This work includes differential forms and the elegant forms of Maxwell's equations, and a chapter on probability and statistics. It also illustrates and proves mathematical relations.

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

    get this book now!*** Do you wonder about the future... what things will be like some day, how long it might take, and what we can do about it? Substantive yet imaginative, readable, occasionally humorous, and science oriented, this book proposes possible future scenarios spanning from the current century to nearly eternity. Most chapters offer a concluding section with recommendations and often, agree or disagree, the author's occasionally inimitable opinions. Some of the recommended actions can be done by individuals, others by nations or other groups, and still others by the entire world. Over 300 references. Discover "What it Means That an Hour’s Work Yields a Week’s Food" (chapter 1). Foresee the "Teeming Cities of Mars" (chap. 21). Learn why it’s "Keyboards Yesterday, Mind Reading Tomorrow" (chap. 3). Have you ever wondered — "Will Artificial Intelligence Threaten Civilization?" (See chap. 12.) Find out what happens "When Genomes Get Cheap" (chap. 6). Prepare for an "Asteroid Apocalypse" (chap. 25). Explore why you would benefit from "Wiki-Wiki-Wikipedia" (chap. 4). How we will "Live Anywhere, Work Anywhere Else" (chap. 2). Realize how the future "Tastes Like the Singularity" (chap. 15). Get smarter with "Smart Pills’n Such" (chap. 5). Experience a "Soylent Spring" (chap. 9). Understand nukes better by "Deconstructing Nonproliferation" (chap. 13). Get ready for a "Space Empire" (chap. 14). What is "Sic Transit Humanitas: The Transcent of Man" (chap. 26)? There’s global warming, and there’s "Warm, Poison Planet" (chap. 17). But let’s not forget about "Big Ice" (chap. 22). Things may really grow on trees with "New Plant Paradigms" (chap. 24). We all have "Questions" (chap. 31). And much more! This book is aimed at the reader who is interested in the future, and intrigued by science and technology.

    It is not a mere reorganization of the data into a new kind of library catalogue, nor another specualtive philosophy about the nature of man and language, but rather a rigorus explication of our intuitions about our language in terms of an overt axiom system, the theorems derivable from it, explicit results which may be compared with new data and other intuitions, all based plainly on an overt theory of the internal structure of languages; and it may well provide an opportunity for the application of explicity measures of simplicity to decide preference of one form over another form of grammar.

    Nobel-Prize-winner Claude Cohen-Tannoudji and his colleagues have written this book to eliminate precisely these difficulties. Fourteen chapters provide a clarity of organization, careful attention to pedagogical details, and a wealth of topics and examples which make this work a textbook as well as a timeless reference, allowing to tailor courses to meet students' specific needs. Each chapter starts with a clear exposition of the problem which is then treated, and logically develops the physical and mathematical concept. These chapters emphasize the underlying principles of the material, undiluted by extensive references to applications and practical examples which are put into complementary sections. The book begins with a qualitative introduction to quantum mechanical ideas using simple optical analogies and continues with a systematic and thorough presentation of the mathematical tools and postulates of quantum mechanics as well as a discussion of their physical content. Applications follow, starting with the simplest ones like e.g. the harmonic oscillator, and becoming gradually more complicated (the hydrogen atom, approximation methods, etc.). The complementary sections each expand this basic knowledge, supplying a wide range of applications and related topics as well as detailed expositions of a large number of special problems and more advanced topics, integrated as an essential portion of the text.

    With a balanced blend of case excerpts and author commentary, Samaha guides you as you hone your critical thinking and legal analysis skills. You'll see the principles, defenses, and elements of crime at work as you progress through the book-and you'll learn about the general principles of criminal liability and its defenses, as well as the elements of crimes against persons property, society, and crimes against the state. Featuring the latest topics and court cases, as well as many study tools to help you do well in this course, Samaha's CRIMINAL LAW is a text you will want to keep as a valuable reference even after you graduate and begin your career in the criminal justice field of your choosing.

    Python, an easy-to-learn and increasingly popular object-oriented language, allows readers to become comfortable with the fundamentals of programming without the troublesome syntax that can be challenging for novices. With the knowledge acquired using Python, students gain confidence in their skills and learn to recognize the logic behind developing high-quality programs. Starting Out with Python discusses control structures, functions, arrays, and pointers before objects and classes. As with all Gaddis texts, clear and easy-to-read code listings, concise and practical real-world examples, detail-oriented explanations, and an abundance of exercises appear in every chapter. This text is intended for a one-semester introductory programming course for students with limited programming experience.

    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

    In keeping with previous versions, the 3rd edition uses numerous historical vignettes that show the evolution of the field. New pedagogical features--"Roadmaps" showing the development of a given topic, and "Design Boxes" giving examples of design decisions--will make the 3rd edition even more practical and user-friendly than before. The 3rd edition strikes a careful balance between classical methods of determining compressible flow, and modern numerical and computer techniques (such as CFD) now used widely in industry & research. A new Book Website will contain all problem solutions for instructors.

    Manufacturing Engineering and Technology describes both time-tested and modern methods of manufacturing engineering materials, and sets the standard for introducing readers to the scope and variety of manufacturing processes.

    Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

    Practical Algebra is an easy andfun-to-use workout program that quickly puts you in command of allthe basic concepts and tools of algebra. With the aid of practical, real-life examples and applications, you'll learn: * The basic approach and application of algebra to problemsolving * The number system (in a much broader way than you have known itfrom arithmetic) * Monomials and polynomials; factoring algebraic expressions; howto handle algebraic fractions; exponents, roots, and radicals;linear and fractional equations * Functions and graphs; quadratic equations; inequalities; ratio, proportion, and variation; how to solve word problems, andmore Authors Peter Selby and Steve Slavin emphasize practical algebrathroughout by providing you with techniques for solving problems ina wide range of disciplines--from engineering, biology, chemistry, and the physical sciences, to psychology and even sociology andbusiness administration. Step by step, Practical Algebra shows youhow to solve algebraic problems in each of these areas, then allowsyou to tackle similar problems on your own, at your own pace.Self-tests are provided at the end of each chapter so you canmeasure your mastery.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    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.

    Now, two of the world's most eminent thinkers -- University of London physicists David Bohm, a former protege of Einstein's and one of the world's most respected quantum physicists, and Stanford neurophysiologist Karl Pribram, one of the architects of our modern understanding of the brain -- believe that the universe itself may be a giant hologram, quite literally a kind of image or construct created, at least in part, by the human mind. This remarkable new way of looking at the universe explains not only many of the unsolved puzzles of physics, but also such mysterious occurrences as telepathy, out-of-body and near death experiences, "lucid" dreams, and even religious and mystical experiences such as feelings of cosmic unity and miraculous healings.