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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
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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.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:55
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Free access to Intel® Compilers, Performance libraries, Analysis tools and more...

Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
作者: 管理 最后更新时间: 2019/03/21 - 12:00
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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
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Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

作者: Vadim Pirogov (Intel) 最后更新时间: 2019/03/21 - 12:00
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在英特尔® 数学核心函数库中引入 DNN 基元

    深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用,最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用,达到甚至超过了人类能力。如今,DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题,如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。

作者: Vadim Pirogov (Intel) 最后更新时间: 2019/03/21 - 12:08
博客

Big Datasets from Small Experiments

作者: Andrey Vladimirov 最后更新时间: 2019/07/04 - 18:46