Graph Neural Network (GNN) Course

The Graph Neural Network (GNN) Course is designed to provide comprehensive, in-depth training in Graph Neural Network (GNN). Prerequisites Basic knowledge of Python programming Understanding of machine learning fundamentals, including supervised and unsupervised learning Familiarity with deep learning concepts such as neural networks, tensors, activation functions, and backpropagation Knowledge of graph basics, including nodes and edges, is helpful but not mandatory Experience with PyTorch is recommended, but not mandatory What Will You Learn Introduction to Graph Neural Networks (GNNs) Graph Theory Fundamentals Graph Data Structures and Representations Graph Representation Learning Node, Edge, and Graph Embeddings Message Passing Neural Networks Graph Convolutional Networks (GCNs) Graph Attention Networks (GATs) GraphSAGE and Graph Isomorphism Networks (GINs) Heterogeneous Graph Neural Networks Node Classification and Graph Classification Link Prediction Techniques Working with PyTorch Geometric (PyG) Building GNN Models with Deep Graph Library (DGL) Training, Evaluating, and Optimizing GNN Models Graph Data Preprocessing and Feature Engineering Who Should Enroll in Graph Neural Network Course? The program covers foundational concepts through to advanced techniques to ensure you gain job-ready expertise.