Summary

In this chapter, we 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, we delved into stacked RBMs, that is, deep belief networks, and we trained a DBN consisting of three RBM layers on the MNIST dataset.

In the next chapter, we will explore another model using an unsupervised learning paradigm – autoencoders.