Copulas VS gretel-synthetics

Compare Copulas vs gretel-synthetics and see what are their differences.

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Copulas gretel-synthetics
1 4
504 533
3.2% 4.9%
7.3 7.3
5 days ago 17 days ago
Python Python
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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.

Copulas

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

gretel-synthetics

Posts with mentions or reviews of gretel-synthetics. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-03.
  • Ask HN: If we train an LLM with “data” instead of “language” tokens
    1 project | news.ycombinator.com | 16 Aug 2023
    Hey there! Co-founder of Gretel.ai here, and I think I can provide some insights on this topic.

    Firstly, the concept you're hinting at is not purely traditional ML. In traditional machine learning, we often prioritize feature extraction and engineering specific to a given problem space before training.

    What you're describing and what we've been working on at Gretel.ai, is leveraging the power of models like Large Language Models (LLMs) to understand and extrapolate from vast amounts of diverse data without the need for time-consuming feature engineering. Here's a link to our open-source library https://github.com/gretelai/gretel-synthetics for synthetic data generation (currently supporting GAN and RNN-based language models), and also our recent announcement around a Tabular LLM we're training to help people build with data https://gretel.ai/tabular-llm

    A few areas where we've found tabular or Large Data Models to be really useful are:

  • Libraries for synthetic data?
    4 projects | /r/algotrading | 3 May 2023
    you can try QuantGAN: https://github.com/PakAndrey/QuantGANforRisk also try DoppelGANger https://github.com/gretelai/gretel-synthetics/tree/master/src/gretel_synthetics/timeseries_dgan
  • Which open source tool for generating synthetic data sets?
    1 project | /r/MLQuestions | 17 Oct 2022
  • Gretel-synthetics: open-source library to create synthetic datasets
    1 project | news.ycombinator.com | 22 Feb 2021

What are some alternatives?

When comparing Copulas and gretel-synthetics you can also consider the following projects:

CTGAN - Conditional GAN for generating synthetic tabular data.

gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

SDV - Synthetic data generation for tabular data

adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

ydata-synthetic - Synthetic data generators for tabular and time-series data

AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.

genalog - Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.

RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!