  The Cherenkov Telescope Array (CTA) will be the world's leading ground-based
gamma-ray observatory allowing us to study very high energy phenomena in the
Universe. CTA will produce huge data sets, of the order of petabytes, and the
challenge is to find better alternative data analysis methods to the already
existing ones. Machine learning algorithms, like deep learning techniques, give
encouraging results in this direction. In particular, convolutional neural
network methods on images have proven to be effective in pattern recognition
and produce data representations which can achieve satisfactory predictions. We
test the use of convolutional neural networks to discriminate signal from
background images with high rejections factors and to provide reconstruction
parameters from gamma-ray events. The networks are trained and evaluated on
artificial data sets of images. The results show that neural networks trained
with simulated data can be useful to extract gamma-ray information. Such
networks would help us to make the best use of large quantities of real data
coming in the next decades.
