15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Scientists
- AI Developers
- Machine Learning Engineers
- Researchers
- Network Analysts
- Bioinformatics Specialists
- Anyone needing GNNs skills
Session Objectives
- Understand the fundamentals of Graph Neural Networks (GNNs).
- Master graph convolution networks (GCNs) for node classification.
- Utilize graph attention networks (GATs) for relational learning.
- Implement graph embedding techniques for network analysis.
- Design and build GNN models for various network data applications.
- Optimize GNN models for performance and scalability.
- Troubleshoot and address common GNN implementation challenges.
- Implement model evaluation and validation techniques for GNNs.
- Integrate GNN models into real-world systems.
- Understand how to handle large-scale graph data.
- Explore advanced GNN architectures (e.g., GraphSAGE, RGCN).
- Apply real world use cases for GNNs in various domains.
- Leverage GNN libraries for efficient model implementation.
About the Course
Unlock the power of relational data with our Graph Neural Networks (GNNs) Training Course. This program is designed to equip you with the essential skills to analyze and model network data, enabling you to build powerful applications that leverage the interconnected nature of data. In today's data-driven world, mastering GNNs is crucial for developing innovative solutions in various fields, from social network analysis to drug discovery. Our GNNs training course offers hands-on experience and expert guidance, empowering you to implement state-of-the-art graph-based models.
This network data modeling training delves into the core concepts of graph neural networks, covering topics such as graph convolutions, message passing, and node and graph embedding. You'll gain expertise in using industry-standard libraries and tools to analyze and model network data, meeting the demands of modern graph-based AI projects. Whether you're a data scientist, AI developer, or researcher, this Graph Neural Networks (GNNs) course will empower you to build powerful graph-based models.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of Graph Neural Networks (GNNs).
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Subtopic 1.2:
Overview of graph convolutions, message passing, and graph embeddings.
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Subtopic 1.3:
Setting up a GNNs development environment.
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Subtopic 1.4:
Introduction to GNNs libraries and tools.
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Subtopic 1.5:
Best practices for GNNs.
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Subtopic 2.1:
Implementing GCNs for node classification tasks.
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Subtopic 2.2:
Utilizing spectral and spatial graph convolutions.
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Subtopic 2.3:
Designing and building GCN models for graph data.
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Subtopic 2.4:
Optimizing GCNs for node-level predictions.
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Subtopic 2.5:
Best practices for GCNs.
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Subtopic 3.1:
Implementing GATs for relational learning.
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Subtopic 3.2:
Utilizing attention mechanisms for node interactions.
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Subtopic 3.3:
Designing and building GAT models for graph data.
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Subtopic 3.4:
Optimizing GATs for edge-level predictions.
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Subtopic 3.5:
Best practices for GATs.
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Subtopic 4.1:
Implementing graph embedding techniques (Node2Vec, DeepWalk).
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Subtopic 4.2:
Utilizing graph embeddings for node and graph representations.
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Subtopic 4.3:
Designing and building embedding models for network analysis.
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Subtopic 4.4:
Optimizing graph embeddings for downstream tasks.
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Subtopic 4.5:
Best practices for graph embeddings.
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Subtopic 5.1:
Designing GNN models for specific network data applications.
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Subtopic 5.2:
Implementing model architectures for various graph tasks.
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Subtopic 5.3:
Utilizing graph data preprocessing techniques.
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Subtopic 5.4:
Optimizing model design for graph data.
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Subtopic 5.5:
Best practices for GNN model design.
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Subtopic 6.1:
Optimizing GNN models for performance and scalability.
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Subtopic 6.2:
Utilizing batching and sampling techniques for large graphs.
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Subtopic 6.3:
Implementing distributed GNN training.
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Subtopic 6.4:
Designing scalable GNN solutions.
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Subtopic 6.5:
Best practices for model optimization.
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Subtopic 7.1:
Debugging common GNN implementation issues.
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Subtopic 7.2:
Analyzing model performance and stability.
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Subtopic 7.3:
Utilizing troubleshooting techniques for model improvement.
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Subtopic 7.4:
Resolving common GNN challenges.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing evaluation metrics for GNN tasks.
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Subtopic 8.2:
Utilizing cross-validation techniques for graph data.
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Subtopic 8.3:
Designing and building model validation pipelines.
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Subtopic 8.4:
Optimizing model evaluation strategies.
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Subtopic 8.5:
Best practices for model evaluation.
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Subtopic 9.1:
Integrating GNN models into real-world applications.
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Subtopic 9.2:
Utilizing APIs and deployment tools for GNNs.
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Subtopic 9.3:
Implementing real-time graph-based systems.
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Subtopic 9.4:
Optimizing models for deployment environments.
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Subtopic 9.5:
Best practices for integration.
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Subtopic 10.1:
Implementing techniques for handling large-scale graph data.
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Subtopic 10.2:
Utilizing graph partitioning and distributed processing.
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Subtopic 10.3:
Designing and building scalable graph processing pipelines.
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Subtopic 10.4:
Optimizing data handling for large graphs.
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Subtopic 10.5:
Best practices for large graphs.
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Subtopic 11.1:
Implementing GraphSAGE for inductive learning.
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Subtopic 11.2:
Utilizing Relational Graph Convolutional Networks (RGCNs).
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Subtopic 11.3:
Designing and building advanced GNN models.
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Subtopic 11.4:
Optimizing advanced architectures for specific tasks.
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Subtopic 11.5:
Best practices for advanced architectures.
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Subtopic 12.1:
Implementing GNNs for social network analysis.
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Subtopic 12.2:
Utilizing GNNs for drug discovery and bioinformatics.
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Subtopic 12.3:
Implementing GNNs for recommendation systems.
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Subtopic 12.4:
Utilizing GNNs for fraud detection.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing PyTorch Geometric for GNN implementation.
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Subtopic 13.2:
Implementing GNN models with Deep Graph Library (DGL).
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Subtopic 13.3:
Designing and building GNN pipelines with libraries.
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Subtopic 13.4:
Optimizing library usage for efficient implementation.
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Subtopic 13.5:
Best practices for library implementation.
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Subtopic 14.1:
Implementing model interpretability techniques for GNNs.
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Subtopic 14.2:
Utilizing visualization tools for graph-based explanations.
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Subtopic 14.3:
Designing and building interpretable GNN models.
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Subtopic 14.4:
Optimizing model transparency.
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Subtopic 14.5:
Best practices for model interpretability.
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Subtopic 15.1:
Emerging trends in graph neural networks.
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Subtopic 15.2:
Utilizing transformer-based GNNs.
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Subtopic 15.3:
Implementing GNNs for dynamic graphs and temporal networks.
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Subtopic 15.4:
Best practices for future GNN applications.
