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rjlipton.com | ||
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nickhar.wordpress.com
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| | | | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | |
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weisser-zwerg.dev
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| | | | | A series about Monte Carlo methods and generating samples from probability distributions. | |
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nhigham.com
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| | | | | A $latex p$th root of an $latex n\times n$ matrix $LATEX A$ is a matrix $LATEX X$ such that $latex X^p = A$, and it can be written $latex X = A^{1/p}$. For a rational number $latex r = j/k$ (where $latex j$ and $latex k$ are integers), defining $latex A^r$ is more difficult: is... | |
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chipnetics.com
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| | | There is a lot to remember in data science! It touches everything from alignment, to data wranging, data analytics, storytelling and visuals. This python cheat sheet is a quick reference to get a fast boost into many of these areas. | ||
