From playlist Week 9: Social Networks
Graph Neural Networks, Session 1: Introduction to Graphs
Examples of Graph representation of data Motivation for doing machine learning on Graphs
From playlist Graph Neural Networks (Hands-on)
Graph Neural Networks, Session 2: Graph Definition
Types of Graphs Common data structures for storing graphs
From playlist Graph Neural Networks (Hands-on)
Graph Data Structure 1. Terminology and Representation (algorithms)
This is the first in a series of videos about the graph data structure. It mentions the applications of graphs, defines various terminology associated with graphs, and describes how a graph can be represented programmatically by means of adjacency lists or an adjacency matrix.
From playlist Data Structures
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Network Analysis. Lecture 3. Random graphs.
Erdos-Reni random graph model. Poisson and Bernulli distributions. Distribution of node degrees. Phase transition, gigantic connected component. Diameter and cluster coefficient. Configuration model Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture3.pdf
From playlist Structural Analysis and Visualization of Networks.
(ML 13.3) Directed graphical models - formalism (part 1)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
Probabilistic Graphical Models (PGMs) In Python | Graphical Models Tutorial | Edureka
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka "Graphical Models" video answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural
From playlist Machine Learning Algorithms in Python (With Demo) | Edureka
Ruslan Salakhutdinov: "Learning Hierarchical Generative Models, Pt. 1"
Graduate Summer School 2012: Deep Learning, Feature Learning "Learning Hierarchical Generative Models, Pt. 1" Ruslan Salakhutdinov, University of Toronto Institute for Pure and Applied Mathematics, UCLA July 23, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-school
From playlist GSS2012: Deep Learning, Feature Learning
Willem van den Boom - Bayesian Learning of Graph Substructures
Willem van den Boom (National University of Singapore) presents "Bayesian Learning of Graph Substructures, 5 August 2022.
From playlist Statistics Across Campuses
Stanford CS330 Deep Multi-Task & Meta Learning - Transfer Learning, Meta Learning l 2022 I Lecture 3
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu​ Chelsea Finn Computer
From playlist Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
(ML 13.4) Directed graphical models - formalism (part 2)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
Jason Morton: "An Algebraic Perspective on Deep Learning, Pt. 2"
Graduate Summer School 2012: Deep Learning, Feature Learning "An Algebraic Perspective on Deep Learning, Pt. 2" Jason Morton, Pennsylvania State University Institute for Pure and Applied Mathematics, UCLA July 20, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-scho
From playlist GSS2012: Deep Learning, Feature Learning
AMMI Course "Geometric Deep Learning" - Lecture 6 (Graphs & Sets II) - Petar Veličković
Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July-August 2021 by Michael Bronstein (Imperial College/Twitter), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind) Lecture 6: General attributed graphs •
From playlist AMMI Geometric Deep Learning Course - First Edition (2021)
Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Generative Model Basics - Unconventional Neural Networks p.1
Hello and welcome to a series where we will just be playing around with neural networks. The idea here is to poke around with various neural networks, doing unconventional things with them. Doing things like trying to teach a sequence to sequence model math, doing classification with a gen
From playlist Unconventional Neural Networks
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 13B : Belief Nets
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]