January 2019
Intermediate to advanced
342 pages
9h 17m
English
Content preview from Intelligent Projects Using Python
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Contrastive divergence
One of the ways to compute the expectation of a joint probability distribution is to generate a lot of samples from the joint probability distribution by Gibbs sampling and then take the mean value of the samples as the expected value. In Gibbs sampling, each of the variables in the joint probability distribution can be sampled, conditioned on the rest of the variables. Since the visible units are independent, given the hidden units and vice versa, you can sample the hidden unit as 
and then the visible unit activation given the hidden unit as . We can then take the sample as one sampled from the joint probability distribution. ...
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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.