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Profiling Python Code with Intel Vtune Amplifier

Even experienced Python programmers fall into coding traps once in a while. To help find out what’s really going on, watch this video where Intel’s Lead Technical Consulting Engineer Kevin O’Leary provides a hands-on demo showing how to use Intel VTune Amplifier to find hot spots in Python code that …

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Using Intel’s Math Kernel Library for Eigenvectors

Eigenvalues and eigenvectors are key in many machine learning tasks from facial recognition to vibration analysis. In this video, Slashdot Media Contributing Editor Rick Leinecker demonstrates what eigenvalues and eigenvectors are, their role in developing code, and how Intel’s Math Kernel Library (MKL) can accelerate their use and adoption.

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Deep Learning, Python and Intel Libraries: A Chat with Intel’s Kent Moffat

Automating the drudgery of repetitive tasks is one of the key applications of Deep Learning, a subset of machine learning. In this interview with Go Parallel Editor Michael Krieger, Intel Sr Product Manager Kent Moffat discusses how developers can take advantage of the new Intel Distribution for Python and libraries that …

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Screaming Fast Analytics with Intel Performance Libraries

Taming the Data Deluge with high-level algorithms and low-level primitives Machine Learning and Big Data demand powerful tools to tame the data beast. Intel has created two libraries, the Math Kernel Library (MKL) and Data Analytics Acceleration Library (DAAL) to serve most every developer’s need. Watch this brief video to …

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Using Intel Performance Primitives (IPP) for Fast Fourier Transforms (FFTs)

Video Presentation shows how easy Fourier Transforms are, and how fast they can execute Fourier Transforms are difficult to do, and the code usually takes a lot of time to execute. But Intel’s Performance Primitives take away the hassle, according to this video by Slashdot Media Contributing Editor Rick Leinecker

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Best Approaches to Multithreading with OpenMP

Multithreading has a lot of facets to cover to be successful. In this video, Slashdot Media Contributing Editor Rick Leinecker examines several targets and methodologies to consider as you get underway.

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Functional Programming in Python – Tutorial Video Shows How

Video tutorial puts Python through some of its functional paces In this companion to his recent blog, Slashdot Media Contributing Editor David Bolton demonstrates how Python can be used for functional tasks including iterators, list comprehension and generators. You can see David’s companion blog article here!

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OpenMP: Past the Basics

So, you’ve started tinkering with OpenMP to help parallelize your code. Now what?  This video by Slashdot Media Contributing Editor Rick Leinecker points OpenMP newbies in the right direction to go beyond parallelizing for loops, and demonstrates how to avoid data race conditions while you’re doing it.  

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Python, Pattern, Program! Machine Learning for Multiprocessing Simplified

Using “Pattern” Library with Python2 to speed coding of machine learning apps

In the third installment of his Machine Learning series, Slashdot Media Contributing Editor David Bolton takes a look at the “Pattern” library from U. Antwerp, designed to accelerate development of machine learning applications for multiprocessor environments

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Why Parallelize Code? Add One Line, Get 4x Boost

Video demo show how to accelerate computations by simple addition of parallelization pragma. Thanks to tools like OpenMP, parallelizing code to speed execution can be as simple as adding a single line of code. The results can be dramatic, as demonstrated in this video showing prime number calculation by Slashdot …

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