Фильтры

Article

Case Study – Using the Intel® Deep Learning SDK for Training Image Recognition Models

In this case study, we explore LeNet*,one of the prominent image recognition topologies for handwritten digit recognition, and show how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
Автор: Meghana R. (Intel) Последнее обновление: 24.01.2018 - 15:35
File Wrapper

Parallel Universe Magazine - Issue 26, October 2016

Автор: админ Последнее обновление: 12.12.2018 - 18:08
Article

Brain Tumor Segmentation using Fully Convolutional Tiramisu Deep Learning Architecture

The aim of the work was to implement, train and evaluate the quality of automated brain tumor multi-label segmentation technique for Magnetic Resonance Imaging based on Tiramisu deep learning architecture.
Автор: Kocot, Szymon Последнее обновление: 26.03.2019 - 16:20
File Wrapper
Article

Optimize HEVC Decoding Efficiency on High-end NUMA Systems

Intel® Xeon® Scalable processors have advanced scalability features to gain workload performance increases
Автор: Björn Taubert (Intel) Последнее обновление: 12.06.2019 - 15:48
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.
Автор: Sunny G. (Intel) Последнее обновление: 15.10.2019 - 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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 15.10.2019 - 19:55