This repository provides a Chinese translation of the "practicalAI" machine learning course, aimed at developers and researchers seeking to learn practical ML implementation with PyTorch. It offers a comprehensive set of Jupyter notebooks covering fundamental ML algorithms to advanced deep learning topics, all runnable directly in a browser via Google Colab without local setup.

How It Works

The project leverages Jupyter notebooks to deliver its content, focusing on PyTorch implementations of various machine learning models. The approach emphasizes product-level, object-oriented programming for ML, moving beyond basic tutorials. By using Google Colab, it eliminates the need for local environment configuration, allowing users to run and experiment with code directly in their browser, with automatic saving to Google Drive.

Quick Start & Requirements

• Run notebooks directly in Google Colab by replacing github.com with colab.research.google.com/github/ in the notebook URL.
• No local setup required; all code runs in a browser.
• Access notebooks via nbviewer for viewing without execution.

Highlighted Details

• Covers a wide range of ML topics: from basic Python, NumPy, Pandas, and linear models to advanced CNNs, RNNs, GANs, recommendation systems, and reinforcement learning.
• Focuses on PyTorch implementations.
• Emphasizes object-oriented programming for machine learning.
• All content is translated into Chinese.

Maintenance & Community

• Open to contributions for improving notebooks and translations.
• A list of contributors is provided for specific notebooks.

Licensing & Compatibility

• The repository does not explicitly state a license.

Limitations & Caveats

• The licensing is not specified, which may impact commercial use or integration into closed-source projects.
• Some notebooks are listed without assigned translators, potentially indicating incomplete contributions or review.