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Deep Neural Networks in a Mathematical Framework

Deep Neural Networks in a Mathematical Framework

Paperback

Series: Springerbriefs in Computer Science

General Computers

ISBN10: 3319753037
ISBN13: 9783319753034
Publisher: Springer Nature
Published: Apr 3 2018
Pages: 84
Weight: 0.33
Height: 0.21 Width: 6.14 Depth: 9.21
Language: English

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. 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. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.

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General Computers