  Deep neural networks are a powerful technique that have found ample
applications in several branches of Physics. In this work, we apply machine
learning algorithms to a specific problem of Cosmic Ray Physics: the estimation
of the muon content of extensive air showers when measured at the ground. As a
working case, we explore the performance of a deep neural network applied to
the signals recorded by the water-Cherenkov detectors of the Surface Detector
Array of the Pierre Auger Observatory. We apply deep learning architectures to
large sets of simulated data. The inner structure of the neural network is
optimized through the use of genetic algorithms. To obtain a prediction of the
recorded muon signal in each individual detector, we train neural networks with
a mixed sample of light, intermediate and heavy nuclei. When true and predicted
signals are compared at detector level, the primary values of the Pearson
correlation coefficients are above 95\%. The relative errors of the predicted
muon signals are below 10\% and do not depend on the event energy, zenith
angle, total signal size, distance range or the hadronic model used to generate
the events.
