Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1028
Book Description
Mathematical Reviews
School Science and Mathematics
National Union Catalog
Author:
Publisher:
ISBN:
Category : Union catalogs
Languages : en
Pages : 672
Book Description
Includes entries for maps and atlases.
Publisher:
ISBN:
Category : Union catalogs
Languages : en
Pages : 672
Book Description
Includes entries for maps and atlases.
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Book Description
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 and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Book Description
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 and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
The National union catalog, 1968-1972
Library of Congress Catalogs
Algebre
Author: Michel Queysanne
Publisher:
ISBN:
Category : Algebra
Languages : fr
Pages : 616
Book Description
Publisher:
ISBN:
Category : Algebra
Languages : fr
Pages : 616
Book Description
Subject Catalog
Author: Library of Congress
Publisher:
ISBN:
Category : Subject catalogs
Languages : en
Pages : 612
Book Description
Publisher:
ISBN:
Category : Subject catalogs
Languages : en
Pages : 612
Book Description
Library of Congress Catalog
Author: Library of Congress
Publisher:
ISBN:
Category : Subject catalogs
Languages : en
Pages : 624
Book Description
A cumulative list of works represented by Library of Congress printed cards.
Publisher:
ISBN:
Category : Subject catalogs
Languages : en
Pages : 624
Book Description
A cumulative list of works represented by Library of Congress printed cards.