Lectures related to Differential Operators: Gradient and Divergence

Explores the optimization of neural networks using Stochastic Gradient Descent (SGD) and the concept of dual risk versus empirical risk.

Covers vector calculus concepts like gradient, divergence, and curl with applications in physics and engineering.

Explores how sources and sinks affect the divergence of vector fields.

Explores vectorial calculus, divergence, curl, gradient, implicit functions, and local inversion theorems.

Covers surface integrals, the divergence theorem, and Stocks' theorem through examples and analogies.

Explores surface integrals using parameterization and the divergence theorem, with practical examples included.