Title: Neural Network Arena: Investigating Long-Term Dependencies in Deep Models
Language: English
Authors: Brantner, Hannes 
Qualification level: Diploma
Advisor: Grosu, Radu 
Issue Date: 2021
Number of Pages: 113
Qualification level: Diploma
Abstract: 
This work should help to objectively compare various machine learning models used to process regularly sampled time-series data. It should outline the weaknesses and strengths of the benchmarked models and determine their primary domain of use. Moreover, as there are many models benchmarked, their relative expressivity across various application domains can be compared reasonably well. Another aim is to provide an overview of what architectures are currently available and how they can be implemented. Furthermore,the implemented benchmark suite should be reusable for future projects in the machinelearning domain.
Keywords: machine learning
URI: https://doi.org/10.34726/hss.2021.88580
http://hdl.handle.net/20.500.12708/17037
DOI: 10.34726/hss.2021.88580
Library ID: AC16165350
Organisation: E191 - Institut für Computer Engineering 
Publication Type: Thesis
Hochschulschrift
Appears in Collections:Thesis

Files in this item:

Show full item record

Page view(s)

34
checked on Jun 1, 2021

Download(s)

40
checked on Jun 1, 2021

Google ScholarTM

Check


Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.