Preprint Technical Note Version 1 This version is not peer-reviewed

A Beginner's Tutorial of Restricted Boltzmann Machines

Version 1 : Received: 19 March 2020 / Approved: 23 March 2020 / Online: 23 March 2020 (05:51:32 CET)

How to cite: Cheng, Y. A Beginner's Tutorial of Restricted Boltzmann Machines. Preprints 2020, 2020030337 (doi: 10.20944/preprints202003.0337.v1). Cheng, Y. A Beginner's Tutorial of Restricted Boltzmann Machines. Preprints 2020, 2020030337 (doi: 10.20944/preprints202003.0337.v1).

Abstract

Restricted Boltzmann machines (RBMs) are the building blocks of some deep learning networks. However, despite their importance, it is our perception that some very important derivations about the RBM are missing in the literature, and a beginner may feel RBM very hard to understand. We provide here these missing derivations. We cover the classic Bernoulli-Bernoulli RBM and the Gaussian-Bernoulli RBM, but leave out the ``continuous'' RBM as it is believed not as mature as the former two. This tutorial can be used as a companion or complement to the famous RBM paper ``Training restricted Boltzmann machines: An introduction'' by Fisher and Igel.

Subject Areas

Restricted Boltzmann machines; artificial intelligence; deep learning

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