Lectures related to Bayesian optimization

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Explores the use of momentum in gradient descent to enhance speed and stability.

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.