Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Democratizing deep learning through knowledge transfer

Lars Hulstaert (Microsoft)
13:45–14:25 Thursday, 11 October 2018
Implementing AI, Models and Methods
Location: Westminster Suite
Secondary topics:  Computer Vision, Deep Learning models, Text, Language, and Speech

Who is this presentation for?

  • Data scientists

Prerequisite knowledge

  • A basic understanding of machine learning principles
  • Familiarity with deep learning (useful but not required)

What you'll learn

  • Understand how to use transfer learning to leverage big datasets and train deep learning models with limited amounts of data


Transfer learning allows data scientists to leverage insights from large labeled datasets. The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where only little labelled data is available. Lars Hulstaert explains what transfer learning is and demonstrates how it can boost your NLP or CV pipelines.

Topics include:

  • A definition of transfer learning:
  • Three reasons why transfer learning is a critical skill as a data scientist
  • Transfer learning basic concepts
  • Applications of transfer learning in NLP and CV
  • A set of guidelines to enable transfer learning in your problem domain
Photo of Lars Hulstaert

Lars Hulstaert


Lars Hulstaert is a data scientist at Microsoft. Previously, he studied machine learning at Cambridge University and Ghent University.