What is the difference between KL and KLD?

KL (Kullback-Leibler) divergence and KLD (Kullback-Leibler divergence) refer to the same concept in information theory, where KL is often used as a shorthand notation. It measures the difference between two probability distributions, typically a true distribution and an approximate distribution, quantifying how much information is lost when the approximate distribution is used to represent the true one. There is no inherent difference between KL and KLD; they are interchangeable terms used in the context of statistical analysis and machine learning.