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Ask HN: What are the best resources today for learning AI/LLMs

20 points by geph2021 ↗ HN
I know of a few resources, but I'm sure there are others and would love to gather some pointers from the HN crowd. These come to mind:

https://course.fast.ai/

https://www.deeplearning.ai/courses/

Andrej Karpathy youtube and github:

https://www.youtube.com/@AndrejKarpathy/videos

11 comments

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gnabgib
Related:

“If you learn all of these, you’ll know 90% of what matters” (313 points, 2 months ago, 110 comments) https://news.ycombinator.com/item?id=40397806

ASK: How do I learn more about LLMs and ML? (35 points, 4 months ago, 17 comments) https://news.ycombinator.com/item?id=39950683

0xEF
Low-effort replies like this need to die. Did you read through the comments on either link you lazily dug up? OP is looking for a structured learning, not a jumble of 30 academic papers with questions surrounding the legitimacy. OP also already linked Karpathy's videos favored by your second link.

Maybe next time instead of trying to prove a question has been answered, just answer the damned question with fresh or relevant information. Welcome to an aggregate site. Expect repetition.

jrvieira
you're complaining to a bot
0xEF
Sigh. I'll see myself out.
[deleted]
(comment deleted)
vasili111
Links that you have provided are good for learning how deep learning in general and LLMs in particular work. But if you are interested in only building the products based on existing models (like GPT models from OpenAI) you will not need those details of inner work and how those model are created. In that case learn from OpenAI documentation, Azure OpenAI documentation, Azure AI services documentation and etc.
nextos
I'd say Kevin Murphy's two-volume PML book is a wonderful outline of almost everything that concerns ML these days: https://probml.github.io/pml-book. Note there is a ton of companion code to recreate figures and discuss concepts. My other favorite, https://d2l.ai, is much simpler mostly aiming at modern CNNs and LLMs. It is really polished, and code is embedded within the text.

AI is very broad. I think the future is neurosymbolic, and these two books only cover a tiny part of symbolic, mostly concerned with probabilistic and causal models. See Murphy vol 2 sections V-VI. Lots of interesting ideas for symbolic AI can be found in the SAT and theorem proving literature.