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Apache-2.0

《精通数据科学:从线性回归到深度学习》配套代码

《精通数据科学:从线性回归到深度学习》一书的配套代码和数据

建议对Python比较熟悉的读者参考feature_pep_8分支上的代码,因为后者更加工整,符合PEP 8的规范。

本书由人民邮电出版社出版,网购地址为:

本书还配套免费的视频课程,观看地址为:

如果发现书中有纰漏之处,请在这里勘误,谢谢大家。

如果大家对本书有赞赏、建议、批评之声,请在豆瓣上留下你们的看法,再次谢谢大家。

作者想说的话

即使有严重的自夸嫌疑,但我还是想说:这是一本非常不错的书,推荐大家购买。

李国杰院士和韩家炜教授在读过这本书之后,亲自为其作序,在此再次向两位大佬表示感谢。

目录

  • 第1章 数据科学概述

    • 1.1 挑战
    • 1.2 机器学习
    • 1.3 统计模型
    • 1.4 关于本书
  • 第2章 Python安装指南与简介:告别空谈

    • 2.1 Python简介
    • 2.2 Python安装
    • 2.3 Python上手实践
    • 2.4 本章小结
  • 第3章 数学基础:恼人但又不可或缺的知识

    • 3.1 矩阵和向量空间
    • 3.2 概率:量化随机
    • 3.3 微积分
    • 3.4 本章小结
  • 第4章 线性回归:模型之母

    • 4.1 一个简单的例子
    • 4.2 上手实践:模型实现
    • 4.3 模型陷阱
    • 4.4 模型持久化
    • 4.5 本章小结
  • 第5章 逻辑回归:隐藏因子

    • 5.1 二元分类问题:是与否
    • 5.2 上手实践:模型实现
    • 5.3 评估模型效果:孰优孰劣
    • 5.4 多元分类问题:超越是与否
    • 5.5 非均衡数据集
    • 5.6 本章小结
  • 第6章 工程实现:计算机是怎么算的

    • 6.1 算法思路:模拟滚动
    • 6.2 数值求解:梯度下降法
    • 6.3 上手实践:代码实现
    • 6.4 更优化的算法:随机梯度下降法
    • 6.5 本章小结
  • 第7章 计量经济学的启示:他山之石

    • 7.1 定量与定性:变量的数学运算合理吗
    • 7.2 定性变量的处理
    • 7.3 定量变量的处理
    • 7.4 显著性
    • 7.5 多重共线性:多变量的烦恼
    • 7.6 内生性:变化来自何处
    • 7.7 本章小结
  • 第8章 监督式学习: 目标明确

    • 8.1 支持向量学习机
    • 8.2 核函数
    • 8.3 决策树
    • 8.4 树的集成
    • 8.5 本章小结
  • 第9章 生成式模型:量化信息的价值

    • 9.1 贝叶斯框架
    • 9.2 朴素贝叶斯
    • 9.3 判别分析
    • 9.4 隐马尔可夫模型
    • 9.5 本章小结
  • 第10章 非监督学习:聚类与降维

    • 10.1 K-means
    • 10.2 其他聚类模型
    • 10.3 Pipeline
    • 10.4 主成分分析
    • 10.5 奇异值分解
    • 10.6 本章小结
  • 第11章 分布式机器学习:集体力量

    • 11.1 Spark简介
    • 11.2 最优化问题的分布式解法
    • 11.3 大数据模型的两个维度
    • 11.4 开源工具的另一面
    • 11.5 本章小结
  • 第12章 神经网络:模拟人的大脑

    • 12.1 神经元
    • 12.2 神经网络
    • 12.3 反向传播算法
    • 12.4 提高神经网络的学习效率
    • 12.5 本章小结
  • 第13章 深度学习:继续探索

    • 13.1 利用神经网络识别数字
    • 13.2 卷积神经网络
    • 13.3 其他深度学习模型
    • 13.4 本章小结

代码说明

针对技术书籍,最好的阅读方法是对照每一章的示例代码,动手实现所讨论的模型。这样会极大加深自己对模型的理解和实践能力,否则就会像读小说一样,阅读时感觉不错,但实际使用时就无从下手了。

若想要使用这份代码,请先按照本书第2章和第11章的指南,安装相关的开源软件。

需要注意的是,为了节省篇幅、突出重点,正文中所展示的代码是基于Linux系统下的Python 2.7,而配套代码则兼容Python 3和Windows系统。

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