33:21[Deep Graph Learning] 6.1 Overview of supervised generative GNNs
A 33-minute video from BASIRA Lab: “[Deep Graph Learning] 6.1 Overview of supervised generative GNNs”.
BASIRA Lab
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20:03[Deep Graph Learning] 6.2 Self-supervised/unsupervised generative GNNs
A 20-minute video from BASIRA Lab: “[Deep Graph Learning] 6.2 Self-supervised/unsupervised generative GNNs”.
12:28[Deep Graph Learning] 6.3 Unconditional sequential graph generation
A 12-minute video from BASIRA Lab: “[Deep Graph Learning] 6.3 Unconditional sequential graph generation”.
35:55[Deep Graph Learning] 6.4 Unconditional one-shot graph generation
A 36-minute video from BASIRA Lab: “[Deep Graph Learning] 6.4 Unconditional one-shot graph generation”.
9:30[Deep Graph Learning] 6.5 Supervised conditional generation on graphs
A 10-minute video from BASIRA Lab: “[Deep Graph Learning] 6.5 Supervised conditional generation on graphs”.
32:47[Deep Graph Learning] 6.6 Generative Graph U-Net
A 33-minute video from BASIRA Lab: “[Deep Graph Learning] 6.6 Generative Graph U-Net”.
53:35[Deep Graph Learning] 1.1 Graph types
A 54-minute video from BASIRA Lab: “[Deep Graph Learning] 1.1 Graph types”.
1:04:19[Deep Graph Learning] 1.2 The graph matrix: from topology to resilience
A 64-minute video from BASIRA Lab: “[Deep Graph Learning] 1.2 The graph matrix: from topology to resilience”.
23:46[Deep Graph Learning] 1.3 Graph learning tasks (node, edge and graph based)
A 24-minute video from BASIRA Lab: “[Deep Graph Learning] 1.3 Graph learning tasks (node, edge and graph based)”.
51:48[Deep Graph Learning] 2.1 The logic behind graph-based learning
A 52-minute video from BASIRA Lab: “[Deep Graph Learning] 2.1 The logic behind graph-based learning”.
23:49[Deep Graph Learning] 2.2 The evolving landscape of feature embedding
A 24-minute video from BASIRA Lab: “[Deep Graph Learning] 2.2 The evolving landscape of feature embedding”.
21:34[Deep Graph Learning] 2.3 Shallow graph node embedding
A 22-minute video from BASIRA Lab: “[Deep Graph Learning] 2.3 Shallow graph node embedding”.
43:09[Deep Graph Learning] 2.4 Analyzing a single GCN layer
A 43-minute video from BASIRA Lab: “[Deep Graph Learning] 2.4 Analyzing a single GCN layer”.
46:08[Deep Graph Learning] 2.5 Generalized GCN node and layer updates
A 46-minute video from BASIRA Lab: “[Deep Graph Learning] 2.5 Generalized GCN node and layer updates”.
1:06:25[Deep Graph Learning] 3.1 GCN training and loss optimization
A 66-minute video from BASIRA Lab: “[Deep Graph Learning] 3.1 GCN training and loss optimization”.
32:20[Deep Graph Learning] 3.2 GNN inductive capability & graph-based learning
A 32-minute video from BASIRA Lab: “[Deep Graph Learning] 3.2 GNN inductive capability & graph-based learning”.
22:24[Deep Graph Learning] 3.3 Graph pooling & embedding aggregation
A 22-minute video from BASIRA Lab: “[Deep Graph Learning] 3.3 Graph pooling & embedding aggregation”.
12:35[Deep Graph Learning] 3.4 GCN layer operations
Advanced
A 13-minute video from BASIRA Lab: “[Deep Graph Learning] 3.4 GCN layer operations”.
19:03[Deep Graph Learning] 3.5 Global and local aggregation methods
A 19-minute video from BASIRA Lab: “[Deep Graph Learning] 3.5 Global and local aggregation methods”.
