… were only sparsely used in the past since they are hard to train when using … using Root
Mean Square Propagation (RMSProp) it is possible to efficiently learn multi-layer neural networks…

… In this study, we removed the additional hyper-parameter from RMSProp and observed that
… and results obtained during training and testing the deep neural network. Table 2 consists of …

… the deep neural networks’ training procedure. All models in this paper are trained using
RmsProp … All weights for neurons in neural networks were initialized from a centered Normal …

… CIDDS dataset and apply a deep learning approach, Long-Short… using deep neural network
to classify attacks in IoT networks [5]. … In this study, we found rmsprop optimizer is suitable for …

… The present study uses principal component analysis based deep neural network model using
… For backpropagation the Root mean square propagation (RMSprop) error was used. The …

… the network using mini-batch stochastic gradient descent with momentum, dropout [10],
RMSprop and batch normalization [2]. To tune the hyperparameters, we used a grid search, …

… with a generative deep neural network (DNN). The goal is then to estimate the weights and
biases of the DNN. … L-BFGS-B converges faster and is more stable than ADAM and RMSProp. …

… Thus, the DBN with RMSProp learning-based classification … , the pre-trained network is
refined by supervised training to … of a deep neural network with the values of pre-trained DBN …

… , RMSProp as the optimizer algorithm, no convolutional layer set … , which are distance measures
of distributions compared to … automatic search convolutional neural networks to avoid the …

… But it suggested that the applicability of RMSPROP is good for training the Chili Dataset, …
convolutional neural networks based multiclass plant disease detection and classification using …