Functional analysis

Weighted space

In functional analysis, a weighted space is a space of functions under a weighted norm, which is a finite norm (or semi-norm) that involves multiplication by a particular function referred to as the weight. Weights can be used to expand or reduce a space of considered functions. For example, in the space of functions from a set to under the norm defined by: , functions that have infinity as a limit point are excluded. However, the weighted norm is finite for many more functions, so the associated space contains more functions. Alternatively, the weighted norm is finite for many fewer functions. When the weight is of the form , the weighted space is called polynomial-weighted. (Wikipedia).

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Metric spaces -- Proofs

This lecture is on Introduction to Higher Mathematics (Proofs). For more see http://calculus123.com.

From playlist Proofs

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Complete metric space: example & proof

This video discusses an example of particular metric space that is complete. The completeness is proved with details provided. Such ideas are seen in branches of analysis.

From playlist Mathematical analysis and applications

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What is a metric space ?

Metric space definition and examples. Welcome to the beautiful world of topology and analysis! In this video, I present the important concept of a metric space, and give 10 examples. The idea of a metric space is to generalize the concept of absolute values and distances to sets more gener

From playlist Topology

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Weighted graph as a metric space -- Proofs

This lecture is on Introduction to Higher Mathematics (Proofs). For more see http://calculus123.com.

From playlist Proofs

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What is a Vector Space?

This video explains the definition of a vector space and provides examples of vector spaces.

From playlist Vector Spaces

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Introduction to Metric Spaces

Introduction to Metric Spaces - Definition of a Metric. - The metric on R - The Euclidean Metric on R^n - A metric on the set of all bounded functions - The discrete metric

From playlist Topology

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What is a metric space? An example

This is a basic introduction to the idea of a metric space. I introduce the idea of a metric and a metric space framed within the context of R^n. I show that a particular distance function satisfies the conditions of being a metric.

From playlist Mathematical analysis and applications

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Dimensions (1 of 3: The Traditional Definition - Directions)

More resources available at www.misterwootube.com

From playlist Exploring Mathematics: Fractals

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Fooling intersections of low-weight halfspaces - Rocco Servedio

Computer Science/Discrete Mathematics Seminar I Topic: Fooling intersections of low-weight halfspaces Speaker: Rocco Servedio Affiliation: Columbia University Date: October 30, 2017 For more videos, please visit http://video.ias.edu

From playlist Mathematics

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Automorphism group of the moduli space of parabolic vector bundles by David Alfaya Sanchez

DISCUSSION MEETING ANALYTIC AND ALGEBRAIC GEOMETRY DATE:19 March 2018 to 24 March 2018 VENUE:Madhava Lecture Hall, ICTS, Bangalore. Complex analytic geometry is a very broad area of mathematics straddling differential geometry, algebraic geometry and analysis. Much of the interactions be

From playlist Analytic and Algebraic Geometry-2018

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Lecture 2/16 : The Perceptron learning procedure

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 2A An overview of the main types of neural network architecture 2B Perceptrons: The first generation of neural networks 2C A geometrical view of perceptrons 2D Why the learning works 2E What perceptrons can't do

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

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Lecture 2C : A geometrical view of perceptrons

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 2C : A geometrical view of perceptrons

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

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Mumford-Tate Groups and Domains - Phillip Griffiths

Phillip Griffiths Professor Emeritus, School of Mathematics March 28, 2011 For more videos, visit http://video.ias.edu

From playlist Mathematics

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Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]

For cool updates on AI research, follow me at https://twitter.com/iamvriad. Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-

From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton

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Miroslav Englis: Analytic continuation of Toeplitz operators

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b

From playlist Analysis and its Applications

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Leslie Saper : L2-cohomology and the theory of weights

Abstract : The intersection cohomology of a complex projective variety X agrees with the usual cohomology if X is smooth and satisfies Poincare duality even if X is singular. It has been proven in various contexts (and conjectured in more) that the intersection cohomology may be represente

From playlist Topology

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Martina Hofmanova: Global solutions to elliptic and parabolic Φ4 models in Euclidean space

Abstract: I will present some recent results on global solutions to singular SPDEs on ℝd with cubic nonlinearities and additive white noise perturbation, both in the elliptic setting in dimensions d=4,5 and in the parabolic setting for d=2,3. A motivation for considering these equations is

From playlist Probability and Statistics

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Topology: Metric Spaces

This video is about metric spaces and some of their basic properties.

From playlist Basics: Topology

Related pages

Functional analysis | Norm (mathematics)