  At the CERN Large Hadron Collider experiment, the non-resonant double Higgs
production via vector-boson fusion represents a unique mean to probe the VVHH
(V=Z, W$^{\pm}$) Higgs self-coupling at the current center of mass energies.
Such a rare signal cannot be separated efficiently from huge backgrounds by
applying a few-observables cut-based selection. Indeed, in this work, a Deep
Learning algorithm is used to decide whether an event is more signal- or
background-like. In particular, we report on two main aspects: results of a
hyper-parameters parallel scanning strategy to distribute the training process
across multiple nodes on the ReCaS-Bari data center computing resources and the
discriminating performance of a Deep Neural Network architecture.
