Symmetric matrices - eigenvalues & eigenvectors
Free ebook http://tinyurl.com/EngMathYT A basic introduction to symmetric matrices and their properties, including eigenvalues and eigenvectors. Several examples are presented to illustrate the ideas. Symmetric matrices enjoy interesting applications to quadratic forms.
From playlist Engineering Mathematics
Diagonalizing a symmetric matrix. Orthogonal diagonalization. Finding D and P such that A = PDPT. Finding the spectral decomposition of a matrix. Featuring the Spectral Theorem Check out my Symmetric Matrices playlist: https://www.youtube.com/watch?v=MyziVYheXf8&list=PLJb1qAQIrmmD8boOz9a8
From playlist Symmetric Matrices
Differential Equations | Convolution: Definition and Examples
We give a definition as well as a few examples of the convolution of two functions. http://www.michael-penn.net http://www.randolphcollege.edu/mathematics/
From playlist Differential Equations
In this video we construct a symmetric group from the set that contains the six permutations of a 3 element group under composition of mappings as our binary operation. The specifics topics in this video include: permutations, sets, groups, injective, surjective, bijective mappings, onto
From playlist Abstract algebra
Linear Algebra - Lecture 41 - Diagonalization of Symmetric Matrices
In this lecture, we investigate the diagonalization of symmetric matrices.
From playlist Linear Algebra Lectures
Symmetric Groups (Abstract Algebra)
Symmetric groups are some of the most essential types of finite groups. A symmetric group is the group of permutations on a set. The group of permutations on a set of n-elements is denoted S_n. Symmetric groups capture the history of abstract algebra, provide a wide range of examples in
From playlist Abstract Algebra
Symmetric Matrix | Don't Memorise
This video explains the concept of a Symmetric Matrix. ✅To learn more about, Matrices, enroll in our full course now: https://infinitylearn.com/microcourses?utm_source=youtube&utm_medium=Soical&utm_campaign=DM&utm_content=IBgXO5qvbrg&utm_term=%7Bkeyword%7D In this video, we will learn:
From playlist Matrices
Adam Skalski: Translation invariant noncommutative Dirichlet forms
Talk by Adam Skalski in Global Noncommutative Geometry Seminar (Europe) http://www.noncommutativegeometry.nl/ncgseminar/ on April 28, 2021
From playlist Global Noncommutative Geometry Seminar (Europe)
Lec 22 | MIT 18.085 Computational Science and Engineering I
Fourier expansions and convolution A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Markus Reineke - Cohomological Hall Algebras and Motivic Invariants for Quivers 4/4
We motivate, define and study Donaldson-Thomas invariants and Cohomological Hall algebras associated to quivers, relate them to the geometry of moduli spaces of quiver representations and (in special cases) to Gromov-Witten invariants, and discuss the algebraic structure of Cohomological H
From playlist 2021 IHES Summer School - Enumerative Geometry, Physics and Representation Theory
31. Eigenvectors of Circulant Matrices: Fourier Matrix
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k This lecture conti
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Peter Pivovarov: Random s-concave functions and isoperimetry
I will discuss stochastic geometry of s-concave functions. In particular, I will explain how a ”local” stochastic isoperimetry underlies several functional inequalities. A new ingredient is a notion of shadow systems for s-concave functions. Based on joint works with J. Rebollo Bueno.
From playlist Workshop: High dimensional spatial random systems
Tess Smidt: "Euclidean Neural Networks for Emulating Ab Initio Calculations and Generating Atomi..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics "Euclidean Neural Networks* for Emulating Ab Initio Calculations and Generating Atomic Geometries *also called Tensor Field Networks and 3D
From playlist Machine Learning for Physics and the Physics of Learning 2019
Properties of Dirichlet Convolution with Prof. Omar!
Part 2 with Prof. Omar: https://youtu.be/8v_sh7JMUS0 Prof. Omar's video covers a very interesting function called the Möbius function, which has special properties related to the Dirichlet convolution. A video on the fall 2014 Caltech-Harvey Mudd Math Competition (CHMMC) power round prob
From playlist Challenge Problems
Lecture 4: Equivariant CNNs I (Euclidean Spaces) - Maurice Weiler
Video recording of the First Italian School on Geometric Deep Learning held in Pescara in July 2022. Slides: https://www.sci.unich.it/geodeep2022/slides/GroupEquivariantConvolutionalNetworksOnEuclideanSpaces.pdf
From playlist First Italian School on Geometric Deep Learning - Pescara 2022
Matrices: Transpose and Symmetric Matrices
This is the third video of a series from the Worldwide Center of Mathematics explaining the basics of matrices. This video deals with matrix transpose and symmetric matrices. For more math videos, visit our channel or go to www.centerofmath.org
From playlist Basics: Matrices
Introduction to Graph Neural Networks and What We Can Implement in the Wolfram Language
I will give a quick review of the ideas of graph neural networks (GNN), then overview the potential types of GNN and show more details of algorithms that can be implemented in the Wolfram Language. I will then show how to implement these types of GNN in the Wolfram Language.
From playlist Wolfram Technology Conference 2022
In this video, I provide some intuition behind the concept of convolution, and show how the convolution of two functions is really the continuous analog of polynomial multiplication. Enjoy!
From playlist Real Analysis
Lecture 16: Fast Convolution, Low Pass Filter Approximations, Integral Images (US 6,457,032)
MIT 6.801 Machine Vision, Fall 2020 Instructor: Berthold Horn View the complete course: https://ocw.mit.edu/6-801F20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63pfpS1gV5P9tDxxL_e4W8O In this lecture, Prof. Horn discusses sampling and aliasing, integral images, Fou
From playlist MIT 6.801 Machine Vision, Fall 2020