Gradient

Related lectures (32)

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Covers the differentiability of functions of multiple variables and the significance of directional derivatives and gradients.

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

Explores continuity equations and internal entropy production in various coordinate systems, emphasizing scalar functions and vector quantities.

Explores curl in 2D vector fields, demonstrating its rotation concept through examples and its application in physics.

Explores gradient, divergence, and integral theorems in vector calculus.

Explores the use of momentum in gradient descent to enhance speed and stability.

Covers the concepts of vector differentiation and convex functions.

Introduces gradient, Laplacian, Taylor formula, polynomial approximations, extrema, and Taylor series expansions in multiple variables.

Explores the connection between Riemannian and Euclidean gradients and Hessians at critical points.

Explores generalisations of functions and the concept of gradients in advanced analysis.