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@StochasterAI

Stochaster

Proponents of the future.

Being assortments of mathematical brillinace and bright programming, Artificial Neural Networks are algorithms from THE BOOK.

We maintain two main repos:

  1. Stochaster Framework for Neural Networks. Stochaster allows you to study neural networks by creating them, tweaking them and expanding their capabilities.
    • This framework is developed to provide a thorough understanding on NNs both while using them in your code, or reading our source-code. More technical info available in the Stochaster Repo.
  2. Stochaster Lectures on Neural Networks. These lectures cover ANNs in various levels of depth and complexity with full explanations on the underlying mathematics.
    • Lectures contain PyTorch models followed by ground-up implementations on them used on example datasets (such as MNIST), and dive deep into their linear algebra and calculus and cover more advanced model-specific domains if needed (such as graph theory in GNNs).

Finally, this is our email.

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  1. Stochaster Public

    Studying, teaching or building neural networks? Stochaster is here to make it easier!

    Python 3

  2. Notebooks Public

    Collection of Jupyter Notebooks on neural networks using PyTorch and Stochaster involving detailed mathematics.

    Jupyter Notebook

Repositories

Showing 3 of 3 repositories
  • Stochaster Public

    Studying, teaching or building neural networks? Stochaster is here to make it easier!

    Python 3 MIT 0 0 0 Updated Jun 27, 2024
  • .github Public
    0 0 0 0 Updated Feb 28, 2024
  • Notebooks Public

    Collection of Jupyter Notebooks on neural networks using PyTorch and Stochaster involving detailed mathematics.

    Jupyter Notebook 0 GPL-3.0 0 0 0 Updated Feb 27, 2024