knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of DeepOperators is to provide pre-trained deeply learned boolean operators.
The %&%
and %|%
operators turn plain old business logic into "deep learning", "machine learning", and "AI" problems. Because business folk sometimes dictate how problems are solved, DeepOperators enables the ~~programmer~~ Data Scientist to use "deep learning" when it is required but it would otherwise be more practical to use built-in logical operators.
You can install the development version of DeepOperators from GitHub with:
# install.packages("remotes") remotes::install_github("ellisvalentiner/DeepOperators")
This is a basic example of the DeepOperator functions:
library(DeepOperators) # using the deep AND operator, %&% TRUE %&% FALSE TRUE %&% TRUE # using the deep OR operator, %|% TRUE %|% FALSE FALSE %|% FALSE
Additionally DeepOperator provides function to automatically re-train the deep operators.
train_deep_or() train_deep_and()
This package was inspired by Fizz Buzz in Tensorflow.
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