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 hyper-parameters, 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.

本文是一份深度学习相关的实用指南，包含了一些常用的超参数建议，特别是关于基于反向传播梯度和基于梯度优化的学习算法方面。讨论了如何处理允许调整多个超参数时可以获得更有趣结果的事实，并总结描述了成功高效地训练和调试大规模深度神经网络的实践方法，最后探讨了更深层次结构的训练困难的开放性问题。