This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
This tutorial was created for the MLibrary 2.0 workshop series. If you're following this tutorial on your own you may want to review the contents of the resources page before you begin. When you're finished, please help us to improve by taking this brief
LOTSE ist ein Navigations- und Schulungssystem, dass neben der Zielgruppe Wissenschaftler/-innen speziell für Studierende entwickelt wurde. Es bietet Ihnen Hilfe beim Erlernen wissenschaftlicher Arbeitstechniken und unterstützt Sie bei allen Arbeitsschr
Tom Griffith: This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science. The first section mentions several useful general references, and the others provide supplementary readings on specific topics. If you would like to suggest some additions to the list, contact Tom Griffiths.
The Low Tech Library consists of books about the necessary skills for getting by if the extremely complex - and potentially fragile - global high-tech economy comes temporarily (or permanently) unraveled.