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Article

Using Intel® MKL in your Python* program

Some instructions and a simple example showing how to call Intel® MKL from Python*,
Автор: TODD R. (Intel) Последнее обновление: 10.12.2018 - 13:29
Article

Android开发中的多线程编程技术

多线程这个令人生畏的“洪水猛兽”,很多人谈起多线程都心存畏惧。在Android开发过程中,多线程真的很难吗?多线程程序的“麻烦”源于它很抽象、与单线程程序运行模式不同,但只要掌握了它们的区别,编写多线程程序就会很容易了。下面让我们集中精力开始学习吧!

Автор: Последнее обновление: 23.06.2019 - 18:50
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java socket 多线程网络传输多个文件

     由于需要研究了下用 java socket 传输文件,由于需要传输多个文件,因此,采用了多线程设计。客户端每个线程创建一个 socket 连接,每个 socket 连接负责传输一个文件,服务端的ServerSocket每次 accept 一个 socket 连接,创建一个线程用于接收客户端传来的文件。

Автор: Последнее обновление: 25.05.2018 - 09:00
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Unleash the Parallel Performance of Python* Programs

[updated 10/5/2018]

Автор: Anton Malakhov (Intel) Последнее обновление: 05.10.2018 - 18:24
Article

Manage Deep Learning Networks with Caffe* Optimized for Intel® Architecture

How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
Автор: Andres Rodriguez (Intel) Последнее обновление: 11.03.2019 - 13:17
Article

Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
Автор: Sunny G. (Intel) Последнее обновление: 08.05.2018 - 10:50
Article

安装英特尔® Theano*软件优化包和支持工具

Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel®...
Автор: Sunny G. (Intel) Последнее обновление: 08.05.2018 - 10:50
Article

借助针对英特尔® 架构优化的 Caffe* 管理深度学习网络

如何面向英特尔® 架构优化 Caffe*,训练深度网络模型及部署网络。
Автор: Andres Rodriguez (Intel) Последнее обновление: 11.03.2019 - 13:17
Article

Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

Overview
Автор: JON J K. (Intel) Последнее обновление: 30.05.2018 - 07:00
Article

Profiling TensorFlow* workloads with Intel® VTune™ Amplifier

This tutorial will show how to combine the data provided by TensorFlow*.timeline with options available in one of the most powerful performance profilers for Intel® Architecture – Intel® VTune™ Amplifier.
Автор: Alexandr Kurylev (Intel) Последнее обновление: 21.03.2019 - 09:54