This 3-credit-hour, 16-week course covers the fundamentals of deep learning. Students will gain a principled understanding of the motivation, justification, and design considerations of the deep neural network approach to machine learning and will complete hands-on projects using TensorFlow and Keras.
Module 1: Introduction to Deep Feedforward Networks
Sigmoidal output units
Module 2: Regularization for Deep Learning
Module 3: Optimization for Training Deep Models
Optimization algorithms: Gradient, Hessian-Free, Newton