Restricted Boltzmann Machine (RBM)
A Restricted Boltzmann Machine (RBM) is a type of generative stochastic artificial neural network that is used to learn a probability distribution over its set of inputs. RBMs are characterized by their structure, which consists of a layer of visible units (representing input data) connected to a layer of hidden units (representing features extracted from the data), with no intra-layer connections—hence the term "restricted."
This bipartite graph structure allows RBMs to efficiently model complex, high-dimensional data by capturing correlations between input features. Training an RBM involves adjusting the weights between visible and hidden units based on the differences between input data and the network's reconstruction of that data, a process typically achieved through contrastive divergence. Once trained, RBMs can be used to generate new data samples similar to the training data, perform dimensionality reduction, feature learning, and classification.
RBMs have been effectively applied in various AI and ML tasks. In collaborative filtering, RBMs can learn to recommend items to users based on the learned probability distribution of user preferences and item features. For example, an RBM trained on user ratings of movies can generate recommendations by inferring users' preferences for unseen movies.
In the field of deep learning, RBMs can be used as building blocks for deeper neural networks called Deep Belief Networks (DBNs), where multiple RBM layers are stacked to learn hierarchical representations of data.
This approach has been particularly successful in areas such as image and speech recognition, where the hierarchical structure of RBMs can capture complex patterns at multiple levels of abstraction. Additionally, RBMs have been used in dimensionality reduction, where the hidden layer's activations serve as a compressed representation of the input data, facilitating tasks like feature extraction and data visualization in high-dimensional spaces.