  Imaging Atmospheric Cherenkov Telescopes (IACT) of TAIGA astrophysical
complex allow to observe high energy gamma radiation helping to study many
astrophysical objects and processes. TAIGA-IACT enables us to select gamma
quanta from the total cosmic radiation flux and recover their primary
parameters, such as energy and direction of arrival. The traditional method of
processing the resulting images is an image parameterization - so-called the
Hillas parameters method. At the present time Machine Learning methods, in
particular Deep Learning methods have become actively used for IACT image
processing. This paper presents the analysis of simulated Monte Carlo images by
several Deep Learning methods for a single telescope (mono-mode) and multiple
IACT telescopes (stereo-mode). The estimation of the quality of energy
reconstruction was carried out and their energy spectra were analyzed using
several types of neural networks. Using the developed methods the obtained
results were also compared with the results obtained by traditional methods
based on the Hillas parameters.
