This intermediate-level chapter has shown you the basic theory behind how RBMs work and their applications. We paid special attention to a Bernoulli RBM that operates on input data that may follow a Bernoulli-like distribution in order to achieve fast learning and efficient computations. We used the MNIST dataset to showcase how interesting the learned representations are for an RBM, and we visualized the learned weights as well. We concluded by comparing the RBM with a very simple AE and showed that both learned high-quality latent spaces while being fundamentally different models.
At this point, you should be able to implement your own RBM model, visualize its learned components, and see the learned latent space by projecting (transforming) the input data and looking at the hidden layer projections. You should feel confident in using an RBM on large datasets, such as MNIST, and even perform a comparison with an AE.
The next chapter is the beginning of a new group of chapters...
