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Concept

Gradient

Related lectures (32)

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Covers the analysis of scalar fields, including divergence, gradient, and Laplacian.

Covers methods of demonstrations and the Taylor formula for functions of several variables.

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

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 differentiability in functions and the role of gradients in directional derivatives.

Explores differentiable functions, gradients, and tangent planes in two-variable functions.

Explores the linear properties of gradients and the importance of understanding scalar products and Jacobian matrices.

Explores simple examples of partial differentiation, demonstrating how to calculate partial derivatives and gradients in various scenarios.

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Explores differentiability, gradients, and the Mean Value Theorem for functions.

Explores differentiability, gradients, and the Mean Value Theorem in open sets.

Explores gradient in scalar fields, directional derivatives, and level sets.

Covers a review of vector calculus and the Maxwell equations in electromagnetism.

Introduces differential operators, gradient, and divergence in vector fields.

Explains necessary conditions for convergence in optimization problems.