An auto-encoder is a sequential neural network, consisting of two components, the encoder and the decoder.

Let’s say we were dealing with images. Our encoder would extract features from the image which would reduce some components like its height and width, but makes a latent representation for the image. This latent representation just means the neural network only captures the most relevant characteristics of the input.

The decoder is the part of the neural network which learns how to reconstruct the data from the encoded version. …

Ashley C

Innovator and AI enthusiast

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