Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
Version 3.0.0 of this project ends support for Python 2.7, 3.3, and 3.4. We recommend migrating your projects to Python 3.5 or newer as soon as possible. We'll be happy to answer any questions you ...
This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning ...
A Tutorial on how to Connect Python with Different Simulation Software to Develop Rich Simheuristics
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.
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