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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无监督学习技术
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9.3 练习题
1. 如何定义聚类？你能列举几种聚类算法吗？
2. 聚类算法的主要应用有哪些？
3. 描述两种使用 K-Means 时选择正确数目的集群的技术。
4. 什么是标签传播？为什么要实施它，如何实现？
5. 你能否说出两种可以扩展到大型数据集的聚类算法？两个寻找高密度区域的算法？
6. 你能想到一个主动学习有用的示例吗？你将如何实施它？
7. 异常检测和新颖性检测有什么区别？
8. 什么是高斯混合模型？你可以将其用于哪些任务？
9. 使用高斯混合模型时，你能否列举两种技术来找到正确数量的集群？
10. 经典的 Olivetti 人脸数据集包含 400 张灰度的 64
×
64 像素的人脸图像。每个图像
被展平为大小为 4096 的一维向量。40 个不同的人被拍照（每个 10 次），通常的
任务是训练一个模型来预测每个图片中代表哪个人。使用 sklearn.datasets.
fetch_olivetti_faces() 函数来加载数据集，然后将其拆分为训练集、验证
集和测试集（请注意，数据集已缩放到 0 到 1 之间）。由于数据集非常小，你可能
希望使用分层抽样来确保每组中每个人的图像数量相同。接下来，使用 K-Means
对图像进行聚类，确保你拥有正确的集群数（使用本章中讨论的一种技术）。可视
化集群：你在每个集群中看到了相似面孔吗？
11. 继续使用 Olivetti 人脸数据集，训练分类器来预测每张图片代表哪个人，并在验证
集上对其进行评估。接下来，将 K-Means 用作降维工具，然后在简化集上训练分 ...
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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.