Offers an advanced exploration of the theoretical foundations, architectures, and techniques essential for the design and optimization of deep learning models. Broadly discusses convolutional and recurrent neural networks, attention mechanisms, generative models, and transformer networks. Offers hands-on experience in implementing, optimizing, and deploying deep learning solutions to real-world problems. Highlights current challenges and the latest trends in the field.
Offering history
Term
Sections
Enrolled
Capacity
Full
Open seats/section
Spring 2026
2
34
60
57%
13.0
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 68% · W 0% · Th 0% · F 0%
Common patterns: T (68% of sections), async (32% of sections)
— in patterns, R means Thursday