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Overview for machine-learning


Applying Deep Learning To Airbnb Search

This discussion is about the use of machine learning at Airbnb. It's a success story about how machine learning helps Airbnb's search ranking to find guests the best possible options while rewarding the most deserving hosts. Ranking at Airbnb is a quest to understand the needs of the guests and the quality of the hosts to strike the best match possible. The talk discusses the work done in applying neural networks in an attempt to break out of that plateau and also focuses on the elements found useful in applying neural networks to a real life product. 

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How Malwarebytes Leverages Big Data, AI

In this talk Sujay Kulkarni Manager, Data Engineering at Malwarebytes discusses how Malwarebytes leverages big data, AI and the cloud to protect millions of companies and individuals globally from the world’s most harmful internet threats and malware. They collect billions of records each day on a millisecond by millisecond basis, and apply advanced machine learning and AI to identify and profile harmful threats and their sources. 

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Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases

Miguel Angel Campo highlights how the data team from Century Fox developed a system based on Collaborative (Deep) Metric Learning (CML) to predict the purchase probabilities of new theatrical releases. He explaind how they trained and evaluated the model using a large dataset of customer histories spanning multiple years, and tested the model for a set of movies that were released outside of the training window. Initial experiments show gains relative to models that don't train on collaborative preferences.
 

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Dynamic Pricing of Lyft Rides Using Streaming

Learn how Lyft is using ML models and streaming infrastructure for low latency, reliability and scalability in pricing their rides. Amar Pai a software engineer at Lyft takes you through how they use Beam for streaming and lessons they have learned so far. 

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