The book focuses on techniques of lasting value and explains them precisely in terms of a simple abstract machine. The book presents all major programming paradigms in a uniform framework that shows their deep relationships and how and where to use them together.After an introduction to programming concepts, the book presents both well-known and lesser-known computation models ("programming paradigms"). Each model has its own set of techniques and each is included on the basis of its usefulness in practice. The general models include declarative programming, declarative concurrency, message-passing concurrency, explicit state, object-oriented programming, shared-state concurrency, and relational programming. Specialized models include graphical user interface programming, distributed programming, and constraint programming. Each model is based on its kernel language—a simple core language that consists of a small number of programmer- significant elements. The kernel languages are introduced progressively, adding concepts one by one, thus showing the deep relationships between different models. The kernel languages are defined precisely in terms of a simple abstract machine. Because a wide variety of languages and programming paradigms can be modeled by a small set of closely related kernel languages, this approach allows programmer and student to grasp the underlying unity of programming. The book has many program fragments and exercises, all of which can be run on the Mozart Programming System, an Open Source software package that features an interactive incremental development environment.

    Domain-driven design (DDD) combined with functional programming is the innovative combo that will get you there. In this pragmatic, down-to-earth guide, you'll see how applying the core principles of functional programming can result in software designs that model real-world requirements both elegantly and concisely - often more so than an object-oriented approach. Practical examples in the open-source F# functional language, and examples from familiar business domains, show you how to apply these techniques to build software that is business-focused, flexible, and high quality.Domain-driven design is a well-established approach to designing software that ensures that domain experts and developers work together effectively to create high-quality software. This book is the first to combine DDD with techniques from statically typed functional programming. This book is perfect for newcomers to DDD or functional programming - all the techniques you need will be introduced and explained.Model a complex domain accurately using the F# type system, creating compilable code that is also readable documentation---ensuring that the code and design never get out of sync. Encode business rules in the design so that you have "compile-time unit tests," and eliminate many potential bugs by making illegal states unrepresentable. Assemble a series of small, testable functions into a complete use case, and compose these individual scenarios into a large-scale design. Discover why the combination of functional programming and DDD leads naturally to service-oriented and hexagonal architectures. Finally, create a functional domain model that works with traditional databases, NoSQL, and event stores, and safely expose your domain via a website or API.Solve real problems by focusing on real-world requirements for your software.What You Need: The code in this book is designed to be run interactively on Windows, Mac and Linux.You will need a recent version of F# (4.0 or greater), and the appropriate .NET runtime for your platform.Full installation instructions for all platforms at fsharp.org.

    Aiming to tell the dark secrets of computer arithmetic, this title is suitable for library developers, compiler writers, and lovers of elegant hacks.

    This practical guide not only shows you how to search, analyze, and explore data with Elasticsearch, but also helps you deal with the complexities of human language, geolocation, and relationships.If you're a newcomer to both search and distributed systems, you'll quickly learn how to integrate Elasticsearch into your application. More experienced users will pick up lots of advanced techniques. Throughout the book, you'll follow a problem-based approach to learn why, when, and how to use Elasticsearch features.Understand how Elasticsearch interprets data in your documentsIndex and query your data to take advantage of search concepts such as relevance and word proximityHandle human language through the effective use of analyzers and queriesSummarize and group data to show overall trends, with aggregations and analyticsUse geo-points and geo-shapes--Elasticsearch's approaches to geolocationModel your data to take advantage of Elasticsearch's horizontal scalabilityLearn how to configure and monitor your cluster in production

    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.

    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

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    The author's take on the essence of Java as a new programming language and the thorough introduction to Java's features make this a worthwhile tutorial. Thinking in Java begins a little esoterically, with the author's reflections on why Java is new and better. (This book's choice of font for chapter headings is remarkably hard on the eyes.) The author outlines his thoughts on why Java will make you a better programmer, without all the complexity. The book is better when he presents actual language features. There's a tutorial to basic Java types, keywords, and operators. The guide includes extensive source code that is sometimes daunting (as with the author's sample code for all the Java operators in one listing.) As such, this text will be most useful for the experienced developer. The text then moves on to class design issues, when to use inheritance and composition, and related topics of information hiding and polymorphism. (The treatment of inner classes and scoping will likely seem a bit overdone for most readers.) The chapter on Java collection classes for both Java Developer's Kit (JDK) 1.1 and the new classes, such as sets, lists, and maps, are much better. There's material in this chapter that you are unlikely to find anywhere else. Chapters on exception handling and programming with type information are also worthwhile, as are the chapters on the new Swing interface classes and network programming. Although it adopts somewhat of a mixed-bag approach, Thinking in Java contains some excellent material for the object-oriented developer who wants to see what all the fuss is about with Java.

