Autoencoders are feed-forward, non-recurrent neural networks, which learn by unsupervised learning. They have an inherent capability to learn a compact representation of data. They are at the centre of deep belief networks and find applications in image reconstruction, clustering, machine translation, and much more.
Some types of Auto-Encoders are -
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Vanilla Auto-Encoders
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Sparse Auto-Encoders
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Denoising Auto-Encoders
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Convolution Auto-Encoder
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Stacked autoencoder
