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In my last article, I looked at NumPY and some of its uses in numerical simulations. Although NumPY does provide some really robust building blocks, it is a bit lacking in more sophisticated tools.
Lots of tips and tricks available on the NumPY Web site, which is well worth a look, especially as you start out. This short introduction should get you started in thinking of Python as a viable ...
Want to get better performance with Python? Here's how to use NumPy to toe the 'invisible line' of data and memory transfers and optimize efficiency.
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How-To Geek on MSNRegression in Python: How to Find Relationships in Your Data
The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...
[Zoltán] sends in his very interesting implementation of a NumPy-like library for micropython called ulab. He had a project in MicroPython that needed a very fast FFT on a micro controller, and ...
NumPy, the Python package for scientific computing, is an adolescent with prospects for a prolific maturity.
With Python and NumPy getting lots of exposure lately, I'll show how to use those tools to build a simple feed-forward neural network.
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