Question

What do you understand by Boltzmann Machine?

Answer 1

A Boltzmann machine (also known as stochastic Hopfield network with hidden units) is a type of recurrent neural network. In a Boltzmann machine, nodes make binary decisions with some bias. Boltzmann machines can be strung together to create more sophisticated systems such as deep belief networks. Boltzmann Machines can be used to optimize the solution to a problem.

Some important points about Boltzmann Machine-

1. It uses a recurrent structure.
2. It consists of stochastic neurons, which include one of the two possible states, either 1 or 0.
3. The neurons present in this are either in an adaptive state (free state) or clamped state (frozen state).
4. If we apply simulated annealing or discrete Hopfield network, then it would become a Boltzmann Machine.

Answer 2

One of the most basic Deep Learning models is a Boltzmann Machine, resembling a simplified version of the Multi-Layer Perceptron. This model features a visible input layer and a hidden layer -- just a two-layer neural net that makes stochastic decisions as to whether a neuron should be on or off. Nodes are connected across layers, but no two nodes of the same layer are connected.