8 ms·
https://d2l.ai https://d2l.ai (Zhang et al) is a very nice book with the right amount of theory and tons of code. If you want a bit more than just DL, and bett
by nextos 4y ago
https://d2l.ai https://d2l.ai (Zhang et al) is a very nice book with the right amount of theory and tons of code.
If you want a bit more than just DL, and better foundations, https://probml.github.io/pml-book https://probml.github.io/pml-book (Murphy) is the place to begin with.
Personally, I think a broader perspective, such as the one offered by the second one (Murphy) is the way to go.
- hedgehog0 4y agoMurphy is really long…
- sloaken 4y agoThose both look real good, and price effective for those of us just wanting to explore. Thank you.
- jstx1 4y agoAnti-recommend Murphy's book as a place for anyone to begin with. I can barely understand what it's trying to say about the topics I already know.
- hedgehog0 4y agoWhat would be your recommendations?
- nextos 4y agoTry Deisenroth et al.: https://mml-book.github.io https://mml-book.github.io
- hedgehog0 4y agoIt seems quite simple… maybe a little too easier than “understanding machine learning”?
- medo-bear 4y agoits a good foundational book. dont underestimate foundations
- hedgehog0 4y agoThank you. I also learned that Caltech has a course and book called “Learning from Data” which also seems to focus on foundations.
- hedgehog0 4y agoI'm a math graduate student, so some of the earlier chapters are relatively easy for me, hence the comment.
- medo-bear 4y agoyep i was in the same boat. and one of the things that would have save me a lot of time was not being dismissive of fundamental chapters. you will go through them quickly, but they might contain one or more insights that will be new or a useful refresher. also theoretical ml is rather math heavy (even for math graduates) so the word 'fundamentals' might be a bit deciving
- hedgehog0 4y agoYeah I agree. To be honest, I'm more into theoretical ML. Do you have any other similar recommendations?
- medo-bear 4y agodepends on what you think you know and what you want to learn. murphy focuses on a bayesian perspective, which is more useful from a theoretical pow. other books take a frequentist perspective, which is the dominant view in practice, unless you are using something like stan. think of it like theoretical vs experimental physics
- hedgehog0 4y agoWhat do you think of Murphy approach in practice/engineering.
- medo-bear 4y agowhat do you mean ?
- hedgehog0 4y agoYou said Murphy is more Bayesian, so I was wondering that was Bayesian approach working in practice?
- medo-bear 4y agooh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing [1] https://mc-stan.org/ https://mc-stan.org/
- nextos 4y agoOr when you have just moderate amounts of data.