  Over the past few years, we have seen fundamental breakthroughs in core
problems in machine learning, largely driven by advances in deep neural
networks. At the same time, the amount of data collected in a wide array of
scientific domains is dramatically increasing in both size and complexity.
Taken together, this suggests many exciting opportunities for deep learning
applications in scientific settings. But a significant challenge to this is
simply knowing where to start. The sheer breadth and diversity of different
deep learning techniques makes it difficult to determine what scientific
problems might be most amenable to these methods, or which specific combination
of methods might offer the most promising first approach. In this survey, we
focus on addressing this central issue, providing an overview of many widely
used deep learning models, spanning visual, sequential and graph structured
data, associated tasks and different training methods, along with techniques to
use deep learning with less data and better interpret these complex models ---
two central considerations for many scientific use cases. We also include
overviews of the full design process, implementation tips, and links to a
plethora of tutorials, research summaries and open-sourced deep learning
pipelines and pretrained models, developed by the community. We hope that this
survey will help accelerate the use of deep learning across different
scientific domains.
