KEY TOPICS For each new device or circuit, the authors describe the principle of the operation, give thorough examples, and then show its actual application. An excellent reference on modern digital systems.

    As a professional C++ developer and former Harvard teaching fellow, I know what you need to know to be a great C++ programmer, and I know how to teach it, one step at a time. I know where people struggle, and why, and how to make it clear. I cover every step of the programming process, including:Getting the tools you need to program and how to use them*Basic language feature like variables, loops and functions*How to go from an idea to code*A clear, understandable explanation of pointers*Strings, file IO, arrays, references*Classes and advanced class design*C++-specific programming patterns*Object oriented programming*Data structures and the standard template library (STL)Key concepts are reinforced with quizzes and over 75 practice problems.

    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.

    In its most simple form, earned value equates to fundamental project management. This is not a new book, but rather it is an updated book. Authors Quentin Fleming and Joel Koppelman have made some important additions. In many cases, there will be no changes to a given section. But in other sections, the authors have made substantial revisions to what they had described in the first edition. Fleming and Koppelman’s goal remains the same with this update: describe earned value project management in its most fundamental form, for application to all projects, of any size or complexity. Writing in an easy-to-read, friendly, and humorous style characteristic of the best teachers, Fleming and Koppelman have identified the minimum requirements that they feel are necessary to use earned value as a simple tool for project managers. They have also witnessed the use of simple earned value on software projects, and find it particularly exciting. Realistically, a Cost Performance Index (CPI) is the same whether the project is a multibillion-dollar high-technology project, or a simple one hundred thousand-dollar software project. A CPI is a CPI … period. It is a solid metric that reflects the health of the project. In every chapter, Fleming and Koppelman stick with using simple stories to define their central concept. Their project examples range from peeling potatoes to building a house. Examples are in round numbers, and most formulas get no more complicated than one number divided by another. Earned Value Project Management—Second Edition may be the best-written, most easily understood project management book on the market today. Project managers will welcome this fresh translation of jargon into ordinary English. The authors have mastered a unique "early-warning" signal of impending cost problems in time for the project manager to react.

    The book's strong reader orientation makes the law accessible, interesting, and relevant, and the cases, content, and features of the Twelfth Edition have been thoroughly updated to represent the latest developments in business law. An excellent assortment of included cases ranges from precedent-setting landmarks to important recent decisions, and ethical, global, and corporate themes are integrated throughout. In addition, numerous features and exercises help you master key concepts and apply what you've learned to real-world issues, and the book offers an unmatched range of support resources--including innovative online review tools.

    As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.Expanded from Tyler Akidau's popular blog posts Streaming 101 and Streaming 102, this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You'll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.You'll explore:How streaming and batch data processing patterns compareThe core principles and concepts behind robust out-of-order data processingHow watermarks track progress and completeness in infinite datasetsHow exactly-once data processing techniques ensure correctnessHow the concepts of streams and tables form the foundations of both batch and streaming data processingThe practical motivations behind a powerful persistent state mechanism, driven by a real-world exampleHow time-varying relations provide a link between stream processing and the world of SQL and relational algebra

    The overall vision, framework, and pedagogy that made the previous editions so popular has been retained, making this a highly comprehensive IS text. Accomplished authors Ralph Stair and George Reynolds continue to expose their readers to clear learning objectives that are reinforced by timely, real-world business examples and hands-on activities. Regardless of their major, students can use this book to understand and practice fundamental IS principles so that they can function more efficiently and effectively as workers, managers, decision makers, and organizational leaders.

    This class-tested and successful textbook synthesizes the established facts of immunology into a comprehensible, coherent, and up-to-date account of how the immune system works, rather than presenting immunology as a chronology of experiments and discoveries. Emphasizing the human immune system the text has been designed to break down the barriers which often divide basic and clinical immunology. The reader-friendly text, section and chapter summaries, and full-color illustrations make the book accessible and easily understandable to students. The Immune System is adapted from Immunobiology by Janeway, Travers & Walport.

    Meant for students and professionals, it includes explanations using case studies. The design methodology is divided into conceptual, logical, and physical.

