He was one of the leading pioneers of the greatest revolution in twentieth-century science: quantum mechanics. The youngest theoretician ever to win the Nobel Prize for Physics, he was also pathologically reticent, strangely literal-minded and legendarily unable to communicate or empathize. Through his greatest period of productivity, his postcards home contained only remarks about the weather.Based on a previously undiscovered archive of family papers, Graham Farmelo celebrates Dirac's massive scientific achievement while drawing a compassionate portrait of his life and work. Farmelo shows a man who, while hopelessly socially inept, could manage to love and sustain close friendship.The Strangest Man is an extraordinary and moving human story, as well as a study of one of the most exciting times in scientific history.'A wonderful book . . . Moving, sometimes comic, sometimes infinitely sad, and goes to the roots of what we mean by truth in science.' Lord Waldegrave, Daily Telegraph

    With all our media now traveling a single network, an unprecedented potential is building for centralized control over what Americans see and hear. Could history repeat itself with the next industrial consolidation? Could the Internet—the entire flow of American information—come to be ruled by one corporate leviathan in possession of “the master switch”? That is the big question of Tim Wu’s pathbreaking book.As Wu’s sweeping history shows, each of the new media of the twentieth century—radio, telephone, television, and film—was born free and open. Each invited unrestricted use and enterprising experiment until some would-be mogul battled his way to total domination. Here are stories of an uncommon will to power, the power over information: Adolph Zukor, who took a technology once used as commonly as YouTube is today and made it the exclusive prerogative of a kingdom called Hollywood . . . NBC’s founder, David Sarnoff, who, to save his broadcast empire from disruptive visionaries, bullied one inventor (of electronic television) into alcoholic despair and another (this one of FM radio, and his boyhood friend) into suicide . . . And foremost, Theodore Vail, founder of the Bell System, the greatest information empire of all time, and a capitalist whose faith in Soviet-style central planning set the course of every information industry thereafter.Explaining how invention begets industry and industry begets empire—a progress often blessed by government, typically with stifling consequences for free expression and technical innovation alike—Wu identifies a time-honored pattern in the maneuvers of today’s great information powers: Apple, Google, and an eerily resurgent AT&T. A battle royal looms for the Internet’s future, and with almost every aspect of our lives now dependent on that network, this is one war we dare not tune out.Part industrial exposé, part meditation on what freedom requires in the information age, The Master Switch is a stirring illumination of a drama that has played out over decades in the shadows of our national life and now culminates with terrifying implications for our future.

    We Are the Nerds is a riveting look deep inside this captivating, maddening enterprise–whose army of highly engaged (obsessed?) users have been credited with everything from solving cold case crimes to seeding alt-right fury and helping to land Donald Trump in the White House. We Are the Nerds is a gripping start-up business narrative: the story of how Reddit’s founders, Steve Huffman and Alexis Ohanian, rose up from their suburban childhoods to become millionaires and create an icon of the digital age–before seeing the site engulfed in controversies and nearly losing control of it for good. Based on Christine Lagorio’s exclusive access to founders Alexis Ohanian and Steve Huffman, We Are the Nerds is also a compelling exploration of the way we all communicate today–and how we got here. Reddit and its users have become a mirror of the Internet: it has dingy corners, shiny memes, malicious trolls, and a sometimes heart-melting ability to connect people across cultures, oceans, and ideological divides.

    If you're a business or IT professional, this short hands-on guide teaches you how to pull and transform data with SQL in significant ways. You will quickly master the fundamentals of SQL and learn how to create your own databases.Author Thomas Nield provides exercises throughout the book to help you practice your newfound SQL skills at home, without having to use a database server environment. Not only will you learn how to use key SQL statements to find and manipulate your data, but you'll also discover how to efficiently design and manage databases to meet your needs.You'll also learn how to:Explore relational databases, including lightweight and centralized modelsUse SQLite and SQLiteStudio to create lightweight databases in minutesQuery and transform data in meaningful ways by using SELECT, WHERE, GROUP BY, and ORDER BYJoin tables to get a more complete view of your business dataBuild your own tables and centralized databases by using normalized design principlesManage data by learning how to INSERT, DELETE, and UPDATE records

     With the creation of their two radically different processes—the Daguerreotype and the Talbotype—these two giants of early photography  changed the world and how we see it.Drawing on a wide range of original, contemporary sources and featuring plates in colour, sepia and black and white, many of them rare or previously unseen, Capturing the Light by Roger Watson and Helen Rappaport charts an extraordinary  tale of genius, rivalry and human resourcefulness in the quest to produce the world's first photograph.

