We’ll dive into recommendation systems to understand the business problem and how deep learning can play a key role in solving this problem. We will go over a hands on example of creating and training a recommendation model using PyTorch, and explore model design and optimizations. Attendees will learn how to apply deep learning to the problem of recommendations and ranking, and how they can leverage PyTorch to rapidly implement recommendation systems for various business use cases.


  • Understand the problem of content recommendation, and how it applies to e-commerce, media services, and content services.
  • Understand how PyTorch can be used to prototype, optimize, and deploy neural networks.
  • Learn how models are trained, evaluated, and optimized for specific business needs.



Hagay Lupesko – Engineering Leader | Facebook

Hagay has been busy building software for the past 15 years and still enjoys every bit of it (literally). He engineered and shipped products across various domains: from 3D medical imaging, through global scale web systems, and up to deep learning systems used at scale by engineers and scientists world-wide. He is currently based in the Silicon Valley, in sunny California, and focuses on democratizing AI and Machine Learning.

May 26 @ 09:00
09:00 — 09:30 (30′)

Day 2 | 19th of May – Machine Learning

Hagay Lupesko – Engineering Leader | Facebook