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1. |
Record Nr. |
UNINA9910782783203321 |
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Autore |
Beaudouin Denis |
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Titolo |
Charles Beaudouin [[electronic resource] ] : une histoire d'instruments scientifiques / / Denis Beaudouin ; photos d'instruments de Chloé Beaudouin |
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Pubbl/distr/stampa |
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Paris, : EDP Sciences, 2005 |
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ISBN |
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1-281-80066-X |
9786611800666 |
2-7598-0311-2 |
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Descrizione fisica |
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1 online resource (297 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Scientific apparatus and instruments - France - History |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. [283]-285). |
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Nota di contenuto |
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Sommaire; Préface; Première partie : Histoire d'un constructeur : un artisan des sciences dans la concentration industrielle du XXe siècle; Deuxième partie : Les instruments construits par Charles Beaudouin 1903-1975; Troisième partie : Une brève histoire des constructeurs d'instruments scientifiques sur la montagne Sainte-Geneviève; Conclusion : la métamorphose de l'instrument; Bibliographie; Remerciements; Crédits photographiques; |
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Sommario/riassunto |
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A book on the adventure of Charles Beaudouin, a builder of scientific instruments. This book takes us on a fascinating journey, choc-a-block with new discoveries, twists and turns not only scientific but also technological and financial as we revisit the extraordinary French scientific and industrial adventure of the 20th century. |
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2. |
Record Nr. |
UNINA9910492147403321 |
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Autore |
Lavrač Nada |
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Titolo |
Representation Learning : Propositionalization and Embeddings / / by Nada Lavrač, Vid Podpečan, Marko Robnik-Šikonja |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (175 pages) |
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Disciplina |
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Soggetti |
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Data mining |
Artificial intelligence - Data processing |
Numerical analysis |
Data Mining and Knowledge Discovery |
Data Science |
Numerical Analysis |
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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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Introduction to Representation Learning -- Machine Learning Background -- Text Embeddings -- Propositionalization of Relational Data -- Graph and Heterogeneous Network Transformations -- Unified Representation Learning Approaches -- Many Faces of Representation Learning. |
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Sommario/riassunto |
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This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common |
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underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions. |
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