Showing 1-20 of 45 projects
Bayesian methods and probabilistic programming in Python with PyMC
A comprehensive list of PyTorch-related content on GitHub, including models, libraries, and tutorials.
A comprehensive collection of deep learning, reinforcement learning, and machine learning resources for vibe coders.
Continuously updated machine learning and deep learning notes and demos by a dedicated researcher.
A powerful Bayesian modeling and probabilistic programming library for Python developers.
A deep universal probabilistic programming library for Python and PyTorch, enabling Bayesian machine learning.
A probabilistic language based on pattern matching and constraint propagation for procedural generation.
Python code for a book on probabilistic machine learning, with support for various ML frameworks.
A book series on probabilistic machine learning for developers building AI-powered applications.
A Python library for probabilistic time series modeling and forecasting, useful for data scientists and ML engineers.
Edward is a probabilistic programming language in TensorFlow for deep generative models and variational inference.
Probabilistic reasoning and statistical analysis tools built on TensorFlow for data scientists and ML researchers.
Scalable neural forecasting algorithms for time-series data, with support for various deep learning models.
Unofficial PyTorch implementation of an image super-resolution model using iterative refinement.
Improved Denoising Diffusion Probabilistic Models for AI applications
Python library for Causal AI and Bayesian networks
A probabilistic programming library powered by NumPy and JAX for Bayesian inference and MCMC sampling.
Turing.jl is a powerful Bayesian inference library for probabilistic programming in the Julia language.
Fast, accurate, and scalable probabilistic data linkage with support for multiple SQL backends.
Course notes for CS228: Probabilistic Graphical Models, focusing on SCSS
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