  Deep learning based on artificial neural networks is a powerful machine
learning method that, in the last few years, has been successfully used to
realize tasks, e.g., image classification, speech recognition, translation of
languages, etc., that are usually simple to execute by human beings but
extremely difficult to perform by machines. This is one of the reasons why deep
learning is considered to be one of the main enablers to realize the notion of
artificial intelligence. In order to identify the best architecture of an
artificial neural network that allows one to fit input-output data pairs, the
current methodology in deep learning methods consists of employing a
data-driven approach. Once the artificial neural network is trained, it is
capable of responding to never-observed inputs by providing the optimum output
based on past acquired knowledge. In this context, a recent trend in the deep
learning community is to complement pure data-driven approaches with prior
information based on expert knowledge. In this work, we describe two methods
that implement this strategy, which aim at optimizing wireless communication
networks. In addition, we illustrate numerical results in order to assess the
performance of the proposed approaches compared with pure data-driven
implementations.
