It has been winning converts from dynamic language enthusiasts as well as users of traditional compiled languages. The former appreciate the robustness and efficiency that Go's lightweight type system brings to their code; the latter find Go's simplicity and fast tools a refreshing change. Thanks to its well-designed standard libraries and its excellent support for concurrent programming, Go is fast becoming the language of choice for distributed systems. The Go Programming Language is the definitive book on Go for the working programmer. It assumes no prior knowledge of Go, nor any other specific programming language, so you'll find it an accessible guide whether you come from JavaScript, Ruby, Python, Java, or C++. The book will quickly get you started using Go effectively from the beginning, and by the end, you will know how to use it well to write clear, idiomatic and efficient programs to solve real-world problems. You'll understand not just how to use its standard libraries, but how they work, and how to apply the same design techniques to your own projects. The earlier chapters will introduce you to the basic concepts of Go programming---numbers, strings, functions---while at the same time presenting important computer science concepts like recursion, and useful examples of graphics, UTF-8, and error handling. The chapters on methods and interfaces will show you a new way to think about object-oriented programming; the chapter on concurrency explains why concurrency is so important in modern programming, and how Go helps you handle it well. You'll also learn about Go's pragmatic but effective approach to testing; how to build, test, and manage projects using the go tool, and the art of metaprogramming using reflection. The book contains hundreds of interesting and practical examples that cover the whole language and a wide range of applications. The code samples from the book are available for download from gopl.io.

    Building on two widely acclaimed previous editions, Craig Larman has updated this book to fully reflect the new UML 2 standard, to help you master the art of object design, and to promote high-impact, iterative, and skillful agile modeling practices.Developers and students will learn object-oriented analysis and design (OOA/D) through three iterations of two cohesive, start-to-finish case studies. These case studies incrementally introduce key skills, essential OO principles and patterns, UML notation, and best practices. You won’t just learn UML diagrams - you’ll learn how to apply UML in the context of OO software development.Drawing on his unsurpassed experience as a mentor and consultant, Larman helps you understand evolutionary requirements and use cases, domain object modeling, responsibility-driven design, essential OO design, layered architectures, “Gang of Four” design patterns, GRASP, iterative methods, an agile approach to the Unified Process (UP), and much more. This edition’s extensive improvements include:- A stronger focus on helping you master OOA/D through case studies that demonstrate key OO principles and patterns, while also applying the UML- New coverage of UML 2, Agile Modeling, Test-Driven Development, and refactoring- Many new tips on combining iterative and evolutionary development with OOA/D- Updates for easier study, including new learning aids and graphics- New college educator teaching resources- Guidance on applying the UP in a light, agile spirit, complementary with other iterative methods such as XP and Scrum- Techniques for applying the UML to documenting architectures- A new chapter on evolutionary requirements, and much moreApplying UML and Patterns, Third Edition, is a lucid and practical introduction to thinking and designing with objects - and creating systems that are well crafted, robust, and maintainable.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

    They aren't conscious of all the choices they make, like how they format their source, the names they use for variables, or the kinds of loops they use. They're focused entirely on problems they're solving, solutions they're creating, and algorithms they're implementing. So they write code in the way that seems natural, that happens intuitively, and that feels good.But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on how software ought to be created.Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.Praise for Perl Best Practices from Perl community members:"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide." -- Randal Schwartz"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book." -- Peter Scott"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too." -- Andy Lester"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years." -- Bill Odom"Finally, a means to bring lasting order to the process and product of real Perl development teams." -- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable P

    Focused on helping students understand and construct proofs and expanding their mathematical maturity, this best-selling text is an accessible introduction to discrete mathematics. Johnsonbaugh's algorithmic approach emphasizes problem-solving techniques. The Seventh Edition reflects user and reviewer feedback on both content and organization.

    You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online.As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and PygalMake 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progressesWork with data to generate interactive visualizationsCreate and customize simple web apps and deploy them safely onlineDeal with mistakes and errors so you can solve your own programming problemsIf you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code!

    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

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Commercial game development expert Robert Nystrom presents an array of general solutions to problems encountered in game development. For example, you'll learn how double-buffering enables a player to perceive smooth and realistic motion, and how the service locator pattern can help you provide access to services such as sound without coupling your code to any particular sound driver or sound hardware. Games have much in common with other software, but also a number of unique constraints. Some of the patterns in this book are well-known in other domains of software development. Other of the patterns are unique to gaming. In either case, Robert Nystrom bridges from the ivory tower world of software architecture to the in-the-trenches reality of hardcore game programming. You'll learn the patterns and the general problems that they solve. You'll come away able to apply powerful and reusable architectural solutions that enable you to produce higher quality games with less effort than before. Applies classic design patterns to game programming. Introduces new patterns specific to game programming. Brings abstract software architecture down to Earth with approachable writing and an emphasis on simple code that shows each pattern in practice. What you'll learn Overcome architectural challenges unique to game programming Apply lessons from the larger software world to games. Tie different parts of a game (graphics, sound, AI) into a cohesive whole. Create elegant and maintainable architecture. Achieve good, low-level performance. Gain insight into professional, game development. Who this book is forGame Programming Patterns is aimed at professional game programmers who, while successful in shipping games, are frustrated at how hard it sometimes is to add and modify features when a game is under development. Game Programming Patterns shows how to apply modern software practices to the problem of game development while still maintaining the blazing-fast performance demanded by hard-core gamers. Game Programming Patterns also appeals to those learning about game programming in their spare time. Hobbyists and aspiring professionals alike will find much to learn in this book about pathfinding, collision detection, and other game-programming problem domains.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set.

