Learning Graph Neural Networks
With Janani Ravi
Liked by 43 users
Duration: 2h 13m
Skill level: Intermediate
Released: 7/29/2024
Course details
Graph neural networks—neural networks capable of working with graph data structures—apply deep learning to data structures to reveal fresh insights from their graphs. In this course, learn about the different use cases of graph modeling and how to train a graph neural network and evaluate its results. Instructor Janani Ravi starts with some background on graphs, including terminology and graph types. She then introduces graph machine learning concepts and the basics of graph neural networks. The last half of the course consists of exercises to help you set up and train graph neural networks using PyTorch Geometric, visualize graphs using NetworkX, and training a graph convolutional network for node labeling using the Cora dataset.
Skills you’ll gain
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Meet the instructor
Learner reviews
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Samoua Alsamoua
Samoua Alsamoua
MSc Researcher @ Karadeniz Technical University Software Engineering: Artificial intelligence, Deep learning and heuristic Algorithms
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Eddie Jenkins, CBE
Eddie Jenkins, CBE
Data Scientist | ML Development, Deployment, Strategy, and Stakeholder Communication | Specialty in Pricing, Segmentation, Forecasting, and Churn…
Contents
What’s included
- Practice while you learn 1 exercise file
- Test your knowledge 5 quizzes
- Learn on the go Access on tablet and phone
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