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A new version of Python package GPopt
is available on PyPI. GPopt
is a package for stochastic optimization based on Gaussian process regressors (for now, the name GP*
is ‘unfortunate’). This type of optimization is particularly useful for tuning machine learning models’ hyperparameters.
The main change in GPopt
’s v0.3.0
is: the user can now choose a different surrogate model (see this excellent book for more details on the procedure).
You’ll find below a link to a notebook showcasing the use of GPopt
for tuning Boosted Configuration Networks (BCN version 0.7.0).
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