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Much of the innovative programming that powers the Internet, creates operating systems, and produces software is the result of “open source” code, that is, code that is freely distributed—as opposed to being kept secret—by those who write it. Leaving source code open has generated some of the most sophisticated developments in computer technology, including, most notably, Linux and Apache, which pose a significant challenge to Microsoft in the marketplace. As Steven Weber discusses, open source’s success in a highly competitive industry has subverted many assumptions about how businesses are run, and how intellectual products are created and protected. Traditionally, intellectual property law has allowed companies to control knowledge and has guarded the rights of the innovator, at the expense of industry-wide cooperation. In turn, engineers of new software code are richly rewarded; but, as Weber shows, in spite of the conventional wisdom that innovation is driven by the promise of individual and corporate wealth, ensuring the free distribution of code among computer programmers can empower a more effective process for building intellectual products. In the case of Open Source, independent programmers—sometimes hundreds or thousands of them—make unpaid contributions to software that develops organically, through trial and error. Weber argues that the success of open source is not a freakish exception to economic principles. The open source community is guided by standards, rules, decisionmaking procedures, and sanctioning mechanisms. Weber explains the political and economic dynamics of this mysterious but important market development.

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Learn to use open source software to automate the processing of scanned and digital documents without the use of barcodes or separator sheets.

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Extend the power of your infrastructure and applications with Salt modules About This Book Get the most up-to-date practical resource on writing new Salt modules and extending Salt Learn through use cases and encounter both commonly-used modules as well as advanced ones Effectively troubleshoot problems and hiccups encountered while building and putting modules to work Who This Book Is For This book is for both new and existing Salt developers who are looking to build and write new Salt modules. Some prior Python development experience is expected. What You Will Learn Understand the working of Salt's Loader system Write several of the most common types of Salt modules Interact between different kinds of modules and build new ones Submit open source modules upstream to the Salt project Make Salt interact with third-party services and applications In Detail Salt already ships with a very powerful set of tools, but that doesn't mean that they all suit your needs perfectly. By adding your own modules and enhancing existing ones, you can bring the functionality that you need to increase your productivity. Extending SaltStack follows a tutorial-based approach to explain different types of modules, from fundamentals to complete and full-functioning modules. Starting with the Loader system that drives Salt, this book will guide you through the most common types of modules. First you will learn how to write execution modules. Then you will extend the configuration using the grain, pillar, and SDB modules. Next up will be state modules and then the renderers that can be used with them. This will be followed with returner and output modules, which increase your options to manage return data. After that, there will be modules for external file servers, clouds, beacons, and finally external authentication and wheel modules to manage the master. With this guide in hand, you will be prepared to create, troubleshoot, and manage the most common types of Salt modules and take your infrastructure to new heights! Style and approach This book follows a step-by-step tutorial-based approach explaining the different types of modules, from fundamentals to complete and full-functioning modules.

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Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks.The book will take a how-to approach, focusing on recipes that demonstrate Stylecop code analysis.Stylecop Code Analysis How-to is intended for developers and project managers who are looking for an all-encompassing overview of Stylecop to display or enforce their specific coding conventions. It is assumed that you have a minimal understanding of integration technologies such as MSBuild and Nant. You should also have some knowledge on how to program in C# and LINQ. An available version of Visual Studio 2008 professional or superior is also required to follow the first chapters of this book.

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Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow. About This Book Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. What You Will Learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to ...

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