Many high-performance Python frameworks, such as NumPy, scikit-learn, and PyTorch, rely on primitives implemented in Cython and C++ to achieve optimal performance.

In this tutorial, we will explore how to implement custom kernels in Cython and C++ and integrate them into Python projects. Using Linear Regression model trained with Normal Equations method as an example, we will demonstrate how to accelerate numerical computations by writing efficient kernels in Cython and C++. We will also discuss when implementing custom kernels is beneficial and when existing optimized libraries offer the best performance.

This tutorial is aimed at intermediate Python users. At the same time C++ knowledge is advantageous but not mandatory.