  We present a new regression model for the determination of parton
distribution functions (PDF) using techniques inspired from deep learning
projects. In the context of the NNPDF methodology, we implement a new efficient
computing framework based on graph generated models for PDF parametrization and
gradient descent optimization. The best model configuration is derived from a
robust cross-validation mechanism through a hyperparametrization tune
procedure. We show that results provided by this new framework outperforms the
current state-of-the-art PDF fitting methodology in terms of best model
selection and computational resources usage.
