This book covers a range of genres, including action, adventure, and puzzle games complete with professional quality sound effects and visuals. It discusses game design theory and features practical examples of how this can be applied to making games that are more fun to play. Game Maker allows games to be created using a simple drag-and-drop interface, so you don't need to have any prior coding experience. It includes an optional programming language for adding advanced features to your games, when you feel ready to do so. You can obtain more information by visiting book.gamemaker.nl. The authors include the creator of the Game Maker tool and a former professional game programmer, so you'll glean understanding from their expertise. The book also includes a DVD containing Game Maker software and all of the game projects that are created in the book—plus a host of professional-quality graphics and sound effects that you can use in your own games.

    individual whose contributions to the scientific, business and cultural realms are extensive."—BookpageBefore slim laptops that fit into briefcases, computers looked like strange, alien vending machines. But in "the most staggering burst of technical invention by a single person in high-tech history" (BusinessWeek​) Steve Wozniak invented the first true personal computer. Wozniak teamed up with Steve Jobs, and Apple Computer was born, igniting the computer revolution and transforming the world. Here, thirty years later, the mischievous genius with the low profile treats readers to a rollicking, no-holds-barred account of his life—for once, in the voice of the wizard himself.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    However, successful applications have a habit of growing. Eventually the development team ends up in what is known as monolithic hell. All aspects of software development and deployment become painfully slow. The solution is to adopt the microservice architecture, which structures an application as a services, organized around business capabilities. This architecture accelerates software development and enables continuous delivery and deployment of complex software applications.Microservice Patterns teaches enterprise developers and architects how to build applications with the microservice architecture. Rather than simply advocating for the use the microservice architecture, this clearly-written guide takes a balanced, pragmatic approach. You'll discover that the microservice architecture is not a silver bullet and has both benefits and drawbacks. Along the way, you'll learn a pattern language that will enable you to solve the issues that arise when using the microservice architecture. This book also teaches you how to refactor a monolithic application to a microservice architecture.

    It is very readable and the stress many beginners face about memorizing arcane syntax typically presented by other programming languages will not affect you at all. Conversely, you will be able to concentrate on learning concepts and paradigms of programming. This book shall introduce you to an easy way to learn Python in just 7 days and in this time, be able to complete your own projects! By reading the book and implementing what you learn herein, you will realize just why major institutions like NASA, Google, Mozilla, Yahoo, Dropbox, IBM, Facebook and many others prefer to use python in their core products, services and business processes. Let

    It contains sample programs to illustrate practical solutions to the type of real-world problems professional developers encounter.

    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.

    Drawing on Lopp's management experiences at Apple, Netscape, Symantec, and Borland, this book is full of stories based on companies in the Silicon Valley where people have been known to yell at each other. It is a place full of dysfunctional bright people who are in an incredible hurry to find the next big thing so they can strike it rich and then do it all over again. Among these people are managers, a strange breed of people who through a mystical organizational ritual have been given power over your future and your bank account.Whether you're an aspiring manager, a current manager, or just wondering what the heck a manager does all day, there is a story in this book that will speak to you.

    This cookbook includes more than 100 recipes to help you take advantage of REST, HTTP, and the infrastructure of the Web. You'll learn ways to design RESTful web services for client and server applications that meet performance, scalability, reliability, and security goals, no matter what programming language and development framework you use.Each recipe includes one or two problem statements, with easy-to-follow, step-by-step instructions for solving them, as well as examples using HTTP requests and responses, and XML, JSON, and Atom snippets. You'll also get implementation guidelines, and a discussion of the pros, cons, and trade-offs that come with each solution.Learn how to design resources to meet various application scenariosSuccessfully design representations and URIsImplement the hypertext constraint using links and link headersUnderstand when and how to use Atom and AtomPubKnow what and what not to do to support cachingLearn how to implement concurrency controlDeal with advanced use cases involving copying, merging, transactions, batch processing, and partial updatesSecure web services and support OAuth

