Written by two of the field's true pioneers, Spacetime Physics can extend and enhance coverage of specialty relativity in the classroom. This thoroughly up-to-date, highly accessible overview covers microgravity, collider accelerators, satellite probes, neutron detectors, radioastronomy, and pulsars.  The chapter on general relativity with new material on gravity waves, black holes, and cosmology.

    Subsequent sections deal with integrating factors; dilution and accretion problems; linearization of first order systems; Laplace Transforms; Newton's Interpolation Formulas, more.

    From Simon & Schuster, This Book Needs No Title is Raymond Smullyan's budget of living paradoxes—the author of What is the Name of This Book?Including eighty paradoxes, logical labyrinths, and intriguing enigmas progress from light fables and fancies to challenging Zen exercises and a novella and probe the timeless questions of philosophy and life.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    With problems and solutions. Includes 99 illustrations.

    Now, from a Silicon Valley hero and bestselling technology writer comes a pointed critique of the hype surrounding computers and their real benefits, especially in education. In High-Tech Heretic, Clifford Stoll questions the relentless drumbeat for "computer literacy" by educators and the computer industry, particularly since most people just use computers for word processing and games--and computers become outmoded or obsolete much sooner than new textbooks or a good teacher.As one who loves computers as much as he disdains the inflated promises made on their behalf, Stoll offers a commonsense look at how we can make a technological world better suited for people, instead of making people better suited to using machines.

    . . . Brian Clegg attempts to restore something of the lost wonder of air travel . . . even as Leonardo, so fascinated by science, might have done . . . leav[ing] his readers improved for the journey and filled with a renewed sense of curiosity toward the wonders out their window.”—Wall Street Journal“An eye-spy book for adults . . . fitting into that publishing niche somewhere between hard science and Schott’s Miscellany that was so successfully exploited by books such as The Cloudspotter’s Guide.” —London Times Book of the WeekEvery moment of your airplane journey is an opportunity to experience science in action—Inflight Science will be your guide. Brian Clegg explains the ever-changing view from your window seat and suggests entertaining experiments to calculate how far away you are from distant objects and the population of the towns you fly over. You'll learn why the coastline is infinite in length, the cause of thunderstorms, and why there's absolutely no chance of getting stuck on an airline vacuum toilet!Packed full of amazing insights from physics, chemistry, engineering, geography, and more, Inflight Science is a voyage of scientific discovery perfect for any journey.Brian Clegg is the author of several popular science titles, including Before the Big Bang and the forthcoming How to Build a Time Machine (2011), both from St. Martin's Press.

    After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layerperceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

    All chapters are supplemented by thought-provoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering. "Mathematicians wishing a self-contained introduction need look no further." — American Mathematical Monthly.

    Indeed, such equations are crucial to mathematical physics. Although simplifications can be made that reduce these equations to ordinary differential equations, nevertheless the complete description of physical systems resides in the general area of partial differential equations.This highly useful text shows the reader how to formulate a partial differential equation from the physical problem (constructing the mathematical model) and how to solve the equation (along with initial and boundary conditions). Written for advanced undergraduate and graduate students, as well as professionals working in the applied sciences, this clearly written book offers realistic, practical coverage of diffusion-type problems, hyperbolic-type problems, elliptic-type problems, and numerical and approximate methods. Each chapter contains a selection of relevant problems (answers are provided) and suggestions for further reading.

    Emphasis on developing algebraic skills is extended to the exercises--including both drill problems and applications. The authors work through examples and explanations with a blend of rigor and accessibility. In addition, they have refined the flow, transitions, organization, and portioning of the content over many editions to optimize learning for readers. The table of contents covers a wide range of topics efficiently, enabling readers to gain a diverse understanding.

    Designed for experienced software developers ready to advance their status, Exam Ref focuses on the critical-thinking and decision-making acumen needed for success at the Microsoft Specialist level.Focus on the expertise measured by these objectives:Manage Program FlowCreate and Use TypesDebug Applications and Implement SecurityImplement Data AccessThis Microsoft Exam Ref:Organizes its coverage by exam objectives.Features strategic, what-if scenarios to challenge you.Includes a 15% exam discount from Microsoft. (Limited time offer)

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points.The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencesNow current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.

    Thoroughly updated, this edition offers new, faster techniques for solving differential equations generated by the emergence of high-speed computers.