Summary

The chapter covered the major unsupervised learning algorithms. We went through algorithms best suited for dimension reduction, clustering, and image reconstruction. We started with the dimension reduction algorithm PCA, then we performed clustering using k-means and self-organized maps. After this we studied the restricted Boltzmann machine and saw how we can use it for both dimension reduction and image reconstruction. Next the chapter delved into stacked RBMs, that is, deep belief networks, and we trained a DBN consisting of three RBM layers on the MNIST dataset. Lastly, we learned about variational autoencoders, which, like GANs, can generate images after learning the distribution of the input sample space.

This chapter, along with chapters 6 and 9, covered models that were trained using unsupervised learning. In the next chapter, we move on to another learning paradigm: reinforcement learning.