Being assortments of mathematical brillinace and bright programming, Artificial Neural Networks are algorithms from THE BOOK.
We maintain two main repos:
- 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.
- 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.
