PRML
prml
PRML | prml | |
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1 | 1 | |
11,250 | 1,862 | |
- | - | |
0.0 | 0.0 | |
almost 2 years ago | almost 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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PRML
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Probabilistic Machine Learning, Kevin Murphy (2nd edition, 2021)
It's a regression as far as code readability goes for fairly straightforward reasons: almost everything in Matlab is a matrix. Matrices are not first class citizens in Python, and it matters. I use Python a hell of a lot more than Matlab, but for examining how an algorithm works, Matlab wins. Go look at these PRML collections in Python and Matlab and see if you disagree:
https://github.com/ctgk/PRML
https://github.com/PRML/PRMLT
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
What are some alternatives?
retrolab - JupyterLab distribution with a retro look and feel 🌅
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
simfin-tutorials - Tutorials for SimFin - Simple financial data for Python
rethinking-numpyro - Statistical Rethinking (2nd ed.) with NumPyro
football-crunching - Analysis and datasets about football (soccer)
feature-engineering-tutorials - Data Science Feature Engineering and Selection Tutorials
iotSDR - GitHub Repository for iotSDR open source material
Jupyter-PowerShell - Jupyter Kernel for PowerShell