[Feature Request]: Add Deep Belief Networks (DBN) in Deep Learning

[pavitraag]

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Feature Description

Deep Belief Networks (DBNs) are a type of generative graphical model that consist of multiple layers of stochastic, latent variables. These networks are composed of layers of Restricted Boltzmann Machines (RBMs) where each layer is trained to capture high-level features from the data learned by the previous layer. DBNs can be fine-tuned with backpropagation and are used for tasks such as classification, regression, and dimensionality reduction.

Use Case

Integrating Deep Belief Networks into the project would significantly enhance its capability to learn and model complex, hierarchical representations of data. This feature would be particularly advantageous for tasks requiring deep feature extraction and high-level abstraction, such as image recognition, speech processing, and natural language understanding. By leveraging DBNs, the project can improve its accuracy and robustness in predictive modeling, making it more effective in solving complex machine learning problems.

Benefits

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Priority

High

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[github-actions]

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 u please assign me this

[github-actions]

Hello @pavitraag! Your issue #3757 has been closed. Thank you for your contribution!