Understanding Graph Convolutional Networks
Graph Neural Networks have become a hotter and hotter topic in recent years. Since 2014, approaching deep learning with graph-structured data has become less and less niche, and many improvements have been made in algorithms that make predicting on graph-structured data possible. However, even within the term “Graph Neural Networks”, there are a variety of vastly different approaches, and lots of hype. So, what can GNNs do for you?
In this discussion, we’ll focus on:
• Breaking down the terminology – what are Graph Neural Networks versus Graph
Convolutional Networks, and what are some other kinds of GNNs?
• Dissecting the intuition behind Graph Convolutional Networks – what makes them work?
• How to get started embedding and predicting on benchmark biological datasets with an implementation of a Graph Convolutional Network.
Speaker: Sidney Arcidiacono, Data Science Intern at GreenLight Biosciences.
Website: https://www.aicamp.ai/event/eventdeta...
The code labs: https://github.com/sidneyarcidiacono/...