If you’re doing work in statistics, data science, or machine learning, the odds are high you’re using Python. And for good reason, too: The rich ecosystem of libraries and tooling, and the convenience ...

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One of the big surprises of the past few years has been the spectacular rise in the use of Python* in high-performance computing applications. With the latest releases of Intel® Distribution for ...

Python libraries that can interpret and explain machine learning models provide valuable insights into their predictions and ensure transparency in AI applications. A Python library is a collection of ...

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What is it about Python—the language, the ecosystem, the development processes around them—that has made it into such a favorite for data science? Python has long enjoyed growing popularity in many ...

Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...

However, Python data analytics and machine learning packages are rapidly evolving and maturing as will be discussed next. Intel has also included pyDAAL, a new Python version of their Data Analytics ...

Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room to ...