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	ग्राफ़ न्यूरल नेटवर्क	सीएनएन छवि वर्गीकरण (CNN Image Classification)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017	2016
प्रवर्तक	Kipf, T.N. & Welling, M.	He, K. et al. (ResNet); Tan, M. & Le, Q.V. (EfficientNet)
प्रकार	Deep learning on graph-structured data	Deep convolutional neural network (supervised)
मौलिक स्रोत	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR.
उपनाम	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	CNN — Görüntü Sınıflandırma (ResNet / VGG / EfficientNet), convolutional neural network image classifier, deep image classification, ResNet / VGG / EfficientNet
संबंधित	4	5
सारांश	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	CNN image classification uses deep convolutional architectures such as ResNet (He et al., 2016), VGG and EfficientNet (Tan & Le, 2019) to sort images into categories. Stacked convolutional layers learn a hierarchy of visual features directly from pixels, and skip (residual) connections prevent the vanishing-gradient problem in very deep networks.