This article , co-authored by Pierre Gentine and Ryan Abernathey, replaces the typically used physically based assumptions with deep learning in subgrid-scale modeling, in large eddy simulations of turbulence. They show that the latter performs better in a priori (offline) tests of atmospheric turbulence. The deep neural networks model also captures key statistics of turbulence in a posteriori (online) tests.