  The IceCube Neutrino Observatory is able to measure the all-flavor neutrino
flux in the energy range between 100 GeV and several PeV. Due to the different
features of the neutrino interactions and the geometry of the detector, all
high-level analyses require a selection of suitable events as a first step.
However, presently, no algorithm exists that gives a generic prediction of an
event's underlying interaction type. One possible solution to this is the use
of deep neural networks similar to the ones commonly used for 2D image
recognition. The classifier that we present here is based on the modern
InceptionResNet architecture and includes multi-task learning in order to
broaden the field of application and increase the overall accuracy of the
result. We provide a detailed discussion of the network's architecture, examine
the performance of the classifier for event type classification and explain
possible applications in IceCube.
