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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.