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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2
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前言
借助机器学习，你可以完成以下和更多任务：
•
细分客户并为每个群体找到最佳的营销策略。
•
根据类似客户的购买记录，为每个客户推荐产品。
•
检测哪些交易可能是欺诈性的。
•
预测明年的收入。
无论出于何种原因，你都决定学习机器学习并将其实现在你的项目中。好主意！
目标与方法
本书假设你对机器学习一无所知，其目标是为你提供实现能够从数据中学习的程序所需
的概念、工具和直觉。
我们将介绍大量技术，从最简单和最常用的技术（例如线性回归）到一些经常赢得比赛
的深度学习技术。
本书不是实现每种算法的玩具版本，而是使用可用于生产环境的 Python 框架：
•
Scikit-Learn 非常易于使用，它有效地实现了许多机器学习算法，因此成为学习机器
学习的重要切入点。Scikit-Learn 由 David Cournapeau 于 2007 年创建，现在由法国
计算机科学和自动化研究所的一个研究小组领导。
•
TensorFlow 是用于分布式数值计算的更复杂的库。通过将计算分布在数百个 GPU（图
形处理单元）服务器上，它可以有效地训练和运行大型神经网络。TensorFlow（TF）
是由 Google 创建的，并支持许多大型机器学习应用程序。它于 2015 年 11 月开源，2.0
版本于 2019 年 11 月发布。
•
Keras 是高层深度学习API，使训练和运行神经网络变得非常简单。它可以在
TensorFlow、Theano 或微软 Cognitive Toolkit（以前称为 CNTK）之上运行。 ...
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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.