Personal Information
Entreprise/Lieu de travail
Moscow, Russian Federation Russian Federation
Profession
Data Scientist
Secteur d’activité
Technology / Software / Internet
À propos
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
Mots-clés
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Tout plus
Présentations
(3)J’aime
(21)Modern Recommendation for Advanced Practitioners part2
Flavian Vasile
•
il y a 4 ans
Modern Recommendation for Advanced Practitioners
Flavian Vasile
•
il y a 4 ans
Recent Trends in Personalization: A Netflix Perspective
Justin Basilico
•
il y a 4 ans
Neighbor methods vs matrix factorization - case studies of real-life recommendations (Gravity LSRS2015 RECSYS 2015)
Domonkos Tikk
•
il y a 8 ans
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Alexandros Karatzoglou
•
il y a 10 ans
Parallelize R Code Using Apache Spark
Databricks
•
il y a 6 ans
FlinkML: Large Scale Machine Learning with Apache Flink
Theodoros Vasiloudis
•
il y a 8 ans
Fast ALS-Based Matrix Factorization for Recommender Systems
David Zibriczky
•
il y a 8 ans
Steffen Rendle, Research Scientist, Google at MLconf SF
MLconf
•
il y a 9 ans
Winning Kaggle 101: Introduction to Stacking
Ted Xiao
•
il y a 8 ans
Distributed Coordinate Descent for Logistic Regression with Regularization
Илья Трофимов
•
il y a 8 ans
Building a real time, solr-powered recommendation engine
Trey Grainger
•
il y a 11 ans
Enabling Python to be a Better Big Data Citizen
Wes McKinney
•
il y a 8 ans
word2vec, LDA, and introducing a new hybrid algorithm: lda2vec
👋 Christopher Moody
•
il y a 8 ans
Linear models for data science
Brad Klingenberg
•
il y a 8 ans
SparkR + Zeppelin
felixcss
•
il y a 8 ans
Mining of massive datasets using locality sensitive hashing (LSH)
J Singh
•
il y a 10 ans
LSH
Hsiao-Fei Liu
•
il y a 10 ans
Feature Importance Analysis with XGBoost in Tax audit
Michael BENESTY
•
il y a 9 ans
Introducing DataFrames in Spark for Large Scale Data Science
Databricks
•
il y a 9 ans
10 R Packages to Win Kaggle Competitions
DataRobot
•
il y a 9 ans
Personal Information
Entreprise/Lieu de travail
Moscow, Russian Federation Russian Federation
Profession
Data Scientist
Secteur d’activité
Technology / Software / Internet
À propos
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
Mots-clés
alternating-least-squares
svd
recommender-system
big data
matrix-factorization
minhash
lsh
lshr
Tout plus