Course Description
This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high …
This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.
Course Info
Learning Resource Types
notes
Lecture Notes
theaters
Lecture Videos
assignment
Problem Sets
grading
Projects with Examples
auto_stories
Readings
Deep learning allows models to perform tasks on photos such as edge detection, colorization, in-painting, and more. (Courtesy of Bar et al. Used under CC BY.)
