Cross stitch some of the most famous castles in Wales. Bilingual with clear directions and charts.

Welsh Castles in Cross-Stitch / Cestyll Cymru mewn Croesbwyth

Crafts, Hobbies & Home

James, Gareth

James, Iona

Crafts & Hobbies

Y Lolfa

Brooklyn-based artist Rachel Harrison examines the condition of looking, wanting and having via works and installations that hybridize the sculptural, the painterly and the photographic. With a finely tuned balance of sharp wit, wry sympathy and sensitive insight, she layers unusual materials into a whole. Currents 30 combines an exhibition catalogue with various artist projects.

Rachel Harrison: Currents 30: Milwaukee Art Museum

Arts & Photography

Gordon, David

Kelsey, John

Individual Artists

Milwaukee Art Museum

Product Description 
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. 
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.\nThis Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.\nReview 
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University) 
 From the Back Cover 
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. 
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning co

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Computers & Technology

Witten, Daniela

Hastie, Trevor

Tibshirani, Robert

Computer Science

Springer

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. 
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.\nThis Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

We here at Hashtag Blogger have teamed up with internet sensation Rumena Begum to provide you with ‘Blogging Made Easy’. We have written the guides in this book to help you make the most out of life as a blogger in 2016 and help you take your success to the next level by attracting more visitors and increasing your online exposure. Whether you’re starting up a new blog, have done blogging for years, or you’re running a blog for your business. We’re going to cover everything you need to know in order to have a successful online blog. We’ll also explain how you can outreach and get noticed by brands looking for collaborations.

Blogging Made Easy

Business & Money

Sharma, Amen

Rosales, Romeo Martin J.

Begum, Rumena

Marketing & Sales

CreateSpace Independent Publishing Platform

<div> <div>Norwich City Miscellany is packed with fascinating facts, figures, trivia, stats, stories, and anecdotes all relating to the history of Norwich City. From memorable matches and favorite sons, the book follows no set order, chronological or otherwise, but has plenty to keep any fanatic coming back for more--and is fully endorsed by the club.</div> </div>

Norwich City On This Day: History, Facts and Figures from Every Day of the Year

Humor & Entertainment

Puzzles & Games

Pitch Publishing Ltd

So, you want to get better at poker? You are committed to this and you are prepared to spend time on it. You ask advice and what do people tell you? Probably something along the lines of “just keep working on it and you'll get there.” This is wrong. “Just working on it” will purposeful practice. Purposeful practice is the gold standard for anyone who wishes to take full advantage of the remarkable adaptability of the human brain and is the fast track route to improving your poker skills. This book will give you: * A clear theoretical understanding of the science of purposeful practice * Numerous techniques by which this can be adapted to improvement at poker * Specific exercises designed to create an infallible Plan for Poker Improvement

Purposeful Practice for Poker: The Modern Approach to Studying Poker

Cardner, Dr. Patricia

D&B Publishing

You've reached the business end of the tournament and you're gunning for glory. However, you are now playing for the big bucks where one mistake could cost you thousands if not tens of thousands of dollars. You'll undoubtedly feel nervous. Are you technically prepared? The Final Table will teach you how to make great decisions at every stage. It will also set out a program so you can learn how to continuously improve your final table strategy every single day.- How do you apply pressure as the chip leader?- What is risk premium and why is it so important?- What's the best way to study the complexities of final table play?The Final Table features detailed analysis of over 100 hand examples at different stages of play. These vary from full ring to short-handed and heads-up play and cover the most common preflop and postflop situations.

The Final Table: Play your best poker when the most is at stake

An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

An Introduction to Statistical Learning&nbsp;provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and&nbsp; astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote&nbsp;An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Taylor, Jonathan

Programming Languages

See the best price to sell, buy, or rent books by James, Gareth. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by James, Gareth

See the best price to sell, buy, or rent books by James, Gareth. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Welsh Castles in Cross-Stitch / Cestyll Cymru mewn Croesbwyth

Rachel Harrison: Currents 30: Milwaukee Art Museum

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Blogging Made Easy

Norwich City On This Day: History, Facts and Figures from Every Day of the Year

Purposeful Practice for Poker: The Modern Approach to Studying Poker

The Final Table: Play your best poker when the most is at stake

An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)