The allure of money is especially strong. Coins represent real value. The warmth of silver and the weight of gold are irresistible to some. Coins travel throughout the world and through time itself, representing and absorbing history as they pass from one person to the next. Oh the stories coins could tell if they only had voices.Coin-collecting is a relaxing and inexpensive (although it can be very expensive!) hobby. If you buy properly, coins can be an excellent place to park your money for a rainy day, and if you buy the right coins and the market improves, you may even be able to make a profit on your collection. In fact, n"umismatics" (the fancy term for coin collecting) offers more riches than you may realize. This rewarding hobby also opens the door to some serious life skills, such asHistoryGeographyObservational skillsOrganizational skillsAnalytical tools.This book is designed to appeal to collectors at every level, from beginner to advanced. This is not a hardcore coin book; rather, "Coin Collecting For Dummies" is a great general reference that points you in different directions for further investigation. Perhaps the most important goal of this book is to get you excited - and to keep you excited - about coin collecting. "Coin Collecting For Dummies" covers all these topics and more: Deciding what to collectStoring your collection correctlyFinding out about repaired, restored, and recolored coinsPricing world coinsFinding a good coin dealerBuying at auctionUnderstanding that condition equals valueExploring the wild side with rare, expensive, and esoteric coinsSelling your coinsNo one needs coins, but if you decide to collect them, you certainly need this book. Coin collecting can sometimes be a confusing maze of choices sprinkled with little traps along the way. Sure, you can go it alone, but why not make your journey into numismatics a lot easier by picking the brain of an expert collector and learning from the mistakes of others.

    Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

    Written by two of the best-known names in categorical logic, Conceptual Mathematics is the first book to apply categories to the most elementary mathematics. It thus serves two purposes: first, to provide a key to mathematics for the general reader or beginning student; and second, to furnish an easy introduction to categories for computer scientists, logicians, physicists, and linguists who want to gain some familiarity with the categorical method without initially committing themselves to extended study.

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    The vision behind Clojure is of a radically simple language framework holding together a sophisticated collection of programming features. Learning Clojure involves much more than just learning the mechanics of the language. To really get Clojure you need to understand the ideas underlying this structure of framework and features. You need this book: an accessible introduction to Clojure that focuses on the ideas behind the language as well as the practical details of writing code.

    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

    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.

    More and more often, those companies look to technology consultants to fulfill their needs. There are real advantages to being a consultant. You make contacts with a lot of different people; you get exposure to many industries; and most important, unlike a software developer in the IT department for a brick-and-mortar company, as a technology consultant, you are the profit center…so long as you are billing. Consulting can be hugely rewarding—but it’s easy to fail if you are unprepared. To succeed, you need a mentor who knows the lay of the land. Aaron Erickson is your mentor, and this is your guidebook. Erickson has done it all—from Practice Leadership to the lowest level project work. In The Nomadic Developer, he brings together his hardwon insights on becoming successful and achieving success through tough times and relentless change. You’ll find 100% practical advice and real experiences—his own and annotations from those in the trenches. In addition, renowned consultants—such as David Chappell, Bruce Eckel, Deborah Kurata, and Ted Neward—share some of their hard-earned lessons. With this useful guidebook, you can Objectively assess whether the consultant’s life makes sense for you Break into thebusiness and build a career path that works Avoid the Seven Deadly Firms by identifying unscrupulous technology consultancies and avoiding their traps and pitfalls Understand the business models and mechanics that virtually all consulting firms use Master secret consulting success tips that are typically left unstated or overlooked Gain a competitive advantage by adding more value than your competitors Continue your professional development so you stay billable even during bad times Profit from both fixed-bid and time-and-materials projects Build a personal brand that improves your resiliency no matter what happens

    It demonstrates how to encourage, develop, and foster the processes which seem to come naturally to mathematicians.

    It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript.This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data.Learn HTML, CSS, JavaScript, and SVG basicsDynamically generate web page elements from your data—and choose visual encoding rules to style themCreate bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layoutsUse smooth, animated transitions to show changes in your dataIntroduce interactivity to help users explore data through different viewsCreate customized geographic maps with dataExplore hands-on with downloadable code and over 100 examples

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    Who was Grace Hopper? A software tester, workplace jester, cherished mentor, ace inventor, avid reader, naval leader—AND rule breaker, chance taker, and troublemaker. Grace Hopper coined the term “computer bug” and taught computers to “speak English,” and throughout her life succeeded in doing what no one had ever done before. Delighting in difficult ideas and in defying expectations, the insatiably curious Hopper truly is “Amazing Grace” . . . and a role model for science- and math-minded girls and boys.

    It offers a comprehensive and accessible treatment of the theory of algorithms and complexity—the elegant body of concepts and methods developed by computer scientists over the past 30 years for studying the performance and limitations of computer algorithms. The book is self-contained in that it develops all necessary mathematical prerequisites from such diverse fields such as computability, logic, number theory and probability.