DeepTab is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

Classification in TabularDataset

This repository employs two different architectures of Tabular Transformer models for Operating System fingerprinting from three different datasets.

Stabilization of classification ML-models using synthetic data with outliers on Open Data.

Tabular Data Processing lightgbm, tabnet, resnet

Production-grade causal uplift modeling on 14M rows, benchmarks S-Learner, T-Learner, and FT-Transformer challengers on the Criteo dataset, with Optuna tuning, MLflow tracking, FastAPI + Docker + Google Cloud Run serving, and a Streamlit dashboard.

Code for in-progress paper "Deep Learning for Credit Risk: Evaluating Feature Tokenizer Transformers Against Industry Baselines"