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1. |
Record Nr. |
UNINA990005052450403321 |
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Autore |
Piton, Camille |
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Titolo |
Les Lombards : en France & à Paris / par C. Piton |
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Pubbl/distr/stampa |
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Paris : Libr. Champion, 1892-1893 |
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Descrizione fisica |
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Disciplina |
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Locazione |
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Collocazione |
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332.109 PIT 1 (1) |
332.109 PIT 1 (2) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1.: 1892 2.: Leurs marques - Leurs poids-monnaie - Leurs sceaux de plomb [...] - 1893 |
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2. |
Record Nr. |
UNINA9910805685203321 |
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Autore |
Thiel Sonja |
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Titolo |
AI in Museums : Reflections, Perspectives and Applications |
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Pubbl/distr/stampa |
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Bielefeld, : transcript, 2023 |
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©2023 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (321 pages) |
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Collana |
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Altri autori (Persone) |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Frontmatter -- Contents -- Foreword -- Introduction -- Part 1: Reflections -- The Role of Culture in the Intelligence of AI -- Why AI Cannot Think -- AI and Art -- The Hidden Costs of AI -- Dead End or Way Out? -- Power, Data and Control -- Managing AI -- Museum-AI Assemblages -- Part 2: Perspectives -- AI with Museums and Cultural Heritage -- Troubleshoot? -- Digital Curation and AI -- Teaching Provenance to AI -- The Funding Program LINK-AI and Culture -- Discovering Culture with AI -- Post-Truth -- Impostor Syndrome -- Part 3: Applications -- Algorithmic Exhibition-Making -- Evaluating the Blackbox -- Clouds of Symbols -- xCurator -- Say the Image, Don't Make It -- CHIM-Chatbot in the Museum -- With AI to Art! -- Exploring Beyond the Exhibits -- Tracking the Visitor -- Symotiv -- Notes on Contributors -- Abstracts |
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Sommario/riassunto |
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Artificial intelligence is becoming an increasingly important topic in the cultural sector. While museums have long focused on building digital object databases, the existing data can now become a field of application for machine learning, deep learning and foundation model approaches. This goes hand in hand with new artistic practices, curation tools, visitor analytics, chatbots, automatic translations and tailor-made text generation. With a decidedly interdisciplinary approach, the volume brings together a wide range of critical reflections, practical perspectives and concrete applications of artificial |
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intelligence in museums, and provides an overview of the current state of the debate. |
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3. |
Record Nr. |
UNINA9910921008103321 |
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Autore |
Lobianco Antonello |
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Titolo |
Julia Quick Syntax Reference : A Pocket Guide for Data Science Programming / / by Antonello Lobianco |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2024 |
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ISBN |
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Edizione |
[2nd ed. 2024.] |
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Descrizione fisica |
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1 online resource (239 pages) |
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Collana |
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Professional and Applied Computing Series |
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Disciplina |
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Soggetti |
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Julia (Computer program language) |
Computer programming |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Part 1. Language Core -- 1. Getting Started -- 2. Data Types and Structures -- 3. Control Flow and Functions -- 4. Custom Types -- E1: Shelling Segregation Model - 5. Input – Output -- 6. Metaprogramming and Macros -- 7. Interfacing Julia with Other Languages -- 8. Efficiently Write Efficient Code. - 9 Parallel Computing in Julia - Part 2. Packages Ecosystem -- 10. Working with Data -- 11. Scientific Libraries -- E2: Fitting a forest growth model - 12 – AI with Julia – E3. Predict house values - 13. Utilities. Appendix: Solutions to the exercises. |
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Sommario/riassunto |
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Learn the Julia programming language as quickly as possible. This book is a must-have reference guide that presents the essential Julia syntax in a well-organized format, updated with the latest features of Julia’s APIs, libraries, and packages. This book provides an introduction that reveals basic Julia structures and syntax; discusses data types, control flow, functions, input/output, exceptions, metaprogramming, performance, and more. Additionally, you'll learn to interface Julia with other programming languages such as R for statistics or Python. At a more applied level, you will learn how to use Julia packages for data analysis, numerical optimization, symbolic computation, and machine learning, and how to present your results in dynamic documents. The |
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Second Edition delves deeper into modules, environments, and parallelism in Julia. It covers random numbers, reproducibility in stochastic computations, and adds a section on probabilistic analysis. Finally, it provides forward-thinking introductions to AI and machine learning workflows using BetaML, including regression, classification, clustering, and more, with practical exercises and solutions for self-learners. What You Will Learn Work with Julia types and the different containers for rapid development Use vectorized, classical loop-based code, logical operators, and blocks Explore Julia functions: arguments, return values, polymorphism, parameters, anonymous functions, and broadcasts Build custom structures in Julia Use C/C++, Python or R libraries in Julia and embed Julia in other code. Optimize performance with GPU programming, profiling and more. Manage, prepare, analyse and visualise your data with DataFrames and Plots Implement complete ML workflows with BetaML, from data coding to model evaluation, and more. Who This Book Is For Experienced programmers who are new to Julia, as well as data scientists who want to improve their analysis or try out machine learning algorithms with Julia. |
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