The interplay between physics and deep learning is typically divided into two themes.

The first is “physics for deep learning,” where techniques from physics are brought to bear on understanding dynamics of learning. The second is “deep learning for physics,” which focuses on application of deep learning techniques to physics problems. I will present a more nuanced view of this interplay with examples of how the structure of physics problems have inspired advances in deep learning and how it yields insights on topics such as inductive bias, interpretability, and causality.