Comments (5)
感谢贡献那么优秀的笔记! 错误地方我直接提交pull了,也不确定改的对不对
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week6.md
"""
11.4 查准率和查全率之间的权衡
参考视频: 11 - 4 - Trading Off Precision and Recall (14 min).mkv
在之前的课程中,我们谈到查准率和召回率,作为遇到偏斜类问题的评估度量值。在很多应用中,我们希望能够保证查准率和召回率的相对平衡。
在这节课中,我将告诉你应该怎么做,同时也向你展示一些查准率和召回率作为算法评估度量值的更有效的方式。继续沿用刚才预测肿瘤性质的例子。假使,我们的算法输出的结果在0-1 之间,我们使用阀值0.5 来预测真和假。
"""
其中recall一下翻译成查全率,一下翻译成召回率。建议修改统一一下
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版本:机器学习个人笔记完整版v5.3-A4打印版
页码:P132
问题:此页中,关于记号
“正则化的那一项只是排除了每一层
这里
“$s_{l}$ +1 层”这让人费解,实际上这里应该说的是第 l、第 l+1 层吧?
另外,对应的,此处的损失函数的正则化部分,第二个累和号上界应该是
theta 矩阵是以第 j +1层的激活单元数量(
不知理解是否有误,还望告知。
from coursera-ml-andrewng-notes.
版本:机器学习个人笔记完整版v5.3-A4打印版
页码:P150
问题:公式漏了字符
这里的损失函数漏掉了 y^{i}_{test}
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机器学习个人笔记完整版v5.35-A4打印版.pdf
页码:P112
问题:多了一对小括号。后面那个正则化项是不该包含在外层的累加里的
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Related Issues (20)
- ML-Exercise2中正则化代价函数的说明问题 HOT 8
- 练习4中加入正则化的反向传播函数 HOT 5
- 在线笔记链接挂了 HOT 4
- PPT Lecture4.pptx 有书写错误,紫色标识 HOT 1
- Incomplete code for Exercise 2 (Logistic Regression), Regularization HOT 4
- excuse me but is there an original version in English? it would be so nice. A million thanks! HOT 4
- 请问是否有吴恩达老师的AI for medicine的课件呢 HOT 1
- 4.2 的内容应该放在 4.6 后面
- code/ex6-SVM/3- search for the best parameters.ipynb 参数的位置反了
- 1. logistic_regression_v1.ipynb
- 1
- ex5-biasVSvariance的练习中moduleTNC.minimize报错
- ex1中代价函数中 * 似乎应该改为 @
- 笔记在线阅读建议添加返回按键 HOT 1
- ML-Exercise2.ipynb正则化逻辑回归创建特征多项式小修改 HOT 1
- 提取码错误 HOT 1
- 9.2 代价函数正则项中s_l +1 应该为 s_(l+1)
- 个人笔记 v5.51-A4 打印版 PDF 中的 7.3 正则化线性回归 HOT 6
- ml HOT 3
- exp4: 为什么计算第三层参数的梯度时,不需要乘sigmoid_gradient(z3) HOT 2
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