How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
1. Robots and ASTRO
This work demonstrates the experiments to train and test the deep learning AlexNet* topology with the Intel® Optimization for TensorFlow* library using CIFAR-10 classification data on Intel® Xeon® Scalable processor powered machines. These experiments were conducted with options set at compile time and run time.
This case study evaluates the ability of TensorFlow* Object Detection API to solve a real-time problem such as traffic light detection on Intel® Xeon® processor-based CPU machines.
Learn about Capsule networks (CapsNet), are an advanced approach to previous neural network designs
This article provides an effective and robust approach to detect and classify metal defects using computer vision and machine learning.
Building a Black Box Model Using Transfer Learning Introduction
Apache* MXNet* v1.2.0 optimized with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)Apache* MXNet community announced the v1.2.0 release of the Apache MXNet deep learning framework. One of the most important features in this release is the Intel optimized CPU backend: MXNet now integrates with Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) to accelerate neural network operators: Convolution, Deconvolution, FullyConnected, Pooling, Batch Normalization,...
本案例研究评估了 TensorFlow* 对象检测 API 处理一个实时任务的能力，即在基于英特尔® 至强® 处理器的机器上进行交通灯检测。