This repository contains an implementation of a Restricted Boltzmann Machine (RBM), a type of artificial neural network used for unsupervised learning tasks such as dimensionality reduction, feature learning, and collaborative filtering. RBMs have been widely used in various domains, including recommendation systems, image recognition, and natural language processing.
- Unsupervised Learning: RBMs learn to represent input data in a lower-dimensional feature space without the need for labeled training data.
- Probabilistic Model: RBMs model the joint probability distribution of visible and hidden units using the Boltzmann distribution.
- Efficient Training: Training RBMs typically involves techniques like Contrastive Divergence (CD) or Gibbs Sampling, which efficiently approximate the gradient of the log-likelihood function.
- Applications: RBMs can be applied to various tasks, including collaborative filtering, dimensionality reduction, feature learning, and generating new data samples.
- Python 3.x
- NumPy
- Matplotlib (for visualization, optional)
- Tensorflow
https://www.coursera.org/learn/building-deep-learning-models-with-tensorflow