It discusses cloud concepts and highlights various design patterns and best practices. This documentation is offered for free here as a Kindle book, or you can read it in PDF format at https://aws.amazon.com/whitepapers/.

    This hands-on guide provides invaluable insight for creating successful test-driven development processes. With source code and examples featured in both C# and .NET, the book walks you through the TDD methodology and shows how it is applied to a real-world application. You'll witness the application built from scratch and details each step that is involved in the development, as well as any problems that were encountered and the solutions that were applied.Clarifies the motivation behind test-driven development (TDD), what it is, and how it works Reviews the various steps involved in developing an application and the testing that is involved prior to implementing the functionality Discusses unit testing and refactoring Professional Test-Driven Development with C# shows you how to create great TDD processes right away.

    

    In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming—anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

    With this concise book, you will learn how to use the latest version of this W3C standard to retrieve and manipulate the increasing amount of public and private data available via SPARQL endpoints. Several open source and commercial tools already support SPARQL, and this introduction gets you started right away.Begin with how to write and run simple SPARQL 1.1 queries, then dive into the language's powerful features and capabilities for manipulating the data you retrieve. Learn what you need to know to add to, update, and delete data in RDF datasets, and give web applications access to this data.Understand SPARQL’s connection with RDF, the semantic web, and related specificationsQuery and combine data from local and remote sourcesCopy, convert, and create new RDF dataLearn how datatype metadata, standardized functions, and extension functions contribute to your queriesIncorporate SPARQL queries into web-based applications

    It presents a learning journey, not definitive guidance. It describes the experiences of a development team with no prior CQRS proficiency in building, deploying (to Windows Azure), and maintaining a sample real-world, complex, enterprise system to showcase various CQRS and ES concepts, challenges, and techniques.The development team did not work in isolation; we actively sought input from industry experts and from a wide group of advisors to ensure that the guidance is both detailed and practical.The CQRS pattern and event sourcing are not mere simplistic solutions to the problems associated with large-scale, distributed systems. By providing you with both a working application and written guidance, we expect you’ll be well prepared to embark on your own CQRS journey.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    Build hands-on expertise through a series of lessons, exercises, and suggested practices—and help maximize your performance on the job.Provides in-depth, hands-on training you take at your own pace Focuses on job-role-specific expertise for using HTML5, JavaScript, and CSS3 to begin building modern web and Windows 8 apps Features pragmatic lessons, exercises, and practices Creates a foundation of skills which, along with on-the-job experience, can be measured by Microsoft Certification exams such as 70-480 Coverage includes: creating HTML5 documents; implementing styles with CSS3; JavaScript in depth; using Microsoft developer tools; AJAX; multimedia support; drawing with Canvas and SVG; drag and drop functionality; location-aware apps; web storage; offline apps; writing your first simple Windows 8 apps; and other key topics

    Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website.Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithmsDevelop a unit testing framework for debugging bandit algorithmsGet additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials

    The patterns apply to the development process as well as to architecture and coding practices. The content is based on a presentation developed by Scott Guthrie and delivered by him at the Norwegian Developers Conference (NDC) in June of 2013 (part 1, part 2), and at Microsoft Tech Ed Australia in September 2013 (part 1, part 2). Many others updated and augmented the content while transitioning it from video to written form. Who should read this book Developers who are curious about developing for the cloud, are considering a move to the cloud, or are new to cloud development will find here a concise overview of the most important concepts and practices they need to know. The concepts are illustrated with concrete examples, and each chapter includes links to other resources that provide more in-depth information. The examples and the links to additional resources are for Microsoft frameworks and services, but the principles illustrated apply to other web development frameworks and cloud environments as well. Developers who are already developing for the cloud may find ideas here that will help make them more successful. Each chapter in the series can be read independently, so you can pick and choose topics that you're interested in. Anyone who watched Scott Guthrie's "Building Real World Cloud Apps with Windows Azure" presentation and wants more details and updated information will find that here. Assumptions This ebook expects that you have experience developing web applications by using Visual Studio and ASP.NET. Familiarity with C# would be helpful in places.

    Freeman, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyLearning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time.Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

    Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.