2.0.0), tfruns (>= 1.0), magrittr, zeallot, methods, R6, rlang

person("Tomasz", "Kalinowski", role = c("ctb", "cph", "cre"),
person("Daniel", "Falbel", role = c("ctb", "cph"), email = "daniel@rstudio.com"),
2.6.0), tfruns (>= 1.0), magrittr, zeallot, glue, methods, R6,
Author: Tomasz Kalinowski [ctb, cph, cre],

Suggests: ggplot2, testthat (>= 2.1.0), knitr, rmarkdown, callr,

person("RStudio", role = c("ctb", "cph", "fnd")),
person("Google", role = c("ctb", "cph", "fnd")),

Imports: generics (>= 0.0.1), reticulate (> 1.22), tensorflow (>
2.7.0), tfruns (>= 1.0), magrittr, zeallot, glue, methods, R6,

Imports: generics (>= 0.0.1), reticulate (> 1.22), tensorflow (>=
2.8.0), tfruns (>= 1.0), magrittr, zeallot, glue, methods, R6,

Title: R Interface to the Keras Deep Learning Library
Authors@R: c(person("Taylor", "Arnold", email = "taylor.arnold@acm.org", role = c("aut", "cre")))
Description: Provides a consistent interface to the 'Keras' Deep Learning Library
directly from within R. 'Keras' (see <https://keras.io/> for more information)
provides specifications for describing dense neural networks, convolution
neural networks (CNN) and recurrent neural networks (RNN) running on top
of either 'TensorFlow' (<https://www.tensorflow.org/>)  or 'Theano'
Python and R are automatically handled correctly, even when the default
choices would otherwise lead to errors. Includes complete R documentation
SystemRequirements: Python (>= 2.7); keras <https://keras.io/> (>=

directly from within R. 'Keras' provides specifications for describing dense
neural networks, convolution neural networks (CNN) and recurrent neural networks
(RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between

Authors@R: c(person("Taylor", "Arnold", email = "tarnold2@richmond.edu", role = c("aut", "cre")), person("Andrie", "de Vries", email = "apdevries@gmail.com", role = c("aut")))

Title: A High-Level R Interface for Neural Nets
Authors@R: person("Pete", "Mohanty", role = c("aut", "cre"), email = "pete.mohanty@gmail.com")
Description: Adds a high-level interface for 'keras' neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from 'keras'.
kms() accepts a number of parameters (like loss and optimizer) and splits the data into sparse test and training matrices. kms() returns a single object with predictions, a confusion matrix, and function call details.