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This highly topical book is a concise and accessible account of the relationship between technology and work. Firstly, it reviews and critically assesses a variety of recent approaches to the social and cultural dimensions of technology. Secondly, it examines the implications of these new approaches for existing ideas about the nature of technology and work organization. At the core of much thinking about technology is the assumption that the technical character and capacity of artefacts is given. The enduring image of deus ex machina captures the idea that it is the essential capacity 'within' a technology which, in the end, accounts for the way we organize ourselves, our work and other life experiences. Recent work in the sociology of technology, by contrast, sets out relativist and constructivist accounts of technology, which begin to challenge this central assumption. The Machine at Work includes a reinterpretation of the Luddites; a review of the social processes of development in information technology; a reassessment of theories of the role of technology in work; and an analysis of the common limitations of some constructivist and feminist perspectives on technology. The book argues that only a commitment to a particular conception of constructivism enables the kind of radical rethinking about technology and work relations that is needed. This engaging and informative text will be of interest to students in a range of subject areas – from sociology, organizational theory and behaviour, to industrial relations, management and business studies.

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 15% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new cameras and image acquisition interfaces, 3D sensors and technologies, 3D object recognition and 3D image reconstruction. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13, a trial version of which is available from the authors' website.

Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide  As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions.  The  AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam  delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture.  From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam.  You’ll also find:  An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms  AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam  is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning.

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For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision. With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.

Third — третий альбом британской группы Soft Machine, изданный в 1970 году. Издание на виниле. Группа глубже ушла в джаз и современную электронную музыку на этом альбоме, который ведущий нью-йоркский культурный еженедельник Village Voice назвал эпохальным достижением. Это двойной альбом великолепной музыки, где на каждой стороне содержится одна композиция, две из них написал Рэтлидж и по одной — Хоппер и Уайетт, существенный вклад в альбом внесли также столпы Кентерберийской сцены Элтон Дин и Джимми Хастингс. Музыка альбома развивает идеи джаз-фьюжна, ярко заявившего о себе на альбоме Майлса Дэвиса Bitches Brew, выпущенном несколькими месяцами ранее. Альбом обозначил переход группы от психоделического рока к джазу и стал заметной вехой в истории Кентерберийской сцены. Состав музыкантов, участвовавших в записи альбома Third — один из самых сильных в истории Soft Machine. Альбом отличает великолепное музыкальное взаимодействие между Майком Рэтлиджем на клавишных, Робертом Уайеттом на барабанах, Хью Хоппером на бас-гитаре и новым членом группы Элтоном Дином на саксофоне. Альбом поднялся до 18 места в британском чарте альбомов и продержался там 6 недель.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.