October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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机器学习概览
｜
37
人均 GDP
（
美元
）
生活满意度
全部数据上的线性模型
部分数据上的线性模型
部分数据上的归一化线性模型
图 1-23：正则化降低了过拟合的风险
1.5.6 欠拟合训练数据
你可能已经猜到了，欠拟合和过拟合正好相反。它的产生通常是因为对于底层的数据结
构来说，你的模型太过简单。例如，用线性模型来描述生活满意度就属于欠拟合。现实
情况远比模型复杂得多，所以即便是对于用来训练的示例，该模型产生的预测都一定是
不准确的。
解决这个问题的主要方式有：
•
选择一个带有更多参数、更强大的模型。
•
给学习算法提供更好的特征集（特征工程）。
•
减少模型中的约束（例如，减少正则化超参数）。
1.5.7 退后一步
现在你已经对机器学习有了一定了解。不过讲了这么多概念，你可能有点晕，我们暂且
退后一步，纵观一下全局：
•
机器学习是关于如何让机器可以更好地处理某些特定任务的理论，它从数据中学习，
而无须清晰地编码规则。
•
机器学习系统有很多类型：有监督和无监督，批量的和在线的，基于实例的和基于
模型的，等等。
•
在一个机器学习项目中，你从训练集中采集数据，然后将数据交给学习算法来计算。
如果算法是基于模型的，它会调整一些参数来将模型适配于训练集（即对训练集本
身做出很好的预测），然后算法就可以对新的场景做出合理的预测。如果算法是基于
实例的，它会记住这些示例，并根据相似度度量将它们与所学的实例进行比较，从
而泛化这些新实例。
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.