… deep supervised learning (also recapitulating the history of backpropagation), unsupervised
learning, reinforcement learning & … for short programs encoding deep and large networks. …

… 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-…

… experimentation with more sophisticated and deep neural architectures than was previously
… of the potential of deep learning. This book discusses neural networks from this modern …

… deep learning. They’ve been developed further, and today deep neural networks and deep
learning … concepts of neural networks, including modern techniques for deep learning. After …

… 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. …

… We show that distributing deep learning models is an … We compare the results with a deep
learning model trained on … performance of distributing deep learning models compared to …

… 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…

… deep learning models is desired. Therefore, a multitask learning architecture using deep
learning networks for … State-of-the-art deep learning models are studied, including 1) recurrent …