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Learn PyTorch basics: tensors, autograd, neural networks, and more with practical examples and tutorials. Beginner-friendly.

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PyTorch Fundamentals

Deep Learning

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.

Prerequisites

Before diving into PyTorch fundamentals, ensure you have the following prerequisites:

  • Basic understanding of Python programming language.
  • Familiarity with machine learning concepts.

Reference

This repository draws inspiration from the incredible Paperspace - PyTorch 101 Tutorial Series and EffectivePyTorch.

Table of Contents

  1. PyTorch Basics
  2. From Automatic Differentiation to Curve Fitting
  3. Encapsulate Your Model with Modules
  4. Broadcasting: The Good and the Ugly
  5. Take Advantage of Overloaded Operators
  6. Optimizing Runtime with TorchScript
  7. Building Efficient Custom Data Loaders
  8. Achieving Numerical Stability in PyTorch
  9. Faster Training with Automatic Mixed Precision
  10. Building Neural Networks with PyTorch
  11. Advanced Usage of PyTorch
  12. Memory Management and Multi GPU Usage

Getting Started

Clone the repository and dive into PyTorch fundamentals.

git clone https://github.com/your_username/pytorch-fundamentals.git
cd pytorch-fundamentals

Contributing

We welcome contributions from the community! If you have suggestions, bug reports, or want to add new tricks to the repository, follow these steps:

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature/new-trick.
  3. Make your changes and commit: git commit -m 'Add new trick: Feature Name'.
  4. Push to the branch: git push origin feature/new-trick.
  5. Open a pull request.

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