prml
pyprobml
| prml | pyprobml | |
|---|---|---|
| 1 | 3 | |
| 1,862 | 6,282 | |
| - | 1.2% | |
| 0.0 | 6.2 | |
| almost 2 years ago | 5 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | MIT License |
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prml
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Best Possible Book Recommended for Machine Learning [Discussion] [D] [Recommendation]
For me it was definitely the book Pattern Recognition and Machine Learning by Christopher Bishop. It is heavily Bayesian but it gives you a broad overview and depth to understanding current models once you’re done with it. I have repo full of Python code for the models if you’re interested: https://github.com/gerdm/prml
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?
PRML - PRML algorithms implemented in Python
numpyro - Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
jaxopt - Hardware accelerated, batchable and differentiable optimizers in JAX.
rethinking-numpyro - Statistical Rethinking (2nd ed.) with NumPyro
machine-learning-experiments - 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
lucid - A collection of infrastructure and tools for research in neural network interpretability.
lightwood - Lightwood is Legos for Machine Learning.
KoboldAI-Runpod - This is just a simple set of notebooks to load koboldAI and SillyTavern Extras on a runpod with Pytorch 2.0.1 Template
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
stable-diffusion-dreambooth-colab - Dreambooth for colab