Feature description

Feature Request: Add Deep Belief Network (DBN) Implementation

Description

I would like to contribute a Deep Belief Network (DBN) implementation using stacked Restricted Boltzmann Machines (RBMs) to the neural network module.

Key Features

• Pure NumPy implementation with no external deep learning frameworks (no PyTorch, TensorFlow, etc.)
• Layer-wise unsupervised pretraining using Contrastive Divergence (CD-k)
• Implements Gibbs sampling for probabilistic binary units
• Manual weight updates and gradient computation for educational transparency
• Includes usage example demonstrating training and reconstruction

Purpose

• Adds a generative probabilistic model to the repository, complementing existing discriminative models
• Provides educational value by showing fundamental unsupervised learning algorithms
• Minimal dependencies (only NumPy required)
• Follows repository coding standards (PEP8, docstrings, examples)

Implementation Details

• File location: neural_network/deep_belief_network.py
• Includes both RBM and DeepBeliefNetwork classes
• Fully documented with comprehensive docstrings
• Includes working example in __main__ block

I am ready to submit a pull request following the contribution guidelines. Please let me know if this addition would be welcomed!

References

• Based on original DBN/RBM research and educational implementations
• Extension from the code developed by me for Intrusion Detection System (IDS) project: https://github.com/Adhithya-Laxman/IDS-For-WiFi-using-Federated-DBN-RBM