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Graph Neural Networks: Basics, Codes and Simulations for AI
Basics GNNs, GNN Explainer & PyNeuraLogic through 100 + Resources: Code Implementations in Python (StellarGraph & PyG)
Created by Junaid Zafar, offered on Udemy
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Student feedback
6.3/10To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 9291 students which left 66 reviews at an average rating of 4.09, which is average.
Course length
9/10We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 2 hours 10 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 4 hours 29 minutes.
Overall score
6.9/10This course currently has a bestcourses score of 6.9/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.
Description
Graph AI carries immense potential for us to explore, connect the dots and build intelligent applications using the Internet of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their area to the students. The purpose of this course is to unfold the basics to the cutting-edge concepts and technologies in this realm.
Graphs are all around us; real-world objects are often defined in terms of their connections to other things. A set of objects, and the connections between them, are naturally expressed as a Graph Neural Network (GCN). Recent developments have increased their capabilities and expressive power. They have profound applications in the realm of AI, fake news detection, traffic prediction to recommendation systems.
This course explores and explains modern AI graph neural networks. In this course, we look at what kind of data is most naturally phrased as a graph, and some common examples. Then we explore what makes graphs different from other types of data, and some of the specialized choices we have to make when using graphs. We then build a modern GNN, walking through each of the parts of the model and gradually to state-of-the-art AI GNN models. Finally, we provide a GNN playground where you can play around with a real-world task and dataset to build a stronger intuition of how each component of an AI GNN model contributes to the predictions it makes.
The topics of this course include:
1. Introduction to Graph Machine Learning.
2. Internet of Behaviors.
3. Homographic Intelligence.
4. Graphs Basics and Eigen Centrality.
4. Graph Neural Networks.
5. Graph Attention Networks.
6. Building a Graph Neural Network
7. GNNs Predictors by Pooling Information.
8. Graph AI and its code implementations in Python.
9. Multi- Graphs and Hyper- Graphs in AI 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. Mixed Grain Aggregators.
18. Multimodal Graph AI.
19. 100+ Resources on Graph Neural Networks
What you will learn
- Fundamentals Graph AI using Internet of Behaviors
- Basics and implementation of Graph Neural Networks
- How to a create a Graph Neural Network, its training, optimization and testing
- AI Graph feature learning and prediction using FastGCN, gated and mixed grain architectures.
- How to derive an AI sub- graph from Graph Neural Networks
- How create a Graph AI model?
Requirements
- No prior experience in programming is required. You will learn everything you need to know from the very basics
