A comprehensive toolkit and benchmark for tabular data learning, featuring 35+ deep methods, more than 10 classical methods, and 300 diverse tabular datasets.

ML models + benchmark for tabular data classification and regression

Repository for TabICL: A Tabular Foundation Model for In-Context Learning on Large Data

R.L. methods and techniques.

Tabular methods for reinforcement learning

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models

Implementations of various RL and Deep RL algorithms in TensorFlow, PyTorch and Keras.

[ICML 2024] BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model

Modular Names Classifier, Object Oriented PyTorch Model

First task for my Reinforcement Learning class in Deusto. The research paper the main RL algorithms applied on the Frozen Lake env provided by GymOpenAI. Paper is avaible at:

Advanced CLI tool for automating Machine Learning (AutoML) using state-of-the-art deep learning models to apply transfer learning with multiple tuning methods and architecture modifications to pretrained models for image and text datasets, with end-to-end training for tabular and time series datasets.

This is a python script file that translates tree-graph information stored in a .txt file to complicated LaTeX code, which can be compiled into a pretty tree graph in LaTeX editor (ex. Overleaf).