  Deep learning algorithms vary depending on the underlying connection
mechanism of nodes of them. They have various hyperparameters that are either
set via specific algorithms or randomly chosen. Meanwhile, hyperparameters of
deep learning algorithms have the potential to help enhance the performance of
the machine learning tasks. In this paper, a tuning guideline is provided for
researchers who cope with issues originated from hyperparameters of deep
learning models. To that end, four types of deep learning algorithms are
investigated in terms of tuning and data mining perspective. Further, common
search methods of hyperparameters are evaluated on four deep learning
algorithms. Normalization helps increase the performance of classification,
according to the results of this study. The number of features has not
contributed to the decline in the accuracy of deep learning algorithms. Even
though high sparsity results in low accuracy, a uniform distribution is much
more crucial to reach reliable results in terms of data mining.
