AWS re:Invent 2016: Using MXNet for Recommendation Modeling at Scale (MAC306)
For many companies, recommendation systems solve important machine learning problems. But as recommendation systems grow to millions of users and millions of items, they pose significant challenges when deployed at scale. The user-item matrix can have trillions of entries (or more), most of which are zero. To make common ML techniques practical, sparse data requires special techniques. Learn how to use MXNet to build neural network models for recommendation systems that can scale efficiently to large sparse datasets.
What to Expect from the Session
Background on recommender systems and machine
Learn how to implement them on MXNet using p2
instances and the AWS Deep Learning AMI.
Explore several types of recommender systems, including
advanced deep learning ideas.
Learn tricks for handling sparse data in MXNet.
Systems & Machine Learning
from mxreco import NegativeSamplingDataIter
train_data = NegativeSamplingDataIter(
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