What are Deep Belief Networks?
Deep Belief Networks (DBNs) are a type of generative graphical model composed of multiple layers of stochastic, latent variables. They harness the principles of unsupervised learning, making them a powerful tool in the field of Deep Learning.
Structure
DBNs are typically structured as a stack of Restricted Boltzmann Machines (RBMs). Each RBM layer learns to represent data in a hierarchical manner, where higher layers capture more abstract features. This multi-layer architecture allows for effective representation learning.
Training
DBNs are trained using a two-phase process. The first phase is unsupervised pre-training, where each layer is trained one at a time, allowing the model to learn lower-level features progressively. The second phase, fine-tuning, involves supervised training utilizing backpropagation to adjust the weights of the network for specific tasks.
Applications
While initially gaining popularity for image and speech recognition tasks, DBNs can also be applied in various fields like natural language processing and game AI. Their ability to learn complex representations makes them versatile for various machine learning challenges.
Conclusion
Deep Belief Networks are a significant development in the realm of Artificial Intelligence, combining generative models with hierarchical learning, which continues to influence current deep learning architectures.