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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .
Both autocoder, and the unrelated autocode, a term of the same era used in the UK for languages of a higher level, derive from the phrase automatic coding.This referred generally to programs which eased the burden of producing the numeric machine language codes of programs. [2] "
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Related to autoencoders is the NeuroScale algorithm, which uses stress functions inspired by multidimensional scaling and Sammon mappings (see above) to learn a non-linear mapping from the high-dimensional to the embedded space. The mappings in NeuroScale are based on radial basis function networks.
The word "uno" means "one" in Italian and was chosen to mark a major redesign of the Arduino hardware and software. [7] The Uno board was the successor of the Duemilanove release and was the 9th version in a series of USB-based Arduino boards. [8] Version 1.0 of the Arduino IDE for the Arduino Uno board has now evolved to newer releases. [4]