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 :...

What is  Information Theory ? Information theory  is a mathematical framework developed by  Claude Shannon  in the 1940s to understand how information is measured, transmitted, and compressed. At its core, it deals with questions like: How much information is in a message? How can we represent that information efficiently? How can we reduce noise when transmitting information? Key Concept: Entropy Entropy  is a measure of uncertainty or unpredictability. Think of it like this: A fair coin (50% heads, 50% tails) has  high entropy  because it’s unpredictable. A coin that always lands on heads has  zero entropy  because it’s completely predictable. In deep learning,  entropy  tells us how much uncertainty there is in the model’s prediction. What is the  Information Bottleneck ? Imagine you're trying to compress an image to send over the internet. You want to remove unnecessary parts (like background noise) but keep the important c...