thu-ml/zhusuan
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
This library helps machine learning researchers and practitioners build and experiment with advanced probabilistic models, especially those involving deep neural networks. It takes a description of your model's structure and data, and provides tools to estimate its parameters and make predictions. This is ideal for researchers developing new deep generative models or Bayesian deep learning architectures.
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Use this if you are a machine learning researcher or data scientist needing to implement and experiment with probabilistic deep learning models and advanced Bayesian inference algorithms like Variational Inference, Importance Sampling, or Hamiltonian Monte Carlo.
Not ideal if you primarily work with standard deterministic neural networks for supervised tasks or need a more out-of-the-box solution for common machine learning problems without deep customization of probabilistic aspects.
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2,220
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Language
Python
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MIT
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Last pushed
Dec 17, 2022
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