2 个仓库
Awesome GitHub RepositoriesConvolutional Layers
Neural network layers that apply convolution operations to graph-structured data.
Distinct from Graph: Focuses on the specific implementation of convolutional layers for graphs rather than a general graph layer
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Convolutional Layers. Refine with filters or upvote what's useful.
Awesome Convolutional Layers GitHub Repositories
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- tkipf/pygcn
tkipf/pygcn
5,404在 GitHub 上查看pygcn 是一个用于实现图卷积网络（GCN）的 PyTorch 库和框架。它提供了用于半监督节点分类和从图结构数据生成节点嵌入的工具。 该系统根据邻域模式和局部相似性将图节点转换为低维向量。它通过利用少量标记示例和整体图拓扑结构，实现节点标签的预测。 该库涵盖了关系数据分析和半监督图学习。它包括用于消息传递、邻接变换和对称归一化的计算原语。
A collection of PyTorch implementations for building and training graph convolutional networks on graph-structured data.
Python - packtpublishing/hands-on-graph-neural-networks-using-python
PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python
1,044在 GitHub 上查看This repository serves as an educational resource for implementing graph neural networks using Python. It provides a collection of structured code examples and tutorials designed to guide developers through the process of building and training machine learning models that operate on complex, interconnected datasets. The project covers the core mechanics of graph-based deep learning, including message-passing architectures, feature aggregation, and the stacking of convolutional layers. It demonstrates how to represent non-Euclidean data as static graphs and how to manage memory during training
Builds deep learning models by layering operations that perform localized spectral or spatial filtering across graph-structured data.
Jupyter Notebook
