Relational Databases (part 1 of 6)
The essential concepts of relational databases. Part of a larger series teaching programming. Visit codeschool.org
From playlist Relational Databases
From playlist Week 9: Social Networks
Intro to Relational - Graph Convolutional Networks
Join my FREE course Basics of Graph Neural Networks (https://www.graphneuralnets.com/p/basics-of-gnns/?src=yt)! This video presents Relational Graph Convolutional Networks (R-GCNs) as a way to apply GCNs to heterogeneous graphs. A simple Twitter graph is used to demonstrate the concepts.
From playlist Graph Neural Networks
Relational Databases (part 6 of 6)
The essential concepts of relational databases. Part of a larger series teaching programming. Visit codeschool.org
From playlist Relational Databases
Graph Neural Networks, Session 2: Graph Definition
Types of Graphs Common data structures for storing graphs
From playlist Graph Neural Networks (Hands-on)
Relational Databases (part 4 of 6)
The essential concepts of relational databases. Part of a larger series teaching programming. Visit codeschool.org
From playlist Relational Databases
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Networking
Molecular Noise, Non-stationarity and Memory in Single-enzyme Kinetics by Arti Dua
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL ORGANIZERS: Debashish Chowdhury (IIT-Kanpur, India), Ambarish Kunwar (IIT-Bombay, India) and Prabal K Maiti (IISc, India) DATE: 11 October 2022 to 22 October 2022 VENUE: Ramanujan Lecture Hall 'Fluctuation-and-noise' a
From playlist STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (2022)
Approximation with deep networks - Remi Gribonval, Inria
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
DDPS | The problem with deep learning for physics (and how to fix it) by Miles Cranmer
Description: I will present a review of how deep learning is used in physics, and how this use is often misguided. I will introduce the term “scientific debt,” and argue that, though deep learning can quickly solve a complex problem, its success does not come for free. Because most learnin
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
MIT 6.S191: Recurrent Neural Networks and Transformers
MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Soleimany January 2022 For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 1:59 - Sequence modeling 4:16 - Neurons with recurrence 10
From playlist Introduction to Machine Learning
Yulia Gel (4/28/21): Topological Clustering of Multilayer Networks
Title: Topological Clustering of Multilayer Networks Abstract: Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socio-
From playlist AATRN 2021
DDPS | Deep learning for reduced order modeling
Description: Reduced order modeling (ROM) techniques, such as the reduced basis method, provide nowadays an essential toolbox for the efficient approximation of parametrized differential problems, whenever they must be solved either in real-time, or in several different scenarios. These ta
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Daniel Roberts: "Deep learning as a toy model of the 1/N-expansion and renormalization"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Deep learning as a toy model of the 1/N-expansion and renormalization" Daniel Roberts - Diffeo Institute for Pure and Applied Mathematics, UCLA November 20, 2019
From playlist Machine Learning for Physics and the Physics of Learning 2019
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 2
Recorded during the meeting "Data Assimilation and Model Reduction in High Dimensional Problems" the July 21, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone A kinetic description of a plasma in external and self-consistent fiel
From playlist Numerical Analysis and Scientific Computing