 Some of the most powerful AIs in the 2010s involved sparse autoencoders stacked inside of deep neural networks.The simplest form of an autoencoder is a feedforward, non-recurrent neural network similar to single layer perceptrons that participate in multilayer perceptrons (MLP) – having an input layer, an output layer and one or more hidden layers connecting them – where the output layer has the same number of nodes (neurons) as the input layer, and with the purpose of reconstructing its inputs (minimizing the difference between the input and the output) instead of predicting the target value formula_1 given inputs formula_2.Therefore, autoencoders are unsupervised learning models (do not require labeled inputs to enable learning).