  We discuss the determination of the parton substructure of hadrons by casting
it as a peculiar form of pattern recognition problem in which the pattern is a
probability distribution, and we present the way this problem has been tackled
and solved. Specifically, we review the NNPDF approach to PDF determination,
which is based on the combination of a Monte Carlo approach with neural
networks as basic underlying interpolators. We discuss the current NNPDF
methodology, based on genetic minimization, and its validation through closure
testing. We then present recent developments in which a hyperoptimized
deep-learning framework for PDF determination is being developed, optimized,
and tested.
