Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    It gives you hundreds of solutions to real-world problems, with clear explanations and thousands of lines of code you can use in your own projects.From data structures and algorithms, to integration with cutting-edge technologies, the Ruby Cookbook has something for every programmer. Beginners and advanced Rubyists alike will learn how to program with:Strings and numbersArrays and hashesClasses, modules, and namespacesReflection and metaprogrammingXML and HTML processingRuby on Rails (including Ajax integration)DatabasesGraphicsInternet services like email, SSH, and BitTorrentWeb servicesMultitaskingGraphical and terminal interfacesIf you need to write a web application, this book shows you how to get started with Rails. If you're a system administrator who needs to rename thousands of files, you'll see how to use Ruby for this and other everyday tasks. You'll learn how to read and write Excel spreadsheets, classify text with Bayesian filters, and create PDF files. We've even included a few silly tricks that were too cool to leave out, like how to blink the lights on your keyboard.The Ruby Cookbook is the most useful book yet written about Ruby. When you need to solve a problem, don't reinvent the wheel: look it up in the Cookbook.

    The Web is awash in Ruby code that is now virtually impossible to change or extend. This text helps you solve that problem by using powerful real-world object-oriented design techniques, which it thoroughly explains using simple and practical Ruby examples. Sandi Metz has distilled a lifetime of conversations and presentations about object-oriented design into a set of Ruby-focused practices for crafting manageable, extensible, and pleasing code. She shows you how to build new applications that can survive success and repair existing applications that have become impossible to change. Each technique is illustrated with extended examples, all downloadable from the companion Web site, poodr.info. The first title to focus squarely on object-oriented Ruby application design, Practical Object-Oriented Design in Ruby will guide you to superior outcomes, whatever your previous Ruby experience. Novice Ruby programmers will find specific rules to live by; intermediate Ruby programmers will find valuable principles they can flexibly interpret and apply; and advanced Ruby programmers will find a common language they can use to lead development and guide their colleagues. This guide will help you Understand how object-oriented programming can help you craft Ruby code that is easier to maintain and upgrade Decide what belongs in a single Ruby class Avoid entangling objects that should be kept separate Define flexible interfaces among objects Reduce programming overhead costs with duck typing Successfully apply inheritance Build objects via composition Design cost-effective tests Solve common problems associated with poorly designed Ruby code

    Covers all the topics needed by computer scientists with a sometimes humorous approach that reviewers found refreshing. The goal of the book is to provide a firm understanding of the principles and the big picture of where computer theory fits into the field.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    This is the first book that introduces Lisp as a language for the real world.Practical Common Lisp presents a thorough introduction to Common Lisp, providing you with an overall understanding of the language features and how they work. Over a third of the book is devoted to practical examples, such as the core of a spam filter and a web application for browsing MP3s and streaming them via the Shoutcast protocol to any standard MP3 client software (e.g., iTunes, XMMS, or WinAmp). In other "practical" chapters, author Peter Seibel demonstrates how to build a simple but flexible in-memory database, how to parse binary files, and how to build a unit test framework in 26 lines of code.

    … The book the National Security Agency wanted never to be published." –Wired Magazine "…monumental… fascinating… comprehensive… the definitive work on cryptography for computer programmers…" –Dr. Dobb's Journal"…easily ranks as one of the most authoritative in its field." —PC Magazine"…the bible of code hackers." –The Millennium Whole Earth CatalogThis new edition of the cryptography classic provides you with a comprehensive survey of modern cryptography. The book details how programmers and electronic communications professionals can use cryptography—the technique of enciphering and deciphering messages-to maintain the privacy of computer data. It describes dozens of cryptography algorithms, gives practical advice on how to implement them into cryptographic software, and shows how they can be used to solve security problems. Covering the latest developments in practical cryptographic techniques, this new edition shows programmers who design computer applications, networks, and storage systems how they can build security into their software and systems. What's new in the Second Edition? * New information on the Clipper Chip, including ways to defeat the key escrow mechanism * New encryption algorithms, including algorithms from the former Soviet Union and South Africa, and the RC4 stream cipher * The latest protocols for digital signatures, authentication, secure elections, digital cash, and more * More detailed information on key management and cryptographic implementations

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Often, computer science and computer engineering curricula don't provide students with a concentrated and consistent introduction to the fundamental concepts that underlie all computer systems. Traditional computer organization and logic design courses cover some of this material, but they focus largely on hardware design. They provide students with little or no understanding of how important software components operate, how application programs use systems, or how system attributes affect the performance and correctness of application programs. - A more complete view of systems - Takes a broader view of systems than traditional computer organization books, covering aspects of computer design, operating systems, compilers, and networking, provides students with the understanding of how programs run on real systems. - Systems presented from a programmers perspective - Material is presented in such a way that it has clear benefit to application programmers, students learn how to use this knowledge to improve program performance and reliability. They also become more effective in program debugging, because t

    It is particularly valuable where the document, article, or book to be produced contains a lot of mathematics, and where the user is concerned about typographic quality. TeX software offers both writers and publishers the opportunity to produce technical text of all kinds, in an attractive form, with the speed and efficiency of a computer system.Novice and expert users alike will gain from The TeXbook the level of information they seek. Knuth warns newcomers away from the more difficult areas, while he entices experienced users with new challenges. The novice need not learn much about TeX to prepare a simple manuscript with it. But for the preparation of more complex documents, The TeXbook contains all the detail required.Knuth’s familiar wit, and illustrations specially drawn by Duane Bibby, add a light touch to an unusually readable software manual.The TeXbook is the first in a five-volume series on Computers and Typesetting, all authored by Knuth.

    Until now, all it's been missing is a manual. With this thorough, jargon-free guide, you'll learn how to build web apps that include video tools, dynamic drawings, geolocation, offline web apps, drag-and-drop, and many other features. HTML5 is the future of the Web, and with this book you'll reach it quickly.The important stuff you need to know:Structure web pages in a new way. Learn how HTML5 helps make web design tools and search engines work smarter.Add audio and video without plugins. Build playback pages that work in every browser.Draw with Canvas. Create shapes, pictures, text, and animation—and make them interactive.Go a long way with style. Use CSS3 and HTML5 to jazz up your pages and adapt them for mobile devices.Build web apps with rich desktop features. Let users work with your app offline, and process user-selected files in the browser.Create location-aware apps. Write geolocation applications directly in the browser.

    With this long-awaited revision, the authors continue to present the theory in a concise and straightforward manner, now with an eye out for the practical applications. They have revised this book to make it more accessible to today's students, including the addition of more material on writing proofs, more figures and pictures to convey ideas, side-boxes to highlight other interesting material, and a less formal writing style. Exercises at the end of each chapter, including some new, easier exercises, help readers confirm and enhance their understanding of the material. *NEW! Completely rewritten to be less formal, providing more accessibility to todays students. *NEW! Increased usage of figures and pictures to help convey ideas. *NEW! More detail and intuition provided for definitions and proofs. *NEW! Provides special side-boxes to present supplemental material that may be of interest to readers. *NEW! Includes more exercises, including many at a lower level. *NEW! Presents program-like notation for PDAs and Turing machines. *NEW! Increas

    " 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...