August 2022
Intermediate to advanced
394 pages
12h 32m
Chinese
Content preview from 金融人工智能：用Python实现AI量化交易
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24
｜
第
1
章
In [88]: freq['sum'].describe().astype(int)
➋
Out[88]: count 1024
mean 12
std 3
min 2
25% 10
50% 12
75% 15
max 26
Name: sum, dtype: int64
➊
将
0
和
1
的频率相加。
➋
显示相加后的汇总统计信息。
数据量和多样性
在基于神经网络的预测任务中，用于训练神经网络的可用数据的数据量和多
样性对其预测性能起决定性作用。本节中的假设示例表明，相较于在数据量
较大且具有多样性的数据集上训练的神经网络，在数据量较小且较为单一的
数据集上训练的相同神经网络，其表现会差
10
个百分点以上
。考虑到人工
智能从业者和公司经常为小到
1/10
个百分点的改进而奋斗，
10
个百分点可
以被认为是巨大的差异了。
1.3.3
大数据
更大的数据集和大数据集有什么区别？十多年来，
大数据
一词已被用于表示许多事物。就
本书的目的而言，我们可以将
大数据
定义为在数量、种类和速度方面足够丰富的
大数据
集
，它可以对人工智能算法进行适当的训练，从而使得与基线算法相比，人工智能算法在
预测任务上表现得更好。
之前使用的较大的数据集在实际应用中仍然很小。但是，它足以完成指定的目标。训练人
工智能算法所需要的数据集的数量和种类主要由特征及标签数据的结构和特点所决定。
在这种情况下，假设零售银行会使用基于神经网络的信用评分分类方法。给定内部数据，
负责的数据科学家设计了
25
个分类特征
，每个特征都可以采用
8
个不同的值，由此产生 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.