The main algorithm of speech recognition has changed to DNN (Deep Neural Network). Without internet, the speech recognition service in your mobile devices nearly useless, very few times it can listen to what you said and work.With support for the SSSE3 instruction set on Intel’s CPU, we could easy run a DNN based speech recognition application without the internet. Adding direct SSSE3 support...
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.
As Deep Neural Network (DNN) applications grow in importance in various areas including internet search engines and medical imaging, Intel teams are working on software solutions to accelerate these workloads that will become available in future versions of Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL). This technical preview demonstrates...
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.
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®...
In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Machine learning can take very large amounts of data to predict possible outcomes with a high degree of accuracy. The second-generation Intel® Xeon Phi processor has the processor performance and memory bandwidth to address complex machine learning applications.
Machine learning has been around for a while, so even if you haven’t worked on it as a developer, you’re probably very familiar with it as a consumer. When you add something to your cart in Amazon, and see a list of other recommended products that you might also like—that's an example of machine learning. Essentially, machine learning is the development of computer programs that can learn and...