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LinkedIn Learning

Learning Graph Neural Networks

via LinkedIn Learning

Overview

Learn about the use cases of graph modeling and find out how to train graph neural networks and evaluate its results.

Syllabus

Introduction
  • Introducing graph neural networks
  • Prerequisites
1. Understanding Graphs
  • Undirected and directed graphs
  • Other graph types
  • Graph representations
2. Introducing Graph Machine Learning
  • Prediction tasks with graphs
  • Approaches to graph machine learning
  • Challenges of using graphs in machine learning
3. Introducing Graph Neural Networks
  • Graph neural networks intuition
  • Understanding the structure of GNNs
  • The graph neural network architecture
  • Message passing transformation and aggregation
  • Training a GNN
4. Representing Graphs in PyTorch Geometric
  • Introducing PyTorch Geometric
  • Exercise: Set up the Colab environment and libraries
  • Exercise: Setting up a graph data structure in PyG
  • Exercise: Visualizing graphs and exploring graph methods
  • Exercise: Visualizing and exploring a directed graph
  • Exercise: Exploring the cora dataset
  • Exercise: Mini batches of data
  • Exercise: Representing heterogeneous graphs in PyG
5. Performing Node Classification Using GNNs
  • Exercise: The CiteSeer dataset for node classification
  • Exercise: Setting up a DNN as a baseline model
  • Exercise: Training the baseline model
  • Exercise: Setting up a graph convolutional network
  • Exercise: Training a GCN
Conclusion
  • Summary and next steps

Taught by

Janani Ravi

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