With the advent of programmable shaders, a wide variety of new algorithms have arisen and evolved over the past few years. This edition discusses current, practical rendering methods used in games and other applications. It also presents a solid theoretical framework and relevant mathematics for the field of interactive computer graphics, all in an approachable style. The authors have made the figures used in the book available for download for fair use.: Download Figures

    This work represents the views of a mathematician on the analogies between computing machines and the living human brain.

    It is suitable for a one or two semester university course on AI, as well as for researchers in the field.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    A practical approach to test-driven development for Ruby on Rails using RSpec and related tools.

    A dirty little secret assembly language programmers rarely admit to, however, is that what you really need to learn is machine organization, not assembly language programming. Write Great Code Vol I, the first in a series from assembly language expert Randall Hyde, dives right into machine organization without the extra overhead of learning assembly language programming at the same time. And since Write Great Code Vol I concentrates on the machine organization, not assembly language, the reader will learn in greater depth those subjects that are language-independent and of concern to a high level language programmer. Write Great Code Vol I will help programmers make wiser choices with respect to programming statements and data types when writing software, no matter which language they use.

    This book brings you up-to-date with the latest changes in this crucial service.The fifth edition covers BIND 9.3.2, the most recent release of the BIND 9 series, as well as BIND 8.4.7. BIND 9.3.2 contains further improvements in security and IPv6 support, and important new features such as internationalized domain names, ENUM (electronic numbering), and SPF (the Sender Policy Framework).Whether you're an administrator involved with DNS on a daily basis or a user who wants to be more informed about the Internet and how it works, you'll find that this book is essential reading.Topics include:What DNS does, how it works, and when you need to use it How to find your own place in the Internet's namespace Setting up name servers Using MX records to route mail Configuring hosts to use DNS name servers Subdividing domains (parenting) Securing your name server: restricting who can query your server, preventing unauthorized zone transfers, avoiding bogus servers, etc. The DNS Security Extensions (DNSSEC) and Transaction Signatures (TSIG) Mapping one name to several servers for load sharing Dynamic updates, asynchronous notification of change to a zone, and incremental zone transfers Troubleshooting: using nslookup and dig, reading debugging output, common problems DNS programming using the resolver library and Perl's Net::DNS module

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    But when you start writing code as part of a team, you need to think harder about the decisions you make. This book will help you do that.When you write code in a team setting, will other developers be able to understand what you did? Did you organize your code in such a way that it's easy to adapt and extend? Will your code survive once you're gone? Maintainable JavaScript doesn't just recommend a set of guidelines, it examines guidelines being used and recommended by others in the industry, so you get a feel for what's common in well-run JavaScript projects. Save a copy of this book now. It's the only resource that focus solely on JavaScript conventions.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms.Inside, you’ll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works.Topics include:Data Structures and AlgorithmsStrings and TextNumbers, Dates, and TimesIterators and GeneratorsFiles and I/OData Encoding and ProcessingFunctionsClasses and ObjectsMetaprogrammingModules and PackagesNetwork and Web ProgrammingConcurrencyUtility Scripting and System AdministrationTesting, Debugging, and ExceptionsC Extensions

    But the state-of-the-art has moved forward dramatically since Meyers last updated this book in 1997. (For instance, there s now STL. Design patterns. Even new functionality being added through TR1 and Boost.) So Meyers has done a top-to-bottom rewrite, identifying the 55 most valuable techniques you need now to be exceptionally effective with C++. Over half of this edition s content is new. Templates broadly impact C++ development, and you ll find them everywhere. There s extensive coverage of multithreaded systems. There s an entirely new chapter on resource management. You ll find substantial new coverage of exceptions. Much is gained, but nothing s lost: You ll find the same depth of practical insight that first made Effective C++ a classic all those years ago. Bill Camarda, from the July 2005 href="http://www.barnesandnoble.com/newslet... Only

    Internet and World Wide Web How to Program, 4e introduces students with little or no programming experience to the exciting world of Web-Based applications. The book has been substantially revised to reflect today's Web 2.0 rich Internet application-development methodologies. A comprehensive book that teaches the fundamentals needed to program on the Internet, this text provides in-depth coverage of introductory programmming principles, various markup languages (XHTML, Dynamic HTML and XML), several scripting languages (JavaScript, PHP, Ruby/Ruby on Rails and Perl); AJAX, web services, Web Servers (IIS and Apache) and relational databases (MySQL/Apache Derby/Java DB) -- all the skills and tools needed to create dynamic Web-based applications. The text contains comprehensive introductions to ASP.NET 2.0 and JavaServer Faces (JSF). Hundreds of live-code examples of real applications throughout the book available for download allow readers to run the applications and see and hear the outputs.The book provides instruction on building Ajax-enabled rich Internet applications that enhance the presentation of online content and give web applications the look and feel of desktop applications. The chapter on Web 2.0 and Internet business exposes readers to a wide range of other topics associated with Web 2.0 applications and businesses After mastering the material in this book, students will be well prepared to build real-world, industrial strength, Web-based applications.

    *A comprehensive overview of hardware and software issues make this a "must-have" for electrical and computer engineers*Contains new material on RISC processors, performance analysis, multiprocessors and memory systems*New two-color design and illustrations illuminate the text

    With this book, you'll discover why Scala is ideal for highly scalable, component-based applications that support concurrency and distribution.Programming Scala clearly explains the advantages of Scala as a JVM language. You'll learn how to leverage the wealth of Java class libraries to meet the practical needs of enterprise and Internet projects more easily. Packed with code examples, this book provides useful information on Scala's command-line tools, third-party tools, libraries, and available language-aware plugins for editors and IDEs.Learn how Scala's succinct and flexible code helps you program fasterDiscover the notable improvements Scala offers over Java's object modelGet a concise overview of functional programming, and learn how Scala's support for it offers a better approach to concurrencyKnow how to use mixin composition with traits, pattern matching, concurrency with Actors, and other essential featuresTake advantage of Scala's built-in support for XMLLearn how to develop domain-specific languagesUnderstand the basics for designing test-driven Scala applications