Szilard Pafka

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algor...

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Performance of various open source GBM implementations

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A minimal benchmark of various tools (statistical software, databases etc.) for working with tabular data of moderately large sizes (interactive data analysis).

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Playing with various deep learning tools and network architectures

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Latency numbers every data scientist should know (aka the pyramid of analytical tasks) - the order of magnitude of computational time for the most common analytical tasks (SQL-like data munging, linea...

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Size of datasets used for analytics based on 10 years of surveys by KDnuggets.

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GBM intro talk (with R and Python code)

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GBM multicore scaling: h2o, xgboost and lightgbm on multicore and multi-socket systems

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Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

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Some thoughts on how to use machine learning in production

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