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  2. Kunstig intelligens
  3. Machine learning

Sparse Estimation with Math and Python

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

Sparse Estimation with Math and Python

- 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 Python 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 Pyth (https://www.springer.com/gp/book/9789811578762)
  • Sparse Estimation with Math and R

Vis mere
Tjek vores konkurrenters priser
Normalpris
kr 335
Fragt: 39 kr
6 - 8 hverdage
20 kr
Pakkegebyr
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 Python 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 Pyth (https://www.springer.com/gp/book/9789811578762)
  • Sparse Estimation with Math and R

Vis mere
Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 246
ISBN-13: 9789811614378
Indbinding: Paperback
Udgave:
ISBN-10: 9811614377
Kategori: Machine learning
Udg. Dato: 31 okt 2021
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 31 okt 2021
Forfatter(e): Joe Suzuki
Forfatter(e) Joe Suzuki

Kategori Machine learning

ISBN-13 9789811614378

Sprog Engelsk

Indbinding Paperback

Sider 246

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 31 okt 2021

Oplagsdato 31 okt 2021

Forlag Springer Verlag, Singapore

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