zhangxiann/PyTorch_Practice resource

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1397 stars · 293 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of PyTorch learning notes with 25 chapters organized into an 8-week study plan, accompanied by runnable Python code examples and an online GitBook ebook. It covers tensors, autograd, data loading, model building and training, visualization with TensorBoard, regularization, and applications like ResNet, object detection, GANs, and RNNs.

Use cases

• learn pytorch from scratch with a structured plan
• understand tensors and autograd in pytorch
• learn dataloader and transforms for image preprocessing
• understand loss functions and optimizers in pytorch
• learn to visualize training with tensorboard
• study resnet source code and finetuning
• learn to implement rnn manually in pytorch

When to choose

• you want a structured, week-by-week PyTorch curriculum with runnable code
• you already know basic machine/deep learning concepts and want PyTorch specifics
• you prefer Chinese-language tutorials with an accompanying ebook

When to avoid

• you need up-to-date coverage of the latest PyTorch APIs (notes last updated ~2020)
• you are a complete beginner to machine learning
• you need production-grade reference implementations rather than educational examples

Facets

learning-resource · maturity maintenance

machine-learning deep-learning developer-tools deep-learning machine-learning tutorials computer-vision python cross-platform pytorch tutorial notes ebook chinese deep-learning-course tensorboard gan rnn

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