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[2025-10-18]: The code and models have been released! We have improved the training resolution of the models and achieved better results compared to the original paper. Both old weights (for ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

An exercise-driven course on Advanced Python Programming that was battle-tested several hundred times on the corporate-training circuit for more than a decade. Written by David Beazley, author of the ...

So, you want to learn Python? That’s cool. A lot of people are getting into it these days because it’s used for all sorts of things, from building websites to analyzing data. If you’re looking for a ...

Abstract: Simulation is an excellent tool to study real-life systems with uncertainty. Discrete-event simulation (DES) is a common simulation approach to model time-dependent and complex systems.