整体进度

| 章节 | 贡献者 | 进度 |
| --- | --- | --- |
| [七、Web 技术](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/7.md) | - | - |
| [超文本传输协议](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/7.1.md) | @miaoxiaozui2017 | 100% |
| [八、处理文本](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/8.md) | - | - |
| [python 字符串方法](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/8.1.md) | @miaoxiaozui2017 | 100% |
| [正则表达式](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/8.2.md) | @miaoxiaozui2017 | 100% |
| [regex 和 python](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/8.3.md) | @miaoxiaozui2017 | 100% |
| [九、关系数据库和 SQL](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/9.md) | - | - |
| [关系模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/9.1.md) | @miaoxiaozui2017 | 100% |
| [SQL](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/9.2.md) | @miaoxiaozui2017 | |
| [SQL 连接](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/9.3.md) | @miaoxiaozui2017 | |
| [十、建模与估计](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/10.md) | - | - |
| [模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/10.1.md) | | |
| [损失函数](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/10.2.md) | | |
| [绝对损失和 Huber 损失](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/10.3.md) | | |
| [十一、梯度下降与数值优化](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/11.md) | - | - |
| [使用程序最小化损失](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/11.1.md) | | |
| [梯度下降](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/11.2.md) | | |
| [凸性](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/11.3.md) | | |
| [随机梯度下降法](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/11.4.md) | | |
| [十二、概率与泛化](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/12.md) | - | - |
| [随机变量](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/12.1.md) | | |
| [期望和方差](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/12.2.md) | | |
| [风险](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/12.3.md) | | |
| [十三、线性模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.md) | - | - |
| [预测小费金额](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.1.md) | | |
| [用梯度下降拟合线性模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.2.md) | | |
| [多元线性回归](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.3.md) | | |
| [最小二乘-几何透视](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.4.md) | | |
| [线性回归案例研究](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/13.5.md) | | |
| [十四、特征工程](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/14.md) | - | - |
| [沃尔玛数据集](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/14.1.md) | | |
| [预测冰淇淋评级](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/14.2.md) | | |
| [十五、偏方差权衡](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/15.md) | - | - |
| [风险和损失最小化](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/15.1.md) | | |
| [模型偏差和方差](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/15.2.md) | | |
| [交叉验证](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/15.3.md) | | |
| [十六、正则化](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/16.md) | - | - |
| [正则化直觉](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/16.1.md) | | |
| [L2 正则化：岭回归](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/16.2.md) | | |
| [L1 正则化：LASSO 回归](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/16.3.md) | | |
| [十七、分类](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.md) | - | - |
| [概率回归](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.1.md) | | |
| [Logistic 模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.2.md) | | |
| [Logistic 模型的损失函数](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.3.md) | | |
| [使用逻辑回归](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.4.md) | | |
| [经验概率分布的近似](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.5.md) | | |
| [拟合 Logistic 模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.6.md) | | |
| [评估 Logistic 模型](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.7.md) | | |
| [多类分类](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/17.8.md) | | |
| [十八、统计推断](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.md) | - | - |
| [假设检验和置信区间](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.1.md) | | |
| [置换检验](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.2.md) | | |
| [线性回归的自举（真系数的推断）](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.3.md) | | |
| [学生化自举](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.4.md) | | |
| [P-HACKING](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/18.5.md) | | |
| [十九、向量空间回顾](https://github.com/apachecn/ds100-textbook-zh/blob/master/docs/19.md) | | |

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章节	贡献者	进度
七、Web 技术	-	-
超文本传输协议	@miaoxiaozui2017	100%
八、处理文本	-	-
python 字符串方法	@miaoxiaozui2017	100%
正则表达式	@miaoxiaozui2017	100%
regex 和 python	@miaoxiaozui2017	100%
九、关系数据库和 SQL	-	-
关系模型	@miaoxiaozui2017	100%
SQL	@miaoxiaozui2017
SQL 连接	@miaoxiaozui2017
十、建模与估计	-	-
模型
损失函数
绝对损失和 Huber 损失
十一、梯度下降与数值优化	-	-
使用程序最小化损失
梯度下降
凸性
随机梯度下降法
十二、概率与泛化	-	-
随机变量
期望和方差
风险
十三、线性模型	-	-
预测小费金额
用梯度下降拟合线性模型
多元线性回归
最小二乘-几何透视
线性回归案例研究
十四、特征工程	-	-
沃尔玛数据集
预测冰淇淋评级
十五、偏方差权衡	-	-
风险和损失最小化
模型偏差和方差
交叉验证
十六、正则化	-	-
正则化直觉
L2 正则化：岭回归
L1 正则化：LASSO 回归
十七、分类	-	-
概率回归
Logistic 模型
Logistic 模型的损失函数
使用逻辑回归
经验概率分布的近似
拟合 Logistic 模型
评估 Logistic 模型
多类分类
十八、统计推断	-	-
假设检验和置信区间
置换检验
线性回归的自举（真系数的推断）
学生化自举
P-HACKING
十九、向量空间回顾

整体进度 #2

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miaoxiaozui2017 commented on Feb 20, 2020

miaoxiaozui2017 commented on Feb 22, 2020

miaoxiaozui2017 commented on Mar 3, 2020

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