Deep Learning By Example

Introduction to autoencoders

An autoencoder is yet another deep learning architecture that can be used for many interesting tasks, but it can also be considered as a variation of the vanilla feed-forward neural network, where the output has the same dimensions as the input. As shown in Figure 1, the way autoencoders work is by feeding data samples (x₁,...,x₆) to the network. It will try to learn a lower representation of this data in layer L2, which you might call a way of encoding your dataset in a lower representation. Then, the second part of the network, which you might call a decoder, is responsible for constructing an output from this representation . You can think of the intermediate lower representation that the network learns from ...