There are a ton of great explainers of what graph neural networks are. However, I find that a lot of them go pretty deep into the math pretty quickly. Yet, we still are faced with that age-old problem: where are all the pics?? As such, just as I had attempted with Bayesian deep learning, I’d like to try to demystify graph deep learning as well, using every tool I have at my disposal to minimize the number of equations and maximize intuition using pictures. Here’s my attempt, I hope you find it useful!

Read in full here:

https://ericmjl.github.io/essays-on-data-science/machine-learning/graph-nets/

This thread was posted by one of our members via one of our news source trackers.