Description: Negative Log-Likelihood is a loss function used in generative models to evaluate how well the model fits the observed data. This metric is based on the idea of maximizing the likelihood of the data, which means finding the model parameters that make the observed data most probable. Mathematically, negative log-likelihood is calculated as the negative logarithm of the likelihood function, turning the maximization problem into a minimization one, thus facilitating its use in optimization algorithms. This function is particularly useful in contexts where data may follow complex distributions, such as in mixture models or in generative adversarial networks (GANs). Negative log-likelihood allows researchers and developers to effectively tune their models, providing a clear measure of how well the model adapts to real data. Its implementation in various libraries has enabled data scientists and machine learning engineers to efficiently use this loss function in their projects, improving the quality of generative models and their ability to learn complex patterns in data.
