KL Divergence: The Mathematical Tool to Measure the Difference Between Two Worlds

This episode explains the Kullback-Leibler (KL) divergence, a mathematical tool for measuring the difference between two probability distributions.

It details its use to evaluate and improve the performance of AI models, including identifying prediction errors, particularly those concerning rare but critical classes. The original article proposes best practices for integrating the KL divergence into model development, including visualization of distributions and regular iteration. Finally, it highlights the importance of customizing models using industry-specific data to reduce divergence and improve accuracy.