  At low energies, the microscopic characteristics and changes of physical
systems as viewed at different distance scales are described by universal scale
invariant properties investigated by the Renormalization Group (RG) apparatus,
an efficient tool used to deal with scaling problems in effective field
theories. We employ an information-theoretic approach in a deep learning setup
by introducing an artificial neural network algorithm to map and identify new
physical degrees of freedom. Using deep learning methods mapped to a genuine
field theory, we develop a mechanism capable to identify relevant degrees of
freedom and induce scale invariance without prior knowledge about a system. We
show that deep learning algorithms that use an RG-like scheme to learn relevant
features from data could help to understand the nature of the holographic
entanglement entropy and the holographic principle in context of the AdS/CFT
correspondence.
