Covers transformer architecture and subquadratic attention mechanisms, focusing on efficient approximations and their applications in machine learning.
Provides a review of linear algebra concepts crucial for convex optimization, covering topics such as vector norms, eigenvalues, and positive semidefinite matrices.
