There are actually 91 errors described in the article, but number 90 looks nicer in the title. The article is intended for C/C++ programmers, but developers working with other languages may also find it interesting.
(This work was done by Vivek Lingegowda during his internship at Intel.)
Tim Mattson (Intel) has authored an extensive series of excellent videos as in introduction to OpenMP*.
This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
An article addressing thread and task parallelism. This article can be used to optimize framework methodology. Written by Andrew Binstock--Principal Analyst at Pacific Data Works LLC and lead author of "Practical Algorithms for Programmers."
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.