toxikitty_

A lot of data scientists these days (me included) are former academics with backgrounds in numerical simulation in fields like chemistry, physics, mechanical engineering etc.

They live and breath numerical linear algebra and are comfortable reading advanced theoretical books or papers.

It's easy for them to pick up the basics needed to pass interviews and find a data science job. How would they go about adding some rigor to their understanding of ML and statistics?

> We note that there exist heuristic classical algorithms that can solve most instances of Chimera structured problems in a timescale comparable to the D-Wave 2X. However, we believe that such solvers will become ineffective for the next generation of annealers currently being designed.

From the abstract.