Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniquesTopics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

    More than 250 challenging practice questions have been completely revised to closely model the format, tone, topics, and difficulty of the real exam. An integrated study system based on proven pedagogy, exam coverage includes step-by-step exercises, special Exam Watch notes, On-the-Job elements, and Self Tests with in-depth answer explanations to help reinforce and teach practical skills.Praise for the author:"Finally A Java certification book that explains everything clearly. All you need to pass the exam is in this book."--Solveig Haugland, Technical Trainer and Former Sun Course Developer"Who better to write a Java study guide than Kathy Sierra, the reigning queen of Java instruction? Kathy Sierra has done it again--here is a study guide that almost guarantees you a certification "--James Cubeta, Systems Engineer, SGI"The thing I appreciate most about Kathy is her quest to make us all remember that we are teaching people and not just lecturing about Java. Her passion and desire for the highest quality education that meets the needs of the individual student is positively unparalleled at SunEd. Undoubtedly there are hundreds of students who have benefited from taking Kathy's classes."--Victor Peters, founder Next Step Education & Software Sun Certified Java Instructor"I want to thank Kathy for the EXCELLENT Study Guide. The book is well written, every concept is clearly explained using a real life example, and the book states what you specifically need to know for the exam. The way it's written, you feel that you're in a classroom and someone is actually teaching you the difficult concepts, but not in a dry, formal manner. The questions at the end of the chapters are also REALLY good, and I am sure they will help candidates pass the test. Watch out for this Wickedly Smart book."-Alfred Raouf, Web Solution Developer, Kemety.Net"The Sun Certification exam was certainly no walk in the park but Kathy's material allowed me to not only pass the exam, but Ace it "--Mary Whetsel, Sr. Technology Specialist, Application Strategy and Integration, The St. Paul Companies

    A golden opportunity. A nightmare he can’t escape.If Arthur Mallory plays one more steaming pile of garbage disguised as a virtual role-playing game, he might hang up his gaming hat for good. When he discovers the new “deep dive” game Worlds Unbound, he decides it might be worth a try. If it sucks, he'll just return it the next day and cancel the financing.The game is everything he could have wanted, even without elves or dwarves or character creation. The magic system has him hooked.He’s willing to overlook the mangled sleep schedule and crippling nightmares if it means feeling that power running through his veins. But when an accident leaves his headset broken, he realizes he might be cut off from the magic – and the game – for good.He’s not going to let that happen.

    For more than a decade, well-known consultant and business professor Wayne Winston has been teaching corporate clients and MBA students the most effective ways to use Microsoft Excel for data analysis, modeling, and decision making. Now this award-winning educator shares the best of his classroom experience in this practical, business-focused guide. Each chapter advances your data analysis and modeling expertise using real-world examples and learn-by-doing exercises. You also get all the book’s problem-and-solution files on CD—for all the practice you need to solve complex problems and work smarter with Excel.Learn how to solve real business problems with Excel!Create best, worst, and most-likely scenarios for sales Calculate how long it would take to recoup a project’s startup costs Plan personal finances, such as computing loan terms or saving for retirement Estimate a product’s demand curve Simulate stock performance over a year Determine which product mix will yield the greatest profits Interpret the effects of price and advertising on sales Assign a dollar value to customer loyalty Manage inventory and order quantities with precision Create customer service queues with short wait times Estimate the probabilities of equipment failure Model business uncertainties Get new perspectives on data with PivotTable dynamic views Help predict quarterly revenue, outcomes of sporting events, presidential elections, and more! On the CD:Practice files for all the book’s exercises Solutions for problem sets Fully searchable eBook A Note Regarding the CD or DVDThe print version of this book ships with a CD or DVD. For those customers purchasing one of the digital formats in which this book is available, we are pleased to offer the CD/DVD content as a free download via O'Reilly Media's Digital Distribution services. To download this content, please visit O'Reilly's web site, search for the title of this book to find its catalog page, and click on the link below the cover image (Examples, Companion Content, or Practice Files). Note that while we provide as much of the media content as we are able via free download, we are sometimes limited by licensing restrictions. Please direct any questions or concerns to booktech@oreilly.com.

