Summary

In this chapter, we’ve had an extensive look at a new generation of deep learning models: autoencoders. We started with the vanilla autoencoder, and then moved on to its variants: sparse autoencoders, denoising autoencoders, stacked autoencoders, and convolutional autoencoders. We used the autoencoders to reconstruct images, and we also demonstrated how they can be used to clean noise from an image. Finally, the chapter demonstrated how autoencoders can be used to generate sentence vectors and images. The autoencoders learned through unsupervised learning.

In the next chapter, we will delve deeper into generative adversarial networks, another interesting deep learning model that learns via an unsupervised learning paradigm.