8b. Loss Functions | Cross-Entropy, KL Divergence, in under 2 hours

Unlock the secrets of core machine learning and deep learning concepts with this comprehensive lecture on Loss Functions. This session focuses on the two most crucial and often confused loss functions: Cross-Entropy and Kullback-Leibler (KL) Divergence We'll start with the fundamentals: What is a Loss Function and why is it the engine that drives model training? The essential distinction between cost and loss functions. In-depth explanation and mathematical derivation of Cross-Entropy Loss , covering its applications for both binary classification (Binary Cross-Entropy) and multi-class classification (Categorical Cross-Entropy). A clear, intuitive breakdown of KL Divergence , how it measures the difference between two probability distributions, and its critical role in advanced models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Detailed comparison: When to use Cross-Entropy vs. KL Divergence in practical scenarios. By the end of this single, focused lecture, you'll have everything you need to confidently choose, implement, and understand the most important loss functions in your Machine Learning and Deep Learning projects.