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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

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? Are you psychologically 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?* Should you play to win or play to make the most money?* What's the best way to study all this?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.

Norwich City On This Day revisits all the most magical and memorable moments from the Canaries' rollercoaster past, mixing in a maelstrom of quirky anecdotes and legendary characters to produce an irresistibly dippable diary - with an entry for every day of the year. From the club's formation in 1902 to the Premier League era, City fans have witnessed promotions and relegations, European adventures and Cup runs, hard times and hard-fought local derbies - all featured here. Timeless greats such as Duncan Forbes and Martin Peters, Ron Ashman, Kevin Keelan, Darren Huckerby and Mark Bowen all loom larger than life. Revisit 9th February 1980, when Justin Fashanu volleyed home the Goal of the Season against champions Liverpool. 18th March 1959: an FA Cup semi for the Third Division giantkillers of Man U and Spurs. Or 20th October 1993: City become the first and only British team to win away at Bayern Munich.

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, marketing, and  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 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.

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 not help you. Randomly analysing hands, watching poker on TV or vaguely looking at equity equations won't cut it. The only approach that will work is the right sort of practice based on a relatively new area of psychological investigation - the science of expertise. ----- This book identifies precisely what this "right sort of practice" entails. It is based around what has become known as 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

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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.