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Product Details 基本信息 ISBN-13 书号 9783662549827 Author 作者 Deisenroth, Rainer Format 版本 精装 Publisher 出版社 Springer Vieweg Publication Date 出版日期 2017-11-22 Language 语种 英语
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Product Details 基本信息 ISBN-13 书号 9781108470049 Author 作者 Marc Peter Deisenroth Format 版本 精装 Pages Number 页数 398页 Publisher 出版社 Cambridge University Press Publication Date 出版日期 2020-04-23 Language 语种 英语 Book Contents 内容简介 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussi
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图书信息 书号: 9781108455145 作者: Marc Peter Deisenroth 装帧: 平装-胶订 页数: 398页 出版社: Cambridge University Press 尺寸: 25.4 x 17.8 x 2.5 cm 出版日期: 2020-04-23 重量: 908g 语种: 其它(含多语) 内容简介 The fundamental mathematical tools needed to unde
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【现货】Mathematics for Machine Learning
Product Details 基本信息 ISBN-13 书号 9781108455145 Author 作者 Marc Peter Deisenroth Pages Number 页数 398页 Publisher 出版社 Cambridge University Press Publication Date 出版日期 20200423 Product Dimensions 商品尺寸 0 x 0 x 0 cm Book Description 内容简介 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models an
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