  Extensive air showers created by high-energy particles interacting with the
Earth atmosphere can be detected using imaging atmospheric Cherenkov telescopes
(IACTs). The IACT images can be analyzed to distinguish between the events
caused by gamma rays and by hadrons and to infer the parameters of the event
such as the energy of the primary particle. We use convolutional neural
networks (CNNs) to analyze Monte Carlo-simulated images from the telescopes of
the TAIGA experiment. The analysis includes selection of the images
corresponding to the showers caused by gamma rays and estimating the energy of
the gamma rays. We compare performance of the CNNs using images from a single
telescope and the CNNs using images from two telescopes as inputs.
