February 2018
Beginner to intermediate
258 pages
5h 47m
English
Content preview from R Deep Learning Projects
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Variational Autoencoders
Variational Autoencoders (VAE) are a more recent take on the autoencoding problem. Unlike autoencoders, which learn a compressed representation of the data, Variational Autoencoders learn the random process that generates such data, instead of learning an essentially arbitrary function as we previously did with our neural networks.
VAEs have also an encoder and decoder part. The encoder learns the mean and standard deviation of a normal distribution that is assumed to have generated the data. The mean and standard deviation are called latent variables because they are not observed explicitly, rather inferred from the data.
The decoder part of VAEs maps back these latent space points into the data. As before, we need ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.