Limitations of RBMs
- Slow Training: Training RBMs can be computationally expensive, especially for large datasets.
- Limited Learning Capacity: Single RBMs have a limited capacity for learning complex relationships. They are often used as initial layers in DBNs to overcome this limitation.
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.