Description: 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

Title: Clustering of Short Texts by Mixture of Unigrams and Its Deep
person("Martin", "D'Ippolito", email = "martinmy69@gmail.com", role = c("aut","cre")),
person("Anderlucci", "Laura", email = "laura.anderlucci@unibo.it", role = "aut"),
person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "aut"))
Description: Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <arXiv:1902.06615v2>). There are also functions for graphical representation of clusters obtained.
Imports: skmeans, extraDistr, dplyr, Rfast, entropy, ggplot2,
Packaged: 2021-01-20 15:19:45 UTC; dippolitom

Description: Functions providing an easy and intuitive way for fitting and clusters data using the Mixture of Unigrams models by means the Expectation-Maximization algorithm (Nigam, K. et al. (2000). <doi:10.1023/A:1007692713085>), Mixture of Dirichlet-Multinomials estimated by Gradient Descent (Anderlucci, Viroli (2020) <doi:10.1007/s11634-020-00399-3>) and Deep Mixture of Multinomials whose estimates are obtained with Gibbs sampling scheme (Viroli, Anderlucci (2020) <doi:10.1007/s11222-020-09989-9>). There are also functions for graphical representation of clusters obtained.
Imports: skmeans, extraDistr, dplyr, Rfast, entropy, ggplot2, graphics,
Packaged: 2021-03-03 20:17:09 UTC; dippolitom