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ApaBila / Final_Report_GSoC2020.md
Created Aug 30, 2020
Final Work Product GSoC 2020
View Final_Report_GSoC2020.md

Validation of Neural Network Packages - Salsabila Mahdi

Google Summer of Code 2020

Abstract (from proposal) The purpose of this GSoC project is to validate neural network packages that perform regression. It is a follow-up of a GSoC 2019 project in which we examined 49 packages::algorithms but we were short of time to publish the results in a clean format. We intend to correct some glitches in the 2019 code and use a more recent code prepared by one mentor during winter 2020 that is more flexible and permits an evaluation of all packages::algorithms in one run. Our NNbenchmark package,

@ApaBila
ApaBila / Final_Report_NN_SM.md
Last active Aug 30, 2020
Gist to present my work for GSoC2019
View Final_Report_NN_SM.md

Neural Network Package Validation 2 - Salsabila Mahdi

Google Summer of Code

Abstract (from proposal)
The purpose of this project is to verify the convergence of the training algorithms provided in 69 Neural Network R packages available on CRAN to date. Neural networks often must be trained with second order algorithms and not with the first order algorithms as many packages seem to use instead. Due to the large number of packages to validate, the work has been split among two students. Being Student 2, I will validate 34 packages and prepare one article to be published in the R-Journal. At the end of the program, a package will be made available to Neural Network package authors and maintainers to verify and test new algorithms by themselves. The results of this project could be used to make better neural network packages in the future, improve the ones currently being used, or simply know how the neural network packages actually perform.

WORK COMPLETED