Callie Daniels' mother has one dying wish. She wants neighboring rancher, Garrett O'Neill, to marry her seventeen year old daughter. It's not supposed to be a conventional marriage.Garrett O'Neill would keep Callie's ranch safe and Callie would go away to college. & ;& ;Four years later, Callie comes home with stars in her eyes and happily ever after in her heart. She plans to make their marriage real until finds the new housekeeper, Sylvie in Garrett's arms. & ;& ;Heartbroken, Callie takes off to her own house, which they closed up four years ago. She stays there with Old Henry, a longtime friend and ranch hand, until her new horse injures her. Garrett forces Callie to move back in to his house. & ;& ;Callie finally gets her heart's desire. Alone in a line shack, they make sweet love for the first time. Unfortunately the love light is gone from Garret's eyes the next morning.

Callie Daniels' mother has one dying wish. She wants neighboring rancher, Garrett O'Neill, to marry her seventeen year old daughter. It's not supposed to be a conventional marriage.Garrett O'Neill would keep Callie's ranch safe and Callie would go away to college. Four years later, Callie comes home with stars in her eyes and happily ever after in her heart. She plans to make their marriage real until finds the new housekeeper, Sylvie in Garrett's arms. Heartbroken, Callie takes off to her own house, which they closed up four years ago. She stays there with Old Henry, a longtime friend and ranch hand, until her new horse injures her. Garrett forces Callie to move back in to his house. Callie finally gets her heart's desire. Alone in a line shack, they make sweet love for the first time. Unfortunately the love light is gone from Garret's eyes the next morning

Finalist in the 2012 RONE Awards FOUR STARS from InD'tale Magazine February 2013 edition Top Pick Five Stars- The Romance Reviews Sexy, Cowboy, Stamos Walker runs a work release program for nonviolent male prisoners. When Joy Courtland shows up on his doorstep, he assumes she's the nanny. He hands her his son and rushes out to work. Joy, a convicted felon, bonds with little Dillon immediately. Stamos isn't happy when he returns and finds out Joy is not the nanny. While trying to get Joy back to prison through the proper channels, Stamos realizes Joy is not a killer. He doesn't want her to go back to prison. Stamos is willing to try everything he can to keep Joy safe even if it means marriage

Five years ago, hunky rodeo star Stetson Scott left town, never looking back, convinced he had no reason to stay. He unknowingly left behind more than just a love, so much more. Storm McCrory's heart was ripped out that same day. Now Stetson is back in town. He's shocked to find not just Storm McCrory living in poverty, but his son. The son he never knew. Both shunned by most of the town. Will Storm allow Stetson to help her and be a father to his son, or will her stubborn pride get in the way of their true love?

Hailed as the greatest novel of the Mexican Revolution, The Underdogs recounts the story of an illiterate but charismatic Indian peasant farmer’s part in the rebellion against Porfirio Díaz, and his subsequent loss of belief in the cause when the revolutionary alliance becomes factionalized. Azuela’s masterpiece is a timeless, authentic portrayal of peasant life, revolutionary zeal, and political disillusionment.

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.

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

During the Cold War, freedom of expression was vaunted as liberal democracy’s most cherished possession—but such freedom was put in service of a hidden agenda. In The Cultural Cold War, Frances Stonor Saunders reveals the extraordinary efforts of a secret campaign in which some of the most vocal exponents of intellectual freedom in the West were working for or subsidized by the CIA—whether they knew it or not. Called "the most comprehensive account yet of the [CIA’s] activities between 1947 and 1967" by the New York Times, the book presents shocking evidence of the CIA’s undercover program of cultural interventions in Western Europe and at home, drawing together declassified documents and exclusive interviews to expose the CIA’s astonishing campaign to deploy the likes of Hannah Arendt, Isaiah Berlin, Leonard Bernstein, Robert Lowell, George Orwell, and Jackson Pollock as weapons in the Cold War. Translated into ten languages, this classic work—now with a new preface by the author—is "a real contribution to popular understanding of the postwar period" (The Wall Street Journal), and its story of covert cultural efforts to win hearts and minds continues to be relevant today.