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英文字典中文字典相关资料:


  • PyG Documentation — pytorch_geometric documentation
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
  • PyG 2. 0: Scalable Learning on Real World Graphs
    In this paper, we present Pyg 2 0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities
  • pyg-team pytorch_geometric - GitHub
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
  • GitHub Pages - PyG
    PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits
  • torch-geometric · PyPI
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
  • PyG Overview - NVIDIA Docs
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
  • Installation — pytorch_geometric documentation
    NVIDIA currently recommends the NVIDIA PyG Container on NGC as the most reliable way to use cuGraph integration with PyG For other installation methods, refer to the cuGraph GNN repository and or the RAPIDS installation guide
  • pyg-nightly · PyPI
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data
  • Introduction by Example — pytorch_geometric documentation
    We shortly introduce the fundamental concepts of PyG through self-contained examples For an introduction to Graph Machine Learning, we refer the interested reader to the Stanford CS224W: Machine Learning with Graphs lectures





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