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Learn how to utilize multi-core, high-memory machines to dramatically accelerate your computations in R and Python, without any complex or time-consuming setup.
We all know that calculating error bounds on metrics derived from very large data sets has been problematic for a number of reasons. In more traditional statistics one can put a confidence interval or error bound on most metrics (e.g., mean), parameters (e.g., slope in a regression), or classifications (e.g., confusion matrix and the Kappa statistic).
For many machine learning applications, an error bound could be very important.…Continue
It seems like more and more companies are very interested in either, improving or setting up their analytical capabilities. All these companies are quite attracted to Hadoop, Spark or other similar solutions, not necessarily because they solve real problems they’re facing, but because they are…Continue