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    Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior.How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions.It's time to start thinking big.In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate.Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    

    Human beings have long been both fascinated and appalled by randomness. On the one hand, we love the thrill of a surprise party, the unpredictability of a budding romance, or the freedom of not knowing what tomorrow will bring. We are inexplicably delighted by strange coincidences and striking similarities. But we also hate uncertainty's dark side. From cancer to SARS, diseases strike with no apparent pattern. Terrorists attack, airplanes crash, bridges collapse, and we never know if we'll be that one in a million statistic. We are all constantly faced with situations and choices that involve randomness and uncertainty. A basic understanding of the rules of probability theory, applied to real-life circumstances, can help us to make sense of these situations, to avoid unnecessary fear, to seize the opportunities that randomness presents to us, and to actually enjoy the uncertainties we face. The reality is that when it comes to randomness, you can run, but you can't hide. So many aspects of our lives are governed by events that are simply not in our control. In this entertaining yet sophisticated look at the world of probabilities, author Jeffrey Rosenthal--an improbably talented math professor--explains the mechanics of randomness and teaches us how to develop an informed perspective on probability.

    

    Have you ever opened a Kindle book to find that the font started out way too small or way too large? Have you tried to change to a different font while reading and discovered you couldn't? Have you jumped to a new chapter in a Kindle book and seen that the chapter heading lost its formatting? Has a Kindle completely ignored formatting you knew was in the book? According to Amazon, the simplest way to publish your Kindle book is to upload an HTML file you've saved from Microsoft Word or another app. By itself, that method can bring you maybe 80% of the way to a well-formatted, trouble-free ebook. But what about the other 20%? In this follow-up to his bestselling -From Word to Kindle, - Aaron Shepard takes your saved HTML as a starting point and tells how to quickly tweak and tune it to avoid common problems. Assuming no knowledge of HTML, he introduces the basics of the language, then reveals how to use find-and-replace and macros to touch up an entire book in seconds! If you're serious about Kindle publishing and you're technically inclined -- but not a full-fledged geek -- Aaron provides the tips you need to bring your Kindle book to the next level, making it something truly to be proud of. ///////////////////////////////////////////////// Aaron Shepard is a foremost proponent of the new business of profitable self publishing, which he has practiced and helped develop since 1998. He is the author of -Aiming at Amazon, - -POD for Profit, - -Perfect Pages, - and Amazon's #1 and #2 bestselling paid books on Kindle formatting, -From Word to Kindle- and -Pictures on Kindle.- ///////////////////////////////////////////////// CONTENTS Getting Started 1 WORKING WITH HTML HTML and Kindle HTML Export HTML Editing HTML Processing HTML Basics HTML Checking HTML Cleanup HTML Testing 2 HTML FIXES Fixes for Fonts Fixes for Paragraphs Fixes for Headings Fixes for Line Breaking Fixes for Pictures Fixes for Navigation ///////////////////////////////////////////////// SAMPLE Here are some of the things you can accomplish through changes in HTML. * Adjust bookmarks so headings retain proper formatting when jumped to. * Remove settings that stop the user from choosing their own. * Keep fonts from appearing much too small or much too large when the book is opened. * Make sure indents and other spacing stays relative to larger and smaller font sizes. * Avoid line breaks that leave short words dangling at the ends of lines or paragraphs. * Make up for features lost in translation from your word processor, like nonbreaking hyphens. * Stop -ghost hyphens- from appearing in the middle of words. * Keep pages of text from disappearing for some users. * Prevent the Kindle from applying its own defaults in place of your settings.

    They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.

    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.

    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

    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.

    And Harry J.Friedman is very good at it. Someone once said, "Harry is the kind of guy who would sell someone a kickstand and then a $3,000 bicycle to go on top of it. "What makes him and his bookon retail selling so unique is his uncanny ability to turn shoppers into buyers, and teach others how to do the same. "No Thanks, I'm Just Looking" will give you the inside scoop on how to skyrocket your career with a system of practical money-making steps that are easy to learn and fun to use. No theoretical mumbojumbo oroutdated 1940s methodologies.Unlike most sales trainers who are obsessed with handling objections and tricky closing techniques, Harryconcentrates more on "front-end selling." He'll show you how to get past customer resistance - that "I don't like you and I don't trust you" defensive shield thatcustomers are seemingly born with. And he'll show you how to probe more effectively so you can find outexactly what your customers want so that all you have to do is deliver it. The essence of front-end selling is having your customers say "I'11 take it" before you everhave to ask "Will you buy it?"The fun really begins, however, when you learn his unique version of the trial close - how to routinely add on additional merchandise, while you're closing thesale on the main item. This will put you in control of the sale and set you up for the final step which will reduce returns, build referrals and increase your repeat business.This book will save you countless hours of trial and error experience and allow you to focus on the things that really work. It will become your retail bible as you read it over and over. And each time you do,you'll turn up another batch of gems that just keep making you money.

    Employing an irresistible cast of dragon-riding Vikings, lizard-throwing giants, and feuding aliens, the renowned illustrator Grady Klein and the award-winning statistician Alan Dabney teach you how to collect reliable data, make confident statements based on limited information, and judge the usefulness of polls and the other numbers that you're bombarded with every day. If you want to go beyond the basics, they've created the ultimate resource: "The Math Cave," where they reveal the more advanced formulas and concepts.Timely, authoritative, and hilarious, The Cartoon Introduction to Statistics is an essential guide for anyone who wants to better navigate our data-driven world.

    In this informative and motivating book, you'll discover: What it means to be The Innovative Admin How you can unleash your mind to think innovatively Techniques you can use to bring out the administrative leader inside you Ways you can tap into your creativity and initiative to get ahead The Innovative Admin empowers you to become the best you can be by enhancing your capacity for innovation. Read it and grow your administrative career to exciting new levels. Innovation isn't just for technology - it's the key to your career future.

    Jones and George are dedicated to the challenge of "Making It Real" for students. The authors present management in a way that makes its relevance obvious even to students who might lack exposure to a "real-life" management context. This is accomplished thru a diverse set of examples, and the unique, and most popular feature of the text, the "Manager as a Person" Chapter 2. This chapter discusses managers as real people with their own personalities, strengths, weaknesses, opportunities, and problems and this theme is carried thru the remaining chapters. This text also discusses the importance of management competencies--the specific set of skills, abilities, and experiences that gives one manager the ability to perform at a higher level than another in a specific context. The themes of diversity, ethics, globalization, and information technology are integrated throughout.