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Szilard Pafka

Login: szilard

Company: null

Location: Santa Monica, California

Bio: physics PhD, chief (data) scientist, meetup organizer, datascience.la, (visiting) professor

Blog: https://www.linkedin.com/in/szilard

Member of

Repositories

2018.erum.io: Homepage of the 2018 event
app-consumer-loan: null
benchm-databases: A minimal benchmark of various tools (statistical software, databases etc.) for working with tabular data of moderately large sizes (interactive data analysis).
benchm-dl: Playing with various deep learning tools and network architectures
benchm-dplyr-dt: null
benchm-ml: 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 algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
benchm-R-mysql: null
BigDataDayLA2015-DataScience: List of talks from the Data Science Track of Big Data Day LA 2015 (annual free conference)
datascience-1slide: Data Science in 1 Slide
datascience-course-historical: 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".
datascience-latency: 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, linear and non-linear supervised learning etc.) with the typically available tools on commodity hardware.
dataset-sizes-kdnuggets: Size of datasets used for analytics based on 10 years of surveys by KDnuggets.
dplyr: Plyr specialised for data frames: faster & with remote datastores
event-BigDataCampLA2014: null
GBM-meltdown: The Effect of the Linux Kernel Page-Table Isolation (KPTI) Patch (Meltdown Vulnerability) on GBMs
GBM-multicore: GBM multicore scaling: h2o, xgboost and lightgbm on multicore and multi-socket systems
GBM-perf: Performance of various open source GBM implementations
GBM-tune: Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions
GBM-workshop: Code (and other materials) for an introductory talk/workshop on GBMs (developed originally for an R-Ladies Meetup)
h2o-experiments: null
h2o-scoring--OLD: Various options for deploying h2o.ai models to production (scoring new data)
kaggle-scripts-R-pydata: Kaggle scripts: R vs pydata + most popular R and Python packages for Machine Learning
LA-data-meetups: null
LightGBM: A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.
meetup-presentations_budapest: R-Ladies Budapest - This is the collection of code, presentations and additional materials created by the Budapest R-Ladies community
ml-algos-perf: Performance of Machine Learning Algorithms - playground for experimentation in order to understand their performance characteristics as a function of the attributes of the datasets used for training
ml-hacks: null
ml-prod: Some thoughts on how to use machine learning in production
MLprod-1slide: Machine Learning in Production in 1 Slide
ML-scoring: Compare the scoring speed of several open source machine learning libraries.
ml-x1: Machine learning tools on monster EC2 X1 instance (128 cores, 2 TB RAM)
mxnet_shiny: Image Classification using MXNetR
RMySQL: An R interface for MySQL
shinyvalidinp: null
shinyvalidinp-demo: null
student-data-science-project-1-kaggle: Sample student project for the Data Science course I was teaching at CEU's MSc in Business Analytics https://github.com/szilard/teach-data-science-msc-analytics-ceu
student-data-science-project-2: Sample student project for the Data Science course I was teaching at CEU's MSc in Business Analytics https://github.com/szilard/teach-data-science-msc-analytics-ceu
student-data-science-project-3: Sample student project for the Data Science course I was teaching at CEU's MSc in Business Analytics https://github.com/szilard/teach-data-science-msc-analytics-ceu
survey-ml-tools: Quick informal survey at the Los Angeles Machine learning meetup about tools used for machine learning.
talk-DataVisLA-intro: null
talk-GALA-DScourse: null
talk-LARUG-munging: null
talks: A list of recent talks by Szilard at various meetups, conferences etc. (link to slides/code/video etc.).
teach-data-science-msc-analytics-ceu: Materials for a short introductory/intermediate Data Science course taught in the MSc in Business Analytics program at the Central European University
teach-data-science-UCLA-master-appl-stats: Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA
teach-ML-CEU-master-bizanalytics: Machine Learning #1 and #2 courses at CEU Master of Science in Business Analytics
useR2016-subm: null
xgboost: 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, Flink and DataFlow
xgboost-adv-workshop-LA: Advanced workshop on XGBoost with Tianqi Chen in Santa Monica, June 2, 2016

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This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.