{"id":36527,"date":"2024-11-01T09:49:15","date_gmt":"2024-11-01T09:49:15","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36527"},"modified":"2024-11-01T11:52:50","modified_gmt":"2024-11-01T11:52:50","slug":"deep-learning-pytorch-course-graph-neural-networks","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36527\/","title":{"rendered":"Deep Learning PyTorch Course, Graph Neural Networks"},"content":{"rendered":"

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Table of Contents<\/h2>\n
    \n
  1. 1. Introduction<\/a><\/li>\n
  2. 2. Overview of Graph Neural Networks (GNN)<\/a><\/li>\n
  3. 3. Applications of Graph Neural Networks<\/a><\/li>\n
  4. 4. Implementing GNN in PyTorch<\/a><\/li>\n
  5. 5. Conclusion<\/a><\/li>\n
  6. 6. Further Reading<\/a><\/li>\n<\/ol>\n

    1. Introduction<\/h2>\n

    \n In recent years, the field of deep learning has rapidly advanced due to various new research and technological developments. Among them, Graph Neural Networks (GNN) are receiving increasing attention and showing promising results in various fields. This course aims to explain the concept of GNN, how it works, and how to implement it using PyTorch. Ultimately, it aims to provide a deep understanding of the types of problems that GNN is well-suited to solve.\n <\/p>\n

    2. Overview of Graph Neural Networks (GNN)<\/h2>\n

    \n Graph Neural Networks are a neural network structure based on nodes and edges in unstructured data. Unlike traditional neural networks, GNN can learn both the features and connectivity of nodes while considering the structure of the graph. GNN is primarily used for tasks such as node classification, link prediction, and graph classification.\n <\/p>\n

    2.1 Basic Components of GNN<\/h3>\n

    \n The main components of a GNN are as follows:<\/p>\n