This monograph explores advances in representation learning, a crucial area of machine learning that transforms data into compact tabular representations to capture semantic properties and relations. It discusses propositionalization and embedding approaches, with a focus on unifying these techniques and providing insights through examples and Python code. The book is of interest to a broad audience, including data scientists, machine learning researchers, and developers seeking practical AI solutions.

Format: Paperback / softback
Length: 163 pages
Publication date: 11 July 2022
Publisher: Springer Nature Switzerland AG

This comprehensive monograph delves into the realm of representation learning, a cutting-edge field within machine learning. It explores modern data transformation techniques aimed at converting diverse data modalities, such as texts, graphs, and relations, into compact tabular representations that effectively capture their semantic properties and relationships. The monograph specifically focuses on two key approaches: propositionalization, rooted in relational learning and inductive logic programming, and embedding, which has gained prominence with recent advancements in deep learning. By providing a unified perspective on these diverse representation learning techniques, the authors aim to facilitate understanding and provide insights through selected examples and sample Python code. This monograph appeals to a broad audience, including data scientists, machine learning researchers, students, developers, software engineers, and industrial researchers interested in practical AI solutions.

Introduction

Representation learning is a fundamental aspect of machine learning, as it enables machines to understand and interpret complex data. Traditional machine learning algorithms rely on hand-crafted features, which may not be generalizable to new data or may suffer from information loss. Representation learning, on the other hand, seeks to learn compact and meaningful representations that can capture the essential features of the data and facilitate subsequent tasks such as classification, regression, and clustering.

Propositionalization Approaches

Propositionalization approaches are rooted in relational learning and inductive logic programming. These techniques aim to represent data as a set of propositions, where each proposition represents a fact or a relation between entities. Propositionalization techniques are particularly useful for dealing with structured data, such as relational databases and XML files. They can be used to convert complex data into a form that is easier to manipulate and analyze.

Embedding Approaches

Embedding approaches have gained popularity with recent advances in deep learning. These techniques involve mapping high-dimensional data into a lower-dimensional space, where the dimensions are chosen to capture important features of the data. Embedding approaches have been successful in various applications, such as image recognition, speech recognition, and natural language processing.

Unifying Perspective on Representation Learning

The monograph establishes a unifying perspective on representation learning techniques developed in these various areas of modern data science. It provides a comprehensive overview of the key concepts, algorithms, and applications of representation learning. The authors also discuss the challenges and limitations of representation learning and provide insights into future research directions.

Conclusion

In conclusion, this monograph is a valuable resource for anyone interested in representation learning. It provides a comprehensive overview of the state-of-the-art techniques, discusses their applications, and highlights the challenges and opportunities in this field. The monograph is written in a clear and accessible style, making it suitable for both researchers and practitioners.

Weight: 285g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030688196
Edition number: 1st ed. 2021