jaxopt
pyprobml
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jaxopt | pyprobml | |
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1 | 3 | |
888 | 6,257 | |
1.6% | 1.7% | |
7.8 | 6.2 | |
3 days ago | 4 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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jaxopt
pyprobml
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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
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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
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
torchopt - TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
machine-learning-experiments - 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
lucid - A collection of infrastructure and tools for research in neural network interpretability.
PyNeuraLogic - PyNeuraLogic lets you use Python to create Differentiable Logic Programs
PRML - PRML algorithms implemented in Python
symbolicai - Compositional Differentiable Programming Library
lightwood - Lightwood is Legos for Machine Learning.