I want to make something in this area(LLM). Can you recommend any books?

Books? No, not really. Maybe others will have better suggestions for newcomers, sorry. Are you talking research novelty or just applying current methods to a given task?

The latter is covered well by Andrej Karpathy's videos and by just playing around with current models and other tutorials in a small test environment. You don't need to know very much, there's a lot of low-hanging fruit.

For the former, the field is moving rapidly and most of the innovations are coming from papers. Any book that claims to cover deep learning is almost inevitably outdated. Find a university or institution near you and see if they have an undergraduate reading group on deep learning that is open to the public to attend. Mine does, and it's really helpful for staying up to date with the latest ideas. "Probabilistic Machine Learning" by Murphy contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning (even if it contains virtually no deep learning in it), and I would hope that any student or colleague of mine would be familiar with most of it. But I'm not sure it's good to learn from, and picking all that up takes a while to be honest.

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nextos 2 hours ago | parent | next [-]

> "Probabilistic Machine Learning" by Murphy [...] even if it contains virtually no deep learning in it

This is confusing. Are you referring to the old 2012 version?

Volumes 1 & 2 (2022-3) contain a substantial amount of deep learning [1], including relatively recent developments.

There's also a new RL volume getting written, with some drafts deposited in arXiv [2].

[1] https://probml.github.io/pml-book

[2] https://arxiv.org/pdf/2412.05265

	▲	hodgehog11 2 hours ago \| parent [-]
		I was mostly referring to Volume 1 (not advanced topics). You have a point that Volume 2 definitely contains more. To be honest, I was mostly covering myself from a "that's not real deep learning" criticism; "relatively recent developments" is pretty generous if you're active in the field. Given its rapidity, anything over a few years old is essentially considered classical. It's almost impossible to have a book that is up-to-date with the state of the art here. These are very nice volumes though (RL one is good too), and Murphy should be commended for the amount of work in here. It's probably as good a compendium as one can expect.

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jdw64 2 hours ago | parent | prev [-]

I've read the books you mentioned(Probabilistic Machine Learning). I guess there's nothing left but papers, right? Thanks for the advice.