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What is a Restricted Boltzmann Machines( RBMs) ?
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network capable of learning a probability distribution across its inputs. It was invented by paul smolensky in 1986.
This algorithm is useful for dimensional reduction, categorization, collaborative filtering, feature learning, topic modelling, and even many body quantum physics. This algorithm has two layer first is visible layer or input layer and second is hidden layer
In the above diagram each circle represents a neuron-like unit which is called a node where the calculations take place. The nodes are connected with each other no one two nodes are connected with the same layer.
The restriction in a restricted Boltzmann machine is that there is no intra-layer communication. Each node is a computational node that receives input and makes stochastic judgments about whether or not to transmit that input.
All visible nodes take low level features from data to learn. For example suppose grayscale image all visible mode would receive one pixel value for each pixel in one image
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