Boltzmann Machines are a class of stochastic neural networks that model complex probability distributions over binary vectors. They consist of interconnected units (neurons) that make probabilistic decisions about their activation states, enabling the network to learn and represent intricate patterns in data.
Key Features:
- Symmetric Connections: Each unit in a Boltzmann Machine is connected to every other unit, forming a fully connected network.
- Stochastic Units: Units make probabilistic decisions about their activation states, introducing randomness into the network’s behavior.
- Energy-Based Model: The network operates by minimizing an energy function, with lower energy states representing more probable configurations.