Eat Pytorch In 20 Days: Study PyTorch with notebooks

Understand the project

Practical overview

Use it for a paced introduction to PyTorch or as a notebook reference when working through model-building and training concepts.

Learners with some machine-learning and deep-learning background who want structured PyTorch study.
Practitioners seeking notebook examples for common modeling and training tasks.

Study PyTorch modeling workflows for structured, image, text and time-series data.
Learn tensors, automatic differentiation, computation graphs and PyTorch API layers.
Look up examples for data loading, model layers, loss functions, visualization and training.
Explore recommendation and advertising model examples in the additional curriculum.

Before you choose

The source repository is public and identifies the project license as Apache-2.0.

The guide assumes prior exposure to machine learning and deep learning, including training simple models.
The README recommends cloning the repository and using Jupyter for interactive study.
Some notebook lessons require separate datasets; the README describes obtaining and placing a dataset archive.
The main README and lesson descriptions are in Chinese.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the guide’s notebooks and datasets for use in Jupyter.

Some machine-learning and deep-learning background, including experience building a simple model with Keras, TensorFlow, or PyTorch.
Jupyter.
The project’s dataset archive, obtained using the acquisition method described in the README.

Clone the repository locally; the README recommends its Gitee mirror for faster downloads in China: the command below.
Documented command
```
git clone https://gitee.com/Python_Ai_Road/eat_pytorch_in_20_days
```
Follow the README’s instruction to obtain the eat_pytorch_datasets.zip download link by replying pytorch to the WeChat public account 算法美食屋.
Download and extract the dataset archive, then move the extracted data into the eat_pytorch_in_20_days directory.
Open the notebooks in Jupyter and study them interactively, following the day-by-day plan or finding examples by topic.

The repository and its datasets are arranged for interactive study of the notebooks, which progress through PyTorch workflows, concepts, and APIs.

The README estimates 30 minutes to two hours of study per day; completing the main plan is presented as a 20-day course.
Dataset download details are provided through the named WeChat account rather than a direct download URL.
Readers are expected to have prior machine-learning and deep-learning fundamentals.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 15 October 2026
Original website title: lyhue1991/eat_pytorch_in_20_days · Official homepage title · retained in its source language