Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.

Armadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net

Fast implementation of BERT inference directly on NVIDIA (CUDA, CUBLAS) and Intel MKL

Julia bindings to the FFTW library for fast Fourier transforms

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

Optimized inference engine for OpenNMT models

A package for Multiple Kernel Learning in Python

Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices

Fast Unit Root Tests and OLS regression in C++ with wrappers for R and Python

End-to-end speech recognition using TensorFlow

NumPy-based Python interface to Intel (R) MKL FFT functionality

Redistribute Intel MKL as a crate

Deep Learning With C++

Least squares adjustment software

Make available to Julia the sparse functionality in MKL

PyTorch implementation of the Complex Steerable Pyramid

Foundational library for Kernel methods in pattern analysis and machine learning

automatic color-grading

A third-party distribution of Multiwfn for gfortran, 100% free!

Optimized tensorflow wheels binaries build for macos

Python wrapper for Intel Math Kernel Library (MKL) matrix multiplication

Basic Linear Algebra Subprograms for .Net

Numerical computing library for linear algebra and task-based parallelism.

Tools to run and parse MKL verbose mode

CBLAS and LAPACKE API bindings to OpenBLAS and MKL

The repository targets the OpenCL gemm function performance optimization. It compares several libraries clBLAS, clBLAST, MIOpenGemm, Intel MKL(CPU) and cuBLAS(CUDA) on different matrix sizes/vendor's hardwares/OS. Out-of-the-box easy as MSVC, MinGW, Linux(CentOS) x86_64 binary provided. 在不同矩阵大小/硬件/操作系统下比较几个BLAS库的sgemm函数性能，提供binary，开盒即用。

Fully setting up and installing R in Windows / Debian / Ubuntu (Version: 3.5.1)

This repository houses the Statslabs.Matrix Linear Algebra Library for use while learning C++ from Bjarne Stroustrup's book 'The C++ Programming Language (4th Edition)'

A fortran 90 implementation of a parallel eigensolver for symmetric tridiagonal matrices