Deep Neural Networks in a Mathematical Framework

Description: Deep Neural Networks in a Mathematical Framework Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Anthony L. Caterini, Dong Eui Chang Format: Paperback Publisher: Springer International Publishing AG, Switzerland Imprint: Springer International Publishing AG ISBN-13: 9783319753034, 978-3319753034 Synopsis 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. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural [url] SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.

Price: 45.74 GBP

Location: Aldershot

End Time: 2024-11-07T09:26:24.000Z

Shipping Cost: 26.16 GBP