majidraimi / pytorch-course

🔥⚡️ Learn PyTorch in <2 weeks! From Zero to Mastery course offers a fast-track journey into deep learning. Master fundamentals, workflow, neural networks, computer vision, custom datasets, modularization, transfer learning, and complete projects. Hands-on examples and 🚀 projects for accelerated PyTorch proficiency!

PyTorch Course: From Zero to Mastery

Welcome to the PyTorch Course: From Zero to Mastery! 🚀 This course is designed to take you from a beginner level to a proficient user of PyTorch, a powerful deep learning library. Whether you are new to deep learning or have some prior experience, this course will provide you with the knowledge and skills needed to work effectively with PyTorch.

Course Description 📚

This course covers a wide range of topics related to PyTorch and deep learning. Each section consists of detailed explanations, code examples, and hands-on exercises to reinforce your understanding. The course includes the following modules:

1. PyTorch Fundamentals | Section Two 🎯
2. PyTorch Workflow 🚀🔧
3. PyTorch Neural Network Classification 🧠🔢
4. PyTorch Computer Vision 🖼️👁️‍🗨️
5. PyTorch Custom Datasets 📦🔢
6. PyTorch Going Modular 🧩🔧
7. PyTorch Transfer Learning 🔄➕
8. Milestone Project 1: PyTorch Experiment Tracking 📊🚀
9. Milestone Project 2: PyTorch Paper Replicating 📄🚀
10. Milestone Project 3: Model Deployment 🚀🔧

Prerequisites 📋

To get the most out of this course, it is recommended to have a basic understanding of Python programming. Familiarity with concepts such as arrays, matrices, and calculus will also be helpful, but not mandatory. Prior experience with deep learning frameworks is not required.

Installation 💻

To run the code examples and complete the exercises in this course, you need to have PyTorch installed on your machine. Follow the steps below to set up your environment:

1. Install Python (version 3.6 or higher) from the official Python website: https://www.python.org/downloads/
2. Install PyTorch by following the instructions provided in the official PyTorch documentation: https://pytorch.org/get-started/locally/

Additional dependencies required for specific sections of the course will be mentioned within each section's README file.

Usage 🚀

Each section of the course is organized into separate folders, containing the necessary code files, datasets, and README files with detailed instructions. Start with the first section and progress through the course sequentially to build a solid foundation.

To begin a section, navigate to the corresponding folder and follow the instructions provided in the README file. The README file will outline the concepts covered, explain the code structure, and guide you through any exercises or assignments.

Feel free to modify the code examples, experiment with different parameters, and explore additional functionalities. This will help deepen your understanding of PyTorch and improve your overall learning experience.

Contribution 👥

This course is developed and maintained by [Your Name]. Contributions in the form of bug fixes, improvements, or additional exercises are welcome. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your contribution.
3. Make the necessary changes and additions.
4. Test your changes to ensure they work as expected.
5. Submit a pull request with a clear description of your changes.

Support 🆘

If you encounter any issues, have questions, or need further clarification, please feel free to reach out by [email/creating an issue/contacting the course instructor].

Acknowledgments 🙏

We would like to express our gratitude to the PyTorch community for their continuous support and the developers who have contributed to the various libraries and resources that make this course possible.

License 📝

This course is released under the MIT License. You are free to use, modify, and distribute the code and materials in this course for personal or commercial purposes.