Recommendation Systems Training in Oxford

Recommendation Systems Training in Oxford

Recommender systems (also known as: recommendation systems, recommendation platform or recommendation engine) training.

Oxford

Oxford
7200 The Quorum Oxford Business Park North, Garsington Road
Oxford, OXF OX4 2JZ
United Kingdom
Oxfordshire GB
Oxford
The top-quality Oxford Business Park Centre is located in a modern building in Oxford's premier business district, just inside the ring road and a 40 minute drive from London. The park offers a thriving environment for businesses of all sizes, working in a range of sectors including engineering,... Read more

Recommendation Systems Course Events - Oxford

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
dsstne Amazon DSSTNE: Build a recommendation system Oxford 7 hours Wed, 2018-05-09 09:30 £1100 / £1375
dsstne Amazon DSSTNE: Build a recommendation system Oxford 7 hours Fri, 2018-06-29 09:30 £1100 / £1375
dsstne Amazon DSSTNE: Build a recommendation system Oxford 7 hours Tue, 2018-08-21 09:30 £1100 / £1375
dsstne Amazon DSSTNE: Build a recommendation system Oxford 7 hours Fri, 2018-10-19 09:30 £1100 / £1375

Course Outlines

Code Name Duration Outline
dsstne Amazon DSSTNE: Build a recommendation system 7 hours

Amazon DSSTNE is an open-source library for training and deploying recommendation models. It allows models with weight matrices that are too large for a single GPU to be trained on a single host.

In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.

By the end of this training, participants will be able to:

  • Train a recommendation model with sparse datasets as input
  • Scale training and prediction models over multiple GPUs
  • Spread out computation and storage in a model-parallel fashion
  • Generate Amazon-like personalized product recommendations
  • Deploy a production-ready application that can scale at heavy workloads

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

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