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怎样理解置信水平与误差幅度的关系？ - 知乎
先搞清楚置信水平和误差幅度两个概念。 书中前文有述“特定的样本中随机变异落在给定误差幅度内的概率大小称为置信水平”。 误差幅度的英文单词margin of error，margin意思是界限、幅度，error是误差。 影响误差的因素很多，我们只能做到尽量减少。但只要误差幅度在一定范围，统计就是有效的 ...
95%的置信区间是有5%的误差范围（margins of error）吗？
统计方面问题，希望大神帮忙解答 95%的置信区间是有5%的误差范围（margins of error）吗？
哪里有标准的机器学习术语 (翻译)对照表？ - 知乎
学习机器学习时的困惑，“认字不识字”。很多中文翻译的术语不知其意，如Pooling，似乎90%的书都翻译为“…
Players complain that the difficulty of "Black Myth: Wukong ... - 知乎
Players complain that the difficulty of "Black Myth: Wukong" is too high, "I get killed in less than 10 seconds after starting the fight," will such difficulty discourage players? Why set such a high difficulty?
如何看待最近关于深度学习generalization error bound ... - 知乎
Some follow-up. 最近Bartlett更新了他们的paper， [1706.08498v2] Spectrally-normalized margin bounds for neural networks 用一个新的他们自己定义的矩阵norm替代了原来的1-norm，然后说明PAC-Bayesian的那篇paper结论很容易由他们现在的结论推出来。 。。
神经网络中，设计loss function有哪些技巧? - 知乎
这就是为什么他们会有名称，如 Contrastive Loss, Margin Loss, Hinge Loss or Triplet Loss。 与其他损失函数（如交叉熵损失或均方误差损失）不同，损失函数的目标是学习直接预测给定输入的一个标签、一个值或一组或多个值，rank loss的目标是预测输入之间的相对距离。
CPU 的 “Margin for error” 高好还是低好？那段英文注解是什么意思？
昨天在 cpubenchmark 网站查询 AMD X3421 评分，看到 “Margin for error : High” 不明白指的是容错还是…
Hello, is it accurate to measure height with a tape measure ... - 知乎
The measurer can stand on either side of the person being measured. Adjust the measurer's head so that the upper edge of the auricle is level with the lowest point of the orbital margin, then move the horizontal board of the height measuring device to the top of the person being measured until it is appropriately loose. This can measure the ...
How to evaluate the YONEX REGNA racket?-zhihu
The 16/19 string pattern provides a larger margin for error, with a relatively larger sweet spot. Especially in backhand performance, it allows traditional single-handed backhand flat shots to perform better, with the racket providing a good "grip" feel.
2024年对比学习 (contrastive learning)有没有深入的理论分析和相关研究？ - 知乎
We also try to introduce representation learning into the language modeling regime, achieving 72.3% on the GSM8K dataset by fine-tuning a 7B model using matrix cross-entropy loss, with a margin of 3.1% over the standard cross-entropy loss.
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