Applications of RBMs
- Dimensionality Reduction: RBMs can reduce the number of dimensions in the data, capturing the most relevant features.
- Collaborative Filtering: RBMs are used in recommendation systems to predict user preferences based on previous interactions.
- Feature Learning: RBMs can learn features from the input data that can be used in other machine-learning tasks.
- Image Recognition: RBMs can be used to pre-train layers in deep neural networks for tasks such as image recognition.
Restricted Boltzmann Machine : How it works
A Restricted Boltzmann Machine (RBM), Introduced by Geoffrey Hinton and Terry Sejnowski in 1985, Since, It become foundational in unsupervised machine learning, particularly in the context of deep learning architectures. They are widely used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modelling.