Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers optimization methods without constraints, including gradient and line search in the quadratic case.
Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.
Covers the Finite Element Method, discussing the derivation of the equation of motion and exploring mass and stiffness matrices.
Covers the derivation of the equation of motion, interpolation, Newton's equation, and energy conservation in finite element modeling.
Explores numerical methods for solving ODE systems, stability regions, and absolute stability importance.
Explores error estimation in numerical methods for solving ordinary differential equations, emphasizing the impact of errors on solution accuracy and stability.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Introduces the Euler Forward Method for ODEs, focusing on error analysis and stability.
