Reducing the Dimensionality of Data with Neural Networks

Overview

This repository contains Python scripts for reducing the dimensionality of data using neural networks. Two main techniques are implemented: Restricted Boltzmann Machine (RBM) and autoencoders. These techniques are commonly used for feature learning and dimensionality reduction tasks in machine learning.

Files

• RBM.py: Implementation of a Restricted Boltzmann Machine (RBM) for dimensionality reduction.
• auto_encoder.py: Implementation of an autoencoder for dimensionality reduction and data reconstruction.
• train_test_MNIST.py: Training and Testing script for validating the implemented neural network models on MNIST dataset.
• utilsnn.py: Utility functions for neural network operations, such as image preprocessing.

Instructions

To use the provided scripts:

1. Make sure you have Python installed on your system.
2. Install the required dependencies.