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stefan-marr.de | ||
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tonybaloney.github.io
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| | | | | [AI summary] Python 3.13 introduces a new JIT compiler using a copy-and-patch approach, aiming to improve performance by compiling bytecodes into machine code at runtime. | |
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sedimental.org
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| | | | | Thoughts on FOSS and fintech, layered by Mahmoud Hashemi | |
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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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djalil.chafai.net
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| | | This post is devoted to few convex and compact sets of matrices that I like. The set \( {\mathcal{C}_n} \) of correlation matrices. A real \( {n\times n} \) matrix \( {C} \) is a correlation matrix when \( {C} \) is symmetric, semidefinite positive, with unit diagonal. This means that \[ C_{ii}=1, \quad C_{ji}=C_{ji},\quad \left\geq0 \] for every \(... | ||
