The RBM is a generative model, which can randomly produce visible data values when some latent or hidden parameters are supplied to it. In this chapter, we have discussed the concept and mathematical model of the Boltzmann machine, which is an energy-based model. The chapter then discusses and gives a visual representation of the RBM. Further, this chapter discusses CRBM, which is a combination of Convolution and RBMs to extract the features of high dimensional images. We then moved toward popular DBNs that are nothing but a stacked implementation of RBMs. The chapter further discusses the approach to distribute the training of RBMs as well as DBNs in the Hadoop framework.
We conclude the chapter by providing code samples for both the models. The next chapter of the book will introduce one more generative model called autoencoder and its various forms such as de-noising autoencoder, deep autoencoder, and so on.