As big data and deep learning gain more prominence in both industry and academia, the time seems ripe for a class focused exclusively on the theory and practice of deep learning, both to understand why deep learning has had such a tremendous impact across so many disciplines, and also to spur research excellence in deep learning at BYU.
This class will be a graduate-level coding class. Students will be exposed to the theoretical aspects of deep learning (including derivatives, regularization, and optimization theory), as well as practical strategies for training large-scale networks, leveraging hardware acceleration, distributing training across multiple machines, and coping with massive datasets. Students will engage the material primarily through weekly coding labs dedicated to implementing state-of-the-art techniques, using modern deep learning software frameworks. The class will culiminate with a substantial data analysis project.