Program Outline
Introduction to Graph Neural Networks
This NVIDIA DLI course introduces the basic concepts, models, and applications of graph neural networks. It is a strong entry point for teams exploring graph-based representation learning and relational AI use cases.
Delivery
Virtual, On-site, or Hybrid
Duration
2 hours
Product
PyTorch, Deep Graph Library
Role
Data Scientist, Developer
NVIDIA
Graph Neural NetworksRelationship-aware deep learning
NVIDIA Graph AI
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners who already understand core deep learning and want to extend into graph-based modeling and relational AI techniques.
Overview
Program Summary
Official NVIDIA DLI course introducing the concepts, models, and applications of graph neural networks.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Understand graph deep learning concepts
+
Module 1
Understand graph deep learning concepts
Learn how graph neural networks represent relationships, structure data differently, and apply to real-world AI scenarios.
- Graph deep learning foundations
- Core graph model concepts
2Module 2
Explore applied graph AI applications
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Module 2
Explore applied graph AI applications
Review common graph-neural-network use cases and begin experimenting with graph tooling for applied AI workloads.
- Graph neural network applications
- Experimentation with graph tooling
Coverage Areas
Topic Coverage
Coverage Item 1
Graph deep learning foundations
Coverage Item 2
Core graph model concepts
Coverage Item 3
Graph neural network applications
Coverage Item 4
Experimentation with graph tooling
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your relationship-heavy use case as the framing scenario
- •Add fraud, recommendation, or knowledge graph examples
- •Extend into advanced graph ML implementation planning
