Preface

Deep learning is a subfield of artificial intelligence that has been growing rapidly in recent years. It involves training artificial neural networks with a large amount of data, enabling them to learn and make predictions or decisions with high accuracy. In recent years, there have been numerous findings and research in deep learning that have expanded its capabilities and improved its performance.

One recent finding in deep learning is the development of generative adversarial networks (GANs), which have revolutionized the field of image generation. GANs can generate realistic images by training two neural networks—a generator and a discriminator—that compete against each other. Another notable breakthrough in deep learning is the development of transformer networks, which have improved natural language processing (NLP) tasks such as language translation and sentiment analysis.

Furthermore, deep learning has been applied to various fields, including healthcare, finance, and robotics. For instance, deep learning models have been used to diagnose diseases from medical images with high accuracy, predict stock prices, and improve robot perception and control.

Overall, recent findings and research in deep learning have expanded its applications and improved its performance, making it a promising tool for solving complex problems in various domains. This book presents new examples and paradigms of the application of deep learning in various fields, particularly in achieving the United Nations Sustainable Development Goals.

> **Manuel Domínguez-Morales** Associate Professor at University of Seville, Architecture and Computer Technology Department, Seville, Spain

> **Javier Civit-Masot and Luis Muñoz-Saavedra** Postdoc at University of Seville, Architecture and Computer Technology Department, Seville, Spain

> > **Robertas Damaševičius**  Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania

Section 1