15:29[Deep Graph Learning] 4.1 Point, batch and mini-batch gradient descent
A 15-minute video from BASIRA Lab: “[Deep Graph Learning] 4.1 Point, batch and mini-batch gradient descent”.
49:33[Deep Graph Learning] 4.2 Batching and GNN sampling methods
A 50-minute video from BASIRA Lab: “[Deep Graph Learning] 4.2 Batching and GNN sampling methods”.
6:45[Deep Graph Learning] 4.3 Recap on GNN sampling methods
Advanced
A 7-minute video from BASIRA Lab: “[Deep Graph Learning] 4.3 Recap on GNN sampling methods”.
16:36[Deep Graph Learning] 4.4 GNN batch normalization layer
A 17-minute video from BASIRA Lab: “[Deep Graph Learning] 4.4 GNN batch normalization layer”.
21:35[Deep Graph Learning] 4.5 Generalized GNN layer and Dropout
A 22-minute video from BASIRA Lab: “[Deep Graph Learning] 4.5 Generalized GNN layer and Dropout”.
20:46[Deep Graph Learning] 4.6 GNN inductive vs transductive learning
A 21-minute video from BASIRA Lab: “[Deep Graph Learning] 4.6 GNN inductive vs transductive learning”.
33:53[Deep Graph Learning] 5.1 Node permutation invariance in GNNs
A 34-minute video from BASIRA Lab: “[Deep Graph Learning] 5.1 Node permutation invariance in GNNs”.
13:38[Deep Graph Learning] 5.2 Node permutation equivariance in GNNs
A 14-minute video from BASIRA Lab: “[Deep Graph Learning] 5.2 Node permutation equivariance in GNNs”.
37:44[Deep Graph Learning] 5.3 GNN expressiveness
A 38-minute video from BASIRA Lab: “[Deep Graph Learning] 5.3 GNN expressiveness”.
20:54[Deep Graph Learning] 5.4 Graph Isomorphism Network Expressive nets
A 21-minute video from BASIRA Lab: “[Deep Graph Learning] 5.4 Graph Isomorphism Network Expressive nets”.
8:45[Deep Graph Learning] 6.7 Evaluation measures for generative GNNs
A 9-minute video from BASIRA Lab: “[Deep Graph Learning] 6.7 Evaluation measures for generative GNNs”.
50:32[Deep Learning Matrix Reloaded] From spanning sets to deep matrix factorization (part 2)
Advanced
A 51-minute video from BASIRA Lab: “[Deep Learning Matrix Reloaded] From spanning sets to deep matrix factorization (part 2)”.
27:43[Deep Learning Matrix Reloaded] Supervised learning expressed into matrices (part 1)
A 28-minute video from BASIRA Lab: “[Deep Learning Matrix Reloaded] Supervised learning expressed into matrices (part 1)”.
47:23[Intuitive Deep Learning] 1.1 Supervised linear and non-linear regression coded in matrices
A 47-minute video from BASIRA Lab: “[Intuitive Deep Learning] 1.1 Supervised linear and non-linear regression coded in matrices”.
20:04[Intuitive Deep Learning] 1.2 Supervised linear and nonlinear multiregression coded in matrices
A 20-minute video from BASIRA Lab: “[Intuitive Deep Learning] 1.2 Supervised linear and nonlinear multiregression coded in matrices”.
30:36[Intuitive Deep Learning] 1.3 Supervised linear and non-linear classifiers coded in matrices
A 31-minute video from BASIRA Lab: “[Intuitive Deep Learning] 1.3 Supervised linear and non-linear classifiers coded in matrices”.
1:17:02[Intuitive Deep Learning] 1.4 Spanning sets & matrix factorization for data representation | PCA
A 77-minute video from BASIRA Lab: “[Intuitive Deep Learning] 1.4 Spanning sets & matrix factorization for data representation | PCA”.