There have been numerous proposals for deep neural networks for tabular data, such as rectangular data sets (e.g., data frames). To date, none have really worked well and take far too long to train. TabPFN is a model that emulates a Bayesian approach and trains a deep learning model on a prior of simulated tabular datasets.  Version 2 was released this year and offers several significant advantages, but also has one notable disadvantage. I'll introduce this model and show an example.
Max Kuhn
Posit
TabPFN: A Deep-Learning Solution for Tabular Data
There have been numerous proposals for deep neural networks for tabular data, such as rectangular data sets (e.g., data frames). To date, none have really worked well and take far too long to train. TabPFN is a model that emulates a Bayesian approach and trains a deep learning model on a prior of simulated tabular datasets.  Version 2 was released this year and offers several significant advantages, but also has one notable disadvantage. I'll introduce this model and show an example.