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[D] Open Exposition Problems in Machine Learning

In his paper “A Beginner’s Guide to Forcing,” Timothy Chow introduced the idea of an “open exposition problem,” which is a concept that has not yet been explained in a totally clear way. The online journal Distill is trying to tackle open exposition problems in machine learning, which I feel is really important.

So what do you guys think still isn’t explained well in ML? What topics confuse you or your students?

Timothy Chow’s paper: http://timothychow.net/forcing.pdf

Distill: https://distill.pub/

submitted by /u/turing_machines
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.