  Deep Neural Networks (DNNs) are a powerful and flexible tool for information
extraction and modeling. In this study, we use DNNs to extract the Sivers
functions by globally fitting Semi- Inclusive Deep Inelastic Scattering (SIDIS)
and Drell-Yan (DY) data. To make predictions of this Transverse
Momentum-dependent Distribution (TMD), we construct a minimally biased model
using data from COMPASS and HERMES. The resulting Sivers function model,
constructed using SIDIS data, is also used to make predictions for DY
kinematics specific to the valence and sea quarks, with careful consideration
given to experimental errors, data sparsity, and complexity of phase space.
