CS 6304 - Deep Learning

Credits: 3 Class: 3 Lab: 0
Prerequisite(s): CS 5310, or departmental approval
The course presents a comprehensive introduction to the foundations of deep learning. Topics include different components of a deep learning model such as fully connected network, convolutional neural networks, Recurrent Neural Networks, and long short-term memory Neural Networks; tuning and optimization methods such as drop out, batch normalization, Adam, Stochastic gradient descent; and more. Case studies from healthcare, text mining, computer vision, and natural language processing will be discussed as well as tools to build deep learning models such as Tensor Flow and Keras.