In my experience, the reason is less due to speed and more because there's a perception that training algorithms for things like RBMs still involve a certain am

In my experience, the reason is less due to speed and more because there's a perception that training algorithms for things like RBMs still involve a certain amount of "black magic" in tuning parameters, deciding when it's converged, etc, in contrast to linear/logistic regression or support vector machines, where you can basically turn a crank and get an answer out.