Deep Learning.

Catalog description

This course provides students with an in-depth look at deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Students learn to tackle practical issues that arise during the life cycle of data, both in the cloud and on the Edge. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students complete several substantive programming assignments using relevant modern tools and frameworks.