|
|
Sep 07, 2024
|
|
MSDS 634 - Deep Learning Unit(s): 2
This course introduces students to a range of topics and concepts in deep learning including the foundation neural networks, most common neural network architectures such as MLP, convolutional neural networks, and recurrent neural networks to name a few. Advanced topics such as generative models, geometric deep learning and graph neural networks are also covered. Students learn practical aspects of deep learning and using pytroch for creation/training/inference of various networks. Intuition, mathematical notions, and the practical aspects are emphasized throughout the course to build a solid theoretical and practical foundation of deep learning.
Prerequisite: MSDS 630 with a minimum grade of C- and MSDS 689 with a minimum grade of C- Restriction: Level Restricted to Graduate; Field of study restricted to Data Science Major. College of Arts and Sciences
Add to Portfolio (opens a new window)
|
|
|