It’s the latest in a long line of “Are you cool enough to use me?” programming languages, APIs, and toolkits. In that sense, it lands squarely in the tradition of Rails, and Ajax, and Hadoop, and even to some degree iPhone programming and HTML5.Dig a little deeper, and you’ll hear that Node.js (or, as it’s more briefly called by many, simply “Node”) is a server-side solution for JavaScript, and in particular, for receiving and responding to HTTP requests. If that doesn’t completely boggle your mind, by the time the conversation heats up with discussion of ports, sockets, and threads, you’ll tend to glaze over. Is this really JavaScript? In fact, why in the world would anyone want to run JavaScript outside of a browser, let alone the server?The good news is that you’re hearing (and thinking) about the right things. Node really is concerned with network programming and server-side request/response processing. The bad news is that like Rails, Ajax, and Hadoop before it, there’s precious little clear information available. There will be, in time — as there now is for these other “cool” frameworks that have matured — but why wait for a book or tutorial when you might be able to use Node today, and dramatically improve the maintainability.

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.

    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.

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.

    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.

    Completely self-contained, heavily illustrated, and mathematically accessible, it has a scope of application that is not limited to the solution of specialized problems. Digital Image Fundamentals. Image Enhancement in the Spatial Domain. Image Enhancement in the Frequency Domain. Image Restoration. Color Image Processing. Wavelets and Multiresolution Processing. Image Compression. Morphological Image Processing. Image Segmentation. Representation and Description. Object Recognition.

    This title is aimed at courses in applied econometrics, political methodology, and sociological methods or a one-year graduate course in econometrics for social scientists.

    This text provides a simple and straightforward introduction to econometrics for the beginner. The book is designed to help students understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. In each of the editions, I have tried to incorporate major developments in the field in an intuitive and informative way without resort to matrix algebra, calculus, or statistics beyond the introductory level. The fourth edition continues that tradition.

    By the time you're halfway through this fast-paced, hands-on guide, you'll have built a series of useful projects, including a complete ZigBee wireless network that delivers remotely sensed data.Radio networking is creating revolutions in volcano monitoring, performance art, clean energy, and consumer electronics. As you follow the examples in each chapter, you'll learn how to tackle inspiring projects of your own. This practical guide is ideal for inventors, hackers, crafters, students, hobbyists, and scientists.Investigate an assortment of practical and intriguing project ideasPrep your ZigBee toolbox with an extensive shopping list of parts and programsCreate a simple, working ZigBee network with XBee radios in less than two hours -- for under $100Use the Arduino open source electronics prototyping platform to build a series of increasingly complex projectsGet familiar with XBee's API mode for creating sensor networksBuild fully scalable sensing and actuation systems with inexpensive componentsLearn about power management, source routing, and other XBee technical nuancesMake gateways that connect with neighboring networks, including the Internet

    Designed for graduate and advanced undergraduate students, it includes the contributions of leading thinkers, educators, and practitioners who provide an in-depth and objective appraisal of why and how we organize health care the way we do; the enormous impact of health-related behaviors on the structure, function, and cost of the health care delivery system; and other emerging and recurrent issues in health policy, health care management, and public health. To update this book with the rapid changes that have occurred in health care through November 2013, a separate chapter, the Affordable Care Act (ACA) Supplement, is available to students and instructors as a downloadable PDF.This text is divided into five sections, in order to provide some coherence to this broad terrain. Part I, The Current U.S. Health Care System, addresses major characteristics and issues, including reform, financing, and comparative health care systems. This section now includes multiple new charts and tables providing concrete health care data. Part II, Population Health, focuses on health behavior, including health care models, public health policy and practice, risk factors, facilitating healthy lifestyle practices, and access to care. Part III, Medical Care Delivery, addresses integrated health models, delivering high-quality health care, health care costs and value, and comparative effectiveness. Part IV, Support for Medical Care Delivery, concerns governance and management issues, including accountability, the health workforce, and information technology. Part V, The Future of Health Care Delivery in the United States, includes a new 5-year trend forecast.Key Features: Includes major provisions of the Patient Protection and Affordable Health Care Act of 2010Each chapter includes these special features: key concepts; extensive mapping resources; key words; learning objectives; discussion questions; and case studiesCovers the newest models of care, such as Accountable Care Organizations and Integrated Delivery SystemsExamines new ways of conceptualizing and assessing health care, including comparative effectiveness researchFeatures contributions by leading scholars and key figures within the U.S. health care system, including John Billings, JD; Carolyn M. Clancy, MD; C. Tracy Orleans, PhD; and Michael S. Sparer, PhD, JDContains new coverage of health reform, developing countries, population health, public health and catastrophic events, and a broadened discussion of the health care workforceAffordable Care Act (ACA) Supplement available to students and instructors as a downloadable PDF. Available to Instructors: Instructor's Guide (updated to reflect content from ACA supplement)PowerPoint PresentationsImage BankTest Bank (updated to reflect content from ACA supplement)"

    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.

    D.S. Malik is ideal for a one-semester course focused on data structures. Clearly written with the student in mind, this text focuses on Data Structures and includes advanced topics in C++ such as Linked Lists and the Standard Template Library (STL). This student-friendly text features abundant Programming Examples and extensive use of visual diagrams to reinforce difficult topics. Students will find Dr. Malik's use of complete programming code and clear display of syntax, explanation, and example easy to read and conducive to learning.

    " From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required just the ability to program in a modestly extended version of C. " CUDA by Example, " written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance. Major topics covered includeParallel programmingThread cooperationConstant memory and eventsTexture memoryGraphics interoperabilityAtomicsStreamsCUDA C on multiple GPUsAdvanced atomicsAdditional CUDA resources All the CUDA software tools you ll need are freely available for download from NVIDIA.http: //developer.nvidia.com/object/cuda-by-e...

    This time, they introduce readers to Einstein's special relativity and Maxwell's classical field theory. Using their typical brand of real math, enlightening drawings, and humor, Susskind and Friedman walk us through the complexities of waves, forces, and particles by exploring special relativity and electromagnetism. It's a must-read for both devotees of the series and any armchair physicist who wants to improve their knowledge of physics' deepest truths.