With recent advances in hardware, frameworks, and research, Deep Learning has emerged as an indispensable technique for solving many data science and AI problems over the last few years. Like any tool, however, it is important to understand when and how to apply it, how to frame your problem in a manner that allows you to apply the tool effectively, as well as what decisions and compromises the machine learning practitioner must make to apply the model on production data and in production systems. In this talk, we will present the lessons we’ve learned developing a deep learning model to handle the distinctive problem eBay faces in recommender systems. We will specifically address the following topics:
Daniel Galron is a research scientist & engineer at eBay, where he leads efforts on applying deep learning methods for recommender systems. He earned a PhD in computer science from NYU, where he worked on machine learning methods for machine translation.
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