  Deep learning techniques, namely convolutional neural networks (CNN), have
previously been adapted to select gamma-ray events in the TAIGA experiment,
having achieved a good quality of selection as compared with the conventional
Hillas approach. Another important task for the TAIGA data analysis was also
solved with CNN: gamma-ray energy estimation showed some improvement in
comparison with the conventional method based on the Hillas analysis.
Furthermore, our software was completely redeveloped for the graphics
processing unit (GPU), which led to significantly faster calculations in both
of these tasks. All the results have been obtained with the simulated data of
TAIGA Monte Carlo software; their experimental confirmation is envisaged for
the near future.

Instrumentation and Methods for Astrophysics;Distributed, Parallel, and Cluster Computing;Machine Learning;Machine Learning