Anyone involved in the work or study of today's tourism industry must consider all these factors together and their effect on it.This updated and revised Eleventh Edition of Tourism presents a comprehensive introduction to travel and tourism, while continuing the tested approach of successful previous editions. New and revised coverage, integrating the latest developments in the tourism industry, includes:Profiles of such industry leaders as J. W. Marriott, Jr, of Marriott International, Inc., James Rasulo of Walt Disney Parks and Resorts, and Francesco Frangialli, Secretary-General of the United Nations World Tourism Organization Lively and fun "Global Insights" look at emerging areas of importance in tourism Demographic trends, such as how the many new travelers from BRIC nations (Brazil, Russia, India, and China) recreate and seek culture New information on transportation options, including high-speed rail and river cruises New consideration of the use of the Internet, particularly Web 2.0 (podcasting, social networks, and blogs), in tourism research, marketing, and promotion Updates on passport, visa, and governmental policies Expanded treatment of crisis management Moving easily between theory and practice, Tourism, Eleventh Edition offers an unparalleled discussion of recreational travel today, and is suitable both as a lively learning tool for students and a reliable go-to reference for tourism professionals.

    It presents the principles, paradigms, designs and implementations of modern programming languages, and contains increased coverage of the object-oriented programming paradigm. The book also covers semantics and Java.

    Everyone has heard of computer programmers but few people realize there are nearly as many people behind the scenes with job titles such as Software Tester, Software Quality Assurance Engineer, Software Test Engineer, and Software Test Technician. Microsoft alone hires hundreds of people for these positions each year. There are also many companies whose sole purpose is providing software test consulting and software testing services. The first edition of Software Testing was published in November 2000. Although the processes and techniques used in testing computer software are timeless, this title will be brought up-to-date by adding a chapter that specifically deals with testing software for security bugs and revisiting the rest of the book to update examples and references.

    The authors provide intuition, description, and analysis of fundamental data structures and algorithms. Numerous illustrations, web-based animations, and simplified mathematical analyses justify important analytical concepts. Key Features of the Fourth Edition: * Updates to Java 5.0 include new sections on generics and other Java 5.0 features, and revised code fragments, examples, and case studies to conform to Java 5.0. * Hundreds of exercises, including many that are new to this edition, promote creativity and help readers learn how to think like programmers and reinforce important concepts. * New case studies illustrate topics such as web browsers, board games, and encryption. * A new early chapter covers Arrays, Linked Lists, and Recursion. * A new final chapter on Memory covers memory management and external memory data structures and algorithms. * Java code examples are used extensively, with source code provided on the website. * Online animations and effective in-text art illustrate data structures and algorithms in a clear, visual manner. Access additional resources on the web www.wiley.com/college/goodrich): * Java source code for all examples in the book * Animations * Library (net.datastructures) of Java constructs used in the book * Problems database and search engine * Student hints to all exercises in the book * Instructor resources, including solutions to selected exercises * Lecture slides

    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

    This book covers the core concepts of operating systems without any unnecessary jargon or text. The authors put you on your way to mastering the fundamental concepts of operating systems while you also prepare for today's emerging developments.Covers the core concepts of operating systems Bypasses unnecessary and wordy text or jargon Encourages you to take your operating system knowledge to the next level Prepares you for today's emerging developments in the field of operating systems Operating Systems Concepts Essentials is a soup-to-nuts guide for all things involving operating systems!

    " 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...

    Through the book’s many examples, you’ll quickly learn how OCaml stands out as a tool for writing fast, succinct, and readable systems code.Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. In the book’s third section, you’ll delve deep into the details of the compiler toolchain and OCaml’s simple and efficient runtime system.Learn the foundations of the language, such as higher-order functions, algebraic data types, and modulesExplore advanced features such as functors, first-class modules, and objectsLeverage Core, a comprehensive general-purpose standard library for OCamlDesign effective and reusable libraries, making the most of OCaml’s approach to abstraction and modularityTackle practical programming problems from command-line parsing to asynchronous network programmingExamine profiling and interactive debugging techniques with tools such as GNU gdb

    The book will teach you about:* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data* Deep learning, a powerful set of techniques for learning in neural networksNeural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

    R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    Some books try to give you copy-and-paste instructions for how to deal with every single system issue that may arise, but How Linux Works actually shows you how the Linux system functions so that you can come up with your own solutions. After a guided tour of filesystems, the boot sequence, system management basics, and networking, author Brian Ward delves into open-ended topics such as development tools, custom kernels, and buying hardware, all from an administrator's point of view. With a mixture of background theory and real-world examples, this book shows both "how" to administer Linux, and "why" each particular technique works, so that you will know how to make Linux work for you.

    Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

    This one's a keeper." --Robert C. Martin "If you want to be an expert in the state of the art in TDD, you need to understand the ideas in this book."--Michael Feathers Test-Driven Development (TDD) is now an established technique for delivering better software faster. TDD is based on a simple idea: Write tests for your code before you write the code itself. However, this simple idea takes skill and judgment to do well. Now there's a practical guide to TDD that takes you beyond the basic concepts. Drawing on a decade of experience building real-world systems, two TDD pioneers show how to let tests guide your development and "grow" software that is coherent, reliable, and maintainable. Steve Freeman and Nat Pryce describe the processes they use, the design principles they strive to achieve, and some of the tools that help them get the job done. Through an extended worked example, you'll learn how TDD works at multiple levels, using tests to drive the features and the object-oriented structure of the code, and using Mock Objects to discover and then describe relationships between objects. Along the way, the book systematically addresses challenges that development teams encounter with TDD--from integrating TDD into your processes to testing your most difficult features. Coverage includes - Implementing TDD effectively: getting started, and maintaining your momentum throughout the project - Creating cleaner, more expressive, more sustainable code - Using tests to stay relentlessly focused on sustaining quality - Understanding how TDD, Mock Objects, and Object-Oriented Design come together in the context of a real software development project - Using Mock Objects to guide object-oriented designs - Succeeding where TDD is difficult: managing complex test data, and testing persistence and concurrency

    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.