Foundations and Practice of Deep Learning for Artificial Intelligence. 3 credits

Catalog description

This course will cover an introduction to neural networks and deep learning, including theoretical motivation and practical implementations. We will cover basic building blocks of designing, training and fine-tuning and monitoring deep networks, including explainability and hands-on exercises and programming assignments in Pytorch. We will discuss supervised, self-supervised, and unsupervised learning approaches and cover applications areas of deep neural networks in computer vision, natural language processing and understanding, deep reinforcement learning, generative deep learning, and adversarial learning.Offered by Engineering & Computing. May not be repeated for credit.