It covered jazz, of course, but it also included Davis’s ruminations on race, politics and culture. Fascinated, Hef sent the writer—future Pulitzer-Prize-winning author Alex Haley, an unknown at the time—back to glean even more opinion and insight from Davis. The resulting exchange, published in the September 1962 issue, became the first official Playboy Interview and kicked off a remarkable run of public inquisition that continues today—and that has featured just about every cultural titan of the last half century.To celebrate the Interview’s 50th anniversary, the editors of Playboy have culled 50 of its most (in)famous Interviews and will publish them over the course of 50 weekdays (from September 4, 2012 to November 12, 2012) via Amazon’s Kindle Direct platform. Here is the interview with the novelist and philosopher Ayn Rand from the March 1964 issue.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Bringing together the very best from the clever people at The Economist, Go Figure explains the mind-boggling, the peculiar and the profound, things you might always have quietly wondered about and yet more you didn't know you didn't know.Figure out why so many Koreans are called Kim, how bitcoin mining works, why eating insects makes sense and how to get ahead under a dictator - a treat for the knowing, the uninitiated and the downright curious.

    but it doesn't have to be. Using the advice and wisdom of 75 successful entrepreneurs, let this book be the roadmap to more success, wealth and fulfillment in your life. The experts highlighted in this book are now iconic investors, super successful entrepreneurs, financial planners, bestselling authors, and more, but they didn't start out that way. They are living proof that you can truly come from any background or situation to ultimately reach a high level of success. All that it takes to find true wealth are the simple actions laid out in this book. This step-by-step guide teaches: - The money secrets of the rich - How to reprogram your mind for massive success - The common traits and skills of the wealthy - A money plan and list of priorities to focus on - The key mistakes that are holding you back - Where to begin so you can take your finances and career to the next level As successful entrepreneur David Wood says, "Wealth is a choice." The choice is yours to make. Take control. Make money. Live wealthy. For free training videos & resources for the book, visit: MakeMoneyLiveWealthy.com

    How advances in technology is affecting banking

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    So when a school in Irvine has a sudden drug problem, they bring him in. As luck would have it, he moves in across the street from Vivienne; the attraction is mutual and instant. But life throws many curveballs at them in the form of the drug issues at his school, his daughter’s illness, and Vivienne’s stalker ex. They turn to each other for comfort, and her to him for protection, but things are messy. Will they work past their problems and come together? *Daddy Next Door has no cheating and a HEA. Includes 4 bonus books.*

    The book makes clear that applied microeconometrics is about the estimation of marginal and treatment effects, and that parametric estimation is simply a means to this end. It also clarifies the distinction between causality and statistical association. The book focuses specifically on cross section and panel data methods. Population assumptions are stated separately from sampling assumptions, leading to simple statements as well as to important insights. The unified approach to linear and nonlinear models and to cross section and panel data enables straightforward coverage of more advanced methods. The numerous end-of-chapter problems are an important component of the book. Some problems contain important points not fully described in the text, and others cover new ideas that can be analyzed using tools presented in the current and previous chapters. Several problems require the use of the data sets located at the author's website.

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.

    Shilov, Professor of Mathematics at the Moscow State University, covers determinants, linear spaces, systems of linear equations, linear functions of a vector argument, coordinate transformations, the canonical form of the matrix of a linear operator, bilinear and quadratic forms, Euclidean spaces, unitary spaces, quadratic forms in Euclidean and unitary spaces, finite-dimensional algebras and their representations, with an appendix on categories of finite-dimensional spaces.The author begins with elementary material and goes easily into the advanced areas, covering all the standard topics of an advanced undergraduate or beginning graduate course. The material is presented in a consistently clear style. Problems are included, with a full section of hints and answers in the back.Keeping in mind the unity of algebra, geometry and analysis in his approach, and writing practically for the student who needs to learn techniques, Professor Shilov has produced one of the best expositions on the subject. Because it contains an abundance of problems and examples, the book will be useful for self-study as well as for the classroom.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Kennedy, Jr.’s gang. He had his own famed “Rat Pack,” made up of hard drinking, womanizing individuals like himself—guys like Dean Martin, Sammy Davis, Jr., and Peter Lawford—but the guy “Ol’ Blue Eyes” really wanted to hang with was Lawford’s brother-in-law, the real chairman of the board, John F. Kennedy.In Sinatra and the Jack Pack, Michael Sheridan delves deep into the acclaimed singer’s relationship with the former president. He shares how Sinatra emerged from a working class Italian family and carved out a unique place for himself in American culture, and how Kennedy, also of immigrant stock, came from a privileged background of which the young Frank could only have dreamed.By the time the men met in the 1950s, both were thriving—and both liked the good life. They bonded over their mutual ability to attract beautiful women, male admirers, and adoring acolytes. They also shared a scandalous secret: each had dubious relationships with the mafia. It had promoted Frank’s career and helped Kennedy buy votes. FBI Director J. Edgar Hoover had, over two decades, compiled detailed and damning dossiers on their activities.From all accounts the friendship thrived. Then, suddenly, in March 1962, Frank was abruptly ejected from JFK’s gang. This unique volume tells why. It will release shortly after a television documentary inspired by the book airs, is filled with a beloved cast of characters, and is the compelling, untold story of a tumultuous relationship between two American icons.Skyhorse Publishing, as well as our Arcade imprint, are proud to publish a broad range of books for readers interested in history--books about World War II, the Third Reich, Hitler and his henchmen, the JFK assassination, conspiracies, the American Civil War, the American Revolution, gladiators, Vikings, ancient Rome, medieval times, the old West, and much more. While not every title we publish becomes a New York Times bestseller or a national bestseller, we are committed to books on subjects that are sometimes overlooked and to authors whose work might not otherwise find a home.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.