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Condition: New. 2022nd edition NO-PA16APR2015-KAP.
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Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
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Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 44.30
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Kernel Methods for Machine Learning With Math and Python : 100 Exercises for Building Logic
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Kernel Methods for Machine Learning With Math and Python : 100 Exercises for Building Logic
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Condition: New. 2022. 1st ed. 2022. paperback. . . . . . Books ship from the US and Ireland.
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Kernel Methods for Machine Learning with Math and R
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Mai 2022, 2022
ISBN 10: 9811903972 ISBN 13: 9789811903977
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.The book¿s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.
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Kernel Methods for Machine Learning with Math and Python
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore Mai 2022, 2022
ISBN 10: 9811904006 ISBN 13: 9789811904004
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs.The book¿s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysistopics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 220 pp. Englisch.
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Kernel Methods for Machine Learning with Math and R : 100 Exercises for Building Logic
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811903972 ISBN 13: 9789811903977
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs.The book's main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
-
Kernel Methods for Machine Learning with Math and Python : 100 Exercises for Building Logic
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2022
ISBN 10: 9811904006 ISBN 13: 9789811904004
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs.The book's main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysistopics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
-
Kernel Methods for Machine Learning with Math and R : 100 Exercises for Building Logic
Language: English
Published by Springer Nature Singapore, 2022
ISBN 10: 9811903972 ISBN 13: 9789811903977
Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book¿s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
-
Kernel Methods for Machine Learning with Math and R : 100 Exercises for Building Logic
Language: English
Published by Springer Nature Singapore, 2022
ISBN 10: 9811903972 ISBN 13: 9789811903977
Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book¿s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
