An auto-encoder is an artificial neural network used for learning efficient codings. The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data. This means it is being used for dimensionality reduction. More specifically, it is a feature extraction method. Auto-encoders use three or more layers: An input layer. For example, in a face recognition task, the neurons in the input layer could map to pixels in the photograph.
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- An auto-encoder is an artificial neural network used for learning efficient codings. The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data. This means it is being used for dimensionality reduction. More specifically, it is a feature extraction method. Auto-encoders use three or more layers: An input layer. For example, in a face recognition task, the neurons in the input layer could map to pixels in the photograph. A number of considerably smaller hidden layers, which will form the encoding. An output layer, where each neuron has the same meaning as in the input layer. If linear neurons are used, then an auto-encoder is very similar to PCA.
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- An auto-encoder is an artificial neural network used for learning efficient codings. The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data. This means it is being used for dimensionality reduction. More specifically, it is a feature extraction method. Auto-encoders use three or more layers: An input layer. For example, in a face recognition task, the neurons in the input layer could map to pixels in the photograph.
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