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.

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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.

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Namshik Kim

physicist, data scientist

Data Scientist

Vancouver, BC, Canada.