Manifolds #5: Tangent Space (part 1)
Today, we introduce the notion of tangent vectors and the tangent vector space at a point on a manifold.
From playlist Manifolds
Manifolds 1.3 : More Examples (Animation Included)
In this video, I introduce the manifolds of product manifolds, tori/the torus, real vectorspaces, matrices, and linear map spaces. This video uses a math animation for visualization. Email : fematikaqna@gmail.com Code : https://github.com/Fematika/Animations Notes : http://docdro.id/5koj5
From playlist Manifolds
What is a Manifold? Lesson 6: Topological Manifolds
Topological manifolds! Finally! I had two false starts with this lesson, but now it is fine, I think.
From playlist What is a Manifold?
Today, we take a look at charts, their transition maps, and coordinate functions.
From playlist Manifolds
What is a Manifold? Lesson 2: Elementary Definitions
This lesson covers the basic definitions used in topology to describe subsets of topological spaces.
From playlist What is a Manifold?
Today, we begin the manifolds series by introducing the idea of a topological manifold, a special type of topological space which is locally homeomorphic to Euclidean space.
From playlist Manifolds
[Quiz] Eigenfaces, Domain adaptation, Causality, Manifold Hypothesis, Denoising Autoencoder
Machine Learning Quiz questions explained. Luckily, Ms. Coffee Bean found Tim Elsner @daidailoh (Twitter) “brave” enough to explain some of our latest questions in the AI Coffee Break quiz: ► https://www.youtube.com/c/AICoffeeBreak/community (Tim is not speaking for his employer.) The Mac
From playlist AI Coffee Break Quiz question answers
Marc Mézard: "Machine learning with neural networks: the importance of data structure"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Machine learning with neural networks: the importance of data structure" Marc Mézard - Ecole Normale Supérieure Abstract: The success of deep neural networks still
From playlist Machine Learning for Physics and the Physics of Learning 2019
Ensembles and Structured Data in Physics and Inference (Lecture 3) by Marc Mézard
INFOSYS-ICTS TURING LECTURES ARTIFICIAL INTELLIGENCE: SUCCESS, LIMITS, MYTHS AND THREATS SPEAKER: Marc Mézard (Director of Ecole normale supérieure - PSL University ) DATE: 06 January 2020, 16:00 to 17:30 VENUE: Chandrasekhar Auditorium, ICTS-TIFR, Bengaluru Lecture 1 (Public Lecture)
From playlist Infosys-ICTS Turing Lectures
Shulman Lectures - Paul Frosh: The Saturation of Media
The Shulman Lectures in Science and the Humanities - Elemental Media Paul Frosh is a professor in the Department of Communication and Journalism at the Hebrew University of Jerusalem. His research spans visual culture, media aesthetics, consumer culture, media witnessing, and cultural memo
From playlist Whitney Humanities Center
Frédéric Touzet : Codimension one foliation with pseudo-effective conormal bundle - lecture 1
Let X be a projective manifold equipped with a codimension 1 (maybe singular) distribution whose conormal sheaf is assumed to be pseudoeffective. Basic examples of such distributions are provided by the kernel of a holomorphic one form, necessarily closed when the ambient is projective. Mo
From playlist Virtual Conference
Proper Actions and Representation Theory Part 2
Professor Toshiyuki Kobayashi, University of Tokyo, Japan
From playlist Distinguished Visitors Lecture Series
In this #SHORTS video, we offer a brief idea of what a (smooth) manifold is. Smooth manifolds, topological manifolds, Riemannian manifolds, complex manifolds, are some of the main objects in the vast field of geometry. These spaces are (topological) spaces that are locally Euclidean. 👍 To
From playlist All Videos
Wei Zhu: "LDMnet: low dimensional manifold regularized neural networks"
New Deep Learning Techniques 2018 "LDMnet: low dimensional manifold regularized neural networks" Wei Zhu, Duke University, Mathematics Abstract: Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data ar
From playlist New Deep Learning Techniques 2018
I define topological manifolds. Motivated by the prospect of calculus on topological manifolds, I introduce smooth manifolds. At the end I point out how one needs to change the definitions, to obtain C^1 or even complex manifolds. To learn more about manifolds, see Lee's "Introduction to
From playlist Differential geometry
Lecture 10: Meshes and Manifolds (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Dalimil Mazáč - Bootstrapping Automorphic Spectra
I will explain how the conformal bootstrap can be adapted to place rigorous bounds on the spectra of automorphic forms on locally symmetric spaces. A locally symmetric space is of the form H\G/K, where G is a non-compact semisimple Lie group, K the maximal compact subgroup of G, and H a di
From playlist Quantum Encounters Seminar - Quantum Information, Condensed Matter, Quantum Field Theory
The Two-Dimensional Discrete Fourier Transform
The two-dimensional discrete Fourier transform (DFT) is the natural extension of the one-dimensional DFT and describes two-dimensional signals like images as a weighted sum of two dimensional sinusoids. Two-dimensional sinusoids have a horizontal frequency component and a vertical frequen
From playlist Fourier
Diophantine Inheritance and dichotomy for P - adic measures by Shreyasi Datta
PROGRAM SMOOTH AND HOMOGENEOUS DYNAMICS ORGANIZERS: Anish Ghosh, Stefano Luzzatto and Marcelo Viana DATE: 23 September 2019 to 04 October 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Ergodic theory has its origins in the the work of L. Boltzmann on the kinetic theory of gases.
From playlist Smooth And Homogeneous Dynamics