An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower-dimensional latent representation, then decodes the latent representation back to an image.
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This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.
An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower-dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.
To learn more about autoencoders
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