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游戏行业的人工智能设计(第一部分)

The gaming industry has seen great strides in game complexity recently. Game developers are challenged to create increasingly compelling games. This series explores important Artificial Intelligence (AI) concepts and how to optimize them for multi-core.
Authored by admin Last updated on 12/12/2018 - 18:00
Article

游戏行业的人工智能设计(第二部分)

Designing AI for Games. Intelligent agents need to identify points of interest in the game world. This article shows how to identify and optimize points of interest and provides ways of organizing them for multi-threading.
Authored by admin Last updated on 12/12/2018 - 18:00
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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.
Authored by Gennady F. (Blackbelt) Last updated on 07/05/2019 - 14:55
Video

英特尔® 数据分析加速库(DAAL)

数据分析在各个行业和科技领域广泛运用,可帮助决策、查找各种模式、发现各种理论等。

Authored by Wei D. (Intel) Last updated on 01/18/2019 - 00:50
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面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

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.
Authored by Last updated on 07/06/2019 - 16:40
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应用蚁群优化算法 (ACO) 实施交通网络扩展

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.
Authored by Sunny G. (Intel) Last updated on 07/05/2019 - 19:13
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腾讯* 在基于英特尔® 至强® 处理器的游戏内购买推荐系统中使用机器学习

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.
Authored by Nguyen, Khang T (Intel) Last updated on 12/12/2018 - 18:00
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深度学习助力虚拟癌症筛选

本演示重点介绍了京都大学医学研究生院的研究成果。该大学对超过 4 亿种蛋白质的数据集进行了研究。 此次演示使用相关的算法来分析化合物的虚拟库,并根据相关的化学和其他属性,预测哪些化合物可能适用于特定的蛋白质。其研究成果有助于更快地确定治疗方案。

Authored by IDZSupport K. Last updated on 01/17/2019 - 19:37
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通过基因组分析进行临床患者筛查试验

QIAGEN 可执行基因组测序并对排序 DNA 进行分析, 以便帮助确定患有特定病变(相对于参考基因组)的患者,借助 cryo-EM 和 LAMMPS 的输出结果确定具有相关性的蛋白质。通过快速发现具有合适蛋白质的患者,同时满足临床试验的标准,借助分析加快获得临床试验的效果、降低成本、提供更加有针对性的治疗,以及减少不必要的副作用。

Authored by IDZSupport K. Last updated on 01/17/2019 - 01:57