contrastive divergence

Contrastive divergence is a learning algorithm used in machine learning, particularly for training models like restricted Boltzmann machines. It works by comparing two states: the original data and a sample generated from the model. The idea is to find differences between these states to update the model's parameters, improving its ability to generate similar data. Essentially, it adjusts the model based on how far off its guesses are from actual examples, enhancing its performance over time by learning from mistakes. This method allows the model to learn complex patterns in data efficiently.