Welcome to the PyTorch Fundamentals! This repository is designed to help you master PyTorch from beginner to advanced levels with practical examples and tutorials. Whether you're just starting your deep learning journey or looking to enhance your skills, you'll find concise explanations and hands-on exercises to guide you through the world of PyTorch.
Before diving into PyTorch fundamentals, ensure you have the following prerequisites:
- Basic understanding of Python programming language.
- Familiarity with machine learning concepts.
This repository draws inspiration from the incredible Paperspace - PyTorch 101 Tutorial Series and EffectivePyTorch.
- PyTorch Basics
- From Automatic Differentiation to Curve Fitting
- Encapsulate Your Model with Modules
- Broadcasting: The Good and the Ugly
- Take Advantage of Overloaded Operators
- Optimizing Runtime with TorchScript
- Building Efficient Custom Data Loaders
- Achieving Numerical Stability in PyTorch
- Faster Training with Automatic Mixed Precision
- Building Neural Networks with PyTorch
- Advanced Usage of PyTorch
- Memory Management and Multi GPU Usage
Clone the repository and dive into PyTorch fundamentals.
git clone https://github.com/your_username/pytorch-fundamentals.git
cd pytorch-fundamentalsWe welcome contributions from the community! If you have suggestions, bug reports, or want to add new tricks to the repository, follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature/new-trick. - Make your changes and commit:
git commit -m 'Add new trick: Feature Name'. - Push to the branch:
git push origin feature/new-trick. - Open a pull request.
- GitHub Resource 1 - Description of the resource.
- GitHub Resource 2 - Description of the resource.
Explore more learning materials to deepen your understanding of PyTorch:
- Learning Material 1 - Description of the material.
- Learning Material 2 - Description of the material.