Well, I was diagnosed 24 years ago, and I still learned something new on every page.”—Michael Kinsley, Vanity Fair columnist and author of Old Age: A Beginner’s Guide Here is the book that John Vine and his wife, Joanne, wish they could have consulted when John was first diagnosed with Parkinson’s disease—a nontechnical, personal guide written from the patient’s perspective. Relying on his experiences over the past 12 years, John writes knowledgeably about all aspects of the disease. John also interviewed other Parkinson’s patients and their partners, whose stories and advice he includes throughout the book. “I wish we’d had John Vine’s book when my brother-in-law was diagnosed. The book is highly informative, unflinchingly honest, and reassuringly optimistic. It’s just what the doctor should have ordered.”—Cokie Roberts, best-selling author and political commentator on ABC News and NPR “John Vine details, in a compelling and accessible way, his experience with Parkinson’s disease. His book is an extraordinary guide to living successfully with Parkinson’s, and a must read for all who want to better understand the condition. Although diagnosed with Parkinson’s, my father lived an active and productive life until his death at age 94. As the book makes clear, while each patient’s journey is unique, common approaches are indispensable in treating the symptoms of the disease.”—Eric H. Holder, Jr. served as the 82nd Attorney General of the United States from 2009 to 2015 “John Vine has written the best primer I’ve ever read for newly diagnosed Parkinson’s patients and their families. It helps them cope with the shock of diagnosis, gives them (jargon-free) the scientific basics they need to know, describes the symptoms they may experience (making clear that every case is different) and catalogs the resources available to navigate living with Parkinson’s. John humanizes the book by describing his own experience and that of 22 other patients and their partners. I’d urge every neurologist to have copies of Vine’s primer on hand to help new PD on their journey forward.”—Morton Kondracke, author of Saving Milly: Love, Politics and Parkinson’s Disease and a member of the Founders' Council of the Michael J. Fox Foundation “My husband has PD, and I devoured this book. It’s wise, wonderfully readable, and, above all, helpful. Since John Vine has PD, he speaks with great authority about the challenges, both physical and psychological. If you have Parkinson’s, live with someone who has it, or just know someone battling the disease, A Parkinson’s Primer is for you.”—Lesley Stahl, award-winning television journalist on the CBS News program 60 Minutes “This is a remarkable book describing the personal experiences of many individuals, including the author, living with Parkinson’s disease. It captures the fact that although there are many possible symptoms in this disease, each person experiences different symptoms and copes with them in various ways. The thoughtful and insightful comments and coping strategies should be helpful for persons with PD, and their partners, regardless of the stage of the disease.”—Stephen Grill, MD, PhD, Director of the Parkinson’s & Movement Disorders Center of Maryland John M. Vine is a lawyer at Covington & Burling LLP in Washington, DC, where he is the senior member and former head of the firm’s employee benefits group. He was diagnosed with Parkinson’s in 2004.

    Digital technologies—with hardware, software, and networks at their core—will in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human.In The Second Machine Age MIT’s Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives.Amid this bounty will also be wrenching change. Professions of all kinds—from lawyers to truck drivers—will be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar.Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape.A fundamentally optimistic book, The Second Machine Age alters how we think about issues of technological, societal, and economic progress.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.Learn how to use a problem's "weight" against itself to:Break down seemingly complex data problems into simplified partsUse alternative data analysis techniques to examine themUse human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problemsLearn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

    Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.In AI Superpowers, Kai-Fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power.Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not.  But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.

    This volume is comprised of the years 1900 through 1959, and includes all of the best known cases of the period, as well as many more lesser-known murders, all presented in a compelling chronological narrative that takes the reader on a grisly journey through the blood-soaked avenues of early twentieth century crime. Featuring: The Peasenhall Murder. The Seal Chart Murder. The Atlanta Ripper. The Villisca Axe Murders. The Axeman of New Orleans. The Green Bicycle Case. Little Lord Fauntleroy. Hinterkaifeck Farm. The St. Aubin Street Massacre. The Wallace Case. The Atlas Vampire. The Brighton Trunk Crime. The Cleveland Torso Murderer. The Horror in Room 1046. Who Put Bella in the Wych Elm? The Pitchfork Murder. The Sodder Children. The Phantom Killer. The Black Dahlia. Somerton Man. The Grimes Sisters. The Boy in the Box. And Much More!

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

    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.

    Written by Susan McMurry, the Study Guide and Solutions Manual provide answers and explanations to all in-text and end-of-chapter exercises.

    Whenyou upgrade to Extra Class, you gain access to the entire Amateur Radio frequency spectrum. Ues this book to ace the top-level ham radio licensing exam. Our expert instruction will lead you through all of the knowledge you need to pass the exam: rules, specific operating skills and more advanced electronics theory.

    This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

    Product Condition: No Defects.

    Over the years sports debates have become muddled by many myths that do not match the numbers generated by those playing the games. In The Wages of Wins, the authors use layman's language and easy to follow examples based on their own academic research to debunk many of the most commonly held beliefs about sports.In this updated version of their book, these authors explain why Allen Iverson leaving Philadelphia made the 76ers a better team, why the Yankees find it so hard to repeat their success from the late 1990s, and why even great quarterbacks like Brett Favre are consistently inconsistent. The book names names, and makes it abundantly clear that much of the decision making of coaches and general managers does not hold up to an analysis of the numbers. Whether you are a fantasy league fanatic or a casual weekend fan, much of what you believe about sports will change after reading this book.

    The result is described as “a thought-provoking visual feast”.