Graph Neural Network The Graph Neural Network training course, which covers graph representation to complete mastery of an introductory level of GNN , is published by the popular Udemy Academy. In recent years, the graph neural network (GNN) has gained increasing popularity in various fields due to its high expressive power and excellent performance.
Graph structures allow us to capture data with complex structures and relationships, and GNN provides us with the opportunity to study and model this complex data representation for tasks such as classification, clustering, link prediction, and robust representation. Considering that this is a relatively new concept and it will be hard to learn from conferences and articles, this course can help you .
This course provides complete introductory material for learning graph neural network, and by completing this course, you will have a good understanding of the subject in theory and in practice. In this training course, you will be taught both mathematical topics and how to code them in the Python programming language. Now if you want to start learning about Graph Neural Networks (GNN) and implementing GNN models in PyTorch Geometric ; This course will be suitable for you and you can download and watch it from the Downloadly site .
Who is this course suitable for:
- Engineering graduate students
- Graduate students of computer science
- Data Scientists
- Python developers are interested in learning graph neural network
- Deep learning engineers
- Machine learning engineers
- Signal processing engineers
- People interested in learning neural networks
What you will learn in the Graph Neural Network course:
- Chart display tutorial
- Graph Neural Network (GNN)
- Chart analysis
- Graph Complexity Network (GCN)
- Graph attention network (GAT)
- Simplifying Graph Convolution (SGC)
- And …
Characteristics of the Graph Neural Network course:
Instructor: Younes Sadat-Nejad
Education level: introductory to advanced
Number of lessons: 26
Duration of education: 4 hours and 29 minutes
Introductory background on machine learning and deep learning
Introductory background on signal processing and data analysis
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