    

    You'll read about humans who are invisible to computers, how a default password once caused a zombie apocalypse and why airlines sometimes give away free tickets. This is also a book on how to prevent, avoid and reduce the impact of such problems. Our lives are increasingly tracked, monitored and categorised by software, driving a flood of information into the vast sea of big data. In this brave new world, humans can't cope with information overload. Governments and companies alike rely on computers to automatically detect fraud, predict behaviour and enforce laws. Inflexible automatons, barely smarter than a fridge, now make life-changing decisions. Clever marketing tricks us into believing that phones, TV sets and even cars are somehow smart. Yet all those computer systems were created by people - people who are well-meaning but fallible and biased, clever but forgetful, and who have grand plans but are pressed for time. Digitising a piece of work doesn't mean there will be no mistakes, but instead guarantees that when mistakes happen, they'll run at a massive scale. The next time you bang your head against a digital wall, the stories in this book will help you understand better what's going on and show you where to look for problems. If nothing else, when it seems as if you're under a black-magic spell, these stories will at least allow you to see the lighter side of the binary chaos. For people involved in software delivery, this book will help you find more empathy for people suffering from our mistakes, and discover heuristics to use during analysis, development or testing to make your software less error prone. <

    The focus of this text is to solve real-world problems using the latest AI techniques. Intelligent systems like expert systems, fuzzy systems, artificial neural networks, genetic algorithms and ant colony systems are discussed in detail with case studies to facilitate in- depth understanding. Since the ultimate goal of AI is the construction of programs to solve problems, an entire chapter has been devoted to the programming languages used in AI problem solving. The theory is well supported by a large number of illustrations and end-chapter exercises. With its comprehensive coverage of the subject in a clear and concise manner this text would be extremely useful not only for undergraduate students, but also to postgraduate students.

    With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.Learn how to apply the tidy text format to NLPUse sentiment analysis to mine the emotional content of textIdentify a document's most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R's tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

    Complete with exclusive photos and stories from his childhood as well as lots of behind-the-scenes fun, Austin's first official book will give you the glimpse into his life you can't get by following him on Twitter. Mahomies, this book is for you!

    Super-intelligence. Upgrades. In book two of The Obsolescence Trilogy, James, Alexa, and the rest of the survivors are coming to terms with the dizzying new reality that they’ve been forced to live in. They soon discover that everyone still alive must face a seemingly impossible choice: either betray their very humanity to survive or watch civilization retreat into a stone age before dwindling out of existence altogether. Set in the near future, Upgrade is the second book in The Obsolescence Trilogy. Plausible, fast-paced and full of rich, insightful characters, this sci-fi thriller will keep you up well past your bedtime! Continue your journey through the world of obsolescence today!

    Today, Stephen Hawking believes that AI “could spell the end of the human race.” At the very least, its development raises complicated moral issues with powerful real-world implications—for us and for our machines.In this volume, recording artist Brian Eno proposes that we’re already part of an AI: global civilization, or what TED curator Chris Anderson elsewhere calls the hive mind. And author Pamela McCorduck considers what drives us to pursue AI in the first place.On the existential threat posed by superintelligent machines, Steven Pinker questions the likelihood of a robot uprising. Douglas Coupland traces discomfort with human-programmed AI to deeper fears about what constitutes “humanness.” Martin Rees predicts the end of organic thinking, while Daniel C. Dennett explains why he believes the Singularity might be an urban legend.Provocative, enriching, and accessible, What to Think About Machines That Think may just be a practical guide to the not-so-distant future.

    To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

    What hope do we have against superintelligent machines? But we aren't really on the path to developing intelligent machines. In fact, we don't even know where that path might be.A tech entrepreneur and pioneering research scientist working at the forefront of natural language processing, Erik Larson takes us on a tour of the landscape of AI to show how far we are from superintelligence, and what it would take to get there. Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world. We haven't a clue how to program this kind of intuitive reasoning, known as abduction. Yet it is the heart of common sense. That's why Alexa can't understand what you are asking, and why AI can only take us so far.Larson argues that AI hype is both bad science and bad for science. A culture of invention thrives on exploring unknowns, not overselling existing methods. Inductive AI will continue to improve at narrow tasks, but if we want to make real progress, we will need to start by more fully appreciating the only true intelligence we know--our own.