Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.
Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.
Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.
Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
