A course in modern machine learning via deep learning. Topics include statistical estimation, efficient gradient descent of nonlinear functions, convolutional models, attention-based models, and generative models. Emphasis is placed on maintaining a balance between theory and the ability to produce practically efficient implementation of these techniques leveraging GPU acceleration within a leading deep learning development framework. Practical implementation details also consider techniques for avoiding local optima and improving generalization.
Prerequisite Courses