https://github.com/ellisvalentiner/deepoperators

Deeply Learned Boolean Operators
https://github.com/ellisvalentiner/deepoperators

deep-learning hacktoberfest operators r

Last synced: 5 months ago
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Deeply Learned Boolean Operators

• Host: GitHub
• URL: https://github.com/ellisvalentiner/deepoperators
• Owner: ellisvalentiner
• License: other
• Created: 2019-02-14T22:55:25.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-10-18T06:09:08.000Z (over 6 years ago)
• Last Synced: 2025-04-07T03:32:31.891Z (about 1 year ago)
• Topics: deep-learning, hacktoberfest, operators, r
• Language: R
• Homepage:
• Size: 85 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 6
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

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README

          
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# DeepOperators

[![Travis build status](https://travis-ci.org/ellisvalentiner/DeepOperators.svg?branch=master)](https://travis-ci.org/ellisvalentiner/DeepOperators)

[![Coverage status](https://codecov.io/gh/ellisvalentiner/DeepOperators/branch/master/graph/badge.svg)](https://codecov.io/github/ellisvalentiner/DeepOperators?branch=master)

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:

``` r

# install.packages("remotes")

remotes::install_github("ellisvalentiner/DeepOperators")

```

## Example

This is a basic example of the DeepOperator functions:

```{r}

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.

```{r, eval=FALSE}

train_deep_or()

train_deep_and()

```

## Provenance

This package was inspired by [Fizz Buzz in Tensorflow](http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/).