Lectures related to Gradient Descent Method

Explores distribution and interpolation spaces, showcasing their importance in mathematical analysis and the computations involved.

Covers functions, differentiability, Taylor expansions, and integrals, providing fundamental concepts and practical applications.

Explores the continuity of functions in R² and R³, emphasizing accumulation points and limits.

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

Explores limits, convergence, and boundedness of functions and sequences.

Explores optimization basics such as norms, convexity, and differentiability, along with practical applications and convergence rates.