GNNs, GNN Explainer & PyNeuraLogic by means of 100 + Sources: Code Implementations in Python (StellarGraph & PyG)
What you’ll study
- Fundamentals Graph AI utilizing Web of Behaviors
- Fundamentals and implementation of Graph Neural Networks
- How you can a create a Graph Neural Community, its coaching, optimization and testing
- AI Graph function studying and prediction utilizing FastGCN, gated and combined grain architectures.
- How you can derive an AI sub- graph from Graph Neural Networks
- How create a Graph AI mannequin?
- No prior expertise in programming is required. You’ll study all the pieces it’s worthwhile to know from the very fundamentals
Graph AI carries immense potential for us to discover, join the dots and construct clever functions utilizing the Web of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art outcomes on each node and graph classification duties. Nonetheless, regardless of GNNs revolutionizing graph illustration studying, there may be restricted understanding of their space to the scholars. The aim of this course is to unfold the fundamentals to the cutting-edge ideas and applied sciences on this realm.
Graphs are throughout us; real-world objects are sometimes outlined when it comes to their connections to different issues. A set of objects, and the connections between them, are naturally expressed as a Graph Neural Community (GCN). Latest developments have elevated their capabilities and expressive energy. They’ve profound functions within the realm of AI, pretend information detection, site visitors prediction to advice methods.
This course explores and explains trendy AI graph neural networks. On this course, we take a look at what sort of knowledge is most naturally phrased as a graph, and a few widespread examples. Then we discover what makes graphs totally different from different sorts of knowledge, and among the specialised decisions we now have to make when utilizing graphs. We then construct a contemporary GNN, strolling by means of every of the components of the mannequin and progressively to state-of-the-art AI GNN fashions. Lastly, we offer a GNN playground the place you may mess around with a real-world job and dataset to construct a stronger instinct of how every element of an AI GNN mannequin contributes to the predictions it makes.
The matters of this course embrace:
1. Introduction to Graph Machine Studying.
2. Web of Behaviors.
3. Homographic Intelligence.
4. Graphs Fundamentals and Eigen Centrality.
4. Graph Neural Networks.
5. Graph Consideration Networks.
6. Constructing a Graph Neural Community
7. GNNs Predictors by Pooling Info.
8. Graph AI and its code implementations in Python.
9. Multi- Graphs and Hyper- Graphs in AI utilizing IoB.
10. Design House for a GNNs.
11. Inductive Biases in GNNs.
12. Pytorch Geometric Implementations.
13. Node2Vec Function Studying.
14. FAST GCNs.
15. Gated Graph RNNs.
16. Graph LSTMs
17. Blended Grain Aggregators.
18. Multimodal Graph AI.
19. 100+ Sources on Graph Neural Networks