RBM is an acronym that stands for Restricted Boltzmann Machine. It is a generative stochastic artificial neural network model used in machine learning and deep learning algorithms. RBM is a type of unsupervised learning algorithm that falls under the broader family of Boltzmann machines.
An RBM consists of two layers of neurons, namely visible and hidden neurons, that are connected in a bipartite fashion. The connection between these neurons is undirected, meaning that information can flow bidirectionally. The visible layer neurons represent the input data, while the hidden layer neurons capture higher-level features or abstractions learned from the input.
The RBM model learns by adjusting the connection weights between the visible and hidden neurons to maximize the likelihood of the input data. This learning process involves Gibbs sampling, where the network iteratively adjusts the states of its neurons until it reaches a stable configuration.
RBM is primarily used for dimensionality reduction, feature learning, and collaborative filtering tasks. It has found applications in various domains, including recommendation systems, image recognition, and natural language processing. RBM's ability to learn complex hierarchical representations from data makes it a powerful tool in unsupervised learning.
In summary, RBM is a generative stochastic neural network model used for unsupervised learning. It consists of two layers of neurons and is capable of learning high-level features from input data through a process of adjusting connection weights.
