CSC 451 - Fundamentals of Deep Learning

Theory and application of deep learning. Topics include linear and logistic regression, fully-connected neural networks, convolutional neural networks, object localization/detection, neural style transfer, recurrent neural networks, generative adversarial networks, variational auto encoders, and capsule networks. Students are required to complete multiple programming assignments.

Prerequisite(s): 'C' or better in CSC 229, and ( MAT 150 or MAT 139).

3 credit(s).

Last Term Offered: Spring 2020