Graph Attention in DGL

I looked into the implementation of graph attention networks in DGL. DGL is a library for deep learning on graphs. I also talked about how data is processed inside graph attention layers.
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🔗 jupyter notebook I built in this video: github.com/mashaan14/CZcams-...
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• Graph Attention in PyT...
• Graph Attention Networ...
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- my website ➡️ mashaan14.github.io/mashaan/
- my github ➡️ github.com/mashaan14
- my linkedin ➡️ / mashaan
- sponsor me on GitHub Sponsors➡️github.com/sponsors/mashaan14
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📹 Video edit: Adobe Premiere Rush
🎧 Audio enhancement: Adobe Podcast
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Chapters:
00:00 start
00:11 DGL or PyG?
01:23 acknowledgment
01:46 GATConv layer in DGL
03:11 GATConv source code
05:00 start of the code
05:13 GAT layers from DGL
06:20 how data is processed inside GAT
07:35 breaking down the 1st layer
08:48 why concatenation not averaging?
09:41 2nd GAT layer
10:23 evaluate and train functions
10:58 importing Cora dataset
11:31 initialize the model
11:49 training and testing
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#DGL #GNN #GAT #attention #convolution #graphconvolution #ai #deeplearning #machinelearning #python #neuralnetworks #artificialintelligence #pytorch-geometric #jraph #graph #GCN #GNNs #pytorchgeometric #GCNConv #GATConv

[@oneplus383]

Very good explanation. 😊

[@mashaan14]

Glad it was helpful!