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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The array API specification stipulates four numeric constants:
einfnanpi
PyTorch currently supports none.
We should be using pkg_resources (or importlib.resources if our min Python version is 3.7) instead of uses of __file__.
$ get grep '__file__' sklearn/
sklearn/__check_build/__init__.py: local_dir = os.path.split(__file__)[0]
sklearn/datasets/_base.py: module_path = dirname(__file__)
sklearn/datasets/_base.py: module_path = dirname(__file__)
sklearn/datasets/_base.py:
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Could probably be an error instead?
sock.handle is a null pointer here
https://github.com/JuliaLang/julia/blob/5dbf45a2997acd26e0f72abaa320c9d0d54956a1/stdlib/Sockets/src/Sockets.jl#L817 and here https://github.com/JuliaLang/julia/blob/5dbf45a2997acd26e0f72abaa320c9d0d54956a1/stdlib/Sockets/src/Sockets.jl#L821
Would peer = isopen(sock) ? getpeername(sock) : nothing be a safe alternative
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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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