From 13b84109e03fa38f10c757fab12d499176724634 Mon Sep 17 00:00:00 2001 From: Insaf Ashrapov Date: Sat, 28 Mar 2026 09:17:57 +0300 Subject: [PATCH 1/2] Add TabGAN - synthetic tabular data generation library --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 85a1a83..0dd1e04 100644 --- a/README.md +++ b/README.md @@ -160,6 +160,8 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni ## Machine Learning + +- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. _Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python)._ - [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library. From fed92e79f39c84b76106abeab37d0787729e872f Mon Sep 17 00:00:00 2001 From: Insaf Ashrapov Date: Sun, 29 Mar 2026 07:16:00 +0000 Subject: [PATCH 2/2] Remove extra empty line, place TabGAN in alphabetical order Co-Authored-By: Claude Opus 4.6 (1M context) --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 0dd1e04..3cb47e7 100644 --- a/README.md +++ b/README.md @@ -160,8 +160,6 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni ## Machine Learning - -- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. _Libraries for Machine Learning. Also see [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning#python)._ - [catboost](https://github.com/catboost/catboost) - A fast, scalable, high performance gradient boosting on decision trees library. @@ -172,6 +170,7 @@ _Libraries for Machine Learning. Also see [awesome-machine-learning](https://git - [pgmpy](https://github.com/pgmpy/pgmpy) - A Python library for probabilistic graphical models and Bayesian networks. - [scikit-learn](https://github.com/scikit-learn/scikit-learn) - The most popular Python library for Machine Learning with extensive documentation and community support. - [spark.ml](https://github.com/apache/spark) - [Apache Spark](https://spark.apache.org/)'s scalable [Machine Learning library](https://spark.apache.org/docs/latest/ml-guide.html) for distributed computing. +- [TabGAN](https://github.com/Diyago/Tabular-data-generation) - Synthetic tabular data generation using GANs, Diffusion Models, and LLMs. - [xgboost](https://github.com/dmlc/xgboost) - A scalable, portable, and distributed gradient boosting library. ## Natural Language Processing