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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Synthetic data generators for tabular and time-series data
| Date | Stars |
|---|---|
| 2026-07-24 | 1646 |
| 2026-07-25 | 1646 |
| 2026-07-28 | 1646 |
| 2026-07-30 | 1646 |
| 2026-08-06 | 1646 |
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  [](https://pypi.org/project/fg-data-synthetic/)  [](https://pypi.org/project/fg-data-synthetic/)  <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=ab07c7a0-c1ee-481e-9368-baf70185cf40" /> <p align="center"><img width="300" src="https://assets.ydata.ai/oss/ydata-synthetic_black.png" alt="Data Synthetic Logo"></p> Join us on [](https://tiny.ydata.ai/dcai-ydata-synthetic) # Data Synthetic A package to generate synthetic tabular and time-series data leveraging the state of the art generative models. ## 🎊 The exciting features: > These are must try features when it comes to synthetic data generation: > - A new streamlit app that delivers the synthetic data generation experience with a UI interface. A low code experience for the quick generation of synthetic data > - A new fast synthetic data generation model based on Gaussian Mixture. So you can quickstart in the world of synthetic data generation without the need for a GPU. > - A conditional architecture for tabular data: CTGAN, which will make the process of synthetic data generation easier and with higher quality! ## Migration Guide ### 1. Uninstall the old package ```bash pip uninstall ydata-synthetic ``` ### 2. Install the new package ```bash pip install fg-data-synthetic ``` ### 3. Update your imports Find and replace all occurrences of the old import in your codebase: ```python # Before import ydata_synthetic from ydata_synthetic.synthesizers.regular import RegularSynthesizer # After import data_synthetic from data_synthetic.synthesizers.regular import RegularSynthesizer ``` You can use this one-liner to find all affected files: ```bash grep -r "ydata_synthetic" . --include="*.py" ## Synthetic data ### What is synthetic data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. ### Why Synthetic Data? Synthetic data can be used for many applications: - Privacy compliance for data-sharing and Machine Learning development - Remove bias - Balance datasets - Augment datasets > **Looking for an end-to-end solution to Synthetic Data Generation?**<br> > [YData Fabric](https://ydata.ai/products/synthetic_data) enables the generation of high-quality datasets within a full UI experience, from data preparation to synthetic data generation and evaluation.<br> > Check out the [Community Version](https://ydata.ai/ydata-fabric-free-trial). # fg-data-synthetic This repository contains material related with architectures and models for synthetic data, from Generative Adversarial Networks (GANs) to Gaussian Mixtures. The repo includes a full ecosystem for synthetic data generation, that includes different models for the generation of synthetic structure data and time-series. All the Deep Learning models are implemented leveraging Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. Are you ready to learn more about synthetic data and the bext-practices for synthetic data generation? ## Quickstart The source code is currently hosted on GitHub at: https://github.com/Data-Centric-AI-Community/fg-data-synthetic Binary installers for the latest released version are available at the [Python Package Index (PyPI).](https://pypi.org/project/fg-data-synthetic/) ```bash pip install
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dba99c795c954be3, topic:deep-learning, topic:pytorch
matched fp:dba99c795c954be3, topic:synthetic-data, desc:synthetic data, readme:synthetic data
matched fp:dba99c795c954be3, topic:gan
matched fp:dba99c795c954be3, topic:training-data, readme:datasets
