Workshop: Large Scale Real Time Deep Learning Recommender

Description
Speaker
In this workshop, we will focus on learning how to build a real-time deep learning system. We will start with a technical talk that will dive deep into the science of recommender systems. We will cover state-of-the-art methods including deep generative recommender models. After the tech talk, we will host an interactive workshop that offers a hands-on experience.

The interactive workshop offer both theoretical and practical modules. By participating in the workshop you would be able to:

  • Learn the core concepts behind recommender systems
  • Experience building deep learning models with hands-on exercise
  • Build a system that can actually deploy in production
  • Evaluate RealityEngines.AI as a solution to your AI challenges

  • Agenda (US pacific time UTC-7):
  • [10:00 - 10:10am] Welcome and workshop overview
  • [10:10 - 10:45am] Core concepts behind deep-learning based recommender systems including deep generative recommender models
  • [10:45 - 11:45am] Build all the components of the large scale real-time deep learning system
  • [11:45 - 12:00pm] Q&A and wrap up
  • Bindu&Siddartha

    Siddartha Naidu, Director of Research and Co-Founder at RealityEngines.AI. Prior to this he was a Principal Engineer in Amazon. Siddhartha spent almost a decade at Google, where he worked on a number of large-scale machine learning projects from Search ads targeting to newspaper digitization.He has a PhD in Physics from Carnegie Mellon.

    Bindu Reddy CEO and Co-Founder of RealityEngines.AI. she was the General Manager for AI Verticals at AWS, AI. Her organization created and launched Amazon Personalize and Amazon Forecast, the first of their kind AI services that enable organizations to create custom deep-learning models easily. Prior to that, she was the CEO and co-founder of Post Intelligence that was acquired by Uber. Bindu was previously at Google

    • Date: Jun 12, 10:00 (US Pacific Time)
    • Fee: Free
    • Available Seats: 15 (max 300)
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