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[null,"1.","Record Nr.",null,"UNISALENTO991001592949707536"]
[null,null,"Autore",null,"Emo, Andrea"]
[null,null,"Titolo",null,"Quaderni di metafisica : 1927-1981 / Andrea Emo ; a cura di Massimo Dona e Romano Gasparotti ; prefazione di Massimo Cacciari ; saggi integrativi di Massimo Cacciari ... [et al.]"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Bompiani, 2006"]
[null,null,"ISBN",null,"884525626X"]
[null,null,"Descrizione fisica",null,"LXXII, 1674 p. ; 22 cm"]
[null,null,"Collana",null,"Bompiani il pensiero occidentale"]
[null,null,"Disciplina",null,"110"]
[null,null,"Soggetti",null,"Metafisica"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910492147403321"]
[null,null,"Autore",null,"Lavrač Nada"]
[null,null,"Titolo",null,"Representation Learning : Propositionalization and Embeddings / / by Nada Lavrač, Vid Podpečan, Marko Robnik-Šikonja"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-68817-8"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (175 pages)"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Artificial intelligence - Data processing"]
[{"$nulls":4},"Numerical analysis"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Data Science"]
[{"$nulls":4},"Numerical Analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction to Representation Learning -- Machine Learning Background -- Text Embeddings -- Propositionalization of Relational Data -- Graph and Heterogeneous Network Transformations -- Unified Representation Learning Approaches -- Many Faces of Representation Learning."]
[null,null,null,"Sommario/riassunto","This monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers "]
[{"$nulls":4},"and students to developers, software engineers and industrial researchers interested in hands-on AI solutions."]