Explores the sampling theorem, illustrating signal reconstruction and the importance of meeting the Nyquist criterion.
Explores signal sampling theory, interpolation techniques, and the importance of the sampling theorem in signal processing.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.
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.
