Building on his classic first edition, Theodore S. Rappaport reviews virtually every important new wireless standard and technological development, including W-CDMA, cdma2000, UMTS, and UMC 136/EDGE; IEEE 802.11 and HIPERLAN WLANs; Bluetooth, LMDS, and more. Includes dozens of practical new examples, solved step by step.

    The authors, who developed the metaobject protocol andwho were among the group that developed CLOS, introduce this new approach toprogramming language design, describe its evolution and design principles, andpresent a formal specification of a metaobject protocol for CLOS.Kiczales, desRivi?res, and Bobrow show that the "art of metaobject protocol design" lies increating a synthetic combination of object-oriented and reflective techniques thatcan be applied under existing software engineering considerations to yield a newapproach to programming language design that meets a broad set of designcriteria.One of the major benefits of including the metaobject protocol inprogramming languages is that it allows users to adjust the language to better suittheir needs. Metaobject protocols also disprove the adage that adding moreflexibility to a programming language reduces its performance. In presenting theprinciples of metaobject protocols, the authors work with actual code for asimplified implementation of CLOS and its metaobject protocol, providing anopportunity for the reader to gain hands-on experience with the design process. Theyalso include a number of exercises that address important concerns and openissues.Gregor Kiczales and Jim des Rivi?res, are Members of the Research Staff, andDaniel Bobrow is a Research Fellow, in the System Sciences Laboratory at Xerox PaloAlto Research Center.

    Includes many useful applications.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    Jon C. Thompson presents the latest data in thoroughly updated diagnostic and treatment algorithms for all conditions while preserving the popular at-a-glance table format from the previous edition. You'll get even more art from the Netter Collection as well as new radiologic images that visually demonstrate the key clinical correlations and applications of anatomical imaging. For a fast, memorable review of orthopaedic anatomy, this is a must-have.Maintains the popular at-a-glance table format that makes finding essential information quick and convenient.Contains useful clinical information on disorders, trauma, history, physical exam, radiology, surgical approaches, and minor procedures in every chapter.Lists key information on bones, joints, muscles, and nerves in tables correlate to each Netter image.Highlights key material in different colors-pearls in green and warnings in red-for easy reference. Features both plain film and advanced radiographic (CT and MRI) images, along with cross-sectional anatomic plates for an even more thorough visual representation of the material.Includes additional common surgical approaches to give you a broader understanding of techniques.Incorporates reorganized Complicated Arthology tables for large joints, such as the shoulder, knee, and hip, for increased clarity and to incorporate new artwork and additional clinical correlations.Reflects new data and current diagnostic and treatment techniques through updates to the Disorders and Fractures sections and the Physical Exam and Anatomic tables in each chapter.Presents the very latest developments in the field through thoroughly updated diagnostic and treatment algorithms for all clinical conditions.

    It includes detailed examples and exercises.

    In Inventing the Internet, Janet Abbate recounts the key players and technologies that allowed the Internet to develop; but her main focus is always on the social and cultural factors that influenced the Internets design and use. The story she unfolds is an often twisting tale of collaboration and conflict among a remarkable variety of players, including government and military agencies, computer scientists in academia and industry, graduate students, telecommunications companies, standards organizations, and network users.The story starts with the early networking breakthroughs formulated in Cold War think tanks and realized in the Defense Department's creation of the ARPANET. It ends with the emergence of the Internet and its rapid and seemingly chaotic growth. Abbate looks at how academic and military influences and attitudes shaped both networks; how the usual lines between producer and user of a technology were crossed with interesting and unique results; and how later users invented their own very successful applications, such as electronic mail and the World Wide Web. She concludes that such applications continue the trend of decentralized, user-driven development that has characterized the Internet's entire history and that the key to the Internet's success has been a commitment to flexibility and diversity, both in technical design and in organizational culture.

    will mean for us, it also forces readers to challenge their preconceptions. And it manages to do all this in a way that is both entertaining and engaging.” —The New York Times As we approach a great turning point in history when technology is poised to redefine what it means to be human, The Fourth Age offers fascinating insight into AI, robotics, and their extraordinary implications for our species.In The Fourth Age, Byron Reese makes the case that technology has reshaped humanity just three times in history: - 100,000 years ago, we harnessed fire, which led to language. - 10,000 years ago, we developed agriculture, which led to cities and warfare. - 5,000 years ago, we invented the wheel and writing, which lead to the nation state. We are now on the doorstep of a fourth change brought about by two technologies: AI and robotics. The Fourth Age provides extraordinary background information on how we got to this point, and how—rather than what—we should think about the topics we’ll soon all be facing: machine consciousness, automation, employment, creative computers, radical life extension, artificial life, AI ethics, the future of warfare, superintelligence, and the implications of extreme prosperity. By asking questions like “Are you a machine?” and “Could a computer feel anything?”, Reese leads you through a discussion along the cutting edge in robotics and AI, and, provides a framework by which we can all understand, discuss, and act on the issues of the Fourth Age, and how they’ll transform humanity.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Through the lives of Geoff Hinton and other major players, Metz explains this transformative technology and makes the quest thrilling.--Walter Isaacson, author of The Code Breaker Recipient of starred reviews in both Kirkus and Library JournalTHE UNTOLD TECH STORY OF OUR TIMEWhat does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create?With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.Long dismissed as a technology of the distant future, artificial intelligence was a project consigned to the fringes of the scientific community. Then two researchers changed everything. One was a sixty-four-year-old computer science professor who didn't drive and didn't fly because he could no longer sit down--but still made his way across North America for the moment that would define a new age of technology. The other was a thirty-six-year-old neuroscientist and chess prodigy who laid claim to being the greatest game player of all time before vowing to build a machine that could do anything the human brain could do.They took two very different paths to that lofty goal, and they disagreed on how quickly it would arrive. But both were soon drawn into the heart of the tech industry. Their ideas drove a new kind of arms race, spanning Google, Microsoft, Facebook, and OpenAI, a new lab founded by Silicon Valley kingpin Elon Musk. But some believed that China would beat them all to the finish line.Genius Makers dramatically presents the fierce conflict between national interests, shareholder value, the pursuit of scientific knowledge, and the very human concerns about privacy, security, bias, and prejudice. Like a great Victorian novel, this world of eccentric, brilliant, often unimaginably yet suddenly wealthy characters draws you into the most profound moral questions we can ask. And like a great mystery, it presents the story and facts that lead to a core, vital question:How far will we let it go?

    Numerous examples are provided to reinforce the concepts and relevant case studies illustrate the concepts and mechanisms described.

    Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.