README.md

In ellisvalentiner/DeepOperators: Deeply Learned Boolean Operators

DeepOperators

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.

Installation

You can install the development version of DeepOperators from GitHub with:

# install.packages("remotes")
remotes::install_github("ellisvalentiner/DeepOperators")

Example

This is a basic example of the DeepOperator functions:

library(DeepOperators)

# using the deep AND operator, %&%
TRUE %&% FALSE
#> [1] FALSE
TRUE %&% TRUE
#> [1] TRUE

# using the deep OR operator, %|%
TRUE %|% FALSE
#> [1] TRUE
FALSE %|% FALSE
#> [1] FALSE

Additionally DeepOperator provides function to automatically re-train the deep operators.

train_deep_or()
train_deep_and()

Provenance

This package was inspired by Fizz Buzz in Tensorflow.

ellisvalentiner/DeepOperators documentation built on Oct. 16, 2019, 5:56 p.m.