Shay Byrne's Books

Miracle of Fatima Mansions

An Escape from Drug Addiction

By: Shay Byrne

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Miracle of Fatima Mansions

An Escape from Drug Addiction

By: Shay Byrne

The Miracle of Fatima Mansions traces a young man’s terrifying descent into a life of drug addiction, crime and prostitution, against the backdrop of the cultural and sexual revolution of the 60s and 70s. Drawn by the bright lights and emerging drug-scene of ‘Swinging London’, Shay Byrne left his native Dublin to immerse himself in his new found-freedom. Drifting between the vibrant squats in Hampstead Heath, he soon sank into a life of drug addiction and petty-crime. Returning to Ireland, his life took a dramatic turn when he narrowly escaped death during a violent attack at the city’s squalid Fatima Mansions estate. Fatima Mansions represented a leap into a brighter future for the city’s working class. But the dream was short-lived, and by the late 60s it had become synonymous with extreme social depravation, drugs and urban decay. This would be the unlikely location of an epiphany that would transform Shay’s life. Slowly rebuilding his life, the author had to deal with the physical and mental damage caused by long-term drug abuse. The Miracle of Fatima Mansions reveals the true legacy of the 1960s drug culture.

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Page Count

212

Publisher

Maverick House

Published Date

ISBN 10

1908518421

ISBN 13

9781908518422

Advances in Health Care Organization Theory

By: Stephen S. Mick,Patrick D. Shay

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Advances in Health Care Organization Theory

By: Stephen S. Mick,Patrick D. Shay

Explore the evolution of organization theory in the health care sector Advances in Health Care Organization Theory, 2nd Edition, introduces students in health administration to the fields of organization theory and organizational behavior and their application to the management of health care organizations. The book explores the major health care developments over the past decade and demonstrates the contribution of organization theory to a deeper understanding of the changes in the delivery system, including the historic passage of the Patient Protection and Affordable Care Act of 2010. Taking both a micro and macro view, editors Stephen S. Mick and Patrick D. Shay, collaborate with a roster of contributing experts to compile a comprehensive volume that covers the latest in organization theory. Topics include: Institutional and neo-institutional theory Patient-centered practices and organizational culture change Design and implementation of patient-centered care management teams Hospital-based clusters as new organizational structures Application of social network theory to health care

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Page Count

459

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118862775

ISBN 13

9781118862773

Bayesian Analysis in Natural Language Processing

Second Edition

By: Shay Cohen

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Bayesian Analysis in Natural Language Processing

Second Edition

By: Shay Cohen

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the use of statistical techniques in NLP has evolved in several ways. One such example of evolution took place in the late 1990s or early 2000s, when full-fledged Bayesian machinery was introduced to NLP. This Bayesian approach to NLP has come to accommodate various shortcomings in the frequentist approach and to enrich it, especially in the unsupervised setting, where statistical learning is done without target prediction examples. In this book, we cover the methods and algorithms that are needed to fluently read Bayesian learning papers in NLP and to do research in the area. These methods and algorithms are partially borrowed from both machine learning and statistics and are partially developed "in-house" in NLP. We cover inference techniques such as Markov chain Monte Carlo sampling and variational inference, Bayesian estimation, and nonparametric modeling. In response to rapid changes in the field, this second edition of the book includes a new chapter on representation learning and neural networks in the Bayesian context. We also cover fundamental concepts in Bayesian statistics such as prior distributions, conjugacy, and generative modeling. Finally, we review some of the fundamental modeling techniques in NLP, such as grammar modeling, neural networks and representation learning, and their use with Bayesian analysis.

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