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Fits from simple regression to highly customizable deep neural networks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.
| Version: | 1.3.2 |
| Imports: | stats, utils, parallel |
| Suggests: | datasets |
| Published: | 2020-01-16 |
| DOI: | 10.32614/CRAN.package.automl |
| Author: | Alex Boulangé [aut, cre] |
| Maintainer: | Alex Boulangé <aboul at free.fr> |
| BugReports: | https://github.com/aboulaboul/automl/issues |
| License: | GPL-2 | GPL-3 [expanded from: GNU General Public License] |
| URL: | https://aboulaboul.github.io/automl https://github.com/aboulaboul/automl |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | automl results |
| Reference manual: | automl.pdf |
| Vignettes: |
howto_automl.pdf |
| Package source: | automl_1.3.2.tar.gz |
| Windows binaries: | r-devel: automl_1.3.2.zip, r-release: automl_1.3.2.zip, r-oldrel: automl_1.3.2.zip |
| macOS binaries: | r-release (arm64): automl_1.3.2.tgz, r-oldrel (arm64): automl_1.3.2.tgz, r-release (x86_64): automl_1.3.2.tgz, r-oldrel (x86_64): automl_1.3.2.tgz |
| Old sources: | automl archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.