Written for both experienced and new programmers alike, Learning Ruby is a just-get-in-and-drive book -- a hands-on tutorial that offers lots of Ruby programs and lets you know how and why they work, just enough to get you rolling down the road. Interest in Ruby stems from the popularity of Rails, the web development framework that's attracting new devotees and refugees from Java and PHP. But there are plenty of other uses for this versatile language. The best way to learn is to just try the code! You'll find examples on nearly every page of this book that you can imitate and hack. Briefly, this book:Outlines many of the most important features of Ruby Demonstrates how to use conditionals, and how to manipulate strings in Ruby. Includes a section on regular expressions Describes how to use operators, basic math, functions from the Math module, rational numbers, etc. Talks you through Ruby arrays, and demonstrates hashes in detail Explains how to process files with Ruby Discusses Ruby classes and modules (mixins) in detail, including a brief introduction to object-oriented programming (OOP) Introduces processing XML, the Tk toolkit, RubyGems, reflection, RDoc, embedded Ruby, metaprogramming, exception handling, and other topics Acquaints you with some of the essentials of Rails, and includes a short Rails tutorial. Each chapter concludes with a set of review questions, and appendices provide you with a glossary of terms related to Ruby programming, plus reference material from the book in one convenient location. If you want to take Ruby out for a drive, Learning Ruby holds the keys.

    Written In An Objective And User-Friendly Manner, This Best-Selling Text Continues To Captivate Students By Incorporating Personal Drug Use And Abuse Experiences And Perspectives Throughout. Statistics And Chapter Content Have Been Revised To Include The Latest Information On Current Topics.

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

    Fully stocked with solved problemsN950 of themNit shows you how to solve problems that may not have been fully explained in class. Plus you ge"

     Test-Driven JavaScript Development is a complete, best-practice guide to agile JavaScript testing and quality assurance with the test-driven development (TDD) methodology. Leading agile JavaScript developer Christian Johansen covers all aspects of applying state-of-the-art automated testing in JavaScript environments, walking readers through the entire development lifecycle, from project launch to application deployment, and beyond.Using real-life examples driven by unit tests, Johansen shows how to use TDD to gain greater confidence in your code base, so you can fearlessly refactor and build more robust, maintainable, and reliable JavaScript code at lower cost. Throughout, he addresses crucial issues ranging from code design to performance optimization, offering realistic solutions for developers, QA specialists, and testers.Coverage includes - Understanding automated testing and TDD - Building effective automated testing workflows - Testing code for both browsers and servers (using Node.js) - Using TDD to build cleaner APIs, better modularized code, and more robust software - Writing testable code - Using test stubs and mocks to test units in isolation - Continuously improving code through refactoring - Walking through the construction and automated testing of fully functional softwareThe accompanying Web site, tddjs.com, contains all of the book's code listings and additional resources.

    All of the fundamentals of robotics are covered--robotics analysis; including kinematics, kinetics and force control, and trajectory planning of robots; its sub-systems such as actuators, sensors, and vision systems; as well as robotics applications. "Introduction to Robotics" also includes many subjects related to mechatronics, microprocessor actuator control, integration of sensors, vision systems, and fuzzy logic. For practicing mechanical engineers, electronic and electric engineers, computer engineers, and engineering technologists who would like to learn about robotics.

    Offering time-tested problems, conceptual and visual pedagogy, and a state-of-the-art media package, this 11th edition looks to the future of university physics, in terms of both content and approach.

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    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.

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    The book will also be extremely useful for those preparing for other MBA entrance examinations like XAT, SNAP, CMAT, NMAT, etc. Quantitative Aptitude is quite challenging component of the CAT question paper and the other mentioned MBA entrance examinations. In his inimitable style, Arun Sharma, an acknowledged authority on the topic, provides a comprehensive package of theory and practice problems to enable aspirants to attempt questions with extra speed and confidence.

    This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You'll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to:Analyze all types of sequences Use all types of databases Work with DNA and protein sequences Conduct similarity searches Build a multiple sequence alignment Edit and publish alignments Visualize protein 3-D structures Construct phylogenetic trees This up-to-date second edition includes newly created and popular databases and Internet programs as well as multiple new genomes. It provides tips for using servers and places to seek resources to find out about what's going on in the bioinformatics world. Bioinformatics For Dummies will show you how to get the most out of your PC and the right Web tools so you'll be searching databases and analyzing sequences like a pro!

    It offers a comprehensive and accessible treatment of the theory of algorithms and complexity—the elegant body of concepts and methods developed by computer scientists over the past 30 years for studying the performance and limitations of computer algorithms. The book is self-contained in that it develops all necessary mathematical prerequisites from such diverse fields such as computability, logic, number theory and probability.

    The book covers all of the new CompTIA Security+ 2008 exam objectives and maps to the new Security+ 2008 exam. This updated edition features many all-new topics, including topics new to the CompTIA exams like cross site scripting, SQL injection, rootkits, and virtualization, as well as topics of increasing importance in the industry as a whole, like the latest breeds of attackers, Wi-Fi Protected Access 2, and Microsoft Windows Vista security.

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.