Applications of Game Theory in Deep Neural Networks

Over the last decade, deep learning has been a hot topic of discussion due to its learning capabilities from data. As a brand-new area of study within machine learning (ML), the deep learning (DL) notion initially emerged in 2006. To understand several applications of game theory in deep neural networks (DNNs), first let us go through some basic concepts of DL and game theory. Deep learning techniques are a subset of machine learning that is able to classify automatically by learning hierarchical representations in deep architectures. Have you ever wondered how your mobile gallery is automatically organized on the basis of different human faces? This is nothing but the product of DL. Why do we opt for DL in place of ML? In ML, we have to tell machines about the different features that help machines to classify between different species. For example, to classify samples from the mixture of guava and apple, features such as color, size, shape, etc. play an important part. However, in the case of DL, features are picked by a neural network without interference from humans.