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Harvard's just open-sourced their ML Systems textbook. it's extremely practical for not just learning how to build and train models, but to build production systems (the skill that actually matters). topics are cool af:
> building autograd, optimizers, attention, and a mini-pytorch from scratch to truly learn how an ML framework runs. (i love this the most)
> basics of DL, batch sizes, precision, model architectures, and training
> ML performance optimization, HW acceleration, benchmarking, efficiency
so this is not just an intro to machine learning, it's the full package from the beginning to the actual end. right now you can read the book and access the code for free. this is one of the best books I've seen dropping in 2025, so don't sleep on it.
here's the repo (you can find the book link there): github.com/harvard-edge/c…

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