Description: Representation Learning : Propositionalization and Embeddings, Paperback by Lavrac, Nada; Podpecan, Vid; Robnik-sikonja, Marko, ISBN 3030688194, ISBN-13 9783030688196, Like New Used, Free shipping in the US 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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Book Title: Representation Learning : Propositionalization and Embeddings
Number of Pages: Xvi, 163 Pages
Language: English
Publication Name: Representation Learning : Propositionalization and Embeddings
Publisher: Springer International Publishing A&G
Publication Year: 2022
Subject: Probability & Statistics / General, General, Databases / Data Mining, Databases / General
Type: Textbook
Item Weight: 10.1 Oz
Author: VID PodpečAn, Nada Lavrač, Marko Robnik-Sikonja
Item Length: 9.3 in
Subject Area: Mathematics, Computers, Science
Item Width: 6.1 in
Format: Trade Paperback
