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Demystifying Graph Convolutional Neural Network (GCN)
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Welcome to "Demystifying Graph Convolutional Neural Network (GCN)" – a comprehensive 30-minute lecture by Aiswarya Nandakumar, founder of Togo AI Labs and an expert in Graph Neural Networks (GNNs).
In this lecture you will learn,
How convolution operation in GNNs helps extract meaningful features from nodes and their neighbours is similar to how it is done in images.
The essential math equations power GCNs, including adjacency matrices, degree matrices, and normalization.
Explore the architecture of GCNs, learning how they are structured and how they process graph data.
Gain insights into the different operations within GCNs, such as normalization, aggregation, and applying GCN layers.
This lecture is perfect for beginners or those without prior knowledge of GNNs.
Benefit from a hands-on approach with easy-to-understand calculations and examples.
In this lecture you will learn,
How convolution operation in GNNs helps extract meaningful features from nodes and their neighbours is similar to how it is done in images.
The essential math equations power GCNs, including adjacency matrices, degree matrices, and normalization.
Explore the architecture of GCNs, learning how they are structured and how they process graph data.
Gain insights into the different operations within GCNs, such as normalization, aggregation, and applying GCN layers.
This lecture is perfect for beginners or those without prior knowledge of GNNs.
Benefit from a hands-on approach with easy-to-understand calculations and examples.