Machine learning
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
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According to
https://github.com/pytorch/pytorch/blob/64847c7f0b3559c6edc40f001619b80c7dc68ef7/c10/util/Exception.h#L479
all usages of AT_ERROR("msg") should be replace with TORCH_CHECK(false, "msg")
There are currently 29 instances of AT_ERROR being used in c10 codebase:
$ grep AT_ERROR c10 -R|grep -v Exception.h|wc -l
29
It appears that the docs for Logistic Regression differ based on solvers and penalties. The "penalty" parameter states that "The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties," while the "solver" parameter states that "‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty" (attaching some screenshots). This was actually a little unclear to me, as I wasn't sure if the n
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TODO: add a hint somewhere around here: https://github.com/JuliaLang/julia/blob/efa7e4e0fc0dfeafb5f536c5bca504f664a25dfa/base/errorshow.jl#L278-L293
to make it easier for users to discover problems like https://discourse.julialang.org/t/is-there-a-way-to-transpose-an-array-of-any-i-have-a-12x500-array-any-2-i-want/56607?u=tim.holy
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Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.
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