QJieWang
QJieWang
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When using torchstat, I found that it's not possible to analyze models quantized to QUInt8. I consider this to be important. However, are there any other tools available to achieve...
[错误地址](https://github.com/datawhalechina/team-learning-data-mining/blob/master/EnsembleLearning/CH6-%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0%E4%B9%8B%E6%A1%88%E4%BE%8B%E5%88%86%E4%BA%AB/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0%E6%A1%88%E4%BE%8B%E5%88%86%E6%9E%901/%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0-%E6%A1%88%E4%BE%8B%E5%88%86%E6%9E%901.ipynb) #部分特征处理,取众数(首先去除缺失值的数据) for i in range(1,9+1): data.loc[data['public_service_'+str(i)]
[问题地址](https://github.com/datawhalechina/team-learning-data-mining/blob/master/ProbabilityStatistics/03%20%E5%B8%B8%E8%A7%81%E5%88%86%E5%B8%83%E4%B8%8E%E5%81%87%E8%AE%BE%E6%A3%80%E9%AA%8C.md)  2.3之后的所有图片相关maekdown语法地址引用错误,导致图片不能显示。 ``` 如2.3的第一个图片,应该为  而不是  ```
[错误地点](https://github.com/datawhalechina/team-learning-data-mining/blob/master/ProbabilityStatistics/02%20%E6%95%B0%E7%90%86%E7%BB%9F%E8%AE%A1%E4%B8%8E%E6%8F%8F%E8%BF%B0%E6%80%A7%E7%BB%9F%E8%AE%A1.md)  ```  而不是  ```
[文字内容较准](https://github.com/datawhalechina/team-learning-program/blob/master/IntroductionToNumpy/task01%20%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B%E5%8F%8A%E6%95%B0%E7%BB%84%E5%88%9B%E5%BB%BA/04.%20%E6%95%B0%E7%BB%84%E7%9A%84%E5%88%9B%E5%BB%BA.ipynb ) 1. 数组创建中 (b)通过asarray()函数进行创建 array()和asarray()都可以将结构数据转化为 ndarray,但是array()和asarray()主要区别就是当数据源是ndarray 时,array()仍然会 copy 出一个副本,占用新的内存,**但不改变 dtype 时 asarray()不会。**(此处应该改为:”但asarray()不会“) 2. 数组的属性中 numpy.ndarray.itemsize**以字节的形式返回数组中每一个元素的大小**。表达不清楚,可以改为**数组中每个元素的字节数的大小**