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Heterogeneous Graph Neural Networks (HGNNs)

An in-depth guide to Heterogeneous Graph Neural Networks (HGNNs), explaining how they handle different node and edge types, and their applications in complex graphs like knowledge graphs and recommendation systems.

Real-World Implementation of Graph Neural Networks (GNNs)

Step-by-step guide to implementing a GNN using PyTorch Geometric for a real-world dataset, covering everything from data preprocessing to deployment.

Advanced Techniques in Graph Neural Networks (GNNs)

A deep dive into more advanced GNN models, challenges such as over-smoothing, and cutting-edge architectures to tackle scalability and complexity in graph neural networks.

Graph Neural Networks (GNNs) - Introduction and Basics

Introduction to Graph Neural Networks (GNNs), their importance in deep learning for graph data, and basic concepts.

Advanced Applications of Q-Learning Variants - From Theory to Practice

A deep dive into applying advanced Q-Learning variants like Double Q-Learning, Dueling Q-Networks, and hybrid approaches in real-world tasks, focusing on strategies for scaling, implementation, and performance optimization.

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