---

＜E-Book＞
Representation Learning : Propositionalization and Embeddings / by Nada Lavrač, Vid Podpečan, Marko Robnik-Šikonja

Edition	1st ed. 2021.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2021
Language	English
Size	XVI, 163 p. 46 illus., 38 illus. in color : online resource
Authors	*Lavrač, Nada author Podpečan, Vid author Robnik-Šikonja, Marko author SpringerLink (Online service)
Subjects	LCSH:Data mining LCSH:Artificial intelligence -- Data processing  All Subject Search LCSH:Numerical analysis FREE:Data Mining and Knowledge Discovery FREE:Data Science FREE:Numerical Analysis
Notes	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 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 and students to developers, software engineers and industrial researchers interested in hands-on AI solutions HTTP:URL=https://doi.org/10.1007/978-3-030-68817-2

 Similar Items