Ask HN: What are the foundational texts for learning about AI/ML/NN?

Haugeland is GOFAI/cognitive science, not directly relevant to modern deep learning variety of models unless you are doing reinforcement leaning or trees stuff. Russel and Norvig are the typical textbooks for those. Marks and Haykins are all severely out of date.

You are approaching this like an established natural sciences field where old classics = good. This is not true for ML. ML is developing and evolving quickly.

I suggest taking a look at Kevin Murphy's series for the foundational knowledge. Sutton and Barto for reinforcement learning. Mackay's learning algorithms and information theory book is also excellent.

Kochenderfer's ML series are also excellent if you like control theory and cybernetics

https://algorithmsbook.com/ https://mitpress.mit.edu/9780262039420/algorithms-for-optimi... https://mitpress.mit.edu/9780262029254/decision-making-under...

For applied deep learning texts beyond the basics, I recommend picking up some books/review papers on LLMs, Transformers, GANs.

Seminal deep learning papers: https://github.com/anubhavshrimal/Machine-Learning-Research-...

Data engineering/science: https://github.com/eugeneyan/applied-ml

"Neural Networks and Deep Learning", by Michael Nielsen http://neuralnetworksanddeeplearning.com (full text)

The first chapter walks through a neural network that recognizes handwritten digits implemented in a little over 70 lines of Python and leaves you with a very satisfying basic understanding of how neural networks operate and how they are trained.

I personally consider Linear algebra to be foundational in AI/ML. Intro to Linear algebra, Gilbert Strang. And his free course on MIT OCW is fantastic too.

While having strong mathematical foundation is useful, I think developing intuition is even more important. For this, I recommend Andrew Ng's coursera courses first before you dive too deep.

I never took beyond Precalculus in school, thanks for the tip!

"Introduction to Statistical Learning" - https://www.statlearning.com/

(there's also "Elements of Statistical Learning" which is a more advanced version)

AI: A Modern Approach - https://aima.cs.berkeley.edu/

I remember Carmack mentioning in a podcast a list of seminal papers that Ilya Sutskever (@ilyasut) gave to him to learn AI foundations. I would love to see that list.