A Japanese deep-learning book, translated and repo-ified

What it does

This repository houses chapter-by-chapter Python code for Deep Learning from Scratch (Saito Goki’s Japanese textbook, translated to Chinese). It walks through eight chapters of hand-rolled NumPy implementations—perceptrons, backprop, CNNs—plus shared common utilities and dataset loaders. The README also points readers to an errata page on ituring.com.cn and solicits tips via a WeChat tip jar.

The interesting bit

The repo’s real payload isn’t the code—it’s the “电子版” (electronic edition) implied by the description. The README never quite admits it, but the repo name and topics tag suggest a PDF is bundled alongside the exercises, making this a study aid that doubles as a textbook mirror.

Key highlights

• Eight chapter folders (ch01–ch08) with matching source files
• common and dataset modules for shared logic and data handling
• Pure NumPy + Matplotlib, no framework magic
• MIT licensed, explicitly commercial-friendly
• Errata tracked externally on ituring.com.cn

Caveats

• README assumes you own the book; “源代码的解释请参考本书” (see the book for explanations)
• Dependencies are vague: “Python 3.x” with no pinned versions
• A typo in the sample command (train_nueralnet.py) suggests light maintenance

Verdict

Worth a look if you’re studying Saito’s book in Chinese and want runnable code. Skip it if you expect standalone tutorials or modern PyTorch/TensorFlow implementations—you’ll be reading the textbook alongside the repo or not at all.