Explores the optimization of neural networks using Stochastic Gradient Descent (SGD) and the concept of dual risk versus empirical risk.
Explores fundamental groups, homotopy classes, and coverings in connected manifolds.
Explores the Gauss and Green theorems in vector calculus, showcasing their applications through practical examples and geometric interpretations.
Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Covers the definitions of gradient and divergence, including the Cartesian coordinate system and the divergence theorem.
Covers symmetries and conservation laws in fluid dynamics, emphasizing the importance of maximizing symmetries in ideal fluid systems.
