Summary

Autoencoders, one of the most popular and widely applicable generative models, have been discussed in this chapter. Autoencoders basically help two phases: one is the encoder phase and the other is the decoder phase. In this chapter, we elaborated on both of these phases with suitable mathematical explanations. Going forward, we explained a special kind of autoencoder called the sparse autoencoder. We also discussed how autoencoders can be used in the world of deep neural networks by explaining deep autoencoders. Deep autoencoders consist of layers of Restricted Boltzmann machines, which take part in the encoder and decoder phases of the network. We explained how to deploy deep autoencoders using Deeplearning4j, by loading chunks of the input dataset into a Hadoop Distributed File System. Later in this chapter, we introduced the most popular form of autoencoder called the denoising autoencoder and its deep network version known as the stacked denoising autoencoder. The implementation...