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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788621113
Length 576 pages
Edition 1st Edition
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (17) Chapters Close

Preface 1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Ensemble Learning 9. Neural Networks for Machine Learning 10. Advanced Neural Models 11. Autoencoders 12. Generative Adversarial Networks 13. Deep Belief Networks 14. Introduction to Reinforcement Learning 15. Advanced Policy Estimation Algorithms 16. Other Books You May Enjoy

Variational autoencoders

A variational autoencoder (VAE) is a generative model proposed by Kingma and Wellin (in their work Auto-Encoding Variational Bayes, arXiv:1312.6114 [stat.ML]) that partially resembles a standard autoencoder, but it has some fundamental internal differences. The goal, in fact, is not finding an encoded representation of a dataset, but determining the parameters of a generative process that is able to yield all possible outputs given an input data-generating process.

Let's take the example of a model based on a learnable parameter vector θ and a set of latent variables z that have a probability density function p(z;θ). Our goal can therefore be expressed as the research of the θ parameters that maximize the likelihood of the marginalized distribution p(x;θ) (obtained through the integration of the joint probability p(x,z;θ...

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