PyTorch

PyTorch is one of the most important frameworks for modern deep learning, dominating both research and large model development.

Getting Started

Xiaotudui PyTorch Tutorial

• Video: PyTorch Deep Learning Quick-Start Tutorial
• Highlights: Easy to understand; suitable for absolute beginners
• Coverage:
  • Basic concepts
  • Tensor operations
  • Building neural networks
  • Training loop design
  • Real-world project examples

Official and Community Resources

• PyTorch Official Chinese Tutorial: pytorch.ac.cn/tutorials
• Runoob Tutorial: runoob.com/pytorch
• PyTorch Interview Essentials: mstx.cn

Core Concepts

• Tensor: PyTorch's core data structure — a GPU-accelerated multi-dimensional array
  
  
• Autograd: Dynamic computation graph and automatic differentiation
• nn.Module: Modular building block for neural networks
• Training loop: Optimizers, loss functions, learning rate schedulers, and more

Advanced Tensor Operations

Tools and Libraries

• einops: GitHub — Elegant tensor operations (rearrange, reduce, repeat)
• The Tensor Cookbook:
  • Official site
  • Zhihu discussion
  • tensorgrad (GitHub)
  • [Cookbook PDF]

Classic Textbook

• Deep Learning with PyTorch Step-by-Step: A Beginner's Guide (Daniel Voigt Godoy)
  • [PDF (Z-Library)]

Practical Advice

Learning Path

1. Basics: Tensor operations and autograd
2. Model building: nn.Module and loss functions
3. Training loop: Optimizers and learning rate schedulers
4. Advanced features: Distributed training and optimization techniques
5. Project practice: Full end-to-end project development

Development Tips

1. Hands-on every concept: Implement each idea yourself
2. Read the source code: Understand underlying implementation principles
3. Build projects: Solidify knowledge through real-world practice
4. Engage with the community: Contribute to open-source projects

Appendix: Environment Setup

Jupyter Kernel Registration

pip install ipykernel

# Register a kernel
python -m ipykernel install --user --name myenv
python -m ipykernel install --user --name yourname --display-name yourname

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