… wanted to determine whether deep learning could contribute to … PSI-BLAST and deep learning
network architectures, which … the deep network architecture and efficiently train the deep …

… deep learning networks on … of deep learning networks. Thus, this review is expected to
direct the future advances on efficient hardware accelerators and to be useful for deep learning …

… , the weights of a deep network could be initialized to sensible values. A final layer of output
units could then be added to the top of the network and the whole deep system could be fine-…

… deep learning network for multiple-input multiple-output (MIMO) detection. The structure of
the network is … Some trainable parameters are optimized through deep learning techniques to …

… We offer a systematic analysis of the use of deep learning networks for stock market … deep
learning potentially attractive for stock market prediction at high frequencies. Deep learning …

… , where each stage transforms (often in a non-linear way) the aggregate activation of the
network. Deep Learning in NNs is about accurately assigning credit across many such stages. …

… deep learning network (LiftingNet) is proposed to learn features adaptively from raw mechanical
data without prior knowledge. Inspired by convolutional neural network … learning ability. …

… would be many layers deep. This book introduces a broad range of topics in deep learning.
The text … techniques used by practitioners in industry, including deep feedforward networks, …

… a new 3-D approach while applying deep learning technique to extract signature … deep
learning network architecture employed in this study integrating both 2D and 3D CNN networks…

… In this paper, we proposed two deep learning methods to address … A deep learning framework
consisting of two fully … the design of deep learning networks in related medical research. …