  Generative models in deep learning allow for sampling probability
distributions that approximate data distributions. We propose using generative
models for making approximate statistical predictions in the string theory
landscape. For vacua admitting a Lagrangian description this can be thought of
as learning random tensor approximations of couplings. As a concrete
proof-of-principle, we demonstrate in a large ensemble of Calabi-Yau manifolds
that Kahler metrics evaluated at points in Kahler moduli space are
well-approximated by ensembles of matrices produced by a deep convolutional
Wasserstein GAN. Accurate approximations of the Kahler metric eigenspectra are
achieved with far fewer than $h^{11}$ Gaussian draws. Accurate extrapolation to
values of $h^{11}$ outside the training set are achieved via a conditional GAN.
Together, these results implicitly suggest the existence of strong correlations
in the data, as might be expected if Reid's fantasy is correct.
