gretel-python-client VS ydata-synthetic

Compare gretel-python-client vs ydata-synthetic and see what are their differences.

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gretel-python-client ydata-synthetic
3 60
43 1,286
- 4.1%
9.1 7.6
4 days ago 15 days ago
Python Jupyter Notebook
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

gretel-python-client

Posts with mentions or reviews of gretel-python-client. We have used some of these posts to build our list of alternatives and similar projects.

ydata-synthetic

Posts with mentions or reviews of ydata-synthetic. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-05.

What are some alternatives?

When comparing gretel-python-client and ydata-synthetic you can also consider the following projects:

SDV - Synthetic data generation for tabular data

REaLTabFormer - A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.

gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.

DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

Copulas - A library to model multivariate data using copulas.

ReTube - ReImagine Tubing

Conditional-Sig-Wasserstein-GANs

isp-data-pollution - ISP Data Pollution to Protect Private Browsing History with Obfuscation

pytorch-forecasting - Time series forecasting with PyTorch

Differential-Privacy-Guide - Differential Privacy Guide

Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.