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Intel® Data Analytics Acceleration Library

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
Criado por James R. (Blackbelt) Última atualização em 12/12/2018 - 18:00
Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Criado por Gennady F. (Blackbelt) Última atualização em 05/07/2019 - 14:54
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.

Criado por Gennady F. (Blackbelt) Última atualização em 21/03/2019 - 12:28
Article

基于英特尔® 至强™ 处理器 E5 产品家族的多节点分布式内存系统上的 Caffe* 培训

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Criado por Gennady F. (Blackbelt) Última atualização em 05/07/2019 - 14:55
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.
Criado por Sunny G. (Intel) Última atualização em 05/07/2019 - 19:10
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.
Criado por Andres Rodriguez (Intel) Última atualização em 11/03/2019 - 13:17
Article

Using Intel® Data Analytics Acceleration Library on Matlab*

Intel® Data Analytics Acceleration Library (Intel® DAAL) is high performance library, which provides a rich set of algorithms, ranging from the most basic descriptive statisti

Criado por Ying H. (Intel) Última atualização em 08/10/2018 - 02:47
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.
Criado por Última atualização em 06/07/2019 - 16:40
Article

Tencent* Uses Machine Learning for In-Game Purchase Recommendation System on Intel® Xeon® Processors

To enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.
Criado por Nguyen, Khang T (Intel) Última atualização em 09/05/2019 - 13:08
Vídeo

Remove Python* Performance Barriers for Machine Learning

Online content and service providers like Netflix and Amazon have popularized the use of recommendation platforms/engines, that predict a user’s preferences based on historical ratings, collective

Criado por administrar Última atualização em 13/02/2018 - 09:24