Adaptive and automatic gradient boosting computations.

Most recent/important talks given at conferences/meetups

GBM intro talk (with R and Python code)

A curated list of gradient boosting machines (GBM) resources

Advanced workshop on XGBoost with Tianqi Chen in Santa Monica, June 2, 2016

Szilard Pafka's short bio (to go with conference talk abstracts)

Kaggle scripts: R vs pydata + most popular R and Python packages for Machine Learning

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

Code (and other materials) for an introductory talk/workshop on GBMs (developed originally for an R-Ladies Meetup)

Machine Learning #1 and #2 courses at CEU Master of Science in Business Analytics

Machine Learning #1 and #2 courses at CEU Master of Science in Business Analytics

Machine Learning in Production in 1 Slide

Some thoughts on how to use machine learning in production

Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

Compare the scoring speed of several open source machine learning libraries.

Performance of various open source GBM implementations

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

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Inspired by David Donoho's "50 Years of Data Science" (2015) paper, I'm releasing here a course proposal draft I wrote in 2009 for a possible course of "data science".

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 al…

Materials for a short introductory/intermediate Data Science course taught in the MSc in Business Analytics program at the Central European University

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

Data Science in 1 Slide

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

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, li…

Quick informal survey at the Los Angeles Machine learning meetup about tools used for machine learning.

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow