What Is Boltzmann Machine?

Learning and prediction are accomplished by a network of interconnected nodes in a Boltzmann Machine. Named after the great physicist Ludwig Boltzmann, who also gave us Boltzmann's Constant (for more on that, see my previous answer!). Imagine that you are trying to train a computer to identify a specific pattern in a database, such as distinguishing between cat and dog pictures. The "weights" used by a Boltzmann Machine to establish the degree of connectivity between each pair of nodes are chosen at random. After receiving the input data, the machine employs an iterative process called "Gibbs sampling" to fine-tune the weights until the network can accurately predict the correct output (i.e., whether the picture is a cat or a dog) based on the input data. Now, however, comes the exciting part: Complex data patterns can be learned by Boltzmann Machines that a human might miss at first glance. Take, as an illustration, the problem of determining, from a person's viewing habits, which films they will enjoy. A Boltzmann Machine can recognize even the most subtle of data patterns, such as the association between a preference for romantic comedies and a similar preference for feel-good dramas. As a result, the machine can make more precise predictions than it would be able to use a straightforward algorithm based on a small number of readily available variables. Just what is it about Boltzmann Machines that makes them so exceptional? Since they are not given instructions on learning from the data, they are considered an "unsupervised learning" algorithm. On the other hand, "supervised learning" algorithms can only learn from a dataset in which each example has already been labeled. Boltzmann machines can be "stacked" to create more complex neural networks, which is another neat feature. As a result, they can learn more subtle patterns in the data and produce more precise forecasts.