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

Caffe* Scoring Optimization for Intel® Xeon® Processor E5 Series

    In continued efforts to optimize Deep Learning workloads on Intel® architecture, our engineers explore various paths leading to the maximum performance.

作者: Gennady F. (Blackbelt) 最后更新时间: 2019/03/21 - 12:28

BigDL: Distributed Deep Learning on Apache Spark*

As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon...
作者: 最后更新时间: 2019/03/11 - 13:17

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

Learn how to install and use BigDL for training and testing some of the commonly used deep neural network models on Apache Spark.
作者: Sunny G. (Intel) 最后更新时间: 2019/03/11 - 13:17

Training an Agent to Play Pong* Using neon™ Framework

This article showcases the implementation of an agent to play the game Pong* using an Intel® architecture-optimized neon™ framework, and to serve as an introduction to the Policy Gradients algorithm.
作者: Kaustav Tamuly 最后更新时间: 2018/06/18 - 16:10

Binary Neural Networks

In this article we delve into the theory, training procedure, and performance of binary neural networks (BNNs).
作者: Yash Akhauri 最后更新时间: 2019/05/08 - 15:30

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

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
作者: Yash Akhauri 最后更新时间: 2019/03/21 - 12:40

TensorFlow* Sample Codes for Distributed Image Classification

The TensorFlow* image classification sample codes below describe a step-by-step approach to modify the code in order to scale the deep learning training across multiple nodes of HPC data centers.

作者: Michael Steyer (Intel) 最后更新时间: 2019/03/11 - 13:17

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

How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.
作者: Alberto V. (Intel) 最后更新时间: 2019/04/02 - 10:04