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The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to the main functionalities as well as full customization of their diverse aspects.
Jun 15, 2019
Autoencoders are neural networks which perform feature learning on data. Many variants can be found in the literature, but their implementations are scarce,�...
The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to�...
The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to�...
The easiest way to start working with Ruta is to use the autoencode() function. It allows for selecting a type of autoencoder and transforming the feature space�...
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The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to�...
Ruta is an R package which gives uncomplicated access to unsupervised deep structures. Its default backend for neural network training is MXNet. The�...
Implementation of several unsupervised neural networks, from building their architecture to their training and evaluation.
Jan 9, 2023 � new_autoencoder: Create an autoencoder learner. In ruta: Implementation of Unsupervised Neural Architectures � View source: R/autoencoder.R�...
The following demonstrates our first implementation of a basic autoencoder. When using h2o you use the same h2o.deeplearning() function that you would use to�...
Missing: Ruta: | Show results with:Ruta:
