  We explore machine learning methods to detect gravitational waves (GW) from
binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL
networks are trained with gravitational waveforms obtained from BBH mergers
with component masses randomly sampled in the range from 5 to 100 solar masses
and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal
waveforms are injected in public data from the O2 run of the Advanced LIGO and
Advanced Virgo detectors, in time windows that do not coincide with those of
known detected signals. We demonstrate that DL algorithms, trained with GW
signal waveforms at distances of 2000 Mpc, still show high accuracy when
detecting closer signals, within the ranges considered in our analysis.
Moreover, by combining the results of the three-detector network in a unique
RGB image, the single detector performance is improved by as much as 70%.
Furthermore, we train a regression network to perform parameter inference on
BBH spectrogram data and apply this network to the events from the the GWTC-1
and GWTC-2 catalogs. Without significant optimization of our algorithms we
obtain results that are mostly consistent with published results by the
LIGO-Virgo Collaboration. In particular, our predictions for the chirp mass are
compatible (up to 3$\sigma$) with the official values for 90% of events