    Written for 11- to 15-year-olds and assuming no prior computing knowledge, this book uses the wildly successful, low-cost, credit-card-sized Raspberry Pi computer to explain fundamental computing concepts. Young people will enjoy going through the book's nine fun projects while they learn basic programming and system administration skills, starting with the very basics of how to plug in the board and turn it on. Each project includes a lively and informative video to reinforce the lessons. It's perfect for young, eager self-learners—your kids can jump in, set up their Raspberry Pi, and go through the lessons on their own. Written by Carrie Anne Philbin, a high school teacher of computing who advises the U.K. government on the revised ICT Curriculum Teaches 11- to 15-year-olds programming and system administration skills using Raspberry Pi Features 9 fun projects accompanied by lively and helpful videos Raspberry Pi is a $35/£25 credit-card-sized computer created by the non-profit Raspberry Pi Foundation; over a million have been sold Help your children have fun and learn computing skills at the same time with Adventures in Raspberry Pi.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    No longer is execution speed the sole criterion for judging compiled code. Today, code might be judged on how small it is, how much power it consumes, how well it compresses, or how many page faults it generates. In this evolving environment, the task of building a successful compiler relies upon the compiler writer's ability to balance and blend algorithms, engineering insights, and careful planning. Today's compiler writer must choose a path through a design space that is filled with diverse alternatives, each with distinct costs, advantages, and complexities.Engineering a Compiler explores this design space by presenting some of the ways these problems have been solved, and the constraints that made each of those solutions attractive. By understanding the parameters of the problem and their impact on compiler design, the authors hope to convey both the depth of the problems and the breadth of possible solutions. Their goal is to cover a broad enough selection of material to show readers that real tradeoffs exist, and that the impact of those choices can be both subtle and far-reaching.Authors Keith Cooper and Linda Torczon convey both the art and the science of compiler construction and show best practice algorithms for the major passes of a compiler. Their text re-balances the curriculum for an introductory course in compiler construction to reflect the issues that arise in current practice.

    The first edition of this book, Programming Perl, hit the shelves in 1990, and was quickly adopted as the undisputed bible of the language. Since then, Perl has grown with the times, and so has this book.Programming Perl is not just a book about Perl. It is also a unique introduction to the language and its culture, as one might expect only from its authors. Larry Wall is the inventor of Perl, and provides a unique perspective on the evolution of Perl and its future direction. Tom Christiansen was one of the first champions of the language, and lives and breathes the complexities of Perl internals as few other mortals do. Jon Orwant is the editor of The Perl Journal, which has brought together the Perl community as a common forum for new developments in Perl.Any Perl book can show the syntax of Perl's functions, but only this one is a comprehensive guide to all the nooks and crannies of the language. Any Perl book can explain typeglobs, pseudohashes, and closures, but only this one shows how they really work. Any Perl book can say that my is faster than local, but only this one explains why. Any Perl book can have a title, but only this book is affectionately known by all Perl programmers as "The Camel."This third edition of Programming Perl has been expanded to cover version 5.6 of this maturing language. New topics include threading, the compiler, Unicode, and other new features that have been added since the previous edition.

    Indeed, that's what computers are for. You can enlist your own computer to automate all of your project's repetitive tasks, ranging from individual builds and running unit tests through to full product release, customer deployment, and monitoring the system.Many teams try to do these tasks by hand. That's usually a really bad idea: people just aren't as good at repetitive tasks as machines. You run the risk of doing it differently the one time it matters, on one machine but not another, or doing it just plain wrong. But the computer can do these tasks for you the same way, time after time, without bothering you. You can transform these labor-intensive, boring and potentially risky chores into automatic, background processes that just work.In this eagerly anticipated book, you'll find a variety of popular, open-source tools to help automate your project. With this book, you will learn: How to make your build processes accurate, reliable, fast, and easy. How to build complex systems at the touch of a button. How to build, test, and release software automatically, with no human intervention. Technologies and tools available for automation: which to use and when. Tricks and tips from the masters (do you know how to have your cell phone tell you that your build just failed?) You'll find easy-to-implement recipes to automate your Java project, using the same popular style as the rest of our Jolt Productivity Award-winning Starter Kit books. Armed with plenty of examples and concrete, pragmatic advice, you'll find it's easy to get started and reap the benefits of modern software development. You can begin to enjoy pragmatic, automatic, unattended software production that's reliable and accurate every time.