  Machine Learning is an efficient method for analyzing and interpreting the
increasing amount of astronomical data that is available. In this study, we
show, a pedagogical approach that should benefit anyone willing to experiment
with Deep Learning techniques in the context of stellar parameters
determination. Utilizing the Convolutional Neural Network architecture, we give
a step by step overview of how to select the optimal parameters for deriving
the most accurate values for the stellar parameters of stars: T$_{\rm{eff}}$,
$\log g$, [X/H], and $v_e \sin i$. Synthetic spectra with random noise were
used to constrain this method and to mimic the observations. We found that each
stellar parameter requires a different combination of network hyperparameters
and the maximum accuracy reached depends on this combination, as well as, the
Signal to Noise ratio of the observations, and the architecture of the network.
We also show that this technique can be applied to other spectral types in
different wavelength ranges after the technique has been optimized.
