Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
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Updated
May 22, 2019 - Python
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
Fast inference engine for OpenNMT models
Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision
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
Least squares adjustment software
Deep Learning With C++
Make available to Julia the sparse functionality in MKL
Foundational library for Kernel methods in pattern analysis and machine learning
PyTorch implementation of the Complex Steerable Pyramid
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.
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.
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)'
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