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scikit-learn optimized with Intel(R) Data Analytics Acceleration Library

Project description

Optimized implementation of scikit-learn, leveraging Intel® Data Analytics Acceleration Library to achieve highly efficient data layout, blocking, multi-threading, vectorization. Accelerates scikit-learn's K-Means, SVM, PCA, linear & ridge regression, and correlation & cosine distance capabilities. Drop-in replacement that maintains API compatibility with scikit-learn. Additional details can be found in our SciPy 2017 conference proceedings.

One of many Intel® accelerated Python packages and performance library runtimes available on PyPI, and as part of Intel® Distribution for Python.

For latest release updates and security notifications, please subscribe to the Intel® Distribution for Python Community forum.

Free to use and redistribute pursuant to the Intel Simplified Software License.

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Files for intel-scikit-learn, version 0.19.2
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Filename, size intel_scikit_learn-0.19.2-cp27-cp27mu-manylinux1_x86_64.whl (6.4 MB) File type Wheel Python version cp27 Upload date Hashes View hashes
Filename, size intel_scikit_learn-0.19.2-cp27-cp27m-win_amd64.whl (5.8 MB) File type Wheel Python version cp27 Upload date Hashes View hashes
Filename, size intel_scikit_learn-0.19.2-cp35-cp35m-manylinux1_x86_64.whl (6.2 MB) File type Wheel Python version cp35 Upload date Hashes View hashes
Filename, size intel_scikit_learn-0.19.2-cp36-cp36m-macosx_10_12_intel.macosx_10_12_x86_64.whl (6.2 MB) File type Wheel Python version cp36 Upload date Hashes View hashes
Filename, size intel_scikit_learn-0.19.2-cp36-cp36m-manylinux1_x86_64.whl (6.2 MB) File type Wheel Python version cp36 Upload date Hashes View hashes
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