restricted_boltzmann

Package for the implementation of restricted Boltzmann machines

Installation

This package can be easily installed with pip:

pip install git+https://github.com/jzsmoreno/restricted_boltzmann

Features

• Training: Implements Contrastive Divergence (CD) and other training algorithms for RBMs.
• Inference: Support for both Gibbs sampling and deterministic inference methods.
• Customization: Allows the configuration of various hyperparameters such as the number of hidden units, learning rate, and batch size.
• Compatibility: Can be easily integrated with popular deep learning frameworks like TensorFlow or PyTorch for more advanced use cases.

Applications

RBMs are widely used in various real-world applications, including:

• Pattern Recognition: Feature extraction in pattern recognition problems, such as handwriting recognition or pattern classification.
• Recommendation Systems: Collaborative filtering for recommending products, movies, or other items based on user behavior (e.g., movie or book recommendations).
• Radar Target Recognition: Used in radar systems to detect low signal-to-noise ratio (SNR) targets in environments with high noise levels.

Key Features of RBMs

• Unsupervised Learning: RBMs learn from input data without requiring labeled responses.
• Recurrent and Symmetric Structure: The network structure is symmetric, with the same types of connections between visible and hidden layers.
• No Intra-Layer Connections: There are no connections within the visible layer or within the hidden layer, making the model computationally efficient.
• Energy-Based Model: RBMs associate high probability with low-energy configurations.

Contributing

If you'd like to contribute to the development of this package, feel free to fork the repository and submit a pull request. Please make sure your contributions adhere to the coding style and include relevant tests.

License

This package is licensed under the MIT License. See the LICENSE file for more information.