Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.

    In the tradition of Manning's ground breaking "In Action" series, this book comes from right from the source. Written by Bruce Payette, one of principal creators of PowerShell, Windows PowerShell in Action shows you how to build scripts and utilities to automate system tasks or create powerful system management tools to handle the day-to-day tasks that drive a Windows administrator's life. Because it's based on the .NET platform, PowerShell is also a powerful tool for developers and power users.Windows PowerShell in Action was written by Bruce Payette, one of the founding members of the Windows PowerShell team, co-designer of the PowerShell language and the principal author of the PowerShell language implementation. The book enables you to get the most out of the PowerShell environment. Using many examples, both small and large, this book illustrates the features of the language and environment and shows how to compose those features into solutions, quickly and effectively.This book is designed for anyone who wants to learn PowerShell and use it well. Rather than simply being a book of recipes to read and apply, this book gives you the deep knowledge about how PowerShell works and how to apply it.

    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

    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.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Vaughn Vernon couples guided approaches to implementation with modern architectures, highlighting the importance and value of focusing on the business domain while balancing technical considerations.Building on Eric Evans’ seminal book, Domain-Driven Design, the author presents practical DDD techniques through examples from familiar domains. Each principle is backed up by realistic Java examples–all applicable to C# developers–and all content is tied together by a single case study: the delivery of a large-scale Scrum-based SaaS system for a multitenant environment.The author takes you far beyond “DDD-lite” approaches that embrace DDD solely as a technical toolset, and shows you how to fully leverage DDD’s “strategic design patterns” using Bounded Context, Context Maps, and the Ubiquitous Language. Using these techniques and examples, you can reduce time to market and improve quality, as you build software that is more flexible, more scalable, and more tightly aligned to business goals.

    The manner in which computers are now able to mimic human thinking is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the age of machine learning, computers do not strictly need to receive an ‘input command’ to perform a task, but rather ‘input data’. From the input of data they are able to form their own decisions and take actions virtually as a human would. But as a machine, can consider many more scenarios and execute calculations to solve complex problems. This is the element that excites companies and budding machine learning engineers the most. The ability to solve complex problems never before attempted. This is also perhaps one reason why you are looking at purchasing this book, to gain a beginner's introduction to machine learning. This book provides a plain English introduction to the following topics: - Artificial Intelligence - Big Data - Downloading Free Datasets - Regression - Support Vector Machine Algorithms - Deep Learning/Neural Networks - Data Reduction - Clustering - Association Analysis - Decision Trees - Recommenders - Machine Learning Careers This book has recently been updated following feedback from readers. Version II now includes: - New Chapter: Decision Trees - Cleanup of minor errors

    While other nerds were fumbling with password possibilities, this adept break-artist was penetrating the digital secrets of Sun Microsystems, Digital Equipment Corporation, Nokia, Motorola, Pacific Bell, and other mammoth enterprises. His Ghost in the Wires memoir paints an action portrait of a plucky loner motivated by a passion for trickery, not material game. (P.S. Mitnick's capers have already been the subject of two books and a movie. This first-person account is the most comprehensive to date.)

    In the second edition of Network Security, this most distinguished of author teams draws on hard-won experience to explain every facet of information security, from the basics to advanced cryptography and authentication; secure Web and email services; and emerging security standards. Highlights of the book's extensive new coverage include Advanced Encryption Standard (AES), IPsec, SSL, PKI Standards, and Web security.

    Some of its new features are already being implemented by existing browsers, and much more is around the corner. Written by developers who have been using the new language for the past year in their work, this book shows you how to start adapting the language now to realize its benefits on today's browsers. Rather than being just an academic investigation, it concentrates on the practical--the problems HTML5 can solve for you right away. By following the book's hands-on HTML5 code examples you'll learn: new semantics and structures to help your site become richer and more accessiblehow to apply the most important JavaScript APIs that are already implementedthe uses of native multimedia for video and audiotechniques for drawing lines, fills, gradients, images and text with canvas how to build more intelligent web formsimplementation of new storage options and web databaseshow geolocation works with HTML5 in both web and mobile applicationsAll the code from this book (and more) is available at www.introducinghtml5.com. ********There appear to be intermittent problems with the first printing of Introducing HTML5. If you have one of these copies, please email us at ask@peachpit.com with a copy of your receipt (from any reseller), and we'll either provide access to the eBook or send you another copy of the print book -- whichever you prefer. If you'd like the eBook we can add that to your Peachpit.com account. You can set up a free account at www.peachpit.com/join http: //www.peachpit.com/join>. Thanks so much for your understanding!

    In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.

    Their applications range from network routing and computational genomics to public-key cryptography and database system implementation. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject---a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. The exposition is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. Part 1 of the book series covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.

    It's all muscle and no fluff. Discover how the experts tackle Windows 7--and challenge yourself to new levels of mastery! Compare features and capabilities in each edition of Windows 7.Configure and customize your system with advanced setup options.Manage files, folders, and media libraries.Set up a wired or wireless network and manage shared resources.Administer accounts, passwords, and logons--and help control access to resources.Configure Internet Explorer 8 settings and security zones.Master security essentials to help protect against viruses, worms, and spyware.Troubleshoot errors and fine-tune performance.Automate routine maintenance with scripts and other tools. CD includes: Fully searchable eBookDownloadable gadgets and other tools for customizing Windows 7Insights direct from the product team on the official Windows 7 blogLinks to the latest security updates and products, demos, blogs, and user communities For customers who purchase an ebook version of this title, instructions for downloading the CD files can be found in the ebook.

    With more traditional web servers becoming obsolete, having knowledge on servers that achieve high scalability and optimal resource consumption using Node.js is the key to your app development success. Teaching you the essentials to making event-driven server-side apps, this book demonstrates how you can use less space and take less time for communication between web client and server.Contains numerous hands-on examples Explains implementation of real-time apps including Socket.IO and HTML5, and WebSockets Addresses practical Node.js advantages from specific design choices Demonstrates why knowledge and use of JavaScript is beneficial Includes an interactive online component with sample chapters Explains components of stand out apps including brevity and benchmarks Looking to enhance your abilities even further? Smashing Node.js: JavaScript Everywhere makes developing server-side apps accessible with its focus on JavaScript, open source, and easy-to-use language.

    Star Schema: The Complete Reference offers in-depth coverage of design principles and their underlying rationales. Organized around design concepts and illustrated with detailed examples, this is a step-by-step guidebook for beginners and a comprehensive resource for experts.This all-inclusive volume begins with dimensional design fundamentals and shows how they fit into diverse data warehouse architectures, including those of W.H. Inmon and Ralph Kimball. The book progresses through a series of advanced techniques that help you address real-world complexity, maximize performance, and adapt to the requirements of BI and ETL software products. You are furnished with design tasks and deliverables that can be incorporated into any project, regardless of architecture or methodology.Master the fundamentals of star schema design and slow change processingIdentify situations that call for multiple stars or cubesEnsure compatibility across subject areas as your data warehouse growsAccommodate repeating attributes, recursive hierarchies, and poor data qualitySupport conflicting requirements for historic dataHandle variation within a business process and correlation of disparate activitiesBoost performance using derived schemas and aggregatesLearn when it's appropriate to adjust designs for BI and ETL tools