Fortunately, there s a solution: Erik T. Ray s Learning XML, Second Edition. This book presents an outstanding birds-eye view of the XML landscape. It s definitely not a programming book (though it does introduce some key XML programming issues). Rather, it s focused on key ideas you need to understand whatever you want to do with XML. That could be document management, web or print content delivery, application integration, B2B commerce, data storage, internationalization -- you name it.Ray s day job is software developer and XML specialist at O Reilly. There, he s helped to implement a complete publishing solution, using DocBook-XML and Perl to produce books in print, on CD-ROM, and for online delivery. So he understands XML from the real-world point of view of someone with a job to do. His first goal is to take on the big questions. First, What is XML? Ray attacks this question from multiple angles, introducing XML as a general-purpose information storage system, a markup language toolkit, and an open standard (or, increasingly, a collection of standards). What can (and can t) you do with XML? What s the history that led us here? And what tools do you need to get started? Next, he introduces the basic building blocks of XML markup and all XML-derived languages: stuff you ll need to know regardless of your goals. Through easy examples, you ll understand elements, attributes, entities, and processing instructions -- and how they fit together in a well-formed XML document. Then, it s on to representing information with XML -- in other words, understanding the nature and planning the structure of the documents you ll be using. Ray starts simply, then builds on his basic examples to discuss narrative documents with text flows, block and inline elements, and titled sections. Once you can handle those, he discusses more complex information modeling, as used in specialized markup languages such as VML. This edition contains an entirely new chapter on XML Schemas -- what he calls the shepherds that keep documents from straying outside of the herd and causing trouble. Schemas, of course, have become hugely important. This is one of the best plain-English introductions to the topic we ve seen. Ray then turns to presentation, introducing CSS stylesheets, basic usage, rule matching, properties, and more. A little later on, he returns to the subject -- this time with a complete introduction to XSL-FO that illuminates two powerful examples. The first is TEI-XML, a markup language for scholarly documents (Ray presents a Shakespearean sonnet, appropriately coded). The second is the immensely powerful DocBook -- which, as we ve observed, Ray knows inside and out. Learning XML is superbly written. Clear explanations. Simple examples. Great metaphors and analogies. And excellent introductions to nearly every topic that matters, from links to presentation, transformation to internationalization. If you re just starting out with XML, you re lucky to have it. Bill CamardaBill Camarda is a consultant, writer, and web/multimedia content developer. His 15 books include Special Edition Using Word 2000 and Upgrading & Fixing Networks for Dummies, Second Edition.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    But what do we really know about the design process? What leads to effective, elegant designs? The Design of Design addresses these questions. These new essays by Fred Brooks contain extraordinary insights for designers in every discipline. Brooks pinpoints constants inherent in all design projects and uncovers processes and patterns likely to lead to excellence. Drawing on conversations with dozens of exceptional designers, as well as his own experiences in several design domains, Brooks observes that bold design decisions lead to better outcomes. The author tracks the evolution of the design process, treats collaborative and distributed design, and illuminates what makes a truly great designer. He examines the nuts and bolts of design processes, including budget constraints of many kinds, aesthetics, design empiricism, and tools, and grounds this discussion in his own real-world examples--case studies ranging from home construction to IBM's Operating System/360. Throughout, Brooks reveals keys to success that every designer, design project manager, and design researcher should know.

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    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.

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Restructured into six parts, this new edition covers a wide spectrum of software processes from initial requirements solicitation through design and development.

    In it, you’ll learn how to bypass configuration steps so you can focus on your application’s behavior. Spring expert Craig Walls uses interesting and practical examples to teach you both how to use the default settings effectively and how to override and customize Spring Boot for your unique environment. Along the way, you’ll pick up insights from Craig’s years of Spring development experience.

    

    It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it's specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

    This book follows in the tradition of the first two: It delivers new material, organized in bite-sized Items and grouped into themed sections. Readers of the first two books will find some familiar section themes, now including new material, such as exception safety, generic programming, and optimization and memory management techniques. The books overlap in structure and theme, not in content. This book continues the strong emphasis on generic programming and on using the C++ standard library effectively, including coverage of important template and generic programming techniques. Sutter's goal for this third and final book in his set is to present case studies that pull together themes from the previous books. This book also covers important points presented at the C++ Standard Committee where corrections to the Standard have been discussed and accepted.

    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.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    

    "Design Patterns Explained "complements the existing design patterns texts and may perform a very useful role, fitting between introductory texts such as UML Distilled and the more advanced patterns books." James Noble Leverage the quality and productivity benefits of patterns without the complexity! "Design Patterns Explained, Second Edition" is the field's simplest, clearest, most practical introduction to patterns. Using dozens of updated Java examples, it shows programmers and architects exactly how to use patterns to design, develop, and deliver software far more effectively. You'll start with a complete overview of the fundamental principles of patterns, and the role of object-oriented analysis and design in contemporary software development. Then, using easy-to-understand sample code, Alan Shalloway and James Trott illuminate dozens of today's most useful patterns: their underlying concepts, advantages, tradeoffs, implementation techniques, and pitfalls to avoid. Many patterns are accompanied by UML diagrams. Building on their best-selling First Edition, Shalloway and Trott have thoroughly updated this book to reflect new software design trends, patterns, and implementation techniques. Reflecting extensive reader feedback, they have deepened and clarified coverage throughout, and reorganized content for even greater ease of understanding. New and revamped coverage in this edition includesBetter ways to start "thinking in patterns"How design patterns can facilitate agile development using eXtreme Programming and other methodsHow to use commonality and variability analysis to design application architecturesThe key role of testing into a patterns-driven development processHow to use factories to instantiate and manage objects more effectivelyThe Object-Pool Pattern a new pattern not identified by the "Gang of Four"New study/practice questions at the end of every chapter Gentle yet thorough, this book assumes no patterns experience whatsoever. It's the ideal "first book" on patterns, and a perfect complement to Gamma's classic "Design Patterns." If you're a programmer or architect who wants the clearest possible understanding of design patterns or if you've struggled to make them work for you read this book.