  The use of machine learning algorithms in theoretical and experimental
high-energy physics has experienced an impressive progress in recent years,
with applications from trigger selection to jet substructure classification and
detector simulation among many others. In this contribution, we review the
machine learning tools used in the NNPDF family of global QCD analyses. These
include multi-layer feed-forward neural networks for the model-independent
parametrisation of parton distributions and fragmentation functions, genetic
and covariance matrix adaptation algorithms for training and optimisation, and
closure testing for the systematic validation of the fitting methodology.
