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A Practical Guide to Training Restricted Boltzmann Machines

  • Geoffrey E. Hinton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7700)

Abstract

Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide how to set the values of numerical meta-parameters. Over the last few years, the machine learning group at the University of Toronto has acquired considerable expertise at training RBMs and this guide is an attempt to share this expertise with other machine learning researchers.

Keywords

Learning Rate Reconstruction Error Hide Unit Restrict Boltzmann Machine Training Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geoffrey E. Hinton
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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