Is code-of-learn-deep-learning-with-pytorch open source?

Yes — L1aoXingyu/code-of-learn-deep-learning-with-pytorch is an open-source project tracked on heatdrop.

What language is code-of-learn-deep-learning-with-pytorch written in?

L1aoXingyu/code-of-learn-deep-learning-with-pytorch is primarily written in Jupyter Notebook.

How popular is code-of-learn-deep-learning-with-pytorch?

L1aoXingyu/code-of-learn-deep-learning-with-pytorch has 2.9k stars on GitHub.

Where can I find code-of-learn-deep-learning-with-pytorch?

L1aoXingyu/code-of-learn-deep-learning-with-pytorch is on GitHub at https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch.

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L1aoXingyu/code-of-learn-deep-learning-with-pytorch

A PyTorch primer that treats GitHub as the errata page

Companion notebooks for a Chinese deep-learning book, maintained because PyTorch moves faster than print.

★2.9k stars Jupyter Notebook Learning ML Frameworks

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code-of-learn-deep-learning-with-pytorch

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What it does

This repository holds the Jupyter notebook companion code for 深度学习入门之PyTorch, a Chinese introductory deep-learning book. It walks through tensors and autograd up through CNNs, RNNs, GANs, reinforcement learning, computer vision, and NLP — essentially a full-semester syllabus in notebook form.

The interesting bit

The author openly tells buyers to ignore the printed code and treat this repo as the canonical version, since PyTorch’s rapid releases broke the book’s examples. That honesty — plus the author’s admission that their “level is limited” — makes it an unusually humble educational resource in a field crowded with overconfident tutorials.

Key highlights

Broad coverage: 10 chapters spanning fundamentals, classic architectures (VGG, ResNet, DenseNet), optimizers, and applications like fine-tuning and segmentation
Explicitly maintained as post-sale support for book buyers, with content being updated for a second edition
~2,900 stars and Chinese-language focus, with references to MXNet/Gluon materials for cross-framework context
Includes practical topics often skipped in primers: TensorBoard, data I/O pipelines, ONNX export, and multi-GPU training

Caveats

Several advanced sections (autograd functions, model deployment, Deep Dream, image captioning) appear listed but lack linked notebooks, suggesting the syllabus outpaces current content
The author cautions that their expertise has gaps and that some material references other online sources
Transitioning to a second edition, so the repo structure and book alignment are in flux

Verdict

Worth bookmarking if you read Chinese and want a structured, soup-to-nuts PyTorch survey — provided you treat it as course notes, not gospel. Skip it if you need polished libraries or English-first documentation.

Frequently asked

What is L1aoXingyu/code-of-learn-deep-learning-with-pytorch?: Companion notebooks for a Chinese deep-learning book, maintained because PyTorch moves faster than print.
Is code-of-learn-deep-learning-with-pytorch open source?: Yes — L1aoXingyu/code-of-learn-deep-learning-with-pytorch is an open-source project tracked on heatdrop.
What language is code-of-learn-deep-learning-with-pytorch written in?: L1aoXingyu/code-of-learn-deep-learning-with-pytorch is primarily written in Jupyter Notebook.
How popular is code-of-learn-deep-learning-with-pytorch?: L1aoXingyu/code-of-learn-deep-learning-with-pytorch has 2.9k stars on GitHub.
Where can I find code-of-learn-deep-learning-with-pytorch?: L1aoXingyu/code-of-learn-deep-learning-with-pytorch is on GitHub at https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch.