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An auto-encoder is a neural network used for unsupervised learning of efficient codings. The aim of an auto-encoder is to learn a representation (encoding) for a set of data, typically for the purpose of dimensionality reduction. Also, this learned representation can be used for other purposes such as classification or reconstruction.
There are two parts to an auto-encoder:
The encoder: This part of the auto-encoder compresses the input data into a hidden representation.
The decoder: This part of the auto-encoder reconstructs the input data from the hidden representation.
The encoder and decoder are usually implemented as neural networks. The hidden representation learned by the encoder is typically smaller than the input data. This compression is useful because it can reduce the amount of data needed to be stored or transmitted. It can also be used for data denoising or dimensionality reduction.
The hidden representation learned by the encoder can also be used for other tasks such as classification or reconstruction. The hidden representation can be thought of as a compact summary of the input data.
There are many different types of auto-encoders. The most common is the Restricted Boltzmann machine (RBM). RBMs are a type of energy-based model which learn a probability distribution over the input data.
Other types of auto-encoders include denoising auto-encoders, sparse auto-encoders, and convolutional auto-encoders. Denoising auto-encoders are used for data denoising, while sparse auto-encoders are used for learning sparse representations. Convolutional auto-encoders are used for learning features from images.
Auto-encoders can be trained using a variety of different algorithms such as stochastic gradient descent, conjugate gradient, or LBFGS.
Auto-encoders are a powerful tool for unsupervised learning. They can be used for dimensionality reduction, data denoising, and feature learning.
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