Restricted Boltzmann Machine Variants

Restricted Boltzmann Machines (RBMs) are a type of neural network used for unsupervised learning, especially in feature extraction and dimensionality reduction. Variants of RBMs include different architectures and training techniques to improve performance, stability, or applicability. For example, Deep Boltzmann Machines stack multiple RBMs to model complex data patterns, while Gaussian RBMs handle continuous data instead of binary. Other variants incorporate techniques like sparse representations or convolutional structures, enabling better modeling of images, text, or speech. These adaptations enhance the flexibility and effectiveness of RBMs across various data types and tasks.