Linear Algebra for Computer Scientists. 12. Introducing the Matrix
This computer science video is one of a series of lessons about linear algebra for computer scientists. This video introduces the concept of a matrix. A matrix is a rectangular or square, two dimensional array of numbers, symbols, or expressions. A matrix is also classed a second order
From playlist Linear Algebra for Computer Scientists
Understanding Matrices and Matrix Notation
In order to do linear algebra, we will have to know how to use matrices. So what's a matrix? It's just an array of numbers listed in a grid of particular dimensions that can represent the coefficients and constants from a system of linear equations. They're fun, I promise! Let's just start
From playlist Mathematics (All Of It)
How do we add matrices. A matrix is an abstract object that exists in its own right, and in this sense, it is similar to a natural number, or a complex number, or even a polynomial. Each element in a matrix has an address by way of the row in which it is and the column in which it is. Y
From playlist Introducing linear algebra
What is a matrix? Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Matrices
What is an inverse matrix and how do I calculate it? Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Matrices
9A_3 The Inverse of a Matrix Using the Identity Matrix
Continuation of the use of an identity matrix to calculate the inverse of a matrix
From playlist Linear Algebra
This video introduces the identity matrix and illustrates the properties of the identity matrix. http://mathispower4u.yolasite.com/ http://mathispower4u.wordpress.com/
From playlist Introduction to Matrices and Matrix Operations
In this tutorial we take a look at elementary matrices. They start life off as identity matrices to which a single elementary row operation is performed. They form the building blocks of Gauss-Jordan elimination. In a future video we will use the to do LU decomposition of matrices.
From playlist Introducing linear algebra
Mod-01 Lec-21 Projection Theorem in a Hilbert Spaces (Contd.) and Approximation
Advanced Numerical Analysis by Prof. Sachin C. Patwardhan,Department of Chemical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Bombay: Advanced Numerical Analysis | CosmoLearning.org
The computational theory of Riemann–Hilbert problems (Lecture 4) by Thomas Trogdon
Program : Integrable Systems in Mathematics, Condensed Matter and Statistical Physics ORGANIZERS : Alexander Abanov, Rukmini Dey, Fabian Essler, Manas Kulkarni, Joel Moore, Vishal Vasan and Paul Wiegmann DATE & TIME : 16 July 2018 to 10 August 2018 VENUE : Ramanujan Lecture Hall, ICT
From playlist Integrable systems in Mathematics, Condensed Matter and Statistical Physics
Stephanos Venakides: Rigorous semiclassical asymptotics for integrable systems
The title of the lecture is shortened to comply with Youtubes' title policy. The original title of this lecture is "Rigorous semiclassical asymptotics for integrable systems:The KdV and focusing NLS cases". Programme for the Abel Lectures 2005: 1. "Abstract Phragmen-Lindelöf theorem & Sa
From playlist Abel Lectures
Hans G. Feichtinger: Mathematical and numerical aspects of frame theory - Part 2
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
9A_4 The Inverse of a Matrix Using the Determinant
Calculating the inverse of a matrix by use of the determinant of the matrix
From playlist Linear Algebra
Matrix Entanglement by Vaibhav Gautam
PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II
From playlist NUMSTRING 2022
Diffusion and superdiffusion from hydrodynamic projection by Benjamin Doyon
PROGRAM THERMALIZATION, MANY BODY LOCALIZATION AND HYDRODYNAMICS ORGANIZERS: Dmitry Abanin, Abhishek Dhar, François Huveneers, Takahiro Sagawa, Keiji Saito, Herbert Spohn and Hal Tasaki DATE : 11 November 2019 to 29 November 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore How do is
From playlist Thermalization, Many Body Localization And Hydrodynamics 2019
Douglas Lundholm - Spectrum and Ground States of MembraneMatrix Models
https://indico.math.cnrs.fr/event/4272/attachments/2260/2716/IHESConference_Douglas_LUNDHOLM.pdf
From playlist Space Time Matrices
Quantum Circuit Cosmology - S. Carroll - Workshop 1 - CEB T3 2018
Sean Carroll (California Institute) / 17.09.2018 Quantum Circuit Cosmology ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com/InHenriPo
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
An introduction to Invariant Theory - Harm Derksen
Optimization, Complexity and Invariant Theory Topic: An introduction to Invariant Theory Speaker: Harm Derksen Affiliation: University of Michigan Date: June 4, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
Principal axes theorem + orthogonal matrices
Free ebook http://tinyurl.com/EngMathYT A basic introduction to orthogonal matrices and the principal axes theorem. Several examples are presented involving a simplification of quadratic (quadric) forms. A proof is also given.
From playlist Engineering Mathematics
Complete Statistical Theory of Learning (Vladimir Vapnik) | MIT Deep Learning Series
Lecture by Vladimir Vapnik in January 2020, part of the MIT Deep Learning Lecture Series. Slides: http://bit.ly/2ORVofC Associated podcast conversation: https://www.youtube.com/watch?v=bQa7hpUpMzM Series website: https://deeplearning.mit.edu Playlist: http://bit.ly/deep-learning-playlist
From playlist AI talks