Taimre thomas (57 résultats)

paperback. Etat : Very Good. Unmarked trade paperback. Gentle bend on corner of cover.

paperback. Etat : Very Good. No marks inside; solid spine; clean cover. Slight corner/edge wear.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : Very Good. Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting.

Hardcover. Etat : Good. No markings on text.

Etat : New. pp. 188.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. pp. 188 2nd Edition.

Etat : Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Etat : New.

hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : As New. Unread book in perfect condition.

Etat : New.

hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : New.

Etat : acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.…

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Hardback. Etat : New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.…

Etat : New. In English.

Etat : New.

Etat : New.

Etat : New.

Etat : New. In English.

Etat : New.

Hardback. Etat : New. 2nd. Praise for the first edition:"In nine succinct but information-packed chapters, the authors provide a logically structured and robust introduction to the mathematical and statistical methods underpinning the still-evolving field of AI and data science."- Joacim Rocklöv and Albert A. Gayle, International Journal of Epidemiology, Volume 49, Issue 6"This book organizes the algorithms clearly and cleverly. The way the Python code was written follows the algorithm closely-very useful for readers who wish to understand the rationale and flow of the background knowledge."- Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, Volume 77, Issue 4The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science.New in the Second EditionThis expanded edition provides updates across key areas of statistical learning: Monte Carlo Methods: A new section introducing regenerative rejection sampling - a simpler alternative to MCMC. Unsupervised Learning: Inclusion of two multidimensional diffusion kernel density estimators, as well as the bandwidth perturbation matching method for the optimal data-driven bandwidth selection. Regression: New automatic bandwidth selection for local linear regression. Feature Selection and Shrinkage: A new chapter introducing the klimax method for model selection in high-dimensions. Reinforcement Learning: A new chapter on contemporary topics such as policy iteration, temporal difference learning, and policy gradient methods, all complete with Python code. Appendices: Expanded treatment of linear algebra, functional analysis, and optimization that includes the coordinate-descent method and the novel Majorization-Minimization method for constrained optimization.Key Features:Focuses on mathematical understanding.Presentation is self-contained, accessible, and comprehensive.Extensive list of exercises and worked-out examples.Many concrete algorithms with Python code.Full color throughout and extensive indexing.A single-counter consecutive numbering of all theorems, definitions, equations, etc., for easier text searches.…

Etat : New. Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching.