An Introduction to Variational Autoencoders (Paperback)

In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient descent. The framework has a wide array of applications from generative modeling, semi-supervised learning to representation learning.The authors expand earlier work and provide the reader with the fine detail on the important topics giving deep insight into the subject for the expert and student alike. Written in a survey-like nature the text serves as a review for those wishing to quickly deepen their knowledge of the topic. An Introduction to Variational Autoencoders provides a quick summary for the of a topic that has become an important tool in modern-day deep learning techniques.

General

Imprint:	Now Publishers Inc
Country of origin:	United States
Series:	Foundations and Trends (R) in Machine Learning
Release date:	November 2019
First published:	2019
Authors:	Diederik P. Kingma • Max Welling
Dimensions:	234 x 156 x 10mm (L x W x T)
Format:	Paperback
Pages:	102
ISBN-13:	978-1-68083-622-6
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-68083-622-6
Barcode:	9781680836226

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