  The estimation of the guessing probability has paramount importance in
quantum cryptographic processes. It can also be used as a witness for nonlocal
correlations. In most of the studied scenarios, estimating the guessing
probability amounts to solving a semi-definite programme, for which potent
algorithms exist. However, the size of those programs grows exponentially with
the system size, becoming infeasible even for small numbers of inputs and
outputs. We have implemented deep learning approaches for some relevant Bell
scenarios to confront this problem. Our results show the capabilities of
machine learning for estimating the guessing probability and for understanding
nonlocality.
