Interactive Parallel Computing with IPython
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| Interactive Parallel Computing with IPython | orange | |
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| - | 27 | |
| 2,551 | 4,611 | |
| 0.4% | 0.9% | |
| 8.2 | 9.6 | |
| 27 days ago | 6 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later |
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Interactive Parallel Computing with IPython
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Tracking mentions began in Dec 2020.
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
Dask - Parallel computing with task scheduling
glue - Linked Data Visualizations Across Multiple Files
NetworkX - Network Analysis in Python
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
zipline - Zipline, a Pythonic Algorithmic Trading Library
RDKit - The official sources for the RDKit library
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
SymPy - A computer algebra system written in pure Python
NumPy - The fundamental package for scientific computing with Python.
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis