Understanding Entropy and Information Theory in Machine Learning
Introduction 📚 This article explores the fundamental concepts of information theory, which form the mathematical foundation for many machine learning algorithms. Understanding these concepts is crucial for grasping how models process and learn from data. Information Quantity When an event A occurs with probability P(A), the information quantity I(A) measures how much information we gain from observing this event: $ I(A) = -\log P(A)$ Key insight: Rare events carry more information than common ones. This makes intuitive sense - learning that a rare event occurred tells us more than learning about a common event. ...