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Supporting hyperplanes
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Related lectures (29)
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Derivative and Linear Approximation
Covers the concept of approximating a function around a point xo using linear approximation.
Proximal Gradient Descent: Optimization Techniques in Machine Learning
Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
Integration Techniques: Change of Variable
Explores integration by substitution technique with examples and verification.
Optimization Techniques: Gradient Descent and Convex Functions
Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
Vector spaces and norms
Covers vector spaces, differentiable functions, scalar product, and norms in depth.
Fourier Series: Convergence and Dirichlet Theorem
Covers Fourier series convergence, Dirichlet theorem, and applications in signal processing.
Derivability and Limits in Analysis I
Explores derivability, limits computation, and properties of functions in Analysis I.
Differentiable Functions in R²
Covers the representation of differentiable functions in R² and the concept of changes of coordinates through bijections.
Differentiability and Partial Derivatives
Explores differentiability in two variables and the chain rule for compositions.