Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL) 2017 optimized for Advanced Vector Extensions (AVX)-2 and AVX-512 instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors (among others). This article describes how to build Caffe optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks. In addition, various functionalities of Caffe are explored in detail including how to fine-tune, extract and view features of different models, and use the Caffe Python API.
This article helped get you up and running with streaming capabilities with the Intel Aero Compute Board. I gave you an overview of the board itself and showed you how to connect to it. I also showed you which libraries are needed, how I set up Eclipse for my own project, and how to get Wi-Fi up, transfer files, and set up QGroundControl. At this point you are ready to explore other capabilities of the board and streaming.
Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel-optimized Theano with Intel® compilers and Intel® MKL 2017 on CentOS* and Ubuntu* based systems.
In this article I will show you how to use LibRealSense and PCL to generate point cloud data and display that data in the PCL Viewer. This article assumes you have already downloaded, installed both LibRealSense and PCL, and have them set up properly in Ubuntu*.