Does anyone could help me to understand what is the autoencoders?
Autoencoders are a neural network solution to the problem of dimensionality reduction.

The point of dimensionality reduction is to find a lower-dimensional representation of your data. For example, if your data includes people's height, weight, trouser leg measurement and shoe size, we'd expect there to be some underlying size dimension which would capture much of the variance of these variables. If you're familiar with Principal Component Analysis (PCA), this is another example of a dimensionality reduction technique.

Autoencoders attempt to capture a lower-dimensional representation of their data by having a hidden "bottleneck" layer which is much smaller than the dimensionality of the data. The idea is to train a neural network which throws away as much of its dimensionality as possible and can still reconstruct the original data.

Once you have an autoencoder which performs well, by observing the activations at the bottleneck layer, it's possible to see how an individual example scores in each of the reduced dimensions. This may allow us to begin to make sense of what each of the dimensions represents. One can then use these activations to score new examples on this set of reduced dimensions.