Popular GitHub Repositories for Categorical Variables

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Modularized Implementation of Deep RL Algorithms in PyTorch

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

Loopy belief propagation for factor graphs on discrete variables in JAX

Mixed continous/categorical flow-matching model for de novo molecule generation.

A working implementation of the Categorical DQN (Distributional RL).

This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Embed categorical variables via neural networks.

Causing: CAUsal INterpretation using Graphs

Zumbra is a custom programming language built with its own parser, compiler, and virtual machine. It supports function definitions, scoped variables, custom operators, and more, all with a minimalist syntax and a playful soul.

Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.

Samples using context variables and (system) entities in IBM Watson Assistant (formerly Conversation) service on IBM Cloud

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos

Easily manage ChatGPT conversation variables

REGAIN (Regularised Graphical Inference)