Advanced · Neural Networks
Restricted Boltzmann machine (RBM)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A generative neural network that learns probability distributions over input data.
Technical Definition
A generative neural network that learns probability distributions over input data.
How it works
A Restricted Boltzmann Machine (RBM) is a type of stochastic neural network used for generative modeling. It consists of two layers: a visible layer for the input data and a hidden layer for learning representations. The 'restricted' nature means there are no connections within layers, only between them, simplifying learning.
Related Concepts
- Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.
- Boltzmann machine — A type of stochastic recurrent neural network used for generative learning and optimization.
- Generative model — A type of machine learning model that can create new data instances similar to the data it was trained on, or estimate the likelihood of a given data point originating from the training distribution.