Differentiable Functions in R²

Related lectures (29)

Page 3 of 3

Covers Fourier series convergence, Dirichlet theorem, and applications in signal processing.

Covers the definition of derivatives and provides examples of differentiable functions.

Explores differentiability for functions and matrices, covering partial differentiability, the Jacobean matrix, and the chain rule.

Discusses advanced optimization techniques, focusing on faster and projected gradient descent methods in machine learning.

Explains differentiability in LR functions and introduces the Jacobian matrix.

Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.

Covers the derivative of the reciprocal function and its properties.

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Covers supporting hyperplanes for approximating functions' graphs in different dimensions using hyperplanes and linear approximations.