pyprobml
numpyro
Our great sponsors
| pyprobml | numpyro | |
|---|---|---|
| 3 | 2 | |
| 6,257 | 2,039 | |
| 1.7% | 1.8% | |
| 6.2 | 8.7 | |
| 4 months ago | 8 days ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyprobml
-
Best Possible Book Recommended for Machine Learning [Discussion] [D] [Recommendation]
Another great book is Kevin Murphy’s Machine Learning: A probabilistic approach. He just launched the second version of his book and he has a Python repo for the models and graphs: https://github.com/probml/pyprobml
-
Probabilistic Machine Learning, Kevin Murphy (2nd edition, 2021)
This exists actually, it's not complete yet (I think?) but it covers a lot of the material in the book:
https://github.com/probml/pyprobml
numpyro
- Bayesian Analysis with Python
-
Saving the World with Bayesian Modeling
Perhaps an alternative to look into: Numpyro [1] has a JAX backend so can be really fast when compiled; and it can run on GPUs. So that might be helpful for your problem with loads of data.
[1] https://github.com/pyro-ppl/numpyro
What are some alternatives?
prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
PyMC - Bayesian Modeling and Probabilistic Programming in Python
jaxopt - Hardware accelerated, batchable and differentiable optimizers in JAX.
trax - Trax — Deep Learning with Clear Code and Speed
machine-learning-experiments - 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
lucid - A collection of infrastructure and tools for research in neural network interpretability.
BayesianEcosystems_IAP - Notes and code for Bayesian ecosystem modeling IAP course
PRML - PRML algorithms implemented in Python
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
lightwood - Lightwood is Legos for Machine Learning.
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...