autoencoder: Sparse Autoencoder for Automatic Learning of Representative Features from Unlabeled Data

Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

AuthorEugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Date of publication2015-07-02 09:09:12
MaintainerYuriy Tyshetskiy <yuriy.tyshetskiy@nicta.com.au>
LicenseGPL-2
Version1.1

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Files

autoencoder
autoencoder/NAMESPACE
autoencoder/data
autoencoder/data/autoencoder_Ninput=100_Nhidden=25_rho=1e-2.rda
autoencoder/data/autoencoder_Ninput=100_Nhidden=100_rho=1e-2.rda
autoencoder/data/datalist
autoencoder/data/training_matrix_N=5e3_Ninput=100.rda
autoencoder/R
autoencoder/R/autoencoder.R
autoencoder/MD5
autoencoder/DESCRIPTION
autoencoder/man
autoencoder/man/autoencode.Rd autoencoder/man/autoencoder_Ninput=100_Nhidden=25_rho=1e-2.Rd autoencoder/man/autoencoder_Ninput=100_Nhidden=100_rho=1e-2.Rd autoencoder/man/visualize.hidden.units.Rd autoencoder/man/autoencoder-package.Rd autoencoder/man/training_matrix_N=1e4_Ninput=100.Rd autoencoder/man/predict.autoencoder.Rd

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