klotz: machine learning*

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"Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

https://en.wikipedia.org/wiki/Machine_learning

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  1. |streamstats count|eventstats first(count) as fruit_number by fruit|fields - count
  2. BrisquelyBrusque writes "I think what he's getting at is, we'll never have an algorithm that is

    1. fast, distributed, easily deployed
    2. interpretable
    3. able to converge quickly for most problems
    4. robust to noise, outliers, multicollinearity, class imbalance, and the curse of dimensionality
    5. optimized for any combination of numeric variables and factors
    6. self-supervised (no need for extensive parameter tuning)
    7. capable of probability estimates as well as predictions
    8. able to issue predictions for multiple targets
    9. comfortable with structured, unstructured data (text, 2D, 3D, audio, tabular)
    10. open-source

    Besides, a recent analysis by Amazon Web Services found that 50 to 95% of all ML applications in an organization are based on traditional ML (random forests, regression models). That's why these application papers matter -- we're learning to make progress in certain areas where traditional ML fails."
    2020-12-31 Tags: , , , , , by klotz

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