  In this work, we present a new, high performance algorithm for background
rejection in imaging atmospheric Cherenkov telescopes. We build on the already
popular machine-learning techniques used in gamma-ray astronomy by the
application of the latest techniques in machine learning, namely recurrent and
convolutional neural networks, to the background rejection problem. Use of
these machine-learning techniques addresses some of the key challenges
encountered in the currently implemented algorithms and helps to significantly
increase the background rejection performance at all energies.
  We apply these machine learning techniques to the H.E.S.S. telescope array,
first testing their performance on simulated data and then applying the
analysis to two well known gamma-ray sources. With real observational data we
find significantly improved performance over the current standard methods, with
a 20-25\% reduction in the background rate when applying the recurrent neural
network analysis. Importantly, we also find that the convolutional neural
network results are strongly dependent on the sky brightness in the source
region which has important implications for the future implementation of this
method in Cherenkov telescope analysis.
