  Theoretical physicists describe nature by i) building a theory model and ii)
determining the model parameters. The latter step involves the dual aspect of
both fitting to the existing experimental data and satisfying abstract criteria
like beauty, naturalness, etc. We use the Yukawa quark sector as a toy example
to demonstrate how both of those tasks can be accomplished with machine
learning techniques. We propose loss functions whose minimization results in
true models that are also beautiful as measured by three different criteria -
uniformity, sparsity, or symmetry.
