Introducing Autoencoders
In previous chapters, we have seen that neural networks are very powerful algorithms. The power of each network lies in its architecture, activation functions, and regularization terms, plus a few other features. Among the varieties of neural architectures, there is a very versatile one, especially useful for three tasks: detecting unknown events, detecting unexpected events, and reducing the dimensionality of the input space. This neural network is the autoencoder.
Architecture of the Autoencoder
The autoencoder (or autoassociator) is a multilayer feedforward neural network, trained to reproduce the input vector onto the output layer. Like many neural networks, it is trained using the gradient descent algorithm, or one of its modern variations, against a loss function, such as the Mean Squared Error (MSE). It can have as many hidden layers as desired. Regularization terms and other general parameters that are useful for avoiding overfitting or for improving...
