orange VS NumPy

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...

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.

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.

Both tools are ripoffs of a data mining framework named Orange 3

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.

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.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]