  The reconstruction of top-quark pair-production ($t\bar{t}$) events is a
prerequisite for many top-quark measurements. We use a deep neural network,
trained with Monte-Carlo simulated events, to reconstruct $t\bar{t}$ decays in
the lepton+jets final state. Comparing our approach to a widely-used kinematic
fit, we find significant improvements in the correct assignment of jets to the
partons from the decay, and we study the reconstruction performance of several
kinematic top-quark properties. We document our workflow for the optimisation
of the hyperparameters of the deep neural network. This workflow can be
followed by experimental collaborations to retrain the network taking into
account their detailed detector simulations.
