Abstract: Deep nets such as GPT are at the core of the current advances in many systems and applications. Things are moving fast; techniques become obsolete quickly (within weeks). How can we take advantage of new discoveries and incorporate them into our existing work? Are new developments radical improvements, or incremental repetitions of established concepts, or combinations of both?In this tutorial, we aim to bring interested researchers and practitioners up to speed on the recent and ongoing techniques around ML and Deep learning in the context of IR and NLP. Additionally, our goal is to clarify terminology, emphasize fundamentals, and outline problems and new research opportunities.
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