If you use Python* for HPC, __data science, you love the productivity features and the large ecosystem of packages available to quickly build applications. However, getting faster performance from Python that’s close to native code is difficult, requiring software development expertise and tremendous patience.
Well, good news! Intel provides optimized Scikit-learn, the most used Python package for classical machine learning. Get optimizations in Support Vector Machine (SVM), K-Means, with Intel® DAAL for great performance speedups compared to unaccelerated open source scikit-learn. Get faster scikit-learn through Intel® Distribution for Python*Here are some new features:
Want to learn more? Join us for a free webinar: Speed-Up Python* Applications & Soar Core Computations WED., OCT 31, 2018 9:00 AM PDT
We are calling all data Scientists. Come join us and hear more with Intel’s Preethi Venkatesh as she shows us how Intel® Distribution for Python* delivers native-code-quick performance―right out of the box. Find out how to overcome the performance challenges posed by the standard CPython binary, the Intel distribution offers optimized numeric, scientific, and machine learning packages to accelerate Python performance on a wide range of Intel® processors.
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The power of two! Tackle massive computing workloads with the Intel® data Analytics Acceleration Library and Intel Distribution for Python with built-in and accelerated NumPy, SciPy & scikit-learn, for native-code like performance speeds! Shorter compute times & quicker results, by simply switching to a faster Python. #performancepython
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Easy. Imagine that!
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Intel’s commitment to the industry & open source community remains strong. Intel is a member of the INRIA backed scikit-learn consortium launched in Sept’18. Check it out and Read More, here.
Intel provides optimized Scikit-learn, the most used Python package for classical machine learning. Get faster scikit-learn through Intel® Distribution for Python* Sponsored Post.