Authors : Marsan Ma, Nikhil Lopes, Raj Amrit, Hong Lu, Dipankar Biswas, Trent Kyono Leadership: Iris Wang, Madhu Kurup Recommendation and ranking systems power many of the most important experiences on large internet platforms. Yet the models that run in production are rarely the largest models we can train. They are usually compact, latency-sensitive supervised […]
All posts categorized in: Machine Learning
Indeed Open Source: All Things Open 2019 Speakers
We’re excited to have three Indeed representatives presenting at All Things Open this year. Join us in Raleigh, NC October 13-15 for engaging discussions. Sustaining FOSS Projects by Democratizing the Sponsorship Process Tuesday, October 15 | 10:45am | Room 201 Speaker: Duane O’Brien, Indeed head of open source Within a given company, there are typically […]
Recognize Class Imbalance with Baselines and Better Metrics
In my first machine learning course as an undergrad, I built a recommender system. Using a dataset from a social music website, I created a model to predict whether a given user would like a given artist. I was thrilled when initial experiments showed that for 99% of the points in my dataset, I gave […]
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