A type of deep neural network framework made of two neural networks (generator and discriminator) which compete against each other, and use a cooperative zero-sum game to learn. The generator is trained to produce fake data, and the discriminator tries to differentiate the generator’s fake data from real examples.
Published in Chapter:
Understanding Machine Learning Concepts
Javier M. Aguiar-Pérez (University of Valladolid, Spain), María A. Pérez-Juárez (University of Valladolid, Spain), Miguel Alonso-Felipe (University of Valladolid, Spain), Javier Del-Pozo-Velázquez (University of Valladolid, Spain), Saúl Rozada-Raneros (University of Valladolid, Spain), and Mikel Barrio-Conde (University of Valladolid, Spain)
Copyright: © 2023
|Pages: 16
DOI: 10.4018/978-1-7998-9220-5.ch058
Abstract
Artificial intelligence can be seen as the intelligence exhibited by machines. For an artificial intelligence system to be able to take decisions based on the data available, different type of learning methods, such as machine learning, need to be applied. Machine learning is a learning technique that gives machines the ability to learn without being explicitly programmed. It addresses the creation and study of algorithms that are capable of learning from data and making predictions about it. Machine learning algorithms can be divided into different categories including supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. In this article, the authors want to explain what machine learning is, as well as clearly establish the differences and relationship of machine learning with other related concepts, including artificial intelligence and deep learning. In addition, some possible use cases and applications will be named in order to provide the reader with a clear idea of what the potential of machine learning is.