TITLE: Deep learning for knot theory. Classification
QUESTION [9 upvotes]: As far as I know, there is a classification of all prime knots with less than 16 crossings.
It seems that there is already a fast enough algorithm to distinguish a knot from an unknot.
So in principle there is a huge amount of data to implement a deep learning machine which will recognize (and distinguish) knots up to some very good accuracy.
Is it something that mathematicians have tried to do? Any references?

REPLY [7 votes]: Adding to Sean Lawton's answer, there was an arxiv posting yesterday by Davies-Juhász-Lackenby-Tomasev, The signature and cusp geometry of hyperbolic knots that describes a relation between the cusp geometry of a hyperbolic knot, its signature, and its hyperbolic volume. The authors describe how this was found via machine learning.
This is not exactly what the OP asked for (it doesn't speak specifically to classification) but in many ways seems like a more interesting use of technology to discover heretofore unseen relationships.