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LinkedIn Learning

Learning Graph Neural Networks

via LinkedIn Learning

Overview

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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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