. Autoencoders

Note

Learning Objectives

By the end of this chapter, you will be able to do the following:

  • Explain where autoencoders can be applied and their use cases

  • Understand how artificial neural networks are implemented and used

  • Implement an artificial neural network using the Keras framework

  • Explain how autoencoders are used in dimensionality reduction and denoising

  • Implement an autoencoder using the Keras framework

  • Explain and implement an autoencoder model using convolutional neural networks

Note

In this chapter, we will take a look at autoencoders and their applications.

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