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LAIKA Studios & Intel Join Forces to Expand What’s Possible in Stop-Motion Filmmaking

See how LAIKA’s films and the company’s work with Intel’s Applied Machine Learning team and use of Intel® oneAPI tools helps it realize the limitless scope of stop-motion animation.

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Intel’s MLPerf Results for DL training on CPU

Reflecting the broad range of AI workloads, Intel submitted results for MLPerf Training Release v0.7 in June 2020 for three training topologies (MiniGo, DLRM, ResNet-50 v1.5). Results in each case demonstrated that Intel continues to raise the bar for training on general purpose CPUs.

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Optimizing XGBoost Training Performance

Compare the training performance of XGBoost 1.1 on CPU with third-party GPUs and learn more about the optimizations introduced to this popular gradient boosting trees algorithm.

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Accelerate Your scikit-learn Applications

Accelerate a selection of common estimators (for example, logistic regression and singular value decomposition) on Intel architecture using this machine learning library.

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Intel and Facebook Accelerate PyTorch Performance

Harnessing the new bfloat16 capability in Intel® Deep Learning Boost, the team substantially improved PyTorch performance across multiple training workloads on 3rd generation Intel® Xeon® Scalable processors.

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Treatise of Medical Image Processing: COVID-19 Recognition -19

Read about a new proposal that uses an AI-based analytics system to detect COVID-19 from chest X-rays and CT radiographs.

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产品和性能信息

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英特尔的编译器针对非英特尔微处理器的优化程度可能与英特尔微处理器相同(或不同)。这些优化包括 SSE2、SSE3 和 SSSE3 指令集和其他优化。对于在非英特尔制造的微处理器上进行的优化,英特尔不对相应的可用性、功能或有效性提供担保。该产品中依赖于微处理器的优化仅适用于英特尔微处理器。某些非特定于英特尔微架构的优化保留用于英特尔微处理器。关于此通知涵盖的特定指令集的更多信息,请参阅适用产品的用户指南和参考指南。

通知版本 #20110804