Autoencoders
Autoencoders are a type of neural network that learns to compress and then reconstruct input data. They consist of an encoder network that maps the input data to a lower-dimensional latent space representation, and a decoder network that maps the latent space representation back to the original input space. The encoder and decoder networks are trained together to minimize the reconstruction error between the original input and the reconstructed output. Autoencoders can be used for various tasks such as data compression, denoising, and anomaly detection. They are also used as a pre-training step for other deep learning models.
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