Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Fully updated for the new Java SE 6 platform, this no-nonsense tutorial and reliable reference illuminates the most important language and library features with thoroughly tested real-world examples. The example programs have been carefully crafted to be easy to understand as well as useful in practice, so you can rely on them as an outstanding starting point for your own code. Volume I is designed to quickly bring you up to speed on what's new in Java SE 6 and to help you make the transition as efficiently as possible, whether you're upgrading from an earlier version of Java or migrating from another language. The authors concentrate on the fundamental concepts of the Java language, along with the basics of user-interface programming. You'll find detailed, insightful coverage ofJava fundamentalsObject-oriented programmingInterfaces and inner classesReflection and proxiesThe event listener modelGUI programming with SwingPackaging applicationsException handlingLogging and debuggingGeneric programmingThe collections frameworkConcurrencyFor detailed coverage of XML processing, networking, databases, internationalization, security, advanced AWT/Swing, and other advanced features, look for the forthcoming eighth edition of "Core Java(TM), Volume II--Advanced Features "(ISBN: 978-0-13-235479-0).

    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

    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.

    A classic title on the PC revolution originally published in 1986. Featuring Bill Gates, Andy Hertzfeld, Charles Simonyi, Ray Ozzie, Michael Hawley and many more.

    Internet and World Wide Web How to Program, 4e introduces students with little or no programming experience to the exciting world of Web-Based applications. The book has been substantially revised to reflect today's Web 2.0 rich Internet application-development methodologies. A comprehensive book that teaches the fundamentals needed to program on the Internet, this text provides in-depth coverage of introductory programmming principles, various markup languages (XHTML, Dynamic HTML and XML), several scripting languages (JavaScript, PHP, Ruby/Ruby on Rails and Perl); AJAX, web services, Web Servers (IIS and Apache) and relational databases (MySQL/Apache Derby/Java DB) -- all the skills and tools needed to create dynamic Web-based applications. The text contains comprehensive introductions to ASP.NET 2.0 and JavaServer Faces (JSF). Hundreds of live-code examples of real applications throughout the book available for download allow readers to run the applications and see and hear the outputs.The book provides instruction on building Ajax-enabled rich Internet applications that enhance the presentation of online content and give web applications the look and feel of desktop applications. The chapter on Web 2.0 and Internet business exposes readers to a wide range of other topics associated with Web 2.0 applications and businesses After mastering the material in this book, students will be well prepared to build real-world, industrial strength, Web-based applications.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Along the way, we encounter black holes, turtles, snakes, dragons, axe-sharpening, and yak-shaving—and take a guided tour through the theories and methods, both brilliant and misguided, that litter the history of software development, from the famous ‘mythical man-month’ to Extreme Programming. Not just for technophiles but for anyone captivated by the drama of invention, Dreaming in Code offers a window into both the information age and the workings of the human mind.

    The Hacker Playbook provides them their own game plans. Written by a longtime security professional and CEO of Secure Planet, LLC, this step-by-step guide to the “game” of penetration hacking features hands-on examples and helpful advice from the top of the field.Through a series of football-style “plays,” this straightforward guide gets to the root of many of the roadblocks people may face while penetration testing—including attacking different types of networks, pivoting through security controls, privilege escalation, and evading antivirus software.From “Pregame” research to “The Drive” and “The Lateral Pass,” the practical plays listed can be read in order or referenced as needed. Either way, the valuable advice within will put you in the mindset of a penetration tester of a Fortune 500 company, regardless of your career or level of experience.This second version of The Hacker Playbook takes all the best "plays" from the original book and incorporates the latest attacks, tools, and lessons learned. Double the content compared to its predecessor, this guide further outlines building a lab, walks through test cases for attacks, and provides more customized code.Whether you’re downing energy drinks while desperately looking for an exploit, or preparing for an exciting new job in IT security, this guide is an essential part of any ethical hacker’s library—so there’s no reason not to get in the game.

    On Lisp explains the reasons behind Lisp's growing popularity as a mainstream programming language. On Lisp is a comprehensive study of advanced Lisp techniques, with bottom-up programming as the unifying theme. It gives the first complete description of macros and macro applications. The book also covers important subjects related to bottom-up programming, including functional programming, rapid prototyping, interactive development, and embedded languages. The final chapter takes a deeper look at object-oriented programming than previous Lisp books, showing the step-by-step construction of a working model of the Common Lisp Object System (CLOS). As well as an indispensable reference, On Lisp is a source of software. Its examples form a library of functions and macros that readers will be able to use in their own Lisp programs.

    Along with a clear introduction to asynchronous programming, you get an in-depth look at how the async feature works and why you might want to use it in your application.Written for experienced C# programmers—yet approachable for beginners—this book is packed with code examples that you can extend for your own projects.Write your own asynchronous code, and learn how async saves you from this messy choreDiscover new performance possibilities in ASP.NET web server codeExplore how async and WinRT work together in Windows 8 applicationsLearn the importance of the await keyword in async methodsUnderstand which .NET thread is running your code—and at what points in the programUse the Task-based Asynchronous Pattern (TAP) to write asynchronous APIs in .NETTake advantage of parallel computing in modern machinesMeasure async code performance by comparing it with alternatives

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.