國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Sparse estimation with math and pyth...

SpringerLink (Online service)

Sparse estimation with math and python100 exercises for building logic /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sparse estimation with math and pythonby Joe Suzuki.
其他題名:	100 exercises for building logic /
作者:	Suzuki, Joe.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	x, 246 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis.
電子資源:	https://doi.org/10.1007/978-981-16-1438-5
ISBN:	9789811614385$q(electronic bk.)

Sparse estimation with math and python100 exercises for building logic /
Suzuki, Joe.

Sparse estimation with math and python100 exercises for building logic /[electronic resource] :by Joe Suzuki. - Singapore :Springer Singapore :2021. - x, 246 p. :ill. (chiefly col.), digital ;24 cm.

Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.

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.

ISBN: 9789811614385$q(electronic bk.)

Standard No.: 10.1007/978-981-16-1438-5doiSubjects--Topical Terms:

181905
Multivariate analysis.

LC Class. No.: QA278 / .S89 2021

Dewey Class. No.: 519.535

Sparse estimation with math and python100 exercises for building logic /
LDR:02464nmm a2200325 a 4500 001 610485
003 DE-He213
005 20211030105202.0
006 m d
007 cr nn 008maaau
008 220330s2021 si s 0 eng d
020 $a 9789811614385$q(electronic bk.)
020 $a 9789811614378$q(paper)
024 7 $a 10.1007/978-981-16-1438-5 $2 doi
035 $a 978-981-16-1438-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278 $b .S89 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 519.535 $2 23
090 $a QA278 $b .S968 2021
100 1 $a Suzuki, Joe. $3 732855
245 1 0 $a Sparse estimation with math and python $h [electronic resource] : $b 100 exercises for building logic / $c by Joe Suzuki.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a x, 246 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.
520 $a 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.
650 0 $a Multivariate analysis. $3 181905
650 0 $a Estimation theory. $3 181864
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Statistics, general. $3 275684
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-1438-5
950 $a Computer Science (SpringerNature-11645)