PyTorch Fundamentals: Your First Steps into Hands-on Deep Learning

Overview

Introduction to PyTorch fundamentals, covering tensor initialization, operations, indexing, and reshaping.

Install Dependencies

pip install -r requirements.txt

If you're installing torch with CUDA support, make sure to use the correct installation command from PyTorch's official website, as some versions require a specific installation method.

Contents

• What are Tensors?
• Tensor Initialization
• Common Tensor Initialization Methods
• Tensor Type Conversion
• Converting Between NumPy Arrays and Tensors
• Tensor Mathematics and Comparison Operations
• Matrix Multiplication and Batch Operations
• Broadcasting and Other Useful Operations
• Tensor Indexing
• Tensor Reshaping

Code Notebook

Dive into the hands-on examples in this interactive Jupyter notebook.

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Blog Post

Read the full breakdown and insights in the accompanying blog post.

Contributing

We welcome contributions from the community! If you have a new technique or improvement to suggest:

1. Fork the repository
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a pull request

License

This project is licensed under MIT License

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Keywords: AI, Machine Learning, Deep Learning, PyTorch, Generative AI, LLMs, AI Agents