Cancer care is undergoing a shift towards precision healthcare that is enabled by the increased availability and integration of multiple types of data, including genomic, transcriptomic, and histological data. The use and interpretation of diverse, high-dimensional data types for translational research or clinical assignments require significant time and expertise. Moreover, the integration of multiple data types is more resource intensive than the interpretation of individual data types and requires modeling algorithms that can learn from huge numbers of complex features. Deep learning is a sub-field of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across areas of healthcare combined with the availability of highly characterized cancer datasets has accelerated research into the utility of deep learning in analyzing the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning.