  Convolutional Neural Nets, which is a powerful method of Deep Learning, is
applied to classify equation of state of heavy-ion collision event generated
within the UrQMD model. Event-by-event transverse momentum and azimuthal angle
distributions of protons are used to train a classifier. An overall accuracy of
classification of 98\% is reached for Au+Au events at $\sqrt{s_{NN}} = 11$ GeV.
Performance of classifiers, trained on events at different colliding energies,
is investigated. Obtained results indicate extensive possibilities of
application of Deep Learning methods to other problems in physics of heavy-ion
collisions.
