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Hands-On Unsupervised Learning with Python

You're reading from Hands-On Unsupervised Learning with Python

Product type Book

Published in Feb 2019

Publisher Packt

ISBN-13 9781789348279

Pages 386 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Giuseppe Bonaccorso

Table of Contents (12) Chapters

Preface

1. Getting Started with Unsupervised Learning

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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Questions

What's the main difference between soft and hard clustering?
Fuzzy c-means can easily deal with non-convex clusters. Is this statement correct?
Which is the main assumption of a Gaussian mixture?
Suppose that two models achieve the same optimal log-likelihood; however, the first one has an AIC that is double the second one. What does this mean?
Considering the previous question, which model would we prefer?
Why would we want to employ the Dirichlet distribution as the prior for the weights of a Bayesian Gaussian mixture?
Suppose that we have a dataset containing 1,000 labeled samples, whose values have been certified by an expert. We collect 5,000 samples from the same sources, but we don't want to pay for extra labeling. What can we do in order to incorporate them into our model?

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Authors (1)

Giuseppe 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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