    It is the definitive reference guide, now in a second edition. Although the first edition was written in 1978, it continues to be a worldwide best-seller. This second edition brings the classic original up to date to include the ANSI standard. From the Preface: We have tried to retain the brevity of the first edition. C is not a big language, and it is not well served by a big book. We have improved the exposition of critical features, such as pointers, that are central to C programming. We have refined the original examples, and have added new examples in several chapters. For instance, the treatment of complicated declarations is augmented by programs that convert declarations into words and vice versa. As before, all examples have been tested directly from the text, which is in machine-readable form. As we said in the first preface to the first edition, C "wears well as one's experience with it grows." With a decade more experience, we still feel that way. We hope that this book will help you to learn C and use it well.

    At the center of this change are Apple and Google, two companies whose philosophies, leaders, and commercial acumen have steamrolled the competition. In the age of the Android and the iPad, these corporations are locked in a feud that will play out not just in the marketplace but in the courts and on screens around the world.     Fred Vogelstein has reported on this rivalry for more than a decade and has rare access to its major players. In Dogfight, he takes us into the offices and board rooms where company dogma translates into ruthless business; behind outsize personalities like Steve Jobs, Apple’s now-lionized CEO, and Eric Schmidt, Google’s executive chairman; and inside the deals, lawsuits, and allegations that mold the way we communicate. Apple and Google are poaching each other’s employees. They bid up the price of each other’s acquisitions for spite, and they forge alliances with major players like Facebook and Microsoft in pursuit of market dominance.     Dogfight reads like a novel: vivid nonfiction with never-before-heard details. This is more than a story about what devices will replace our phones and laptops. It’s about who will control the content on those devices and where that content will come from—about the future of media in Silicon Valley, New York, and Hollywood.

    More importantly, this book provides a framework for thinking about computer organization and design that will enable the reader to continue the lifetime of learning necessary for staying at the forefront of this competitive discipline. --John Crawford Intel Fellow Director of Microprocessor Architecture, Intel The performance of software systems is dramatically affected by how well software designers understand the basic hardware technologies at work in a system. Similarly, hardware designers must understand the far reaching effects their design decisions have on software applications. For readers in either category, this classic introduction to the field provides a deep look into the computer. It demonstrates the relationship between the software and hardware and focuses on the foundational concepts that are the basis for current computer design. Using a distinctive learning by evolution approach the authors present each idea from its first principles, guiding readers through a series of worked examples that incrementally add more complex instructions until they ha

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    Through computers, the Internet, the media, and even our daily newspapers, we are awash in a seemingly endless stream of charts, maps, infographics, diagrams, and data. Visual Explanations is a navigational guide through this turbulent sea of information. The book is an essential reference for anyone involved in graphic, web, or multimedia design, as well as for educators and lecturers who use graphics in presentations or classes.Jacket design: Dmitry Krasny.Other artwork by Bonnie Scranton, Dmitry Krasny, and Weilin Wu.

    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.

    Sarnoff was convinced that Armstrong’s inventions had the power to change the way societies communicated with each other forever. He would become a visionary captain of the media industry, even predicting the advent of the Internet. In the mid-1930s, however, when Armstrong suspected Sarnoff of orchestrating a cadre of government officials to seize control of the FM airwaves, he committed suicide. Sarnoff had a very different view of who his friend’s enemies were. Many corrupt politicians and corporations saw in Armstrong’s inventions the opportunity to commodify our most ubiquitous natural resource—the air. This early alliance between high tech and business set the precedent for countless legal and industrial battles over broadband and licensing bandwidth, many of which continue to influence policy and debate today.

    It was the dawn of a new era in artificial intelligence: a machine capable of beating the reigning human champion at this most cerebral game. That moment was more than a century in the making, and in this breakthrough book, Kasparov reveals his astonishing side of the story for the first time. He describes how it felt to strategize against an implacable, untiring opponent with the whole world watching, and recounts the history of machine intelligence through the microcosm of chess, considered by generations of scientific pioneers to be a key to unlocking the secrets of human and machine cognition. Kasparov uses his unrivaled experience to look into the future of intelligent machines and sees it bright with possibility. As many critics decry artificial intelligence as a menace, particularly to human jobs, Kasparov shows how humanity can rise to new heights with the help of our most extraordinary creations, rather than fear them. Deep Thinking is a tightly argued case for technological progress, from the man who stood at its precipice with his own career at stake.

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    Simply stated, it asks whether every problem whose solution can be quickly checked by computer can also be quickly solved by computer. The Golden Ticket provides a nontechnical introduction to P-NP, its rich history, and its algorithmic implications for everything we do with computers and beyond. Lance Fortnow traces the history and development of P-NP, giving examples from a variety of disciplines, including economics, physics, and biology. He explores problems that capture the full difficulty of the P-NP dilemma, from discovering the shortest route through all the rides at Disney World to finding large groups of friends on Facebook. The Golden Ticket explores what we truly can and cannot achieve computationally, describing the benefits and unexpected challenges of this compelling problem.