Stochastic Hopfield net Boltzmann machine is nothing but stochastic Hopfield net1. If you did not yet read the post of the Hopfield net in the blog, just go read it. I assume the readers are familiar to it, and directly use many results we had in the post. The magic of deep learning which we have discussed a couple of times works here, too. Such as $\epsilon$-greedy off-policy algorithm2, the stochastic character of the binary units allows the machine occasionally increase its energy to escape from poor local minima.
Binary Hopfield net using Hebbian learning We want to study Hopfield net from the simple case. Hopfield net is a fully connected feedback network. A feedback network is a network that is not a feedforward network, and in a feedforward network, all the connections are directed. All the connections in our example will be bi-directed. This symmetric property of the weight is important property of the Hopfield net. Hopfield net can act as associative memories, and they can be used to solve optimization problems.