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ganGenerativeData:Generate Generative Data for a Data Source
Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.
Maintained by Werner Mueller. Last updated 4 months ago.
1 stars 1.70 scorecran
readMLData:Reading Machine Learning Benchmark Data Sets in Different Formats
Functions for reading data sets in different formats for testing machine learning tools are provided. This allows to run a loop over several data sets in their original form, for example if they are downloaded from UCI Machine Learning Repository. The data are not part of the package and have to be downloaded separately.
Maintained by Petr Savicky. Last updated 10 years ago.
1 stars 1.00 score