What are Deep Belief Networks in AI/ML?
A deep belief network (DBN) is a type of artificial neural network that is composed of multiple layers of hidden units, with connections between the layers but not between units within each layer. DBNs are trained to learn a hierarchical representation of the input data, with each layer learning to represent more abstract features of the data.
DBNs are typically trained using a greedy layer-by-layer approach, where each layer is trained independently of the other layers. This makes DBNs relatively easy to train, compared to other types of deep neural networks.
Key characteristics and components of Deep Belief Networks include:
- Hierarchical Structure: DBNs are composed of multiple layers of nodes, typically organized into three types of layers:
Visible Layer: The input layer that represents observed data (e.g., input features).
Hidden Layers: One or more layers of latent variables, each capturing increasingly abstract and higher-level features.
Top Layer: An additional hidden layer called the "top" layer, often used for generative purposes.
2. Restricted Boltzmann Machines (RBMs): DBNs are usually built by stacking layers of Restricted Boltzmann Machines (RBMs). RBMs are a type of stochastic neural network with binary-valued hidden and visible units. They have a symmetric architecture and are used to model the conditional probability distribution of the data.
3. Unsupervised Learning: DBNs are typically trained using unsupervised learning methods, such as contrastive divergence or persistent contrastive divergence. During training, they learn to capture hierarchical patterns and dependencies in the data without the need for labeled examples.
4. Fine-Tuning: After pretraining the RBMs in a layer-wise manner, DBNs can be fine-tuned using supervised learning techniques like backpropagation. This fine-tuning allows DBNs to be used for classification tasks.
