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Related lectures (40)
Proximal Gradient Descent: Optimization Techniques in Machine Learning
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Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.
Optimization Techniques: Gradient Descent and Convex Functions
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Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.
Differentiable Functions and Lagrange Multipliers
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Covers differentiable functions, extreme points, and the Lagrange multiplier method for optimization.
Fourier Series: Convergence and Dirichlet Theorem
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Covers Fourier series convergence, Dirichlet theorem, and applications in signal processing.
Differentiable Functions in R²
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Covers the representation of differentiable functions in R² and the concept of changes of coordinates through bijections.
Differentiability and Partial Derivatives
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Explores differentiability in two variables and the chain rule for compositions.
Intersection of 2 Cylinders
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Explores the intersection of two cylinders in cylindrical coordinates and the concept of bijectivity.
Derivatives: Definition and Examples
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Covers the definition of derivatives and provides examples of differentiable functions.
Vector Operations: Planes and Lines
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Explores vector operations in 3D space, including plane intersections and distance calculations.
Implicit functions theorem
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Explores the implicit functions theorem, showcasing examples of implicit differentiation and function definition.
Ray Tracing: Fundamentals
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Covers the basics of ray tracing, including ray generation, intersection with geometric shapes, and distance calculations to planes, setting the foundation for implementing a ray tracer.
Differentiability and Tangent Planes
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Covers differentiability in multivariable functions, focusing on tangent planes and their properties.
Differentiability and Limits
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Explores differentiability, limits, and open sets in multivariable functions, with a focus on local minimums and continuity.
Surface Normals: Vector Analysis
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Explains surface normals for parametric and implicit surfaces, focusing on vector analysis and examples with spheres.
Optimization on Manifolds
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Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.
How to Compute Derivatives?: Analysis 1 Lecture 18
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Covers continuity, differentiation, and algebraic operations, including the computation of derivatives for various functions.
Approximation by Smooth Functions
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Discusses approximation by smooth functions and the convergence of function sequences in normed vector spaces.
Analytical Geometry: Mixed Product and Determinant
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Covers the analytical expression of the mixed product and its applications.
Linear Combinations and Basis Characterization
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Explores linear combinations, basis determination, and vector space dimensionality through practical examples and exercises.
Manopt: Optimization Toolbox for Manifolds
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Introduces Manopt, a toolbox for optimization on manifolds, focusing on solving optimization problems on smooth manifolds using the Matlab version.
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