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.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:54

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

How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
作者: Andres Rodriguez (Intel) 最后更新时间: 2019/03/11 - 13:17

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.
作者: 最后更新时间: 2019/07/06 - 16:40

Intel® Math Kernel Library for Deep Neural Networks: Part 2 – Code Build and Walkthrough

Learn how to configure the Eclipse* IDE to build the C++ code sample, along with a code walkthrough based on the AlexNet deep learning topology for AI applications.
作者: Bryan B. (Intel) 最后更新时间: 2018/05/23 - 11:00

Go* for Big Data

Using Intel® Data Analytics Acceleration Library (Intel® DAAL) with the Go* programming language to enable batch, online, and distributed processing
作者: Daniel W. 最后更新时间: 2019/03/05 - 23:50
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Parallel Universe Magazine - Issue 26, October 2016

作者: 管理 最后更新时间: 2018/12/12 - 18:08

Accelerate Machine Learning with Intel® Software Development Tools

Speed up your machine learning application code and turn data into insight and actionable results.

作者: 管理 最后更新时间: 2018/11/09 - 07:12

Transform Enterprise, HPC & AI, Accelerate Parallel Code

作者: 管理 最后更新时间: 2019/07/06 - 16:15

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*,...
作者: James R. (Blackbelt) 最后更新时间: 2019/08/27 - 13:50
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Parallel Universe Magazine - Issue 28, April 2017

作者: 管理 最后更新时间: 2019/09/30 - 16:45