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.

Awesome Tabular Deep Learning for "Representation Learning for Tabular Data: A Comprehensive Survey"

Tabular methods for reinforcement learning

Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL

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

In this we explore into a Question Answering task on structured relational data (Tables) and CSV data

📌Simple Method in optimization problem, step by step with C language implementation.

An implementation of the Quine-McCluskey-Algorithm for Boolean function minimization, written in C.

Code examples for simple reinforcement learning projects

An investigation into tabular classification with deep NNs for ETHZ Machine Learning for Healthcare on the MIT-BIH arrythmia dataset .

tabular method cpp src

Modular Names Classifier, Object Oriented PyTorch Model

The implementation of tabular solution methods in Reinformcement Learning, Sutton's book: Part I

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.

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:

Revisiting tabular and deep reinforcement learning methods.