[Feature Request]: Add Boltzmann Machines in Deep Learning

[pavitraag]

Is there an existing issue for this?

• [X] I have searched the existing issues

Feature Description

Boltzmann Machines are a type of stochastic recurrent neural network that can learn to represent complex distributions over a set of variables. They are named after the Boltzmann distribution and are used for tasks such as dimensionality reduction, feature learning, and generating new data samples. Boltzmann Machines consist of a network of units (neurons) with weighted connections and can be trained using algorithms like contrastive divergence.

Use Case

Integrating Boltzmann Machines into the project would enhance its capability to model and understand complex data distributions. This feature would be particularly beneficial for applications in unsupervised learning, such as clustering, pattern recognition, and anomaly detection. By leveraging the unique properties of Boltzmann Machines, the project could improve its performance in identifying underlying structures within data, making it more versatile and powerful in handling various machine learning tasks.

Benefits

No response

Add ScreenShots

No response

Priority

High

Record

• [X] I have read the Contributing Guidelines
• [X] I'm a GSSOC'24 contributor
• [X] I have starred the repository

[github-actions[bot]]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[pavitraag]

@Ajay-Dhangar can you please assign me this

[github-actions[bot]]

Hello @pavitraag! Your issue #3756 has been closed. Thank you for your contribution!