Led by three renowned internals experts, this classic guide is fully updated for Windows 7 and Windows Server 2008 R2—and now presents its coverage in two volumes.As always, you get critical insider perspectives on how Windows operates. And through hands-on experiments, you’ll experience its internal behavior firsthand—knowledge you can apply to improve application design, debugging, system performance, and support.In Part 1, you will:Understand how core system and management mechanisms work—including the object manager, synchronization, Wow64, Hyper-V, and the registryExamine the data structures and activities behind processes, threads, and jobsGo inside the Windows security model to see how it manages access, auditing, and authorizationExplore the Windows networking stack from top to bottom—including APIs, BranchCache, protocol and NDIS drivers, and layered servicesDig into internals hands-on using the kernel debugger, performance monitor, and other tools

    It presents a learning journey, not definitive guidance. It describes the experiences of a development team with no prior CQRS proficiency in building, deploying (to Windows Azure), and maintaining a sample real-world, complex, enterprise system to showcase various CQRS and ES concepts, challenges, and techniques.The development team did not work in isolation; we actively sought input from industry experts and from a wide group of advisors to ensure that the guidance is both detailed and practical.The CQRS pattern and event sourcing are not mere simplistic solutions to the problems associated with large-scale, distributed systems. By providing you with both a working application and written guidance, we expect you’ll be well prepared to embark on your own CQRS journey.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Written By Two Of Today'S Most Respected Computer Science Educators, Nell Dale And John Lewis, The Text Provides A Broad Overview Of The Many Aspects Of The Discipline From A Generic View Point. Separate Program Language Chapters Are Available As Bundle Items For Those Instructors Who Would Like To Explore A Particular Programming Language With Their Students. The Many Layers Of Computing Are Thoroughly Explained Beginning With The Information Layer, Working Through The Hardware, Programming, Operating Systems, Application, And Communication Layers, And Ending With A Discussion On The Limitations Of Computing. Perfect For Introductory Computing And Computer Science Courses, Computer Science Illuminated, Third Edition's Thorough Presentation Of Computing Systems Provides Computer Science Majors With A Solid Foundation For Further Study, And Offers Non-Majors A Comprehensive And Complete Introduction To Computing.

    As traffic volume and data demands increase, these applications become more complicated and brittle, exposing risks and compromising availability. This practical guide shows IT, devops, and system reliability managers how to prevent an application from becoming slow, inconsistent, or downright unavailable as it grows.Scaling isn't just about handling more users; it's also about managing risk and ensuring availability. Author Lee Atchison provides basic techniques for building applications that can handle huge quantities of traffic, data, and demand without affecting the quality your customers expect.In five parts, this book explores:Availability: learn techniques for building highly available applications, and for tracking and improving availability going forwardRisk management: identify, mitigate, and manage risks in your application, test your recovery/disaster plans, and build out systems that contain fewer risksServices and microservices: understand the value of services for building complicated applications that need to operate at higher scaleScaling applications: assign services to specific teams, label the criticalness of each service, and devise failure scenarios and recovery plansCloud services: understand the structure of cloud-based services, resource allocation, and service distribution

    Based on the authors' intensive summer C++ courses at Stanford University, Accelerated C++ covers virtually every concept that most professional C++ programmers will ever use -- but it turns the traditional C++ curriculum upside down, starting with the high-level C++ data structures and algorithms that let you write robust programs immediately. Once you're getting results, Accelerated C++ takes you under the hood, introducing complex language features such as memory management in context, and explaining exactly how and when to use them. From start to finish, the book concentrates on solving problems, rather than learning language and library features for their own sake. The result: You'll be writing real-world programs in no time -- and outstanding code faster than you ever imagined.

    A focus is placed on the principles and practices of decomposing a complex problem space as well as the implementation patterns and best practices for shaping a maintainable solution space.

    It features excellent examples and access to Addison Wesley's database Web site that includes further teaching, tutorials and many useful student resources.

    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.

    Why is this open-source tool so popular? Because it automates a tedious task: persisting your Java objects to a relational database. The inevitable mismatch between your object-oriented code and the relational database requires you to write code that maps one to the other. This code is often complex, tedious and costly to develop. Hibernate does the mapping for you.Not only that, Hibernate makes it easy. Positioned as a layer between your application and your database, Hibernate takes care of loading and saving of objects. Hibernate applications are cheaper, more portable, and more resilient to change. And they perform better than anything you are likely to develop yourself."Hibernate in Action" carefully explains the concepts you need, then gets you going. It builds on a single example to show you how to use Hibernate in practice, how to deal with concurrency and transactions, how to efficiently retrieve objects and use caching.The authors created Hibernate and they field questions from the Hibernate community every day-they know how to make Hibernate sing. Knowledge and insight seep out of every pore of this book."What's Inside"- ORM concepts- Getting started- Many real-world tasks- The Hibernate application development process

    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.

    It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity. Topics include sets, logic, counting, methods of conditional and non-conditional proof, disproof, induction, relations, functions and infinite cardinality.

    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

    Tracy Kidder got a preview of this world in the late 1970s when he observed the engineers of Data General design and build a new 32-bit minicomputer in just one year. His thoughtful, prescient book, The Soul of a New Machine, tells stories of 35-year-old "veteran" engineers hiring recent college graduates and encouraging them to work harder and faster on complex and difficult projects, exploiting the youngsters' ignorance of normal scheduling processes while engendering a new kind of work ethic.These days, we are used to the "total commitment" philosophy of managing technical creation, but Kidder was surprised and even a little alarmed at the obsessions and compulsions he found. From in-house political struggles to workers being permitted to tease management to marathon 24-hour work sessions, The Soul of a New Machine explores concepts that already seem familiar, even old-hat, less than 20 years later. Kidder plainly admires his subjects; while he admits to hopeless confusion about their work, he finds their dedication heroic. The reader wonders, though, what will become of it all, now and in the future. —Rob Lightner

    This classic book uncovers what interviews are really like at America's top software and computer companies and provides you with the tools to succeed in any situation. The authors take you step-by-step through new problems and complex brainteasers they were asked during recent technical interviews. 50 interview scenarios are presented along with in-depth analysis of the possible solutions. The problem-solving process is clearly illustrated so you'll be able to easily apply what you've learned during crunch time. You'll also find expert tips on what questions to ask, how to approach a problem, and how to recover if you become stuck. All of this will help you ace the interview and get the job you want.What you will learn from this bookTips for effectively completing the job application Ways to prepare for the entire programming interview process How to find the kind of programming job that fits you best Strategies for choosing a solution and what your approach says about you How to improve your interviewing skills so that you can respond to any question or situation Techniques for solving knowledge-based problems, logic puzzles, and programming problems Who this book is for This book is for programmers and developers applying for jobs in the software industry or in IT departments of major corporations.Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that will guide you through all the techniques involved.