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.
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
