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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. https://doi.org/10.20944/preprints202003.0337.v1 Cheng, Y. A Beginner's Tutorial of Restricted Boltzmann Machines. Preprints 2020, 2020030337. https://doi.org/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.
Keywords
Restricted Boltzmann machines; artificial intelligence; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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