AI Practitioner

  What is KL Divergence? Kullback–Leibler Divergence (KL Divergence)  is a fundamental concept in probability theory, information theory, and machine learning. It measures the difference between two probability distributions. In essence,  KL Divergence tells us how much information is lost  when we use one distribution ( Q ) to approximate another distribution ( P ). It’s often described as a measure of "distance" between distributions — but  important : it is  not a true distance  because it is  not symmetric . That means: $KL(P \parallel Q) \neq KL(Q \parallel P)$ Why is KL Divergence Important in Deep Learning? KL Divergence shows up in many core ML/DL areas: Variational Autoencoders (VAE) : Regularizes the latent space by minimizing KL divergence between the encoder's distribution and a prior (usually standard normal). Language Models : Loss functions like  cross-entropy  are tightly related to KL Divergence. Reinforcement Learning :...