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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. 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、. 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？

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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“… 如下图中，点 x_4 被分类正确了，但是它的损失不是0。其实这个道理和SVM中的Margin是一样的，不仅要分类正确，还要使得Margin最大化，所以说hinge loss的另外一种解释。（关于SVM的具体推导，. If you are the second type, you have already succeeded by a large margin just by clearly recognizing that you have this issue

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Approach the problems with a positive mindset

在ML community，可能对比学习理论研究的最黄金时间已经过去了，黄金时间应该是不晚于2020年左右（指能够带来非常多引用的那种，比如Arora的开山作 A theoretical analysis of contrastive. 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. 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?