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What is Understanding Autoencoders in Neural Networks about?

An autoencoder is a type of neural network that compresses an input into a lower-dimensional encoding and then reconstructs the output from this encoding. The goal of an autoencoder is to minimize the reconstruction error between the input and output. It consists of an encoder that compresses the input into a latent-space representation, and a decoder that decompresses this encoding to reconstruct the output. Autoencoders are commonly used for dimensionality reduction and feature learning in an unsupervised

Author

Sreetam Ganguly

Language

EN