Yuyang from Aofeisi Quantum Bit Report | WeChat Official Account QbitAI
The official PyTorch tutorial has been significantly updated:
It now offers a tag index, enhances topic categorization, and is more beginner-friendly.
No longer do you have to face a whole page of tutorial articles in confusion; you can now precisely click where you want to learn.
Netizens have expressed that the update is very timely.
Tag Index: Click Where You Need Help
If you are a complete beginner with PyTorch, the official PyTorch team continues to recommend one of their most popular tutorials: 60-Minute Blitz to PyTorch.
This time, there is a more prominent entry point to ensure you won’t miss it.
The highlight of this update is the quick tag index.
It is no longer a simple classification of CV, NLP, RL, etc., but rather a more detailed division of tutorial topics.
You can select tags to precisely find the tutorials you want.
For example, if you want to see tutorials on model optimization related to computer vision, select the tags “Image/Video” and “Model Optimization” to quickly filter the corresponding teaching content.
Specific PyTorch examples, commonly used APIs in PyTorch, a memo of elements, and GitHub links to tutorials are provided as additional resources, easily found after the tutorial section.
Of course, in addition to updates in interactive experience, the tutorial content aspect has also added new guides, such as:
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Loading Data in PyTorch
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Model Interpretability Using Captum
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How to Use TensorBoard with PyTorch
Complete Resource List
Finally, let’s summarize what aspects are included in the official PyTorch tutorial.
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PyTorch Beginner Tutorial: 60-Minute Blitz
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Image/Video Section (CV)
TorchVision Object Detection Fine-tuning Tutorial
Computer Vision Transfer Learning Tutorial
Adversarial Example Generation
DCGAN Tutorial
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Audio Section
Torchaudio Tutorial
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Text Section (NLP)
Sequence2Sequence Modeling with nn.Transformer and TorchText
Zero to NLP: Name Classification with Character-level RNN
Zero to NLP: Name Generation with Character-level RNN
Zero to NLP: Translation with Sequence2Sequence Networks and Attention
Text Classification with TorchText
Language Translation with TorchText
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Reinforcement Learning
Reinforcement Learning Tutorial
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Deploying PyTorch Models in Production
Deploying PyTorch Models with Flask
Introduction to TorchScript
Loading TorchScript Models in C++
Exporting Models from PyTorch to ONNX and Running with ONNX Runtime
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Frontend API
Introduction to Named Tensors in PyTorch
Final Storage Format of Channels in PyTorch
Using the PyTorch C++ Frontend
Custom C++ and CUDA Extensions
Extending TorchScript with Custom C++ Operators
Extending TorchScript with Custom C++ Classes
Autograd in C++ Frontend
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Model Optimization
Pruning Tutorial
Dynamic Quantization on LSTM Word Language Models
Dynamic Quantization on BERT
Static Quantization with Eager Mode in PyTorch
Quantization Transfer Learning Tutorial for Computer Vision
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Parallel and Distributed Training
Best Practices for Single Machine Model Parallelism
Introduction to Distributed Data Parallelism
Writing Distributed Applications with PyTorch
Introduction to Distributed RPC Framework
(Advanced) Distributed Training with PyTorch 1.0 on Amazon AWS
Implementing Parameter Server with Distributed RPC Framework
Portal
Official PyTorch Tutorial: https://pytorch.org/tutorials/
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