graphkit-learn
A python package for graph kernels, graph edit distances, and graph pre-image problem. (by jajupmochi)
prml
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop (by gerdm)
| graphkit-learn | prml | |
|---|---|---|
| 1 | 1 | |
| 120 | 1,862 | |
| - | - | |
| 7.8 | 0.0 | |
| 2 months ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 only | GNU Affero General Public License v3.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
graphkit-learn
Posts with mentions or reviews of graphkit-learn.
We have used some of these posts to build our list of alternatives
and similar projects.
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[R][P] graphkit-learn: A Python Library for Graph Kernels Based on Linear Patterns
library link: https://github.com/jajupmochi/graphkit-learn
prml
Posts with mentions or reviews of prml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-09.
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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?
When comparing graphkit-learn and prml you can also consider the following projects:
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
shap - A game theoretic approach to explain the output of any machine learning model.
PRML - PRML algorithms implemented in Python
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
rethinking-numpyro - Statistical Rethinking (2nd ed.) with NumPyro