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Article

Caffe* Scoring Optimization for Intel® Xeon® Processor E5 Series

    In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.

Автор: Gennady F. (Blackbelt) Последнее обновление: 21.03.2019 - 12:28
Article

Performance Comparison of OpenBLAS* and Intel® Math Kernel Library in R

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
Автор: Nguyen, Khang T (Intel) Последнее обновление: 06.07.2019 - 16:40
Article

Baidu Deep Neural Network Click-Through Rate on Intel® Xeon® Processors E5 v4

How do new web sites selling products or services appear at the top of the search list? The key is to use the right keywords that people might use to search for their products or services. Baidu1 is the most popular search engine in China. Ad companies can pay Baidu so that their ads appear at the top of the search list.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 05.07.2019 - 14:36
Article

Scale-Up Implementation of a Transportation Network Using Ant Colony Optimization (ACO)

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Автор: Sunny G. (Intel) Последнее обновление: 05.07.2019 - 19:10
Блоги

How Intel® Xeon Phi™ Processors Benefit Machine Learning/Deep Learning Apps and Frameworks

Machine learning can take very large amounts of data to predict possible outcomes with a high degree of accuracy. The second-generation Intel® Xeon Phi processor has the processor performance and memory bandwidth to address complex machine learning applications.
Автор: Pradeep Dubey (Intel) Последнее обновление: 21.03.2019 - 12:40
Блоги

IDF16: Intel® Software Recap

Software, Networking and IoT Create “Best of All Worlds” at Intel Developer Forum 2016
Автор: Последнее обновление: 19.06.2019 - 16:21
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Автор: Последнее обновление: 06.07.2019 - 16:40
Article

Improving Support Vector Machine with Intel® Data Analytics Acceleration Library

Introduction
Автор: Nguyen, Khang T (Intel) Последнее обновление: 07.10.2018 - 07:15
Блоги

Top Ten Intel® Software Developer Stories | October

Find out what's buzzing this month with the October Top Ten!
Автор: Carman, Vicky (Intel) Последнее обновление: 12.12.2018 - 18:00
Видео

Webinar: Deep Learning 101

Deep neural networks are capable of amazing levels of representation power resulting in state-of-the-art accuracy in areas such as computer vision, speech recognition, natural language processing,

Автор: Andres Rodriguez (Intel) Последнее обновление: 24.10.2018 - 15:36