Tag: Boltzmann Machines
Boltzmann Machines are powerful computational models inspired by the principles of statistical mechanics and artificial neural networks. These probabilistic generative models consist of interconnected nodes, or “neurons,” that work collaboratively to learn and represent complex patterns in data. It utilize a process called stochastic learning, where the state of each neuron is updated based on the collective activity of its neighboring neurons. Through iterative training, It can learn intricate dependencies and capture underlying structures in the data, making them valuable in tasks such as pattern recognition, recommendation systems, and unsupervised learning. Their ability to handle large-scale, high-dimensional datasets and extract meaningful representations has made Boltzmann Machines a valuable tool in the field of machine learning and artificial intelligence.