Reducing-the-Dimensionality-of-Data-with-Neural-Networks
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20 hours ago •
nmeripo
nmeripo
Implementation of G. E. Hinton and R. R. Salakhutdinov's Reducing the Dimensionality of Data with Neural Networks (Tensorflow)
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:
- Make sure you have Python installed on your system.
- Install the required dependencies.
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