Today I’m speaking with Yury Gorishniy about the state of the competition between Deep Learning and Gradient Boosted Decision Trees when it comes to tabular datasets, and about a recent paper he published that seems to take a stab at improving the state of deep learning on tabular datasets.We discuss whether or not there exists a gap between deep learning and gradient boosted decision trees, what the future of a gap might look like, and the extent to which the embedding of numerical features can give deep learning architectures a necessary boost in performance.Two of his recent papers are useful in this discussion:On Embeddings for Numerical Features in Tabular Deep Learning - https://arxiv.org/abs/2203.05556Revisiting Deep Learning Models for Tabular Data - https://arxiv.org/abs/2106.11959You can find Yury in the following places:GitHub - https://github.com/Yura52Twitter - https://twitter.com/YuraFiftyTwoEnjoy!

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