  We present the first estimation of the mass and spin magnitude of Kerr black
holes resulting from the coalescence of binary black holes using a deep neural
network. The network is trained on a dataset containing 80\% of the full
publicly available catalog of numerical simulations of gravitational waves
emission by binary black hole systems, including full precession effects for
spinning binaries. The network predicts the remnant black holes mass and spin
with an error less than 0.04\% and 0.3\% respectively for 90\% of the values in
the non-precessing test dataset, it is 0.1\% and 0.3\% respectively in the
precessing test dataset. When compared to existing fits in the LIGO algorithm
software library, the network enables to reduce the remnant mass root mean
square error to one half in the non-precessing case. In the precessing case,
both remnant mass and spin mean square errors are decreased to one half, and
the network corrects the bias observed in available fits.
