  We present a novel method to infer the Dark Matter (DM) content and spatial
distribution within galaxies, based on convolutional neural networks trained
within state-of-the-art hydrodynamical simulations (Illustris TNG100). The
framework we have developed is capable of inferring the DM mass distribution
within galaxies of mass $~10^{11}-10^{13}M_{\odot}$ with very high performance
from the gravitationally baryon dominated internal regions to the DM-rich,
baryon-depleted outskirts of the galaxies. With respect to traditional methods,
the one presented here also possesses the advantages of not relying on a
pre-assigned shape for the DM distribution, to be applicable to galaxies not
necessarily in isolation, and to perform very well even in the absence of
spectroscopic observations
