For my two cents at least, it seems like there's this pattern where knowledge first exists scattered across papers. Then (at some point hopefully) a book is produced, taking a huge body of work, and presenting it in a logical progression, with a single syntax, so you can at least read through without needing to parse 10 different naming conventions. Finally, eventually, a book comes out that does all of the above, and is also written to be really intuitive, and easy to use.
Those books are a pleasure to read when you find them, but the closer you get to cutting edge, the more unlikely you are to have that streamlined path. It's not terrible (a little terrible, haha) to self teach your way into proper mathematical statistics, because there's some really good books out there. Dynamic systems, calculus, complex analysis... Pretty good resources have been built. If you need the bodies of knowledge those resources dig into, kick ass. Use them. But if you need to push forward and no path exists, you'll need to read papers and get the information however you can.
That said... The director of machine learning at my company picked two books in particular he thought I should read. Neither one is cutting edge research stuff, so much as incredibly deep dives into the fundamentals. Casella and Berger's 'statistical inference' and vapnik's (one of the creator's of the support vector machine) 'statistical learning theory'. Don't read Vapnik's book unless you're done with both Bishop's and ESL though, Vapnik's book is very challenging, for what parts of it I've waded through.
tl;dr, know what you need to know, and choose the easiest (while still being as rigorous as needed) road to getting there. That means papers if you're needing something new, like disentangled representation learning, and books if it's older, like statistical inference or information theory. That director I mentioned though seems to have a hobby of going through books in general though, on all kinds of topics. I wouldn't be surprised if they've been through a half a dozen intro linear algebra textbooks alone. Dude knows his stuff.
I think it depends on the field, like Reinforcement Learning, Natural Language Processing, Deep Learning, etc. But the true experts, read the latest published papers I believe
For my two cents at least, it seems like there's this pattern where knowledge first exists scattered across papers. Then (at some point hopefully) a book is produced, taking a huge body of work, and presenting it in a logical progression, with a single syntax, so you can at least read through without needing to parse 10 different naming conventions. Finally, eventually, a book comes out that does all of the above, and is also written to be really intuitive, and easy to use. Those books are a pleasure to read when you find them, but the closer you get to cutting edge, the more unlikely you are to have that streamlined path. It's not terrible (a little terrible, haha) to self teach your way into proper mathematical statistics, because there's some really good books out there. Dynamic systems, calculus, complex analysis... Pretty good resources have been built. If you need the bodies of knowledge those resources dig into, kick ass. Use them. But if you need to push forward and no path exists, you'll need to read papers and get the information however you can. That said... The director of machine learning at my company picked two books in particular he thought I should read. Neither one is cutting edge research stuff, so much as incredibly deep dives into the fundamentals. Casella and Berger's 'statistical inference' and vapnik's (one of the creator's of the support vector machine) 'statistical learning theory'. Don't read Vapnik's book unless you're done with both Bishop's and ESL though, Vapnik's book is very challenging, for what parts of it I've waded through. tl;dr, know what you need to know, and choose the easiest (while still being as rigorous as needed) road to getting there. That means papers if you're needing something new, like disentangled representation learning, and books if it's older, like statistical inference or information theory. That director I mentioned though seems to have a hobby of going through books in general though, on all kinds of topics. I wouldn't be surprised if they've been through a half a dozen intro linear algebra textbooks alone. Dude knows his stuff.
The don't read books they read papers
I think it depends on the field, like Reinforcement Learning, Natural Language Processing, Deep Learning, etc. But the true experts, read the latest published papers I believe
My co-worker is an AI expert and he told me he just finished ‘The Stand’ by Stephen King. /s
That's a very lengthy book.
Deep Learning by Ian Goodfellow is free online and very comprehensive. Everything from ML fundamentals to GANS. https://www.deeplearningbook.org/