Linkedin Learning Advanced Graph Neural NetWorks

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232.34 MB | 00:05:52 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
1  Overview of graph neural networks  (10.49 MB)
2  Prerequisites  (1.55 MB)
1  Message passing in GNNs  (6.62 MB)
2  Aggregation and transformation math  (7.15 MB)
3  Aggregation and transformation math in matrix form  (6.2 MB)
1  Introducing graph attention  (5.47 MB)
2  Computing the attention coefficient  (6.58 MB)
3  Including attention in GNN layers  (4.61 MB)
4  Getting set up with Colab and the PyTorch Geometric library  (5.95 MB)
5  Exploring the Cora dataset  (12.09 MB)
6  Setting up the graph convolutional network  (9.67 MB)
7  Training a graph convolutional network  (13.84 MB)
8  Node classification using a graph attention network  (15.09 MB)
9  Using the GATv2Conv layer for attention  (9.69 MB)
1  Understanding graph classification  (10.04 MB)
2  Exploring the PROTEINS Dataset for graph classification  (9.17 MB)
3  Minibatching graph data  (5.96 MB)
4  Setting up a graph classification model  (9.84 MB)
5  Training a GNN for graph classification  (8.81 MB)
6  Eliminating neighborhood normalization and skip connections  (7.27 MB)
1  A quick overview of autoencoders  (5.23 MB)
2  Introducing graph autoencoders  (4.52 MB)
3  Splitting link prediction data  (12.08 MB)
4  Understanding link splits  (13.76 MB)
5  Designing an autoencoder for link prediction  (12.33 MB)
6  Training the autoencoder  (15.04 MB)
1  Summary and next steps  (2.64 MB)
Ex Files Advanced Graph Neural Networks  (644.04 KB)

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