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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.
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.
Equips readers with the logic required for machine learning and data science
Provides in-depth understanding of source programs
Written in an easy-to-follow and self-contained style
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xii
208 S. 3 s/w Illustr. 29 farbige Illustr. 208 p. 32 illus. 29 illus. in color. |
| ISBN-13: | 9789811904004 |
| ISBN-10: | 9811904006 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Suzuki, Joe |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore |
| Maße: | 235 x 155 x 13 mm |
| Von/Mit: | Joe Suzuki |
| Erscheinungsdatum: | 15.05.2022 |
| Gewicht: | 0,341 kg |
Equips readers with the logic required for machine learning and data science
Provides in-depth understanding of source programs
Written in an easy-to-follow and self-contained style
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xii
208 S. 3 s/w Illustr. 29 farbige Illustr. 208 p. 32 illus. 29 illus. in color. |
| ISBN-13: | 9789811904004 |
| ISBN-10: | 9811904006 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Suzuki, Joe |
| Auflage: | 1st ed. 2022 |
| Hersteller: |
Springer Singapore
Springer Nature Singapore |
| Maße: | 235 x 155 x 13 mm |
| Von/Mit: | Joe Suzuki |
| Erscheinungsdatum: | 15.05.2022 |
| Gewicht: | 0,341 kg |
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