Recently, I have been studying autoencoders. If I understood correctly, an autoencoder is a neural network where the input layer is identical to the output layer. So, the neural network tries to predict the output using the input as golden standard.
What is the usefulness of this model? What are the benefits of trying to reconstruct some output elements, making them as equal as possible to the input elements? Why should one use all this machinery to get to the same starting point?