… In this manuscript, a deep learning model is deployed to predict input slices … fine-tuned two
layers proposed stacked sparse autoencoder (SSAE) model. … Stacked sparse auto encoder …

… The main issue with stacked auto-encoders … learning of compact document representations
with deep networks. In Proceedings of the 25th international conference on Machine learning…

… 1) STL-10 Dataset: The STL-10 dataset is a natural image set for developing unsupervised
feature learning, deep learning, and self-taught learning algorithms. The primary challenge is …

… features from deep neural networks is proposed. A stack of denoising auto-encoders is first
… and find that increasing the number of autoencoders in the network produces more useful …

… Unlike traditional machine learning, deep machine learning extracts features directly from
… auto-encoder together. Compared with shallow neural networks, deep neural networks with …

… The application of deep learning approaches to finance has received a great deal of … a
novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) …

… prediction by using the deep learning algorithms. Therefore, we focus in this paper on
building the stacked auto-encoders (SAE) deep learning algorithm accompanying with a softmax …

… deep learning framework called stacked convolutional sparse auto-encoder, which can
learn … More specifically, the framework is constructed by stacking layers. In each layer, higher …

… deep learning are aware of the effectiveness of evolutionary algorithms in optimizing the
hyper-parameters of deep learning … for stacked auto-encoders that are a class of deep learning …

… A stacked autoencoders based Deep Learning framework for classification of Type 2 …
Deep network classifier can be created by cascading stacked auto encoder with the softmax …