Search results for ""Author Yue Zhang""

While urban preservation is almost as old as cities themselves, it has become increasingly controversial in modern cities. In this book, Yue Zhang presents a cross-national comparative analysis of the politics of urban preservation. Based on comprehensive archival research and more than two hundred in-depth interviews in Beijing, Chicago, and Paris, Zhang finds that urban preservation provides a tool for diverse political and social actors to frame their propositions and advance their favored courses of action. In cities from West to East, divergent political and economic interests have caused urban preservation to become contested. Exploring three of the world’s great cities, Zhang deftly navigates readers through each case study, illustrating the complexities of the politics of urban preservation in each city. In Beijing, urban preservation was integral to promoting economic growth and enhancing the city’s image during the lead-up to the 2008 Olympics; in Chicago, it is used to increase property values and revitalize neighborhoods; and in Paris, it offers a channel for national and municipal governments to compete for control over urban space. Although urban preservation serves various purposes in these cities, Zhang explains how different types of political fragmentation have affected the implementation of preservation initiatives in predictable ways, thus generating distinct patterns of urban preservation. A comparative urban politics study of unusual breadth, The Fragmented Politics of Urban Preservation gives us insight into the complex policy process of urban preservation through which political institutions are intertwined with interests and inclinations, fundamentally shaping the direction of urban development, the physical forms of cities, and the lives of citizens.

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With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

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