Machine learning studies algorithms and statistical models that computers use to perform tasks without explicit instructions [1]. The origin behind these technological advances can be largely traced back to a series of breakthroughs in artificial intelligence, in particular those based on deep learning, where data are processed through the sequential combination of multiple nonlinear layers [3]. Deep learning has accelerated the adoption of artificial intelligence with notable advances in areas ranging from computer vision [4] and natural language processing [5], to scientific applications such as drug discovery [6] and protein folding [7]. Recently, the condensed matter physics, quantum information, statistical physics, and atomic, molecular, and optical physics communities have turned their attention to the algorithms underlying modern machine learning with the objective of making progress in quantum matter research. This recent resurgence of research interest at the intersection between strongly correlated systems and machine learning is shaped in part by the commonalities in the structure of the problems that these seemingly unrelated fields attack.