Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.1 - Why Graphs Published 2021-04-13 Download video MP4 360p Download video MP4 720p Recommendations 20:27 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.2 - Applications of Graph ML 18:40 But what is a neural network? | Chapter 1, Deep learning 1:15:20 Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018) 59:00 An Introduction to Graph Neural Networks: Models and Applications 59:52 MIT 6.S191: Deep Generative Modeling 1:12:20 Theoretical Foundations of Graph Neural Networks 2:37:14 2020 Machine Learning Roadmap (95% valid for 2023) 38:27 ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein 58:12 MIT Introduction to Deep Learning | 6.S191 1:08:06 Deep Learning Basics: Introduction and Overview 57:57 Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition 15:00 Understanding Graph Attention Networks 51:31 11. Introduction to Machine Learning 1:29:00 Graph Node Embedding Algorithms (Stanford - Fall 2019) 23:08 Graphs, Vectors and Machine Learning - Computerphile 51:06 Intro to graph neural networks (ML Tech Talks) 1:54:59 Think Graph Neural Networks (GNN) are hard to understand? Try this two part series.. 1:14:59 AMMI 2022 Course "Geometric Deep Learning" - Lecture 1 (Introduction) - Michael Bronstein 07:50 Machine Learning vs Deep Learning Similar videos 10:31 Stanford CS224W: ML with Graphs | 2021 | Lecture 6.1 - Introduction to Graph Neural Networks 16:53 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 - Reasoning in Knowledge Graphs 27:30 Stanford CS224W: ML with Graphs | 2021 | Lecture 2.1 - Traditional Feature-based Methods: Node 25:21 Stanford CS224W: ML with Graphs | 2021 | Lecture 9.1 - How Expressive are Graph Neural Networks 20:27 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.3 - Choice of Graph Representation​ 27:50 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 8.1 - Graph Augmentation for GNNs 14:44 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.1 - Node Embeddings 18:34 Stanford CS224W: ML with Graphs | 2021 | Lecture 5.1 - Message passing and Node Classification 14:51 Stanford CS224W: ML with Graphs | 2021 | Lecture 17.1 - Scaling up Graph Neural Networks 40:09 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.2 - A Single Layer of a GNN 22:14 Stanford CS224W: ML with Graphs | 2021 | Lecture 13.1 - Community Detection in Networks 20:28 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs 27:10 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 - PageRank 05:51 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 7.1 - A general Perspective on GNNs 18:04 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.3 - Embedding Entire Graphs 35:41 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.3 - Deep Learning for Graphs 29:31 Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 6.2 - Basics of Deep Learning 20:10 Stanford CS224W: ML with Graphs | 2021 | Lecture 2.3 - Traditional Feature-based Methods: Graph More results