Overview of Autoencoders. Autoencoders are a type of neural…

Autoencoders are a type of neural network that can be used to learn a compressed representation of a dataset. They consist of two main parts: an encoder, which maps the input data to a lower-dimensional representation, and a decoder, which maps the lower-dimensional representation back to the original dimensionality. Input layer (m input units) - Encoding layer (n hidden units) - Decoding layer (m output units) where m is the number of input units and n is the number of hidden units. The number of hidden units can be chosen based on the desired level of compression. The output of the decoder is used as the reconstructed input.