Matrix inversion is one of the fundamental operations in linear algebra. It's analogous to division in ordinary algebra. Thus, making it crucial, for example, when solving many linear systems. All essential Python linear algebra libraries support matrix inversion. We will have a look two most popular ones: NumPy and SciPy.

Cholesky decomposition is a widely used matrix factorization method for hermitian positive-definite matrices. It provides many computational benefits for varying algorithms, such as solving linear systems. We go through how to calculate Cholesky decomposition using the essential scientific computation libraries for Python: NumPy & SciPy. Additionally, we go show you a

Singular value decomposition (SVD) is one of the most significant matrix decomposition techniques. It should be in the toolbox of anyone working on linear algebra. Python has many great packages that provide support for basic linear algebra. We will go through some of these, and also see how to create

Principal component analysis PCA provides an intuitive and analytically sound basis for various applications. Image compression is one of the most applied uses of PCA. We are utilizing scikit-learn or sklearn for short to perform the heavy lifting in principal component analysis. No point in reinventing the wheel, by implementing

Python has taken a big role in scientific computation and engineering. Packages, such as NumPy and SciPy, are heavily relied on in various computational applications and day-to-day research. How about computational optimization? We will have a look at the 3 most popular Python packages for computational optimization. More specifically, we

Pitch detection algorithm in python. Using autocorrelation as the dominant frequency detection tool.

Pybind11 provides convient interface for C++ API. This header-only library is lightweight, easy to build, and include in a CMake project. In this post, I'm going to tackle a specific problem of working with pointer arrays in your C++ API.