  Structure function data provide insight into the nucleon quark distribution.
They are relatively straightforward to extract from the world's vast, and
growing, amount of inclusive lepto-production data. In turn, structure
functions can be used to model the physical processes needed for planning and
optimizing future experiments. In this paper a machine learning algorithm
capable of predicting, using a unique set of parameters, the $F_2$ structure
function, for four-momentum transfer $0.055 \leq Q^2 \leq 800.0$ GeV$^2$ and
for Bjorken $x$ from $2.8 \times 10^{-5}$ to the pion threshold is presented.
The model was trained and reproduces the hydrogen and the deuterium data at the
7~\% level, comparable with the average uncertainty of the experimental data.
Extending the model to other nuclei or expanding the kinematic range are
straightforward. The model is at least ten times faster than existing structure
functions parameterizations, making it an ideal candidate for event generators
and systematic studies.
