Comment by ofou

1 month ago

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/

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ofou

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Tsarp  1 month ago

AI engineering is more applied than research and in that regard this list is great.

swyx and teams podcast, newsletter and discord has been the highest signal to noise ratio for keeping up and learning.

  • swyx  1 month ago

    thank you! very kind. much to improve.

    • ofou  1 month ago

      By the way, I think this is a fantastic reading list for creating AI products, and especially for staying updated on the latest in the AI space. However, it feels a bit scattered and might be hard for beginners to follow, IMO.

      I read your book, The Coding Career Handbook, we need something similar for AI Engineering! I really enjoyed it. Thank you for creating and sharing such high-quality multimodal content :)

      1 reply →

rahimnathwani  1 month ago

Of the resources you mention, #2 is probably the best starting point for someone who wants to start building software soon. Karpathy's videos fast.ai's courses may also fit that purpose.

But the other books (#1, #3, #4) seem like they're intended for those who want to understand all the math. Many people don't want (or need) a full understanding of how all this works. They can provide significant value to their employers with some knowledge of how machine learning works (e.g. the basics of CNNs and RNNs), and some intuitions/vibes about SOTA LLMs, even if they don't understand transformers or other modern innovations.