Deep Neural Networks in a Mathematical Framework
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This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks.
| Autor: | Caterini, Anthony L.; Chang, Dong Eui |
| Nakladatel: | Springer International Publishing AG |
| ISBN: | 9783319753034 |
| Rok vydání: | 2018 |
| Jazyk : | Angličtina |
| Vazba: | Paperback / softback |
| Počet stran: | 84 |
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