KL Divergence Explained: Information Theory’s Most Important Metric
When you’re working with probability distributions in machine learning, statistics, or information theory, you’ll inevitably encounter KL divergence. This mathematical concept might seem intimidating at first, but it’s one of the most fundamental tools for comparing distributions and understanding how information flows in systems. Whether you’re training neural networks, analyzing data, or optimizing models, grasping … Read more