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  1. Bog
  2. Matematikkens fundament
  3. Matematisk logik

Sparse Estimation with Math and R

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

Sparse Estimation 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 knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs.  

Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers'' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.

This book is one of a series of textbooks in machine learning by the same author. Other titles are: 

- Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)

- Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762)

- Sparse Estimation with Math and Python


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Tjek vores konkurrenters priser
Normalpris
kr 335
Fragt: 39 kr
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20 kr
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God 4 anmeldelser på
Tjek vores konkurrenters priser
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs.  

Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers'' insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.

This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.

This book is one of a series of textbooks in machine learning by the same author. Other titles are: 

- Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)

- Statistical Learning with Math and Python (https://www.springer.com/gp/book/9789811578762)

- Sparse Estimation with Math and Python


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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 234
ISBN-13: 9789811614453
Indbinding: Paperback
Udgave:
ISBN-10: 9811614458
Kategori: Matematisk logik
Udg. Dato: 5 aug 2021
Længde: 18mm
Bredde: 233mm
Højde: 156mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 5 aug 2021
Forfatter(e): Joe Suzuki
Forfatter(e) Joe Suzuki

Kategori Matematisk logik

ISBN-13 9789811614453

Sprog Engelsk

Indbinding Paperback

Sider 234

Udgave

Længde 18mm

Bredde 233mm

Højde 156mm

Udg. Dato 5 aug 2021

Oplagsdato 5 aug 2021

Forlag Springer Verlag, Singapore

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