Network theory

Network Analysis. Course introduction.

Introduction to the Social Network Analysis course.

From playlist Structural Analysis and Visualization of Networks.

Number Theory: Part 1

Fundamental concepts of prime numbers are discussed. Fermat's & Euler's Theorems are explained. Testing for primality is Analyzed. Chinese Remainder Theorem is presented.

From playlist Network Security

Introduction to SNA. Lecture 1. Introduction to Network Science

Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture1.pdf Introduction to network science. Examples.

From playlist Introduction to SNA

Basic Concepts in Number Theory & Finite Fields: Part 1

It covers Euclid's Algorithm, Euclid's Algorithm: Tabular Method, Modular Arithmetic, Modular Arithmetic Operations, Modular Arithmetic Properties, Group, Cyclic Group, Ring, Field, Finite Fields or Galois Fields, Polynomial Arithmetic, Polynomial Arithmetic with Mod 2 Coefficients.

From playlist Network Security

Neural Network Overview

This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com

From playlist Intro to Data Science

Basic Concepts in Number Theory & Finite Fields: Part 2

It covers Euclid's Algorithm, Euclid's Algorithm: Tabular Method, Modular Arithmetic, Modular Arithmetic Operations, Modular Arithmetic Properties, Group, Cyclic Group, Ring, Field, Finite Fields or Galois Fields, Polynomial Arithmetic, Polynomial Arithmetic with Mod 2 Coefficients.

From playlist Network Security

Lecture 4. Network models.

Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/

From playlist Network Science, 2021

Social Networks 1 Networks

From playlist Week 9: Social Networks

Network Security, Part 1 : Basic Encryption Techniques

Fundamental concepts of network security are discussed. It provides a good overview of secret Key and public key Encryption. Important data encryption standards are presented.

From playlist Network Security

Introduction into spectral networks (Lecture 1) by Lotte Hollands

Program: Quantum Fields, Geometry and Representation Theory ORGANIZERS : Aswin Balasubramanian, Saurav Bhaumik, Indranil Biswas, Abhijit Gadde, Rajesh Gopakumar and Mahan Mj DATE & TIME : 16 July 2018 to 27 July 2018 VENUE : Madhava Lecture Hall, ICTS, Bangalore The power of symmetries

From playlist Quantum Fields, Geometry and Representation Theory

An Introduction to Tensor Renormalization Group by Daisuke Kadoh

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

IMS Public Lecture: Trends in Wireless Communications

Sergio Verdú, Princeton University

From playlist Public Lectures

Introduction to Data Science - Network Analysis

From playlist Introduction to Data Science Course

Some themes in modern chemical reaction network theory by Murad Banaji

DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore

From playlist Mathematical and statistical explorations in disease modelling and public health

Introduction into spectral networks (Lecture 2) by Lotte Hollands

Program: Quantum Fields, Geometry and Representation Theory ORGANIZERS : Aswin Balasubramanian, Saurav Bhaumik, Indranil Biswas, Abhijit Gadde, Rajesh Gopakumar and Mahan Mj DATE & TIME : 16 July 2018 to 27 July 2018 VENUE : Madhava Lecture Hall, ICTS, Bangalore The power of symmetries

From playlist Quantum Fields, Geometry and Representation Theory

[WeightWatcher] Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory

For slides and more information on the paper, visit https://aisc.ai.science/events/2019-11-06 Discussion lead & author: Charles Martin Abstract: Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-train

From playlist Math and Foundations

Emil Saucan (7/29/22): Discrete Morse Theory, Persistent Homology and Forman-Ricci Curvature

Abstract: It was observed experimentally that Persistent Homology of networks and hypernetworks schemes based on Forman's discrete Morse Theory and on the 1-dimensional version of Forman's Ricci curvature not only both perform well, but they also produce practically identical results. We s

From playlist Applied Geometry for Data Sciences 2022

Interdisciplinarity in the Age of Networks - Jennifer Chayes

Jennifer Chayes Managing Director, Microsoft Research New England, Microsoft Research New York May 21, 2013 For more videos, visit http://video.ias.edu

From playlist Mathematics

Statistical mechanics of deep learning - Surya Ganguli

Workshop on Theory of Deep Learning: Where next? Topic: Statistical mechanics of deep learning Speaker: Surya Ganguli Affiliation: Stanford University Date: October 18, 2019 For more video please visit http://video.ias.edu

From playlist Mathematics

A gentle introduction to network science: Dr Renaud Lambiotte, University of Oxford

The language of networks and graphs has become a ubiquitous tool to analyse systems in domains ranging from biology to physics and from computer science to sociology. Renaud will present important properties observed in real-life networked systems, as well as tools to understand and model

From playlist Data science classes