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Literature Review on Deep Neural Networks

Deep Neural Networks serve as the cornerstone technology for several modern artificial intelligence (AI) applications. With the purpose of modeling the complex non-linear association of data, an artificial neural network (ANN) is developed with the multiple hidden layer structure between the input and output layer is referred to as Deep Neural Networks (DNN). The interpolation of the hidden layer in the network heavily relies on the complexity of the system. DNN with a number of hidden layers brings the higher-level discriminative information concerning the input data. This deeper feature hierarchy allows the DNNs to accomplish outstanding performance in several tasks. It has effectively averted the most critical and unique information loss in each layer of the network. The remarkable success of DNN in acoustic modeling, image recognition, and speech recognition task intends to explode DNN for a number of applications such as language modeling, playing games, cancer detection, self-driving cars, pattern-recognition tasks, and visual classification problems. DNN show their supremacy in the visual classification despite it still confront the severe challenges when dealing with the noisy voice signal in the speech recognition task owing to its higher dependency of a priori knowledge that degrades its performance. It also demands a longer training time to tune the sensitive parameter that makes DNN a slow learning model which is one of the significant challenges in the DNN. DNN model has accomplished greater accuracy with the massive amount of training data.