The commercial success of a game is often dependent upon the quality of the AI, yet the engineering of AI is often begun late in the development process and is frequently misunderstood. In this book, Ian Millington brings extensive professional experience to the problem of improving the quality of AI in games. A game developer since 1987, he was founder of Mindlathe Ltd., at the time the largest specialist AI company in gaming. Ian shows how to think about AI as an integral part of game play. He describes numerous examples from real games and explores the underlying ideas through detailed case studies. He goes further to introduce many techniques little used by developers today. The book's CD-ROM contains a library of C++ source code and demonstration programs, and provides access to a website with a complete commercial source code library of AI algorithms and techniques. * A comprehensive, professional tutorial and reference to implement true AI in games.* Walks through the entire development process from beginning to end.* Includes over 100 pseudo code examples of techniques used in commercial games, case studies for all major genres, a CD-ROM and companion website with extensive C++ source code implementations for Windows, and source code libraries for Linux and OS X available through the website.

    It's available on almost every OS--if you master the techniques in this book, you'll never need another text editor. Practical Vim shows you 120 vim recipes so you can quickly learn the editor's core functionality and tackle your trickiest editing and writing tasks. Vim, like its classic ancestor vi, is a serious tool for programmers, web developers, and sysadmins. No other text editor comes close to Vim for speed and efficiency; it runs on almost every system imaginable and supports most coding and markup languages. Learn how to edit text the "Vim way:" complete a series of repetitive changes with The Dot Formula, using one keystroke to strike the target, followed by one keystroke to execute the change. Automate complex tasks by recording your keystrokes as a macro. Run the same command on a selection of lines, or a set of files. Discover the "very magic" switch, which makes Vim's regular expression syntax more like Perl's. Build complex patterns by iterating on your search history. Search inside multiple files, then run Vim's substitute command on the result set for a project-wide search and replace. All without installing a single plugin! You'll learn how to navigate text documents as fast as the eye moves--with only a few keystrokes. Jump from a method call to its definition with a single command. Use Vim's jumplist, so that you can always follow the breadcrumb trail back to the file you were working on before. Discover a multilingual spell-checker that does what it's told.Practical Vim will show you new ways to work with Vim more efficiently, whether you're a beginner or an intermediate Vim user. All this, without having to touch the mouse.What You Need: Vim version 7

    But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.This book examines:Storage engines: Explore storage classification and taxonomy, and dive into B-Tree-based and immutable log structured storage engines, with differences and use-cases for eachDistributed systems: Learn step-by-step how nodes and processes connect and build complex communication patterns, from UDP to reliable consensus protocolsDatabase clusters: Discover how to achieve consistent models for replicated data

    This book explains the technical aspects of hacking, including stack based overflows, heap based overflows, string exploits, return-into-libc, shellcode, and cryptographic attacks on 802.11b.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    You will be able to look over the shoulder of major coding and design experts to see problems through their eyes.This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International.

    Over the past five years, authors Dustin Boswell and Trevor Foucher have analyzed hundreds of examples of "bad code" (much of it their own) to determine why they’re bad and how they could be improved. Their conclusion? You need to write code that minimizes the time it would take someone else to understand it—even if that someone else is you.This book focuses on basic principles and practical techniques you can apply every time you write code. Using easy-to-digest code examples from different languages, each chapter dives into a different aspect of coding, and demonstrates how you can make your code easy to understand.Simplify naming, commenting, and formatting with tips that apply to every line of codeRefine your program’s loops, logic, and variables to reduce complexity and confusionAttack problems at the function level, such as reorganizing blocks of code to do one task at a timeWrite effective test code that is thorough and concise—as well as readable"Being aware of how the code you create affects those who look at it later is an important part of developing software. The authors did a great job in taking you through the different aspects of this challenge, explaining the details with instructive examples." —Michael Hunger, passionate Software Developer

