Titlebook: Deep Learning: Concepts and Architectures; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Computational Intel

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https://doi.org/10.1007/978-3-322-97122-7nalyze the training results of a variety of model structures. While previous studies have applied convolutional neural networks to image or object recognition, our study proposes a specific encoding method that is integrated with deep learning in order to predict the results of future games. The pre

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https://doi.org/10.1007/978-3-322-90228-3works, namely, Convolutional Neural?Networks, Pretrained Unspervised Networks, and Recurrent/Recursive Neural Networks. Applications of each of these architectures?in selected areas such as pattern recognition and image detection are also discussed.

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https://doi.org/10.1007/978-3-322-90228-3omplexity and curvature. We also describe neural networks from the viewpoints of scattering transforms?and share some of the mathematical and intuitive justifications for those. We finally share a technique for visualizing and analyzing neural networks based on concept of Riemann?curvature.

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https://doi.org/10.1007/978-3-322-90228-3utput sentences is provided. Finally, the attention mechanism which is a technique to cope with long-term dependencies and to improve the encoder-decoder performance on sophisticated tasks is studied.

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1860-949X mplementations and case studies, identifying the best designThis book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilaye