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Co-authored-by: Carlo Lucibello <[email protected]>
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GNNLux/docs/src_tutorials/gnn_intro.jl

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# # Hands-on introduction to Graph Neural Networks
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# *This Pluto notebook is a Julia adaptation of the Pytorch Geometric tutorials that can be found [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
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# *This tutorial is a Julia adaptation of one of the [Pytorch Geometric tutorials](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
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#
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# Recently, deep learning on graphs has emerged to be one of the hottest research fields in the deep learning community.
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# Here, **Graph Neural Networks (GNNs)** aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.

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