Autoencoders - Ep. 10 (Deep Learning SIMPLIFIED)

Autoencoders are a family of neural nets that are well suited for unsupervised learning, a method for detecting inherent patterns in a data set. These nets can also be used to label the resulting patterns. Essentially, autoencoders reconstruct a data set and, in the process, figure out its inherent structure and extract its important features. An RBM is a type of autoencoder that we have previously discussed, but there are several others. Autoencoders are typically shallow nets, the most common of which have one input layer, one hidden layer, and one output layer.