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www.jeremykun.com | ||
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gregorygundersen.com
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tiao.io
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| | | | | Suppose we're given a positive semidefinite (PSD) matrix $\mathbf{A} \in \mathbb{R}^{N \times N}$ to which we wish to update by some low-rank matrix $\mathbf{U} \mathbf{U}^\top \in \mathbb{R}^{N \times N}$, $$\mathbf{B} \triangleq \mathbf{A} + \mathbf{U} \mathbf{U}^\top,$$ where the update factor matrix $\mathbf{U} \in \mathbb{R}^{N \times M}$. To be more precise, the low-rank update is rank-$M$ for some $M \ll N$. What is the best way to calculate the Cholesky decomposition of $\mathbf{B}$? Given ...... | |
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thomvolker.github.io
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| | | | | Many different ways of calculating OLS regression coefficients exist, but some ways are more efficient than others. In this post we discuss some of the most common ways of calculating OLS regression coefficients, and how they relate to each other. Throughout, I assume some knowledge of linear algebra (i.e., the ability to multiply matrices), but other than that, I tried to simplify everything as much as possible. | |
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sebastianraschka.com
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| | | Previously, I shared an article using multi-GPU training strategies to speed up the finetuning of large language models. Several of these strategies include... | ||
