Introduction to Deep Learning. 3 credits, 3 contact hours

Catalog description

Prerequisites: ( CS 100 or DS 100 ) and ( CS 301 or CS 375 or DS 340 ). This course is designed to introduce undergraduate students to the fundamentals of deep learning – through the lens of a research engineer. It covers automatic differentiation, optimization algorithms, and regularization for supervised learning, as well as modern deep learning architectures including convolutional networks, recurrent networks, and transformers. The course concludes with applications of deep networks to representation learning and generative models.