Recommendation Systems Training in Edinburgh

Recommendation Systems Training in Edinburgh

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

Edinburgh Training and Conference Venue

Edinburgh Training and Conference Venue
16 St. Mary's Street
Edinburgh, MLN EH1 1SU
United Kingdom
Midlothian GB
Edinburgh Training and Conference Venue
Right in the heart of Edinburgh’s old town, with really easy transport links and Waverley Train Station only five minutes walk away, this is a great location...Read more

Recommendation Systems Course Events - Edinburgh

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh 7 hours Wed, 2018-02-21 09:30 £1100 / £1600
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh Training and Conference Venue 7 hours Fri, 2018-03-16 09:30 £1100 / £1175
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh 7 hours Mon, 2018-05-07 09:30 £1100 / £1600
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh Training and Conference Venue 7 hours Fri, 2018-05-11 09:30 £1100 / £1175
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh 7 hours Tue, 2018-06-26 09:30 £1100 / £1600
dsstne Amazon DSSTNE: Build a recommendation system Edinburgh Training and Conference Venue 7 hours Tue, 2018-07-24 09:30 £1100 / £1175

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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