PyTorch Introduction with Exercises and Additional Theory on Weight Initialization and Optimizers for Deep Neural Networks

This repo contains a presentation in form of a jupyter notebook which introduces the core concepts of PyTorch as of mid 2022. It assumes some base knowledge in back propagation. Furthermore, the repo also includes pytorch exercises with solutions to directly apply the learned knowledge. Last but not least, there is an additional slides set which dives into the theory of weight initialization and optimizers for deep neural networks.

Citation

@misc{Juelg2022,
  author = {Jülg, Tobias},
  title = {PyTorch Introduction with Exercises and Additional Theory on Weight Initialization and Optimizers for Deep Neural Networks},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/juelg/pytorch-intro}}
}