Riemannian gradient descent

Related lectures (32)

Page 3 of 4

Covers exercises related to Laplace's method and complex analysis.

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Covers optimization algorithms, stable matching, and Big-O notation for algorithm efficiency.

Covers power series, Taylor series, and their applications in functions like logarithmic functions.

Explores the composition of Taylor expansions through an example, simplifying complex compositions to identify essential terms.

Covers the derivation of a function near a critical point.

Explores functions, limits, continuity, differentiability, and important notions of limits and divergence.

Covers the concept of developing limited functions and includes physical examples.

Explores power series, Taylor series, convergence criteria, and applications in mathematics.

Covers fixed-point methods and high-order techniques for solving nonlinear equations.