Deep Learning
Deep Learning
Probabilistic generative models composed of multiple layers of stochastic latent variables
Detailed Explanation
Deep Belief Networks (DBNs) are probabilistic generative models composed of multiple layers of stochastic, hidden variables. They learn to represent complex data distributions by stacking Restricted Boltzmann Machines (RBMs). DBNs can be trained in a greedy layer-by-layer fashion, enabling deep hierarchical feature extraction, which improves tasks like classification, recognition, and data generation within the deep learning framework.
Use Cases
- •Enhance image recognition accuracy by using Deep Belief Networks to learn complex feature hierarchies from large datasets.
Related Terms
Other terms in the Deep Learning category