… learning systems towards the discovery of multiple levels of representation. We show that deep
learning … - We propose the use of deep learning neural networks as a means of improving …

… In any neural network, a dense layer is a layer that is deeply … of the layer are connected to
every neuron of its preceding layer. … neural networks in machine learning libraries are actually …

… use of Artificial Neural Networks and Deep Learning in the … and identification through Artificial
Neural Networks for the different … that the use of Artificial Neural Networks in the Visual Arts …

… Results indicate that deep learning increases the performance of models over simple … The
deep learning techniques evaluated in this paper are (1) deep-learning architectures that use …

… develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a
… the convolutional network is suitable for regression-type problems and can effectively learn …

… using a deep architecture of convolutional neural networks (CNNs) for detecting concrete
cracks without calculating the defect features. As CNNs are capable of learning image features …

… Moreover, deep learning could be used with … learning applications for reducing the
dimensionality of the dataset. Similarly, deep neural networks are popular for supervised learning …

… Figure 12 clearly shows that only recent advances in deep learning methods (by which we
mean the combination of batch normalization, dropout and ELUs) allowed our deep ConvNet …

… In this paper, different kinds of deep learning neural networks (DLNN) for short-term output
PV power forecasting have been developed and compared: Long Short-Term Memory (LSTM)…

… Here, we try to understand the feature learning process of NSAE-LCN. As we know, a
neural network is always viewed as a black box. And in the field of fault diagnosis, few papers …