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Silvio Carta 
Machine Learning and the City [EPUB ebook] 
Applications in Architecture and Urban Design

Wsparcie

Machine Learning and the City
Explore the applications of machine learning and artificial intelligence to the built environment
Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning.
Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes:
* An introduction to the probabilistic logic that underpins machine learning
* Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments
* Practical discussions of the consequences of applied machine learning and the future of urban design
Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.

€91.99
Metody Płatności

O autorze

Silvio Carta is an architect and Associate Professor at the University of Hertfordshire, UK. His research interests include digital architecture, data-driven approaches and computational design. Silvio is the author of Big Data, Code and the Discrete City. Shaping Public Realms (Routledge 2019).

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Język Angielski ● Format EPUB ● Strony 672 ● ISBN 9781119749622 ● Rozmiar pliku 320.5 MB ● Redaktor Silvio Carta ● Wydawca John Wiley & Sons ● Opublikowany 2022 ● Ydanie 1 ● Do pobrania 24 miesięcy ● Waluta EUR ● ID 8427383 ● Ochrona przed kopiowaniem Adobe DRM
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