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Document type:
Masterarbeit 
Author(s):
Julian Suk 
E-mail address:
j.suk@gmx.de 
Title:
Application of second-order optimisation for large-scale deep learning 
Abstract:
Deep neural networks have become some of the most prominent models in machine learning due to their flexibility and therefore, their broad applicability. The training of large-scale deep neural networks requires vast computational resources. Stochastic gra- dient descent methods still enjoy great popularity but Hessian-based optimisation tech- niques are on the rise. While computing the second derivative of the loss function is still computationally expensive, a possibly much faster converg...    »
 
Supervisor:
Hans-Joachim Bungartz 
Advisor:
Severin Reiz 
Year:
2020 
Quarter:
2. Quartal 
Year / month:
2020-05 
Month:
May 
Pages:
99 
Language:
en 
University:
TUM