ericmjl/bayesian-deep-learning-demystified
In which I try to demystify the fundamental concepts behind Bayesian deep learning.
This project offers an explanation of Bayesian deep learning, clarifying its core ideas. It takes complex theoretical concepts and transforms them into more accessible insights. The content is designed for machine learning practitioners and researchers who want to grasp the fundamentals of this advanced technique.
122 stars. No commits in the last 6 months.
Use this if you are a machine learning practitioner or researcher looking to understand the foundational concepts of Bayesian deep learning.
Not ideal if you are looking for a plug-and-play code library or a tutorial on implementing a specific Bayesian deep learning model.
Stars
122
Forks
42
Language
CSS
License
MIT
Category
Last pushed
Nov 29, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ericmjl/bayesian-deep-learning-demystified"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks....
probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
google/edward2
A simple probabilistic programming language.