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Introduction to Graph Neural Networks

Introduction to Graph Neural Networks

NVIDIA NIC-NVI-IGNN

USD 30.00
excl. VAT

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.

Industry Partners

NVIDIA
Institution
NVIDIA
The institution that is responsible for the courseware (and lab environment, if included) of this learning product.
E-learning Format
Self-paced (SP)
Language
English
Subscription duration
180 days
The time you will have access to the learning product once it has been activated.
Grace Period
30 days
Grace Period is the time between booking and automatic activation. After completing your order, you usually have 30 days until a product is automatically activated and the official subscription period begins.
Total effort
2 hours
The estimated total effort it will take you to complete the learning product.