A Survey of the Advances in the Applications of Deep Learning Algorithms Across Different Domains

Abstract

Deep learning has revolutionized the modern-day world starting with its application in computer vision such as image classification, face recognition, autonomous vehicle etc. it has been explored in various areas where human beings find it difficult to come up with solutions to the challenges at hand. By the word deep, it implies they are trained with millions, billions of parameters to achieve outstanding results. In this review paper, the fundamentals of deep learning have been discussed extensively starting with the classification, types of activation functions, different deep learning algorithms as well as their applications were also discussed. Recurrent neural network (RNNs) and its variant, convolution neural networks (CNNs) and various architectures, recursive neural networks (RvNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), generative adversarial networks (GANs) and other deep learning were discussed extensively. Some of the findings of researchers for some of these algorithms were highlighted. Based on various paper reviewed and thorough analysis carried out, it was observed that the exploration of deep learnings in this modern-day world has found applications in virtually all fields of life from medicine, academy, transportation, entertainments, particularly the exploration of CNNs, RNNs, and GANs.