  Deep learning has recently become very popular on account of its incredible
success in many complex data-driven applications, such as image classification
and speech recognition. The database community has worked on data-driven
applications for many years, and therefore should be playing a lead role in
supporting this new wave. However, databases and deep learning are different in
terms of both techniques and applications. In this paper, we discuss research
problems at the intersection of the two fields. In particular, we discuss
possible improvements for deep learning systems from a database perspective,
and analyze database applications that may benefit from deep learning
techniques.
