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Maximum Likelihood Learning Overview by Jalel Eddine Hajlaoui is a document available to read on EtoBox.

What is Maximum Likelihood Learning Overview about?

This lecture covers Maximum Likelihood Learning in the context of Deep Generative Models, focusing on how to learn a probability distribution from data. It introduces the concepts of model family, learning objective, and optimization, emphasizing the use of KL-Divergence as a learning objective and its relation to maximizing log-likelihood. The lecture also discusses practical estimation techniques like Monte Carlo methods and gradient descent for optimizing model parameters.

Author

Jalel Eddine Hajlaoui

Language

EN