Deep Neural Networks in a Mathematical Framework
Anthony L. Caterini, Dong Eui Chang
Springer International Publishing AG, 2018
85,95 €On orderDelivery: 2-3 weeks
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.
- ISBN-13
- 9783319753034
- ISBN-10
- 3319753037
- Publisher
- Springer International Publishing AG
- Year
- 2018
- Publication date
- 2018-04-03
- Pages
- 84
- Dimensions
- 156x234x14
- Weight
- 170