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Autoencoders are a type of artificial neural network based machine learning method that learns by compressing input data into a lower-dimensional meaningful representation and reconstructing the input from this representation. First introduced in the 1980s, autoencoders operate within the framework of unsupervised learning and aim to learn important features within the data to construct a low-dimensional representation. In this architecture where input and output data are identical, the training

Autoencoders are a machine learning method based on artificial neural networks that learn by compressing input data into a lower-dimensional, meaningful representation and reconstructing the input from this representation. First introduced in the 1980s, autoencoders operate within the framework of unsupervised learning, with the primary goal of learning important features within the data to construct a low-dimensional representation. In this architecture, where input and output data are identica