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matthewrocklin.com | ||
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tomaugspurger.net
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| | | | | This work is supported by Anaconda, Inc. and the Data Driven Discovery Initiative from the Moore Foundation. This is part three of my series on scalable machine learning. Small Fit, Big Predict Scikit-Learn Partial Fit Parallel Machine Learning You can download a notebook of this post [here][notebook]. In part one, I talked about the type of constraints that push us to parallelize or distribute a machine learning workload. Today, we'll be talking about the second constraint, "I'm constrained by time, and would like to fit more models at once, by using all the cores of my laptop, or all the machines in my cluster". | |
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jakevdp.github.io
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| | | | | [AI summary] This blog post compares the performance of Numba and Cython in optimizing pairwise distance calculations, highlighting Numba's significant speed improvements over previous versions and its ease of use. | |
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stefan-marr.de
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| | | | | A look into the implementation details of CPython's Global Interpreter Lock (GIL) and how they changed between Python 3.9 and the current development branch that will become Python 3.13. | |
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matbesancon.xyz
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| | | Learning by doing: detecting fraud on bank notes using Python in 3 steps. | ||
