Autoencoders
Autoencoders are an extremely powerful tool that can effectively help data scientists to deal with high-dimensional datasets. Although first presented around 30 years ago, in recent years, autoencoders have become more and more widespread in conjunction with the general rise of neural network-based algorithms. Besides allowing us to compact sparse representations, they can also be at the base of generative models, representing the first inception of the famous Generative Adversarial Network (GAN), which is, using the words of Geoffrey Hinton:
An autoencoder is a neural network where the inputs and outputs are basically the same, but that is characterized by a small number of units in the hidden layer. Loosely speaking, it is a neural network that is trained to reconstruct its inputs using a significantly lower number of variables and/or degree of freedom.
Since an autoencoder does...
