Adam Jelley

Improving Recommender Systems with Deep Learning

Deep learning is among the hottest topics in data science today. However, it is still unclear how most businesses can actually apply deep learning to real problems. In this session, we will go beyond the buzzword and share a concrete example of where deep learning has been successfully implemented as part of a productionised recommender system. You will learn how Dataiku improved an online vacation retailer’s recommender system by leveraging pre-trained deep learning models to derive user preference information from images. This transfer learning approach can enable companies to use state-of-the-art machine learning methods without having deep learning expertise.

Adam is a Data Scientist at Dataiku, where he helps clients build and deploy data science solutions across industries. Prior to Dataiku, he worked for Applied Predictive Technologies, using predictive analytics to guide data-driven decisions at some of the UK’s largest retailers. He holds a Masters in Theoretical Physics from the University of Cambridge and studied Data Intensive Science at University College London.

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