CS 6700 - Deep Learning Theory

Credits: (3)
Typically Taught Fall Semester: Full Sem
Course Fee: $25.00
Course Fee Purpose: Course fees are designed to cover the costs of lab equipment maintenance and replacement including desktop and server computer systems and software; consumable materials and supplies; and support for lab aides, student tutors, and online instructional resources.
Description: Deep learning is at the heart of many life-changing applications and areas of interests including language understanding, face recognition, speech synthesis and recognition, object detection, and robotics to name a few. This course introduces the fundamental principles of deep learning and its applications, including multilayer perceptrons, backpropagation, auto-differentiation, optimizers, convolutional networks (CNNs), recurrent networks (RNNs), autoencoders (AEs), and generative adversarial networks (GANs). This course focuses on both understanding deep learning algorithms (their strengths and limitations) and getting acquainted with the current deep learning research landscape.
Pre-requisite(s): CS 6600.