Mastering Machine Learning Algorithms - Second Edition

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Deep Belief Networks

In this chapter, we're going to present two probabilistic generative models that employ a set of latent variables to represent a specific data generation process. Restricted Boltzmann Machines (RBMs), proposed in 1986, are the building blocks of a more complex model, called a Deep Belief Network (DBN), which is capable of capturing complex relationships among features at different levels (in a way not dissimilar to a deep convolutional network). Both models can be used in unsupervised and supervised scenarios as preprocessors or, as is usual with DBN, fine-tuning the parameters using a standard backpropagation algorithm.

In particular, we will discuss:

• Markov random fields (MRF)
• RBM, including the Contrastive Divergence ...