# zhanggyb/nndl

Another Chinese Translation of Neural Networks and Deep Learning

Repository: https://github.com/zhanggyb/nndl
Canonical: https://ross.abutalabs.com/products/nndl
Language: TeX
License: NOASSERTION
License Family: other
Last push: 2020-12-17T09:36:38+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3878, "days_push": 2085, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1248, forks 335 (observed 2026-08-28T04:04:07.779673+00:00)

## What it is
A Chinese translation of Michael Nielsen's free online book 'Neural Networks and Deep Learning', written in LaTeX and compiled to PDF. It originated as the author's learning notes and incorporates work from an earlier translation project.

## Use cases
- learn neural networks and deep learning in Chinese
- read a Chinese version of Nielsen's deep learning book
- study the math behind neural networks with cross-referenced equations
- build the book PDF from LaTeX source
- find a free deep learning textbook translation

## When to choose
- you prefer reading about deep learning fundamentals in Chinese
- you want a nicely typeset PDF with proper math rendering
- you want to study Nielsen's classic book offline

## When to avoid
- you need an English-language resource
- you want up-to-date coverage of modern deep learning architectures like transformers
- you need a hands-on coding course rather than a book

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, machine-learning
- domain: machine-learning, deep-learning, education, tutorials
- platform: cross-platform
- tags: chinese-translation, latex, neural-networks, online-book, pdf

## Member repositories
- zhanggyb/nndl (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:07.779673+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:54.947813+00:00, confidence not recorded.
  - readme: https://github.com/zhanggyb/nndl (fetched 2026-08-28T04:04:07.779673+00:00, sha b6d03101239a)
- Data as of 2026-08-30T08:39:29.467469+00:00.