  Machine learning techniques such as artificial neural networks are currently
revolutionizing many technological areas and have also proven successful in
quantum physics applications. Here we employ an artificial neural network and
deep learning techniques to identify quantum phase transitions from single-shot
experimental momentum-space density images of ultracold quantum gases and
obtain results, which were not feasible with conventional methods. We map out
the complete two-dimensional topological phase diagram of the Haldane model and
provide an accurate characterization of the superfluid-to-Mott-insulator
transition in an inhomogeneous Bose-Hubbard system. Our work points the way to
unravel complex phase diagrams of general experimental systems, where the
Hamiltonian and the order parameters might not be known.

Quantum Gases;Disordered Systems and Neural Networks;Mesoscale and Nanoscale Physics;Quantum Physics