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Explore neural networks in a layer oriented way, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of Percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.
| Version: | 0.1.0 |
| Imports: | methods |
| Published: | 2019-12-20 |
| Author: | Brian Lee Mayer |
| Maintainer: | Brian <bleemayer at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | deep results |
| Reference manual: | deep.pdf |
| Package source: | deep_0.1.0.tar.gz |
| Windows binaries: | r-devel: deep_0.1.0.zip, r-release: deep_0.1.0.zip, r-oldrel: deep_0.1.0.zip |
| macOS binaries: | r-release (arm64): deep_0.1.0.tgz, r-oldrel (arm64): deep_0.1.0.tgz, r-release (x86_64): deep_0.1.0.tgz, r-oldrel (x86_64): deep_0.1.0.tgz |
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