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).
This lecture is on Introduction to Higher Mathematics (Proofs). For more see http://calculus123.com.
From playlist Proofs
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
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
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
This video explains the definition of a vector space and provides examples of vector spaces.
From playlist Vector 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
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
Dimensions (1 of 3: The Traditional Definition - Directions)
More resources available at www.misterwootube.com
From playlist Exploring Mathematics: Fractals
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
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
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]
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]
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
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
From playlist Unlisted LA Videos
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
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
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
This video is about metric spaces and some of their basic properties.
From playlist Basics: Topology