Autoencoders' example uses augment data for machine learning

Until recently, the study of autoencoders had primarily been an academic pursuit, said Nathan White, lead consultant at AIM Consulting. However, there are now many applications where machine learning practitioners should look to autoencoders as their tool of choice. An autoencoder consists of a pair of deep learning networks, an encoder and decoder. The encoder learns an efficient way of encoding input into a smaller dense representation, called the bottleneck layer. After training, the decoder converts this representation back to the original input.