tabular-data

19 projetos partilham este topic do GitHub

Linhas conectam membros que estão mensuravelmente relacionados entre si. O tamanho do ponto reflete estrelas.

🧬 Membros

autogluon

Fast and Accurate ML in 3 Lines of Code

★ 10.6k

vaex

Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular…

★ 8.5k

TabPFN

⚡ TabPFN: Foundation Model for Tabular Data ⚡

★ 7.7k

FLAML

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

★ 4.4k

Alpaca-CoT

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient…

★ 2.8k

Auto-PyTorch

Automatic architecture search and hyperparameter optimization for PyTorch

★ 2.5k

synthetic-data-generator

SDG is a specialized framework designed to generate high-quality structured tabular data.

★ 2.4k

sketch

AI code-writing assistant that understands data content

★ 2.3k

Awesome-FL

Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials,…

★ 2k

pytorch_tabular

A unified framework for Deep Learning Models on tabular data

★ 1.7k

ktrain

ktrain is a Python library that makes deep learning and AI more accessible and easier to apply

★ 1.3k

Copulas

A library to model multivariate data using copulas.

★ 647

Machine-Learning-Roadmap

A roadmap for getting started with Machine Learning

★ 506

Awesome-LLM-Tabular

Awesome-LLM-Tabular: a curated list of Large Language Model applied to Tabular Data

★ 427

carefree-learn

Deep Learning ❤️ PyTorch

★ 409

TabFormer

Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)

★ 368

be_great

A novel approach for synthesizing tabular data using pretrained large language models

★ 365

MIMIC-IV-Data-Pipeline

A customizable pipeline for multimodal data extraction from MIMIC-IV!

★ 346

SDGym

Benchmarking synthetic data generation methods.

★ 309

Medido a partir dos tópicos do GitHub compartilhados por ambos os projetos, ponderado pela raridade de cada tópico.