Notes:

A Restricted Boltzmann Machine (RBM) is a type of artificial neural network that can learn to represent complex data in a compact, efficient manner. RBMs are often used in machine learning applications, particularly in the field of unsupervised learning, where they can be trained to discover hidden patterns and structures in large datasets.

An RBM is composed of two layers of interconnected nodes, known as the visible layer and the hidden layer. Each node in the visible layer represents a single input feature, such as a pixel in an image or a word in a text document. Each node in the hidden layer represents a higher-level feature that is derived from the input data, such as a shape or a concept.

The nodes in the visible and hidden layers are connected by a set of weights that determine how much influence each node has on the others. During training, the weights are adjusted in order to optimize the representation of the input data. This process is known as learning.

One of the key features of RBMs is that they can learn to represent complex data in a way that is efficient and compact. This is because the hidden layer of an RBM is typically much smaller than the visible layer, so it can capture the essential features of the input data without needing to store all the details.

Overall, a Restricted Boltzmann Machine is a type of artificial neural network that is used for unsupervised learning. It is composed of two layers of interconnected nodes, and is able to learn to represent complex data in a compact, efficient manner.

See also:

Boltzmann Machine & Dialog Systems

- Neural networks [5.1] : Restricted Boltzmann machine – definition
- Neural networks [5.2] : Restricted Boltzmann machine – inference
- Neural networks [5.4] : Restricted Boltzmann machine – contrastive divergence
- Neural networks [5.3] : Restricted Boltzmann machine – free energy
- Neural networks [5.7] : Restricted Boltzmann machine – example
- Neural networks [5.5] : Restricted Boltzmann machine – contrastive divergence (parameter update)
- Restricted Boltzmann Machine | Neural Network Tutorial | Deep Learning Tutorial | Edureka
- Neural networks [5.6] : Restricted Boltzmann machine – persistent CD
- Neural networks [5.8] : Restricted Boltzmann machine – extensions
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