    You'll systematically discover each technology in the MEAN stack as you build up an application one layer at a time, just as you'd do in a real project.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyTraditional web dev stacks use a different programming language in every layer, resulting in a complex mashup of code and frameworks. Together, the MongoDB database, the Express and AngularJS frameworks, and Node.js constitute the MEAN stack--a powerful platform that uses only one language, top to bottom: JavaScript. Developers and businesses love it because it's scalable and cost-effective. End users love it because the apps created with it are fast and responsive. It's a win-win-win!About the BookGetting MEAN with Mongo, Express, Angular, and Node teaches you how to develop web applications using the MEAN stack. First, you'll create the skeleton of a static site in Express and Node, and then push it up to a live web server. Next, you'll add a MongoDB database and build an API before using Angular to handle data manipulation and application logic in the browser. Finally you'll add an authentication system to the application, using the whole stack. When you finish, you'll have all the skills you need to build a dynamic data-driven web application.What's InsideFull-stack development using JavaScriptResponsive web techniquesEverything you need to get started with MEANBest practices for efficiency and reusabilityAbout the ReaderReaders should have some web development experience. This book is based on MongoDB 2, Express 4, Angular 1, and Node.js 4.About the AuthorSimon Holmes has been a full-stack developer since the late 1990s and runs Full Stack Training Ltd.Table of ContentsPART 1 SETTING THE BASELINEIntroducing full-stack developmentDesigning a MEAN stack architecturePART 2 BUILDING A NODE WEB APPLICATIONCreating and setting up a MEAN projectBuilding a static site with Node and ExpressBuilding a data model with MongoDB and MongooseWriting a REST API: Exposing the MongoDB database to the applicationConsuming a REST API: Using an API from inside ExpressPART 3 ADDING A DYNAMIC FRONT END WITH ANGULARAdding Angular components to an Express applicationBuilding a single-page application with Angular: FoundationsBuilding an SPA with Angular: The next levelPART 4 MANAGING AUTHENTICATION AND USER SESSIONSAuthenticating users, managing sessions, and securing APIsAPPENDIXESInstalling the stackInstalling and preparing the supporting castDealing with all of the viewsReintroducing JavaScript - available online only

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    Windows 10 (a free update to users of Windows 8 or Windows 7) fixes a number of the problems introduced by the revolution in Windows 8 and offers plenty of new features, such as the new Spartan web browser, Cortana voice-activated "personal assistant," new universal apps (that run on tablet, phone, and computer), and more. But to really get the most out of the new operating system, you're going to need a guide.Thankfully, Windows 10: The Missing Manual will be there to help. Like its predecessors, this book from the founder of Yahoo Tech, previous New York Times columnist, bestselling author, and Missing Manuals creator David Pogue illuminates its subject with technical insight, plenty of wit, and hardnosed objectivity for beginners, veteran standalone PC users, new tablet owners, and those who know their way around a network.

    But the principles and practices of software architecting–what the authors call the “science of hard decisions”–have been evolving for cloud, mobile, and other shifts. Now fully revised and updated, this book shares the knowledge and real-world perspectives that enable you to design for success–and deliver more successful solutions. In this fully updated Second Edition, you will: Learn how only a deep understanding of domain can lead to appropriate architecture Examine domain-driven design in both theory and implementation Shift your approach to code first, model later–including multilayer architecture Capture the benefits of prioritizing software maintainability See how readability, testability, and extensibility lead to code quality Take a user experience (UX) first approach, rather than designing for data Review patterns for organizing business logic Use event sourcing and CQRS together to model complex business domains more effectively Delve inside the persistence layer, including patterns and implementation.

    Today, Intel and other semiconductor firms are abandoning the single fast processor model in favor of multi-core microprocessors--chips that combine two or more processors in a single package. In the fourth edition of "Computer Architecture," the authors focus on this historic shift, increasing their coverage of multiprocessors and exploring the most effective ways of achieving parallelism as the key to unlocking the power of multiple processor architectures. Additionally, the new edition has expanded and updated coverage of design topics beyond processor performance, including power, reliability, availability, and dependability. CD System Requirements"PDF Viewer"The CD material includes PDF documents that you can read with a PDF viewer such as Adobe, Acrobat or Adobe Reader. Recent versions of Adobe Reader for some platforms are included on the CD. "HTML Browser"The navigation framework on this CD is delivered in HTML and JavaScript. It is recommended that you install the latest version of your favorite HTML browser to view this CD. The content has been verified under Windows XP with the following browsers: Internet Explorer 6.0, Firefox 1.5; under Mac OS X (Panther) with the following browsers: Internet Explorer 5.2, Firefox 1.0.6, Safari 1.3; and under Mandriva Linux 2006 with the following browsers: Firefox 1.0.6, Konqueror 3.4.2, Mozilla 1.7.11. The content is designed to be viewed in a browser window that is at least 720 pixels wide. You may find the content does not display well if your display is not set to at least 1024x768 pixel resolution. "Operating System"This CD can be used under any operating system that includes an HTML browser and a PDF viewer. This includes Windows, Mac OS, and most Linux and Unix systems. Increased coverage on achieving parallelism with multiprocessors. Case studies of latest technology from industry including the Sun Niagara Multiprocessor, AMD Opteron, and Pentium 4. Three review appendices, included in the printed volume, review the basic and intermediate principles the main text relies upon. Eight reference appendices, collected on the CD, cover a range of topics including specific architectures, embedded systems, application specific processors--some guest authored by subject experts.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.