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Representation learning = propositio...

Lavrac, Nada.

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Representation learning = propositionalization and embeddings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Representation learning/ by Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja.
Reminder of title:	propositionalization and embeddings /
Author:	Lavrac, Nada.
other author:	Podpecan, Vid.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvi, 163 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-68817-2
ISBN:	9783030688172

Representation learning = propositionalization and embeddings /
Lavrac, Nada.

Representation learningpropositionalization and embeddings /[electronic resource] :by Nada Lavrac, Vid Podpecan, Marko Robnik-Sikonja. - Cham :Springer International Publishing :2021. - xvi, 163 p. :ill., digital ;24 cm.

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.

ISBN: 9783030688172

Standard No.: 10.1007/978-3-030-68817-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Representation learning = propositionalization and embeddings /
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505 0 $a 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.
520 $a 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.
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