  The aim of this project is to recover the CMB anisotropies maps in
temperature and polarized intensity by means of a deep convolutional neural
network (CNN) which, after appropiate training, can remove the foregrounds from
Planck and QUIJOTE data. The results are then compared with those obtained by
COMMANDER, based on Bayesian parametric component separation. The CNN
successfully recovered the CMB signal for both All Sky and Partial Sky maps
showing frequency dependant results, being optimum for central frequencies
where there is less contamination by foregrounds emissions such as galactic
synchrotron and thermal dust emissions. Recovered maps in temperature are
consistent with those obtained by Planck Collaboration, while polarized
intensity has been recovered as a new observable. The polarized intensity maps
recovered from QUIJOTE experiment are novel and of potential interest to the
scientific community for the detection of primordial gravitational waves. The
way forward will be to recover the maps at higher NSIDE and make them available
to the scientific community.
