What you would learn in Artificial Intelligence Graphs using Internet of Behaviors course?
Graph AI is an excellent opportunity to study connections, make connections and develop intelligent applications using the Internet of Behaviors (IoB). A variety of Graph Neural Networks achieved state-of-the-art results for both nodes and task-based graph classification. Although GNNs have revolutionized learning through graph representation, there is not a lot of knowledge of their field to students. This class aims to introduce students to the latest concepts and techniques in this field.
Graphs are everywhere. Real-world objects are usually described in terms of their relationships to other objects. A set of objects and their connections can be expressed naturally in a Graph Neural Network (GCN). Recent advances have enhanced the capabilities of GCNs and their capacity to express themselves, and they can be used in AI and fraud detection, and forecast and recommendation algorithms.
This course explains and explores the current AI graph neural networks. We will look at what type of data is typically referred to as graphs and the most common instances in the course. We then look at the characteristics that distinguish graphs from other kinds of data and the various specialized decisions we must make when working with graphs. We then construct a contemporary GNN by navigating through the various parts of the model before moving towards the latest AI GNN models. We also provide the GNN play area where you can experiment with real-world tasks and data to understand better how each element in any AI GNN model contributes to the predictions it generates.
The subjects of this course are:
1. An introduction to Graph Machine Learning.
2. Internet is full of Behaviors.
3. Homographic Intelligence.
4. Graphs Basics and Eigen Centrality.
4. Graph Neural Networks.
5. Graph Attention Networks.
6. Making the Graph Neural Network
7. GNNs are predictive of GNNs by Pooling Information.
8. Graphic AI and its implementation of code in Python.
9. MultiMulti Graphs and HyperHyper Graphs in AI by using IoB.
10. Design Space for a GNNs.
11. Inductive biases in GNNs.
12. Pytorch Geometric Implementations.
13. Node2Vec Feature Learning.
14. FAST GCNs.
15. Gated Graph RNNs.
16. Graph LSTMs
17. Aggregators for Mixed Grain.
18. Multimodal Graph AI.
Content of the Course:
Fundamentals of Graph AI by using the Internet of Behaviors
The basics and the execution of Graph Neural Networks
How do I make a Graph Neural Network, its training, testing, and optimization
AI Graph feature learning and prediction using FastGCN gated, mixed, and gated grain structures.
How do you derive an AI sub-graph using Graph Neural Networks?
How do you create a Graph AI model?
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