Personal Information
Organization / Workplace
London United Kingdom
Occupation
Software Engineer at Facebook
Tags
online experiments
data science
statistics
machine learning
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A Multi-Armed Bandit Framework For Recommendations at Netflix
Jaya Kawale
•
5 years ago
Academia to Data Science - A Hitchhiker's Guide
Sudeep Das, Ph.D.
•
6 years ago
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search
Sudeep Das, Ph.D.
•
5 years ago
Recent Trends in Personalization: A Netflix Perspective
Justin Basilico
•
4 years ago
Shallow and Deep Latent Models for Recommender System
Anoop Deoras
•
5 years ago
Recommending for the World
Yves Raimond
•
8 years ago
Déjà Vu: The Importance of Time and Causality in Recommender Systems
Justin Basilico
•
6 years ago
Time, Context and Causality in Recommender Systems
Yves Raimond
•
5 years ago
Deep Learning for Recommender Systems
Yves Raimond
•
6 years ago
ML Infra for Netflix Recommendations - AI NEXTCon talk
Faisal Siddiqi
•
6 years ago
Netflix Recommendations Feature Engineering with Time Travel
Faisal Siddiqi
•
6 years ago
Making Netflix Machine Learning Algorithms Reliable
Justin Basilico
•
6 years ago
Correlation, causation and incrementally recommendation problems at netflix september 2018 - university of antwerp
Roelof van Zwol
•
5 years ago
Recommendation at Netflix Scale
Justin Basilico
•
10 years ago
Recommendations for Building Machine Learning Software
Justin Basilico
•
7 years ago
Is that a Time Machine? Some Design Patterns for Real World Machine Learning Systems
Justin Basilico
•
7 years ago
Data Storage Tips for Optimal Spark Performance-(Vida Ha, Databricks)
Spark Summit
•
8 years ago
Everyday I'm Shuffling - Tips for Writing Better Spark Programs, Strata San Jose 2015
Databricks
•
9 years ago
Top 5 Mistakes to Avoid When Writing Apache Spark Applications
Cloudera, Inc.
•
8 years ago
Shared Infrastructure for Data Science
Wes McKinney
•
6 years ago