Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as PyTorch or TensorFlow.
Prerequisite: DS 740
Semesters Offered: Spring 2025
Credits: 3
Degree Level: Master's