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    Synthetic Data Vault (SDV)

    Synthetic Data Vault (SDV)

    Synthetic Data Generation for tabular, relational and time series data

    The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent...
    Downloads: 3 This Week
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    Machine Learning PyTorch Scikit-Learn

    Machine Learning PyTorch Scikit-Learn

    Code Repository for Machine Learning with PyTorch and Scikit-Learn

    Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing...
    Downloads: 5 This Week
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  • 3
    TGAN

    TGAN

    Generative adversarial training for generating synthetic tabular data

    ...Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where TGAN is run. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code listing. In order to be able to sample new synthetic data, TGAN first needs to be fitted to existing data.
    Downloads: 0 This Week
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