Description
Python scientific workloads can use SciPy built with Intel MKL for optimization, linear algebra, signal processing, statistics, integration, and other numerical routines. This is useful for researchers, engineers, students, and data scientists.
It is a scientific computing library. Results and performance depend on versions, numerical assumptions, hardware, and BLAS/MKL behavior, so important work should document the environment.