TingsongYu/PyTorch-Tutorial-2nd
Indexed by TopGit from live GitHub metadata: TingsongYu/PyTorch-Tutorial-2nd has 4.6k stars, written primarily in Jupyter Notebook. 《Pytorch实用教程》(第二版)无论是零基础入门,还是CV、NLP、LLM项目应用,或是进阶工程化部署落地,在这里都有。相信在本书的帮助下,读者将能够轻松掌握 PyTorch 的使用,成为一名优秀的深度学习工程师。
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How active is development on TingsongYu/PyTorch-Tutorial-2nd?
The most recent commit recorded on TingsongYu/PyTorch-Tutorial-2nd was 1.6 years ago, based on the GitHub push timestamp. The repository has 490 forks — one of the better signals of community interest.
How many stars does TingsongYu/PyTorch-Tutorial-2nd have?
TingsongYu/PyTorch-Tutorial-2nd has 4.6k GitHub stars — refresh the page for the live number, or check github.com/TingsongYu/PyTorch-Tutorial-2nd. TopGit mirrors GitHub's count but does not claim minute-by-minute accuracy.
What else is in the AI Tools space?
TingsongYu/PyTorch-Tutorial-2nd is tracked by TopGit under the AI Tools category, alongside 10 GitHub-tagged topics. Trending and Topics pages list peer repositories of comparable stars and language.
What language is TingsongYu/PyTorch-Tutorial-2nd written in?
TingsongYu/PyTorch-Tutorial-2nd is written primarily in Jupyter Notebook. GitHub's language field is based on the largest share of bytes in the default branch.
What topics is TingsongYu/PyTorch-Tutorial-2nd associated with?
GitHub's repository topics for TingsongYu/PyTorch-Tutorial-2nd: "computer-vision", "deepsort", "diffusion-models", "llm", "onnx", "pytorch", "pytorch-tutorial", "qwen", "tensorrt", "yolov5". TopGit's editorial category is AI Tools.
Where do I read more about TingsongYu/PyTorch-Tutorial-2nd?
This TopGit page is a snapshot — the READ ME tab shows the project's own README content (links stripped, images preserved). The GitHub repository at github.com/TingsongYu/PyTorch-Tutorial-2nd is the definitive source.
Why is TingsongYu/PyTorch-Tutorial-2nd categorized under AI Tools?
TopGit places TingsongYu/PyTorch-Tutorial-2nd in the AI Tools category based on its GitHub topics and description (tagged: "computer-vision", "deepsort", "diffusion-models"). Categories are assigned from real repository metadata, not editorial guesswork.
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