Architecture of Deep Learning and Its Applications

Abstract

Recently, Deep Learning (DL) has accomplished enormous prosperity in various areas, like natural language processing (NLP), image processing, different medical issues and computer vision. Both Machine Learning (ML) and DL as compared to traditional methods, can learn and make better and enhanced use of datasets for feature extraction. This paper is divided into three parts. The first part introduces a detailed information about different characteristics and learning types in terms of learning problems, hybrid learning problems, statistical inference and learning techniques; besides to an exhausted historical background about feature learning and DL. The second part is about the major architectures of DL with mathematical equations and clarified examples. These architectures include Autoencoders (AEs), Generative Adversarial Networks (GANs), Deep Belief Networks (DBNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Recursive Neural Networks. The third part of this work represents an overview with detailed explanation about different applications and use-cases. Finally, the fourth part is about hardware/ software tools used with DL. Index Terms— Deep learning, Machine Learning, Neural Network, Network architecture.