Beware the Local Minima

My career would probably be going better if I were not so easily distracted. A recent distraction occurred when I was browsing for some simple Keras code to test an installation of Intel’s PlaidML library. I came across a linear regression performed using Keras but the graph didn’t look quite right. There were more points […]
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Does Intel Python Deliver?

Intel’s distribution of Python, created in collaboration with Anaconda, is billed by the hardware giant as a distribution created for performance. Specifcally, the numerical performance demanded by scientific research and machine learning. Claims are bandied about referring to Python that executes at machine-language speeds, but does Intel Python really deliver? I won’t use the word […]
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Effective use of RevoScaleR Transformations

As mentioned previously, several important RevoScaleR functions include provisions for transforming data within the function itself, rather than require separate steps in addition to the function call. This is advantageous, since it means that large datasets can be read once instead of having to be read repeatedly by several functions. rxImport, rxDataStep, and rxSplit support […]
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Power BI: Creating and Sharing Power BI Templates

Templates are great for word processors and spreadsheets; they save time and enforce consistency. Templates are great for Power BI, too, but Power BI templates must solve a minor complication not faced by its sister Office applications. The measures, columns, and visualizations that collectively constitute a Power BI report are based on a specific data […]
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Using Tensorflow with R

When examining the available selection of machine learning environments, even the most ardent R user may suffer some pangs of Python envy. Most machine learning environments, such as Google’s Tensorflow, are programmed in C++ for maximum performance and maximum utilization of hardware resources such as GPUs (graphics processing units). The Tensorflow API, however, is designed […]
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