Over the past several years, two trends in machine learning have converged to pique the curiosity of artists working with code: the proliferation of powerful open source deep learning frameworks like TensorFlow and Torch, and the emergence of data-intensive generative models for hallucinating images, sounds, and text as though they came from the oeuvre of Shakespeare, Picasso, or just a gigantic database of digitized cats. This talk will review these developments and offer a set of interdisciplinary tools and learning resources for artists and data scientists alike, if ever there was a difference to begin with.