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You're reading from Hands-On Unsupervised Learning with Python Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

Product type Paperback

Published in Feb 2019

Publisher Packt

ISBN-13 9781789348279

Length 386 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Deep Learning

Authors (2):

Giuseppe Bonaccorso

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Table of Contents (12) Chapters

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

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Technical requirements

The code presented in this chapter requires the following:

Python 3.5+ (The Anaconda distribution is highly recommended)
The following libraries:
- SciPy 0.19+
- NumPy 1.10+
- scikit-learn 0.20+
- pandas 0.22+
- Matplotlib 2.0+
- seaborn 0.9+
- TensorFlow 1.5+
- deep-belief-network (https://github.com/albertbup/deep-belief-network)

The examples are available in the GitHub repository, at https://github.com/PacktPublishing/HandsOn-Unsupervised-Learning-with-Python/Chapter08.

The rest of the chapter is locked

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Authors (2)

Bonaccorso

Giuseppe Bonaccorso is Head of Data Science in a large multinational company. He received his M.Sc.Eng. in Electronics in 2005 from University of Catania, Italy, and continued his studies at University of Rome Tor Vergata, and University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt.

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Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

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