Graph Neural Networks Implementation in Python
Offered By: Prodramp via YouTube
Course Description
Overview
Syllabus
- Video Starts
- Video Introduction
- Tutorial Content in Part2
- Graph Representations Techniques
- Adjacency Matrix
- Incidence Matrix
- Degree Matrix
- Laplacian Matrix
- Creating Graph with NetworkX Jupyter notebook
- Graph Visualization with Node classes Jupyter notebook
- Graph Visualization with Node and Edge Labels Jupyter notebook
- Nodes Adjacency List Jupyter notebook
- Bag of Nodes
- Graph Walking Jupyter notebook
- GNN Concepts
- Role of Laplacian Matrix
- Convolution in Images
- Graph vs 2D fixed data types i.e. images, text
- Convolution on Graphs, how?
- Graph Feature Matrix
- Applying Convolution in Graphs
- Node Embeddings
- Message Passing in GNN
- Advantages of Node Embeddings
- GNN Use Cases
- Handling data in PyG Jupyter notebook
- GNN Experiment for Node grouping Jupyter notebook
- Node assignment to proper class Jupyter notebook
- GNN Model visualization with Netron
- Node classification using GNN in PyG
- Graph tSNE Visualization
- GNN Explainer
- Recap
Taught by
Prodramp
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