Kullback Leibler Divergence

Kullback-Leibler (KL) Divergence is a measure of the difference between two probability distributions. It is used to measure how much one probability distribution differs from another one and can be used in a variety of fields, including biology, computer science, and artificial intelligence. In particular, it is a useful tool to measure the performance of machine learning models, as it allows us to measure how far a model's predictions are from the target distribution. By using KL Divergence, we can see if a model is accurately recognizing patterns and if it is capturing the true underlying relationships in its data. Additionally, it can be used to compare two texts, images, or other types of data, to measure their similarity. KL Divergence is an important tool for understanding the difference between probability distributions, which makes it a key tool for predicting, analyzing, and understanding data.