  Parton Distribution Functions (PDFs) model the parton content of the proton.
Among the many collaborations which focus on PDF determination, NNPDF pioneered
the use of Neural Networks to model the probability of finding partons (quarks
and gluons) inside the proton with a given energy and momentum. In this
proceedings we make use of state of the art techniques to modernize the NNPDF
methodology and study different models and optimizers in order to improve the
quality of the PDF: improving both the quality and efficiency of the fits. We
also present the evolutionary_keras library, a Keras implementation of the
Evolutionary Algorithms used by NNPDF.

Computational Physics;High Energy Physics - Phenomenology