Kullback-Leibler Divergence
Kullback-Leibler (KL) Divergence is a statistical measure that quantifies how one probability distribution diverges from a second, reference probability distribution. It is not a true distance metric (as it's asymmetric) but provides a non-negative value, with zero indicating identical distributions. Commonly used in information theory, machine learning, and statistics, it helps compare models, optimize parameters, and assess information loss.
Developers should learn KL Divergence when working on machine learning tasks like model comparison, variational inference, or reinforcement learning, as it's essential for measuring differences between probability distributions. It's particularly useful in natural language processing for topic modeling, in computer vision for generative models, and in data science for evaluating statistical fits, enabling more informed decision-making in probabilistic frameworks.