Deep Neural Networks in a Mathematical Framework
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 algo...
| Main Authors: | , |
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| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2018.
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| Edition: | 1st ed. 2018. |
| Series: | SpringerBriefs in Computer Science,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-75304-1 |