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课程:机器人深度学习 | 英特尔® 人工智能开发人员计划

了解在许多机器人工作负载中使用深度学习算法的基础知识。
作者: David C. (Intel) 最后更新时间: 2019/08/16 - 15:41
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Курс: «Глубинное обучение для робототехники» | Программа Intel® AI Developer Program

Узнайте основы использования алгоритмов глубинного обучения для множества рабочих нагрузок робототехники.
作者: David C. (Intel) 最后更新时间: 2019/08/16 - 15:43
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Curso: Aprendizaje profundo sobre robótica | Intel® AI Developer Program

Conozca las bases para usar algoritmos de aprendizaje profundo en diversas cargas de trabajo de robótica.
作者: David C. (Intel) 最后更新时间: 2019/08/16 - 15:44
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Curso: Compreensão aprofundada sobre Robótica | Programa para desenvolvedores de IA da Intel®

Aprenda os fundamentos do uso de algoritmos de aprendizado profundo em várias cargas de trabalho de robótica.
作者: David C. (Intel) 最后更新时间: 2019/08/16 - 15:45
Article

Introduction to Remote Program Logic under Python*

About this Series

By David Mertz, Ph.D.

作者: 最后更新时间: 2017/06/07 - 09:31
Article

General installation information

Installation prerequisites, tips, and possible problems for the Intel MPI Library
作者: 最后更新时间: 2017/06/07 - 10:46
Article

Using Intel MKL BLAS and LAPACK with PETSc

This document contains instructions for linking to Intel MKL BLAS and LAPACK functions when building the PETSc libraries. also introduce how to enable Sparse Linear operation include Sparse BLAS and Intel® MKL PARDISO and Cluster PARDISO as direct solver in PETSc applications.
作者: Ying H. (Intel) 最后更新时间: 2019/03/27 - 13:20
Article

Using Intel® MKL in your Python* program

Some instructions and a simple example showing how to call Intel® MKL from Python*,
作者: TODD R. (Intel) 最后更新时间: 2018/12/10 - 13:29
Article

Intel® MKL with NumPy, SciPy, MATLAB, C#, Python, NAG and More

The following article explains on using Intel® MKL with NumPy/SciPy, Matlab, C#, Java, Python, NAG, Gromacs, Gnu Octave, PETSc, HPL, HPCC, IMSL etc.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/06/23 - 18:50
Article

Numpy/Scipy with Intel® MKL and Intel® Compilers

This guide is intended to help current NumPy/SciPy users to take advantage of Intel® Math Kernel Library (Intel® MKL).
作者: Vipin Kumar E K (Intel) 最后更新时间: 2018/07/11 - 18:00