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The best resource I have found for learning forecasting using Python.
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An explanation of the fundamental matrix calculus concepts necessary for understanding how deep neural networks are trained.
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A great way to learn the basics of mechanistic interpretability. Offers a structured AI safety curriculum covering transformer interpretability, deep learning fundamentals, and more.
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An online textbook for CMU's MLOps course covering the engineering practices needed to build and deploy ML systems in production.
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A useful introduction to many different math concepts. I found it most helpful for learning about some of the basic concepts in abstract algebra.
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An interesting look at how persona drift can change the behavior and self-identity of LLMs.
Links
A repository of link I find interesting. I try to only add stuff I think is in the 95th+ percentile for value added.