ydata-synthetic VS gretel-python-client

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

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ydata-synthetic gretel-python-client
60 3
1,292 43
2.8% -
7.3 9.1
6 days ago 1 day ago
Jupyter Notebook Python
MIT License Apache License 2.0
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.

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.

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.

What are some alternatives?

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

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

SDV - Synthetic data generation for tabular data

Copulas - A library to model multivariate data using copulas.

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

Conditional-Sig-Wasserstein-GANs

ReTube - ReImagine Tubing

pytorch-forecasting - Time series forecasting with PyTorch

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

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.

Differential-Privacy-Guide - Differential Privacy Guide