  A dramatic progress in the field of computer vision has been made in recent
years by applying deep learning techniques. State-of-the-art performance in
image recognition is thereby reached with Convolutional Neural Networks (CNNs).
CNNs are a powerful class of artificial neural networks, characterized by
requiring fewer connections and free parameters than traditional neural
networks and exploiting spatial symmetries in the input data. Moreover, CNNs
have the ability to automatically extract general characteristic features from
data sets and create abstract data representations which can perform very
robust predictions. This suggests that experiments using Cherenkov telescopes
could harness these powerful machine learning algorithms to improve the
analysis of particle-induced air-showers, where the properties of primary
shower particles are reconstructed from shower images recorded by the
telescopes. In this work, we present initial results of a CNN-based analysis
for background rejection and shower reconstruction, utilizing simulation data
from the H.E.S.S. experiment. We concentrate on supervised training methods and
outline the influence of image sampling on the performance of the CNN-model
predictions.
