Bayesian-Neural-Networks
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Run various Bayesian inference methods using PyTorch with provided example code.
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Free · no card · unsubscribe anytimePytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian-Neural-Networks has 2k stars on GitHub. It has been forked 305 times. Bayesian-Neural-Networks is written mainly in Jupyter Notebook. It has been in active development since 2019. Bayesian-Neural-Networks is available under the MIT license. Its main topics are approximate-inference, bayes-by-backprop, bayesian-inference, bayesian-neural-networks.
What is Bayesian-Neural-Networks?
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Is Bayesian-Neural-Networks open source?
Bayesian-Neural-Networks is an open-source project. It is released under the MIT license.
Is Bayesian-Neural-Networks free?
Yes. Bayesian-Neural-Networks is free and open source — you can use, modify and self-host it.
What license does Bayesian-Neural-Networks use?
Bayesian-Neural-Networks is available under the MIT license.
What language is Bayesian-Neural-Networks written in?
Bayesian-Neural-Networks is written mainly in Jupyter Notebook.
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