An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify a subset of jets that were strongly modified by the interaction with the QGP. In this talk, we will show how deep learning techniques can be applied for this purpose. Samples of $Z$+jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance will be shown. The results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.