    Lambda enables users to develop code that executes in response to events - API calls, file uploads, schedules, etc - and upload it without worrying about managing traditional server metrics such as disk space, memory, or CPU usage. With its "per execution" cost model, Lambda can enable organizations to save hundreds or thousands of dollars on computing costs. With in-depth walkthroughs, large screenshots, and complete code samples, the reader is guided through the step-by-step process of creating new functions, responding to infrastructure events, developing API backends, executing code at specified intervals, and much more. Introduction to AWS Computing Evolution of the Computing Workload Lambda Background The Internals The Basics Functions Languages Resource Allocation Getting Set Up Hello World Uploading the Function Working with Events AWS Events Custom Events The Context Object Properties Methods Roles and Permissions Policies Trust Relationships Console Popups Cross Account Access Dependencies and Resources Node Modules OS Dependencies OS Resources OS Commands Logging Searching Logs Testing Your Function Lambda Console Tests Third-Party Testing Libraries Simulating Context Hello S3 Object The Bucket The Role The Code The Event The Trigger Testing When Lambda Isn’t the Answer Host Access Fine-Tuned Configuration Security Long-Running Tasks Where Lambda Excels AWS Event-Driven Tasks Scheduled Events (Cron) Offloading Heavy Processing API Endpoints Infrequently Used Services Real-World Use Cases S3 Image Processing Shutting Down Untagged Instances Triggering CodeDeploy with New S3 Uploads Processing Inbound Email Enforcing Security Policies Detecting Expiring Certificates Utilizing the AWS API Execution Environment The Code Pipeline Cold vs. Hot Execution What is Saved in Memory Scaling and Container Reuse From Development to Deployment Application Design Development Patterns Testing Deployment Monitoring Versioning and Aliasing Costs Short Executions Long-Running Processes High-Memory Applications Free Tier Calculating Pricing CloudFormation Reusable Template with Minimum Permissions Cross Account Access CloudWatch Alerts AWS API Gateway API Gateway Event Creating the Lambda Function Creating a New API, Resource, and Method Initial Configuration Mapping Templates Adding a Query String Using HTTP Request Information Within Lambda Deploying the API Additional Use Cases Lambda Competitors Iron.io StackHut WebTask.io Existing Cloud Providers The Future of Lambda More Resources Conclusion

    And then we wonder why software is notorious for being delivered late and full of bugs, while other engineers routinely deliver finished bridges, automobiles, electrical appliances, etc., on time and with only minor defects. This book sets out to redress this imbalance. Members of my advanced development team at Adobe who took the course based on the same material all benefited greatly from the time invested. It may appear as a highly technical text intended only for computer scientists, but it should be required reading for all practicing software engineers." --Martin Newell, Adobe Fellow"The book contains some of the most beautiful code I have ever seen." --Bjarne Stroustrup, Designer of C++"I am happy to see the content of Alex's course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book." --Forest Baskett, General Partner, New Enterprise Associates"Paul's patience and architectural experience helped to organize Alex's mathematical approach into a tightly-structured edifice--an impressive feat!" --Robert W. Taylor, Founder of Xerox PARC CSL and DEC Systems Research Center Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software.This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book's value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, and doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system's software components will work together and behave as they must.The book presents a number of algorithms and requirements for types on which they are defined. The code for these descriptions--also available on the Web--is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup.Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book's experienced authors have been teaching and demonstrating for years--that mathematics is good for programming, and that theory is good for practice.

    Built for your brain, this book covers C# 3.0 and Visual Studio 2008, and teaches everything from language fundamentals to advanced topics including garbage collection, extension methods, and double-buffered animation. You'll also master C#'s hottest and newest syntax, LINQ, for querying SQL databases, .NET collections, and XML documents. By the time you're through, you'll be a proficient C# programmer, designing and coding large-scale applications. Every few chapters you will come across a lab that lets you apply what you've learned up to that point. Each lab is designed to simulate a professional programming task, increasing in complexity until-at last-you build a working Invaders game, complete with shooting ships, aliens descending while firing, and an animated death sequence for unlucky starfighters. This remarkably engaging book will have you going from zero to 60 with C# in no time flat.