Graph neural networks (GNN) are a class of artificial neural networks designed to process data that can be represented as graphs. Across various industries, GNNs find suitable applications such as molecular analysis, drug discovery and repurposing, social network analysis, predicting stock market developments, thermodynamics, and even modeling human brain connectomes.
Course Prerequisites
Competency in the Python 3 programminglanguage. Experience with deep neural networks (specifically variations of CNNs)
What you will learn
In this course you'll learn :
- Important concepts of graphs
- How to apply neural networks to graphs
- Various applications of graph neural networks
- How to build and efficiently train GNN-based models
Additional information
The minimum order quantity for NVIDIA self-paced courses is 10.
When adding a course to the shopping cart, a quantity of 10 is automatically added. It may also take 2-3 working days for your course access to be activated. You will receive an email from us with all the necessary information.
When adding a course to the shopping cart, a quantity of 10 is automatically added. It may also take 2-3 working days for your course access to be activated. You will receive an email from us with all the necessary information.
Industry Partners
