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Jingdong book

机器学习中的数学 人工智能深度学习技术丛书

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机器学习中的数学 人工智能深度学习技术丛书

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数学,作为一门基础学科,是学习和研究现代科学技术必须掌握的基本工具。
人工智能是目前计算机科学十分热门的一个领域,而机器学习作为实现人工智能的一种方法,其底层逻辑都是数学。

AI时代,得数学者得天下!

1、全书结合300余幅插图+100余个示例+50余个公式推导,用讲故事和实例的形式介绍人工智能、机器学习中必备的数学基础知识,趣味性较强。

2、全书以初中阶段数学知识为基础,内容由浅入深,涉及数学计算均列出详细步骤,可让读者快速入门。

3、注重数学的友好性,所述概念和术语均以理解为主,并选择比较简单的案例辅助读者理解,对重点内容反复强调。

4、本书大体上由线性代数、高数、概率三部分组成,每一部分仅对机器学习中常见的数学知识进行介绍。对于繁琐的计算以Python编程的方式实现,让程序员们有归属感。


Content Description

《机器学习中的数学》是一本系统介绍机器学习中涉及的数学知识的入门图书,本书从机器学习中的数学入门开始,以展示数学的友好性为原则,讲述了机器学习中的一些常见的数学知识。机器学习作为人工智能的核心技术,对于数学基础薄弱的人来说,其台阶是陡峭的,本书力争在陡峭的台阶前搭建一个斜坡,为读者铺平机器学习的数学之路。

《机器学习中的数学》共19章,分为线性代数、高等数学和概率3个组成部分。第 1 部分包括向量、向量的点积与叉积、行列式、代数余子式、矩阵、矩阵和方程组、矩阵的秩、逆矩阵、高斯—诺尔当消元法、消元矩阵与置换矩阵、矩阵的LU分解、欧几里得距离、曼哈顿距离、切比雪夫距离、夹角余弦等;第2部分包括导数、微分、不定积分、定积分、弧长、偏导、多重积分、参数方程、极坐标系、柱坐标系、球坐标系、梯度、梯度下降算法、方向导数、线性近似、二阶近似、泰勒公式、牛顿法、zuixiao二乘法、求解极值、拉格朗日乘子法、KKT条件、欧拉—拉格朗日方程等;第3部分包括概率、古典概型、几何概型、互斥事件、独立事件、分布函数、离散型分布、连续型分布等。

《机器学习中的数学》内容全面,语言简练,实例典型,实用性强,立足于“友好数学”,与机器学习完美对接,适合想要了解机器学习与深度学习但数学基础较为薄弱的程序员阅读,也适合作为各大高等院校机器学习相关专业的教材。机器学习及数学爱好者、海量数据挖掘与分析人员、金融智能化从业人员等也可选择本书参考学习。


Author Description

孙博,苏州工业园区高技能领军人才,机器学习爱好者,擅长软件算法和软件结构设计。曾在CSDN及多个知名博客网站发表多篇技术文章,深受读者喜爱。目前任公司CTO,主持校企合作实习平台的建设和搞笑的软件培训工作。


Comments

本书从软件开发人员的角度和学习心态出发,由浅入深地总结了机器学习中与数学相关的基本概念和实际应用方法,回避了纯理论的研究和严格的过程推导,并给出了很多问题的实际背景、避免了抽象和枯燥的想象,为读者能快速入门提供了必要的帮助。本书是机器学习中涉及代数、几何、高等数学、概率等主要基础知识的高度概括和总结,可以作为机器学习初学者的案头参考书。

——前东软望海研发中心总经理 赵永生

从根本上说,机器学习是对数学的应用,要想成为优秀的人工智能工程师,必须有深厚的数学知识才行,而这本书浅显易懂,用实例和讲故事的形式,介绍了机器学习中必备的数学基础知识,让你轻松踏入机器学习的大门。

——西安石油大学教授 张群正

数学的发展源自生活,它应该以友好的姿态面对世人,这本书重新勾起了我对数学的兴趣。

——苏州智慧家长教育科技有限公司CTO 邓宏誉


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