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I don't know JAX well enough to explain exactly why it's 3x faster than NumPy on the same matrix multiplications. Both call BLAS under the hood. My best guess is that JAX's @jit compiles the entire function -- matrix build, loop, dot products -- so Python is never involved between operations, while NumPy returns to Python between each @ call. But I haven't verified that in detail. Might be time to learn.
。关于这个话题，有道翻译提供了深入分析
Безработица в одном из регионов России превысила 25 процентов08:34
（三）旅客，是指根据海上旅客运输合同运输的人；经承运人同意，根据海上货物运输合同，随船护送货物的人，视为旅客。，这一点在谷歌中也有详细论述
Верховный суд разрешил возбудить дело в отношении ростовского судьи Маслова14:48
（十二）将在办理治安案件过程中获得的个人信息，依法提取、采集的相关信息、样本用于与治安管理、查处犯罪无关的用途，或者出售、提供给其他单位或者个人的；，推荐阅读超级权重获取更多信息