| 000 | 03273nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-981-16-1438-5 | ||
| 003 | DE-He213 | ||
| 005 | 20240423130132.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 211030s2021 si | s |||| 0|eng d | ||
| 020 |
_a9789811614385 _9978-981-16-1438-5 |
||
| 024 | 7 |
_a10.1007/978-981-16-1438-5 _2doi |
|
| 050 | 4 | _aQ334-342 | |
| 050 | 4 | _aTA347.A78 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 072 | 7 |
_aUYQ _2thema |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aSuzuki, Joe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aSparse Estimation with Math and Python _h[electronic resource] : _b100 Exercises for Building Logic / _cby Joe Suzuki. |
| 250 | _a1st ed. 2021. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021. |
|
| 300 |
_aX, 246 p. 54 illus., 46 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 505 | 0 | _aChapter 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 | _aThe 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 | _aArtificial intelligence. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 |
_aArtificial intelligence _xData processing. |
|
| 650 | 0 | _aStatistics . | |
| 650 | 1 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aMachine Learning. |
| 650 | 2 | 4 | _aData Science. |
| 650 | 2 | 4 | _aStatistics. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811614378 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811614392 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-1438-5 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c185431 _d185431 |
||