  In this follow-up paper, we investigate the use of Convolutional Neural
Network for deriving stellar parameters from observed spectra. Using
hyperparameters determined previously, we have constructed a Neural Network
architecture suitable for the derivation of Teff, log g, [M/H], and vesini. The
network was constrained by applying it to databases of AFGK synthetic spectra
at different resolutions. Then, parameters of A stars from Polarbase, SOPHIE,
and ELODIE databases are derived as well as FGK stars from the Spectroscopic
Survey of Stars in the Solar Neighbourhood. The network model average accuracy
on the stellar parameters are found to be as low as 80 K for Teff , 0.06 dex
for log g, 0.08 dex for [M/H], and 3 km/s for vesini for AFGK stars.
