Neural Networks: Tricks of the Trade pp 437-478

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7700) | Cite as

Practical Recommendations for Gradient-Based Training of Deep Architectures

  • Yoshua Bengio

Abstract

Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyperparameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yoshua Bengio
    • 1
  1. 1.Université de MontréalCanada

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