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:<p>https://course.fast.ai/<p>https://www.deeplearning.ai/courses/<p>Andrej Karpathy youtube and github:<p>https://www.youtube.com/@AndrejKarpathy/videos
Related:<p><i>“If you learn all of these, you’ll know 90% of what matters”</i> (313 points, 2 months ago, 110 comments) <a href="https://news.ycombinator.com/item?id=40397806">https://news.ycombinator.com/item?id=40397806</a><p><i>ASK: How do I learn more about LLMs and ML?</i> (35 points, 4 months ago, 17 comments) <a href="https://news.ycombinator.com/item?id=39950683">https://news.ycombinator.com/item?id=39950683</a>
I'd say Kevin Murphy's two-volume PML book is a wonderful outline of almost everything that concerns ML these days: <a href="https://probml.github.io/pml-book" rel="nofollow">https://probml.github.io/pml-book</a>. Note there is a ton of companion code to recreate figures and discuss concepts. My other favorite, <a href="https://d2l.ai" rel="nofollow">https://d2l.ai</a>, is much simpler mostly aiming at modern CNNs and LLMs. It is really polished, and code is embedded within the text.<p>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.
I would recommend Simon Willison’s blog <a href="https://simonwillison.net/" rel="nofollow">https://simonwillison.net/</a>
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.
You can try this.<a href="https://github.com/rasbt/LLMs-from-scratch">https://github.com/rasbt/LLMs-from-scratch</a>