Connectionist machine learning models became more and more prominent when researchers found out more details about the functioning and structure of our brains. The connectionist machine learning models are also known as Parallel Distributed Processing or PDP models and they have been manufactured using processing units that are highly adjoined.
These particular models are generally used for complex patterns such as human perception and behavior. Similarly, tasks about perception, constraint satisfaction problems, or modeling vision require sufficient computational power. Until the emergence of the GPU of VLSI technology, enough hardware support wasn’t available to run these models and that is why Boltzmann Machines were introduced.
About Boltzmann Machines
Boltzmann Machine is a particular kind of repetitive neural network where you can find some kind of bigotry and the binary decisions are practically made by the available nodes. You can also amalgamate multiple Boltzmann Machines to create complex systems, for example, a deep belief network. The term has been coined using the name of the popular Austrian scientist Ludwig Boltzmann, who was the instigator of the Boltzmann distribution concept during the 20th century.
The ‘father of deep learning, Geoffrey Hinton, a Stanford scientist along with Terry Sejnowski, a professor at Johns Hopkins University formulated and later developed the Boltzmann Machines. Boltzmann Machines uses both the concepts of deep learning and physics.
Different kinds of Boltzmann Machines
There are different types of Boltzmann Machines:
- Deep Boltzmann Machines or DBMS
- Beep Belief Networks or DBNs
- Restricted Boltzmann Machines or RBMS
Although Restricted Boltzmann Machines and Boltzmann Machines have a lot of similarities, the main difference between them is that RBMs does not establish a connection between the concealed and visible nodes.
Any neural network depending on an energy-based model is known as the Restricted Boltzmann Machine. The primary attributes of this deep learning algorithm are that it is probabilistic, productive, and autonomous. There are a total of two layers in RBM: an undirected hidden layer and an input layer.
The fundamental focus of an RBM is to search for the mixed probability distribution which will be able to expand the log-likelihood function. Furthermore, in RBMs, all the concealed nodes are connected to the visible nodes. Due to the restriction on intralayer connectivity, it is referred to as the Restricted Boltzmann Machine.
Features of Restricted Boltzmann Machine
Some of the main features of the Restricted Boltzmann Machines are:
- During the learning process, RBMs try to connect the high probabilities with low energy states and vice versa.
- You will not be able to find any connection between the layers of RBM.
- RBMs are unsupervised learning algorithms which means they will conclude from the input data itself.
- RBMs make use of repetitive and balanced structures.
The Boltzmann Machines can be regarded as a type of improved neural network which has multiple qualities along with a huge number of applications. It is also being used in the technological field. Although it is still being researched and being improved continuously, Restricted Boltzmann Machines can provide numerous advantages when used for optimization or training purposes.