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  3. Machine learning

Kernel Methods for Machine Learning with Math and R

- 100 Exercises for Building Logic
Af: Joe Suzuki Engelsk Paperback

Kernel Methods for Machine Learning with Math and R

- 100 Exercises for Building Logic
Af: Joe Suzuki Engelsk Paperback
Tjek vores konkurrenters priser

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.
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Tjek vores konkurrenters priser
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Tjek vores konkurrenters priser

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.
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 196
ISBN-13: 9789811903977
Indbinding: Paperback
Udgave:
ISBN-10: 9811903972
Kategori: Machine learning
Udg. Dato: 4 maj 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 4 maj 2022
Forfatter(e): Joe Suzuki
Forfatter(e) Joe Suzuki

Kategori Machine learning

ISBN-13 9789811903977

Sprog Engelsk

Indbinding Paperback

Sider 196

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 4 maj 2022

Oplagsdato 4 maj 2022

Forlag Springer Verlag, Singapore

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