Deep learning has revolutionized many research areas. In this talk, we demonstrate how deep learning over programs is used to provide (preliminary) augmented programmer intelligence. In the first part of the talk, we show how deep learning over programs is used to tackle tasks like code completion, code summarization, and captioning. We describe a general path-based representation of source code that can be used across programming languages and learning tasks, and discuss how this representation enables different learning algorithms. In the second part, we describe techniques for extracting interpretable representations from deep models, shedding light on what has actually been learned in various tasks.