I found this to be helpful

https://www.oreilly.com/library/view/ai-engineering/97810981...

While reading AI Engineering[1], I was inspired to take a crack at an idea I've had for a while - diffusion-based LLM. The accessibility of shakespeare.txt[2] and the proliferation of tinyGPT implementations has made vibe-coding LLM research on the brink of possibility. It does help to have a familiarity in the datascience/ML/AI space in order to check and guide appropriately, but it's amazing how well the virtuous cycle is beginning to work.

1. https://www.oreilly.com/library/view/ai-engineering/97810981...

2. https://gist.github.com/blakesanie/dde3a2b7e698f52f389532b4b...

I believe that most of the papers presented here focus on acquiring knowledge rather than deep understanding. If you’re completely unfamiliar with the subject, I recommend starting with textbooks rather than papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube.

[1]: https://www.bishopbook.com [2]: https://www.oreilly.com/library/view/ai-engineering/97810981... [3]: https://d2l.ai [4]: https://udlbook.github.io/udlbook/