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An Introduction to Variational Autoencoders

by Diederik P. Kingma | 28 November 2019
Category: Computer Science
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 reader of a topic that has become an important tool in modern-day deep learning techniques.
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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 reader of a topic that has become an important tool in modern-day deep learning techniques.
Currently out of stock
Delivery in 5 - 7 working days
Eligible for free delivery
239 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

€79.80
Currently out of stock
Delivery in 5 - 7 working days
Eligible for free delivery
239 Reward Points

Any purchases for more than €10 are eligible for free delivery anywhere in the UK or Ireland!

Product Description

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 reader of a topic that has become an important tool in modern-day deep learning techniques.

Product Details

An Introduction to Variational Autoencoders

ISBN9781680836226

Format

PublisherNOW PUBLISHERS (28 November. 2019)

No. of Pages102

Weight164

Language English (United States)

Dimensions 234 x 156 x 9