dc.contributor.author | Fatima, Noor | |
dc.date.accessioned | 2021-05-21T10:08:36Z | |
dc.date.available | 2021-05-21T10:08:36Z | |
dc.date.issued | 2020-06-20 | |
dc.identifier.citation | ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9 (2020) | |
dc.identifier.issn | 2255-2863 | |
dc.identifier.uri | http://hdl.handle.net/10366/146093 | |
dc.description.abstract | Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It's a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network. | |
dc.format.mimetype | application/pdf | |
dc.publisher | Ediciones Universidad de Salamanca (España) | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | Adadelta | |
dc.subject | Adagrad | |
dc.subject | Adam | |
dc.subject | Adamax | |
dc.subject | Deep Learning | |
dc.subject | Neural Networks | |
dc.subject | Nadam | |
dc.subject | Optimization algorithms | |
dc.subject | RMSprop | |
dc.subject | SGD | |
dc.subject | Adadelta | |
dc.subject | Adagrad | |
dc.subject | Adam | |
dc.subject | Adamax | |
dc.subject | Deep Learning | |
dc.subject | Neural Networks | |
dc.subject | Nadam | |
dc.subject | Optimization algorithms | |
dc.subject | RMSprop | |
dc.subject | SGD | |
dc.title | Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms | |
dc.type | info:eu-repo/semantics/article |
Navegar
Todo o repositórioComunidades e ColeçõesPor data do documentoAutoresAssuntosTítulosEsta coleçãoPor data do documentoAutoresAssuntosTítulos