100+ resources via basic GNNs, GNN Explainer and PyNeuraLogic: code implementation in Python (StellarGraph and PyG)
Graph AI has enormous potential for us to explore, connect dots, and create intelligent applications using the Internet of Behavior (IoB). Many graph neural networks achieved state-of-the-art results on both node and graph classification functions. However, despite the fact that GNN has revolutionized graph representation education, students have a limited understanding of their field. The purpose of this course is to unveil the basics of the latest concepts and technologies in this field.
Graphs are all around us; Real-world objects are usually defined in terms of their connection to other things. The set of objects and their connections are naturally expressed as graph neural networks (GCNs). Recent developments have increased their ability and expressive power. They have in-depth applications in the field of AI, from detecting fake news, to traffic forecasting to recommendation systems.
This course explores and explains modern AI graph neural networks. In this course, we look at what types of data are most naturally worded as graphs and look at some common examples. Then we explore how we distinguish graphs from other types of data and how we have to make certain choices when using graphs. We then build the modern GNN by moving from each part of the model and gradually to the most sophisticated AI GNN models. Finally, we provide a GNN playground where you can play with real-world work and datasets to build a strong intuition about how each component of the AI GNN model contributes to the predictions it makes.
Topics covered in this course include:
1. Introduction to Graph Machine Learning.
2. Internet of Behavior.
3. Homographic intelligence.
4. Graph Basics and Ezen Centrality.
4. Graph Neural Network.
5. Graph Attention Network.
6. Creating a graph neural network
7. Predict GNNs by aggregating information.
8. Graph AI and its code implementation in Python.
9. Multi-graphs and hyper-graphs in AI using IoB.
10. Design space for GNN.
11. Motivational bias in GNN.
12. Pythorch geometric implementation.
13. Node2Vec feature teaching.
14. Fast GCNs.
15. Gated Graph RNNs.
16. Graph LSTM
17. Mixed grain aggregators.
18. Multimodal graph AI.
19. 100+ resources on graph neural networks
Basic GNNs, 100+ resources via GNN Explainer and PyNeuraLogic: Code Implementation in Python (StellarGraph & PyG)
This course is free. You will find the coupon below.
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