Highly expressive computational schemes that are able to efficiently represent the entanglement properties of many-particle systems are of interest. In the seemingly unrelated field of machine learning, deep network architectures have exhibited an unprecedented ability to tractably encompass the dependencies characterizing hard learning tasks such as image classification. In the talk I will introduce a Tensor Network based common language between the two disciplines, which allows us to offer bidirectional contributions.