Personal Information
Organização/Local de trabalho
Moscow, Russian Federation Russian Federation
Cargo
Data Scientist
Setor
Technology / Software / Internet
Sobre
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
Marcadores
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Ver mais
Apresentações
(3)Gostaram
(21)Modern Recommendation for Advanced Practitioners part2
Flavian Vasile
•
Há 4 anos
Modern Recommendation for Advanced Practitioners
Flavian Vasile
•
Há 4 anos
Recent Trends in Personalization: A Netflix Perspective
Justin Basilico
•
Há 4 anos
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Alexandros Karatzoglou
•
Há 10 anos
Parallelize R Code Using Apache Spark
Databricks
•
Há 6 anos
FlinkML: Large Scale Machine Learning with Apache Flink
Theodoros Vasiloudis
•
Há 8 anos
Fast ALS-Based Matrix Factorization for Recommender Systems
David Zibriczky
•
Há 8 anos
Steffen Rendle, Research Scientist, Google at MLconf SF
MLconf
•
Há 9 anos
Winning Kaggle 101: Introduction to Stacking
Ted Xiao
•
Há 8 anos
Distributed Coordinate Descent for Logistic Regression with Regularization
Илья Трофимов
•
Há 8 anos
Building a real time, solr-powered recommendation engine
Trey Grainger
•
Há 11 anos
Enabling Python to be a Better Big Data Citizen
Wes McKinney
•
Há 8 anos
word2vec, LDA, and introducing a new hybrid algorithm: lda2vec
👋 Christopher Moody
•
Há 8 anos
Linear models for data science
Brad Klingenberg
•
Há 8 anos
SparkR + Zeppelin
felixcss
•
Há 8 anos
Mining of massive datasets using locality sensitive hashing (LSH)
J Singh
•
Há 10 anos
LSH
Hsiao-Fei Liu
•
Há 10 anos
Feature Importance Analysis with XGBoost in Tax audit
Michael BENESTY
•
Há 9 anos
Introducing DataFrames in Spark for Large Scale Data Science
Databricks
•
Há 9 anos
10 R Packages to Win Kaggle Competitions
DataRobot
•
Há 9 anos
Personal Information
Organização/Local de trabalho
Moscow, Russian Federation Russian Federation
Cargo
Data Scientist
Setor
Technology / Software / Internet
Sobre
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
Marcadores
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Ver mais