A study on Deep Neural Networks framework

Deep neural networks(DNN) is an important method for machine learning, which has been widely used in many fields. Compared with the shallow neural networks(NN), DNN has better feature expression and the ability to fit the complex mapping. In this paper, we first introduce the background of the development of the DNN, and then introduce several typical DNN model, including deep belief networks(DBN), stacked autoencoder(SAE) and deep convolution neural networks(DCNN), finally research its applications from three aspects and prospects the development direction of DNN.