    Over the past five years, authors Dustin Boswell and Trevor Foucher have analyzed hundreds of examples of "bad code" (much of it their own) to determine why they’re bad and how they could be improved. Their conclusion? You need to write code that minimizes the time it would take someone else to understand it—even if that someone else is you.This book focuses on basic principles and practical techniques you can apply every time you write code. Using easy-to-digest code examples from different languages, each chapter dives into a different aspect of coding, and demonstrates how you can make your code easy to understand.Simplify naming, commenting, and formatting with tips that apply to every line of codeRefine your program’s loops, logic, and variables to reduce complexity and confusionAttack problems at the function level, such as reorganizing blocks of code to do one task at a timeWrite effective test code that is thorough and concise—as well as readable"Being aware of how the code you create affects those who look at it later is an important part of developing software. The authors did a great job in taking you through the different aspects of this challenge, explaining the details with instructive examples." —Michael Hunger, passionate Software Developer

    The first edition of this book, Programming Perl, hit the shelves in 1990, and was quickly adopted as the undisputed bible of the language. Since then, Perl has grown with the times, and so has this book.Programming Perl is not just a book about Perl. It is also a unique introduction to the language and its culture, as one might expect only from its authors. Larry Wall is the inventor of Perl, and provides a unique perspective on the evolution of Perl and its future direction. Tom Christiansen was one of the first champions of the language, and lives and breathes the complexities of Perl internals as few other mortals do. Jon Orwant is the editor of The Perl Journal, which has brought together the Perl community as a common forum for new developments in Perl.Any Perl book can show the syntax of Perl's functions, but only this one is a comprehensive guide to all the nooks and crannies of the language. Any Perl book can explain typeglobs, pseudohashes, and closures, but only this one shows how they really work. Any Perl book can say that my is faster than local, but only this one explains why. Any Perl book can have a title, but only this book is affectionately known by all Perl programmers as "The Camel."This third edition of Programming Perl has been expanded to cover version 5.6 of this maturing language. New topics include threading, the compiler, Unicode, and other new features that have been added since the previous edition.

    To leverage the performance and power of multiprocessor programming, also known as multicore programming, programmers need to learn the new principles, algorithms, and tools.The book will be of immediate use to programmers working with the new architectures. For example, the next generation of computer game consoles will all be multiprocessor-based, and the game industry is currently struggling to understand how to address the programming challenges presented by these machines. This change in the industry is so fundamental that it is certain to require a significant response by universities, and courses on multicore programming will become a staple of computer science curriculums.This book includes fully-developed Java examples detailing data structures, synchronization techniques, transactional memory, and more.Students in multiprocessor and multicore programming courses and engineers working with multiprocessor and multicore systems will find this book quite useful.

    This book explains the technical aspects of hacking, including stack based overflows, heap based overflows, string exploits, return-into-libc, shellcode, and cryptographic attacks on 802.11b.

    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.

    Along the way, we encounter black holes, turtles, snakes, dragons, axe-sharpening, and yak-shaving—and take a guided tour through the theories and methods, both brilliant and misguided, that litter the history of software development, from the famous ‘mythical man-month’ to Extreme Programming. Not just for technophiles but for anyone captivated by the drama of invention, Dreaming in Code offers a window into both the information age and the workings of the human mind.

    Discover the “hidden gold” in Python’s standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you’re wondering which lesser known parts in Python you should know about, you’ll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers’ minds in your next code review. If you’ve got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you’ve worked with other programming languages and you want to get up to speed with Python, you’ll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you’ll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." — Mariatta Wijaya, Python Core Developer"This book makes you write better Python code!" — Bob Belderbos, Software Developer at Oracle"Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." — Ben Felder, Pythonista"It's like having a seasoned tutor explaining, well, tricks!" — Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.