Respuesta :
Answer:
Autoencoder
Explanation:
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.
Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties.
Examples are the regularized autoencoders (Sparse, Denoising and Contractive autoencoders), proven effective in learning representations for subsequent classification tasks, and Variational autoencoders, with their recent applications as generative models.
Autoencoders are effectively used for solving many applied problems, from face recognition to acquiring the semantic meaning of words.
Answer:
The correct answer to the following question will be "Auto-encoder".
Explanation:
It is indeed a form of artificial neural net that utilizes in an unmonitored way to practice successful information coding.
- The objective of such an auto-encoder seems to be to acquire a specification (encoding) for a collection of information, usually for reducing degrees of freedom, by teaching the channel to overlook the "noise" message.
- Learn a few things of endogenous model representation and then use it to recreate the object.
