Personal Information
Organización/Lugar de trabajo
Moscow, Russian Federation Russian Federation
Ocupación
Data Scientist
Sector
Technology / Software / Internet
Acerca de
Key skill and competencies:
• Solid background in math and statistics.
• Strong computer science fundamentals - algorithms and data structures.
• Good problem solving and 'hacker' skills - successful performed in Kaggle
competitions and data analysis hackathons.
• Strong knowledge of modern machine learning techniques – regression, tree
ensembles(boosting, bagging), svm, etc.
Technology and frameworks:
• Cluster computing - Apache Spark.
• Extensive experience with R (C/C++ code for resolving bottlenecks + parallel
computing) for data exploration, machine learning, visualization.
• SQL (PostrgresSQL, MSSQL).
• NoSQL (MongoDB, TokuMX). Contributing to development of R drive
Etiquetas
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Ver más
Presentaciones
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(21)Modern Recommendation for Advanced Practitioners part2
Flavian Vasile
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Modern Recommendation for Advanced Practitioners
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Recent Trends in Personalization: A Netflix Perspective
Justin Basilico
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Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Alexandros Karatzoglou
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Hace 10 años
Parallelize R Code Using Apache Spark
Databricks
•
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FlinkML: Large Scale Machine Learning with Apache Flink
Theodoros Vasiloudis
•
Hace 8 años
Fast ALS-Based Matrix Factorization for Recommender Systems
David Zibriczky
•
Hace 8 años
Steffen Rendle, Research Scientist, Google at MLconf SF
MLconf
•
Hace 9 años
Winning Kaggle 101: Introduction to Stacking
Ted Xiao
•
Hace 8 años
Distributed Coordinate Descent for Logistic Regression with Regularization
Илья Трофимов
•
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Building a real time, solr-powered recommendation engine
Trey Grainger
•
Hace 11 años
Enabling Python to be a Better Big Data Citizen
Wes McKinney
•
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word2vec, LDA, and introducing a new hybrid algorithm: lda2vec
👋 Christopher Moody
•
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Linear models for data science
Brad Klingenberg
•
Hace 8 años
SparkR + Zeppelin
felixcss
•
Hace 8 años
Mining of massive datasets using locality sensitive hashing (LSH)
J Singh
•
Hace 10 años
LSH
Hsiao-Fei Liu
•
Hace 10 años
Feature Importance Analysis with XGBoost in Tax audit
Michael BENESTY
•
Hace 9 años
Introducing DataFrames in Spark for Large Scale Data Science
Databricks
•
Hace 9 años
10 R Packages to Win Kaggle Competitions
DataRobot
•
Hace 9 años
Personal Information
Organización/Lugar de trabajo
Moscow, Russian Federation Russian Federation
Ocupación
Data Scientist
Sector
Technology / Software / Internet
Acerca de
Key skill and competencies:
• Solid background in math and statistics.
• Strong computer science fundamentals - algorithms and data structures.
• Good problem solving and 'hacker' skills - successful performed in Kaggle
competitions and data analysis hackathons.
• Strong knowledge of modern machine learning techniques – regression, tree
ensembles(boosting, bagging), svm, etc.
Technology and frameworks:
• Cluster computing - Apache Spark.
• Extensive experience with R (C/C++ code for resolving bottlenecks + parallel
computing) for data exploration, machine learning, visualization.
• SQL (PostrgresSQL, MSSQL).
• NoSQL (MongoDB, TokuMX). Contributing to development of R drive
Etiquetas
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Ver más