Every complete program uses a consistent style, and as programs are analyzed, styles and standards are further explained. Whenever possible, the authors develop the principle of a subject before they introduce the language implementation so the student understands the concept before dealing with the nuances of C++. In addition, a vast array of figures and tables visually reinforce key concepts. By integrating software engineering principles and encouraging the student to resist the temptation to immediately code, the text builds a solid foundation in problem solving.

    This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

    The author was—at first.When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind.Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold.This book will help you understand:• Why big data is important to you and your organization• What technology you need to manage it• How big data could change your job, your company, and your industry• How to hire, rent, or develop the kinds of people who make big data work• The key success factors in implementing any big data project• How big data is leading to a new approach to managing analyticsWith dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.

    Unlike traditional empirical psychology, it is more oriented toward the how than the what. An experiment in mind design is more likely to be an attempt to build something and make it work--as in artificial intelligence--than to observe or analyze what already exists. Mind design is psychology by reverse engineering.When Mind Design was first published in 1981, it became a classic in the then-nascent fields of cognitive science and AI. This second edition retains four landmark essays from the first, adding to them one earlier milestone (Turing's Computing Machinery and Intelligence) and eleven more recent articles about connectionism, dynamical systems, and symbolic versus nonsymbolic models. The contributors are divided about evenly between philosophers and scientists. Yet all are philosophical in that they address fundamental issues and concepts; and all are scientific in that they are technically sophisticated and concerned with concrete empirical research.ContributorsRodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin Minsky, Allen Newell, Zenon W. Pylyshyn, William Ramsey, Jay F. Rosenberg, David E. Rumelhart, John R. Searle, Herbert A. Simon, Paul Smolensky, Stephen Stich, A.M. Turing, Timothy van Gelder

    Illuminating the confidential process of academic evaluation, this text reveals the inner workings of this secretive, powerful and peculiar world.

    Now in its Eleventh Edition, this classic atlas continues to provide students with accurate anatomical images presented in a pedagogically effective, clinically relevant manner. This new edition has been revised to include 100 additional four-color illustrations, additional orientation drawings, and updated terminology.This edition is packaged with a student version of Dynamic Human Anatomy, an interactive electronic supplement which includes 800 images from the Eleventh Edition of Grant's, 8 video clips from Acland's DVD Atlas of Human Anatomy, and 100 USMLE-style review questions in anatomy.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    As it happens, those of us who concluded “the guy’s just nuts” were right: a fair number of those impossible-to-get-along-with employees actually do have full-fledged personality disorders. In Toxic Coworkers, the authors help us to recognize a variety of common personality traits and disorders, understand how they come about, and learn to develop effective strategies for dealing with them. So the next time the narcissist who runs the front desk is bugging you, or you need to squeeze a favor out of the schizoid who handles inventory, you’ll know exactly what to do.

    Beset from all sides by a never-ending barrage of media, how can we ensure that the most accurate information emerges and is heeded? In this book, Cass R. Sunstein develops a deeply optimisticunderstanding of the human potential to pool information, and to use that knowledge to improve our lives.In an age of information overload, it is easy to fall back on our own prejudices and insulate ourselves with comforting opinions that reaffirm our core beliefs. Crowds quickly become mobs. The justification for the Iraq war, the collapse of Enron, the explosion of the space shuttle Columbia--all ofthese resulted from decisions made by leaders and groups trapped in information cocoons, shielded from information at odds with their preconceptions. How can leaders and ordinary people challenge insular decision making and gain access to the sum of human knowledge?Stunning new ways to share and aggregate information, many Internet-based, are helping companies, schools, governments, and individuals not only to acquire, but also to create, ever-growing bodies of accurate knowledge. Through a ceaseless flurry of self-correcting exchanges, wikis, coveringeverything from politics and business plans to sports and science fiction subcultures, amass--and refine--information. Open-source software enables large numbers of people to participate in technological development. Prediction markets aggregate information in a way that allows companies, rangingfrom computer manufacturers to Hollywood studios, to make better decisions about product launches and office openings. Sunstein shows how people can assimilate aggregated information without succumbing to the dangers of the herd mentality--and when and why the new aggregation techniques are soastoundingly accurate.In a world where opinion and anecdote increasingly compete on equal footing with hard evidence, the on-line effort of many minds coming together might well provide the best path to infotopia.

    In most subjects, the majority of twelfth-grade Black students do not have even a "partial mastery" of the skills and knowledge that the authoritative National Assessment of Educational Progress calls "fundamental for proficient work" at their grade. No Excuses marshals facts to examine the depth of the problem, the inadequacy of conventional explanations, and the limited impact of Title I, Head Start, and other familiar reforms. Its message, however, is one of hope: Scattered across the country are excellent schools getting terrific results with high-needs kids. These rare schools share a distinctive vision of what great schooling looks like and are free of many of the constraints that compromise education in traditional public schools. In a society that espouses equal opportunity we still have a racially identifiable group of educational have-nots—young African Americans and Latinos whose opportunities in life will almost inevitably be limited by their inadequate education. When students leave high school without high school skills, their futures—and that of the nation—are in jeopardy. With successful schools already showing the way, no decent society can continue to turn a blind eye to such racial and ethnic inequality.

    Today’s "machine-learning" systems, trained by data, are so effective that we’ve invited them to see and hear for us?and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole?and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands.The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software.In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story.The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

    This and other developments will prompt utilities to undergo the largest changes in their history. Smart Power examines the many facets of this unprecedented transformation. This enlightening book begins with a look back on the deregulatory efforts of the 1990s and their gradual replacement by concerns over climate change, promoting new technologies, and developing stable prices and supplies. In thorough but non-technical terms it explains the revolutionary changes that the Smart Grid is bringing to utility operations. It also examines the options for low-carbon emissions along with the real-world challenges the industry and its regulators must face as the industry retools and finances its new sources and systems. Throughout the book, Peter Fox-Penner provides insights into the policy choices and regulatory reform needed to face these challenges. He not only weighs the costs and benefits of every option, but presents interviews with informed experts, including economists, utility CEOs, and engineers. He gives a brief history of the development of the current utility business model and examines possible new business models that are focused on energy efficiency.Smart Power explains every aspect of the coming energy revolution for utilities in lively prose that will captivate even the most techno-phobic readers.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    This book presents all the important test design techniques in a single place and in a consistent and easy-to-digest format. An immediately useful handbook for test engineers, developers, quality assurance professionals and requirements and systems analysts, it enables you to: choose the best test case design; find software defects in less time and with fewer resources; and develop optimal strategies that help reduce the likelihood of costly errors. It also assists you in estimating the effort, time and cost of good testing. included, helping you to fully understand the practical applications of these techniques. From well-established techniques such as equivalence classes, boundary value analysis, decision tables and state-transition diagrams, to new techniques like use case testing, pairwise testing and exploratory testing, the book is a usful resource for testing professionals seeking to improve their skills and a handy reference for college-level courses in software test design.

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.