Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

NumPy is known for being fast, but could it go even faster? Here’s how to use Cython to accelerate array iterations in NumPy. NumPy gives Python users a wickedly fast library for working with data in ...

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. Over the past few months, the use of the Python programming ...

[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 was ...

Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...

These pages provide a showcase of how to use Python to do computations from linear algebra. We will demonstrate both the NumPy (SciPy) and SymPy packages. This is meant to be a companion guide to a ...

Data science is often cited as one of the main reasons for Python's growing popularity. But while people are definitely using Python for data analysis and machine learning, not many of those using ...

Tired of out-of-memory errors derailing your data analysis? There's a better way to handle huge arrays in Python.