Recommendation Systems Training in Aberdeen

Recommendation Systems Training in Aberdeen

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

Aberdeen - Berry Street

Regus Aberdeen Berry Street
1 Berry Street
Aberdeen, ABD AB25 1HF
United Kingdom
Aberdeenshire GB
Aberdeen - Berry Street
The Berry Street Centre occupies a three-storey 1950s building, which has recently been refurbished to a high specification. It is in a prominent and convenient location in the heart of Aberdeen's busy and thriving retail and business area and close to all amenities. The off-shore oil capital of... Read more

Recommendation Systems Course Events - Aberdeen

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
dsstne Amazon DSSTNE: Build a recommendation system Aberdeen - Berry Street 7 hours Wed, 2018-05-09 09:30 £1100 / £1430
dsstne Amazon DSSTNE: Build a recommendation system Aberdeen - Berry Street 7 hours Thu, 2018-06-28 09:30 £1100 / £1430
dsstne Amazon DSSTNE: Build a recommendation system Aberdeen - Berry Street 7 hours Tue, 2018-08-28 09:30 £1100 / £1430
dsstne Amazon DSSTNE: Build a recommendation system Aberdeen - Berry Street 7 hours Wed, 2018-10-17 09:30 £1100 / £1430

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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