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Code Sample: Intel® AVX512-Deep Learning Boost: Intrinsic Functions

Опубликовано: 2 апреля 2019 г.Автор: Alberto V.

How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.

TensorFlow* Sample Codes for Distributed Image Classification

Опубликовано: 31 октября 2018 г.Автор: Michael Steyer

The TensorFlow* image classification sample codes below describe a step-by-step approach to modify the code in order to scale the deep learning...

Code Sample: Optimizing Binarized Neural Networks on Intel® Xeon® Scalable Processors

Опубликовано: 14 августа 2018 г.Автор: Yash Akhauri

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in...

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Binary Neural Networks

Опубликовано: 8 августа 2018 г.Автор: Yash Akhauri

In this article we delve into the theory, training procedure, and performance of binary neural networks (BNNs).

Training an Agent to Play Pong* Using neon™ Framework

Опубликовано: 16 апреля 2018 г.Автор: Kaustav Tamuly

This article showcases the implementation of an agent to play the game Pong* using an Intel® architecture-optimized neon™ framework, and to serve as...

BigDL: Distributed Deep Learning on Apache Spark*

Опубликовано: 31 января 2017 г., обновлено 20 июля 2017 г.

As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark...

BigDL – Scale-out Deep Learning on Apache Spark* Cluster

Опубликовано: 28 марта 2017 г.Автор: Sunny G.

Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Опубликовано: 29 октября 2015 г.Автор: Gennady F.

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for...