Advanced PyTorch Techniques

Welcome to the Advanced PyTorch Techniques repository! This collection of Jupyter Notebooks is designed for individuals who have a foundational understanding of PyTorch and seek to deepen their knowledge in specific, advanced areas of deep learning. Each notebook dives into a different topic, explaining the concepts and providing implementation examples to illustrate how these techniques can be applied in PyTorch.

Topics Covered

Below is a list of the topics we've explored so far, each encapsulated in its own Jupyter Notebook:

• Dynamic Computation Graphs in PyTorch: Dive into how PyTorch's dynamic computation graph (Autograd) works and how it differs from static graphs, providing flexibility and ease of use for dynamic model architecture adjustments.

• PyTorch Lightning: An overview and implementation guide for PyTorch Lightning, a lightweight PyTorch wrapper that helps organize your deep learning code and makes your models hardware-agnostic.

• Learning Rate Schedulers in PyTorch: Explore different learning rate scheduling techniques to improve your training process in PyTorch, including step decay, exponential decay, and more.

• Mixed Precision Training in PyTorch: Learn how to implement mixed precision training in PyTorch to speed up training times and reduce memory usage while maintaining the accuracy of your models.

Getting Started

To get started with these notebooks, you'll need a working installation of PyTorch. We recommend setting up a virtual environment and installing PyTorch and its dependencies as follows:

pip install torch torchvision torchaudio

Contribution

I welcome contributions! If you have an advanced PyTorch technique or topic you'd like to explore and share, please feel free to submit a pull request with your Jupyter Notebook.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the PyTorch team for creating such a powerful and flexible deep learning framework.
• Thanks to Google Colab for providing the computing resources that make this type of exploration possible.

Enjoy exploring these advanced PyTorch techniques, and I hope you find these notebooks both informative and inspiring for your deep learning projects!