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Summary: Citizen Media and Public Spaces presents a pioneering exploration of citizen media as a highly interdisciplinary domain that raises vital political, social and ethical issues relating to conceptions of citizenship and state boundaries, the construction of publics and social imaginaries, processes of co-optation and reverse co-optation, power and resistance, the ethics of witnessing and solidarity, and novel responses to the democratic deficit. Framed by a substantial introduction by the editors, the twelve contributions to the volume interrogate the concept of citizen media theoretically and empirically, and offer detailed case studies that extend from the UK to Russia and Bulgaria and from China to Denmark and the liminal spaces within which a growing number of refugees now live.

Summary: Introduction: the impact of blogging on the BBC's coverage of war and terrorism The "war and terror" blogosphere. Blogs: "rumour, prejudice and gossip" or "standard" BBC source? Reporting conflict from news needles in digital haystacks Information overload, the 24/7 news cycle and the turn to twitter "Outside the BBC universe"? blogging at the BBC "Can you teach granddad how to dance"? involving the audience on BBC programme blogs "Live blogging" terror: the BBC's coverage of the attacks on Mumbai Reporting conflict: war in Gaza and the limits of the news revolution Conclusion: the two faces of Janus: the future of journalism.

Summary: The Routledge Companion to Alternative and Community Media provides an authoritative and comprehensive examination of the diverse forms, practices and philosophies of alternative and community media across the world. The volume offers a multiplicity of perspectives to examine the reasons why alternative and community media arise, how they develop in particular ways and in particular places, and how they can enrich our understanding of the broader media landscape and its place in society. The 50 chapters present a range of theoretical and methodological positions, and arguments to demonstrate

Summary: 40 Puzzles and Problems in Probability and Mathematical Statistics is intended to teach the reader to think probabilistically by solving challenging, non-standard probability problems. The motivation for this clearly written collection lies in the belief that challenging problems help to develop, and to sharpen, our probabilistic intuition much better than plain-style deductions from abstract concepts. The selected problems fall into two broad categories. Problems related to probability theory come first, followed by problems related to the application of probability to the field of mathematical statistics. All problems seek to convey a non-standard aspect or an approach which is not immediately obvious. The word puzzles in the title refers to questions in which some qualitative, non-technical insight is most important. Ideally, puzzles can teach a productive new way of framing or representing a given situation. Although the border between the two is not always clearly defined, problems tend to require a more systematic application of formal tools, and to stress more technical aspects. Thus, a major aim of the present collection is to bridge the gap between introductory texts and rigorous state-of-the-art books. Anyone with a basic knowledge of probability, calculus and statistics will benefit from this book; however, many of the problems collected require little more than elementary probability and straight logical reasoning. To assist anyone using this book for self-study, the author has included very detailed step-for-step solutions of all problems and also short hints which point the reader in the appropriate direction."--Publisher's website.

Summary: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.