Editing Boltzmann machine (section)

===Restricted Boltzmann machine===
[[File:Restricted Boltzmann machine.svg|thumb|right|alt=Graphical representation of an example restricted Boltzmann machine |Graphical representation of a restricted Boltzmann machine. The four blue units represent hidden units, and the three red units represent visible states. In restricted Boltzmann machines there are only connections (dependencies) between hidden and visible units, and none between units of the same type (no hidden-hidden, nor visible-visible connections).]]
{{Main|Restricted Boltzmann machine}}
Although learning is impractical in general Boltzmann machines, it can be made quite efficient in a restricted Boltzmann machine (RBM) which does not allow intralayer connections between hidden units and visible units, i.e. there is no connection between visible to visible and hidden to hidden units. After training one RBM, the activities of its hidden units can be treated as data for training a higher-level RBM. This method of stacking RBMs makes it possible to train many layers of hidden units efficiently and is one of the most common [[deep learning]] strategies. As each new layer is added the generative model improves.

An extension to the restricted Boltzmann machine allows using real valued data rather than binary data.<ref>{{Citation|title=Recent Developments in Deep Learning| date=22 March 2010 |url=https://www.youtube.com/watch?v=VdIURAu1-aU |archive-url=https://ghostarchive.org/varchive/youtube/20211222/VdIURAu1-aU |archive-date=2021-12-22 |url-status=live|language=en|access-date=2020-02-17}}{{cbignore}}</ref>

One example of a practical RBM application is in speech recognition.<ref>{{cite journal |url=http://research.microsoft.com/pubs/144412/DBN4LVCSR-TransASLP.pdf |title=Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition |journal=Microsoft Research |volume=20 |year=2011|last1=Yu |first1=Dong |last2=Dahl |first2=George |last3=Acero |first3=Alex |last4=Deng |first4=Li }}</ref>