indaba-pracs-2022
pymc-resources
| indaba-pracs-2022 | pymc-resources | |
|---|---|---|
| 1 | 5 | |
| 172 | 1,882 | |
| 0.6% | 0.6% | |
| 0.0 | 3.7 | |
| 29 days ago | 4 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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indaba-pracs-2022
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From Deep Learning Foundations to Stable Diffusion
This year's Deep Learning Indaba had a tutorial on diffusion models in Jax: https://github.com/deep-learning-indaba/indaba-pracs-2022/tr...
pymc-resources
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Bayesian Analysis with Python
As it happens, there's a PyMC implementation of the 1st and 2nd editions of Statistical Rethinking here:
https://github.com/pymc-devs/pymc-resources
(I think the author of the book discussed above, Osvaldo Martin, is the primary or sole contributor for the Rethinking implementations, in fact -- he had a full implementation in his own repo [here](https://github.com/aloctavodia/Statistical-Rethinking-with-P...) before deprecating it in favor of the above-linked one.)
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Predicting the distribution of a variable rather than a point estimate
That course/book has been translated to Python (using PyMC3 for the modeling, but you could also use the Stan examples and run them from Python using CmdStanPy).
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Statistical Rethinking (2022 Edition)
Prof. McElreath has been adding two new videos every week.
Also, for anyone who prefers to use the pythons for the coding, I recommend the PyMC3 notebooks https://github.com/pymc-devs/resources/tree/master/Rethinkin... There is also a discussion forum related to this repo here https://gitter.im/Statistical-Rethinking-with-Python-and-PyM...
- Statistical rethinking, but with examples in python?
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Stan is a state-of-the-art platform for statistical modeling
The Statistical Rethinking book uses R.
For people wanting Python, Jupyter notebooks with Python code examples are here:
* https://github.com/pymc-devs/resources/tree/master/Rethinkin...
What are some alternatives?
PyCBC-Tutorials - Learn how to use PyCBC to analyze gravitational-wave data and do parameter inference.
neural-tangents - Fast and Easy Infinite Neural Networks in Python
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
jaxrl - JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
cookiecutter-pystan
bodywork-pymc3-project - Serving Uncertainty with Bayesian inference, using PyMC3 with Bodywork
brax - Massively parallel rigidbody physics simulation on accelerator hardware.
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