To achieve substantial speed-ups, take advantage of key Intel® architectural innovations and optimized data-science and machine learning libraries via a smart application of NumPy, SciPy, and pandas techniques.

In this session:

• Learn about NumPy aggregations, universal functions, broadcasting, and other techniques used to expose how CPU vectorization works.
• Use these techniques to achieve outsized performance gains by replacing Python* loop-centric or list-comprehension applications with smarter equivalents that are more maintainable, more efficient, and much faster.
• Measure your code’s acceleration to discover achieved performance boost, whether that’s 10x or over 100x.

Download the stand-alone version of the Intel® Distribution for Python* or as part of the AI Tools. The toolkit includes familiar Python tools and frameworks to accelerate end-to-end data science and analytics pipelines.