[ta46290193]

Do you know if the typical ML hiring process is able to distinguish between someone at the MOOC/"Machine Learning with [current popular language/library]" level from someone who's done hundreds of problems in a graduate-level math textbook? I'd love to be convinced because as I said I'd like the excuse to work through the book, but at the same time, as has been suggested elsewhere, there's more practical stuff to focus on too.

[natalyarostova]

Well, obviously it depends, people can be successful from both camps based on other attributes. But I will say when we interview we often try to weed out people who just took a handful of courses that focus on TensorFlow, but lack general science intuition and depth.

...On the other hand, if you're already a dev and want to become an ML Dev, knowing how to do science deployments, work with big data, and familiarity with APIs like TF would be more valuable than knowing how to do proofs.

[ta46290193]

I suppose it's like software development generally. To be good at it you need a sense of what good programming feels like, but advanced books like Knuth's "The Art of Computer Programming" aren't really relevant to the day-to-day work of gluing together libraries and writing code that doesn't go deeper than for loops.

Similarly I suppose most ML work is done with some solid basics but advanced math textbooks aren't really needed, and people actually working at the theoretical advanced math level are rare and in a handful of academic positions or corporate research labs requiring high credentials or other specific qualifications.

Thanks for your input.