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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108
第 4 章
训练模型
到目前为止，我们已经探讨了不同机器学习的模型，但是它们各自的训练算法在很大程
度上还是一个黑匣子。回顾前几章里的部分案例，你大概感到非常惊讶，在对系统内部
一无所知的情况下，居然已经实现了这么多：优化了一个回归系统，改进了一个数字图
片分类器，从零开始构建了一个垃圾邮件分类器，所有这些，你都不知道它们实际是如
何工作的。确实是这样，在许多情况下，你并不需要了解实施细节。
但是，很好地理解系统如何工作也是非常有帮助的。针对你的任务，它有助于快速定位
到合适的模型、正确的训练算法，以及一套适当的超参数。不仅如此，后期还能让你
更高效地执行错误调试和错误分析。最后还要强调一点，本章探讨的大部分主题对于理
解、构建和训练神经网络（本书第二部分）是至关重要的。
本章我们将从最简单的模型之一
—
线性回归模型，开始介绍两种非常不同的训练模型
的方法：
•
通过“闭式”方程，直接计算出最拟合训练集的模型参数（也就是使训练集上的成
本函数最小化的模型参数）。
•
使用迭代优化的方法，即梯度下降（GD），逐渐调整模型参数直至训练集上的成本
函数调至最低，最终趋同于第一种方法计算出来的模型参数。我们还会研究几个梯
度下降的变体，包括批量梯度下降、小批量梯度下降以及随机梯度下降。等我们进
入到第二部分神经网络的学习时，会频繁地使用这几个的变体。
接着我们将会进入多项式回归的讨论，这是一个更为复杂的模型，更适合非线性数据
集。由于该模型的参数比线性模型更多，因此更容易造成对训练数据过拟合，我们将使
用学习曲线来分辨这种情况是否发生。然后 ...
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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.
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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.