Learning Graph Neural Networks

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Janani Ravi

2:13:12

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01 - Introduction

01 - Introducing graph neural networks.mp4

06:14

02 - Prerequisites.mp4

00:44

02 - 1. Understanding Graphs

01 - Undirected and directed graphs.mp4

05:27

02 - Other graph types.mp4

08:17

03 - Graph representations.mp4

04:44

03 - 2. Introducing Graph Machine Learning

01 - Prediction tasks with graphs.mp4

05:51

02 - Approaches to graph machine learning.mp4

06:10

03 - Challenges of using graphs in machine learning.mp4

06:40

04 - 3. Introducing Graph Neural Networks

01 - Graph neural networks intuition.mp4

04:43

02 - Understanding the structure of GNNs.mp4

04:30

03 - The graph neural network architecture.mp4

06:08

04 - Message passing transformation and aggregation.mp4

06:09

05 - Training a GNN.mp4

04:12

05 - 4. Representing Graphs in PyTorch Geometric

01 - Introducing PyTorch Geometric.mp4

02:01

02 - Exercise Set up the Colab environment and libraries.mp4

04:15

03 - Exercise Setting up a graph data structure in PyG.mp4

05:26

04 - Exercise Visualizing graphs and exploring graph methods.mp4

05:31

05 - Exercise Visualizing and exploring a directed graph.mp4

02:42

06 - Exercise Exploring the cora dataset.mp4

06:02

07 - Exercise Mini batches of data.mp4

03:45

08 - Exercise Representing heterogeneous graphs in PyG.mp4

07:31

06 - 5. Performing Node Classification Using GNNs

01 - Exercise The CiteSeer dataset for node classification.mp4

07:43

02 - Exercise Setting up a DNN as a baseline model.mp4

04:11

03 - Exercise Training the baseline model.mp4

04:47

04 - Exercise Setting up a graph convolutional network.mp4

04:41

05 - Exercise Training a GCN.mp4

02:35

07 - Conclusion

01 - Summary and next steps.mp4

02:13

Description

Graph neural networks—neural networks capable of working with graph data structures—apply deep learning to data structures to reveal fresh insights from their graphs. In this course, learn about the different use cases of graph modeling and how to train a graph neural network and evaluate its results. Instructor Janani Ravi starts with some background on graphs, including terminology and graph types. She then introduces graph machine learning concepts and the basics of graph neural networks. The last half of the course consists of exercises to help you set up and train graph neural networks using PyTorch Geometric, visualize graphs using NetworkX, and training a graph convolutional network for node labeling using the Cora dataset.

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Neural Networks

Janani Ravi

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.