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.
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.
Don't get me wrong, I was a quite willing participant with all of this.
This article presents a performance test of Intel® tuning platform composed by Intel® processor, Intel® C++ Compiler XE and Intel® Math Kernel Library (Intel® MKL) applied to usual AI problems, with two existing approaches of artificial intelligence probed.
Connecting the Dots
This blog post is part of a series that describes my summer school project at CERN openlab.
Learn to build a face access control solution, get horrified in a haunted high school, and be sure to register for the Intel® HPC Developer Conference this month.
A new potential use case of deep learning is the use of it to develop a Cryptocurrency Trader Sentiment Detector. I am currently developing a Sentiment Analyzer on News Headlines, Reddit posts, and Twitter posts by utilizing Recursive Neural Tensor Networks (RNTN) to provide insight into the overall trader sentiment.
Announcing new IoT developer kits this month along with tips on which processor is right for your VR projects.
At the Autodesk University event in Las Vegas, November 14-16, civil and commercial/industrial designers and manufacturers who use Autodesk software came together to see The Future of Making Things.