Lecturer outlines core mathematical principle behind generative AI models

Professor Prathosh AP's public lecture series explains the foundational principle of generative AI. The method involves using deep neural networks to transform random noise into data that mimics a target distribution. This is achieved by optimizing network parameters to minimize a statistical divergence between the generated and real data. The process is illustrated with an example using Gaussian noise and a simple linear network. The lecture notes that complex data distributions require more flexible, deep networks to approximate effectively.