    As John invested in these skills his career took off, and he became a highly paid, highly sought-after developer and consultant. Today John helps more than 1.4 million programmers every year to increase their income by developing this unique blend of skills. "If you're a developer, green or a veteran, you owe it to yourself to read The Complete Software Developers Career Guide." - Jason Down, Platform Developer, Ontario, Canada What You Will Learn in This Book How to systematically find and fill the gaps in your technical knowledge so you can face any new challenge with confidence Should you take contract work - or hold out for a salaried position? Which will earn you more, what the tradeoffs are, and how your personality should sway your choice Should you learn JavaScript, C#, Python, C++? How to decide which programming language you should master first Ever notice how every job ever posted requires "3-5 years of experience," which you don't have? Simple solution for this frustrating chicken-and-egg problem that allows you to build legitimate job experience while you learn to code Is earning a computer science degree a necessity - or a total waste of time? How to get a college degree with maximum credibility and minimum debt Coding bootcampssome are great, some are complete scams. How to tell the difference so you don't find yourself cheated out of $10,000 Interviewer tells you, "Dress code is casual around here - the development team wears flipflops." What should you wear? How do you deal with a boss who's a micromanager. Plus how helping your manager with his goals can make you the MVP of your team The technical skills that every professional developer must have - but no one teaches you (most developers are missing some critical pieces, they don't teach this stuff in college, you're expected to just "know" this) An inside look at the recruiting industry. What that "friendly" recruiter really wants from you, how they get paid, and how to avoid getting pigeonholed into a job you'll hate Who Should Read This Book Entry-Level Developers This book will show you how to ensure you have the technical skills your future boss is looking for, create a resume that leaps off a hiring manager's desk, and escape the "no work experience" trap. Mid-Career Developers You'll see how to find and fill in gaps in your technical knowledge, position yourself as the one team member your boss can't live without, and turn those dreaded annual reviews into chance to make an iron-clad case for your salary bump. Senior Developers This book will show you how to become a specialist who can command above-market wages, how building a name for yourself can make opportunities come to you, and how to decide whether consulting or entrepreneurship are paths you should pursue.

    You'll learn how to use Haskell in a variety of practical ways, from short scripts to large and demanding applications. Real World Haskell takes you through the basics of functional programming at a brisk pace, and then helps you increase your understanding of Haskell in real-world issues like I/O, performance, dealing with data, concurrency, and more as you move through each chapter. With this book, you will:Understand the differences between procedural and functional programming Learn the features of Haskell, and how to use it to develop useful programs Interact with filesystems, databases, and network services Write solid code with automated tests, code coverage, and error handling Harness the power of multicore systems via concurrent and parallel programming You'll find plenty of hands-on exercises, along with examples of real Haskell programs that you can modify, compile, and run. Whether or not you've used a functional language before, if you want to understand why Haskell is coming into its own as a practical language in so many major organizations, Real World Haskell is the best place to start.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer winning in games like Jeopardy and go does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Reboot provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better.

     Rails(TM) AntiPatterns identifies these widespread Rails code and design problems, explains why they're bad and why they happen--and shows exactly what to do instead.The book is organized into concise, modular chapters--each outlines a single common AntiPattern and offers detailed, cookbook-style code solutions that were previously difficult or impossible to find. Leading Rails developers Chad Pytel and Tammer Saleh also offer specific guidance for refactoring existing bad code or design to reflect sound object-oriented principles and established Rails best practices. With their help, developers, architects, and testers can dramatically improve new and existing applications, avoid future problems, and establish superior Rails coding standards throughout their organizations.This book will help you understand, avoid, and solve problems withModel layer code, from general object-oriented programming violations to complex SQL and excessive redundancy Domain modeling, including schema and database issues such as normalization and serialization View layer tools and conventions Controller-layer code, including RESTful code Service-related APIs, including timeouts, exceptions, backgrounding, and response codes Third-party code, including plug-ins and gems Testing, from test suites to test-driven development processes Scaling and deployment Database issues, including migrations and validations System design for "graceful degradation" in the real world

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    Work at your own pace through a series of lessons and practical exercises, and then assess your skills with practice tests on CD—featuring multiple, customizable testing options.Maximize your performance on the exam by learning how to:Create database objectsWork with dataModify dataTroubleshoot and optimize queriesYou also get an exam discount voucher—making this book an exceptional value and a great career investment.

    Today, Rails developers and architects need better ways to interface with legacy systems, move into the cloud, and scale to handle higher volumes and greater complexity. In Service-Oriented Design with Ruby and Rails Paul Dix introduces a powerful, services-based design approach geared toward overcoming all these challenges. Using Dix's techniques, readers can leverage the full benefits of both Ruby and Rails, while overcoming the difficulties of working with larger codebases and teams. Dix demonstrates how to integrate multiple components within an enterprise application stack; create services that can easily grow and connect; and design systems that are easier to maintain and upgrade. Key concepts are explained with detailed Ruby code built using open source libraries such as ActiveRecord, Sinatra, Nokogiri, and Typhoeus. The book concludes with coverage of security, scaling, messaging, and interfacing with third-party services. Service-Oriented Design with Ruby and Rails will help you Build highly scalable, Ruby-based service architectures that operate smoothly in the cloud or with legacy systems Scale Rails systems to handle more requests, larger development teams, and more complex code bases Master new best practices for designing and creating services in Ruby Use Ruby to glue together services written in any language Use Ruby libraries to build and consume RESTful Web services Use Ruby JSON parsers to quickly represent resources from HTTP services Write lightweight, well-designed API wrappers around internal or external services Discover powerful non-Rails frameworks that simplify Ruby service implementation Implement standards-based enterprise messaging with Advanced Message Queuing Protocol (AMQP) Optimize performance with load balancing and caching Provide for security and authentication